esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion of Results
### Fibre Reinforcement Efficiency in Flexure  
#### 4.5.1 Contribution for Flexural Strength  
The fibre reinforcement efficiency of mortars developed in this work is analysed in Figure 21, where data referring to mortars from different authors are also included. The flexural strength ratio is presented as the ratio between the flexural strength value from plain mortars and the values from fibre-reinforced mortars. Mortars composed of different types of binders are presented, namely, CEM and geopolimeric binders in Figure 21a and NHL in Figure 21b. The type of fibres used by other authors vary from natural fibres, such as palm, to man-made fibres, such as basalt, glass, PET, PVA and PAN. The influence of the fraction of fibres on the flexural strength ratio of CEM-FRM is positive in most cases. In the NHL-FRMs, that influence showed a high variation and in some cases was even negative, depending on the authors.  
The use of PAN fibres up to 1.00% in volume showed an efficiency level of NHL-FRM higher than the studies using basalt, glass or hemp fibres. The level of efficiency was justified by the good compatibility between the NHL matrix and the surface of the PAN fibre, as well as the tensile strength of the PAN fibres [66].  
Figure 21: Efficiency of fibre reinforcement showed by the normalized flexural strength: **(a)** FRM with Portland cement and Geopolymeric binders [28; 30; 33]; **(b)** FRM with NHL binders [35; 36; 37].
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 6 Conclusions  
In this paper, we have proposed an energy-efficient and reliable multipath routing protocol named ERMR for CRAHNs. Owing to the dynamic nature and unexpected PU behavior in CRAHNs, both energy efficiency and reliability are primarily emphasized, and the well-known performance metrics of throughput and delay are primarily considered in designing ERMR. As a result, the proposed ERMR protocol is on-demand, energy-efficient, robust, and reliable with multipath routing capability. According to our simulation results, the proposed ERMR outperforms the conventional protocol significantly in terms of network throughput, packet delivery ratio, average energy consumption per bit, and average end-to-end delay. In the simulation environment of our performance study, PUs are assumed to be static. As a future work, we plan to consider the mobility of PUs. Also, we are going to exploit other possible metrics in the performance simulation in the near future.  
The authors wish to thank the editor and anonymous referees for their helpful comments in improving the quality of this paper. This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09918974). Correspondence should be addressed to [PERSON] ([EMAIL_ADDRESS]).  
The individual contributions of authors are as follows. [PERSON] developed and simulated the ERMR protocol. [PERSON] directed the research and contributed to the refinement of the algorithm and the interpretation of the simulation results. The paper was drafted by [PERSON] and subsequently revised and approved by [PERSON].  
The authors declare no conflict of interest.
# Seismic Design Procedure for Low-Rise Cold-Formed Steel-Special Bolded Moment Frames
## 4 Test Results
### Components of Column Tip Displacement  
The imposed storey drift at the loading point is composed of four components: beam deformation, column deformation, panel deformation, and slipping with bearing deforma  
Figure 14: Specimen BS_01: (a) LTB (4.0% storey drift ratio); (b) local buckling of the web and flange (4.0% storey drift ratio).  
Figure 15: Specim BS_03: LTB (3.0% storey drift ratio).  
Figure 13: Specimen OS_02: (a) LTB (5.0% storey drift ratio); (b) LTB (6.0% storey drift ratio); (c) local buckling of the flange (6.0% storey drift ratio).  
tion of the bolt group. Based on the instrumentation used in the testing, the imposed storey drift could be decomposed into these four components. As an example, the displacement of each component and applied load for specimen OS_02 are shown in Figure 16. The major inelastic action was delivered from the slipping and bearing of the bolted connection (see Figure 16a). Although inelastic behaviour can be seen from the beam (see Figure 16b), this was because the deformation due to LTB of the beam occurred beyond a storey drift ratio of 4.0% (see Figure 13). A large out-of-plane deformation accumulated in the beam when the storey drift ratio reached 6.0% (see Figure 13b). As a result, the contribution from the beam to the total column tip displacement significantly increased. This increase in beam displacement was not due to the in-plane inelastic deformation; instead, it was delivered by the geometrical change in the beam due to out-of-plane deformation. Specimens OS_01 to BS_02, which reached the beam yielding strength, showed similar results.  
