esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
When the construction of a tunnel results in local cavities and insufficient thickness around the tunnel lining, this causes uneven settlement of the adjacent surface buildings' foundations and possibly unrecoverable damage [1; 2; 3].  
Regarding this problem, much research has been carried out. The estimation of maximum building settlement is developed by the Potts and Addenbrooke method [4], numerical simulation [5], and InSAR monitoring data [6; 7]. However, the monitoring and prediction of settlement is important, but the impact of settlement on the engineering structure is more concerning [8; 9; 10]. Most of the research on structural deformation assessment is carried out on greenfield ground [11], but the evaluation results are conservative [12]. In order to more accurately evaluate the ground movements, the impacts of structural weight [13; 14] and stiffness [15; 16; 17; 18] need to be considered.  
However, most of the existing research focuses on ground movements and monitoring methods, or on the effects of tunnel excavation on the deformation of the superstructure. It is still very rare to study the dynamic response of structures after uneven foundation settlement, especially the study of the seismic response of structures after uneven foundation settlement, which is not found in related literature.  
Settlement is a deformation feature of a building. It is the result of the vertical deformation of the foundation and the interaction of the building. Settlement is mainly divided into two categories: uniform settlement and uneven settlement [19; 20]. Uniform settlement usually occurs when the load on the building and the foundation soil are relatively uniform. Uniform settlement generally does not cause secondary stress on the building structure and does not damage the building components, but excessive settlement can also cause a series of problems in practice. Uneven settlement is a kind of foundation deformation that occurs easily, often jeopardizing the safety of the building structure. In order to study the influence of uneven settlement on the seismic response of the structure, this paper takes a steel frame structure with an uneven foundation settlement as the research object. Different settling areas and settlements are set in the model, and the incremental dynamic analysis (IDA) method is used to calculate the structural seismic response. The seismic vulnerability analysis and evaluation of the structure are carried out by determining the damage index and performance parameters of the structure.
## 3 Results and Discussion
### Plum Tree Pellet Combustion  
Concerning plum tree pellets, the main results are included in Table 10 (in which Position 2 and Position 4 were selected) and Figures 14 and 15. Similar trends were observed compared with pine pellets, except for CO emissions (see Figure 15), whose values presented a constant trend in general, as observed in previous studies during the stable combustion of biomass in a commercial boiler [46].  
Figure 14: Ostwald diagram for plum tree pellets, and experimental results for each level used in the combustion stove.  
Figure 13: Comparison between different feeding rates in the case of pine pellets.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **P2** & & **P4** \\ \hline
**Parameter** & **Average** & **Average Deviation** & **Average** & **Average Deviation** \\ \hline \(\lambda\) & 10.05 & 1.92 & 7.06 & 1.46 \\ \(\eta\), \% & 79.98 & 4.42 & 81.26 & 4.69 \\ \(\alpha\), \% & 20.02 & 4.42 & 18.74 & 4.69 \\ NO, ppm & 40 & 11.2 & 61.4 & 12.9 \\ Calculated n & 9.72 & 1.87 & 6.82 & 1.42 \\ \(\chi\) (x = \(\mathrm{I}_{\mathrm{CO_{2}}}/(\mathrm{V}_{\mathrm{CO_{2}}}+\mathrm{V}_{ \mathrm{CO_{2}}})\)) & 0.908 & 0.029 & 0.932 & 0.038 \\ \hline \end{tabular}
\end{table}
Table 10: _Cont._Regarding the comparison of different levels and feeding ratios for plum tree pellets, the main results are observed in Table 11. It can be observed that, when the extreme cases were compared, there were considerable differences, which were more noticeable in this case with regard to the examples selected for pine pellets. Thus, it can be seen more clearly that the stove works under more optimal conditions when extra feed is selected for plum tree pellets.  
