esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
It also adopts subjective-objective combination of weights for the severity of disaster-causing factors, adopts CRITIC-entropy weights for the exposure of disaster-prone environments and vulnerability of disaster-bearing bodies, adopts AHP subjective weights for the criterion layer, establishes an evaluation index system of urban flood disaster risk, and evaluates the multi-year flood disaster risk of the city using GIS spatial analysis technology. It combines this with the process of urbanization, analyzes urban flood disaster risk at the county scale, and employs zoning with the goal of providing relevant managers with important disaster risk information. This urban flood risk assessment model based on a combination of subjective and objective multi-weight methods fully considers the role of expert experience and objective data, makes up for the shortcomings brought by single weighting and machine learning models, and can simultaneously avoid the subjectivity of results and uncertainty of parameters, thus obtaining more accurate decision-making results and evaluating urban flood disaster risks more scientifically. This study represents an important basic work for disaster prevention and mitigation in plain cities. To date, the application of subjective and objective assignment methods in flood disaster assessment is uncommon; therefore, it is necessary to use this method to carry out research on flood disaster risk assessment, which is of great significance for achieving improvements in urban flood disaster prevention, risk level classification, and the promotion of economic development. Meanwhile, the application of such innovative combination methods also provides ideas for urban flood risk assessment in similar plain cities.
## 2 Related Works
### Sparse Total Variation Based on \(L_{p}\) Quasinorm  
Compared with the \(L_{1}\) norm, the \(L_{p}\) quasinorm has one more degree of freedom; therefore, it can better characterize sparse gradient information. The contours of the anisotropic total variation (ATV) \(\mathbf{R}_{\text{APTV}}(\mathbf{F})=\left\|\mathbf{K}_{h}*\mathbf{F}\right\|_{p}^{p}+\left\| \mathbf{K}_{v}*\mathbf{F}\right\|_{p}^{p}\) (\(0<p\leq 2\)) based on the \(L_{p}\) quasinorm are shown in Figure 1, where the \(L_{1}\) and \(L_{2}\) norms are special cases of the \(L_{p}\) norm. The \(L_{p}\) norm is defined as \(\left\|\mathbf{F}\right\|_{p}=(\sum_{i=1}^{N}\sum_{j=1}^{N}|\mathbf{F}_{ij}|^{p})^{ \frac{1}{p}}\), while the \(L_{p}\) quasinorm is defined as \(\left\|\mathbf{F}\right\|_{p}^{p}=\sum_{i=1}^{N}\sum_{j=1}^{N}|\mathbf{F}_{ij}|^{p}\). In Figure 1, white Gaussian noise is added to the image with a standard deviation of \(\sigma\); as can be seen from the figure, the intersection of the \(L_{2}\) norm contours and fidelity term \(\frac{1}{2}\left\|\mathbf{F}-\mathbf{G}\right\|_{2}^{2}\) is not sparse, whereas the intersection of the \(L_{1}\) norm contours and fidelity term is sparse, but is susceptible to noise pollution. In contrast, the contours of the \(L_{p}\) quasinorm are more robust to noise.  
In light of the abovementioned considerations, previous research extended the ATV based on the \(L_{1}\) norm to \(\text{TV}_{p}(\mathbf{F})\)[36] as given by Equation (6):  
\[\text{TV}_{p}(\mathbf{F})=\left\|\mathbf{\
abla}_{1}\mathbf{F}\right\|_{p}^{p}+\left\| \mathbf{\
abla}_{2}\mathbf{F}\right\|_{p}^{p}\quad(0<p<1), \tag{6}\]  
where \(\text{TV}_{p}(\mathbf{F})\) represents the ATV sparse regularization term based on the \(L_{p}\) quasinorm.
## 3 Controller Design  
### Hierarchical Control Structure  
The 4 WIS/4 WID electric vehicle has four independent steering systems and four in-wheel motors. The chassis integration control of 4 WIS/4 WID electric vehicle is adopted in this paper, so as to give consideration to other performance requirements while achieving target trajectory tracking.  
