esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural-Network-Based Quark-Gluon Plasma Trigger for the CBM Experiment at FAIR
## 2 Materials and Methods
### Neural Network Models  
For MLP networks (shown in Figure 7), a hidden layer with 64 fully-connected neurons complemented by Leaky Rectified Linear Unit (LReLU) [21] activation function is implemented. The number of neurons is determined empirically and remains constant to allow comparison of FC neural networks with varying numbers of layers. LReLU is chosen for its performance, which is akin to the widely used Rectified Linear Unit (ReLU) activation function, but it circumvents issues related to dead neurons [22]. For the learning process, the adaptive moment estimation (ADAM) [23] algorithm is used to optimize the network parameters after each step. The ADAM algorithm updates exponential moving averages of the gradient (\(m_{t}\)) and the squared gradient (\(v_{t}\)), which themselves are estimates of the 1 st moment (the mean) and the 2 nd raw moment (the uncentered variance) of the gradient with the hyper-parameters \(\beta_{1}\), \(\beta_{2}\), which control the exponential decay rates of these moving averages with respective values 0.9 and 0.999. The values for \(\alpha\) and \(\epsilon\) are 0.001 and \(10^{-8}\), respectively [23].  
The architecture of the CNN (shown in Figure 8) is composed of two three-dimensional convolutional layers, each succeeded by a max-pooling layer, and two sequentially arranged fully-connected layers. The initial convolutional layer contains 32 filters of size \(3\times 3\times 3\), with a zero-padding of \(1\times 1\times 1\) and a stride of \(1\times 1\times 1\), thus preserving the spatial dimensions of the input. The convolution is then followed by a max-pooling operation with a filter size of \(2\times 2\times 2\) and stride of \(2\times 2\times 2\), leading to a halving of the spatial dimensions. The second convolutional layer consists of 64 filters of identical size and employs the same padding and stride length as the preceding convolutional layer. It is subsequently followed by a max-pooling layer with an identical filter size and stride length to the previous pooling layer. The resulting \(64\times 5\times 5\times 5\) matrix is then flattened and fed into the fully-connected layers with parameters mirroring those utilized in the MLP architectures.  
Figure 6: (**a**): 3D Convolution. An illustration of the three-dimensional convolution operation applied to the input layer using a single filter, composed of 28 kernels. This process transforms the 28 input channels into a singular output channel. The quantity of output channels directly corresponds to the number of filters used during the convolution. (**b**): 3D Pooling. An illustration of the three-dimensional pooling operation. Despite retaining the original number of channels, the data dimensions are halved. For instance, a \(20\times 20\times 20\) cube is reduced to a \(10\times 10\times 10\) cube through this process.  
Figure 7: From left to right: structure of one-, two- and three-layer fully-connected neural networks used for QGP detection.
# Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder-Decoder Network
## 5 Conclusions  
In this paper, an advanced processing method for microseismic signals (CNN-denoiser) based on the deep neural network is proposed. The performance of the proposed method outperforms the microseismic signals corrupted with various types and intensities of noise for denoising, even when the frequency bands of microseismic signal and noise overlap. The signal and noise components in the data are appropriately recognized and separated, even if the signal is heavily polluted by noise. The results show that the proposed method significantly improves the SNRs with minor changes of the microseismic signal, and also preserves the shape and amplitude characteristics are well. Moreover, generalization ability was validated on the other dataset outside of the training dataset. Compared with existing methods, the CNN-denoiser significantly improves the SNRs and introduces less distortion in the waveform which allows better recovery of the real waveform. Although the motivation of this study is the need for accurate and automated microseismic signal processing, the proposed method can be applied for signal analysis and disaster assessment in geophysical and geotechnical engineering fields, such as hydraulic fracturing, the mining industry, shale-gas exploitation, and earthquake management.  
Writing--original draft preparation, H.Z.; writing--review and editing, C.M., V.P. and Y.Z.; supervision, N.C.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (gran numbers 41807255); State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (grant number SKLGP2018Z016); Sichuan Science and Technology Project (grant number 2019 YJ0465).  
The authors declare no conflict of interest.
