esa-sceva/satcom-chunk-collection huggingface dataset:
# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 3 News Text Summarization Model Based on Framing Theory  
### Overview  
This paper presents a news text summarization model founded on framing theory, utilizing an end-to-end neural network architecture. The model is built around an encoder-decoder core and incorporates key technologies such as frame identification and multi-head attention mechanisms. It comprises two main modules: a frame identification module and a summary generation module. The model's workflow is illustrated in Figure 1.  
The frame identification module is mainly responsible for extracting frame semantic features from the text and predicting the dominant frame category of the news, providing prior frame knowledge for subsequent summary generation.  
The summary generation module consists of two components: a frame-aware encoder and a frame-guided decoder. In the frame-aware encoder, a frame embedding mechanism is introduced to enhance its ability to model frame semantics. In the frame-guided decoder, the frame representation is used as additional input to the decoder, guiding the decoder'sattention mechanism to adaptively focus on frame-relevant text segments, ultimately generating high-quality summaries that closely align with the dominant frame of the news article.
## 5 Performance Characteristics  
For all measurements, the clearance height has been kept constant at a value of 1 mm which corresponds to 2.4% of the blade height. Total-to-static pressure rise characteristic curves were captured at selected rotational speeds (5500 rpm (A) and 9000 rpm (B)) for 20 operating points, see Figure 3a. For both characteristics the stalling flow coefficient is at \(\varphi=0.425\), the maximum pressure rise  
Figure 2: Rotor mesh.  
coefficient is about \(\Psi=0.5\). A further reduction of the mass flow leads to a sudden decrease of the pressure rise coefficient. Low-pass filtered pressure traces being measured by the pressure transducers upstream of the leading edge plane comprise harmonic fluctuations covering approximately one rotor revolution (Figure 3b; B20). Thus, stall is initiated by long length-scale stall cells (LLSC). They become visible in the pressure traces when the flow coefficient is below the stalling flow coefficient (Figure 3a between A18-A19 and B19-B20 respectively).  
At stable operation (B5) the pressure trace is dominated by the blade passing pressure signature (see Figure 3b). The mean blade passing of an entire rotor revolution has been calculated by the phase average of 100 revolutions. The resulting sequence contains pressure fluctuations from 14 blades, whereby two of them are depicted in Figure 3b (top/left) as a black thick line. Additionally, the data from all revolutions are plotted in red. This analysis procedure was then repeated for an operating point at near stall conditions (B18). It is clearly visible that the variation in blade passing pressure signature increases when the compressor stage approaches stall. The question now arises whether any kind of coherent flow structure is responsible for this variation. To answer this question, the difference between the average blade passing signal and the signal for one individual revolution has been evaluated (see Figure 4). The resulting sequence shows significant pressure drops which are known as prestall propagating disturbances. The pressure spikes, highlighted by red triangles (cf. Figures 3b and 4), appear stochastically distributed and with varying intensities. The following chapters concentrate on the flow non-uniformity which does not appear briefly but in a wide operating range before stall.  
Figure 3: Compressor performance (Experiment).
# Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
## 3 Experimental Results  
### Convolutional Neural Network  
First, the data were divided into a train (80%) and a test (20%) sets. Second, the train set was divided further into a training (90%) and a validation (10%) set. Because of the high imbalance of the classes, we used a stratified split to ensure that the fraction of images from the same class was similar in train and validation sets [61]. To prevent data snooping, only the training and validation set were used during model training, and the test set was kept separately the whole time and used only to test the final the model.  
The final model had a performance of 80% accuracy on the test set. Figure 2 shows the confusion matrix of the test set for our final model, and Figure 3 shows the percentage of correct classifications as a bar plot. As expected, performance was best for the melanocytic nevi (nv) class, which had the most images to learn from. It was worst for the actinic keratoses (akiec) class, which had the third least images to learn from, and, thus, is one of the minority classes in the dataset. More training images of the minority classes (i.e., dermatofibroma, vascular lesions, and actinic keratoses) would help the classifier to extract more specific characteristics of these three classes and, thus, improve the overall classification performance.  