The study of chassis integrated control falls into centralized control structure and hierarchical control structure. The centralized control structure is based on the vehicle dynamics characteristics of the whole vehicle, and the subsystems are directly controlled by the centralized controller at the global level. The controller design is generally based on linear or nonlinear models and the design method of multi-input multi-output system (MIMO) is adopted to solve the coupling problem of vehicle longitudinal dynamics and lateral dynamics. However, the design of the centralized controller is difficult and the control algorithm is complex [28].  
To solve the problem of complexity of centralized control algorithm, a hierarchical control structure is adopted to reduce the complexity of the algorithm, as shown in Figure 3. The desired path and the desired vehicle speed are planned by the path planning layer according to the surrounding environment and other information. The path tracking layer tracks the desired path considering the change of vehicle speed to obtain the desired yaw rate. The vehicle dynamics control layer calculates the longitudinal resultant force demand, lateral resultant force demand, and yaw moment demand to achieve the desired response according to the desired yaw rate and desired vehicle speed. The longitudinal tire force and the lateral tire force of each wheel are obtained from the tire force distribution layer via a certain optimization method. The actuator control layer controls the actuators of each subsystem to achieve the vehicle movement control.
# Prediction of RF-EMF Exposure by Outdoor Drive Test Measurements
## 4 Results
### Analysis of Drive Test Data  
The drive test measurements along the route illustrated in Figure 3 recorded 19,834 points in total. The variation of the E-field strength along this route is shown on the left of Figure 6. The cumulative distribution functions (CDF) of Eq900 E-field from each operator (\(E_{Opi}\)) and total Eq900 (\(E_{Total}\)) are compared on the right of Figure 6. The Eq900 for each cellular operator (\(E_{Opi}\)) is calculated using Equation (1). We note here that the E-field represents the total Eq900, which is obtained from the integration of the E-field strength over all the cellular operators (\(E_{Opi}\)), computed as \(E_{Total}=\sqrt{E_{Opi}^{2}+E_{Opi}^{2}+E_{Opi}^{2}+E_{Opi}^{2}}\). We observe that Eq900 from each operator has almost similar distributions. Obviously, the total Eq900 presents higher values for the E-field strength because of the integration over all the operators. However, the CDF plots show that the RF-EMF exposure in the outdoor urban environment is well below the limit given by ICNIRP guidelines (over the frequency range 700 to 2700 MHz, the maximum limit is 61 V/m). The measurement point with higher values \(E_{Opi}\) shown in an area tends to have close BSs of the corresponding operator. The measurement point with low values, on the other hand, tends to be far away from the BS or the antenna is directed elsewhere. Similarly, total Eq900 varies spatially according to the distribution and the density of the BSs in a given area. Moreover, the E-field strength strongly relies on the traffic.  
In order to check the density of the BSs in Paris, we computed the mean distances between each measurement point and its \(n\)-th nearest neighboring BSs. The results given in Table 1 show a dense deployment of BSs in the urban city. This raises the question of how many neighboring BSs significantly influence the local exposure level in an urban environment. To answer this question, we study the impact of a number of neighboring BSs \(N\) on the ANN-based prediction performance of EMF exposure.
