esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Toxicity Analysis  
The MPs mixture solution was prepared at a variable concentration (0.05-5 mg/L DOC), to observe the by-products toxicity. The samples were treated at optimum levels for FeSO\({}_{4}\) and Fe Fenton oxidation to assay the toxicity of by-products formed. Few drops of 1 N sodium hydroxide were added to the filtered effluents to raise the pH (almost neutral): Eradicate the false by-products toxicity effects at low pH. The \(\alpha\) = 0.01, significance was achieved for the analysis, validating the good test quality. Samples causing bioluminescence inhibition and reducing bacterial growth thereby possess toxic effects are presented in red bars in Figure 4. Positive reference control solution, Cr (VI) showed 80.5 \(\pm\) 5.6% inhibition after 30 min contact time, meets the assay validation standards. Initial samples (MPs mixture) were toxic and cause a relative reduction in bioluminescence. Most of the treated effluents for each treatment were non-toxic except FeSO\({}_{4}\) effluent taken from 0.5 mg/L MPs (initial). It is interesting to note that the effluents after the FeSO\({}_{4}\) Fenton treatment had higher effects to promote the bacteria growth. It could be possible due to the homogeneous FeSO\({}_{4}\) solution, in which iron ions dissolved readily into water, acted as a nutrient and thereby proliferated the bacteria growth. A study by [PERSON] et al. (2019) showed similar results as that this study observed.  
Figure 4: Microtax toxicity assay for untreated samples and by-products formed after the FeSO\({}_{4}\) and Fe Fenton oxidation treatment. Bioluminescence inhibition estimate the bacterial growth and above values are shown as % effects on bacteria, evaluating toxic and non-toxic effects of the samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Total Compound** & **Initial DOC** & \multicolumn{2}{c}{**DOCf/i**} \\  & **Conc. (mg/L)** & **(mg/L)** & **FeSO\({}_{4}\)** & **Fe (0)** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} MPs mixture \\ solution \\ \end{tabular} } & 0.05 & 1.86 & 0.42 \(\pm\) 0.01 & 0.30 \(\pm\) 0.01 \\  & 0.5 & 2.35 & 0.54 \(\pm\) 0.05 & 0.28 \(\pm\) 0.02 \\  & 5 & 6.37 & 0.68 \(\pm\) 0.01 & 0.32 \(\pm\) 0.03 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} MPs mixture with \\ humic acid (_w/w_) \\ \end{tabular} } & \multirow{2}{*}{0.5 + 0.5 = 1} & \multirow{2}{*}{2.70} & \multirow{2}{*}{0.46 \(\pm\) 0.07} & \multirow{2}{*}{0.24 \(\pm\) 0.07} \\  & & & & \\ \hline Tap water & n/a & 1.75 & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: DOCf/i factor value after Fenton oxidation runs at optimal levels of designed parameters.
# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 2 Related Work
### Text Classification
significantly improving training efficiency. In sentiment analysis, [PERSON] et al. [31] introduced a TS-GRU architecture that integrates feature-level attention into a two-stage GRU framework, enabling it to model intricate dependencies and improve prediction via sequential attention and decoding mechanisms. To leverage both local and global semantic cues, hybrid deep learning models have been explored. [PERSON] et al. [32] introduced CABO, a composite architecture that integrates CNNs for local feature extraction with BiLSTM and attention mechanisms for contextual comprehension, achieving superior results over single-stream architectures. [PERSON] et al. [33] presented a Transformer-based encoder-decoder framework for multi-label classification, integrating word embeddings, BiLSTM encoding, and Transformer modules to jointly model document relationships and label queries. [PERSON] et al. [34] developed an XLNet-CNN-GRU hybrid model, combining static GloVe and contextual XLNet embeddings, which are processed by parallel CNN and GRU modules with attention mechanisms to improve classification. [PERSON] et al. [35] proposed an ensemble framework combining a GCN and a CNN, where a simplified boosting strategy retrains the CNN on GCN-misclassified samples, and classification is finalized via weighted voting. [PERSON] et al. [36] presented a capsule-based architecture incorporating label embeddings and graph convolutional layers, effectively capturing inter-label correlations and spatial features. [PERSON] et al. [37] developed a multi-channel convolution-capsule network hybrid that replaces pooling operations with capsule routing, preserving information integrity while extracting local features. Their model outperformed BERT in both long- and short-text classification tasks. In summary, hybrid architectures are increasingly favored for their ability to capture complementary semantic representations. This paper proposes a dual-branch hybrid framework that combines a GCNN with a two-layer BiLSTM network. The GCNN branch performs dynamic local feature selection using multi-scale gated convolutions to suppress noise, while the BiLSTM branch incorporates residual connections to maintain semantic completeness during global sequence modeling. An attention-based fusion mechanism further integrates local and global features.
