esa-sceva/satcom-chunk-collection huggingface dataset:
# Task-Aware Feature Composition for Few-Shot Relation Classification
## 5 Experiments
### Experiments on FewRel1.0 (General Domain)
#### 5.1.2 Baselines  
We compare our work with the following baselines:  
* Finetune(CNN). Using a convolution neural network as the basic sentence encoder and Softmax as the classifier, the model is trained with a traditional supervised learning method. During the test, the sentence encoder parameters are fixed, and the classifier's parameters are fine-tuned with a few supporting sentences in the test data.
* Finetune(PCNN). The training method used is the same as Finetune (CNN), except that the sentence encoder is changed to the piecewise pooled convolution neural network encoder (PCNN) proposed by [PERSON] et al. [32].  
kNN(CNN). Adopting CNN as the basic encoder and Softmax as the classifier and training the model using the traditional supervised method. In the test phase, the sentence representations of support and query sentences are acquired through the CNN encoder. Then, the sentence representations are clustered using the K-nearest neighbor algorithm.
* kNN(PCNN). The core idea is the same as kNN(CNN), except that the encoder used is PCNN.
* Meta Network. A meta-network [23] utilizes a high-level _meta-learner_ on top of the traditional classification model.
* GNN [23]. This work considers each support sentence or query sentence as a node in the graph and employs a graph neural network to propagate the information between nodes.
* SNAIL [23]. A meta-learning model that utilizes temporal convolutional neural networks and attention modules for fast learning.
* Proto [23]. This work assumes there exists prototype embedding for each support class, and for each class, they average all support sentence representations in the class as the class prototype, then compute the distance between query and class prototypes.
* Proto-HATT [10]. Incorporating a hybrid attention mechanism into a prototypical framework.
* HAPN [33]. It proposes word-level, feature-level, and instance-level attention to obtain a more discriminative prototype of each class.
* MLMAN [12]. The basic encoder we used in our paper; it iteratively utilizes matching information between query and support sentences to refine the feature representations.
* BERT-PAIR [11]. A pretraining-based model pairs each query instance with all the supporting instances and sends the concatenated sequence to the BERT classification model to obtain the two instances' score expressing the same relation.
* CTEG [25]. A model equipped with an entity-guided attention mechanism and confusion-aware training strategy to learn to decouple easily confused relations.
## 4 Results  
### Simulation Results  
The initial application of this NN was on the simulated dataset. The input parameters consisted of the complex scattering matrix. To reduce dimensionality without compromising information, only the magnitudes of the reflection and transmission coefficients at port 1 were utilized, only at the resonant frequency \(f_{0}=13.56\)\(MHz\): \(|S_{11}(f_{0})|\) and \(|S_{21}(f_{0})|\)  
Figure 10: Data measured at a fixed coil distance of \(z_{0}=4\) mm for six different load conditions. Data regularization is needed due to measurement error.  
Figure 9: Data measured at fixed load condition of \(R=100\)\(\Omega\), for three different distance coil conditions, with \(z_{0}\) 0, 4 and 8 mm. Data regularization is needed due to measurement error.  
The NN output was the distance value, an array of 57 elements. This NN was applied to each of the 229 configurations.  
Figure 11 illustrates the training process for a representative configuration. The observed MSE of approximately 0.05 indicates near-perfect prediction performance on the testing dataset, with an error of approximately 1%. Figure 12 presents the same plot for the configuration exhibiting the worst prediction performance. Even in this case, the MSE = 0.199 and the mean error is about 1.34%, suggesting that, except for a few points, the prediction accuracy is high. The NN's worst performance was consistently observed for non-zero tilt (\(\theta\)) values. It is hypothesized that the angular misalignment between the coils introduces stronger nonlinearities or more complex system behavior, which are more challenging for the NN to model accurately.  
Figure 11: Training of the NN to predict the distance value from the scattering parameters for the configuration 20: \(\theta=0\), \(\Delta x=20\) mm, \(\Delta y=15\) mm. Output and prediction values of the distance. The plot seems random because, for more generalizability, the input and output data were randomized and fed to random order to the NN.  