Figure 2: Classification result (confusion matrix) of the test set on the trained CNN model.
### Scope and Contributions  
Over the years, there has been an ample amount of work carried out in energy markets. Nevertheless, the REM still has some challenges to overcome. The utmost challenge is that retailer charges are fixed averaged prices for the consumers, thus benefits of low-cost power during off-peak load are not truly translated to the consumers [13]. Thus, system operators fail to curtail the peak demands and alter the energy usage pattern of consumers, although consumers are offered different price slabs, like real-time, flat rate, and time-of-use [14]. In the REM, although consumers can change suppliers, the execution and cancellation of the supplier are not free of cost; as a result, this additional cost restricts the number of consumers who avail themselves of such a facility [15]. The problems being faced by consumers in the WEM and REMs can be solved by combined operation of generation, storage, and demand response through introducing local energy markets (LEMs).  
LEMs are decentralized systems operating at the microgrid or community level, focusing on peer-to-peer (P2P) energy trading, local optimization, and demand-response using distributed energy resources (DERs), like solar panels and batteries. They rely on smart grid technologies, IoT, and blockchain for real-time monitoring and transactions, with shorter settlement periods and emerging regulatory frameworks. In contrast, large-scale energy markets (LSEMs) operate at regional or national levels, managing bulk energy generation and transmission using centralized power plants and established market structures (e.g., day-ahead and ancillary services). LSEMs prioritize grid stability, large-scale optimization, and compliance with stringent regulations. The key technical difference lies in their infrastructure, scale, participants, and goals--LEMs emphasize local resilience and sustainability, while LSEMs focus on reliability, efficiency, and macro-level energy policy alignment.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Comparative Discussion  
The existing methods employed for demand forecasting, such as RF regressor, ensemble ARIMA-NN, distributed RF-GB, LSTM grid search, recurrent BiLSTM, and NARX, have some limitations. RF regressor [41] can be prone to overfitting and there are difficulties regarding interpretability, whereas the developed UDAF model can resolve interpretability issues, attaining high performance on various datasets. Ensemble ARIMA-NN [49] is also prone to overfitting and cannot hold for time series data, whereas the developed UDAF model reduces the risk of overfitting by reducing the number of parameters in the model. The training of the LSTM grid search [50] is expensive. In recurrent BiLSTM [43], the BiLSTM can increase the model complexity. The developed UDAF model reduces the model complexity and attains efficient results by considering data beyond historical data. NARX models [44] assume that the future output depends only on past inputs, which may not hold in all cases. In order to solve these above issues, the proposed UDAF model is developed, combining recurrent BiLSTM and NARX, which benefits from the complementary strengths of these two approaches. The BiLSTM captures temporal dependencies, while NARX incorporates autoregressive features and can look beyond historical information and account for external factors that may affect demand. This synergy leads to improved forecasting accuracy and robustness compared to individual methods and reduces the complexity of the model. The results of the demand forecasting models are analyzed in Tables 1-3, indicating that the suggested model performs better than others in terms of MAE, MSE, RMSE, and R-squared. In particular, the proposed UDAF model showcased significantly lower values for MAE, MSE, RMSE, RMSSE, and R-squared, of 1.73, 4.14, 2.03, 0.020 and 0.94, respectively, for a delay of 25.  
Figure 9: Comparative analysis of daily demand forecasting orders based on K-fold: (**a**) MAE, (**b**) MSE (**c**) RMSE, (**d**) RMSE, and (**e**) R-squared.
# Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model
## 2 Materials and Methods
### Introduction of the LSSVM Model
The kernel function has the ability to reduce the computational complexity in high-dimensional space and plays a crucial role in constructing high-performance least-squares support vector machines. Therefore, the final optimization function of LSSVM is:  
\[y(x)=\sum_{i=1}^{n}\xi_{i}K(x,x_{i})+b \tag{9}\]  
And the kernel function adopts the radial basis function (RBF):  
\[\mathbf{K}\big{(}x_{i},x_{j}\big{)}=exp\Bigg{(}-\frac{\left\|x_{i}-x_{j}\right\|^ {2}}{2\sigma^{2}}\Bigg{)} \tag{10}\]  
where \(\sigma\) is the bandwidth parameter of the kernel function, \(\sigma>0\). In the application of the LSSVM model, the selection of the hyperparameters \(c\) and \(\sigma\) plays a crucial role in the overall performance of the model. Therefore, the subsequent discussion addressed the process of determining these parameters.
## 2 Methods
### Land Cover Data  
Two different land cover datasets were used in this study. For the study regions in Canada, we used the land cover products developed by the Canadian Forest Service and Canadian Space Agency joint project, Earth Observation for Sustainable Development of Forests (EOSD), based on Landsat 7 ETM\(+\) data, and represents _circa_ year 2000 conditions. The land cover map represents 23 unique land cover classes mapped at the spatial resolution of 25 m [30]. The accuracy of the land cover classification was found to be 77%, achieving a target accuracy of 80%, with a 90% confidence interval of 74-80% [31]. Land cover products for the study area were acquired from the EOSD data portal ([[http://www4.saforah.org/eosdlcp/nts_prov.html](http://www4.saforah.org/eosdlcp/nts_prov.html)]([http://www4.saforah.org/eosdlcp/nts_prov.html](http://www4.saforah.org/eosdlcp/nts_prov.html))). For the study region in China, we used the land cover products (100 m) at the scale of 1:250,000, developed by the Earth System Scientific Data Sharing Network (ESSDSN). The qualitative accuracy of the land cover classification was found to be 80-90% [32]. The land cover classification product in 2005 was acquired from the ESSDSN portal ([[http://www.geodata.cn/Portal/metadata/viewMetadata.jsp?id=100101-11860](http://www.geodata.cn/Portal/metadata/viewMetadata.jsp?id=100101-11860)]([http://www.geodata.cn/Portal/metadata/viewMetadata.jsp?id=100101-11860](http://www.geodata.cn/Portal/metadata/viewMetadata.jsp?id=100101-11860))). The land cover data were resampled to Landsat spatial resolution (30 m) using the nearest neighbor method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Study Area**} & \multicolumn{2}{c}{**Landsat**} & \multicolumn{2}{c}{**MODIS**} \\  & **Date** & **Path/Row** & **Date** & **Tile** \\ \hline \multirow{3}{*}{Monthly changes over a} & 24 May 2001 & & 17–24 May 2001 & \\  & 11 July 2001 & 37/22 & 4–11 July 2001 & h11v03 \\ \multirow{3}{*}{forested area} & 12 August 2001 & & 5–12 August 2001 & \\  & 19 October 2001 & & 16–23 October 2001 & \\ \multirow{3}{*}{Annual changes of heterogeneous region} & 13 April 2002 & 122/41 & 7–14 April 2002 & h28v06 \\  & 7 November 2002 & & 1–8 November 2002 & \\ \multirow{3}{*}{Changes of heterogeneous region over several years} & 13 May 2001 & & 9–16 May 2001 & \\  & 8 May 2005 & 16/25 & 1–8 May 2005 & h13v04 \\ \multirow{3}{*}{
\begin{tabular}{c} Changes of heterogeneous region over several years \\ \end{tabular} } & 8 September 2009 & & 6–13 September 2009 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Landsat/ Moderate-resolution Imaging Spectroradiometer (MODIS) data used for the three study areas.
