esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussion
### Case 2: BESS versus PHES with CPP DRP  
In this case, the benefit of CPP DRP on capacity sizing optimization problem for both BESS and PHES-based microgrid is investigated, and pareto fronts plotted. Figure 5a,b shows the Pareto front  
Figure 4: Pareto front plots for case 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{5}{c}{BESS-Based Microgrid} & \multicolumn{5}{c}{**PHES-Based Microgrid**} \\ \hline
**LPSP** & **0\%** & **5\%** & **10\%** & **15\%** & **0\%** & **5\%** & **10\%** & **15\%** \\ \hline
**PV capacity (kW)** & 1422 & 1207 & 1003 & 1030 & 1699 & 1806 & 1652 & 1850 \\
**WT capacity (kW)** & 1919 & 2120 & 2063 & 1774 & 1657 & 1376 & 1371 & 1108 \\
**ESS capacity (kWh)** & 6798 & 1800 & 900 & 411 & 7800 & 7546 & 6967 & 6925 \\ \hline
**TPC (US $)** & \(\mathbf{1.38\times 10^{7}}\) & \(\mathbf{1.05\times 10^{7}}\) & \(\mathbf{9.28\times 10^{6}}\) & \(\mathbf{8.06\times 10^{6}}\) & \(\mathbf{9.06\times 10^{6}}\) & \(\mathbf{8.38\times 10^{6}}\) & \(\mathbf{8.03\times 10^{6}}\) & \(\mathbf{7.59\times 10^{6}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Techno-economic analysis for case 1.  
plots with CPP DRP considering BESS and PHES, respectively. For both cases, it can be observed from the pareto plots that an increase in the LPSP value, the TPC decreases, this is due to the fact that the reliability index (LPSP) and planning cost (TPC) are conflicting objective. Table 4 summarizes the cost-benefits analysis for case 2 which involves the economic effects of critical peak pricing (CPP) DRP for the BESS and PHES-based microgrid configuration.  
For the comparative analysis of the two systems configurations at LPSP = 0% (maximum reliability) with the consideration CPP DRP; the selection of PHES as an ESS alternative to BESS in optimum capacity resulted in 34.22% reduction in the total investment costs. This significant cost saving signifies that PHES-based configuration is more economical and preferred investment option compared to BESS-based microgrid.
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 3 Materials and Methods
### Model Overview
#### 3.2.2 Feature Extraction  
Feature extraction is a critical step for text classification in cyberbullying. In the proposed model, we have used TF-IDF and Word2 Vec techniques for feature extraction. TF-IDF is a combination of TF and IDF (term frequency-inverse document frequency), and this algorithm is based on word statistics for text feature extraction. This model considers only the expressions of words that are the same in all texts [72]. Therefore, TF-IDF is one of the most commonly used feature extraction techniques in text detection [16]. Word2 Vec is a two-layer neural net that \"vectorizes\" words to process text. Its input is a corpus of text, and its output is a set of vectors: attribute vectors representing words in that structure [49]. The Word2 Vec method uses two hidden layers of shallow neural networks, continuous bag-of-words (CBOW), and the Skip-gram model to construct a high-dimensional vector  
Figure 3: Overview of Cyberbullying Classification Pipeline.  
for each word [15]. The Skip-gram model is based on a corpus of terms \(w\) and meaning \(c\). The aim is to increase the likelihood of:  
\[\operatorname*{argmax}_{\theta}\ \prod_{w\in\text{T}}[\prod_{c\in c}p(c\mid w; \theta)], \tag{3}\]  
where T refers to text, and \(\theta\) is a parameter of \(p\) (\(c\mid w\); \(\theta\)). Figure 4 illustrates the Word2 Vec model architecture, where CBOW model attempts to find a word based on previous terms, while Skip-gram attempts to find terms that could fall in the vicinity of each word.  