In addition, when different feed rates were compared for the same level (see Figure 16), differences with pine pellets could be found, with negligible differences in air inlet and exhaust gas temperatures, whereas the CO\({}_{2}\) percentage was higher with 125% feed.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Test Parameter**} & **Plum Tree Pellets** & **Plum Tree Pellets** & \multirow{2}{*}{**Comparison**} \\  & **(P = 2, 100\% Feed)** & **(P = 4, 125\% Feed)** & \\ \hline O\({}_{2}\) \% & 18.9 & 14.1 & \(\downarrow\) \\ Air supply temperature, \({}^{\circ}\)C & 43.6 & 49.1 & \(\uparrow\) \\ Flue gas temperature, \({}^{\circ}\)C & 69.2 & 91.7 & \(\uparrow\uparrow\) \\ Ambient temperature, \({}^{\circ}\)C & 18.8 & 18.9 & Constant \\ CO, ppm & 1378 & 329.3 & \(\downarrow\downarrow\) \\ CO, \% & 0.138 & 0.033 & \(\downarrow
## 4 Discussion
### Data Decomposition and Classifier Optimization  
Either the 163 ions or the 3630 new features are too large for classifier training. Data decomposition could eliminate the 'curse of dimensionality' by generating a reasonable number of components. Three decomposition methods were compared: PCA, TSNE, and PLS. PCA is a widely used and unsupervised decomposition method that is suitable for group separation with significant different features. TSNE is also an unsupervised decomposition method, but it can only generate two or three components. PLS is a supervised decomposition method that keeps the features with the most variance and considers features that are relevant to predict the target. In our study, PLS decomposition can separate the malignant and benign groups better than the other two. The individual difference is significant in patients. Therefore, a supervised machine learning method (PLS) should work better than the unsupervised one.  
Major features contributed most to the separation of malignant and benign nodules by PLS decomposition were illustrated in Figure 4. Clearly, _m/z_ 285 was present three times in the 10 ion ratios, and _m/z_ 833.5 and _m/z_ 699.4 were present twice in the 10 ion ratios. They could be good internal standards for the generation of ions ratios for the separation of malignant and benign nodules. However, they are still unknown substances, and further studies must be carried out to identify their structures.  
Training methods and parameters produced classifiers with different performances. Three common training methods were applied to train classifiers: the support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. With the 163 ions as features, RF performed better than SVM and MLP. In comparison, SVM and MLP performed better than RF with the 3630 new features. From an application point of view, the MLP method performs the best, and its parameters optimization were conducted. The solver of weight optimization is one of the key parameters of the MLP classifier. The weight optimization solvers include'sgd', 'adam', and 'lfbfgs'. The best performance was obtained with 'lfbfgs' as the solver, and it has been announced that the solver 'adam' works well on relatively large datasets (with thousands of training samples or more). For small datasets, 'lfbfgs' can converge faster and perform better [23].
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 1 Introduction
### Expected Scientific Novelty and Practical Value  
The proposed DSS offers several innovative features, including near real-time application, job-on-demand scenario analysis, and a configurable interface tailored to different stakeholders. It leverages HPC infrastructure to reduce computation time. By integrating ground-based measurements, satellite data, and traffic information, the system provides a comprehensive framework for urban air quality management.  
From a practical perspective, the DSS has been applied to study NO\({}_{2}\) in the city of Cagliari, Italy, to facilitate evidence-based decision making by providing actionable insights and advanced visualization tools.  
Understanding NO\({}_{2}\) in urban environments is critical, as it is a major air pollutant primarily resulting from vehicle emissions and industrial activities [18]. Over the past 10-15 years, the scientific knowledge of NO\({}_{2}\) and its health effects has significantly advanced. Research has firmly established that NO\({}_{2}\) exposure increases the risk of premature death from respiratory and cardiovascular diseases, with both short- and long-term exposure posing serious health threats [19]. NO\({}_{2}\) exposure is linked to asthma development in children and adults, bronchitis in children, and worsened health for individuals with preexisting respiratory or cardiovascular conditions. Recent studies also differentiate the health impacts of NO\({}_{2}\) from those of other pollutants, such as fine particulate matter (PM2.5), providing clearer insights into its specific risks [20; 21]. Monitoring NO\({}_{2}\) emissions and their dispersion is essential for shaping effective public health policies to reduce exposure and improve urban air quality.  
