esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Formula
It should also be mentioned that the error margins for the exponents in Equation (10) have been reduced to minimum due to the application of the process described in Figure 3:0  
An example of the limitation of the gap size index with different damping ratios for \(T_{j}=1\) s, calculated using Equation (10), is presented in Figure 3:1  
After substituting Equation (9) or Equation (10) into Equation (2), the formula for the separation distance between adjacent buildings preventing their bounding during a specified ground motion is obtained. The comparison between the separation distance based on Equation (1) and on two variants of Equation (2), calculated for the Kobe earthquake for \(T_{j}=1\) s and different values of \(T_{i}\), is shown in Figure 3:2 It can clearly be seen from  
Figure 3:3 Analysis process of the schematic architecture of the CRVK program model. Figure 3:4 The limitation of the gap size index with different damping ratios for \(T_{j}=1\) s.  
the figure that all three formulas are quite accurate when both buildings have different (substantially different) natural vibration periods. On the other hand, when two structures have natural periods close to one another, Equation (1) shows substantially different results. In particular, for the case when \(T_{i}=T_{j}\), the separation distance calculated by Equation (1) is equal to 1.08 cm, whereas the correct value equal to zero is obtained for both formulas based on Equation (2). Further analysis was conducted for the case when \(T_{j}=1\) s and \(T_{j}=0.5\) s, while different values of \(T_{i}\) were applied, by using Equation (2) together with Equation (10), to calculate the required separation distance between adjacent buildings exposed to different earthquakes. The representative results obtained for the Parkfield earthquake are shown in Figure 7 in the form of the peak lateral displacement (red line) and the required separation distance between two adjacent buildings (green line) as a function of \(T_{i}\). These results confirm the expected zero gap size value in the case when both buildings have identical natural vibration periods. Figure 3:5 Comparison of the separation distance between two adjacent buildings by using three equations for \(T_{j}=1\) s and different values of \(T_{i}\)  
Figure 3:6 The peak lateral displacement (red line) and the required separation distance between two adjacent buildings (green line) for: (**a**) \(T_{j}=0.5\) s, (**b**) \(T_{j}=1\) s and different values of \(T_{i}\)
## 7 Conclusions  
This study aimed to elucidate how the use of VR helps increase the effectiveness of safety training in the construction industry. We proposed a research model, based on the TAM, to examine the impact of technology- and usage-specific factors of VR safety training on the effectiveness of training. A PLS-SEM analysis of 248 construction workers in Korea was conducted. The results supported most of our hypotheses. Vividness, interactivity, and perceived severity had a significant effect on trainees' beliefs and attitudes concerning the VR safety training. Furthermore, the trainees' satisfaction regarding the training was crucial in increasing the transfer of the skills and knowledge obtained via safety training.  
Our findings provide several meaningful implications for improving the effectiveness of a VR-based construction safety training. First, creating an immersive virtual environment that creates a feeling of telepresence is critical for enhancing training effectiveness. This could be achieved by providing various sensory stimulations through the use of haptic suits and other related technologies. Also, an interactive virtual environment with a high degree of freedom must be created. Finally, an effort to keep trainees interested and satisfied must be made by providing updates based on trainee feedback.  
Although our study provided meaningful implications, it is not without its limitations. First, our survey was only conducted in Korea. Different countries have different training contents, work practices, and safety regulations. Thus, comparing different cultures may provide a more generalized result and new implications. Another shortcoming of our study is that we analysed a limited number of samples only consisting of construction workers. Future studies could conduct a multi-group analysis between workers and managers, or between construction tasks. Finally, our study fails to consider the possible bias caused by the novelty effect. To mitigate the novelty effect, future studies may need to provide tutorial courses so that the trainee is familiar with the VR before the training begins [64]. Also, a time-series analysis on training effectiveness may provide new implications while eliminating possible bias caused by the novelty effect.  
