esa-sceva/satcom-chunk-collection huggingface dataset:
# Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor
###### Abstract
Text normalization is also a very common step in the analysis of social media messages, where the input text is prone to present problems of misspelling, abbreviation, incorrect grammar, etc. For example, [PERSON] and [PERSON] [10] proposed a system to perform sentiment analysis in Twitter messages using Convolutional Neural Networks (CNN), with text normalization as a preprocessing step. Their method is based on character-level embedding (instead of the most common word-level embedding), with convolutional, max-pooling and fully connected layers, achieving a classification accuracy above 98.1%. However, the normalization step is based on traditional techniques using tokenization, dictionary word replacement, lemmarization and stemming. The models proposed by [PERSON] and [PERSON] [6] deserve special attention. They are based on Deep Learning techniques and, at each time-step, they read a character and produce an entire word, thus being character-based at the input, and word-based at the output. These models obtained a high accuracy performance (one case achieving a 99.8% on the English test set). Unfortunately, they suffered from the so-called _silly_, undetectable or unrecoverable errors. This means that these errors cannot be detected only looking at the produced output. For example, this is the case when normalizing _I'm 12_ as _I am thirteen_, yet the error _Im twenty_ could be detected in the subsequent process. Our hypothesis is  
Figure 1: An example of equivalent phrases producing the same normalized output. that these errors could be due to the use of recurrent word-level models, and they could be avoided to a large extent by a character-level approach. More recently, this model was improved in [11], by using a covering grammar for the given language, with the purpose of avoiding those unrecoverable errors. This grammar constrains the execution of the recurrent neural network, with the particularity that these grammars can be learned automatically from the samples. The present approach has been designed with two main goals in mind. The first one is offering a solution for the text normalization problem that exclusively uses neural networks, taking advantage of the benefits of data-driven solutions. The second goal is to introduce convolutional components in the neural model, substituting its recurrent counterparts and, thus, speeding up the whole process. Moreover, proving the usefulness of such convolutional architecture would help to push even further the idea that CNNs can be used outside of a computer vision framework. The main contributions of this work are as follows: (1) Proposal of a character-based approach for the text normalization problem which does not suffer from undetectable or unrecoverable errors; (2) introduction of a new general-purpose encoder based on causal convolutions, the Causal Feature Extractor (CFE); and (3) a variation of the traditional attention mechanisms, in which a context matrix is generated, instead of a context vector.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## 4 Experimental Results and Analysis
### Evaluation of Imperceptibility  
After the watermark embedding, compared with the original image, the quality of the watermarked image will be reduced. Peak signal-to-noise ratio (PSNR) is the quality evaluation index adopted in most watermarking schemes to evaluate the imperceptibility performance of the algorithm. As for an image with size of \(N\times N\), the PSNR is defined as Equation (15):  
\[\text{PSNR}=10\lg\frac{255^{2}}{\text{MSE}}=10\lg\left[\frac{255^{2}N^{2}}{ \sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{N-1}\left[I(i,j)-I_{\text{w}}(i,j) \right]^{2}}\right](\text{dB}), \tag{15}\]  
where \(I(i,j)\) and \(I_{\text{w}}(i,j)\) are the pixel values on the coordinates of row \(i\) and column \(j\) in the host image and the watermarked image separately.  
Structural similarity index (SSIM) overcomes the disadvantage of PSNR, becoming a useful index of the similarity detection, which is defined as Equation (16):  
\[\text{SSIM}=\frac{2\mu_{I}\mu_{I_{\text{w}}}+C_{1}}{{\mu_{I}}^{2}+{\mu_{I_{ \text{w}}}}^{2}+C_{1}}\frac{2\sigma_{I}\sigma_{I_{\text{w}}}+C_{2}}{\sigma_{I }^{2}+\sigma_{I_{\text{w}}}^{2}+C_{2}}\frac{\sigma_{I_{\text{w}}}+C_{3}}{ \sigma_{I}\sigma_{I_{\text{w}}}+C_{3}}, \tag{16}\]  
where \(\mu_{I}\) and \(\mu_{I_{\text{w}}}\) denote the mean of original image \(I\) and watermarked or restored image \(I_{\text{w}}\), respectively. Furthermore, \(\sigma_{I}\) and \(\sigma_{I_{\text{w}}}\) are the variance of image \(I\) and \(I_{\text{w}}\) separately, while \(\sigma_{I_{\text{w}}}\) is the covariance between \(I\) and \(I_{\text{w}}\). \(C_{1}\), \(C_{2}\), and \(C_{3}\) are positive parameters. The value of SSIM ranges from 0 to 1. When SSIM is equal to 1, \(I\) and \(I_{\text{w}}\) are exactly the same.  
