esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Application Parameters of Hydraulic Slotting Technology in Jointed Coal Reservoirs
## 5 Conclusions  
To investigate appropriate parameters for the application of hydraulic slotting technology in jointed coal reservoirs, we investigated the law governing the influence of conventional application parameters on the hydraulic slotting effect by conducting a numerical simulation and experimental  
Figure 14: Variation curve of gas extraction rate.  
Figure 13: Gas extraction rate of different boreholes.  
testing. Thus, the permeability-enhancing mechanism was clarified. The conclusions drawn from this study are as follows:  
(1) With the impact of the water jet, the cleats and joints in the coal generated stress concentration and initiated fractures. This promoted the formation of a complex fracture network. The optimized arrangement included angles between the borehole and the coal seam strike with an inclination of 20-45\({}^{\circ}\), respectively. The cleats underwent tensile failure, and initiated and extended along the vertical direction of the cleats and joints.  
(2) The water jet pressure and rotation speed determined the shape of the slot. The depth of the slot increased with the water jet pressure or with the decrease of rotation speed; however, the width of the slot was not greatly affected. High water jet pressure and low rotation speed promoted the formation of cracks at the end of the slot, which strengthened the permeability-enhancing effect. However, the coal fragments could more easily peel off from the sides of the seam, and subsequently blocked the borehole. The high water pressure and low rotation speed application parameters were optimized without blocking the borehole.  
(3) The results of field application at the Pingdingshan No.12 Coal Mine revealed that the gas extraction flow after hydraulic slotting was approximately 3-4 times equal to that of conventional boreholes. After optimization, the gas extraction flow was 1.3 times equal to that of conventional hydraulic slotting. An appropriate angle between the cleats and the borehole can more effectively increase the permeability of the coal seam, and achieves higher gas drainage flow, as verified by the analysis results.  
Conceptualization, methodology and formal analysis, Z.G., S.Z. and Y.W.; resources, Y.L.; data curation, S.Z.; writing--original draft preparation, S.Z. and Y.W.; writing--review and editing, Z.G. and Y.L.; software, X.F.; investigation, X.F.; project administration, Y.W. and Y.L.; visualization, S.Z.; supervision, Z.G.; all authors read and approved the final manuscript.  
This study was financially supported by the National Natural Science Foundation of China (NSFC) (Grant No. 51774055 and No. 51625401) and the National Science and Technology Major Projects of China (Grant No. 2016 ZX05045). The authors declare no conflict of interest.
## 4 Discussion
Thus, the effect on site reuse criteria was evaluated while increasing or decreasing the runoff coefficient by 60% and the evapotranspiration coefficient by 45%. Table 7 summarizes the analysis results for all the eight sensitivity parameters and the relevant changes in the criteria for site release for unrestricted reuse. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **PRCC** & **Quartile** & **Assigned Value** \\ \hline External gamma shielding factor & 0.98 & 75\% & \(3.98\times 10^{-1}\) \\ Density of contaminated zone (g-cm\({}^{-1}\)) & 0.86 & 75\% & \(1.72\times 10^{0}\) \\ Runoff coefficient & 0.63 & 75\% & \(6.24\times 10^{-1}\) \\ Kd of \({}^{60}\)Co in contaminated zone (cm\({}^{3}\)-g\({}^{-1}\)) & 0.51 & 75\% & \(1.04\times 10^{1}\) \\ Ev evapotranspiration coefficient & 0.46 & 75\% & \(6.87\times 10^{-1}\) \\ Kd of \({}^{108}\)Ang in contaminated zone (cm\({}^{3}\)-g\({}^{-1}\)) & 0.39 & 75\% & \(1.23\times 10^{1}\) \\ Kd of \({}^{134}\)Cs in contaminated zone (cm\({}^{3}\)-g\({}^{-1}\)) & 0.38 & 75\% & \(2.41\times 10^{1}\) \\ Kd of \({}^{90}\)Sr in contaminated zone (cm\({}^{3}\)-g\({}^{-1}\)) & 0.27 & 75\% & \(6.52\times 10^{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Input parameters derived from the sensitivity analysis. Figure 2: Total exposure dose as a function of time after the NPP decommissioning calculated with RESRAD-ONSITE code for Wolsong NPP site. The result of sensitivity analysis showed that the parameters that have a major impact on the year for site release to facilitate unrestricted reuse were the density of contaminated zone, evapotranspiration coefficient, and external gamma shielding factor. As shown in Figure 3a, if the input for the density of the contaminated zone through sensitivity analysis is doubled, the year for site release for unrestricted reuse was increased to 11.2 years, which is 2.8 times longer than the standard value of 4.07 years, because of the distinct increase in the total exposure dose. This tendency might be caused by the largest impact of external radiation among various exposure pathways on the total exposure dose, together with the increased number of radionuclides contained in the soil due to the increased density of the contaminated zone (see Figure 3b). Furthermore, the groundwater contamination in the NPP site was mainly caused by the leaching of radionuclides from the water infiltration. This is simulated in the RESRAD-ONSITE code as follows [10]:  