**Author Contributions:** Conceptualization, J.W.Y. and J.S.P.; methodology, J.W.Y.; software, J.W.Y.; validation, J.S.P.; formal analysis, J.W.Y.; investigation, J.S.P.; resources, H.J.P.; data curation, J.W.Y.; writing--original draft preparation, J.W.Y.; writing--review and editing, J.S.P.; visualization, J.S.P.; supervision, H.J.P.; project administration, H.J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## 4 Results and Discussion
### Creep of RPC  
#### 4.3.1 Effect of the Axial Stress Ratio on the Creep of RPC  
The stress-dependent strain is the sum of the instantaneous strain and the creep strain, the specific creep is the creep strain caused by unit stress, and the creep coefficient is the ratio of the creep strain to instantaneous strain at loading time. Figure 6 shows the variations in the stress-dependent strain, specific creep, and creep coefficient of the RPC samples subjected to different stress ratios with time. The stress-dependent strain and its growth rate both increase with increasing axial stress ratio. However, the specific creep and the creep coefficient of RPC samples subjected to different axial stress ratios are almost the same. This indicates that the creep of RPC is linear when the axial stress ratio is less than 0.4, which is consistent with the behaviour of normal strength concrete.  
Figure 6: Effect of the axial stress ratio on the stress dependent strain, specific creep, and creep coefficient of RPC: (**a**) Stress-dependent strain; (**b**) Specific creep; (**c**) Creep coefficient.
# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 8 Task 6: Quality Assessment and Dataset
### Dataset  
The Visual Storytelling Dataset (VIST) [110], which is a sequential vision-to-language dataset, has been widely used for storytelling with image data. This dataset consists of 10,117 Flickr albums with 210,819 unique photos. Each album has an average of 20.8 photos, and the average duration of each album is 7.9 h. Stories consist of five images from an album with five descriptions (mainly one sentence per image). Amazon's Mechanical Turk (AMT) workers have chosen, organized, and annotated these images, and the same album is paired with five various stories as a reference. After filtering, the size of the dataset is 50,136 samples and is divided into 3 primary splits: training, validation, and testing. The training size is about 40,098 samples: 4988 for validation and 5050 for testing. The VIST-Edit1 dataset [181] also includes 14,905 human-edited versions of 2981 machine-generated visual stories.  
The AESOP dataset [182] is built with three guiding principles: (i) Creativity Over Perception, (ii) Causal and Coherent Narratives, and (iii) Constrained World Knowledge. In AESOP, stories are composed of three image-text panels, with the visual parts generated by the drag-and-drop interface. In total, 7062 stories containing 21,186 abstract visual scenes with corresponding text were collected. In the VHED (VIST Human Evaluation Data) dataset [183], the story pairs with zero ranking gap are removed, which yields 13,875 story pairs altogether. The authors divided the train-test-validation sets into 11,208, 1351, and 1316 story pairs in an 8:1:1 ratio.  
On the other hand, an image-based cooking dataset [173] is presented for sequential procedural (how-to) text generation, which consists of 16,441 cooking recipes with 160,479 photos (for food, dessert, and recipe topics) associated with different steps. The authors used 80% of the dataset for training, 10% for validation, and 10% for testing their models. Another similar dataset, Recipe1M+ [184], is a new large-scale, structured corpus of over 1 million cooking recipes and 13 million food images. An overview of the datasets and the approaches that utilized these datasets in their study are represented in Table 6.
## 3 Results and Discussion  
### Drug Loading  
For the pre-surface grafting method (Figure 2a), when DOX is loaded into the native MSNs, the colour of particles changed from white to red, indicating the presence of DOX molecules in the sample. According to the data obtained by HPLC, DOX@MSNs achieved an EE of 7.78 \(\pm\) 0.18%, and a DC of 7.22 \(\pm\) 0.153%. This was in accordance with the original data by which this procedure was adapted, reporting an EE of \(\sim\)6.5% by weight [32]. Even though in their original study, [PERSON] et al. did not report their DC result; the obtained DC value of \(\sim\)7% was also in accordance with other literature procedures which have reported DC values of less than 10% [36]. No extra signals were observed in the FT-IR of the DOX@MSN following encapsulation (see Figure S1 in Supplementary Material). This suggests that the loaded DOX content might be just too low to be detected by FT-IR.  