Figure 9 shows the good perceptual transparency of the watermarked image compared with the original image. After SIFT is performed on [PERSON] and [PERSON] images, 1233 keypoints and 1590 keypoints are extracted in these two watermarked images, respectively.  
Figure 9: Imperceptibility of the watermark: (**a**) original [PERSON] image; (**b**) original character watermark \(w_{1}\); (**c**) watermarked [PERSON]; (**d**) original [PERSON] image; (**e**) original logo watermark \(w_{2}\); (**f**) watermarked [PERSON].
## 5 Monitored and Controlled Indicators Critical to Microalgae Cultivation
### Source and Intensity of Lighting  
Light is a key factor influencing the efficiency of biomass production by microalgae [138]. Undoubtedly, sunlight is the most suitable energy source for the cultivation of microalgae [139]. However, solar energy is only available during the day, and its intensity varies considerably depending on the season and atmospheric conditions, such as cloud cover. In temperate climates, the intensity of natural sunlight also varies throughout the year [140]. In most microalgae species, photosynthetic activity increases at light intensities of 200 to 400 \(\upmu\)mol E/m\({}^{2}\)-s, which corresponds to about 10% of the maximum direct sunlight they can receive [141]. Compared to land plants, microalgae generally require less light. _Chlorella vulgaris_, for example, grows well at an illumination of 50-100 W/m\({}^{2}\), which corresponds to about 232-465 \(\upmu\)mol E/m\({}^{2}\)-s in the Photosynthetically Active Radiation (PAR) range (13,000 to 26,000 lux) [142]. For many species, the optimal lighting conditions are in the range of 5000 lux (90 \(\upmu\)mol/m\({}^{2}\)-s) to 13,000 lux (230 \(\upmu\)mol/m\({}^{2}\)-s) and temperatures between 17-20 \({}^{\circ}\)C [143].  
In recent years, daylight technology has developed considerably and includes modern optical fibre systems that enable their application in devices for intensive CO\({}_{2}\) biosynthesis and wastewater treatment with microalgae [144]. As sunlight is only available during the day, hybrid systems that combine natural light with LED lighting have proven to be the optimal solution to ensure the continuous operation of CO\({}_{2}\) capture and wastewater treatment systems.  
Photoinhibition can occur during the midday hours, when light intensity can reach up to 4000 \(\upmu\)mol E/m\({}^{2}\)-s. In diatoms, this phenomenon has been observed at a light intensity of about 10,000 lux (180 \(\upmu\)mol E/m\({}^{2}\)-s), and similar results have been reported for cyanobacteria [145]. _Planktothrix agardhii_ (formerly Oscillaria), for example, exhibits a photoinhibition threshold above 180 \(\upmu\)mol E/m\({}^{2}\)-s [146]. This effect is particularly pronounced in starter ponds, where the low concentration of microalgae biomass is directly exposed to intense solar radiation and the lack of self-shading of the cells promotes photoinhibition.
# A Highly Interactive Honeypot-Based Approach to Network Threat Management
## 3 Central Node Model in High-Interactivity Honeypot System
### Connection Control Module
#### 3.2.1 The Communication Control Module  
The communication control module mainly includes external connection restrictions and data packet suppression functions. We have enhanced the central node's control capabilities over the numerous highly interactive honeypots linked to the system. This upgrade allows us to monitor the real-time status of each honeypot and guarantee a realistic simulation environment for them. More importantly, it ensures the overall security of the system.  
External Connection Restriction: We have imposed restrictions on the honeypots that the central node must connect to, in order to enhance the security of the central node. We use login identity authentication control to ensure that the user of the honeynet is a \"secure user\" who is securely authenticated. IP address filtering is utilized to ensure that only legitimate honeypots are connected to the system. This filtering mechanism also helps in filtering out the collection of redundant information and prevents untrusted sites from accessing the honeypot system. To ensure that the log file recording hacker behavior is not illegally tampered with by hackers, we provide the ability to change the default storage path of the IIS log. These measures guarantee the security of the honeypot system itself. Furthermore, because the stored files themselves are \"baited\", when the hacker wreaks havoc on the website, he may have unknowingly fallen into being countered.  
