esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vivo Evaluation of Decellularized Human Tooth Scaffold for Dental Tissue Regeneration
## 2 Materials and Methods
### Gene Expression Analysis by Quantitative Real Time Polymerase Chain Reaction  
Gene expression levels in samples were evaluated by qPCR. Total RNA was extracted from the samples using a Qiagen RNeasy Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer's instructions. The integrity and concentration of extracted RNA were measured using a NanoDrop ND-2000 spectrophotometer (ThermoScientific, Waltham, MA, USA). cDNA was synthesized from 500 ng of RNA using an oligo d(T)15 primer Maxime RT PreMix kit (Intron Biotechnology, Seoul, Korea) according to the manufacturer's instructions. A qPCR assay was performed with TB Green Premix EX Taq II (Takara Bio, Otsu, Japan) and an ABI 7300 Real-time PCR system (Applied Biosystems, Carlsbad, CA, USA) according to the manufacturer's instructions. Primer information is given in Table 2. The expression levels of each gene were normalized to that of the gene encoding glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the relative gene expression levels were calculated using the \(2^{-\Delta\text{ACI}}\) method [40]. Gene expression levels were calculated relative to their expression levels in the decellularized dHPDL and dHDP control samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Antibodies** & **Catalog Number** & **Host Species** & **Dilution Factor** \\ \hline CD34 & Ab110643 & Rabbit & 1:100 \\ Col XII & Sc-68862 & Rabbit & 1:2000 \\ CP23 & Sc-164031 & Goat & 1:500 \\ DSP & Sc-35386 & Rabbit & 1:500 \\ HN & MAB1281 & Mouse & 1:100 \\ OC & AB10911 & Rabbit & 1:8000 \\ VEGF & Ab183100 & Goat & 1:100 \\ \hline \hline \end{tabular} Abbreviations: CD34, cluster of differentiation 34; Col XII, collagen type XII; CP23, cement-derived protein 23; DSP, dentin sialoprotein; HN, human nuclei; OC, osteoclast; VEGF, vascular endothelial growth factor.  
\end{table}
Table 1: Primary antibodies for immunohistochemistry.
# Optimal Power Allocation with Hybrid Relaying Based on the Channel Condition
## 2 System Model
### The AF and DF Relay Network  
In this subsection, we assume that the first relay \(r_{1}\) considers the AF protocol and the second relay \(r_{2}\) considers the DF protocol.  
In the first time slot, the source transmits the signal \(x_{ad,s}\) with transmit power \(P_{s}\) to the first relay. The received signal \(y_{ad,1}\) at the first relay can be expressed as  
\[y_{ad,1}=h_{1}x_{ad,s}+n_{1}, \tag{1}\]  
where \(h_{1}\) is the channel coefficient from source to the first relay and \(n_{1}\) is the zero-mean additive white Gaussian noise (AWGN) with unit variance at the first relay.  
Figure 1: A hybrid three-hop relay network.  
In the second time slot, the first relay transmits the signal \(x_{ad,1}\) with transmit power \(P_{ad,1}\) to the second relay. The transmitted signal \(x_{ad,1}\) at the first relay is  
\[x_{ad,1}=\beta_{ad}y_{ad,1}, \tag{2}\]  
where \(\beta_{ad}\) is the amplification factor for the AF relay, which can be calculated by using the amplifier gain formula in [29] as  
\[\beta_{ad}=\sqrt{\frac{P_{ad,1}}{P_{s}|h_{1}|^{2}+1}}. \tag{3}\]  
The received signal \(y_{ad,2}\) at the second relay can be expressed as  
\[\begin{split} y_{ad,2}&=h_{2}x_{ad,1}+n_{2},\\ &=\beta_{ad}h_{2}(h_{1}x_{ad,s}+n_{1})+n_{2},\end{split} \tag{4}\]  
where \(h_{2}\) is the channel coefficient from the first relay to the second relay and \(n_{2}\) is the zero-mean AWGN with unit variance at the second relay. The second relay decodes the received signal. In the third time slot, the second relay transmits the signal \(x_{ad,2}\) with transmit power \(P_{ad,2}\) to the destination. The received signal \(y_{ad,d}\) at destination can be expressed as  
\[y_{ad,d}=h_{3}x_{ad,2}+n_{d}, \tag{5}\]  
where \(h_{3}\) is the channel coefficient from the second relay to the destination and \(n_{d}\) is the zero-mean AWGN with unit variance at the destination.  