Following the loading of DOX into the MSNs, the DOX@MSNs was then functionalized with PLH and TAM as per our previous work (see Supplementary Material for full characterization) [16]. The synthesis is a continuous/one-pot reaction, in which the resulting DOX@MSN-PLH was immediately reacted with TAM-TEG-OMs, to yield DOX@MSN-PLH-TAM. It was assumed that the maintenance of red colour indicated that the DOX molecules remained inside the porous channels during the grafting steps. This is as per previous literatures [37; 38], and no obvious red colour of the washing solutions was observed. Therefore, at the end of surface grafting, DOX@MSN-PLH-TAM was assumed to have an EE of 7.78 \(\pm\) 0.18, similar to that of DOX@MSNs. This assumption was considered during the in vitro release experiments.  
The colour change upon DOX loading was also observed for our functionalized MSNs, MSN-PLH and MSN-PLH-TAM; where the colour of the particles changed from white to red upon loading (Figure 3a shows the colour change for the MSN-PLH-TAM system). In Figure 3b, the FT-IR spectra of DOX@MSN-PLH-TAM demonstrated the same peaks as the unloaded MSN-PLH-TAM which was in accordance with our previously published study [16]. No additional signal was obtained; this suggests that the loaded DOX content might be just too low to be detected by FT-IR. This was also consistent for the MSN-PLH vs. DOX@MSN-PLH system (see Figure S2 in Supplementary Material).  
DOX@MSN-PLH achieved an EE of 12.2% while DOX@MSN-PLH-TAM achieved a similar EE of 12.1%. The EE of these functionalized systems was slightly greater than that of DOX@MSN. This may be due to enhanced interactions between the DOX molecules and the hydrophobic PLH molecules due to \(\pi\)-\(\pi\) stacking [36], leading to an increased amount of DOX becoming encapsulated. Interestingly, this increased trend was not observed with the DC of both functionalized systems. DOX@MSN-PLH and MSN-PLH-TAM achieved a DC of \(\approx\)7%, which is very similar to that of the native MSNs.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 4 Results and Analysis
### Effect of Pair Date
Even though the year-long predictability for crops using a growing season image pair is poor, the pair images from the growing season have better predictability during the growing season or nearby dates. The mean NDVI time series for corn and soybean pixels from the observed Landsat and the predicted images based on different pair dates (80, 224, and 283) are computed and extracted. The selected pair dates represent input images collected during an inactive date (DOY 80), a peak growing date (DOY 224) and a senescence date (DOY 263). Figure 10 shows the annual NDVI compared to the observed Landsat NDVI for corn and soybean. Both corn and soybean show good fusion results and NDVI temporal patterns similar to the original Landsat even though only one pair image was used in predicting an entire year. However, the predictability for different pair dates is slightly different, particularly for soybean. In that case, predictions using an image pair from the inactive season are slightly underestimated for the growing season, while those based on growing season pairs are mostly overestimated for the inactive season. A possible reason is that corn is a major crop in the study area (55% for corn and 35% for soybean) and MODIS temporal information mostly reflected corn growth conditions in the mixed MODIS pixels. Corn and soybeans have different growth patterns (see NDVI curves in Figure 8a). Corn is planted earlier and emerges earlier than soybeans. After reaching peak values, corn NDVI values become smaller than soybeans. The mixture of corn and soybeans at MODIS pixel resolution may cause errors when predicting Landsat images using image pairs from different seasons. Figure 9: Comparison of <MAD> (**a**) and <R> (**b**) as a function of pair date for different land cover types from both study sites (the results of corn and soybean are from site 1 and those of forests are from site 2).
