esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Overview of Results  
Human attention maps were much less similar to CNN attention maps than they were between different human tasks. This fits with the widely reported finding that the similarity between human and CNN attention maps is quite low [10; 11; 48; 49; 50]. It also fits with a specific finding that human-human similarity is higher than human-CNN similarity [47]. Thus, in general, our results are in line with previous research. Moreover, human-CNN similarity depended on both human-centered factors investigated in the present study. Concerning the task factor, attention maps derived from drawing were more similar to Grad-CAM than those derived from eye movements, while the specific eye movement task had little impact. Concerning image type, human-CNN similarity in the Dice scores was highest for objects, medium for indoor scenes and low for landscapes. These image-dependent differences match previous findings, namely that human-CNN similarity was higher for abnormal X-rays with a unique relevant area [53] and for animate objects with faces catching human attention [10]. Taken together, the findings indicate that similarity increases when relevant areas are nonambiguous and spatially restricted.  
Our most interesting finding concerns the interaction of both factors: image type reversed the direction of task effects. For objects, drawing was much more similar to Grad-CAM than the two eye movement tasks, while for landscapes, drawing was descriptively least similar to Grad-CAM, although the task differences were not significant here. Thus, when human attention maps are generated manually, their similarity to CNN is modulated by the images to be classified, at least when considering the main focus of attention (the image-dependence disappeared for cross-correlations). This image-dependence might lead to the conclusion that manual selection is only suitable when there is an obvious, correct solution (i.e., specific object) but not when selection is up to human preferences. It needs to be noted, though, that the suitability of an elicitation task cannot be inferred from high similarity per se. This would be circular reasoning when the aim is to _test_ similarity rather than _generate_ it. However, suitability can be inferred from the variability and thus arbitrarily of attention maps, which also was much higher for drawing than eye movements for indoor scenes and landscapes. We will return to the practical implications below.
## 3 Methods
### Normalization of EIA Big Data  
Because KEIDP is calculated as one value for one development project, EIA Big Data are also required to be expressed as a value that can represent one development project. However, unlike the data used to calculate KEIDP, multiple values of EIA Big Data are measured for one project. For example, in a project to collect and process stone from the Geumgang River Basin in Korea that began in 2010, PM10 was measured 16 times before the project and 80 times after the project. Mean values were calculated as representative values of the periods before and after the project. The means were calculated iteratively at a 99% confidence level to exclude errors in the measuring instrument or mistakes by the recorder.  
Normalization was performed for the 21 selected types of EIA Big Data because the units and data sizes were different. The normalization of the EIA Big Data was conducted using Equation (2), with consideration of the before and after periods.  
Figure 2: Research flow chart.  
\[I_{i}^{n}=\frac{I_{i}^{b}-I_{i}^{a}}{I_{i}^{b}-I_{i}^{a}} \tag{2}\]  
where \(I_{i}^{n}\) is the \(i\)-th normalized indicator, \(I_{i}^{b}\) is the \(i\)-th indicator measured before the project, and \(I_{i}^{b}\) is the \(i\)-th indicator measured after the project.  
Through the normalization process, all EIA Big Data were expressed as values between \(-1\) and \(1\). If the normalization result approached \(1\), it indicated that the indicator value decreased significantly after a project. This could be interpreted as indicating that the project was environmentally friendly and/or the constructor actively accepted the results of the EIA. In contrast, if a normalization result was less than \(0\), it signaled that the indicator value increased compared to the situation before the project, which meant that the EIA results pointed to some degree of environmental damage.