Utilizing TF-IDF is weighted by its relative frequency instead of merely counting the words, which would overemphasize frequent words. The TF-IDF features notify the model if a word appears more often in a statement than the entire text corpus does typically. Prior work has found TF-IDF features useful for cyberbullying detection in SM [113]. As with BOW, the TF-IDF vocabulary is constructed during model training and then reused for test prediction. Both BOW and TF-IDF are considered to be simple, proven methods for classifying text [114]. In Equation (4), the mathematical representation by TF-IDF of the weight of a term in a document is given.  
\[W(d,t)=TF(d,t)*\log\left(\frac{N}{df(t)}\right), \tag{4}\]  
In this case, \(N\) is the number of documents and \(d\,f(t)\) is the number of documents in the corpus containing the word \(t\). In Equation (4), the first term enhances the recall, while the second term enhances the word embedding accuracy [52].
## 4 Results
### Experiment with the Data-3  
For the Data-3 dataset without real reference images, visually, MFFA-GAN retained the ability for retinal layered structures and the choroidal system (Figure 10k), which is crucial for diagnosing cases such as initial centralized plasma choroidal retinopathy or subtle choroidal neovascularization. Conventional methods, such as NLM, left residual speckle noise (Figure 11b,c), while deep learning models, such as DCANet, introduced local blurring. In contrast, the multilevel feature fusion of MFFA-GAN preserved fine details, which is crucial for distinguishing normal changes from early pathological changes.  
The visual analysis conclusions are further supported by the ENL and CNR metrics in Table 3. MFFA-GAN achieved a balanced optimization of noise suppression and structure preservation with the highest ENL value (674.836) and better CNR value (5.208). The lower ENL and CNR values (ENL: 26.389-54.281; CNR: 2.762-3.401) of the traditional methods verify their limitations in complex noise scenarios. Among the deep learning models, DCANet had the highest CNR value (5.526), but its ENL (595.731) was 11.7% lower than that of MFFA-GAN, reflecting that it introduced localized noise residuals in contrast enhancement; however, although DRANet had a higher CNR value (5.141), its ENL (222.046) was only 32.9%, indicating that its noise suppression ability was insufficient. The performance of MFFA-GAN confirms that this method demonstrates reliable generalization ability in clinical scenarios without reference images, providing a high-quality image basis for OCT diagnosis.  
Figure 10: Pixel error map of denoised images and real image: (**a**) reference, (**b**) NLM, (**c**) BM3D, (**d**) DnCNN, (**e**) SDSR-OCT, (**f**) DudeNet, (**g**) MDGAN, (**h**) MDRGAN, (**i**) DCANet, (**j**) DRANet, and (**k**) MFFA-GAN.
## 3 Controller Design Based on Legendre Polynomial FAT
### Adaptive Sliding Mode PID Controller Design
#### 3.2.2 Adaptive Sliding Mode PID Control Law and Error-Boundary Control Law  
Based on the above nonlinear sliding mode manifold, the main result of this paper is presented as follows:  
Using the output of the Legendre polynomial FAT for control law (14), the adaptive sliding mode PID control law of this method in Equation (18) can be obtained. At the same time, the adaptive law of approximation coefficient \(P\) and the error-boundary control law are shown in Equations (19) and (20), respectively.  
\[U=k_{d}{}^{-1}\Big{[}k_{p}\dot{e}+k_{i}e+k_{d}\ddot{x}_{d}+k_{d}\hat{H}+hs+\hat {\rho}(K_{1}s+K_{2}|s|^{\alpha}sig(s))\Big{]}+u_{c} \tag{18a}\] \[u_{c}=-k_{d}{}^{-1}hs^{T}sign(s)\] (18b) \[\hat{H}=\hat{P}^{T}\varphi(x)\] (18c) \[\dot{\hat{P}}=\lambda_{1}\Big{[}\varphi(x)s^{T}k_{d}+\varphi(x)(P_{0}B)^{T}E \Big{]}\] (19) \[\dot{\hat{\rho}}=\lambda_{2}\Big{[}s+k_{d}{}^{-1}(E^{T}P_{0}B)^{T}\Big{]}(K_{ 1}s+K_{2}|s|^{\rho}sig(s)) \tag{20}\]  
where, \(\lambda_{1}\) and \(\lambda_{2}\) are the positive constant. \(P_{0}\) and \(Q\) are the positive definite matrices, so that \(A^{T}P_{0}+P_{0}A=-Q\). In Equations (18a) and (20), the discontinuous switching term sign(s) in Equation (14) is replaced by \(K_{1}s+K_{2}|s|^{\alpha}sig(s)\)[27] to reduce the chattering of control voltage.  