## 2 Periodic Haar Piecewise-Linear PHL Transform
### One-Dimensional PHL Transform  
To perform forward and inverse PHL transform, the following matrix equations can be used:  
* Forward transform \[[\text{C(N)}]=[-\frac{1}{2^{k+1}}][\text{PHL(N)}][\text{X(N)}]\] (3)
* Inverse transform \[[\text{X(N)}]=[\text{IPHL(N)}][\text{C(N)}]\] (4)  
where [C(N)]--vector of PHL coefficients (PHL spectrum);  
[X(N)]--vector of sampled signal;  
[PHL(N)]--matrix of forward transform;  
[IPHL(N)]--matrix of inverse transform;  
[\(-\frac{1}{2^{k+1}}\)]--diagonal matrix of normalization.  
\[[-\frac{1}{2^{k+1}}]=diag[1,-\frac{1}{2^{1}},-\frac{1}{2^{2}}(2\;times),- \frac{1}{2^{3}}(4\;times),...,-\frac{1}{2^{k}}(2^{k-1}\;times)] \tag{5}\]  
The first row of the forward transform matrix consists of number one at the first position and the remaining elements are equal to zero. Other rows are composed of derivatives (in a distributive sense) of periodic Haar functions. The matrix for the inverse transform [IPHL(N)] is constructed in such a way that particular rows consist of PHLfunction values calculated for the same argument. For instance, the [PHL(N)] and [IPHL(N)] matrices, for N = 8, are presented below:  
\[[\text{PHL}(8)]=\begin{bmatrix}1&0&0&0&0&0&0\\ 2&0&0&0&-2&0&0&0\\ \sqrt{2}&0&-2\sqrt{2}&0&\sqrt{2}&0&0&0\\ \sqrt{2}&0&0&0&\sqrt{2}&0&-2\sqrt{2}&0\\ 2&-4&2&0&0&0&0\\ 0&0&2&-4&2&0&0&0\\ 0&0&0&0&2&-4&2&0\\ 2&0&0&0&0&2&-4\end{bmatrix} \tag{6}\]  
\[[\text{IPHL}(8)]=\begin{bmatrix}1&0&0&0&0&0&0&0\\ 1&\frac{1}{4}&\frac{\sqrt{2}}{2}&0&2&0&0&0\\ 1&\frac{1}{2}&\sqrt{2}&0&0&0&0&0\\ 1&\frac{3}{4}&\frac{\sqrt{2}}{2}&0&0&2&0&0\\ 1&1&0&0&0&0&0&0\\ 1&\frac{3}{2}&0&\frac{\sqrt{2}}{2}&0&0&2&0\\ 1&\frac{1}{2}&0&\sqrt{2}&0&0&0&0\\ 1&\frac{1}{4}&0&\frac{\sqrt{2}}{2}&0&0&0&2\end{bmatrix} \tag{7}\]  
In this case, according to Equation (5), the diagonal matrix of normalization takes the following form:  
\[[-\frac{1}{2^{k+1}}]=\begin{bmatrix}1&0&0&0&0&0&0&0\\ 0&-\frac{1}{2}&0&0&0&0&0\\ 0&0&-\frac{1}{4}&0&0&0&0\\ 0&0&0&-\frac{1}{4}&0&0&0&0\\ 0&0&0&-\frac{1}{8}&0&0&0\\ 0&0&0&0&0&-\frac{1}{8}&0&0\\ 0&0&0&0&0&-\frac{1}{8}&0\\ 0&0&0&0&0&0&-\frac{1}{8}\end{bmatrix} \tag{8}\]  
It can be observed that:  
\[[-\frac{1}{2^{k+1}}][\text{PHL}(8)][\text{IPHL}(8)]=[\text{I}(\text{N})] \tag{9}\]  
where [I(N)] is the identity matrix.