\[\mathrm{L_{i}}\ =\ \frac{\mathrm{R(t)}}{\mathrm{\rho\cdot A\cdot T(t)\cdot S_{i}(t)}} \tag{2}\]  
where  
\(\mathrm{L_{i}}\) = leach rate for radionuclide i (year\({}^{-1}\))  
\(\mathrm{R(t)}\) = source strength at time t (pCi:year\({}^{-1}\))  
\(\mathrm{\rho}\) = bulk density of the contaminated zone (kg:m\({}^{-3}\))  
\(\mathrm{A}\) = area of the contaminated zone (m\({}^{2}\))  
\(\mathrm{T(t)}\) = thickness of the contaminated zone at time t (m)  
\(\mathrm{S_{i}(t)}\) = average concentration of the i-th principal radionuclide in the contaminated zone available for leaching at time t (Bq:kg\({}^{-1}\))
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 2 Related Work and Background  
### Clustering  
Clustering strategy reduces the redundant information by controlling the data packets transmitted and aggregated by the CHs. The clustering objective is engaged in order to improve the network performance in terms of lifetime enhancement, overhead management, fault tolerance, energy efficiency, the optimal number of CHs computation, increased connectivity, and reduced delay in data throughput. Clustering strategy can be of many types depending on different parameters. The strategies and categorization of clustering are discussed below in detail.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Gibbs sampling
#### Doc2 Vec  
It was proposed by [77] as a straightforward extension to Word2 Vec [86] for extending learning embeddings from words to word sequences. Doc2 Vec is agnostic to the granularity of the word sequence, which can be a word \(n\)-gram, sentence, paragraph, or document. Doc2 Vec also produces sub-par performance compared with vector-averaging methods based on previous studies [46].
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 1 Introduction
An intuitive interpretation is that the decoder first transforms the audio into a representation and then finds the watermark, where Module 3 represents the transformation of the audio to the representation domain. However, we argue that such an architecture needs to have more serious redundancies of design. Firstly, Modules 1 and 3 provide the same service, transforming audio into representations. Secondly, Modules 1 and 2 are invertible transform pairs, which is irrelevant for the watermark-embedding task (e.g., the details of the DCT transform are not of interest in the design of traditional algorithms). These redundancies increase the parametric quantity of the watermarking algorithm and the cost of learning or deploying. To address the above challenges, we propose an audio watermarking algorithm based on adversarial perturbation (AAW, adversarial audio watermarking) in this paper. It is a deep learning-based watermarking algorithm, so it is more flexible to deal with more attacks. In particular, if a specific attack can be constructed as a differentiable mathematical model, it can be combined with AAW to improve the robustness against that attack. Meanwhile, the AAW relies only on a differentiable decoder for watermark embedding and extraction, significantly reducing the redundancy of the model. A tiny perturbation is added to the host audio to conduct the watermark embedding. This tiny perturbation is found by  
Figure 1: A deep learning-based framework for audio watermarking algorithms. The process for embedding is depicted here, so the potential discriminator and noise layer are omitted. optimizing the audio samples. In this paper, we use two different pre-trained decoders: a supervised classification model and an self-supervised contrastive learning model. Our contributions are the following:  
* A new audio watermarking framework is provided, which relies only on a pre-trained differentiable decoder, while the embedding leverages an optimization algorithm to find a tiny perturbation to add to the host audio. * An attack simulation layer is utilized during embedding. Through this layer, attacks are integrated into the optimization objective at watermark embedding. Experiments demonstrate that this approach significantly improves the robustness of watermarking against multiple attacks. * A whitening layer is introduced, which makes the output features of the pre-trained network independent of each other. This layer reduces the failures of the watermark embedding and more flexibly adjusts the quantity of the watermark. The remainder of this paper is organized as follows: Section 2 introduces the related works; Section 3 describes the proposed method; Section 4 contains the experiment and discussion; and the summary of this work is in Section 5.