Based on the above control law, the control system block diagram of the proposed controller in this article is shown in Figure 1.
## 3 Experiments and Results
### Comparative Experiments
#### 3.2.2 Experimental Results on the Rail Fastener Dataset
Figure 10: Visualization results on the rail fastener dataset: (**a**) original image (anomalies with red frames); (**b**) GANomaly; (**c**) f-AnoGAN; (**d**) MemAE; (**e**) SQUID; (**f**) proposed method. Figure 11 demonstrates the significant advantages of the proposed model improvements through a comparative analysis of anomaly detection performance between the baseline model and our method. In the baseline model (a1), anomalous samples (blue) predominantly cluster in the low-score range (0-0.2), exhibiting substantial overlap with normal samples (pink) distributed in the 0.2-0.4 interval, which reveals inadequate discriminative capability. The proposed model (b1) achieves notable separation: normal samples dominate the 0-0.4 range, while anomalous samples concentrate in the 0.6-1.0 interval, with significantly reduced overlap. The t-SNE visualizations (a2/b2) further validate this improvement: the baseline model's feature space shows intermingled normal (pink) and anomalous (blue) samples forming a single cluster, whereas our method demonstrates two distinct clusters--normal samples exhibit compact distribution while anomalous samples form a tight cluster, indicating substantially enhanced feature separability. This advancement primarily benefits from the multi-scale detail preservation capability of the E-JPU module. Notably, our model successfully identifies three defect types (spring clip damage, bolt absence, and foreign object occlusion) through unsupervised learning without requiring prior knowledge. These results align with quantitative metrics (7.5% AUC improvement), highlighting its practical application value. The consistent performance enhancement across both visual and numerical evaluations underscores the effectiveness of our architectural innovations.
## 3 Results
### Global Model with Data from CIESOL and CIEMAT  
In order to develop a model capable of estimating in different locations, data from both CIESOL and CIEMAT was combined as shown in Table 10 to train an ANN following the same architecture as in Figure 27. ANN was selected for the development of the global considering that ANN achieved the best results for an Extrapolated Model.  
The Extrapolated Models struggled to accurately estimate across the entire dataset range. This issue arises because the soiling patterns used for training differ significantly between locations. Specifically, in the CIESOL dataset, which was used to train the Extrapolated Models, there were very few data points where SL fell below 1%. In contrast, the CIEMAT test data generally exhibited lower SL values, with instances below 1% being more common. As a result, the models underperformed in estimating SL values under 1% (see Figures 18, 22 and 26), as these cases were not adequately represented in the training data. To ensure that the model learned from the entire range of soiling losses in both the training and testing datasets, stratification using binning was performed. This technique involves sorting the target variable values, dividing them into 10 bins, and then splitting the data in a way that maintains the distribution of these values in both training and test sets.  
The results obtained are rather promising (see Table 8). This model achieves an MBE of 0.09% (nMBE = 3.41%) and a RMSE of 1.02% (nRMSE = 40.50%). As for the \(R^{2}\) between estimated and observed SLs, a coefficient of determination of 0.63 was obtained (see Figure 28). This means that this Global Model trained with a combined dataset with data from both locations obtains a lower error than any Extrapolated Model (evaluated on  
\begin{table}
\begin{tabular}{l l l} \hline  & **CIESOL** & **CIEMAT** \\ \hline Train set [days] & 200 & 200 \\ Test set [days] & 50 & 50 \\ Total days & 250 & 250 \\ \hline \end{tabular}
\end{table}
Table 10: Train–test split for Global Model datasets CIESOL and CIEMAT.  