By using [PERSON]'s formula in [30], the achievable rate can be obtained as  
\[R_{ad}(P_{ad,1},P_{ad,2})=\log_{2}(1+\gamma_{ad}), \tag{6}\]  
where \(\gamma_{ad}\) is the SNR for the AF-DF relay network and is given by  
\[\gamma_{ad}=\min\left(\frac{P_{s}P_{ad,1}|h_{1}|^{2}|h_{2}|^{2}}{P_{s}|h_{1}|^ {2}+P_{ad,1}|h_{2}|^{2}+1},P_{ad,2}|h_{3}|^{2}\right). \tag{7}\]  
In other words, the SNR for the AF-DF relay network is determined by the minimum values between the harmonic mean of SNRs from the source to the AF relay and from the AF relay to the DF relay, and the SNR from the DF relay to the destination.
# Matching-Based Resource Allocation for Satellite-Ground Network
## 3 System Model
### Network Model  
A satellite-ground network scenario with users, GEO satellites, and LEO satellites is shown in Figure 1. The users are directly connected to the LEO satellite, which is directly connected to the GEO satellite. As the satellite moves frequently, it may eventually lose contact with the user. The duration of the GEO-LEO satellite link is measured in time slot \(t\), with \(\tau\) being the length of a time slot. In each time slot, since the duration of the U2L and L2G connections are both longer than \(\tau\), the topology of the satellite-ground network is essentially considered quasi-static. Use \(\mathcal{U}^{t}=\left\{u_{1}^{t},u_{2}^{t},\ldots,u_{i}^{t},\ldots,u_{N}^{t}\right\}\) to represent the user set in time slot \(t\), \(\forall i\in\{1,2,\ldots,N\}\). The LEO satellite set is represented by \(\mathcal{L}^{t}=\left\{l_{1}^{t},l_{2}^{t},\ldots,l_{j}^{t},\ldots,l_{M}^{t}\right\}\), \(\forall j\in\{1,2,\ldots M\}\). The GEO satellite set is represented by \(\mathcal{G}^{t}=\left\{l_{1}^{t},l_{2}^{t},\ldots,l_{k}^{t},\ldots,l_{k}^{t} \right\}\), \(\forall k\in\{1,2,\ldots K\}\).
## 4 Conclusions  
The heat transfer and friction factor characteristics of turbulent water flow in a circular tube fitted with ball turbulators (BTs) at a Reynolds range of 5000-35,000 are investigated through numerical simulation. The effects of BTs with different ball diameter ratios (\(BDR\) = 0.5, 0.75, and 1) and spacer lengths (\(S\) = 40, 51.77 and 62.5 mm) on the heat transfer rate, friction factor, and thermal performance factor behaviors are investigated under a uniform condition of wall heat flux. The conclusions are listed as follows:  
1. The Nusselt number increases and friction factor decreases as the Reynolds number increases in a tube with BTs. The Nusselt number for an enhanced tube is around 1.26-2.01 times as much as that of the plain tube, while the friction factor sharply increases by approximately 3.74-10.27 times.  
Figure 8: Thermal performance factor versus Reynolds number.  
It is noteworthy that the BTs with the largest ball diameter ratio of \(BDR\) = 1 provide higher friction factors than that of \(BDR\) = 0.75 and 0.5 by around 34.6-46.2% and 51.1-63.4%, respectively, showing that a smaller ball diameter ratio is more able to effectively decrease the friction factor.