Figure 12: Training of the NN to predict the distance value from the scattering parameters for the configuration 210: \(\theta=60\) deg, \(\Delta x=30\) mm, \(\Delta y=20\) mm. Output and prediction values of the distance. The plot seems random because, for more generalizability, the input and output data were randomized and fed to random order to the NN.
# Analytical Prediction of Tunnel Deformation Beneath an Inclined Plane: Complex Potential Analysis
## 3 Mathematical Analysis  
### Asymmetric Stress around the Tunnel  
With population growth, many engineers are tunnelling under the slope. This causes an asymmetric stress distribution on the tunnel wall. The stress on the ground surface is determined by the compression load between the tunnel wall and the slope surface. Using the stress in term of the Airy stress function, Equation (2) can be rewritten as follows:  
\[F=F_{1}+iF_{2}=U-xc-yd \tag{4}\]  
where \(F\),\(c\),\(d\) are the harmonic function. The proposed expressions in Equation (3) can be found in Equation (A1). The final displacement can be proposed in the form:  
\[\frac{\partial^{2}U}{\partial x^{2}}+i\frac{\partial^{2}U}{\partial y\partial x }=z\overline{\varphi^{\prime\prime}(z)}+\varphi^{\prime}(z)+\overline{ \varphi^{\prime}(z)}+\overline{\Psi^{\prime\prime}(z)} \tag{5}\]  
where \(\varphi^{\prime}\)(z) is illustrated in Appendix A.  
According to the Kolosov-Muskhelishvili method, the formulas offer the best method to treat the 2D crack problem (Equation (A1)). The force applied (Equation (A2)) can result in regular and continuous displacements [38]. The stress described by the Airy function can be given as follows:  
\[\sigma_{xx}+\sigma_{yy}=2\Big{(}\varphi^{\prime}(z)+\overline{\varphi(z)} \Big{)} \tag{6}\]  
\[\sigma_{yy}-\sigma_{xx}+2i\tau_{xy}=2\big{\{}\overline{z}\varphi^{\prime\prime }(z)+\Psi^{\prime}(z)\big{\}} \tag{7}\]  
Figure 1: Geometrical representation of a shallow tunnel model beneath an inclined plane.  
Figure 2: Basic displacement of the tunnel wall.  
Equations (6) and (7) indicate the complex potential of the stress. Hence, the stress derivatives give \(\sigma_{xx}=\partial^{2}U/\partial x^{2}\) (horizontal stress), \(\sigma_{yy}=\partial^{2}U/\partial y^{2}\) (vertical stress) and \(\tau_{xy}=\partial^{2}U/\partial x\partial y\) (shear stress).
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Giro
This information is processed to derive the place on the vertical axis where the radio pulses have been reflected and it is able to show if the reflective layer is tilted or if it presents irregularities. Moreover, it can also show if the reflection points move respect to the transmitting source (if the reflective layer is dynamic), providing the apparent drift speed of the reflective layer. The Lowell GIRO Data Center (LGDC) implements a suite of technologies for post-processing, modeling, analysis and dissemination of the acquired and derived data products such as:  
* IRI-based Real-time Assimilative Model (IRTAM), that builds and publishes every 15-min an updated \"global weather\" map of the peak density and height in the ionosphere, as well as a map of deviations from the classic IRI climate;
* Global Assimilative Model of Bottomside Ionosphere Timelines (GAMBIT) Database and Explorer holding 15 years worth of IRTAM computed maps at 15 min cadence;
* More than 17 million ionograms and matching ionogram-derived records of URSI-standard ionospheric characteristics and vertical profiles of electron density;
* More than 10 million records of the Doppler Skymaps showing spatial distributions over the GIRO locations and plasma drifts;
* Data and software for Traveling Ionospheric Disturbance (TID) diagnostics;