This supports policymakers, urban planners, and public health professionals in mitigating air pollution and improving public health outcomes. The system's open, interoperable geo-database ensures accessibility and fosters collaboration among environmental protection agencies and research institutions. By addressing the limitations of existing systems, this research contributes to the development of scalable, data-driven solutions for urban air quality governance, setting a benchmark for future innovations in environmental management.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 5 Experiments and Results
### Dependence of the TARA Method Performance on Trust Distribution Scenarios  
We have conducted several experiments to analyze how scenarios of malicious behavior influence the performance of the TARA method. The percentage of potentially untrusted nodes is fixed in each scenario, ranging from 10% to 50%. Experiments with all combinations of the \(BASE\) and \(MD\) parameters were helpful, but for simplicity and clear comparison, Table 8 shows the results only for \(BASE=8\) and \(MD=512\).  
For scenarios with few untrusted nodes, the TARA method is not as efficient. Its benefit increases with the percentage of untrusted nodes.
# Strengthening of RC Beams with CFC Panels for Improving Impact Resistance
## 5 Analytical Program Verification
### Strain Energy Distribution  
Figure 23 shows the strain energy distribution of all the specimens after releasing a single impact of 3.0 m/s impact velocity, which can indicate each specimen's ability to absorb and dissipate energy. Among the strengthened specimens, CFC panels can help to protect the RC beam from damage or crack propagation during impact events. The strain energy of the strengthened specimens was not concentrated around the steel reinforcement, but was dissipated in the tension region, whereas the strain energy was differently distributed in the conventional RC beam, and it clearly showed that steel reinforcement played an important role in absorbing energy.  
In RC_F3S_CFC, the full bottom and side surface strengthening achieved the best performance in impact response, followed by the full bottom surface strengthening; however, in RC_P3_CFC, the partial bottom and side surfaces' strengthening performed an interesting strain energy distribution. The specimen had the ability to absorb energy in a wide area, which can minimize the damage or crack width that especially propagates in the tension region. Unfortunately, the midspan displacement of RC_P3S_CFC seemed to be higher than that of the other strengthened specimen, which was significantly caused by the interfacial debonding of the CFC panels. In order to observe the full performance of the strengthened specimens, the adhesion methods needed to be improved to prevent the CFC panels from debonding.  
Figure 23: Strain energy distribution of each specimen: (**a**) RC; (**b**) RC_FB_CFC; (**c**) RC_P3S_CFC; (**d**) RC_F3S_CFC.
# Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging
## 4 Conclusions  
With the advantages of flexible observation, the squint SAR with the multi-mode is widely used to accomplish different monitoring missions. The GNLCSA proposed in this paper derived the new perturbation function and the nonlinear chirp scaling function by the method of series reversion. Also, it modifies the procedures of the processing in the range dimension to avoid the tedious range block processing in ENLCSA [1]. In general, from the perspective of the algorithmic framework, the NLCSA and ENLCSA are special forms of the proposed algorithm. The simulation results indicate that the ADOF and the focusing precise have been well improved in the areas apart from the azimuth reference position with the process of the GNLCSA.  
**Acknowledgments: This work was supported by the Chinese National Natural Science Fund (Grant No. 61401480) and the Major Research Plan of the National Natural Science Foundation of China (Grant No. 91438202). The authors would like to thank the anonymous reviewers for their very competent comments and helpful suggestions.**  
**Author Contributions: [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] contributed materials; and [PERSON] wrote the paper.**  
**Conflicts of Interest: The authors declare no conflict of interest.**  
Figure 8: Phase error of targets in the edge (the distance departing from the referenced azimuth position is 2 km and the azimuth resolution is 1 m) varies with the squint angle and wave length. The blue solid line with ’o’, the red dash line with ’*’ and the cyan lineation line with ’\(\
abla\)’ are the results of the wavelength equaling to 0.06 m, 0.03 m and 0.015 m, respectively.