## 2 Model Description
Figure 3 shows the grid independent test results, with the grid type shown in Figure 2 and the grid adaption. Numbers of elements of 75,159, 94,102, 124,772, and 178,272 are tested. The test cases are performed with geometric parameters of \(P_{r}\) = 6, \(B_{r}\) = 0.375, \(a\) = 60\({}^{\circ}\), and Reynolds number of 15,000, where the Reynolds number is defined as \(Re=\rho UD_{h}/\mu\). The results show that the Nusselt number changes negligibly with the number of mesh elements from 124,772. Therefore, to reduce the computation time and cost, the settings at this mesh number are used for further studies. Figure 4 presents a comparison of the simulation results in this study with the experimental results of [PERSON] [24]. In the experimental study, an inclined baffle is placed opposite the heating plate to examine the thermohydraulic behavior. A sketch of the experiment and the dimensions (in millimeters) are shown in Figure 4a. The results show that there is good agreement between the simulation results in the current study and the experimental results. Average errors of 5.89% and 11.47% are found for the comparison of the Nusselt number and pressure drop, respectively. The validation between the numerical approach and the empirical correlations for a smooth solar air heater duct was presented in our previous study [7]. Therefore, it is not displayed here for the sake of brevity. Then, the numerical methodology is constructed for 16 runs according to the orthogonal array of the Taguchi method. Figure 4: _Cont._  
Figure 3: Grid independence test in the case of \(Re=15,000\), \(P_{r}=6\), \(B_{r}=0.375\), and \(a=60^{\circ}\). To evaluate the thermohydraulic performance of a baffled duct compared to a smooth duct, [PERSON]'s Nusselt number equation (\(Nu_{s}\)) and [PERSON]'s friction factor equation (\(f_{s}\)) are used as follows [15]:  
\[Nu_{s}=\frac{(f_{s}/8)(Re-1000)Pr}{1+12.7\sqrt{f_{s}/8}(Pr^{2/3}-1)}\text{ for }3000\ <\ Re\ <\ 5\ \times\ 10^{6}\text{ and }0.5\ <\ \Pr\ <\ 2000, \tag{4}\]  
Figure 1.0  
where \(Pr\) is the Prandtl number and \(Pr=c_{p}\mu/k\). Once obstructions are introduced into the fluid channel, it is certain that both the heat transfer and pressure loss increase. Therefore, the thermohydraulic performance parameter below is used to evaluate the gain and the loss. Additionally, the thermohydraulic performance parameter is maximized in the present study to enhance heat transfer with a moderate pressure loss penalty. The parameter is developed on the basis of equal pumping power:  
Figure 1.1  
Figure 1.2 Validation of the present study with experimental results [24]. (**a**) Nusselt number as a function of Reynolds number; (**b**) pressure loss as a function of the Reynolds number.
###### Abstract  
Uranium could be released into the aquatic ecological environment through various sorts of nuclear-related procedures, which has high toxicity and carcinogenicity even with a trace amount. A novel phosphonic acid functionalized cellulose adsorbent (PVKAP) with a simple synthesis strategy is developed based on pumpkin vine cellulose (PVK) as the substrate material for efficient and selective capturing U(VI). Because of the strong coordination between phosphonic acid groups and U(VI), the adsorption efficiency and adsorption selectivity of modified cellulose to U(VI) are greatly improved. The adsorption behavior follows the Langmuir adsorption model and pseudo-second-order kinetics model. The maximum adsorption capacities (pH = 5, \(T\) = 293 K) of PVK and PVKAP obtained from Langmuir isotherm are 57.2 and 714.3 mg g\({}^{-1}\), and the adsorption equilibrium are reached in 240 and 35 min, respectively. Additionally, PVKAP has a high adsorption selectivity which reached 70.36% for U(VI) in multi-ion condition, and recycling studies have shown that PVKAP has good recyclability. Furthermore, batch adsorption experiments and spectral analysis reveal that the efficient enrichment of U(VI) on PVKAP could mainly attribute to the inner layer complexation. Therefore, this environmentally friendly and simple route prepared PVKAP has good a potential application value for U(VI) enrichment in aqueous media related to nuclear waste.  
