esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
DR was effectively categorized using deep learning techniques like CNN and transfer learning using models like ResNet, VGG, and GoogleNet [20; 31; 32]. In a previous article by [PERSON] [33], the authors employed a variety of SVM kernels with Ant Colony System (ACS) feature selection method. It evaluates a technique for detecting DR using 250, 550, and 750 features. The cubic SVM classifier significantly outperformed all other kernels, with an accuracy of 92.6%, 91.8%, and 93%, respectively, for 250, 550, and 750 attributes. With the APTOS-2019 dataset, a different study by [PERSON] [34] utilized an extreme learning machine (ELM) model and reached an accuracy of 97.27%. A CNN architecture employed for image segmentation tasks is called U-Net. In the work by [PERSON] [35], segmentation was performed utilizing an asymmetric deep learning architecture for DR screening using U-Net networks. CLAHE is used to analyze and improve the green channel images. For APTOS and MESSIDOR, the non-DR detection accuracy was 98.6% and 91.9%, while the PrDR detection accuracy was 96.8% and 95.76%. Data augmentation is the most popular method for addressing imbalance issues with image categorization [36]. It is a collection of strategies for producing more data pieces from existing data to enhance the quantity of data artificially. The images can be improved in various ways, including cropping the image, inverting it horizontally or vertically, resizing it, and rotating it [37]. In a paper by [PERSON] [38], using a data augmentation strategy enhances the standard of transfer learning in classifying DR images. Table 1 shows the summary table of the related works in DR.
# Posture Recognition Using Ensemble Deep Models under Various Home Environments
## 2 Deep Models of CNN
### VGGNet  
VGGNet is ranked second in the ILSVRC 2014, following GoogLeNet, but it is more widely used than GoogLeNet because it has a significantly simpler structure. VGGNet uses a relatively small \(3\times 3\) convolution (conv) filter and a \(1\times 1\) convolution filter, in contrast to AlexNet's \(11\times 11\) filters in the first layer and ZFNet's \(7\times 7\) filters. When the nonlinear rectified linear unit (ReLU) activation function is applied after the \(1\times 1\) convolution, the model becomes more discriminative. Additionally, a smaller filter requires less parameters to be learned and results in higher processing speed. In VGGNet, a maxpool with a \(2\times 2\) kernel size and \(2\) strides, \(2\) fully connected (FC) layers with \(4096\) nodes, \(1\) FC layer with \(1000\) nodes, and \(1\) Softmax layer is used. Two \(3\times 3\) convolution layers and three \(3\times 3\) convolution layers have effective receptive fields of \(5\times 5\) and \(7\times 7\), respectively. By doubling the number of filters after each maxpool layer, the spatial dimensions are reduced, but the network depth is increased. Originally, VGGNet was designed to investigate how errors are affected by the network depth. There are models with layer depths of \(8\), \(11\), \(13\), \(16\), and \(19\) in VGGNet. As the network depth increases, the error decreases, but the error increases if the layer depth exceeds \(19\). VGGNet uses data augmentation of scale jittering for training and is trained with batch gradient descent using four Nvidia Titan Black graphics processing units for approximately three weeks [19]. Figure 1 shows the structure of VGGNet. In an expression with the form L@M \(\times\) N, L, M, and N represent the sizes of the map, the row of the kernel, and the column of the kernel, respectively. The @ is a symbol used to separate the map size and filter size here.
# Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
## 3 Proposed Method
### Features Extraction
#### 3.1.3 IIF Extraction  
For any image in the training dataset, the eight conventional unitary algorithms are applied to get illuminant color vectors \(e_{i}=[R_{i},G_{i},B_{i}]\). Here the subscripts \(i=1,2,\cdots,8\) represent the unitary algorithms described in Section 2, i.e., GW, WP, SoG, GE1, GE2, GGW, PCA-based, and LSR, respectively. These methods are based on different principles to estimate illumination; each method is suitable for some specific image types to achieve better estimation accuracy, but might not be suitable for other image types. Commonly, for a given image, different vectors \(e_{i}\) have different values, and sometimes the differences between two of them are significant. Thus we can consider \(e_{i}\) as a certain metric under corresponding hypothesis. For example, \(e_{1}=[R_{1},G_{1},B_{1}]\) can be regarded as a metric under the GW assumption. Consequently, we can construct an integrated IIF vector for an image \(I\) as follows:  
\[\theta(I)=[\gamma(e_{i},e_{j})],i=1,2,\cdots,8,j=(i+1),(i+2),\cdots,8, \tag{14}\]  
where \(\gamma(e_{i},e_{j})\) is defined as Euclide distance between \(e_{i}\) and \(e_{j}\) in 3D color space.  
Since we just consider illuminant color, rather than illumination intensity, we take Equation (2) to compute the chromaticity \((r_{i},g_{i})\) for the illuminant color vector \(e_{i},i=1,2,\cdots,8\). Alternatively, we may construct another type of integrated IIF vector for an image \(I\) as follows:  
\[\theta(I)=[r_{1},g_{1},r_{2},g_{2},r_{3},g_{3},r_{4},g_{4},r_{5},g_{5},r_{6},g _{6},r_{7},g_{7},r_{8},g_{8}]. \tag{15}\]  
Denoting the image number in the training dataset as \(N\), an IIF matrix for the training dataset is obtained as follows:  
\[\Theta=\big{[}\ \ \theta^{T}(I_{1})\ \ \ \theta^{T}(I_{2})\ \ \ \cdots\ \ \ \theta^{T}(I_{N})\ \ \big{]}^{T}. \tag{16}\]
# Environmental and Economic Aspects of a Containership Engine Performance in Off-Design Conditions
## 3 Methodology
### Validation of 1D/0D Simulation Model  
The validation of the simulation model was performed by comparing the simulation results with engine performance data declared by the engine manufacturer. Figure 6 shows the comparison of BMEP and BSFC at four operating points (R1--full load at 102 rpm, R2--70% of load at 102 rpm, R3--full load at 92 rpm, and R4--load equal to R2 at 92 rpm).  
During the model calibration process, several additional parameters are controlled. For example, the peak cylinder pressure is controlled by reaching a maximum value of 156 bar, the maximum fuel mass injected per cycle at 102 rpm is equal to 160 g and the boost pressure approximately is equal to 3.7 bar. Once the mentioned parameters were matched in the simulation model with a deviation of \(\pm\)5%, the fine model tuning was performed. It  
Figure 6: Validation of simulation results with engine manufacturer data.  
includes the adoption of a heat transfer calibration factor for the piston, cylinder liner, and head to a value of 0.1, enabling the prediction of BSFC values within the deviation range of \(\pm\)5% from the declared data, as shown in Figure 6. The comparison of the simulation results of BMEP and BSFC shown in Figure 6 confirms that the simulation model is validated in 4 different operating points (design engine operation), and it is applied to simulate the operation in off-design conditions. Before that, the soot emission results were compared over different engine loads at a reference engine speed of 102 rpm, Figure 7. The data measured by the manufacturer of soot emission are represented with values of Filter Smoke Number (FSN), while the simulation results are related to the soot mass ratio expressed in ppm units. To achieve simulation results of soot emission that increases as the engine load decreases, the soot formation parameter was calibrated and calibrated values that depend on engine load were applied for the simulation of other engine operating points. The soot oxidation parameter was kept at the default value.  
The simulation results of soot emission matched the reference measured data of FSN well. As the engine load decreases, more soot is emitted because the lower boosting pressure is applied, reducing the quality of cylinder scavenging and turbulence quantities that are responsible for primary and secondary fuel break-up and the fuel/air mixing process.  