## 2 Study Area & Data Sets  
### Kilauea Volcano  
Recent volcanic activity in the Kilauea volcano has mostly occurred on Kflavuea's summit or in the two rift zones (Figure 1). The shallow magmatic system, including magma supply, storage and transport, is closely related to impending eruption activities. The first eruption in Halema'uma'u crater on Kilauea's summit began in March 2008, and the crater was initially 35 m wide. In late 2013, the crater had grown to a much larger size, _i.e._, 160 m wide and 215 m long [23]. The third period of the eruption started recently in April 2015. Since the magma behavior beneath Kilauea's summit results in volcanic eruptions or seismic activity, we need to monitor the surface changes and chemical state using continuous observing systems.  
Figure 1: Shaded relief map of the Kilauea volcano, Hawai‘i, including the summit and rift zones. The white box outlines the area where 3D displacements of surface deformation were measured. Small squares indicate the magma source locations beneath Kilauea’s summit. The red line between N\(\ddot{\mathrm{a}}\)pau and Pu‘\(\dot{\mathrm{o}}\)’\(\ddot{\mathrm{o}}\)’\(\ddot{\mathrm{o}}\) craters represents the Kamoamoa fissure. ERZ, East Rift Zone; SWRZ, Southwest Rift Zone.  
To understand the complex magma system of the KAlauea volcano, numerous studies have been conducted using geodetic data [6,7,16,19,20,22]. Surface deformation over the volcanic region is closely related to dynamic changes in magma conditions. Many of these studies investigated the characteristics of the magma sources beneath the KAlauea caldera through modeling approaches [19,20,24]. In particular, [PERSON] and [PERSON] (2012) suggested that the shallow magmatic system at KAlauea's summit area can be divided into four distinct and interconnected magma reservoirs [20]. Small black squares represent the surface locations of different magma sources beneath KAlauea's summit [20]. Most of the magma bodies lie within 5 km below the surface. Among them, the modeled result of a point source at 1.5 km depth is mostly consistent with other studies [22,25]. However, disagreement exists with regard to the number of sills and their positions associated with other sources. Thus, an investigation of the surface deformation during periods of unrest is necessary to reveal the magma system of KAlauea.
## 4 Experimental Setup
### Parameter Configurations  
Experiments are executed on a computer equipped with an Intel Core i7-10700F CPU running at 2.9 GHz and an NVIDIA GeForce RTX 3070 GPU with 8 GB of VRAM, which are implemented in Python 3.11 with Pytorch 2.1.2.  
The detailed network structure of the STAGCN and its output feature size of each module are listed in Table 2, and there are five spatial-temporal layers stacked. The input size is \(32\times 60\times 7\times 3\) (i.e., batch size \(\times\) time window length \(\times\) number of nodes \(\times\) number of features). A dropout layer is used to the output of the AGCN to prevent overfitting. Node embedding is initialized randomly, and the embedding dimension is 2. The number of subsequences, \(S\), and time window length, \(T\), are both 60.  
We choose the Adam optimizer to optimize the model, and a learning rate decay strategy is adopted, which decreases the learning rate by multiplying 0.1 every 20 epochs. The model is trained for 100 epochs, and a grid search is used to select the optimized parameters of learning rate and batch size. The searching space of the learning rate is set to [0.0001, 0.0005, 0.001, 0.005]. The searching space of the batch size is set to [16; 32; 64; 128]. The best parameters of the STAGCN model are selected through the validation set, which is set as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Task** & **Training Set** & **Testing Set** \\ \hline Task 1 & C4 and C6 & C1 \\ Task 2 & C1 and C6 & C4 \\ Task 3 & C1 and C4 & C6 \\ \hline \end{tabular}
\end{table}
Table 1: The tool wear prediction tasks for the PHM2010 dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Number of** & **STAGCN** & **Output Feature** \\
**Components** & **Size** \\ \hline
1 & Input & \(32\times 60\times 7\times 3\) \\
2 & Embedding layer & \(32\times 32\times 7\times 3\) \\
3 & Spatial–temporal & Bi-Mamba & \(32\times 32\times 7\times 3\) \\  & layer & AGCN & \(32\times 32\times 7\times 64\) \\
4 & Fully connected layer & \(32\times 105\) \\
5 & Fully connected layer & \(32\times 1\) \\ \hline \end{tabular}
\end{table}
Table 2: The detailed structure of the STAGCN.