3. The \(PEC\) values indicate that a smaller ball diameter ratio and a smaller spacer length are preferred. The computational results indicate that when a certain spacer length is used, a larger ball diameter ratio leads to a higher heat transfer rate and with it a higher flow resistance.
4. From the velocity contours, it is concluded that the flow characteristics are closely related to the use of BTs. The simulation results also show that the flow velocity of fluid near the wall in the tube with BTs is significantly improved, compared with the plain tube, when the same Reynolds number is used.  
[PERSON] and [PERSON] conceived and designed the numerical simulation; [PERSON], [PERSON] and [PERSON] performed the simulation and analyzed the data; [PERSON] and [PERSON] provided the necessary machines and devices; [PERSON] wrote the paper.  
This study is sponsored by the National Natural Science Foundation of China (No. 51722504), Guangdong Science and Technology Planning Project (No. 2017A010104006 and No. 2017 KZ010105), and Guangdong Science Fund for Distinguished Young Scholars (No. 2015A030306013).  
The authors declare no conflict of interest.
## 3 Results and Discussion
However, the grating groove density should not be too large to avoid the diffraction angle being too close to \(90^{\circ}\). In the configurations, a better SMSR was achieved in the long-wavelength region of the spectrum, which is also due to the grating diffractive angle of long output wavelength being relatively large. In the longer output wavelength region, it arrives at the edge of material gain, which will lead to a drop in the SMSR. The threshold current as a function of wavelength of external-cavity laser was investigated. Figure 5 compares the threshold currents of different external-cavity configurations. It can be observed that the variation trend of threshold current was consistent. The threshold current decreased significantly at an intermediate tuning range due to the high material gain and increased when the output wavelength was changed toward the long or short wavelength. With the same diffraction grating, the higher first diffraction efficiency means stronger feedback intensity, which may effectively reduce the mirror loss and then decrease the threshold current. Compared with configuration B, configuration A showed a lower threshold current due to the higher first diffraction efficiency. The minimum threshold currents of configuration A and B were 50 mA and 84 mA, respectively. The minimum threshold current of configuration C was 37 mA, which was the lowest in the three configurations. It can be observed that higher grating resolution can significantly decrease the threshold current. Figure 4: The side mode suppression ratio (SMSR) versus tuning wavelength for configuration A, B, and C, respectively. Figure 6 shows the output power of the ECDLs, which was changed with the wavelength and measured by a power meter (Thorlabs, S146C, Newton, NJ, USA). The injection current was 300 mA and the measuring temperature was 20 \({}^{\circ}\)C. The output power of configuration B was significantly decreased due to the low first diffraction efficiency, which was reported in our previous article [18]. Thus it is not given here. For configuration A with the grating of 600 lines/mm, the maximum output power was 49.9 mW. For configuration C with the grating of 1200 lines/mm, the maximum output power was 48.9 mW. As can be observed in the figures, the number of grating lines had little influence on the output power of the ECDL with a similar first diffraction efficiency.
# Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis
## 2 Materials and Methods
### Identification of Impact Frequency Using Hammer Pressure and Sound Signals  
To assess the effectiveness of the proposed MCSA techniques for impact frequency identification, we first need to establish reference values that can be compared with the ones obtained from the analysis of the motor currents. To this end, we independently analyze the hammer pressure and sound signals to determine and verify a reference value for the target impact frequency during the SP stage.  
Figures 4 and 5 show the time and frequency domain plots of P\({}_{h}\) during the NP-stage and SP-stage, respectively. Figure 4a and its zoom in Figure 4b shows a stable pressure signal with a DC value around 3.55 bar. The frequency spectrum of this signal shown in Figure 4c confirms the absence of frequency components associated with percussive action. Conversely, Figure 5a,b show that significant oscillations in the P\({}_{h}\) values appear during the SP stage. Figure 5c shows the frequency spectra, evidencing the appearance of one dominant frequency component at 14.10 Hz followed by a second harmonic frequency at 28.15 Hz. Considering that the rest of the harmonic content only shows multiples of the dominant component (the impact frequency in this case), the frequency spectra in the figures are limited to 30 Hz.  