## 4 Cloud Application Management Patterns
### Move/Update Management Without Downtime Pattern  
**Icon:** the icon of the move/update management without downtime pattern is depicted in Figure 13.  
**Driving Question:**_How can an application component be moved to a different environment or updated to a new software, middleware, or hardware version, if downtime is inacceptable?_  
**Context**: A _stateless application component_, which has no internal state, but relies on external data and is part of a _composite application_. The component must not be unavailable for the timeframe in which it is updated/moved.  
**Challenges**: due to the provisioning time of the new component and the time it takes the old component to finish processing request, both components must be operated during the switch from one environment/version to the other. This is especially challenging, if the application component depends on external data that has to be shared by the old and the new components.  
**Solution**: as depicted in Figure 14, replicate and synchronize required data to be accessed by the old and the new component and provision and operate both versions concurrently. Then, instantly switch from the old to the new version of the component.  
Figure 14: Abstract Management Flow of the Move/Update without Downtime Pattern.  
Figure 13: Icon of the Move/Update Management without Downtime Pattern.  
**Result**: because the data accessed by both components is synchronized, a consistent component behavior is ensured. The switch between both versions is done instantly. The old component is given time to finish any processing that may have been assigned to it prior to the switch. Then, additional data that was not needed directly in the beginning, for example, log information or archived data, is moved. Finally, the old component is no longer needed and can be deprovisioned.  
**Variations**: if the managed component does not access external data, the corresponding activities in the management flow to replicate and synchronize the data accessed by the managed application component can be omitted.  
**Known uses**: Significant efforts are being made to migrate virtual machines between multiple runtime environments without downtime [44, 45]. [46] gives an overview of the tasks that have to be covered and their order to achieve this.
## 4 Performance Evaluation
### Delay Added to the System  
Based only on the performance of the models presented in Section 4.2, one would think that the most suitable models are the RL-based models. However, a decisive factor in selecting the implementation model is the added delay due to the processing time. While offline training models act only as an intelligent switch when onboard, models with online training require an added delay due to the time needed for training. In this sense, Figures 10 and 11 present a performance evaluation obtained as a function of the training time performed online.  
Figure 10 shows that despite DDQL being the most complex algorithm, it requires only 4.94 min of training to obtain an Average Capacity Gap (see Equation (16)) that is fewer than 18 Mbps. On the other hand, it is observed that QL requires at least 48 min to start obtaining an Average Capacity Gap that is close to 18 Mbps. However, even after 50 min of QL training, QL performance continues to have significant oscillations in which an Average Capacity Gap of up to 134.78 Mbps is obtained.  
Moreover, we can compare the performance obtained by using an RL algorithm concerning an SL algorithm that was trained offline and defined the Offline Training Gap (\(OTG\)), which will allow us to measure performance improvement. This is obtained as the difference between the Average Capacity Gap of an model trained offline (\(KPI_{1,offline}\)) and another model trained online (\(KPI_{1,model}\)).  
\[OTG=KPI_{1,offline}-KPI_{1,model} \tag{19}\]  
In that sense, Figure 11 shows the performance improvement between the RL algorithms and an SL algorithm (CNN that was trained offline, see Table 3). DDQL needs only 3.8 min of online training to achieve a performance similar to CNN, and after 4.9 min, a performance of 27.89 Mbps or higher than that of the CNN is obtained. In contrast, QL requires at least 21 min of training to obtain a performance similar to CNN, and due to the high oscillations of QL after 21 min, a performance that is lower than CNN can be obtained.  
Figure 10: Average capacity gap for online-trained algorithms depending on training time. Note: The times obtained depend on the computer features used. If computer performance is increased, the time required will be considerably reduced.  