## 3 System Model
### LS-MU-MIMO-OFDM with Precoding  
Let us consider a single isolated LS-MU-MIMO-OFDM cell with one BS and \(U\) terminals. The BS has \(N_{t}\) TX antennas, and each terminal has only one RX antenna. For the LS-MU-MIMO-OFDM system, a cyclic prefix (CP) is necessary to remove the inter-symbol interference (ISI). If the CP is longer than the multipath delay spread, individual frequency carriers experience a flat fading channel, and the received \(k^{th}\) frequency domain signal vector \(\mathbf{Y}[k](=[Y_{1}[k],Y_{2}[k],\ldots,Y_{K}[k]]^{T})\) can be represented as follows:  
\[\mathbf{Y}[k]=\sqrt{P_{tx}}\mathbf{H}[k]\mathbf{X}[k]+\mathbf{N}[k], \tag{1}\]  
where \(\mathbf{Y}[k]\) is the \(U\times 1\) received vector for each terminal, \(P_{tx}\) is the total TX power for the forward link, and \(\mathbf{H}[k]\) is the \(U\times N_{t}\) small scale i.i.d. Rayleigh fading channel matrix between \(N_{t}\) BS antennas and \(U\) terminals, \(\mathbf{X}[k]\) is the \(N_{t}\times 1\) signal vector, and \(\mathbf{N}[k]\) is the \(U\times 1\) additive white Gaussian noise (AWGN) vector. Note that \(N_{t}\) is much larger than \(U\) in the LS-MIMO system, and \(U\) message signals must be mapped to \(N_{t}\) antennas. It is also worth noting that power allocation scheme is easier to use in the LS-MIMO-OFDM systems due to the channel hardening effect [5]. The precoding process can reduce the IUI, there are two representative precoding schemes, which are matched filtering (MF) and zero forcing (ZF), for LS-MIMO systems. Even though singular value decomposition (SVD) [40] and geometric mean decomposition (GMD) [41] are also very elegant schemes to reduce the IUI, they are not applicable to LS-MIMO-OFDM because both schemes require full downlink channel information at all RXs, and thus are not fit for the point-to-multipoint communication systems such as LS-MIMO-OFDM.  
The advantages of MF precoding are its simplicity and good availability for distributed LS-MIMO systems. Therefore, the MF (\(\mathbf{W}=N_{t}^{-1}\mathbf{H}^{H}\)) precoding [1] is used in this paper.  
The transmitted signal \(\mathbf{X}[k]\) can be represented as follows:  
\[\mathbf{X}[k]=\zeta\mathbf{W}[k]\mathbf{S}[k], \tag{2}\]  
where \(\zeta\) is the TX power normalization factor, which is approximated as \(\zeta\approx\sqrt{\frac{N_{t}}{K}}\), \(\mathbf{W}[k]\) is the \(N_{t}\times U\) precoding matrix to reduce the IUI, and \(\mathbf{S}[k]\) is the \(U\times 1\) message signal vector.  
The system parameters are summarized in Table 1. We use the the system parameters which are in current LTE system [1].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(T_{sl}\) & Slot length & 0.5 ms \\ \(T_{p}\) & Pilot length in one slot & 0.214 ms \\ \(T_{s}\) & Symbol duration & 71.4 us \\ \(T_{g}\) & Guard Interval (GI) & 4.7 us \\ \(T_{u}\) & Symbol without GI & 66.7 us \\ \(T_{d}\) & Delay spread & 4.7 us \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.
# The Design of a High-Intensity Deuteron Radio Frequency Quadrupole Accelerator
## 1 Introduction  
Since the 1990s, deuteron radio frequency quadrupole (RFQ) accelerators have attracted international interest due to their wide range of applications, encompassing accelerator-driven nuclear energy systems (ADSs) [1] and spallation neutron sources (SNSs) [2]. In particular, RFQs operating in high duty factor or even CW mode have become a research hotspot. The Spiral-2 CW RFQ was designed to accelerate deuterons from 0.04 MeV to 1.5 MeV; in 2016, the commissioning of a facility with the beam showed that it could accelerate 1.34 mA CW He\({}^{2+}\)[3]. The Phase II SARAF RFQ was designed to accelerate 5 mA deuterons from 0.04 MeV to 2.54 MeV; in 2017, the commissioning of a facility with the beam showed that it could accelerate 1.15 mA CW D\({}^{+}\)[4]. The \"973\" RFQ was intended to accelerate deuterons from 0.05 MeV to 1 MeV; in 2018, experiments substantiated its capability to accelerate 1.78 mA CW H\({}_{2}\)+ [5,6]. The IFMIF CW RFQ was designed to accelerate 125 mA deuterons from 0.1 MeV to 5 MeV, and the facility successfully accelerated 125 mA deuterons at a duty factor of 0.1% in 2019 [7,8]. Several other CW RFQs, such as the FRIB RFQ [9] and CMIF RFQ [10], have also demonstrated notable achievements. All of these RFQs have played a significant role in the advancement of accelerator science and technology.  