## 2 Related Works
### DL-Based User Profiling Research  
DL-based user profiling leverages advancements in neural network technologies, such as CNNs, RNNs, and LSTMs, which excel in extracting information for tasks like text classification. [PERSON] et al. [17] proposed a dynamic coherence-based modeling (DCBM) algorithm based on semantic analysis. This method improves user profiles by analyzing the consistency of user interaction history, making them more aligned with actual user preferences. [PERSON] et al. [18] conducted a review on detecting fake accounts in social networks. They found that most existing studies focus on three aspects: account features, behavioral features, and content features. They also suggested that future research should pay more attention to the application of DL techniques. As for the main focus of this paper, researchers have applied DL methods extensively to mainstream OSN platforms such as Weibo and Twitter, producing significant results across diverse social issues [19]. For instance, during the COVID-19 pandemic, ML and DL methods were used for sentiment classification [20], topic analysis [21], rumor detection [22], and stance prediction [23]. [PERSON] et al. [24] analyzed public discourse on the Russia-Ukraine conflict, identifying cross-platform differences in sentiment and extracting emotional insights via semantic network graphs. [PERSON] et al. [25] examined the spatial distribution of floods in southern China in 2020 and their association with public sentiment, contributing to emotion classification research. [PERSON] et al. [26] employed BERT (bidirectional encoder representations from transformers) and structural topic models to analyze young people's perspectives on \"involution\" and their collective resistance behaviors online. [PERSON] et al. [27] selected the top three topics related to migrant workers on Weibo, applying LDA modeling and regression analysis to investigate emotional divergence. [PERSON] [28] proposed a novel method combining sentiment polarity classification with VADER and TextBlob and emotional prediction using DistilBERT-powered RNNs for mental health groups.  
Despite the increasing adoption of cutting-edge algorithms such as deep learning and reinforcement learning in various profiling tasks, ML methods and their variants still maintain relevance [29]. Historical studies underscore the wide applicability of AI-based user profiling approaches in predicting user attributes, identifying features, and analyzing stance and emotions. However, research into profiling \"risky behaviors\" or constructing user risk profiles based on OSNs remains limited, presenting a significant opportunity for future exploration.
# Privacy Policy Analysis of Banks and Mobile Money Services in the Middle East
## 5 Evaluation
### Privacy Compliance
#### 5.1.11 Data Minimization/Retention  
The compliance of this principle was low by both banks and mobile money services. Only 56% and 43% of banks and mobile money services, respectively, clearly stated that only a minimum amount of user information will be collected, and that it will only be retained for the duration required to fulfill the purpose.
## 4 Methodology
### Meshing and Boundary Conditions  
4.3.1. Meshing  
In this paper, a numerical simulation study is conducted on the winnowing flow field of the film stalk separation device. The geometric region of the solution is the geometric space body of the film impurity separation device. The material inlet and air inlet ports are in front of the roller screen. The holes at the back of the roller screen and on the drum are set as the material outlet and air outlet, respectively, which are modeled by SW, and their simplified diagrams are shown in Figure 7. The dimensional parameters of the model are set as follows: Drum diameter/mm: 1200, drum length/mm: 4000, size of air inlet/mm: 600 \(\times\) 150, material inlet size/mm: 400 \(\times\) 180.  
The meshing type of geometrical model of the film stalk separation device employs Tgrid hybrid mesh in good applicability. In the mesh settings, the Relevance Center is set to Fine, Interval Size Seed is set to Active Assembly, Smoothing is set to Medium, Transition is set to Slow, and Span Angle Center is set to Fine. The meshing process generates a total of 1,090,656 nodes, 3,463,343 cells, and 3,460,376 faces. The generated mesh is depicted in Figure 8.
## 1 Introduction
[16] proposed convolutional neural networks (CNNs) combined with NIR spectroscopy for tobacco leaf maturity level; experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves in three categories (upper, middle, and lower position) with accuracies of 96.18%, 95.2%, and 97.31%. [PERSON] et al. [17] applies the basic convolution block, the residual block, and the inverted residual block into the network architecture designs, proposed TeaNet, TeaResnet, and TeaMobilenet for tea quality control, which reach up to a 100% accuracy rate. [PERSON] et al. [18] used DenseNet to predict the soil organic matter (SOM) content based on visible and near-infrared spectroscopy, which achieved a result with a coefficient of determination (R\({}^{2}\)) = 0.892 \(\pm\) 0.004 and a ratio of performance to deviation (RPD) = 3.053 \(\pm\) 0.056 in validation. [PERSON] et al. [19] presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIR spectroscopy. Compared with five popular classification models, 1D-SE-ResNet achieves classification accuracy of 93.72%. This paper proposes a SE-DenseNet method; this model combines the DenseNet and Squeeze-and-Excitation (SE) module, which can realize the automatic extraction of spectral features. The established qualitative and quantitative models based on SE-DenseNet achieve good determination results, which realized the nondestructive determination of mechanical strength of coniferous wood by NIR spectroscopy.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 3 Methodology
### RefineNet  
As illustrated in Figure 2, the proposed RefineNet model shown in Figure 1 is a shallow UNet with encoder and decoder architecture. Let \(Conv\), \(ReLu\), and \(DeConv\) denote a convolution layer, a \(ReLu\) activation function, and a transposed convolution layer, respectively. The encoder part consists of two layers of \(Conv+ReLu\), while the decoder part has two layers of \(DeConv+ReLu\). To better preserve the feature information, the encoder and decoder are connected by three \(Conv+ReLu\) layers. In Figure 2, the input channels, output channels, convolution kernel size, strides, and zero-paddings are denoted in brackets below the \(Conv\) and \(DeConv\) layers.  