* HR2006 ray tracing software mated to the \"realistic\" IRTAM ionosphere. In cooperation with the URSI Ionosonde Network Advisory Group (INAAG), the LGDC promotes cooperative agreements with the ionosonde observatories of the world to accept and process real-time data of HF radio monitoring of the ionosphere, and to promote a variety of investigations that benefit from the global-scale, prompt, detailed, and accurate descriptions of the ionospheric variability. GIRO provides access to Automatic Real-Time Ionogram Scaler with True height (ARTIST) software, which is an \"autoscaler\" of ionogram traces. Use of real-time ARTIST-derived data as input to ionospheric models and associated algorithms (e.g., ray tracing and optimal frequency allocation) started in 1988 when the USAF Weather Agency (AFWA) established a network of 18 digisondes. Since then, other agencies have built similar networks: (i) the Digital Atmospheric Server (DIAS) became operational in 2004 with 6 contributing digisondes located in Europe; and (ii) the Jindalee Operational Radar Network of the Australian government installed 11 digisondes to provide data to their real time ionospheric model. GIRO data have also been used to update the Boeing Plasma Interaction Model (PIM) in support of spacewalk scheduling for the International Space Station. Figure 3: Evolution of digisonde sounders since 1969.
## 3 Results and Discussion
### Free Radical Scavenging Activity upon the Addition of Five Peptides in Chi-Aroma Baijiu  
Following a three-year soaking period and three months of aging, DMPO was employed as a trap to capture free radicals. These free radicals combine with \({}^{\alpha}\)C of \({}^{\alpha}\)C=N groups on DMPO molecules to form free radical adducts [37; 38]. The identification of free radical types was contingent upon the g value and ultrafine cleavage of these adducts. Two types of free radical compounds were identified: hydroxyl radical adducts (DMPO-OH, g = 2.0038, \({}^{\alpha}\)N = 15.81 G, \({}^{\alpha}\)H = 15.6 G) and alkyl radical adducts (DMPO-R, g = 2.0038, \({}^{\alpha}\)N = 15.88 G, \({}^{\alpha}\)H = 22.5 G), as shown in Figure 4A. [PERSON] [39] also identified the presence of free radicals in red wine using electron paramagnetic resonance (EPR). Upon the addition of a 0.1 mg/mL peptide solution, the total spin of free radicals in Chi-aroma Baijiu was changed (Table 1). Depending on the total amount of spin, the hydroxyl radical clearance rate was almost 100%. The alkoxy radical scavenging rates of peptides PV, PA, GL, DF, and GSH were 10.84%, 11.54%, 36.37%, 27.27%, 35.66%, and 34.27%, respectively, as shown in Figure 4B.  
Figure 3: Free radical scavenging capacity of five peptides Pro-Ala (PA), Pro-Val (PV), Gly-Leu (GL), Asp-Phe (DF), and Pro-Leu (PL). Different letters represent statistically different values at \(p<0.05\).  
The total quenching of hydroxyl radicals may be due to peptides containing acidic functional groups that react with OH- to yield H\({}_{2}\)O [40]. Hydroxyl radicals can target both the amino acid side chains and the peptide chain backbone, resulting in the generation of several peptide radical derivatives [41]. The sulfhydryl (-SH) group of GSH provides hydrogen and free-radical-pairing electrons, thereby contributing to a strong free radical scavenging effect [33]. Moreover, they can react with peptide side chains containing amino acid residues such as valine (Val) and leucine (Leu) to form 3- and 4-hydroperoxyaline or 4- and 5-hydroperoxyleucine, thus leading to the complete quenching of hydroxyl radicals in Baijiu [42]. Therefore, the five peptides obtained from distillers' grain filtrate exhibit potential as effective radical scavengers.
can interact, such as peptides and proteins. These findings provide potential insights into the application of peptides in regulating the flavor of Baijiu. It is essential to explore the sensory changes in Baijiu resulting from the influence of peptides on characteristic volatile substances through sensory evaluation in future studies.