Figure 17 summarises the displacement components at the column tip. The specimens where the dominant mechanism is bolt slippage (see Figure 17a-d) show that slippage and bearing deformation in the bolted connection were the major components. On the other hand, for specimen BS_03, the major component of the deformation was the beam, and the deformation due to the bolted connections was minor (see Figure 17e). As explained, BS_03 was the specimen designed not to cause bolt slippage. According to these results, bolted beam-to-column connections that followed the proposed design procedure showed the expected structural behaviours. Inelastic action was mainly contributed by the slippage and bearing action at the bolted connection, which absorbed the energy. The LTB observed in the beam during testing can be prevented when the maximum moment at the bolted connection in the ultimate limit state is equal to or less than the beam strength. [PERSON] and [PERSON] [3] proposed a formula that can calculate the maximum moment in the bolted connection; therefore, this failure mode can also be controlled.  
Figure 16: Displacement of each component (OS_02): (**a**) slipping and bearing; (**b**) beam; (**c**) panel; (**d**) column.
## 5 Experiment and Results  
### Dataset Setup  
In our experiment, we evaluate models using three datasets: the meter reading area detection dataset, the meter reading recognition dataset, and the Pascal VOC dataset [44].  
For the meter reading area detection and meter reading recognition datasets, we utilized the floodX [45] dataset in this experiment. The floodX dataset was collected over two days during flash flood experiments in an urban setting, where 21 sensors, such as radars, ultrasonics, and cameras, were installed to monitor flooding-related variables. The environmental conditions during data collection included overcast, direct sunlight, cloudy conditions, and nighttime, thus providing a robust test for vision-based meter detection and recognition. Since the floodX dataset only contains meter reading annotations without specific locations, we employed LabelImg to annotate the reading areas and positions, thereby obtaining two required datasets.  
The meter reading area detection dataset aims to localize the meter reading area, whereas the meter reading recognition dataset focuses on recognizing the digits within the detected area. The data collected on the first day was utilized as the training set, and the second day was used for testing. The meter reading area detection dataset comprises 23,365 images, including 12,716 training images. On the other hand, the meter reading recognition dataset consists of 46,632 images, with 25,337 images in the training set. Figure 15 illustrates a few sample images from the dataset.  
The PASCAL VOC dataset is a collection of images originally created for the European Conference on Computer Vision (ECCV) project and comprises 20 different object categories, such as person, car, bird, and cat, among others. The dataset consists of 9963 images, with 5011 images in the training and validation set and 4952 images in the test set. Each image in the dataset is annotated with bounding boxes and class labels for the objects present in the image. Additionally, some images are also annotated with segmentation masks for each object.  
The PASCAL VOC dataset is widely recognized as a benchmark for object detection, providing a standardized set of images and annotations to evaluate a model's performance. This enables us to assess our model's generalization ability and competitiveness in the field. Figure 16 showcases some of the images included in the dataset.  
Figure 15: Some of the images in the floodX dataset.
## 3 Results
### Predictive Models
The values of the coefficients selected for the final Model-3 (see Equation (6)) corresponds with the \(\lambda\) with minimum cross-validated deviance plus one standard deviation (\(\lambda\) =0.0087; deviance = 14.45; SE = 4.25). \[Y(\textit{Model\_3})=-12.37\ +\ 1.99\ \textit{Roll\ Acc\ ratio}\left(\frac{peak}{rms}\right)+\ 0.32\ \textit{Yaw\ Acc\ Max}\ \left(\frac{rad}{s^{2}}\right) \tag{9}\]  
The classification accuracies achieved by the three models for identifying cases of _loss of control_ are showed in Table 2. The 'advanced' model Model-3 was found to be the best model (AUC = 1.00), predicting correctly 100% (n = 11) and 100% (n = 154) of _loss of control_ and _full control_ cases, respectively. The 'basic' and 'extended' models obtained very high accuracy (AUC = 0.988 for Model-1 and AUC = 0.998 for Model-2), correctly predicting 90.9% and 100% of _loss of control_ cases and 99.4% and 98.1% of _full control_ cases, respectively. The \(p\)-values for the Hosmer-Lemeshow tests (ranged between 0.648 to 1.00) also indicated good fit of all models. Table 2 also shows the best decision threshold determined by the Youden index for each model, e.g., for Model-1, a probability higher than 0.442 will classify the braking case as _loss of control_. Figures 7-9 show the ROC curve and the scatter plot of _loss of control_ probability by subject and skill level for the 165 braking trials in each the three models; the green area of the scatter plots corresponds to the values considered by each respective model as full control and the _controllability_ markers (green and red) the binary rating of the coders (full control and loss of control). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictor** & **Threshold** & **Specificity** & **Sensitivity** & **AUC (CI)** & **Hosmer-Lemeshow Test** \\ \hline Model-1 & 0.442 & 0.994 & 0.909 & 0.988 (0.968–1.00) & \(\chi\)2(8) = 5.991; \(p\) = 0.648 \\ Model-2 & 0.157 & 0.981 & 1 & 0.998 (0.994–1.00) & \(\chi\)2(8) = 0.449; \(p\) = 1.00; \\ Model-3 & 0.275 & 1 & 1 & 1.00 (1.00–1.00) & \(\chi\)2(8) = 0.000; \(p\) = 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction Performance and best thresholds for the models. Figure 6: Ten-fold cross-validation on the simulated data sets. Red line: mean deviance; Blue line: bounds for deviances within one standard error. Figure 8: Model-2: ROC and scatter plot of _loss of control_ probability by subject for the 165 braking trials. Figure 7: Model-1: ROC and scatter plot of _loss of control_ probability by subject for the 165 braking trials. Figure 9: Model-3: ROC and scatter plot of _loss of control_ probability by subject for the 165 braking trials.
# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
###### Abstract  
Image-based spectral models assist in estimating the yield of maize. During the vegetative and reproductive phenological phases, the corn crop undergoes changes caused by biotic and abiotic stresses. These variations can be quantified using spectral models, which are tools that help producers to manage crops. However, defining the correct time to obtain these images remains a challenge. In this study, the possibility to estimate corn yield using multispectral images is hypothesized, while considering the optimal timing for detecting the differences caused by various phenological stages. Thus, the main objective of this work was to define the ideal phenological stage for taking multispectral images to estimate corn yield. Multispectral bands and vegetation indices derived from the Planet satellite were considered as predictor variables for the input data of the models. We used root mean square error percentage and mean absolute percentage error to evaluate the accuracy and trend of the yield estimates. The reproductive phenological phase R2 was found to be optimal for determining the spectral models based on the images, which obtained the best root mean square error percentage of 9.17% and the second-best mean absolute percentage error of 7.07%. Here, we demonstrate that it is possible to estimate yield in a corn plantation in a stage before the harvest through Planet multispectral satellite images.  
corn phenological stages; remote monitoring; yield prediction; spatial distribution of yield 1  
[PERSON] Junior  
1 Institute of Aggarian Sciences, Federal University of Uberlandia, Monte Carmelo 38500-000, Brazil;  
1 [EMAIL_ADDRESS] (C.A.M.d.A.J.); [EMAIL_ADDRESS] (G.D.M.); [EMAIL_ADDRESS] (D.J.M.)  
In this study, the possibility to estimate corn yield using multispectral images is hypothesized, while considering the optimal timing for detecting the differences caused by various phenological stages. Thus, the main objective of this work was to define the ideal phenological stage for taking multispectral images to estimate corn yield. Multispectral bands and vegetation indices derived from the Planet satellite were considered as predictor variables for the input data of the models. We used root mean square error percentage and mean absolute percentage error to evaluate the accuracy and trend of the yield estimates. The reproductive phenological phase R2 was found to be optimal for determining the spectral models based on the images, which obtained the best root mean square error percentage of 9.17% and the second-best mean absolute percentage error of 7.07%. Here, we demonstrate that it is possible to estimate yield in a corn plantation in a stage before the harvest through Planet multispectral satellite images.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Filtering Step** & **Parameter** & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** \\ \hline Raw input data & Total area * (ha) & 595,355 & 595,355 & 595,355 & 595,355 & 595,355 & 595,355 & 595,355 \\ \hline After filtering for & Filtered area (ha) & 440,327 & 453,636 & 443,059 & 442,949 & 442,526 & 450,206 & 463,443 \\ minimum data & Filtered area (\%) & 73.96\% & 76.20\% & 74.42\% & 74.40\% & 74.33\% & 75.62\% & 77.84\% \\ \hline After filtering for & Filtered area (ha) & 4060.0 & 2659.6 & 3376.5 & 3618.0 & 3271.6 & 3540.7 & 3691.9 \\ crop type & Filtered area (\%) & 0.68\% & 0.45\% & 0.57\% & 0.61\% & 0.55\% & 0.59\% & 0.62\% \\ \hline After filtering for & Above threshold ** & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline After filtering for & Filtered area (ha) & 3902.7 & 2392.2 & 3166.3 & 3392.8 & 3061.7 & 3245.7 & 3274.1 \\ shape index & Filtered area (\%) & 0.66\% & 0.40\% & 0.53\% & 0.57\% & 0.51\% & 0.55\% & 0.55\% \\  & Above threshold ** & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline After filtering for & Filtered area (ha) & 3902.7 & 2161.8 & 2948.4 & 3060.9 & 2858.5 & 2948.9 & 2955.1 \\ valid crop & Filtered area (\%) & 0.66\% & 0.36\% & 0.50\% & 0.51\% & 0.48\% & 0.50\% & 0.50\% \\ rotation systems & Above threshold ** & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline After filtering for & Filtered area (ha) & 2268 & 990 & 1628 & 1890 & 1683 & 1611 & 1718 \\ pixels with at least & Filtered area (\%) & 0.38\% & 0.17\% & 0.27\% & 0.32\% & 0.28\% & 0.27\% & 0.29\% \\
50\% coverage & Above threshold ** & Yes & No & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}  
* According to CORINE 2018 land cover data for arable agricultural land in the continental biogeoregion of Croatia (codes 211 and 212). ** The minimum threshold of 0.25% for training and test data according to the entire study area was determined according to [PERSON] [27].  