To provide a clearer description of the RefineNet model, we denote the \(Conv+ReLu\) and \(DeConv+ReLu\) layers as \(CR\) and \(DCR\), respectively. Therefore, given an input image \(I\), the outputs of different layers in the encoder can be expressed as follows:  
\[E_{i}=CR(E_{i-1}),i=\{1,2\} \tag{8}\]  
where \(E_{0}=I\). According to the parameters labelled in the \(Conv\) layer, the input image is gradually downsampled using strided convolution, while the feature channels are increased. Subsequently, each \(E_{i},i=\{0,1,2\}\) is passed through a \(CR\) layer to connect with the decoder, as follows:  
\[S_{i}=CR(E_{i}),i=\{0,1,2\} \tag{9}\]  
Figure 2: The architecture of the RefineNet model.  
In contrast to the encoder, the decoder restores the details of the image by adding the feature maps from the encoder and the upsampled feature maps using transposed convolutions, which can be expressed as:  
\[D_{i}=S_{i}+DCR(D_{i+1}),i=\{0,1\} \tag{10}\]  
where \(D_{2}=S_{2}\). Therefore, in the decoder part, the feature maps are gradually upsampled using transposed convolution, while the feature channels are decreased.  
To be more specific, consider an input image \(I\) with a resolution of \(256\times 256\times 3\). The image's size is gradually downscaled from \(256\times 256\) to \(64\times 64\), while the feature channels are expanded from 3 to 64. Following this, in the decoder part, the image's size is upsampled from \(64\times 64\) to \(256\times 256\), while the feature channels are reduced back to the original 3. Finally, the output of the refine network, denoted by \(D_{0}\), has the same image size as the input image \(I\).
###### Abstract  
The dry aboveground biomass (AGB) is an important parameter in assessing crop growth and predicting yield. This study aims to ascertain the optimal methods for the spectroscopic estimation of winter oilseed rape (WOR) biomass. The different fertilizer-N gradients WOR were planted to collect biomass data and canopy hyperspectral data in two years of field experiments. Correlation analyses and partial least squares regression (PLSR) were performed between canopy hyperspectral data and AGB, and the linear and non-linear regression models simulated the quantitative relation between the vegetation indices (VIs) and AGB at four different growth stages (seeding, bolting, flowering, and pod stage). The results indicated that VIs that were derived from canopy hyperspectral data could estimate AGB accurately: (1) At the seeding and bolting stage, the CIred edge showed excellent performance with the higher accuracy (R\({}^{2}\) ranged from 0.60-0.95) as compared to the other six VIs (Green chlorophyll index (Clgreen), normalized difference vegetation index (NDVI), Green normalized difference vegetation index (GNDVI), ratio vegetation index (RVI), DVI, and soil adjusted vegetation index (SAVI)); (2) Correlation analyses and PLSR can effectively extract the feature wavelengths (800 nm and 1200 nm) for biomass estimation. The modified vegetation indices NDVI (800, 1200) significantly improved AGB estimation accuracy (R\({}^{2}\) > 0.80, RMSE < 1530 kg/hm\({}^{2}\), RPD > 2.3) without saturation phenomenon at the total for four stages, and retained good robustness and reduced the influence of flower and pod for estimating AGB; (3) it was vital to pay more attention to the near-infrared (NIR) bands that could represent WOR growth phenology, and selecting suitable VIs and modeling algorithms could also have a relatively large effect on the success of AGB estimation. The overall results indicated that WOR AGB could be reliably estimated by canopy hyperspectral data, although the plant architecture and coverage of WOR were significantly different during its entire growing period.  