Figure 27: Architecture developed for the ANN model.  
CIEMAT test dataset). At the same time, \(R^{2}\) showed similar results to the Local Models (trained on the CIESOL training dataset and tested on the CIEMAT test dataset).
## 3 Path Loss Models
### Okumura-Hata Model  
The Okumura-Hata model stands as a frequently employed empirical model for path loss prediction within the realm of wireless communication systems, specifically designed for urban and suburban environments. Variants of this model have been tailored to address diverse frequency bands and environmental scenarios. Presented in (6) is the foundational representation of the Okumura-Hata model, serving as a valuable reference for its general form [38]:  
\[\text{{Path Loss}}\ (L)=A+B\times\log 10(f)-C+\left(D\times\log 10(h)\right)+E \times\left(\log 10(h)\right)^{2}+F\times\log 10(d), \tag{6}\]  
where \(A\), \(B\), \(C\), \(D\), \(E\), and \(F\) are model-specific constants that depend on factors like the frequency of operation, the type of environment (urban or suburban), and the receiver height; \(f\) is the frequency of the signal in MHz; \(h\) is the height of the transmitting antenna in meters; and \(d\) is the distance between the transmitter and receiver in kilometers.  
The constants \(A\), \(B\), \(C\), \(D\), \(E\), and \(F\) vary depending on the specific version of the Okumura-Hata model and the frequency range in use.
## 3 Materials and Methods
### Hybrid Transformer-CNN Segmentation Networks
#### 3.2.3 ALL-MLP Decoder  
The final component of HTCSNet is the ALL-MLP decoder, which facilitates the fusion of the hierarchical features \(F_{cl}\), \((i=1,2,3,4)\), and \(F_{ti}\)\((i=1,2,3,4)\) derived from both the CNN-based and transformer-based encoder branches to achieve the final polyp segmentation map. This decoder adopts a structure that resembles the UNet architecture, with a series of upsampling and concatenation operations that facilitate the integration of multiscale features. The concatenated features from the CNN and transformer branches are fused through a series of MLP layers, which serve as a way for the global and local features' fusion. This fusion results in a comprehensive feature representation \(F_{fi}\)\((i=1,2,3,4)\) that encapsulates both the fine-grained details and the broader context of the polyp image. The ALL-MLP decoder employs bilinear upsampling to gradually upscale the fused features \(F_{fi}\)\((i=1,2,3,4)\), aligning them with the original image dimensions. At each upsampling stage, the decoder concatenates the upscaled features with the corresponding features from the encoder branches, which are further enriched by the MLP-based fusion. This iterative process culminates in the generation of the final segmentation map S, which is produced by upsampling \(F_{fi1}\) and concatenating it with the shallowest features \(F_{cl}\) and \(F_{t1}\), followed by an MLP layer that refines the segmentation outcome. Finally, semantic segmentation of the image is performed.  
Figure 4: The architecture of designed convolutional modules.
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 2 Related Work  
### Image-Based Captioning and Medical Report Generation  
Most of the work on image-based captioning is based on the classical structure of CNN + LSTM, which aims to generate real sentences or relevant paragraphs of a topic to summarize the visual content in an image or video [11; 12; 13; 14]. With the development of computer vision and natural language processing technology, many works combine radiology images and free text to automatically generate radiology reports to help clinical radiologists make a quick and meaningful diagnosis [15]. Radiology report generation takes X-ray images as input and generates descriptive reports to support inference of better diagnostic conclusions beyond disease labels. Many radiology report generation methods follow the practice of image captioning models [16; 17; 18]. For example, ref. [19] adopted an encoder-decoder architecture and proposed a hierarchical generator and attention mechanism to generate long reports. [PERSON] et al. [20] fused the visual features and semantic features of the previous sentence through the attention mechanism, used the fused features to generate the next sentence, and then generated the whole report in a loop. [PERSON] et al. [21] proposed an embedding network with text and image as input to jointly learn text and image information and train the CNN-LSTM architecture end-to-end, which was then combined with a multi-level attention model to generate a chest X-ray report. [PERSON] et al. [22] recorded important information during the generation process and then further assisted the generation of radiology reports by providing memory-driven transformers. [PERSON] et al. [23] used reinforcement learning to exploit structural information between and within reports to generate high-quality radiology reports. [PERSON] et al. [24] combined self-key sequence training and reinforcement learning to optimize the emergence of disease keywords in radiology reports. [PERSON] et al. [25] adopted the CNN-RNN framework to generate radiological reports describing detected diseases based on visual features on chest X-ray image datasets.  