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer
### Relative Position of Double Gratings  
In order to better describe the relative position of the two gratings, a three-dimensional coordinate system (\(x\),\(y\),\(z\)) is established. The slit, two gratings, and spectral images are described in this three-dimensional coordinate system.  
The slit is a line segment that passes (0,0,\(h\))(0,\(-a\),\(h\)), (0,\(a\),\(h\)) and is parallel to the \(y\)-axis. The length of the slit is \(2a\), and the height relative to the center of the aperture is \(h\). In order to simplify the description, the slit width of the slit is omitted here.  
The grating 1 is a plane that passes (0,0,0)(0,\(d\),0) line segment, which can rotate around the \(y\)-axis. The grooves of the grating 1 are parallel to the \(y\)-axis. The grating 2 is a plane that crosses the (0,0,0)(0,\(-d\),0) line segment, which can rotate around the \(y\)-axis. The grooves of the grating 2 are parallel to the \(y\)-axis.The relative position of grating 1 and grating 2 is shown in Figure 10.  
Regardless of the tilt of the image plane, the two bands of interest (\(\lambda_{1}\),\(\lambda_{2}\)) and (\(\lambda_{3}\),\(\lambda_{4}\)) can be imaged separately by rotating grating 1 and grating 2 around the \(y\)-axis. The imaging areas are \(x=0\), \(y\in(-a\),\(+a\)), \(z\in(-h1,-h2)\) area and \(x=0\), \(y\in(-a\),\(+a\)), \(z\in(-h3,-h4)\) area. The non-interesting bands will overlap with the interesting bands. For how to deal with the overlap problem, see Section 2.4, and for the size of the rotation angle of the gratings, see Section 2.5.  
Figure 5 shows the position of the slit and the two spectra in the three-dimensional coordinate system.  
Figure 4: Dual-gratings spectrometer with H\(\alpha\) line, H-line, and K-line of ionized calcium.
# High Resolution Imaging Using Micro-Objectives Fabricated by 2-Photon-Polymerization
## 2 Fabrication
However, there are slight surface imperfections, mainly due to residual polymer deposits that were not completely removed during the development process. For further validation, Figure 1d shows a cross-section of a full sphere cut by the FIB. Compared to the structure produced with the unadapted geometry, see Figure 1b, the optimized design shows a significant improvement in shape accuracy [20]. These shape fidelity improvements enable the fabrication of micro-spheres using 2 PP that match the geometric dimensions of commercially available micro-spheres. The high-resolution properties of conventionally fabricated micro-spheres are well described in the literature [9; 23; 24]. The successful fabrication of 2 PP micro-spheres with identical dimensions to their conventional counterparts allows us, for the first time, a direct comparison of their imaging properties. This provides a reliable demonstration of the high-resolution capabilities of micro structures fabricated using 2 PP technology. To evaluate and compare imaging performance, micro-spheres with a diameter of 10 \(\upmu\)m were fabricated using 2 PP. These micro-spheres were designed with a minimal contact area of less than 1 \(\upmu\)m\({}^{2}\) between the micro-sphere and the substrate, ensuring reduced optical interference from the substrate, as shown in Figure 1d. The diameter variations for the 2 PP micro-spheres are in the range of the surface roughness of 10 nm and thereby one order of magnitude smaller than the diameter variations of the used PS micro-spheres from microparticles GmbH with \(\pm\)100 nm. It should be noted that the diameter variations observed for the 2 PP-fabricated micro-spheres are within the range of the measured surface roughness (approximately 10 nm). Such minimal dimensional deviations are negligible in comparison to the micro-spheres' overall size and have no significant impact on their optical performance. After fabrication, the micro-spheres were carefully detached from the substrate and transferred to the sample for imaging experiments. For the high resolution imaging tests, a BluRay  
Figure 2: Schematic illustration of an easy adaptation of the design geometry for the fabrication of a shape fidelity micro sphere. (**a**) Schematic cross-section of the design geometry \(S(r)\) for a micro sphere, the voxel geometry \(h(r)\) and the area to be removed in the center of the design geometry. (**b**) Resulting adapted geometry \(D(r)\). disc surface was chosen as the sample. The characteristic pits and lands on the disc surface provide a challenging test pattern for evaluating the resolution of optical systems.