It should be noted that the DC component in the P\({}_{h}\) signal appears in the spectra (for frequency equal to zero); however, this DC component is irrelevant for the analysis, and it is only included to keep consistency with the plotted signal in the time domain and could be suppressed by subtracting the mean value to the raw reading of the sensor.  
The sound signal captured with the video recorder is used to confirm the impact frequency found by analyzing the hammer pressure signal. In this case, the sound signal is not synchronized with the rest of the sensors in the setup, and we delimit the different drilling stages by the visual inspection of the video feed. Figure 6 plots the spectrum of the sound signal captured during the SP stage, which shows similar frequency components to the one derived from the hammer pressure signal. Since the camera's microphone includes band-pass filters designed to capture frequencies in the range between 20 Hz and 20 kHz (corresponding to the typical listening range of the human ear), the amplitude of the impact frequency at 14.10 Hz appears to be lower than the  
Figure 4: Hammer pressure signal analysis during NP-stage: (**a**) P\({}_{h}\) signal; (**b**) zoom of P\({}_{h}\) signal; (**c**) frequency spectrum P\({}_{h}\) signal.  
harmonic located around 28.15 Hz. Despite the apparent distortion in the spectrum of the sound signal, and given the restricted scope of the target problem, the spectrum of the sound signal allows us to corroborate that the impact frequency of the DTH hammer in the experiment is 14.10 Hz.
# On the Robustness of No-Feedback Interdependent Networks
## 1 Introduction
In order to improve the robustness of the interdependent networks, many advanced methodologies have been proposed, such as designing coupling preference based on the topology and especially the degree distribution of subnetworks [8; 30], adding redundancy of key nodes or edges [31; 32], and restoring some key nodes after failure [33]. In addition, other properties related to interdependent networks have also been studied, such as propagation [34; 35; 36; 37], competition [38], resilience [39] and gaming [40].In the end, for the theory to model the cascading failures in interdependent networks, spreading process and in particular, mean-field approximation [41; 42] has been recently studied, in addition to the commonly used generating functions and percolation theory. Although dramatic advances have been made in the literature, the available tools and theories for the research of interdependent networks are insufficient. For example, when modeling the dependency link, the characteristics of a real system have not been fully considered, resulting in the oversimplification of the properties of the dependency link. Many models in previous studies were built on the assumption that dependencies are bidirectional and assumed that nodes would inevitably fail after their dependent nodes fail, but this is not the case. Furthermore, failure modes in previous studies that have been considered to inform the perspective of one subnetwork rather than the whole interdependent networks. Such assumptions may lead to a misunderstanding of the operating characteristics of real interdependent systems. In order to facilitate the description, we defined a new failure mode and developed a framework to analyze the robustness of interdependent systems from the perspective of the different characteristics of dependency links. The rest of this paper is organized as follows. Section 2 proposes an interdependent networks model based on the description of the failure modes of nodes and evaluation indicators of robustness. Numerical simulation and analysis on the cascading failure and collapse threshold are provided in Section 3 from two aspects: coupling strength and dependency strength. In addition, the influence of these two factors on the robustness of interdependent networks is also discussed. Finally, Section 4 concludes this paper and puts forward several directions for future work.