winter oilseed rape; aboveground biomass; canopy hyperspectral data; correlation analysis; partial least squares regression +
Footnote †: journal: Applied Soft 2019
## 3 Results
### Adherence of Participants to Using Global Positioning Systemsand Fitness Tracking Technology
Compare Levels of Access to Cardiac Healthcare Services between Patients Who Lived in Areas with High (Metropolitan) and Low (Rural)  
The FollowMee(tm) application collected geospatial data. The mean accuracy of GPS captured data points was at 16.2 m (95% CI, 15.92-16.45 m, median 7 m) compared to the mean accuracy of satellite-captured data which was within 1832.7 m (95% CI, 1774--1891 m, median 2059 m). For rural participants, the mean accuracy of GPS data points was within 98 m (95% CI, 92.6-103.5 m, median = 9 m, with 96% of the GPS data captured) and for metropolitan participants the mean accuracy of GPS data capture was 33.5 m (95% CI, 31.2-35.9, median 6 m, 98.1% GPS-captured). Based upon the summary of data availability stratified by the accuracy of the data point, only data points with an accuracy of \(\leq\)20 m were retained for the spatial analysis (81.3% of all data available). This was a balance between enough rigor and utilising the data available. Access to cardiac related health services in the first 30 to 90 days post-discharge is presented in Table 3. In the first 30 days 67% of participants had visited their GP (metro 25% vs. rural 37.5%), 72% had visited a pharmacy, 28% had present to an emergency department and 16.7% had seen their cardiologists. By 90 days 25% had attended cardiac rehabilitation (metro 16.7% vs. rural 8.3%) and there was no GPS recorded attendance to see a cardiologist during the 30-90-day period. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Number of Participants That Visited Location 1,2**} & \multicolumn{3}{c}{**Number of Participants That Visited Location 1,2**} \\
**Health Service Type** & **Location** & \multicolumn{3}{c}{**0-30 Days**} & \multicolumn{3}{c}{**31-90 Days**} \\  & **Provided n (\%)** & \multicolumn{3}{c}{**n (\%)**} & \multicolumn{3}{c}{**n (\%)**} \\ \cline{3-7}  & **All** & **Metropolitan** & **Rural/Remote** & **All** & **Metropolitan** & **Rural/Remote** \\ \hline Gen Practitioner & 16 (88.9) & 10 (62.5) & 4 (25.0) & 6 (37.5) & 7 (43.8) & 2 (12.5) & 5 (31.3) \\ Card rhab & 12 (66.7) & 2 (16.7) & 1 (8.3) & 1 (8.3) & 3 (25.0) & 2 (16.7) & 1 (8.3) \\ Cardiologist & 18 (100) & 3 (16.7) & 0 (0.0) & 3 (16.7) & 0 (0.0) & 0 (0.0) & 0 (0.0) \\ Pharmacy & 18 (100) & 13 (72.2) & 5 (27.8) & 8 (44.4) & 1 (61.1) & 3 (16.7) & 8 (44.4) \\ Community Health Service & 3 (16.7) & 3 (0.00) & 1 (33.3) & 2 (66.7) & 2 (66.7) & 0 (0.0) & 2 (66.7) \\ ED & 18 (100) & 5 (27.8) & 1 (5.6) & 4 (22.2) & 3 (16.7) & 0 (0.0) & 3 (16.7) \\ Pathology & 18 (100) & 11 (61.1) & 5 (27.8) & 6 (33.3) & 3 (16.7) & 2 (11.1) & 1 (5.6) \\ \hline \hline \end{tabular} The participant’s attendance was recorded if they entered a zone within a 50 m radius at the provided locations. Some health services were co-located. Gen Practitioner = general practitioner; Card rhab = cardiac rehabilitation; ED = emergency department.  
\end{table} 
Table 3: Participant access to services at 30 and 90 days.