## 3 Results and Discussion
### Effect of Different Factors on Properties of Fired Bricks
#### 3.3.6 Sintering Temperature  
The sintering temperature ensures the quality of fired bricks [41]. The influence of firing temperatures between 900 \({}^{\circ}\)C and 1100 \({}^{\circ}\)C on the compressive strength and water absorption of sintered bricks is shown in Figure 9a. With the increase of sintering temperature, the compressive strength of sintered bricks also increases. When the temperature rises from 900 \({}^{\circ}\)C to 950 \({}^{\circ}\)C, the compressive strength does not increase significantly, which is due to the lower sintering degree of the brick. When the temperature rises to 1100 \({}^{\circ}\)C, the compressive strength is 19.6 MPa, 72.5% higher than that at 900 \({}^{\circ}\)C. The water absorption decreases with the increase of the sintering temperature, from 22.8% at 900 \({}^{\circ}\)C to 18.6% at 1100 \({}^{\circ}\)C. This is because at a higher sintering temperature, glass powder and other fluxes will form more liquid phase to fill the pores in the structure, making the structure more compact and reducing the porosity, thus increasing the compressive strength and reducing the water absorption [42].  
The influence of sintering temperature on the bulk density and linear shrinkage of sintered bricks is shown in Figure 9b. The results indicate that with an increase of sintering temperature, the bulk density and linear shrinkage also increase. When the firing temperature reached 1100 \({}^{\circ}\)C, the bulk density and linear shrinkage reached 1.64 g/cm\({}^{3}\) and 5.14% respectively.  
Considering the performance of sintered brick, when the glass powder content is 10 wt%, the molding moisture is 20%, the molding pressure is 25 MPa, the heating mode is heat preservation at 1100 \({}^{\circ}\)C for 2 h, the heating rate is 10 \({}^{\circ}\)C/min and the firing temperature is 1100 \({}^{\circ}\)C, the sintered brick has the optimal performance. Under this condition, the compressive strength is 19.6 MPa, and the water absorption rate is 18.62%, which meets the MU15 grade requirements of Chinese Standards (GB 5101-2017) [43] (compressive strength \(>\) 15 MPa and water absorption \(<\) 19%). The linear shrinkage is 5.14%, and the bulk density is 1.64 g/cm\({}^{3}\), which also meet the general requirements.
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Training Methods
#### 4.5.2 Diverse Inpaintings  
Recent research on image inpainting has started to pivot towards diversification of the generated results. Previous image inpainting methods could produce seemingly real complete images. However, these models operate under the inherent assumption that a given incomplete image should correspond to only one complete image. They optimize the network by comparing the differences between the generated and actual full images. Such an assumption may overlook the potential of multiple valid completions for a given incomplete image, thereby potentially limiting the model's versatility and adaptability. Similar to art inpainting, different artists undoubtedly achieve varied inpainting results for the same piece, yet all outcomes are valid. To achieve diverse inpaintings, [PERSON] et al. [22] introduced a diversified image inpainting method to generate multiple plausible solutions for missing image regions. The main challenge faced by the learning-based method is that each label usually has only one real training instance. To overcome this difficulty, a framework with two parallel paths was proposed. As illustrated in Figure 11, one is a VAE-based reconstruction path, leveraging not only the known image region information but also imposing a smooth prior on the latent space of the region to be restored. The other is a generation path, predicting the latent prior distribution of the missing region based on visible pixels, from which different outcomes can be sampled. The latter does not aim to guide the output to reconstruct instance-specific hidden pixels but allows the plausibility of the results to be driven by an auxiliary discriminator network, resulting in highly variable generated content.  
[PERSON] et al. [122] designed two types of SPDNorm to control the confidence of the generated content in the region to be restored. As illustrated in Figures 12 and 13, one is Hard SPDNorm, and the other is Soft SPDNorm. Hard SPDNorm provides a map where values become smaller towards the center. When the model predicts pixel values in the missing region, the output features operate with this map. Soft SPDNorm, on the other hand, is learned by a CNN. Experimental analyses showed that the learned values hover around 0.5, diverging from the authors' initial intent.  
