esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Tool Force  
The simulation showed the effects of rake angle and belt motion on tool force (Figure 4). The average dragutf force (\(Fx\)) increased as the rake angle increased (Figure 4a). The minimal values of \(Fx\) for the regular tools and the belt tools were 126.7 N and 110 N, respectively, at the rake angle of 30\({}^{\circ}\). Then, the belt tools showed a lower increase rate of \(Fx\) than the regular tools. The \(Fx\) values for the belt tools were 13.18%, 25.21%, 37.98%, and 44.64% lower than those for the regular tools at the rake angles of 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 75\({}^{\circ}\), respectively. These results indicate a significant reduction in dragutf force as influenced by the belt motion, and the reduction is higher when the rake angle is larger.  
Figure 4: Tool force: (**a**) Average dragutf force; (**b**) Average vertical force (negative vertical force is in an upward direction).  
The simulation also showed that the average vertical force (\(Fz\)) had an opposite trend as the rake angle increased (Figure 4b). The maximal values of \(Fz\) were 61.2 N and 58.4 N for the regular tools and belt tools, respectively, at the rake angle of 30\({}^{\circ}\). Then the belt tools showed a lower decrease rate of \(Fz\) than the regular tools. The \(Fz\) values for the belt tools were 8.01% and 1575.0% higher than those for the regular tools at the rake angles of 45\({}^{\circ}\) and 60\({}^{\circ}\), respectively. The \(Fz\) for the regular tools at the rake angle of 75\({}^{\circ}\) had a reversed direction upwards, but the \(Fz\) for the belt tools remained downward. These results indicate that the belt motion tends to increase the downward vertical force when the rake angle is large, but the effect is dependent on the rake angle.  
The force change is partly attributed to the change in the patterns of soil deformation and movement. The increase in rake angle turns the patterns from \"cutting\" into \"pushing\" [35; 40; 43]. This transition means the direction in which the tool applies forces to the soil and the direction in which these forces cause the soil to move have changed [1]. Consequently, the inevitable effects change the equilibrium of the soil, which increases the tool force. However, the belt motion changes the range of disturbance, the particle velocity distribution, and the soil deformation and movement patterns. All these changes are conducive to reducing the draught force. They are discussed in the following sections.  
On the other hand, although the downward vertical forces were increased by the belt motion, they were no larger than those for the regular tools with a rake angle of 30\({}^{\circ}\). A larger vertical force is harmful due to the negative effects of excessive downward penetration and soil compaction [39]. However, a vertical force within a reasonable range is acceptable, and can be counterbalanced by soil reactions. Therefore, concerns are mainly focused on the reasons for the change in draught force in the following discussions.
## 3 Materials and Methods
### Sample Characteristics  
While designed and prepared at MIT, the execution of the experiment spanned multiple universities to amass a larger dataset, enhancing the study's validity and applicability. The study sample consisted of students and faculty from three institutions: the Massachusetts Institute of Technology (MIT) in the USA, Tecnologico de Monterrey in Mexico, and the University of Applied Sciences of The Hague in the Netherlands. This culturally diverse participant pool included 84 individuals with an average age of 23.8 years (SD = 6.77), ranging from 18 to 64 years. Approximately two-thirds of the participants were male. Teams were predominantly mixed-gender (\(n=18\)), with male-only teams constituting a smaller portion (\(n=10\)); there were no female-only teams. Participants were required to have a working knowledge of English for effective communication in international teams.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.12. Aboveground Live Biomass Map in the Amazon Basin  
Based on forest plot data and remote-sensing-derived metrics collected from 1990 to 2000, [PERSON] et al. [5] mapped biomass spatial distribution in the Amazon basin over this period at 1 km resolution using the decision tree approach and a regression model, with an uncertainty greater than 70%.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 5 Results