## 7 Raim
### Global and Local Test  
The structure of a RAIM algorithm comes from the statistical detection theory [2]. Two hypothesis tests are posed: Global and Local Test. The first one tests if a failure exists and the second one identifies which is the failed satellite, under the assumption to have at most one failure at a time. In the residual method, the first step is to compute the residual between predicted and measured PRs [26], \(\hat{\mathbf{r}}=\mathbf{H}\boldsymbol{\Delta\hat{x}}-\boldsymbol{\Delta\rho}\), given by  
\[\hat{\mathbf{r}}=\mathbf{H}(\mathbf{H}^{\mathbf{T}}\boldsymbol{\Sigma}^{- \mathbf{1}}\mathbf{H})^{-1}\mathbf{H}^{\mathbf{T}}\boldsymbol{\Sigma}^{-1} \boldsymbol{\Delta\rho}-\boldsymbol{\Delta\rho}=-R\boldsymbol{\Delta\rho}\]  
For the Weighted Least Squares (WLS) solution, if the PR errors are normally distributed with covariance matrix \(\boldsymbol{\Sigma}\), then the residuals are distributed as \(\hat{\mathbf{r}}\sim N(0,\mathbf{R}\mathbf{E}\mathbf{R}^{\mathbf{T}})\)[27]. The first test performed is the global test, which checks if a failure exists by calculating the value for the test statistic \(T=\hat{\mathbf{r}}^{\mathbf{T}}\boldsymbol{\Sigma}^{-1}\hat{\mathbf{r}}\). If the error is a Gaussian variable with zero-mean, \(T\) follows a Chi-square distribution with \(n-p\) degree of freedom (DOF), where \(n\) is the number of measurements and \(p\) the number of parameters to be estimated. In other words, \(n-p\) is the number of redundant measurements. Therefore, once we get \(T\), we need to compare it against a threshold value. To do this, we have to establish a certain level of false alarm probability (\(\boldsymbol{\alpha}\)) and missed detection (\(\beta\)). If the current calculated \(T\) fails the test, the local test is performed to identify which is the faulty satellite. The elements of the residual vector \(\hat{\mathbf{r}}\) are normalised by the diagonal elements of the covariance matrix of the residual and finally, we get \(\hat{\mathbf{w}}\sim N(0,1)\). The fault exclusion is performed with the null and alternative hypotheses test with \(H_{0}\): \(|w_{k}|\leq n_{1-(\alpha_{0}/2)}\), where \(\alpha_{0}\) is the false alarm probability of the local test. The values of the parameters \(\boldsymbol{\alpha}\), \(\beta\) and \(\alpha_{0}\) are linked together and, once two of them are fixed the third is obtained.  
The \(\beta\) parameter is involved for both global and local test (through the non-centrality parameter [28]). The complete sequence of the tests is shown in Figure 10.
# Tampered and Computer-Generated Face Images Identification Based on Deep Learning
## 5 Discussion  
The obtained results from the first experiment (Section 4.2) proved that the proposed TFID model performed well on the balanced dataset through the four-fold cross-validation with an accuracy of 83% and an AUC value of 0.89. Although the SE-ResNet-50 model achieved higher accuracy compared to the TFID with a classification accuracy and an AUC value of 84.7 and 0.89, respectively, the TFID model showed its potential in tampered face identification. The accuracy of the two mentioned models stayed under 85%, and it could not improve to above 85%, because the limited number of the tampered dataset (4200 images) made the model stop learning useful features. This issue can be solved in the future by expanding the tampered dataset or pre-train the model with denoising criteria to force convolutional layers to learn important general features that are useful for reconstructing the input signal.  