Figure 11: Network model diagram.  
Figure 12: Hard SPDNorm.  
Figure 13: Soft SPDNorm.  
Other works have attempted to diversify inpainting results. [PERSON] et al. [123] treated the diversified image inpainting task as a known marginal probability distribution and joint probability distribution, aiming to solve the conditional probability distribution problem. Their model comprises a manifold projection module, an encoding module, and a generation module. Although some level of variation has been achieved, the quality of the completion is limited by divergence in training. In contrast, [PERSON] et al. [124] directly optimized the log likelihood in the discrete space through transformers without any auxiliary assumptions.
## 2 Methodologies
### Engine Management System and Control Variables
In this case, the distribution between the two loops was assessed through a simplified energy balance based on the measured temperature levels in the intake and EGR circuits, neglecting heat losses and changes of constant pressure specific heat with temperature. Therefore, overall EGR mass flow rate was divided into two contributions (Equation (4)), where M\({}_{\rm HP\;EGR}\) is recirculated by the high-pressure loop and M\({}_{\rm LP\;EGR}\) by the low-pressure circuit. \[\rm M_{EGR}=M_{\rm HP\;EGR}+M_{\rm LP\;EGR}. \tag{4}\]  
Equation (5) identifies LP EGR loop contribution referred to the overall EGR mass flow rate, which allows us to weight the input from each circuit, depending on engine operating mode [21]. \[\rm LP_{proportion}=M_{\rm LP\;EGR}/M_{EGR} \tag{5}\]  
A non-dimensional EGR rate, namely the Charge Dilution Index (CDI), was also evaluated, in order to normalize the effects of engine load on EGR and to compare f\({}_{\rm EGR}\) in different working conditions. This parameter was calculated according to Equation (6) [24], where the oxygen concentration at the engine intake (\(\rm X_{O2\;i}\)) accounts for the dilution effect, while the oxygen content in the exhaust/recirculated gases (\(\rm X_{O2\;e}\)) depends on the engine load. \[\rm CDI=EGR\;level/Load\;level=[(X_{O2\;a}-X_{O2\;i})/(X_{O2\;i}-X_{O2\;e})]. \tag{6}\]  
Further details on this index and its calculation are presented in References [21; 22; 24].
# Sentiment Analysis of Twitter Data
## 1 Introduction
As a result, both SA and OM can be used interchangeably to convey the same concept in many cases. However, other scholars have argued that they are different since they were developed to solve different problems. For instance, [PERSON] et al. (2013) claimed that OM is designed to assess whether or not a given piece of text contains an opinion and is used to address the subjective analysis problem. On the other hand, SA refers to the analysis and prediction of the sentiment polarity of text data ([PERSON] et al., 2014). **RQ2**: Why was Twitter selected as the primary target platform for the study of SA? Twitter has a significant number of active users, and Twitter API makes it simple to collect vast quantities of opinionated text data. In addition, the users come from a variety of backgrounds, including common individuals, celebrities, politicians, etc. In addition, the \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Description** \\ \hline TSA & Twitter-based Sentiment Analysis \\ SNS & Social Networking Service \\ SA & Sentiment Analysis \\ OM & Opinion Mining \\ NLP & Natural Language Processing \\ NB & Naive Bayes \\ SVM & Support Vector Machine \\ POS & Part of Speech \\ BN & Bayesian Network \\ ME & Maximum Entropy \\ DAG & Directed Acyclic Graph \\ NN & Neural Network \\ PSO & Particle Swarm Optimization \\
3 NN & 3-Nearest Neighbors \\ PCA & Polarity Classification Algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The description of abbreviation. collected corpus includes a wide range of distinct materials from several domains, which allows easy access to textual information in a variety of languages [15]. **RQ3**: What are the challenges that TSA is facing? TSA has several significant challenges. Given that a tweet can only be a maximum of 140 characters long, text length is an extremely crucial one. Different from previous research on evaluating the long text of the document, analyzing the sentiment of short length text presents a new challenge for TSA. Topic relevance is another difficulty, which refers to the categorization of tweets into certain topics. This contributes to the efficiency of the fine-grained TSA tasks. In addition, text pre-processing techniques are also essential for TSA. Preprocessing the raw dataset is a prerequisite for model creation, therefore various methods, such as removing punctuation, stop-word removal, stemming, and lemmatization, etc., have been introduced accordingly [14].