The NO\({}_{\text{X}}\) emission sub-model has two constants that can be modified during the calibration steps. The first constant named NO\({}_{\text{X}}\) Kinetic Multiplier was set to the default unit value, while the NO\({}_{\text{X}}\) Postprocessing Multiplier was increased from the default value of 0.64 to 0.80 so that the maximum NO\({}_{\text{X}}\) emissions at R1 and R3 operating conditions do not exceed the threshold of 14.4 g/kWh, as defined by Trier II standard for ship engines with rotational speeds below 130 rpm.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Laboratory Tests**} & \multicolumn{2}{c}{**Field Research**} \\ \cline{2-5}  & **Master Data (Ideal)** & **Data Distorted** & **Dataset Z1** & **Dataset Z2** \\ \hline Type of network & MLP & MLP & MLP & MLP \\ \hline No. of chosen networks & 5 & 5 & 5 & 5 \\ \hline No. of hidden layers & 1 & 1 & 1 & 1 \\ \hline No. of neurons & 4–10 & 4–10 & 6–10 & 4–9 \\ \hline Activation function for the hidden layer & linear & linear & Linear—2 Logistics—2 Exponential—1 & Linear—2 Logistics—1 \\ \hline Activation function for the output layer & linear & linear & linear & linear \\ \hline Number of points (total) & 105 & 105 & 87 & 141 \\ \hline Training set (no. of points) & 75 & 75 & 61 & 99 \\ \hline Validation set & 15 & 15 & 13 & 21 \\ \hline Test set & 15 & 15 & 13 & 21 \\ \hline Maximum error [mm] in the validation and test set & \(1\cdot 10^{-4}\) & \(1\cdot 10^{-4}\) & \(3\cdot 10^{-2}\) & \(4\cdot 10^{-3}\) \\ \hline Mean error \(\mathrm{m_{p}}\) [mm] of the ANN and analytical (similarity and gradient) model & \(3\cdot 10^{-2}\) & \(1\cdot 10^{-1}\) & 1.09 & 3.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of the ANNs used for laboratory and field data.
## 4 Results and Discussions
### Landslide Susceptibility Mapping
Firstly, landslides are predominantly distributed in the mountainous and hilly regions with elevations between 400 and 1000 m, especially on convex slopes within the 10\({}^{\circ}\)-30\({}^{\circ}\) range. The lithology is primarily composed of intermediate-acidic volcanic rocks, particularly andesite and  
Figure 8: Landslide susceptibility zoning map of machine learning models. play a crucial role in the formation of landslides. Additionally, the fault structures within the geological body significantly influence the development of joint fissures, with higher frequency ratios observed closer to faults. Regarding hydrological environmental factors, areas with a Topographic Wetness Index (TWI) between 5 and 10 are less favorable for landslide development, while regions near rivers show significantly higher frequency ratios compared to areas farther from rivers. Lastly, human engineering activities have a significant impact on landslide occurrence, with areas closer to roads exhibiting higher frequency ratios. The distribution of high susceptibility areas in Figure 8 and the statistical analysis results in Table 2 indicate that the proportions of landslides in the very-high- and high-susceptibility zones predicted by the I-RF, I-BPNN, II-RF, and II-BPNN models are 90.05%, 77.22%, 92.57%, and 84.41%, respectively. These prediction results are consistent with the geological, topographical, and anthropogenic factors revealed by the frequency ratio analysis, thereby validating their effectiveness and reliability in landslide susceptibility assessment.