###### Abstract  
Feature extraction is a collection of the necessary detailed information from the given source, which holds the information for further analysis. The quality of the fused image depends on many parameters, particularly its directional selectivity and shift-invariance. On the other hand, the traditional wavelet-based transforms produce ringing distortions and artifacts due to poor directionality and shift invariance. The Dual-Tree Complex Wavelet Transforms (_DTCWT_) combined with Stationary Wavelet Transform (_SWT_) as a hybrid wavelet fusion algorithm overcomes the deficiencies of the traditional wavelet-based fusion algorithm and preserves the directional and shift invariance properties. The purpose of _SWT_ is to decompose the given source image into approximate and detailed sub-bands. Further, approximate sub-bands of the given source are decomposed with _DTCWT_. In this extraction, low-frequency components are considered to implement Texture Energy Measures (_TEM_), and high-frequency components are considered to implement the absolute-maximum fusion rule. For the detailed sub-bands, the absolute-maximum fusion rule is implemented. The texture energy rules have significantly classified the image and improved the output image's accuracy after fusion. Finally, inverse _SWT_ is applied to generate an extended fused image. Experimental results are evaluated to show that the proposed approach outperforms approaches reported earlier. This paper proposes a fusion method based on _SWT_, _DTCWT_, and _TEM_ to address the inherent defects of both the Parameter Adaptive-Dual Channel Pulse coupled neural network (PA-DCPCNN) and Multiscale Transform-Convolutional Sparse Representation (MST-CSR).  
multi-focus image fusion (_MIF_); _SWT_; _DTCWT_; _TEM_; quality evaluation metrics; image quality
## 4 Discussion
### Research Limitations and Further Investigations  
While it is acknowledged that there is an inherent difference in text difficulty and topic between fictional and non-fictional texts, the disparity in reading difficulty levels between the two text types used in this study was substantial. Additionally, there were differences in text length between the two text types. Therefore, it is possible that the observed differences between text types may not be solely attributable to the nature of the texts themselves.  
This study primarily focused on reading behaviors in elementary school children, employing identical texts across all age groups. Although this approach facilitated an exploration of age-related variations in reading strategies, a ceiling effect in which older children demonstrated comparable eye movement patterns was observed. Thus, the generalizability of these findings to older or more advanced readers may be limited. Further research should consider examining proficient readers engaging with age-appropriate texts.  
The current study only analyzed the behavioral aspects of reading, including voluntary eye movement patterns and reading comprehension scores, in typically developing children. Regarding ocular following responses (OFRs), prior research has revealed that there are no age-related differences in non-voluntary eye movements [50]. Moreover, when comparing preterm children with their peers with dyslexia in the fourth and fifth grades, they showed similar performance in reading comprehension, despite differences in eye movement patterns [51]. Examining both voluntary eye movements and non-voluntary eye movements during reading across children of various age groups, including those with developmental disorders, and assessing their language and higher-older cognition skills that support reading comprehension [7], can enhance understanding of the cognitive mechanisms at play in reading.  
The texts used in this study were composed in the participants' native language. Previous research investigating reading processing in adult L1 readers, as well as adult and child L2 learners, has shown that a longer time spent reading is associated with poor comprehension in adult L1 readers. In contrast, extended reading time is linked to improved comprehension for adult L2 readers, and no relationship between eye movements and reading comprehension was observed in child L2 readers [52]. These findings suggest that the results may differ when applied to L2 learners. Therefore, it is recommended that future studies delve into the eye movements and reading performance of L2 learners across various age groups and reading proficiency levels, which could offer valuable insights into the relevance and applicability of the findings in this study in diverse linguistic and educational contexts.
## 2 Methods and Materials
### Data Acquisition
#### 2.2.3 Image Augmentation  
The YOLOv5 model we used is a deep learning-based method that requires a large number of training images to achieve good detection results. However, to achieve high economic efficiency, many efforts are made to avoid manufacturing defects in the production process. At the same time, artificially creating a large number of defects can lead to a waste of resources. Therefore, the number and type of defective samples that can be collected are usually limited, leading to model over-fitting and performance degradation. Fortunately, image augmentation can overcome this problem by producing some more diversified versions of images from the given image dataset. Taking into account the characteristics of the solder joints on aviation plugs, the following methods for image augmentation are employed in this paper.  
**Image graying:** Converting images from RGB to grayscale is intended to make predictions about images without relying on any color information. This makes it easier for the model to extract information from the Image context rather than the color and can improve the model's ability to generalize.  