## 2 Materials and Methods
### Thermal Characterization  
The thermogravimetric analysis (TGA) (fibers and composites) and differential scanning calorimetry (DSC) (composites) analyses were performed simultaneously using TA Instruments SDT Q600 equipment (TA Instruments, New Castle, DE, USA). A 15 \(\pm\) 0.5 mg sample was evaluated in an inert atmosphere of N\({}_{2}\) at 60 mL\(\cdot\)min\({}^{-1}\), using an alumina pan, under a heating rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\). The temperature range analyzed was 20 to 600 \({}^{\circ}\)C for the fibers and 20 to 400 \({}^{\circ}\)C for composites and neat polymers (PHB and PLA).  
The degree of crystallinity (% X\({}_{\text{c}}\)) was obtained compared to the 100% crystalline polymer. The crystallinity of each analyzed material, composite and neat polymer, was determined using Equation (5), where \(\Delta\text{H}^{\circ}_{\text{m}}\) is the theoretical melting enthalpy for 100% crystalline PHB, whose value is equal to 146 J\(\cdot\)g\({}^{-1}\)[37], \(\Delta\text{H}_{\text{m}}\) is the melting enthalpy, in J\(\cdot\)g\({}^{-1}\), of each sample obtained from the DSC test, and w is the PHB weight fraction in the composite.  
\[\%\,\text{X}_{\text{c}}=\frac{\Delta\text{H}_{\text{m}}\times 100}{\Delta\text{H}^{ \circ}_{\text{m}}\times\text{w}} \tag{5}\]
## 4 Numerical Examples
### Single-Edge Notched Tensile Test  
This widely adopted phase field validation test involves a 1 mm \(\times\) 1 mm square plate with a 0.5 mm horizontal notch at the left edge midpoint. Boundary conditions include a  
Figure 3: Nonlinear iterative solution flowchart.  
fixed bottom edge and vertical displacement loading at the top. The numerical simulations were implemented using the COMSOL Multiphysics platform. A four-node plane stress element mesh is adopted, with refined meshing in the crack propagation region. The minimum element size is \(h=0.002\) mm, and the total number of elements is 69,682, as shown in Figure 4.  
The material parameters adopt the elastic medium data from reference [25]: elastic modulus \(E=0.0075\)mm, Poisson's ratio \(v=0.3\), critical energy release rate \(G_{c}=2.7\)kN/m, and characteristic length \(\ell_{0}=0.0075\)mm (controlling the diffuse width of the crack). The crack propagation paths at different stages are shown in Figure 5, where the crack propagates straight in a direction perpendicular to the loading direction. The color scale is explicitly defined as follows: blue (\(\phi=0\)) denotes the intact material, blue to red (\(0<\phi<1\)) represents progressive damage accumulation, and red (\(\phi=1\)) indicates complete fracture.  
In the single-edge notched tensile test simulation, the phase field variable evolution captures the crack initiation and progressive propagation process. Crack nucleation initiates at the notch tip, followed by linear propagation perpendicular to the loading axis. The crack trajectories remain smooth and singular across loading steps, exhibiting gradual extension without bifurcation or diffuse crack patterns. This linear propagation behavior persists throughout the entire process, demonstrating excellent agreement with dynamic damage evolution characteristics reported by [PERSON] et al. [32] and [PERSON] et al. [33]. These consistencies validate the accuracy and reliability of the proposed numerical methodology.  
To investigate the numerical stability of the phase field method, the single-edge notched tensile model was discretized using four meshes with varying refinement levels. With the phase field characteristic length fixed at \(\ell_{0}=0.0075\)mm, convergence was assessed by controlling the \(\ell_{0}/h\) ratios (1.25, 1.5, 2, and 2.5). Figure 6 depicts crack propagation length versus computational steps for different mesh refinements. Initially, crack extension curves diverge due to mesh sensitivity; however, as computational steps progress, the curves converge, demonstrating diminished mesh dependence post-refinement. These results confirm that the phase field method produces stable numerical solutions with reduced mesh sensitivity under appropriate refinement, validating its numerical robustness in the proposed framework.  