**Keywords:** phosphonic acid; cellulose; U(VI); adsorption selectivity; biomass
## 3 Results
### Planimetric Accuracy  
The results of analysis of planimetric accuracy are shown in Table 4 and Figure 17. Two results are given for the ROAMER and the RIEGL, Column I before fine-tuning and Column II after fine-tuning (see Section 2.1.3 for details).  
The planimetric accuracy was not analyzed for the Sensei as the reference targets could not be reliably measured from the Sensei point clouds (details in Section 2.1.3). Planimetric accuracy as a function of distance from the trajectory is shown in Figure 18. Fine-tuning has a significant effect on the ROAMER system's performance.  
Figure 18: Planimetric accuracy as a function of distance from the trajectory; with the linear trend lines fitted to the observed errors in the two driving directions.  
Figure 17: The planimetric accuracy of the tested MLS systems in two driving directions. The left column stands for counter-clockwise, CCW, and the one on the right for clockwise, CW, direction.  
With properly calibrated high-end MLS systems, the planimetric accuracy in good GNSS conditions is high, and in the present study it was within the limits of the used reference data accuracy, _i.e._, about 2 cm. Accuracy deteriorates as the distance to the trajectory increases, but very moderately, when the system calibration is in order.  
The results for the ROAMER in Figures 16 and 18 show very clearly how accuracy is affected when there are problems with calibration (ROAMER I) and how re-calibration using collected point cloud data can improve the performance (ROAMER II). Similar performance improvement can be expected when errors in trajectory, caused by satellite signal outtakes or IMU disturbances, for example, are compensated for using strip adjustments or control targets, for instance.
## Materials and Methods
### Prediction Models
#### 2.3.3 Partial Least Squares Regression (PLSR) Model  
Compared with the abovementioned approaches, machine-learning methods can effectively solve the collinearity problems existing in environmental variables to ensure that the proposed soil prediction model can have an expected immense effect. [PERSON] et al. (2018) proposed a new multivariate statistical analysis method, namely, PLSR, which integrates the advantages of multiple linear regression, principal component, and typical correlation analyses to effectively solve the multicollinearity among environmental factors. PLSR is a popular modeling technique used in chemometric and quantitative spectral analyses. It is based on a linear transition from numerous original descriptors to a new variable space based on a small number of orthogonal factors (P  
The SVM model exhibits several parameters. Cost (\(c\)) represents the penalty associated with errors larger than epsilon (\(\varepsilon\)). Increasing cost value causes a close fitting to the training data. The kernel gamma (\(\gamma\)) parameter controls the shape of the separating hyperplane. Similarly, increasing gamma usually increases the number of support vectors. For epsilon (\(\varepsilon\)), no penalty is associated with points predicted within the distance epsilon (\(\varepsilon\)) from the actual value in training the regression function. A decreasing epsilon forces close fitting to the training data. The nu (\(\
u\)) parameter indicates a lower bound on the number of support vectors to use, which is given as a fraction of total calibration samples, and an upper bound on the fraction of training samples, which are errors [70].  
In addition, two versions of SVM regression, namely, \"epsilon(\(\varepsilon\))-SVR\" and \"nu(\(\
u\))-SVR,\" are commonly used in the SVM model. The original SVM formulations for regression (SVR) use parameter cost (c) and epsilon (\(\varepsilon\)) to apply a penalty to the optimization for points that are incorrectly predicted. SVR is replaced by another parameter, nu (\(\
u\)), which applies a slightly different penalty. The main incentive for \"nu (\(\
u\))\" version of SVM is its highly meaningful interpretation, which is attributed to \"nu (\(\
u\))\" representing an upper bound on the fraction of training samples, which are errors (poorly predicted), and a lower bound on the fraction of samples, which are support vectors. Epsilon (\(\varepsilon\)) and nu (\(\
u\)) are the different versions of the penalty parameter. The same optimization problem is solved in either case [70].