**Image blurring:** Due to platform movement, camera shake, and depth of field, the captured images of solder joints may appear blurred. Therefore, we use mean filtering and median filtering to simulate solder joints with varying degrees of clarity in aviation plugs, which can improve the recognition ability for blurred images.  
**Horizontal flipping:** As can be seen, the aviation plug is a left-right symmetrical structure, which means that the horizontal flapping cannot change its appearance. Therefore, the horizontal flipping is adopted to expand the images in this paper.  
**HSV jittering:** Converting the image from RGB space to HSV space, and then changing the values of the three channels, including hue, saturation, and value. In this way, different scenes and illumination conditions can be simulated without destroying the key information of the image, thus, increasing the diversity of the training images.  
Figure 6: The annotation example for ROI extraction (**a**) and quality assessment (**b**).  
**Mosaic [38]:** The four training samples are randomly scaled and distributed, then merged into one image and sent to the model for training. Without any additional computational load, this method allows four images to be fed into the model at a time, and, thus, can learn more robust features. Moreover, as the images are scaled, some small objects may appear in the merged image. This will greatly improve the ability of the model to detect small targets.  
Taking the ROI extraction as an example, the augmented results are shown in Figure 7. Although these generated images don't change the image structure too much, they could change the spatial and semantic information of the objects. Meanwhile, in practice, the above data augmentation methods are used in conjunction with each other with a certain probability to improve the number and diversity of the dataset.
## Materials and Methods  
### PlanetScope Imagery  
The foundation for successful image feature extraction is based on the selection of optimal imagery [31]. With 150+ SmallSats (4-kg) in a 475 km altitude sun-synchronous orbit (\(\sim\)98\({}^{\circ}\) inclination), the PS Dove constellation provides four-band multispectral coverage over major landmasses and coastal areas at \(\sim\)3.7 m ground sample distance and a geolocation accuracy of \(\sim\)10 m [32] (Table 1; Figure 2). Two limitations identified with PS imagery include the radiometric differences between the different PS Dove \"flocks\" and the low signal-to-noise ratio for high-accuracy detection and mapping of the coastal bentnos [33]. Despite lower spectral fidelity and variable radiometric quality, PS data provide higher spatial resolution over publicly available image datasets and a much higher temporal resolution (near-daily) over existing imagery providers. The collection capacity of the Dove constellation is 340 million km\({}^{2}\)/day. This is particularly useful in marine and coastal applications when cloud-free and clear water column conditions are needed for continuous monitoring for detecting changes in benthic cover, tracking sediment plumes and water quality, or assessing post-storm damage. Obtaining a cloud-free observation with calm, clear water conditions and minimal sunlight and turbidity is critical for achieving accuracy in benthic habitat classification. Therefore, the high temporal cadence makes PS imagery well-suited to this task, as daily observations dramatically increase the likelihood of capturing scenes with optimal water column clarity, which has been successfully applied to similar use cases [34; 30; 35; 36]. However, each PS scene has a relatively small footprint (i.e., swath) (\(\sim\)20 \(\times\) 12 km), and therefore, to cover a large area, numerous individual scenes acquired at different times must be selected and mosaicked together. Comparing or combining observations from different times is often challenging due to the highly variable nature of the ocean surface and atmospheric conditions. To partially mitigate this, a compositing and normalization method was developed that seamlessly combined the suite of scenes that were chosen based on optimal water clarity and atmospheric conditions.  
Figure 1: Comparison of multiple scale benthic habitat data around the island of Carriacou, Grenada: (**a**) PlanetScope Dove Classic imagery (4 m); (**b**) Global-scale Landsat-derived (30 m) five-class coral reef map; (**c**) National-scale PlanetScope-derived (4 m) thirteen-class benthic habitat map.