Figure 4: Single-edge notched tensile test model.
## 3 Results  
Straw Return Aggravates Northern Corn Leaf Blight in Fuxin Region with Minimal Effects on Fusarium Stalk Rot  
To investigate the impact of conservation tillage with maize straw return on the disease incidence in subsequent crops, we selected several fields where this practice had been implemented since 2020 as the study sites (see Table 1 for the specific locations). Fields without straw return served as the control group. Maize was planted in spring 2023, and disease surveys for northern corn leaf blight and _Fusarium_ stalk rot were conducted in autumn at the locations listed in Table 4. Northern corn leaf blight was prevalent across all the surveyed regions. The disease index was notably high in Beipiao City, Chaoyang (48.58 for non-straw return vs. 51.52 for straw return) and Fumeng County, Fuxin (35.64 for non-straw return vs. 42.78 for straw return), indicating severe disease occurrence in these areas. In other regions, the disease index showed minor increases or remained relatively unchanged after straw return. Notably, Fumeng County, Fuxin, exhibited the most significant increase in the disease index for northern corn leaf blight, with a nearly 20% rise under straw return conditions.  
\begin{table}
\begin{tabular}{c c} \hline
**Disease Severity Level** & **Symptom Description** \\ \hline
1 & Few lesions on leaves, lesion area \(\leq\)5\% of the total leaf area \\
3 & Sparse lesions, lesion area accounting for 6–10\% of the leaf area \\
5 & Moderate lesions, lesion area accounting for 11–30\% of the leaf area \\
7 & Abundant lesions, lesions merging, lesion area accounting for 31–70\% of the leaf area \\
9 & Leaves nearly covered by lesions, leading to withering \\ \hline \end{tabular}
\end{table}
Table 3: Grading criteria for northern corn leaf blight severity.  
For _Fusarium_ stalk rot, infected plants were observed at all the survey sites except Kangping County, Shenyang, where no incidence was recorded. However, in these regions, stalk rot remains prevalent, with a slight increase in disease incidence observed in Fumeng County and Fengcheng areas, indicating its persistent impact.  
In conclusion, straw return was associated with an aggravation of northern corn leaf blight in Fuxin, highlighting a potential drawback of this conservation practice in certain regions.
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 4 Discussion
### Comparative Experiment  
The network framework proposed in this paper has verified its effectiveness on FER2013, Oulu_CASIA, and RAF-DB datasets and shown that it achieves better recognition accuracy. Table 3 shows the implementation of the proposed method on the FER2013 dataset. The experiment compares the recognition effects of some other classic networks and mainstream networks. Among them, the CNN+SVM [29] method uses a convolutional neural network and replaces the SoftMax function at the bottom of the classification network with SVM. The switch is simple, but it is useful for classification tasks. The ARM (ResNet18) [30] method developed a lightweight module to solve the Padding Erosion problem through the auxiliary module method to improve the performance of facial expression recognition. In addition, for the classic network Inception, VGG [31] and the baseline network ResNet used in this paper, the method is also carried out under the same equipment conditions. Finally, compared with FER (2021) [32], using the newer results obtained by various optimization strategies, the method proposed in this paper shows better recognition performance.  
Table 4 shows the implementation of the method proposed in this paper on the Oulu_CASIA dataset. The experiment compares some other methods and compares them with some classic depth methods. Deep temporal appearance and geometry network-DTAGN [34] is a two-stream network; a layer of convolutional neural network is used to extract the texture information of the image, and a layer of network is used to focus on the geometric feature changes composed of key points. However, this method is mainly used to recognize  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Method** & **Parameter Quantity(M)** & **Accuracy** \\ \hline  
dynamic expressions, so the recognition accuracy is not high. VGG finetune [35] is to fine-tune the classic VGG network, first using large datasets of other tasks for pre-training, and then fine-tuning the expression data. Peak piloted deep network [36] uses samples with greater expression intensity to guide the correct classification of samples with less intensity to improve the recognition rate of expressions with less intensity. FaceNet2 ExpNet [37] uses face recognition datasets to first pre-train the network to retain the basic features of the face, and then uses expression tags to supervise the network to learn expression-related features. Since the identity information of the face is mostly reflected in the facial features, this method has achieved better results.  