Data Packet Suppression Function: We filter and suppress the data packet information from the honeypots to the central node, in order to filter out useless information and potential attacks on the central node. The central node receives and stores data packets, which are initially \"baited\" packets that have undergone specialized processing by the honeypots [11]. The central node then proceeds to authenticate the IP and verify that the packet format meets its requirements before accepting the data for further processing. The specific workflow is shown in Figure 3 below.  
Figure 2: Information Capture Module It is worth noting that in order to ensure high interactivity.
## 6 Conclusions  
In this paper, we demonstrated a W-band-coordinated MISO photonic mmWave communication system using an NNWSC-based receiver. The NNWSC-based receiver directly converts waveform signals into QAM symbols, eliminating the need for matched filtering, down-sampling, and cascaded equalizers. Trained to perform NM-CAP demodulation and down-conversion and mitigate ISI, IBI, and STO, this receiver outperforms conventional receivers in terms of BER, particularly in high SNR and high-interference environments. The experimental results show that the NNWSC-based receiver maintains low BER across various configurations and demonstrates strong adaptability to changes in roll-off factors and sub-band spacing. In contrast to conventional receivers, which suffer from cascading filter errors and performance degradation in high-ISI and high-IBI conditions, the NNWSC-based receiver exhibits minimal interference accumulation. Additionally, the receiver shows promise in managing STO in coordinated MISO systems. Future work should focus on optimizing the NNWSC-based receiver for low-SNR environments and improving robustness under dynamic STO conditions, further advancing high-capacity wireless networks.  
Conceptualization, K.L. and J.Z.; data curation, K.L.; formal analysis, K.L.; funding acquisition, J.Z.; investigation, K.L., B.D., Y.L. ([PERSON]) and Y.L. ([PERSON]); methodology, K.L. and J.Z.; project administration, J.Z.; resources, K.L. and J.Z.; software, K.L. and Z.L.; validation, K.L. and Z.L.; visualization, K.L., F.W. and Y.H.; writing--original draft, K.L.; writing--review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China (62235005 and 62171137) and the Natural Science Foundation of Shanghai (24 ZR1490500).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 4 Experimental Results
### Price Forecasting
#### 4.3.3 Ablation Study  
In the ablation experiments, XGBoost+TCN-Att and Timegan+TCN-Att are univariate price prediction, while XGBoost+TCN-Att and the proposed method are multi-variate forecasting as shown in Table 6. The R\({}^{2}\) of the proposed method is higher than that of several other univariate and multivariate prediction methods. Although the accuracy of proposed method is similar to that without TimeGAN, the method presented may avoid overfitting and better reflect the relationships among time-series, ensuring stable forecasting performance.
## 4 Analyses of Numerical Simulation Results
### Compatibility of Negative-Curvature Ramp Under Off-Design Conditions
#### 4.2.2 Variation in Outlet Pressure  
The relationship between the total pressure loss coefficients and the static pressure ratio under design inlet conditions is shown in Figure 22. The total pressure loss coefficient curves of both cascades indicate that the flow losses of the improved supersonic compressor cascade are lower than those of the baseline cascade for the whole range of back exit pressures. The total pressure loss coefficient is reduced by 4.6% under the design conditions and 5.2% at the maximum static pressure ratio. This can be attributed to the fact that the intensity and position of the first-passage shock are not dependent on the increase in back exit pressure until the last-passage normal shock reaches the throat of the cascade passage. The maximum outlet-to-inlet static pressure ratio approaches 2.9, as shown in Figure 23. The pre-shock Mach numbers are reduced through compression waves, resulting in the disappearance of the boundary layer separation bubble. As for the large back exit pressure, the first passage shock moves away from the vane's leading edge. Then, a stronger separation shockwave is formed, and the maximum static pressure ratio reaches \(p_{2}/p_{1}=3.04\). The ramp profile hinders low-momentum fluids from moving downward within the boundary layer and shows less flow loss in the improved cascade. In addition, the maximum static pressure ratio of the improved cascade is about 1.6% higher than that of the baseline cascade, indicating that the improved model has potential for high-stage loading.  
Figure 23: Mach number contours with different static pressure ratios.  
Figure 22: Variation in total pressure loss coefficient at different static pressure ratios.