### Comparison of Machine Learning Models for Classification  
In this subsection, we compare the performances of the seven (MLP, KNN, ridge, SVM, logistic regression (_one_\(\times\)_one_ and _one_\(\times\)_all_), LDA, and XGBoost) machine learning models for the classification task. The objective was to evaluate the effectiveness of each model in accurately classifying the target variables and identifying the most appropriate model. Table 12 summarizes the results and Figure 9 elucidates them better. The results with decimation yielded higher-performance models, such as XGBoost, ridge, and logistic regression, which showed the highest accuracies across the preprocessing methods, where they achieved over 96% accuracy. On the other hand, lower-performance models, such as SVM and MLP, achieved accuracies below 60%. These models generally showed a higher standard deviation with decimation, indicating less repeatability in the performances across different runs. The performance without decimation resulted in a significant improvement, where KNN and logistic regression (both types) achieved a 100% accuracy across all the preprocessing methods. This suggests that having less-spread data significantly aided in stabilizing and improving the performance. Conversely, for MLP, the accuracy decreased across the board, which might suggest overfitting issues or a poor interaction between the model complexity and reduced data variance through decimation. Moreover, a notable decrease in the standard deviation for most models indicates an increased consistency in the results with decimation. The results with decimation were faster because they involved fewer data points, which facilitated the discovery of a relationship between the feature vector \(\mathbf{x}\) and predicted class \(y_{p}\). The standard deviation was directly related to the preprocessing chain, which affected the consistency and variability of the model outcomes. This understanding should
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Relationship Between Evaluation Indicators and Forecasting Performance  
Our third case study examines the relationship between evaluation indicators and forecasting performance, focusing on how regional forecasting characteristics influence the time-varying weights in our fusion methodology. This analysis provides deeper insights into the adaptability of our weighting approach across different operational scenarios and regional characteristics.
## 4 Discussion
### Correlation between the Ionic Irradiation and Hardness and Young Modulus  
The discrepancies in the hardness decreases induced by the irradiation of He and Ar ions demonstrate that an ionic correlation can be established for the hardness reduction by ion irradiation. The obtained results revealed that such a correlation depends on the nuclear energy loss and the self-restoration induced by electronic energy deposition. It was found that, in the ion irradiation scenario, the hardness reduction originated from the displacement damages caused by the nuclear energy deposition [5; 11; 12; 17]. In this regard, Table 2 shows that the nuclear energy loss for Ar ion irradiation is about 80 times larger than that of the He ion irradiation, resulting in a significant damage level. Moreover, the numbers of the total displacements for irradiations of 0.5 MeV He ions and 2.0 MeV Ar ions were 164 and 5960, respectively. It was thus inferred that, at the same deposited nuclear energy, displacement levels caused by ballistic effects with Ar ion irradiation are significantly more than those caused by He ion irradiation. Meanwhile, as the mass and energy of the ion increased, the displacement damage intensified. This is because, in a highly dense displacement cascade, the atomic bonds would be severely disrupted, thereby decreasing the effective displacement energy of atoms [18].  
The obtained results showed that, as the irradiation fluence of He and Ar ions increased, the corresponding mean Young modulus increased slightly and uniformly. In other words, within an acceptable margin of error, there is no meaningful correlation in the modulus increase between the He and Ar ion irradiation. It was thus concluded that the variations of Young modulus can be determined by mean ring size, density, the transformation of [BO4] to [BO3] ions and network depolymerization in the glass structure [15; 19; 20]. It is worth noting that the last three factors also affect the hardness. Therefore, the mean ring size may be the main affecting factor for variations of the modulus in nonionic environments. The Raman spectra results showed that the reduction of the mean ring size induced by the heavier ion irradiation was greater than that of the lighter ion irradiation [11; 21].  
Figure 9: Distribution of modulus against electronic energy deposition in borosilicate glass (the magenta curve is the fitting curve of the modulus variation caused by Ar and He ion irradiation).