Table 5 shows the comparison of methods on the RAF-DB dataset. The first is the DLP-CNN [38] method, which aims to solve the problem of multiple modal recognition problem. Secondly, ResNet was used as the baseline network and tested under the same equipment conditions. In contrast, the multi-channel recognition network MFE and the complete algorithm MAWNet proposed in this paper achieved better recognition results.
## 1 Introduction
### PAR Sensors  
Our installations also include data from broadband PAR sensors, measuring across the full visible wavelength band (400-700 nm). These measurements are often standard at flux towers for estimation of vegetation properties. To enable separation of absorbed PAR from ground vegetation and forest canopy it is necessary to measure four PAR fluxes: (1) incoming above the canopy measured by an upward-looking sensor (\(PAR_{0}\)); (2) total reflected PAR, including the forest canopy direct reflection, ground direct reflection, and canopy transmission of the ground reflection, measured by a downward-looking sensor (\(RPAR_{eco}\)); (3) PAR transmission through the canopy measured by an upward-looking sensor below canopy and one meter from the ground (\(TPAR\)); and (4) reflection from the ground measured at the same location as TPAR by a downward-looking sensor (\(RPAR_{g}\)). Complete sets of PAR sensors have not yet been mounted at all our sites and only one site currently enables measurement of all these fluxes (Table 4). For the open sites without tree canopies (Stordalen mire and Fajemyr bog), \(RPAR_{eco}\) cannot be distinguished from \(RPAR_{g}\) since it is impractical to position sensors below the canopies of herbs and mosses.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Name** & \multicolumn{3}{c}{**No. of PAR sensors**} \\  & & \(PAR_{0}\) & \(RPAR_{eco}\) & \(TPAR\) & \(RPAR_{g}\) \\ \hline
1 & Abisko forest & 1 & 1 & 4 & 1 \\
2 & Stordalen mire & 1 & 1 & - & - \\
3 & Norunda forest & 1 & 1 & 14 & - \\
4 & Fajemyr bog & 1 & 1 & - & - \\
5 & Hyytiala forest & - & - & - & - \\ \hline \end{tabular}
\end{table}
Table 4: Currently installed sensors for estimation of PAR fluxes. Abbreviations of PAR fluxes are given in the text.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Name** & \begin{tabular}{c} **Height above** \\ **canopy (m)** \\ \end{tabular} & \
\begin{tabular}{c} **View azimuth** \\ **angle (degrees)** \\ \end{tabular} &
\begin{tabular}{c} **Sensor** \\ **FOV** \\ **canopy level (m\({}^{2}\))** \\ \end{tabular} \\ \hline
1 & Abisko forest & 8 & 261 & 60 & 1,961 \\
2 & Abisko mire & 8 & 313 & 60 & 1,961 \\
3 & Norunda forest & 38 & 135 & 25 & 1,384 \\
4 & Fajemyr bog & 10 & 276 & 60 & 3,064 \\
5 & Hyytiala forest & 15 & 70 & 60 & 6,894 \\ \hline \end{tabular}
\end{table}
Table 3: Geometry information multispectral measurements or reflected radiation.
## 3 Methodology
### The STAGCN-Based Tool Wear Prediction Method  
The overall process of the STAGCN-based tool wear prediction method is shown in Figure 4. The method is mainly composed of four parts: a data processing module, a time-domain feature construction module, a feature extraction module by spatial-temporal layers, and a prediction module by two fully connected layers.  
Figure 4: The process of the tool wear prediction framework based on the STAGCN.