In this project, the RFQ accelerator was designed to accelerate 10 mA CW D\({}^{+}\) ions from 0.04 MeV up to 2.10 MeV. Following this, the propelled beam was introduced into a medium-energy beam transport (MEBT) system and subsequently directed into a drift tube linac (DTL). Three-dimensional simulations were performed employing CST [11] and COMSOL [12], encompassing electromagnetic (EM) and vacuum analyses. The graphical representation of the RFQ modeling, realized through the utilization of SolidWorks [13], is shown in Figure 1. Spanning a length of approximately 3.8 m, the RFQ exhibits a well-defined quadrilateral structure. The provision of power and maintenance of the necessary vacuum level (10\({}^{-6}\) Pa) for the cavity are achieved by means of two power couplers and a set of five pumps.
## 3 Materials and Methods
### Depression Detection Model
In this work, building on the results obtained in previous work on the detection of mental disorders in text [26], we explore the use of FastText [40] and compare it with cutting-edge approaches based on transformer models [68]. Thus, the post \(p_{i}\) is converted into a numerical representation \(E_{i}\), whose length is determined by the dimensionality \(D\) of the word embeddings obtained by the chosen method. This numerical representation \(E_{i}\) encapsulates valuable syntactic and semantic information from the post. The feature vector \(A_{i}\) and the embedding vector \(E_{i}\) are combined into a unified vector \(V_{i}\), which includes information from both lexicon-based features and word embeddings:  
\[V_{i}=A_{i}\oplus E_{i} \tag{3}\]  
In this way, for each post \(p_{i}\), a vector \(V_{i}\) is computed. This comprehensive vector fuses semantic and contextual insights from word embeddings with affective and lexical information conveyed through the lexicon-based features. This combination of information has the potential to enable a broader knowledge, thus, empowering more informed and accurate depression detection in the textual data. The resulting vector \(V_{i}\) is then fed into the machine learning classifier, which returns a prediction of the depression severity level \(l_{i}\) expressed in the post. Moreover, we seek to identify the most practical combination of word embedding models and lexicon-based features (**RQ3**). To this aim, we propose two additional models: a lexicon-based feature model and a distributional representation model. The first model relies solely on linguistic cues and emotional expressions of lexicon-based features; that is, it uses only the vector \(A_{i}\) to perform the prediction \(l_{i}\). The second model emphasizes contextual information and semantic relationships captured by distributional representations, using only the vector \(E_{i}\) to predict the depression severity level \(l_{i}\). Exploring these models allows us to identify the specific features and word embedding techniques that lead to better performance.
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 2 Oleaginous microorganisms
### Sugar Conversion into SCO and Regulatory Mechanisms in Sugar Assimilation
Finally, polyunsaturated fatty acids are synthesized by the action of desaturates and elongases located in the ER. In general, the lipids produced by fungi and microalgae are more unsaturated than those of yeasts. Once the carbon source is depleted in the medium, oleaginous microorganisms use their own storage lipids as an energy source for maintenance purposes or, under certain circumstances, as carbon source for the production of new lipid-free cell material [26; 44; 52]. In the case of microalgae, lipid turnover can occur in cultures growing under CO\({}_{2}\) starvation [27]. Lipases and hydrolases are the enzymes involved in the degradation of lipids, while the Fas released are catabolized through the process of \(\beta\)-oxidation towards acetyl-CoA with the participation of oxidases [44]. Significant lipid production from sugars is possible only under specific environmental conditions otherwise carbon metabolism is directed to the production of other compounds, such as low molecular weight metabolites (i.e., organic acids, sugar alcohols, ethanol, etc.) that are excreted in the growth environment. This metabolic shift may be due to the response in nutrient insufficiency, pH change, oxygen concentration, among other conditions [50; 52; 134; 135; 136; 137; 138; 139]. The cost of SCO production is critically affected by the productivity of oleaginous microorganisms, especially their ability to accumulate lipids. In this context, studies using genetic engineering in the different stages of de novo synthesis and in different oleaginous microorganisms have been developed [140; 141]. It has been shown that overexpression of genes involved in FA, G3P and TAG synthesis considerably increases lipid accumulation [1; 57; 133; 142; 143]. Among the over-expressed enzymes that have been shown to affect lipid accumulation are ACL [122], ACC [131; 144; 145; 146], ME [147; 148], FASI, FASII [149] and some desaturases [150; 151].