Our work is mainly similar to that of [PERSON] et al. [26], who proposed a fully distinguishable end-to-end structural model, which mainly consists of three complementary modules for classifier, generator, and interpreter, which increase the linguistic fluency and clinical accuracy of generated reports. However, it does not add the association between disease labels, and lacks the association between disease labels in the classification. We use the association between labels learned from the text knowledge base to promote the semantic alignment between disease labels and images, which can better show the correlation between disease labels in classification, improve the accuracy of label classification, and further improve the accuracy of report generation.
## 2 Results
### Tissue Analysis
#### 2.3.2 Micronutrients  
Plants grown in the aquaponic system had significantly higher leaf tissue micronutrient contents for the majority of elements, Mn (DF = 1; \(\chi^{2}\) = 8.54; \(p\)! 0.05), Cu (DF = 1; \(\chi^{2}\) = 185.41; \(p\)! 0.05), and Zn (DF = 1; \(\chi^{2}\) = 71.12; \(p\)! 0.05), analyzed than those in the hydroponic system, which, on the other hand, yielded significantly higher (DF = 1; \(p\)! 0.05) tissue contents for B and Fe (Table 3).  
\begin{table}
\begin{tabular}{l l l l} \hline
**Treatments** & **Plant Height (cm)** & **Fresh Weight (g)** & **Dry Weight (g)** \\ \hline T1 & 17.06 \(\pm\) 0.48a & 9.83 \(\pm\) 1.85a & 4.98 \(\pm\) 1.06a \\ T2 & 15.93 \(\pm\) 0.45a & 5.57 \(\pm\) 0.53a & 2.53 \(\pm\) 0.28b \\ \hline \end{tabular} Values shown are mean \(\pm\) S.E. Means followed by the same lowercase letters in the same column are not significantly different (\(p\)! 0.05) following comparison using the Mann–Whitney test for aquaponic (T1) and hydroponic systems (T2).  
\end{table}
Table 1: Growth parameters (mean \(\pm\) SE) of \(H\). _odoratissimum_ grown in aquaponic and hydroponic systems for six weeks under greenhouse conditions.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{2}{l}{**Quantity (mg/kg)**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{2-7}  & **C** & **N** & **P** & **K** & **Ca** & **Mg** \\ \hline T1 & 462,300 \(\pm\) 2655.81a & 11,350 \(\pm\) 317.54a & 1650 \(\pm\) 28.87a & 6600 \(\pm\) 115.47a & 6850 \(\pm\) 433.01a & 747.5 \(\pm\) 28.33a \\ T2 & 454,350 \(\pm\) 4070.32a & 16,000 \(\pm\) 1096.96b & 3400 \(\pm\) 346.41b & 22,850 \(\pm\) 11,113.10a & 8000 \(\pm\) 635.08a & 1950 \(\pm\) 86.60b \\ \hline \end{tabular} Means followed by the same lowercase letters in the same column are not significantly different (\(p\)! 0.05) following the Mann–Whitney test comparison. T1, aquaponic; T2, hydroponic.  