## 3 Results & Discussion
### X-ray Crystallography
#### 3.2.2 Degree of Crystallinity and Thickness of Lamellar Structure Evaluated by SAXS
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Crystallinity**} & \multicolumn{4}{c}{**Thickness of lamellar Structure [nm]**} \\ \cline{3-5}  & & **Long Period** & **Core-Crystalline** & **Core-Amorphous** & **Intermediate Layer** \\  & & **(\(L_{0}\))** & **(\(d_{c}\))** & **(\(d_{a}\))** & **(\(d_{tr}\))** \\ \hline VPE & 80.47 & 20.80 & 15.49 & 2.27 & 1.52 \\
0/s & 80.49 & 20.70 & 15.53 & 2.21 & 1.48 \\
25/s & 80.58 & 20.70 & 15.60 & 2.18 & 1.46 \\
50/s & 79.57 & 19.80 & 14.62 & 2.30 & 1.44 \\
100/s & 70.70 & 19.80 & 14.7 and the thickness of core-amorphous layer was lower than at the high shear rate (100/s). For the correlation between the thickness of intermediate layer (\(d_{tr}\)) and elongation at break (Figure 7b), a positive correlation was detected with a high correlation coefficient (R\({}^{2}\) = 0.89). The reduction of the thickness of the intermediate layer was found in all shear-treated samples with the decrease of the elongation at break. It is worth noting that shear-treated HDPE at 100/s showed the most shortening of the thickness of the intermediate layer and elongation at break, as compared to VPE and the other shear-treated samples. This suggests that shear treatment not only affected the decreasing elongation at break, the thickness of the lamellar structure, especially in the intermediate layer, was also decreased in the HDPE.
# Detection of Induced GNSS Spoofing Using S-Curve-Bias
## 4 Experiments
### Ratio Test Detection Method  
As a comparison, the intermediate spoofing detection algorithm based on the ratio test metric (called the ratio method) [33] is also used in this paper. As in [33], the detection metric of the ratio method is defined by the following formula:  
\[R_{d}=\frac{E_{i}^{\frac{d}{2}}+L_{i}^{\frac{d}{2}}}{\beta P_{i}} \tag{24}\]  
where \(E_{i}^{\frac{d}{2}}\), \(L_{i}^{\frac{d}{2}}\) and \(P_{i}\) represent the early, late and prompt correlator output over the in-phase branch, the superscript \(\frac{d}{2}\) denotes the correlator spacing between the early/late correlator and prompt correlator, \(\beta\) is the correlation main peak slope. For more details, refer to [33]. According to the theory presentedin [33], the threshold, e.g., \(\gamma^{\prime}\), can be derived with a given false detection probability. Therefore, if \(|R_{d}|>\gamma^{\prime}\), it is decided that the receiver has been attacked by a spoofing.  
It is worth pointing out that the ratio method detects spoofing by finding the distortion of correlator output at a given timing. In other words, it detects spoofing based on the static distortions of the correlator output. In contrast, the proposed algorithm declares an induced spoofing attack by detecting the dynamic changes of the SCB.
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 4 Methodology
### Data Collection and Feature Extraction  
To build and evaluate the phishing detection framework, two datasets were utilized:  
* Dataset 1 (Real-World Dataset): A dataset consisting of 5000 emails, evenly distributed with 2500 phishing emails (sourced from PhishTank and OpenPhish archives) and 2500 benign emails (collected from public corporate email traffic and open datasets);
* Dataset 2 (Synthetic Phishing Dataset): A synthetic dataset containing 1000 phishing emails generated using templates based on real-world phishing attack patterns, including domain spoofing, urgent calls-to-action, credential harvesting requests, and misleading hyperlinks.  
Dataset 1 was split using an 80/20 ratio (4000 for training and 1000 for testing). Dataset 2 was reserved exclusively for external validation without prior model exposure. Features extracted included domain age, presence of HTTPS, URL/IP pattern analysis, email header metadata, and text-based features derived from natural language processing. Feature extraction is a critical preprocessing step that transforms raw email data into structured information for the RL agent. We focused on extracting both content-based and header-based features, including the following:  
* URL Features: Length of URLs, number of subdomains, presence of IP addresses, and suspicious domain patterns;
* HTML Structure: Presence of embedded scripts, hidden form fields, and iframe usage;
* Sender Reputation: Mismatched \"From\" and \"Reply-To\" addresses, domain age, and SPF/DKIM validation status;
* Keyword Patterns: Occurrence of phishing-related keywords like \"urgent\", \"verify your account\", and \"password reset\".  
Let \(F\) represent the feature set extracted from each email:  
\[F_{i}=f1,f2,f3,\dots,fn\]  
where the following variables are used:  
\(F_{i}\) represents an individual feature (e.g., URL length and sender reputation score).  
\(n\) is the total number of extracted features.  
These features were normalized to ensure uniform data distribution before being fed into the RL agent.