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## 3 Results  
### Comparison between the Performance of GPU-Accelerated AD and CPU AD Algorithms  
In the preliminary step, we compared GPU-accelerated AD and CPU AD algorithms. By repeatedly executing each algorithm 1000 times, we derived average times of solveAD as representative values, thereby minimizing the impact of external factors. For fixed values  
Figure 4: The two-layer skin model consists of the epidermis and dermis and corresponding optical parameters. \(f_{m}\)—volume fraction of melanin; \(a\)—scattering factor, where \(a=\mu_{s}\) (\(\lambda\) = 500 nm); \(b\)—scattering power; \(f_{Ray}\)—fraction of Rayleigh scattering; \(f_{Hb}\)—volume fraction of deoxyhemoglobin; \(f_{HbO2}\)—volume fraction of oxyhemoglobin; \(f_{turbb}\)—volume fraction of bilirubin; \(f_{COO2}\)—volume fraction of reduced cytochrome oxidase; \(f_{COO2}\)—volume fraction of oxidized cytochrome oxidase; \(d_{epi}\)—epidermis thickness; \(d_{d\sigma}\)—dermis thickness.  
of absorption coefficient \(muA=0.1/\)cm, scattering coefficient \(muS=25/\)cm, and a number of fluxes \(m=20\), we found that the GPU-accelerated AD algorithm was roughly seven times faster than the CPU AD algorithm (time per sample of \(1.5\times 10^{-4}\) s vs. \(1.1\times 10^{-3}\) s) (Figure 5). As shown in Figure 5, time per sample stabilized at around 1000 samples for GPU, while time per sample for CPU changed only marginally with the number of samples.
## 4 Experimental Considerations  
### Experimental Validation  
This study examines how varying initial electron energies and phases influence radiation characteristics in tightly focused, linearly polarized laser fields. The findings demonstrate that precise control over the driving laser's initial phase enables an effective  
Figure 8: Electron energy spectra corresponding to varying initial phases: (**a**) \(\phi_{0}=0\); (**b**) \(\phi_{0}=\pi/4\); (**c**) \(\phi_{0}=\pi/2\).  
Figure 9: Spectra of electrons at different initial energies: (**a**) initial energy \(gm_{0}=20\); (**b**) initial energy \(gm_{0}=40\); (**c**) initial energy \(gm_{0}=60\).  
manipulation of (1) the spatial profile of emitted radiation, and (2) key temporal and spectral properties. Notably, initial phase adjustment serves as a powerful tool for pulse structure modulation, enabling selective generation of single- or double-peak temporal spectra through controlled phase variation. Meanwhile, the pulse width will appear to change periodically; synchronized with the laser half-period. In the influence on the spectrum, the phase modulation of the harmonic intensity can be realized, which is maximum at \(\phi_{0}=k\pi\) and almost disappears at \(\phi_{0}=(k+1/2)\pi\), enhancing the odd harmonic selectivity, and, at the same time, the reconstruction of the spectral shape is realized. In the linearly polarized laser field, the modulation of the electron initial energy in the harmonic order, intensity, spatial distribution, and time domain also has a significant effect on the harmonic radiation characteristics. As the electron's initial energy increases, the harmonic intensity will rise sharply and the time-domain pulse width is compressed; at the same time, the compression of the harmonic spatial distribution leads to a more collimated radiation of the high-energy electrons, which is well applied in high-resolution imaging.
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 2 Research Method
### Multi-Scale Feature Fusion Hybrid Convolutional Attention Model(MUSCA)  
MUSCA is a residual mixed convolutional attention model based on multi-scale multi-layer perceptron fusion. The structure of MUSCA proposed in this article for the bird singing classification task is shown in Figure 2.  