## 4 Design of Two-Time-Scale SMO for Five-Phase IM
### Mathematical Model of Five-Phase IM  
The mathematical models of the five-phase IM are presented and discussed in several research studies [51]. The dynamic model of the motor can be described using the following differential equations.  
\[\left\{\begin{array}{c}\frac{di_{\text{s}}}{dt}=-\frac{R}{\partial L_{\text{ s}}}i_{\text{s}\alpha}+\frac{L_{m}}{\partial L_{\text{s}}L_{\text{r}}}A_{r} \lambda_{r\alpha}+\frac{L_{m}}{\partial L_{\text{s}}L_{\text{r}}}\omega\lambda _{r\beta}+\frac{1}{\partial L_{\text{s}}}v_{\text{s}\alpha}\\ \frac{di_{\text{s}\beta}}{dt}=-\frac{R}{\partial L_{\text{s}}}i_{\text{s} \beta}+\frac{L_{m}}{\partial L_{\text{s}}L_{\text{r}}}A_{r}\lambda_{r\beta}- \frac{L_{m}}{\partial L_{\text{s}}L_{\text{r}}}\omega\lambda_{r\alpha}+\frac {1}{\partial L_{\text{s}}}v_{\text{s}\beta}\\ \frac{d\lambda_{r\beta}}{dt}=L_{m}A_{r}i_{\text{s}\alpha}-A_{r}\lambda_{r \alpha}-\omega\lambda_{r\beta}\\ \frac{d\lambda_{r\beta}}{dt}=L_{m}A_{r}i_{\text{s}\beta}+\omega\lambda_{r \alpha}-A_{r}\lambda_{r\beta}\\ \frac{d\lambda_{\text{s}\alpha}}{dt}=-A_{s}i_{\text{s}\alpha}+\frac{1}{L_{ \text{is}}}V_{\text{s}x}\\ \frac{d\lambda_{\text{s}\alpha}}{dt}=-A_{s}i_{\text{s}\beta}+\frac{1}{L_{ \text{is}}}V_{\text{s}y}\end{array}\right. \tag{22}\]  
Based on the dynamic model of the motor and singular perturbation theory, the slow variables are represented by the rotor-flux \(\alpha\)-\(\beta\) components (\(\lambda_{\text{r}\alpha}\), \(\lambda_{r\beta}\)), whereas the fast variables are presented by the stator current \(\alpha\)-\(\beta\) components (\(i_{\text{s}\alpha}\), \(i_{\text{s}\beta}\)). Consequently, the singularly perturbed model of (22), with \(\zeta=\sigma L_{\text{s}}L_{r}/L_{m}\), \(x=\left(\begin{array}{cc}\lambda_{r\alpha}&\lambda_{r\beta}\end{array} \right)^{t}\), \(z=\left(\begin{array}{cc}i_{\text{s}\alpha}&i_{\text{s}\beta}&i_{\text{s} \alpha}&i_{\text{s}y}\end{array}\right)^{t}\), can be formulated by  
\[\left\{\begin{array}{c}\zeta\dot{z_{1}}=-L_{m}A_{r}z_{1}+A_{r}x_{1}+\omega x _{2}+\frac{L_{r}}{L_{m}}(v_{\text{s}\alpha}-R_{\text{s}}z_{1})\\ \zeta\dot{z_{2}}=-L_{m}A_{r}z_{2}+A_{r}x_{2}-\omega x_{1}+\frac{L_{r}}{L_{m}}( v_{\text{s}\beta}-R_{\text{s}}z_{2})\\ \dot{x_{1}}=L_{m}A_{r}z_{1}-A_{r}z_{1}-\omega x_{2}\\ \dot{x_{2}}=L_{m}A_{r}z_{2}+\omega x_{1}-A_{r}x_{2}\\ \dot{z_{3}}=-A_{s}i_{\text{s}x}+\frac{1}{L_{\text{is}}}v_{\text{s}x}\\ \dot{z_{4}}=-A_{s}i_{\text{s}y}+\frac{1}{L_{\text{is}}}v_{\text{s}y}\end{array}\right. \tag{23}\]  
where \(A_{r}=R_{r}/L_{r}\) and \(A_{\text{s}}=R_{\text{s}}/L_{\text{s}}\).
# Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
## 2 Related Work  
### Pretrained Models in Medical Visual Question Answering  
Recent advances in large-scale pretrained models have significantly advanced medical visual question answering (Med-VQA) through cross-modal learning paradigms. In vision-language joint modeling [12; 13], domain-specific pretrained models achieve semantic alignment between medical images and text via contrastive learning strategies [14]. PubMedCLIP [15], a medical variant of CLIP [16], establishes cross-modal shared semantic spaces through contrastive pretraining on millions of medical image-text pairs. Its visual encoder extracts clinically relevant, anatomically-aware features that provide high-quality image representations for downstream VQA tasks. For textual modeling, RadBERT [17] implements domain-adaptive pretraining on radiology report corpora, enhancing radiological term comprehension through masked language modeling and contrastive learning tasks. This model dynamically encodes hierarchical semantic structures in medical questions while preserving contextual dependencies. Notably, MedFuse [18] addresses medical data scarcity through hierarchical feature fusion architectures, where pretrained EfficientNet image features interact with BioClinicalBERT text embeddings via gated fusion mechanisms,  
Figure 1: A schematic diagram of a traditional hierarchical medical visual question answering framework. Medical images and fine-grained hierarchical medical questions are fed into an image encoder and a text encoder. The encoded features are then input into a Transformer-based fusion module for multi-modal feature integration, and finally, an MLP classification layer is employed to predict the answer candidates for the corresponding medical question.  
demonstrating superior performance in pneumonia detection compared to general-purpose models. Benchmark studies on VQA-Rad [19] further validate the advantages of medical-specific pretraining over generic models like ViLBERT [20], particularly in fine-grained reasoning, where pretrained models better capture correlations. However, existing approaches predominantly employ single-layer decoding architectures to process hierarchical questions, failing to synergize the semantic advantages of pretrained models with hierarchical diagnostic logic. This limitation results in high-level semantic conflicts and low-level feature confusion, creating innovation opportunities for our hierarchical prompting and decoding framework design.
## 3 The End-to-End Instrument Performance Simulation System (EIPS) Framework
### EIPS Components
#### 3.2.2 Atmospheric Radiative Transfer Model: The HT-FRTC  
The theoretical background and development of the HT-FRTC is given in detail in [19]; here, we will give a brief description of important functionalities of HT-FRTC in the context of development of our EIPS framework. The HT-FRTC is a principal component (PC) based model and can be used for calculating transmittance(s), radiance(s) and Jacobian(s) across the EM spectrum from UV to MW represented by their principal components [19]. The code can be run for any user-defined vertical atmospheric pressure grid [19]. Absorption properties of atmospheric trace gases and it's treatment in the HT-FRTC is given and explained in [19].  
The simulations using the HT-FRTC model can be performed in two modes: line-by-line and fast. But in the EIPS, only the fast mode of the HT-FRTC has been incorporated, with which many  
Figure 3: A schematic block diagram of the EIPS Framework, showing three important component models of the framework.  
thousands of atmospheric profiles can be easily processed and a full line-by-line like resolution output can be produced. The HT-FRTC is, to our knowledge, unique in allowing fast simulations at a full line-by-line resolution which are sensor independent and therefore simplify studies of different instrument configurations. Thousands of profiles can be easily processed. In a future operational NWP data assimilation systems, setting a fast code will have to be used in order to be able to process the expected large data volumes in a timely manner. The accuracy of the fast code has been evaluated by comparing its results with line-by-line code results for the profiles used in this study. These profiles are independent from the profiles used for the training of the fast code, which are sampled from a database of ECMWF atmospheric profiles as explained in more detail in [19]. As can be seen in Figure 4a, the fast code is nearly unbiased against the line code with maximum biases of around 1 millikelvin. The standard deviation between the two codes is larger, but does not exceed 20 millikelvin (see Figure 4b). We consider this level of accuracy to be acceptable for the purposes of the studies within the EIPS framework. The accuracy of the line-by-line code will itself be limited in turn by the uncertainties in the knowledge of the spectroscopy, which is difficult to quantify and beyond the scope of this paper.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 3 Propagation Modeling
### Log Distance  
Log distance is a commonly used model for both indoor and outdoor environments. It has a few parameters to characterize the propagation, and therefore, it is relatively easy to implement. Although it lacks the accuracy of sophisticated models, it is not computationally demanding [28]. The log-distance model is presented in Equation (1) [29].  