Therefore, the selection between implementing SL or RL will depend on the features and requirements of the service. If low processing latency is required, it is recommended to implement an SL algorithm that can be trained offline and deployed onboard the satellite (Figure 3). However, it will require knowledge of the onboard traffic demand, which will add complexity to the payload's design. On the other hand, if the priority is to meet all traffic demands while avoiding wasting resources,the implementation of an RL algorithm that could be trained online every time the system's requirements change drastically is recommended (Figure 2).
## 2 Related Work  
Sherman recognition and classification of objects using CNN-based models are the most popular cases of technology use [13]. However, the simple use of CNNs typically requires a substantial amount of training data and is time-consuming. Nevertheless, the recent advent of an object detection technique that classifies objects through bounding boxes has significantly improved the accuracy of object recognition algorithms [8]. A region-based convolutional neural network (R-CNN), which forms the basis of object detection, is used first to extract the region-of-interest from the candidate group and then to determine the individual algorithm groups.  
One advantage of R-CNNs is that they can quickly extract regions with relatively high accuracy, even with smaller datasets. Starting with the R-CNN algorithm in 2013, object detection techniques have developed into various algorithms, including Fast R-CNN, Faster R-CNN, YOLO (You Only Look Once), and RefineDet [14]. Notably, Faster R-CNN is slightly slower than other existing algorithms; however, in terms of accuracy, models based on Faster R-CNN show superior performance, compared to most models [15; 16]. Accordingly, in this study, the Faster R-CNN framework was utilized.  
AI algorithms for dental panoramic images are used to obtain various properties in various applications, such as tooth shape, tooth age, dental plaque, landmark detection, orthodontics, and osteoporosis disease [7; 8; 9; 10; 11; 12]. [PERSON] et al. proposed a novel algorithm to classify the tooth age of infants and toddlers by applying dental panoramic radiography [7]. Their algorithm successfully analyzes dental panoramic radiographs through a basic CNN by analyzing the teeth of infants and toddlers but suffers from manual placement of the bounding box. In a study in 2017, automated tooth detection and numbering was conducted by a CNN that used a heuristic method to detect teeth [9]. Advances in the teeth detection field have led to heuristic techniques, thereby significantly increasing the accuracy of tooth shape determination [10].  
An algorithm for diagnosing the jaw region through dental panoramic radiography was first developed in 2018. Specifically, [PERSON] and [PERSON] [11] proposed an algorithm for identifying and finding jaw tumors in a panoramic image and further validated the proposed algorithm through activation maps. Further, [PERSON] et al. [12] conducted a study in which they found and analyzed mandible lesions by using object detection algorithms, including YOLO and DetectNet [17].
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 3 Proposed Method
### Merging Feature Subsets and Reducing Dimensions with PCA  
In above steps, two feature subsets are obtained. \(Subset1\) is a strong-irrelevance subset which contains features with strong irrelevance among them. \(Subset2\) is a strong-relevance subset which consists of features which have strong correlation with fault categories. First, an intersection operation is carried between \(Subset1\) and \(Subset2\) to obtain a final subset \(F-Subset\). \(F-Subset\) contains the common elements of \(Subset1\) and \(Subset2\). Next, the variance of each feature is calculated and then the five features with the largest variance are selected. The reason of selecting the features with the five largest variances is that the smaller a variance of the feature, the less information this feature contains. Therefore, the five features with the largest variance means that almost all information is retained. Finally, PCA is employed to reduce dimensions of the \(F-Subset\). The details are given in Algorithm 5.  
```
Input:\(Subset1\) and \(Subset2\). Output:\(F-Subset\).
1. Merge \(Subset1\) and \(Subset2\) with an intersection operation to obtain \(F-Subset\).
2. for each \(f_{i}\) in \(F-Subset\)
3. employ PCA to select five features with the largest variance
4. endfor
```  
**Algorithm 5** Merging feature subsets.