## 2 Results
### Comparative Analysis of VOC Profiles and Implications for Conservation  
A comparative analysis of VOCs based on peak area integration highlights the distinct degradation mechanisms of each book (Table 1). The 16 th-century book exhibited high levels of long-chain alkanes (e.g., tetradecane at 12.48% and hexadecane at 10.45%) and moderate levels of aldehydes (e.g., decanal at 4.76% and nonanal at 2.36%), indicating that oxidative degradation is the primary pathway, consistent with the stability of rag-based paper. The minimal concentration of furfural (0.11%) further supports the chemical resilience of this material against acid hydrolysis, demonstrating the durability of rag-based paper in historical artifacts.  
In contrast, the 19 th-century book showed a higher concentration of furfural (0.31%), reflecting its vulnerability to acid-catalyzed hydrolysis due to the inherent acidity of wood pulp paper [12]. This increased susceptibility to hydrolytic degradation in the 19 th-century book underscores the rapid deterioration that acidic materials face, especially in environments where preservation conditions may fluctuate. The relatively lower levels of aldehydes in this book compared to the 16 th-century book suggest that oxidation is a secondary degradation pathway, less significant in the chemically unstable environment created by acidic wood pulp.  
These findings are relevant for heritage science as they reveal how historical manufacturing methods and material choices directly impact the longevity of cultural artifacts. The chemically stable nature of rag-based paper used in the 16 th-century book suggests that such materials are inherently better suited for long-term preservation, with oxidative degradation occurring at a relatively slow pace. This durability makes rag-based artifacts more resilient to environmental fluctuations and less dependent on rigorous conservation efforts.  
Conversely, the findings for the 19 th-century book emphasize the inherent vulnerability of wood pulp paper to acidic degradation. The high furfural content highlights a pressing need for targeted conservation strategies, especially for collections dominated by 19 th-century and early 20 th-century materials [12]. Preservation efforts for such books may require more stringent environmental controls to limit acid hydrolysis, such as maintaining stable humidity levels, reducing temperature fluctuations, and possibly applying deacidification treatments to neutralize the acidic compounds within the paper fibers.  
Additionally, the presence of menthol and synthetic compounds in both books points to historical or contemporary preservation interventions, reflecting an ongoing awareness of the need to mitigate microbial damage and extend paper longevity [13; 14; 15]. These findings underline the role of active preservation treatments and monitoring, especially for vulnerable wood pulp-based collections, and suggest that conservation strategies can benefit from regular VOC profiling to assess the effectiveness of these treatments over time.
## 2 Materials and Methods
### Statistical Analysis  
Data are presented as mean \(\pm\) SD using IBM SPSS Statistics for Windows, Version 27.0., Armonk, NY, USA. The variables' normal distribution was confirmed by [PERSON]'s test and variance homogeneity by [PERSON]'s test. Descriptive analysis was obtained for all variables. Student's \(t\)-test for independent samples with effect size estimation was used for comparing tops and bases. The guidelines used for the interpretation of [PERSON]'s d effect size were that a small effect = 0.2, a medium effect = 0.5, and a large effect = 0.8 [27]. The significance level was set at \(p\leq\) 0.05.  
A deep artificial neural network model was then developed to assess the relationship between the dependent variables and gymnasts' \(\text{FV}_{\text{imb}}\). Considering that 28 gymnasts participated in this work and that each gymnast performed two jumping performance evaluations, the neural network accounted for 56 inputs, since each subject represents an independent input. The structure was developed in Python (version 3.9.2) through the TensorFlow library (Google), using an input layer with 15 variables, representing the dependent variables, 3 hidden layers (hyperbolic tangent, sigmoid, and linear rectified activation functions, respectively) with 1000 neurons each, and the output layer with 1 variable, representing the independent variable (\(\text{FV}_{\text{imb}}\), Figure 1).  