\[PL=PL_{d_{0}}+10\ n\ \log\left(\frac{d}{d_{0}}\right)+X_{g} \tag{1}\]  
where \(PL\) is the path loss (dB), \(d\) is the distance (meter) between the transceivers, \(n\) is the path loss exponent, \(X_{g}\) is the shadow fading with mean (\(\mu\)) zero and standard deviation \(\sigma\) (dB), and \(PL_{d_{0}}\) is the loss at a reference distance (usually 1 m) from the transmitter.
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 3 Case Study
### FE Modeling of Elements
#### 3.3.4. Elastomeric Bearings  
Under each precast post-tensioned girder of the superstructure, elastomeric bearings have been positioned and are considered free to move. The schematic models of the bridge components are shown in Figure 6. The horizontal stiffness of the elastomeric bearings is significantly lower when compared to the adjacent substructure and superstructure. In both horizontal directions, the elastomeric bearings demonstrate a behavior that can be accurately described using non-linear elements (elastic-perfectly plastic model). At the same time, the yield force aligns with the frictional force that arises at the interface between the concrete surface and the bearings. The bridge elastomeric bearings have a shear modulus of \(G_{\text{bearing}}=0.90\) N/mm\({}^{2}\) with a friction coefficient set at 0.4 (2013). For this type of bridge, bearings are deemed sacrificial components, requiring inspection for damage and subsequent replacement in the aftermath of a significant seismic event.  
Figure 6: Analytical model of the bridge components: (**a**) modeling of abutment; (**b**) modeling of abutent cap (schematic model referring to details in original drawings).
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 3 Results
### Correlation Analysis between Canopy SIF and Cotton Yield at Different Growth Periods
#### 3.2.2 Correlation Analysis of Cotton Canopy SIF with AGB and LAI at Different Growth Periods  
As can be seen from Figure 4, the correlation between cotton canopy SIF, AGB, and LAI was the best in the flowering and boll period, followed by the flowering period and the budding period, and it was the smallest during the boll period. In terms of the correlation between cotton canopy SIF and AGB, the correlation first increased and then decreased with the increase in time. The correlation coefficient between SIF and AGB reached the maximum value (r = 0.80) at 14:00-15:00 in the flowering and boll period, followed by that in the flowering period (r = 0.79). In terms of the correlation between cotton canopy SIF and LAI, the correlation first increased and then decreased with the increase in time. The correlation coefficient between SIF and AGB reached the maximum value (r = 0.85) at 14:00-15:00 in the flowering and boll period, followed by that in the flowering period (r = 0.71). The results showed that canopy SIF was significantly correlated with AGB and LAI during the four key reproductive periods, with the highest correlation at 14:00-15:00.
## 4 Discussion
### Performance of Data Categories  
Among the various categorization approaches, the 'all' category (no filtration) shows the poorest results, with RMSLE values as high as 0.49 and R\({}^{2}\) values as low as 0.08. Categorizing according to seasonality does not seem to be effective either, with the AW and SS categories exhibiting RMSLE values in the ranges 0.37-0.52 and 0.37-0.48, respectively. When in situ data are categorized based on location, the WLO category performed moderately better, with RMSLE values ranging from 0.17 to 0.21, while the HH category showed slightly higher RMSLE values from 0.31 to 0.44. The most promising results are obtained when in situ data are categorized according to TSI. The EH and OM categories have relatively low average RMSLE values of 0.18 and 0.20, respectively. The superior performance for the EH category is not entirely surprising, given that the higher ChI-\(a\) concentrations yield more intense spectral signatures (higher reflectance). This observation is supported by other performance metrics, such as MAE and MAPE.