The MUSCA begins with a \(7\times 7\) convolutional layer to down-sample the input features and generate a feature map with 64 channels, which is then fed into the hybrid convolutional attention block. Within this block, some input feature maps undergo down-sampling with a stride of 2, reducing the feature map size to decrease computational load and filter out irrelevant features. The model then utilizes two hybrid convolutional attention blocks with skip connections to form a residual block, allowing the network to learn residual mappings instead of direct mappings [11]. This design facilitates easier gradient flow through the network, addressing issues of vanishing and exploding gradients. Through multiple convolutional down-sampling and residual operations, the model generates feature maps at large, medium and small scales. These multi-scale feature maps undergo global average pooling, and the resulting features are passed through fully connected layers to obtain classification probabilities for multiple categories. These probabilities are normalized to the 0-1 range using the sigmoid activation function. The multi-scale predictions are concatenated, and the combined output is compared to the one-hot encoded true labels using binary cross-entropy loss. This step reduces inter-class distance and weakens irrelevant features. The design idea refers to the backbone network of YOLOv3; by down-sampling  
Figure 2: Structure of MUSCA. Conv2d 1*2 Stride 2 denotes a convolution kernel of length 1 and width 2, Stride is 2.  
the image and combining deconvolution up-sampling, the multi-scale features are spatially aligned to achieve multi-scale fusion [16]. Similar to this, our method also uses multi-scale feature maps, and the global average pooling features from large, medium and small scales are sent to the multi-scale feature fusion layer. Global average pooling is used to reduce the spatial dimension of the feature map while retaining the channel information. It also helps to avoid overfitting by reducing the number of parameters. The layer integrates, weighs and scales features from different scales and then maps the aggregated features to the final category probability through the fully connected layer.
## 5 Results and Discussions  
### Comparison Experiments  
The prediction results of our ST-RANet model for the outbound passenger flow of a station in the SubwayBJ dataset are shown in Figure 10.  
We compare the ST-RANet model with the following five baseline models using the same time periods of the dataset SubwayBJ. Their respective features and parameter settings are described below.  
HA: A basic and simple forecasting method that uses the average of historical observed values as the predicted value for future values, applicable to different types of data and industries.  
SARIMA: A model commonly used in time series analysis, capable of taking into account seasonality and trend. The parameters to adjust the suitable data are order = (p,d,q) = (3,0,5), seasonal_order = (P,D,Q,s) = (1,0,0,336), where the seasonal period is 336 that is, the number of time slices in a week.  
GRU: A type of RNN that has low model complexity and high flexibility, capable of providing high prediction accuracy. The optimal training parameters are set: the batch size is 32, the number of epochs in the training stage is 1000, and the learning rate is 0.001.  
Figure 10: Prediction results of ST-RANet on SubwayBJ.  
DeepST: A spatio-temporal data prediction model using CNN, which performs well in human flow predicting problems. The parameter setting is the same as ST-RANet.  
ST-ResNet: An excellent model for predicting human traffic flow in space and time, with the same parameter settings as ST-RANet.  
Table 3 and Figure 11 show the prediction performances of our ST-RANet model and other baseline models on SubwayBJ. Compared with the three time series models: HA, SARIMA, and GRU, it reduced RMSE and MAE by more than 78% and 87%, respectively. The MAPE indicator shows that less than 1% absolute error proportion is occupied, which reduces by 22-27%. Compared with the spatio-temporal model DeepST and ST-ResNet, our model also shows significant improvements in all metrics.  
As seen from Tables 2 and 3, high prediction results have been achieved with relatively small parameter values. With the resource allocation described above, the model training time is about 6000 s, which is less than one hour. The prediction time is only 0.3 s. This indicates that our model has low complexity and high expressiveness.  
Figure 11: Comparisons among different models on SubwayBJ.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE(\%)** \\ \hline HA [8] & 115.56 & 50.40 & 25.70 \\ SARIMA [10] & 127.33 & 66.63 & 27.93 \\ GRU [23] & 96.82 & 67.17 & 23.20 \\ DeepST [29] & 56.04 & 29.01 & 0.91 \\ ST-ResNet [31] & 56.60 & 27.37 & 1.24 \\
**ST-RANet** & **54.24** & **26.84** & **0.78** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons among different models on SubwayBJ.