In the second experiment (Section 4.3), six new datasets (1/1, 1/5, 1/10, 1/25, 1/50, 1/100) were created based on the original TFID dataset by changing the proportion of the tampered images to real images. After that, these datasets were used to train two original models (TFID and SE-ResNet-50), and six extensions of TFID model (TFID-SA, TFID-CW, TFID-XGB, SE-ResNet-50-SA, SE-ResNet-50-CW, and SE-ResNet-50-XGB) to investigate the performance of the eight models on the IDP. The TFID-XGB performance exceeded other models and achieved robust results with different settings. Moreover, the AUC value was at 0.92, and it was 1/100 even in the most severe imbalanced scenario. Therefore, it showed that the TFID-XGB classifier obtained a robust performance on the IDP. Although the proposed TFID-XGB model achieved equivalent performance to the state-of-the-art pre-trained SE-ResNet-50-XGB model, it outperformed the SE-ResNet-50-XGB model in terms of computational complexity. With a simpler architecture, TFID-XGB model requires significantly lesser testing time but achieves similar performance to the SE-ResNet-50-XGB model. Therefore, it is a better choice for practical applications that are sensitive to the computing power.  
Finally, we applied the proposed model to try to identify the GAN-generated images, which is an emerging method in image forgery. The TFID-XGB classifier and the SE-ResNet-50-XGB model performed well on the GAN-generated dataset.  
Through numerous experiments, the proposed framework proved to have the potential to be applied in practical applications to reduce the labor cost in manually checking the increasing number of manipulated images. The proposed model can identify images, which are forged manually by humans or generated automatically by a computer. Therefore, it also plays an important role in digital image security.
# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
## 2 Measurement and Prediction Methods
### Wind Field Prediction by WRF  
Considering that mesoscale simulations are often used to conduct wind resource assessment of wind farm sites, accuracy of mesoscale model is crucial for accurate power prediction. In the current paper, LiDAR measurements were used to validate results of mesoscale simulations. For the mesoscale simulation, the Weather Research and Forecasting (WRF Ver. 3.4.1) model developed by the National Center of Atmospheric research (NCAR) was employed [21]. The computation domain has five levels of horizontal nesting, i.e., 18 km-6 km-2 km-667 m-222 m, with the higher resolution in the region where LiDAR measurements were conducted (cf. Figure 1). Other than wind resource assessment for wind energy [18], WRF-based simulations are extensively used in wind engineering applications [22; 23]. The results from the three grid resolutions of 2 km, 667 m, and 222 m were compared with the measurements. As the initial and boundary conditions of the simulations, National Center for Environmental Prediction (NCEP) final operational global analysis data (NCEP-FNL 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), 6-hourly) data was used [24]. However, for the sea surface temperature, Operational Sea Surface Temperature and Ice Analysis (OSTIA) data was used [25]. OSTIA provides near-real time global sea surface temperature at the grid resolution of 1/20\({}^{\circ}\) (\(\sim\)6 km).
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 1 Introduction
This paper reports in detail on experiences from the design and live deployment of an Environmental IoT targeting specific local and regional environmental applications (hillslope to river catchment) using inexpensive off-the-shelf technologies and deploying the system for a particular environmental issue: flood and pollution monitoring and alerts in rural environments. Our central hypothesis is that our combination of IoT technology coupled with Cloud Computing enables a paradigm shift in our understanding and management of the natural environment, especially related to understanding ecosystem interdependencies, in times of unprecedented environmental change. The key contribution of this paper is an evaluation of this hypothesis in terms of the technological, scientific, and methodological aspects:  
* Technological: the presentation of a complete end-to-end systems architecture embracing cloud and IoT infrastructure designed to operate effectively in the target operational environment;
* Scientific: the assessment of the potential impact of IoT/cloud technology on environmental and earth sciences;
* Methodological: a reflection on the methodological approaches required to facilitate the desired cross-disciplinary conversation to develop and utilise an Environmental IoT infrastructure. The paper is structured as follows. Section 2 discusses the target deployment environment for the Environmental IoT infrastructure, highlighting the key challenges being addressed by the infrastructure. Section 3 then provides a more in-depth assessment of the related work, supporting our view that the application of IoT principles in the natural environment has been limited in scope and vision. Section 4 presents in detail the design and deployment of the Environmental IoT platform, and Section 5 then discusses the experiences of this work. Section 6 provides a more high-level discussion around the technological, scientific, and methodological aspects discussed above. Finally, Section 7 presents concluding remarks.