## 3 Results and Discussion
### Identification of the Biomass Burning Tracer Levoglucosan
It is also predominantly emitted as insoluble K through the resuspension of particulate matter in the atmosphere. K emissions are also associated with sea salt particles and cooking aerosols. Figure 5: (**a**) Diurnal patterns of 3-h integrated brown carbon at 370 nm (BrC\({}_{370}\)), levogluc, and mannosan; (**b**) correlation of levoglucosan and BrC\({}_{370}\); (**c**) diurnal patterns of mean hourly potassium ions (K\({}^{+}\)) and elemental potassium (K); (**d**) correlation of K\({}^{+}\) and K during the measurement period. Ref. [42] confirmed that levoglucosan and soluble Cd, Cs, K, Rb, and Tl can be used as tracers for BB. This study observed a strong correlation (r\({}^{2}\) = 0.86) between observed K and K\({}^{+}\) (Figure 5d), indicating that K\({}^{+}\) accounted for approximately 54% of the total K concentration. In this study, the measured K\({}^{+}\) showed a low correlation of 0.28 with BrC\({}_{370}\) and also exhibited weak correlations with the sum of levoglucosan and manosan (r\({}^{2}\) = 0.25). The observed K\({}^{+}\) in this study is believed to be influenced by coastal influences located approximately 2.8 km from the measurement site as well as the impact of resuspended dust originating from a four-lane road. These results underscore the importance of exercising caution when utilizing K\({}^{+}\) as an indicator for BB markers. In addition, BB had the most pronounced influence on BrC observed during the measurement period. Furthermore, this confirms that levoglucosan and mannosan can be effectively used as BB tracers.
# Oxidatively Modified Proteins: Cause and Control of Diseases
## 2 Overview of Enzymatic Post-Translational Modifications  
Enzymatic PTMs, such as phosphorylation, acetylation, glycosylation, and ubiquitination, regulate protein function and interaction [14; 15; 16]. Enzymes (kinases, transferases) catalyze the covalent modification of an amino acid side chain and sometimes require co-substrates (e.g., ATP). While some enzymatic PTMs lead to activation or inhibition of intracellular proteins and are critical events in signal transduction [17; 18; 19; 20; 21], other enzymatic PTMs are essential for protein stability and regulation. For instance, the tumor suppressor p53 is stabilized by phosphorylation and regulated by an array of PTMs in more than 36 different amino acids [22; 23; 24]. Enzymatic PTMs transform the p53 protein folding, stability, and function. Although defective regulation leads to misfolding and malfunction of the protein, incorrect folding of p53 alone does not necessarily lead to cell cycle defects and tumor growth. Still, enzymatic PTMs are causative in the dysregulation of kinases, malfunction or misfolding of proteins, and aggregation of proteins that ultimately can cause diseases.  
In Alzheimer's disease (AD), aberrant enzymatic activity promotes hyperphosphorylation of the tubulin-associated unit (tau) protein leading to its oligomerization and aggregation [25]. However, aggregation of amyloid-\(\upbeta\) is a commonly assumed primary pathological process, as the amyloid hypothesis states that this aggregate promotes tau modification [26; 27]. Amyloid-\(\upbeta\) aggregates enable redox-active metal ions to bind, which catalyzes the production of ROS [28]. The increased amount of intracellular ROS is then self-amplified via mitochondria. Hence, ROS and their subsequent non-enzymatic modifications on proteins causing, for instance, oxidation, chlorination, or nitration are intertwined with enzymatic PTMs processes.
## 3 Results  
Table 3 demonstrates the DSC for segmentation. The mean DSC of five folds was 0.7528. The segmentation model created in Fold 3 was used to extract the prostate.  
Table 4 demonstrates the overall ADCmap and T2 WI for segmentation and normal volume accuracy. There was no significant difference between overall segmentation accuracy and normal volumes or between T2 WI and ADCmap images. Figure 4 shows the AUROC for each dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Overall Accuracy (\%)**} & \multicolumn{1}{c}{**AUROC \({}^{\text{c}}\)**} \\ \hline \multirow{2}{*}{Segmentation volume} & ADCmap 1  & 64.68 \(\pm\) 3.97 & 0.70 \(\pm\) 0.05 \\  & T2 WI 2  & 66.64 \(\pm\) 6.72 & 0.71 \(\pm\) 0.07 \\ \hline \multirow{2}{*}{Normal volume} & ADCmap & 69.60 \(\pm\) 2.89 & 0.69 \(\pm\) 0.08 \\  & T2 WI & 64.72 \(\pm\) 8.16 & 0.63 \(\pm\) 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall accuracy and AUROC of the classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** & **Mean** \\ \hline DSC & 0.6981 & 0.7462 & 0.7897 & 0.7421 & 0.7879 & 0.7528 \\ \hline \hline \end{tabular}
\end{table}
Table 3: DSC for segmentation.  