## 2 Materials and Methods
### Experimental Design
#### 2.5.4 Additional Experiments  
In our endeavor to develop a more efficient and lightweight model for plant disease detection, we carried out supplementary experiments to verify the performance and robustness of the WD-YOLO model. We employed visual comparisons using heatmaps  
\begin{table}
\begin{tabular}{l c} \hline
**Hyperparameter** & **Value** \\ \hline Input resolution & 640 \(\times\) 640 \\ Batch size & 32 \\ Maximum epochs & 500 \\ Early stopping patience & 10 epochs \\ Initial learning rate & 0.01 \\ Optimizer & SGD \\ Momentum & 0.937 \\ Weight decay & 0.0005 \\ Learning rate schedule & Cosine annealing \\ Warmup epochs & 3 \\ Warmup momentum & 0.8 \\ Warmup bias learning rate & 0.1 \\ \hline \end{tabular}
\end{table}
Table 2: Training hyperparameters for all experiments.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **DSConv** & **WTConcat** & **Distillation** & **Data Augmentation** \\ \hline YOLOv10n & & & & **Augmentation** \\ YOLO-DA & & & & **✓** \\ YOLO-DS & ✓ & & & **✓** \\ YOLO-WT & & ✓ & & **✓** \\ WD-noDis & ✓ & ✓ & & **✓** \\ WD-noDA & ✓ & ✓ & ✓ & \\ WD-YOLO & ✓ & ✓ & ✓ & \\ \hline \end{tabular} Note: The symbol “✓” indicates that the corresponding module is included in the model on the left.  
\end{table}
Table 3: Different improvement schemes.  
generated from the YOLOv10l, YOLOv10n, and WD-YOLO models to assess the impact of the newly integrated DSConv convolutional module. Utilizing the gradient-weighted class activation mapping (Grad-CAM) technique [20], we generated heatmaps from images within the PlantDoc dataset. This allowed us to closely examine how each model processes and prioritizes features critical to identifying plant diseases.  
Moreover, the robustness of the WD-YOLO model was rigorously tested by introducing various types of noise, including Gaussian and salt-and-pepper noise, into the input images. We evaluated the model's resilience by observing changes in key metrics, such as mAP50, mAP50-95, accuracy, and recall rates. This comprehensive analysis provided insights into the model's ability to maintain operational stability under adverse conditions.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 3 Naming Scheme of SWIM Data
### TLC Structure in Naming Scheme  
In the proposed naming scheme, each OID consists of a set of variable-size information elements (IE) [26], and each IE is coded as a type-length-content (TLC). The size of the type and length fields is fixed, for example, 1 octet, and the size of the content field is variable. The type field indicates the type of element, and the length field defines the size of the content field included in the TLC. TLC may contain multiple sub-TLCs in its content field [27].  
As an example, a content owner or naming authority may assign an aviation flight plan with an OID, such as \"institutionTLC-sortTLC-subsortTLC-titleTLC-formTLC-segdivisionTLC\". The character \"_\" is used to separate elements for presentation purposes and does not belong to OID [28]. This content OID naming includes the content owner's organization ID, category ID, sub-category ID, title ID, where the ID is just the specified number. Another content owner can use different conventions to name their content objects.  
TLC naming meets uniqueness and durability requirements. As long as the content owner has a globally unique organization ID, the local content OID can be guaranteed to be globally unique. Because OIDs have the feature of not referencing locations, OIDs can be stored anywhere, which ensures the persistence of naming. Even if the object changes its management domain, the same OID can be used.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label Field** & **Attribute Value** & \multicolumn{4}{c}{**Attribute Value**} \\ \hline \multirow{3}{*}{Entity domain label} & Provider & Pro 1 & Pro 2 & Pro 3 & Pro 4 \\  & Resource Category & Aviation & Flight & Meteorological & Other \\  & Area & Area A & Area B & Area C & Area D \\ \hline \multirow{2}{*}{Behavior domain label} & Popularity & Level 1 & Level 2 & Level 3 & Level 4 \\  & Credibility & A & B & C & D \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of attribute label classification.