## 3 Results
### Dynamic Changes in Volatiles of Three Chimonanthus Teas During Storage  
Different volatiles also exhibited varying accumulation patterns in scented Chimonanthus tea. Using a heatmap, we visualized these accumulation patterns of various volatiles in three scented Chimonanthus teas during storage (Figures S5-S7). In the scented LLYT-Chimonanthus tea samples, the content percentage of pentanol and linalool consistently increased during 56 days of storage, while the content percentage of 3-phenylpropanol, cinnamyl alcohol, phenylmethyl acetate, \(\gamma\)-phenylpropyl acetate, (E)-cinnamyl acetate, and 2,3-dihydrobenzofuran continuously declined (Figure S5, Table S6). In the scented DHJZ-Chimonanthus tea samples, the content percentage of 3-octenol, linalool oxide I, linalool oxide II, linalool oxide IV, 4-terpineol, 3-methyl-butanal, phenylethanal, styrene, \(\beta\)-myrcene, pulegone, and 4-amino-2,6-dimethylphenol continued to rise throughout the 56 days of storage, whereas (E)-cinnamaldehyde, phenylmethyl acetate, (E)-cinnamyl acetate, and indole consistently diminished (Figure S6, Table S7). In the scented HSMF-Chimonanthus tea samples, the content percentage of linalool steadily increased over the storage, while the content percentage of R-limonene, phenylmethyl acetate, and \(\alpha\)-ionone showed a continuous decrease (Figure S7, Table S8). Among the three scented Chimonanthus teas, the content percentage of methyl salicate showed only a slight increase after 14 days of storage, followed by a significant decline during the subsequent storage period. It is worth noting that the increase in the content percentage of some compounds, such as pentanol, linalool, and linalool oxides, during storage may be due to their significantly lower loss rate compared to other compounds.
## 2 Feature Extraction
### GLCM and Haralick Features  
In this step, ROIs are analyzed by GLCM, the most popular second-order statistical feature, proposed by [PERSON] [42] in 1973. Haralick feature extraction is completed in two steps. In the first step, the co-occurrence matrix is calculated and in the second, the texture features, which are very useful in a variety of imaging applications, particularly in biomedical imaging, are computed based on the co-occurrence matrix.
## 4 Experimental Setup
### Hgs  
The Hybrid-Genetic Search algorithm [4] was first proposed as an algorithmic framework to address three VRP variants: multi-depot VRP, periodic VRP and multi-depot periodic VRP, with capacitated vehicles and constrained route duration. In [6], this framework was extended to incorporate time-constrained VRPs, including VRP with time windows. _PyVRP_ is an open-source, state-of-the-art VRP solver that implements HGS in C++ through a Python interface. The pseudocode in Algorithm 1 shows the Hybrid-Genetic Search algorithm structure as implemented in _PyVRP_[5]. HGS is an evolutionary algorithm that manages both a feasible and infeasible population. Crossover and a local search procedure generate a novel solution for each iteration. A simple Ordered Crossover (OX) is used [31]. During local search, a neighborhood improvement operator is then systematically applied, followed by a repair phase, when the resulting solution is infeasible. Repair increases the penalty values by a factor of 10 and calls the improvement operator again. The process is repeated with a penalty increase of 100 if the offspring remains infeasible. Three main neighborhoods are evaluated: Swap and relocate, 2-opt inter routes, and intra routes. HGS attempts to maintain a static proportion of feasible/infeasible solutions to repair infeasible solutions. Parent solutions are selected based on their quality and their contribution to population diversity. Whenever the population size exceeds a threshold, the remaining solutions are removed until the minimum allowed size is reached. The objective of the algorithm is to find a feasible solution with the minimum total travel times.  
```
Initialize population while number of iterations without improvement \(<\)\(It_{NI}\) and time \(<\)\(T_{max}\)do  Select parent solutions \(P_{1}\) and \(P_{2}\);  Generate offspring \(C\) by crossing \(P_{1}\) and \(P_{2}\);  Educate offspring \(C\) using local search; if\(C\) is infeasible then  Insert \(C\) into infeasible subpopulation;  Repair with probability \(P_{rep}\); endif if\(C\) is feasible then  Insert \(C\) into feasible subpopulation; endif if maximum subpopulation size is reached then  Select survivors; endif  Adjust penalty parameters for violating feasibility conditions if best solution has not been improved during \(It_{div}\) iterations then  Diversify population; endif endwhile  Return best feasible solution;
```  
**Algorithm 1** Hybrid-Genetic Search (HGS).