Table 5 displays the precision and recall values for the ADCmap and T2 WI in segmentation and normal volume. The ADCmap exhibited the highest values for precision for GS \(\geq\) 7 and recall for GS \(<\) 7 in the segmentation volume. The ADCmap demonstrated the highest values for GS \(\geq\) 7 and GS \(<\) 7 compared to the ADCmap and T2 WI.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing
#### 3.3.6 Style Transfer  
DDPM's generative capabilities can be applied to artistic tasks, such as style transfer. By manipulating the denoising diffusion process, DDPM can alter the style of an image while preserving its content, contributing to the creation of visually appealing and stylized images [56; 57].
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
## 3 Multi-State Fuzzy BN
### Inference Based on Fuzzy BN  
(1) Inference of Fuzzy Fault State of Leaf Nodes  
If interval probability of root nodes is fuzzy, the probability of node \(S_{i}\) in fault state \(F_{k}\) is recorded as \(\overline{P}(S_{i,\,F_{k}})\), while interval fuzziness of leaf node \(L\) in fault state \(F_{k}\) is  
\[\begin{array}{rcl}\overline{P}(L=F_{k})&=&\sum\limits_{\begin{array}{c}x_{1 },\ldots,\,S_{n},\,Y_{1},\,\ldots,\,Y_{m},\,F_{k}\end{array}\\ &&y_{1},\ldots,y_{m}\\ &=&\sum\limits_{\begin{array}{c}p\alpha(L)\end{array}}\overline{P}(L=F_{k} \big{|}pa(L))\prod\limits_{j=1}^{m}\sum\limits_{\begin{array}{c}p\alpha(Y_{ j})\end{array}}\overline{P}(Y_{j}\big{|}pa(Y_{j}))\prod\limits_{i=1}^{n} \overline{P}(S_{i,\,F_{k}})\end{array} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(S_{1}\) & \(S_{2}\) & \(\ldots\) & \(S_{n}\) & \(P(F\)=F\({}_{1}|S_{1},\,S_{2},\ldots,\,S_{n})\) & \(\ldots\) & \(P(F\)=F\({}_{k}|S_{1},\,S_{2},\ldots,\,S_{n})\) \\ \hline
0 & 0 & \(\ldots\) & 0 & \({P_{1}}^{1}\) & \(\ldots\) & \({P_{1}}^{k}\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
1 & 1 & \(\ldots\) & 1 & \({P_{m^{n}}}^{1}\) & \(\ldots\) & \({P_{m^{n}}}^{k}\) \\ \hline \end{tabular}
\end{table}
Table 1: CPT of multi-state fuzzy BN.  
Figure 3: Membership function of triangular fuzzy number.  
where, \(pa(L)\) represents the father node of leaf node \(L\) and \(pa(Y_{j})\) indicates the father node of intermediate node \(Y_{j}\).  
(2) Fuzzy fault inference of faults of root nodes  
The inference of fuzzy state of root nodes is to calculate posteriori probability of BN according to the state of leaf nodes. It is also known as backward inference based on BN, that is, the fuzzy interval of probability of leaf node is solved by calculating the obtained posteriori probability of BN.  
If leaf node \(L\) is in the fault state \(F_{k}\), the posteriori probability of root node \(S_{i}\) in the fault state \(F_{k}\) is shown as  
\[\overline{P}(S_{i}=S_{i,F_{k}}|L=F_{k})=\frac{\overline{P}(S_{i}=S_{i,F_{k}},L =F_{k})}{\overline{P}(L=F_{k})} \tag{9}\]  
where, \(\overline{P}(S_{i}=S_{i,F_{k}},L=F_{k})\) is the fuzzy joint probability of root node \(S_{i}=S_{i,F_{k}},L=F_{k}\). Because of  
\[\overline{P}(L=T_{k}\big{|}S_{i}=S_{i,F_{k}})=\frac{\overline{P}(S_{i}=S_{i,F_ {k}},L=T_{k})}{\overline{P}(S_{i}=S_{i,F_{k}})} \tag{10}\]  
Therefore, by combining with Formulas (9) and (10), the following formula is obtained.  
\[\overline{P}(S_{i}=S_{i,F_{k}}|L=T_{k})=\frac{\overline{P}(S_{i}=S_{i,F_{k}}) }{\overline{P}(L=T_{k})}\overline{P}(L=T_{k}\big{|}S_{i}=S_{i,F_{k}}) \tag{11}\]