## 3 Experimental Results
### Computed Tomography Dose Index Volume (CTDI\({}_{\text{vol}}\)) Correlations  
The dataset includes CTDI\({}_{\text{vol}}\) values ranging from 1.1 mGy to 60.2 mGy. As shown in Table 3, linear and multiple linear regression and the Pearson correlation analysis were performed to analyze the relationship between CTDI\({}_{\text{vol}}\) and the patient parameters BMI, weight and age after removing a maximum of seven outliers. A significant correlation was seen between patient weight, BMI and CTDI\({}_{\text{vol}}\) across all investigated CT exams. As can be seen in Table 3, there was a significant (\(p<0.05\)) strong linear relationship between CTDI\({}_{\text{vol}}\) and BMI and weight, especially in the cardiac angiogram and cardiac calcium score exams, as indicated by the \(r\) and \(p\)-value. Age did not demonstrate any linear relationship with CTDI\({}_{\text{vol}}\) across all CT exams. Using multiple regression resulted in a higher correlation with the output variables than when using simple regression in chest and abdomen/pelvis CT exams. This increase was evident when comparing the R\({}^{2}\), MAPE and \(r\) values of the multiple linear regression models with their values in the best respective simple linear regression models. The multiple linear regression models in cardiac angiogram and cardiac calcium score exams exhibited correlation values similar to those of the best respective simple linear regression models.
# Analyzing Key Features of Open Source Software Survivability with Random Forest
## 3 Analyzing the Relationship Between OSS Survivability and Features
### Building the Analysis Model  
This study aimed to build an analysis model to predict OSS survivability by adopting the random forest method. Random forest demonstrates robust performance even when dealing with imbalanced data distributions. As shown in Figure 1, the collected data were significantly imbalanced, particularly in the LoC feature, which exhibited a much larger scale compared to other features such as star or contributor. This imbalance could lead to over-representation or under-representation of certain features during model training, potentially biasing predictions. Random forest mitigated this by employing bootstrap sampling, where individual decision trees are trained on randomly selected subsets of the data, ensuring a balanced reflection of diverse feature distributions.  
Additionally, random forest is highly resilient to nonlinear relationships. Factors influencing OSS survivability often involve complex interactions and nonlinear dependencies that traditional linear- or polynomial-based models struggle to capture. Random forest partitions the data into multiple segments through its tree-based structure and learns  
Figure 3: Measured results of the issue retention rate.  
Figure 2: Issue survival function generation for OSSs.  
independently within each segment, effectively capturing these nonlinear relationships. For example, while polynomial regression models high-order polynomials globally, random forest creates localized partitions that better reflect the underlying data dynamics. We further illustrate random forest's mechanisms:  
1. **Bootstrap Sampling**: Each decision tree is trained on a subset of the original data, \[D_{t}=\{(X_{r},y_{r})\}_{r=1}^{N_{t}},\quad N_{t}\leq N\] where \(N\) is the total number of samples in the dataset \(D\), and \(N_{t}\) is the number of samples in the bootstrapped dataset \(D_{t}\). Here, \(X_{r}\) represents the features, and \(y_{r}\) is the corresponding target value for the \(r\)-th sample.
2. **Feature Sampling**: At each node, a random subset of features is selected: \[F_{t}\subseteq F,\quad|F_{t}|=\texttt{max\_features}\] where \(F\) represents the set of all feature types in the dataset, and \(F_{t}\) is the subset of features randomly selected for the current node. This ensures that no single feature dominates the model, promoting diversity in the decision trees.
3. **Prediction Aggregation**: Final predictions are obtained by averaging the outputs of all decision trees: \[\hat{y}=\frac{1}{T}\sum_{t=1}^{T}h_{t}(x),\] where \(T\) is the total number of trees, and \(h_{t}(x)\) represents the prediction of tree \(t\).  
Given these advantages, random forest is expected to be more suitable for datasets characterized by imbalanced distributions and nonlinear relationships, making it a powerful tool for predicting OSS survivability. To validate this, the performance of random forest was compared with that of the polynomial regression model.