The sample was randomly divided into training and test sets (70% and 30% of the subjects, respectively). In the model training, the root mean square propagation adaptive learning algorithm was used, with a loss function based on the mean square error and a learning rate of 0.001. A limit of 200.000 epochs was established for training, and a checkpoint function was used to store the information of the best weight configuration (by monitoring the root mean square error), thus avoiding overtraining. The final model reached a value of 0.0001 of the latter error after 58.480 epochs, with R\({}^{2}\) = 0.97. Training, test sets, and all subjects' performance were measured using the coefficient of determination of linear regressions between targets and output values (Figure 2). The model interpretation was based on the Shapley additive explanations values (SHAPs), which are aligned with human intuition and allow to discriminate among model output classes with improved generalization and representation of the nonlinear systems' behavior, like human athletic performance [28; 29].  
Figure 1: Model structure of the artificial neural network used. Input layer with 15 variables, representing the dependent variables, 3 hidden layers with 1000 neurons each, and the output layer with 1 variable (F-V imbalance), representing the independent variable.
## 2 Materials and Methods
### Model Evaluation and Validation  
The paper uses four evaluation indicators to assess the accuracy of the 3 PG-MT-LSTM model: \(R^{2}\), \(RMSE\), \(MAE\), and \(MAPE\) (Equations (10)-(13)). Among them, \(y_{i}\) represents the observed biomass value at time \(i\), \(\hat{y_{i}}\) represents the estimated biomass value at time \(i\), \(\overline{y}\) represents the average value of the observed biomass.  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(\hat{y_{i}}-\overline{y}\right)^{2}}{\sum_{i=1} ^{n}\left(y_{i}-\overline{y}\right)^{2}} \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y_{i}}-y_{i}\right)^{2}} \tag{11}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert\hat{y_{i}}-y_{i}\rvert \tag{12}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left\lvert\frac{\hat{y_{i}}-y_{i}}{y_{i}}\right\rvert \times 100\% \tag{13}\]  
\(R^{2}\) measures the proportion of the variance in the dependent variable that is predictable from the independent variable. The closer \(R^{2}\) is to 1, the better the model performance. _RMSE_, _MAE_, and _MAPE_ measure the error between the predicted and observed values. When _MAE_ and _MAPE_ are used together, they provide a comprehensive evaluation of the model's fit to data of different scales. In the context of this paper's data, using both metrics can help to assess the model's performance in simulating the stand biomass of different ages. Combining the above metrics can provide a more comprehensive evaluation of a regression model's accuracy.
## 2 Traditional Canny Algorithm and Its Improvement
### Traditional Canny Algorithm
#### 2.1.2 Limitations  
The advantage of the traditional Canny algorithm is that it can balance noise suppression and edge location accuracy, but there are some difficult problems to solve. The oversmoothing problem of the Gaussian filter: the standard Gaussian filter will blur the step edge while reducing noise, resulting in the loss of weak edge; limitations of fixed thresholds: global thresholds can produce broken edges when illumination is uneven; and pixel level accuracy is insufficient: the error of traditional methods in sub-micron level measurement tasks can reach 5-8% [14].
## 3 Results
### Vegetation Health
#### 3.1.2 PhenoCam Validation  
A correlation between the Landsat-derived GPP and the PhenoCam GCC was found at ten of the eleven sites. For the AT sites, the average \(\mathrm{r^{2}}=0.81\), CDT average \(\mathrm{r^{2}}=0.81\), and PCT average \(\mathrm{r^{2}}=0.47\), with all the correlations exceeding the 99% confidence level. There was variation in the GPP and GCC fit between specific PhenoCam sites, with forested eastern locations such as the AT NEON D02.SERC.DP1.00033 PhenoCam site having a stronger correlation (\(\mathrm{r^{2}}=0.872\)) and western sites such as the PCT Sagehen PhenoCam site having a weaker correlation (\(\mathrm{r^{2}}=0.5\)) (Figure 4). Sites within the Eastern Temperate Forest and Northern Forest ecoregions had a better fit, while the Californian Mediterranean and Northwestern Mountainous Forest ecoregions did not perform as strongly [73].