## 5 Discussion
### Areas for Further Improvement
* Reinforcement learning for dynamic thresholding: while our current adaptive method uses statistical boundaries, future enhancements could involve reinforcement learning to learn optimal threshold adjustments in real time, tailored to evolving sensor behavior.
* Temporal modeling extension: Although the hybrid Autoencoder already captures short-term dependencies, incorporating additional layers of GRUs or bidirectional LSTMs may further improve its capability to learn long-term temporal patterns, especially under subtle or gradual anomalies.
* Ensemble-based hybrid detection: the classification phase could be enhanced by ensembling multiple classifiers (e.g., SVM, Random Forest, Deep Neural Networks) alongside XGBoost, using majority voting or stacking to boost anomaly classification robustness in diverse deployment scenarios.  
In summary, the proposed hybrid framework--integrating a Transformer-based Autoencoder with Isolation Forest and XGBoost--demonstrates strong potential for anomaly classification in real-time Wireless Sensor Network applications. It supports high granularity in distinguishing malfunctioning nodes from intrusion attempts, which is critical for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
reliable forest fire monitoring. The architecture's modular design and use of open-source tools also ensure its reproducibility and extensibility in future deployments.
###### Abstract
In the meantime, there have been few demonstrations on the developments in semiconductor detectors on the basis of photoconductive TPA phenomena, where current-to-voltage amplification occurs sequentially, and the weak current is converted to a moderate output voltage via transimpedance amplification circuits within the detector. Such semiconductor devices do not require an external current amplifier or the sensitivity of photocurrent detection electronics, depending on their photocurrent detection schemes--that is, the autocorrelator sensitivity is dependent only on the TPA sensitivity of the PDs. [PERSON] et al. previously conducted sensitive autocorrelation measurement based on TPA using a commercial photoconductive GaP detector with a wide spectral response ranging from 600 to 1100 nm [27]. To fully resolve IAC for sub-10 fs pulses, we showed that the detector should have a bandwidth of at least \(\sim\)100 kHz. Moreover, the study showed comparable output signals between the second harmonic generation using a BBO crystal and the TPA process using a GaP PD; the sensitivity between the SHG and TPA measurements differing by a factor of three. This study entailed the development of a real-time autocorrelator equipped with gain-controllable photoconductive GaP, GaAsP, and Si detectors, where reverse-biased PDs were illuminated to induce the photocurrent excited by TPA. It delivered readouts of real-time AC and IAC traces at a refresh rate of 1-10 Hz. GaAsP and Si photodiodes were used in this study because they are suitable for pulse measurement up to 1.1 \(\upmu\)m and 1.5 \(\upmu\)m bands, respectively. GaP was selected as a reference photodiode for the detection of high-power pulses in the near-infrared (\(\sim\)800 nm) wavelength band and for the comparison of its characteristics with those of other semiconductor PDs. An internal operational amplifier circuit in a compact detector converted the photocurrent into an electric voltage (transimpedance type), controlling the amplification gain with 10 dB increments by choosing one of internal feedback resistors. While changing the PD of a commercial GaP detector to GaAsP and Si PDs, we investigated the spectral responses of those TPA-based photodetectors to determine their short or long wavelength limits and efficiency, allowing for TPA-based pulse measurement, by using a femtosecond light source tunable from 650 to 1300 nm. Based on the TPA spectral responses, we determined the long wavelength limits to be near 970 and 1300 nm for the GaP and GaAsP detectors, respectively, while the short wavelength limit was found to be near 1170 nm for the Si detector. We found that the spectral response based on TPA-based detectors followed the linear response curve of the corresponding semiconductor PD. These photoconductive detectors are simple and robust sensors with a high signal-to-noise ratio (SNR) and are highly competitive in diagnosing the pulse characteristics of ultrafast fiber lasers and light sources near IR wavelengths.