\end{table} 
Table 1: Total coverage of training/test data based on peak LAI and FAPAR data per study year for each filtering step.
## 4 Three-Dimensional Magnetic Field Modeling  
### Applied GSFC Software for 3D Combined Gravity-Magnetic Modeling  
The GSFC-M (Geological Space Field Calculation, modified) program is intended to compute 3D gravity and magnetic fields in complex physical-geological conditions [17; 34]. This program has been intended for computing gravity field (observed values, free air, and Bouguer), magnetic field components (\(\Delta Z\), \(\Delta X\), \(\Delta Y\)), and total magnetic field \(\Delta T\), as well as second derivatives of the gravitational potential under conditions of uneven terrain relief and oblique magnetization. Three-dimensional horizontal polygonal prisms approximate the geological space. Each body geometrically can be described by 50 points; the number of employed bodies in the calculated model is practically unlimited (up to 1000). The geological bodies can be prepared for calculation, besides the 3D variant, as 2.5D and infinite along the strike. Types of the bodies may be the following: (1) closed, (2) left-hand non-closed, (3) right-hand non-closed, and (4) open.  
The program has the following main advantages besides those already mentioned: (1) simultaneous computing of gravity and magnetic fields, (2) description of the terrain relief by irregularly placed characteristic points, (3) computation of the effect of the earth-air boundary by the method of selection directly in the process of interpretation, (4) modeling of the selected profiles flowing over rugged relief or at various arbitrary levels (using characteristic points), (5) simultaneous modeling of several profiles, (6) description of a large number of geological bodies and fragments [34]. The basic algorithm realized in the GSFC program is the solution of the direct 3D problem of gravity and magnetic prospecting for horizontal polygonal prism limited in the strike direction. In the developed algorithm, integration over a volume is realized on the surface, limiting the anomalous body. The combined potential field modeling is carried out by an iteration process, where geological bodies' geometrical and physical properties may change. An important peculiarity of applied modeling is the possibility of calculating the 3D gravity-magnetic effects from the bodies lying outside the plane of the geological section [34].
# Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data
## 2 Data and Methods
### Spatial Analysis Methods
#### 2.9.2 Least Squares and Geographically Weighted Regression Models  
In the field of regression analysis, traditional ordinary linear regression models tend to presuppose global consistency among variables. However, this approach ignores the local heterogeneity or region-specific characteristics that variables may exhibit. While Ordinary Least Squares (OLS) can provide reasonable and effective estimates for quantifying the effects of color complexity and coordination on affective scores in many statistical contexts, with corresponding regression coefficients and significance levels, the inherent heterogeneity and spatial autocorrelation of spatial data limit the applicability of OLS. In contrast, geographically weighted regression (GWR) is capable of capturing the heterogeneity of spatial data, thereby being a powerful tool for studying spatial nonstationarity. The GWR model reveals the local spatial relationships at a specific scale and the driving factors. This model incorporates the geographic location of the data as a component of the regression parameters and considers the impacts of spatial weights in a comprehensive manner. In this study, we employed both the OLS and GWR models to calculate the color complexity and coordination indexes and the six categories of emotional perception values. These calculations were based on the six categories of emotional perception values derived from related studies. We then conducted a regression analysis on the explanatory variables and the explained variables. The aim of this analysis was to explore the relationship between the color complexity and coordination of streetscapes in the Lixia District of Jinan City and the emotional perceptions of its residents. The calculation formula is as follows:  
\[Y_{i}=\beta_{0}(\mathcal{U}_{i}+\mathcal{V}_{i})+\sum_{k}\beta_{k}(\mathcal{U }_{i}+\mathcal{V}_{i})\mathcal{X}_{ik}+\varepsilon_{i} \tag{12}\]  
In this model, \(Y_{i}\) denotes the affective perception score of cell \(i\); \((\mathcal{U}_{i},\ \mathcal{V}_{i})\) represents the horizontal and vertical coordinates of the spatial location of cell \(i\); \(\beta_{0}(\mathcal{U}_{i}+\mathcal{V}_{i})\) signifies the model regression intercept of change; \(\beta_{k}(\mathcal{U}_{i}+\mathcal{V}_{i})\) is the regression coefficient of the explanatory variables; \(\mathcal{X}_{ik}\) is the \(k\)th explanatory variable on cell \(i\); and \(\varepsilon_{i}\) is the random error term of the model.
# Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
## 4 Results and Performance Evaluation
### Performance by Sparse Coding
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**RMSE of Number of Steps Estimation**} \\ \hline \multicolumn{2}{c}{**Data**} & \multicolumn{5}{c}{**Estimation Methods**} \\ \hline
**Variable** & **Attribute** & **Mean** & **CF** & **RM** & **KNN** & **RMD** \\ \hline \multirow{4}{*}{Sleep Data} & to + 0, 1, 2 & 18.299 & 1.615 & 1.896 & 1.624 & 1.298 \\ \cline{2-7}  & ls + 0, 1, 2 & 154.122 & 17.717 & 20.807 & 17.821 & 14.240 \\ \cline{2-7}  & ds + 0, 1, 2 & 70.048 & 10.106 & 11.868 & 10.165 & 8.122 \\ \hline Blood Sugar mg/dL & bs + 0, 1, 2 & 147.820 & 29.813 & 32.294 & 28.302 & 27.029 \\ \hline Heart Rate & hr + 0, 1, 2 & 88.260 & 8.631 & 10.137 & 8.683 & 6.938 \\ \hline Blood Pressure & sb + 0, 1, 2 & 118.915 & 9.840 & 10.468 & 12.088 & 8.829 \\ \cline{2-7} mmHg & db + 0, 1, 2 & 83.175 & 7.507 & 7.876 & 7.406 & 6.933 \\ \hline Number of Steps & ns + 0, 1, 2 & 8864.1 & 1383.2 & 1624.4 & 1391.4 & 1111.7 \\ \hline \multirow{4}{*}{Temperature (\({}^{\circ}\)C)} & tm + 0, 1, 2 & 15.262 & 1.491 & 1.867 & 1.504 & 1.068 \\ \cline{2-7}  & lt + 0, 1, 2 & 7.215 & 0.341 & 0.423 & 0.340 & 0.262 \\ \cline{2-7}  & ht + 0, 1, 2 & 18.661 & 2.475 & 2.815 & 2.525 & 1.955 \\ \hline Humidity (\%) & hu + 0, 1, 2 & 78.140 & 10.837 & 12.727 & 10.901 & 8.711 \\ \hline Average & & 805.33 & 123.63 & 144.80 & 124.39 & 99.76 \\ \hline \multicolumn{7}{c}{Avg.} & Avg. 0.897 & 0.811 & 0.941 & 1.194 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMSE and turnaround time of each missing value estimation methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multicolumn{5}{c}{**Model**} \\ \cline{2-5}  & **MDM** & **S\_MDM** & **D\_MDM** & **SD\_MDM** \\ \hline Accuracy & 75.37\% & 71.46\% & 69.51\% & 66.47\% \\ \hline Parameter & 15,157 & 11,578 & 10,578 & 7578 \\ \hline \multirow{4}{*}{Turnaround Time (sec)} & Max. & 1.356 & 1.156 & 1.014 & 0.814 \\ \cline{2-5}  & Min. & 1.077 & 0.866 & 0.781 & 0.611 \\ \cline{2-5}  & Avg. & 1.194 & 0.918 & 0.844 & 0.784 \\ \hline Data Size & & 2.7 MB & 2.2 MB & 1.9 MB & 1.3 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance according to the sparse coding of multimodal deep learning models.