\end{table}
Table 2: Tissue nutrient contents of shoots (mean \(\pm\) SE) of \(H\). _odoratissimum_ grown in aquaponic and hydroponic systems for six weeks under greenhouse conditions.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{2}{l}{**Quantity (mg/kg)**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{2-7}  & **Na** & **Mn** & **Fe** & **Cu** & **Zn** & **B** \\ \hline T1 & 14,445\(\pm\) 707.25a & 103.8 \(\pm\) 8.78a & 147.67\(\pm\) 25.85a & 17.1 \(\pm\) 0.75a & 116 \(\pm\) 6.93a & 15.43 \(\pm\) 0.60a \\ T2 & 4570 \(\pm\) 300.22b & 78.15 \(\pm\) 0.08b & 168.5 \(\pm\) 22.81a & 5.1 \(\pm\) 0.46b & 444.4 \(\pm\) 4.91b & 48.07 \(\pm\) 2.63b \\ \hline \end{tabular}
\end{table}
Table 3: Tissue nutrient contents of shoots (mean \(\pm\) SE) of \(H\). _odoratissimum_ grown in aquaponic and hydroponic systems for six weeks under greenhouse conditions.
## 3 Results
### Heavy Metal Contents
#### 3.1.2 Crop Samples  
The statistical parameters of heavy metal contents in 95 crop samples from the Xinping area are listed in Table 2.  
The contents of five heavy metals in maize, walnut, and rice samples are described, respectively, as follows.  
Among 40 maize samples, the contents of As are all lower than the detection limit of 0.1 \(\upmu\)g/g. The Cd contents in 35 samples are lower than the detection limit of 10 ng/g. The contents of Cr vary between 0.10 and 0.26 \(\upmu\)g/g. The Hg contents range from the detection limit of 1 ng/g (in nine samples) to 1.35 ng/g. The contents of Pb vary between 0.04 and 0.13 \(\upmu\)g/g.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Crops Samples** & \multicolumn{3}{c}{**Maize**} & \multicolumn{3}{c}{**Walnut**} & \multicolumn{3}{c}{**Rice**} \\ \hline
**Statistical** & **Min.** & **Median** & **Max.** & **Min.** & **Median** & **Max.** & **Min.** & **Median** & **Max.** \\ \hline As & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & 0.13 & \textless{}0.1 & 0.11 & 0.20 \\ Cd & \textless{}10 & \textless{}10 & 26 & \textless{}10 & \textless{}10 & 14.13 & \textless{}10 & 33.5 & 560 \\ Cr & 0.10 & 0.15 & 0.26 & 0.51 & 0.70 & 0.85 & 0.24 & 0.32 & 1.36 \\ Hg & \textless{}1 & 1.1 & 1.35 & \textless{}1 & \textless{}1 & \textless{}1 & 2.7 & 3.3 & **4** \\ Pb & 0.04 & 0.07 & 0.13 & 0.03 & 0.08 & 0.19 & 0.06 & 0.09 & 0.16 \\ \hline \hline \end{tabular}  
* Notes: The units of As, Cr, and Pb are \(\upmu\)g/g except Cd and Hg, which are in ng/g.  
\end{table}
Table 2: Statistical parameters of analytical values of crop samples in the Xinping area.  
Figure 2: Histograms with box plots of heavy metal contents and pH of surface soil samples from the Xinping area.  
Among 40 walnut samples, the contents of As in 39 samples are lower than the detection limit of 0.1 \(\upmu\)g/g. The contents of Cd range from the detection limit of 10 ng/g (in 38 samples) to 14.13 ng/g. The Cr contents vary between 0.51 and 0.85 \(\upmu\)g/g. The contents of Hg are all lower than its detection limit of 1 ng/g. The Pb contents vary between 0.03 and 0.19 \(\upmu\)g/g.  
Among 15 rice samples, the contents of As range from the detection limit of 0.1 \(\upmu\)g/g (in six samples) to 0.20 \(\upmu\)g/g. The Cd contents vary from the detection limit of 10 ng/g (in four samples) to 560 ng/g. The contents of Cr range from 0.24 to 1.36 \(\upmu\)g/g. The contents of Hg vary between 2.7 and 4 ng/g. The contents of Pb vary from 0.06 to 0.16 \(\upmu\)g/g.