###### Abstract  
The challenge of optimizing the distribution path for location logistics in the cold chain warehousing of fresh agricultural products presents a significant research avenue in managing the logistics of agricultural products. The goal of this issue is to identify the optimal location and distribution path for warehouse centers to optimize various objectives. When deciding on the optimal location for a warehousing center, various elements like market needs, supply chain infrastructure, transport expenses, and delivery period are typically taken into account. Regarding the routes for delivery, efficient routes aim to address issues like shortening the overall driving distance, shortened travel time, and preventing traffic jams. Targeting the complex issue of optimizing the distribution path for fresh agricultural products in cold chain warehousing locations, a blend of this optimization challenge was formulated, considering factors like the maximum travel distance for new energy trucks, the load capacity of the vehicle, and the timeframe. The Location-Route Problem with Time Windows (LRTPWs) Mathematical Model thoroughly fine-tunes three key goals. These include minimizing the overall cost of distribution, reducing carbon emissions, and mitigating the depletion of fresh agricultural goods. This study introduces a complex swarm intelligence optimization algorithm (MODRL-SIA), rooted in deep reinforcement learning, as a solution to this issue. Acting as the decision-maker, the agent processes environmental conditions and chooses the optimal course of action in the pool to alter the environment and achieve environmental benefits. The MODRL-SIA algorithm merges a trained agent with a swarm intelligence algorithm, substituting the initial algorithm for decision-making processes, thereby enhancing its optimization efficiency and precision. Create a test scenario that mirrors the real situation and perform tests using the comparative algorithm. The experimental findings indicate that the suggested MODRL-SIA algorithm outperforms other algorithms in every computational instance, further confirming its efficacy in lowering overall distribution expenses, carbon emissions, and the depletion of fresh produce in the supply chain of fresh agricultural products.  
deep reinforcement learning; Vehicle Routing Problem; location problem; swarm intelligence optimization algorithm; fresh agricultural product supply chain +
Footnote †: journal: Applied Sciences
## 3 Natural Compounds Inhibiting De Novo Fatty Acid Synthesis and Modification
### Other Natural Compounds with Inhibitory Activities against FASN  
Mechanistic studies of the cancer-preventive effects of flavonoids suggested their activities in blocking fatty acid synthesis [39, 65]. An in vitro study using 15 types of flavonoids revealed that 9 compounds, including quercetin and other similar flavonoids such as morin, luteolin and kaempfer, could efficiently inhibit FASN [38]. Another study by [PERSON] et al. also demonstrated that flavonoid extracts from four plants showed significant inhibition of FASN activity when tested in different cancer cell lines [66].  
Emodin, a naturally occurring anthraquinone, could inhibit FASN activity [67]. Consistently, this compound also exhibited antiproliferative and proapoptotic activities in cells of different malignancies, including breast cancer, liver cancer, prostate cancer, leukemia and colon cancer [68, 69, 70, 71, 72]. A recent study linked the anticancer activity of emodin to its inhibition of FASN [27]. The authors demonstrated that emodin could concurrently suppress FASN activity, downregulate its protein expression, and induce apoptosis of colon cancer cells.  
\(\alpha\)-mangostin, a natural xanthone extracted from mangosteen pericarp, has a variety of biological functions, including anticancer activity [73, 74]. [PERSON] et al. demonstrated that, unlike EGCG, \(\alpha\)-mangostin inhibits FASN in a competitive manner regarding acetyl-CoA and a non-competitive manner regarding malonyl-CoA [75]. An intracellular study also demonstrated that \(\alpha\)-mangostin could act as a potent inhibitor to downregulate FASN expression and block its activity, leading to reduced levels of intracellular fatty acid and eventually cancer cell apoptosis [19].
# MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss
###### Abstract
[13] used high-dimensional local binary patterns to obtain local features of human faces, and used densely connected convolutional neural networks to extract human faces. [PERSON] et al. [14] proposed a robust facial recognition method for occlusion based on the pairwise differential siamese network, which explicitly establishes the relationship between some occluded facial section and the occluded feature. [PERSON] et al. [12] decomposed feature embeddings, set different confidence values for the decomposed sub-embeddings, and aggregated the sub-embeddings for identity recognition. However, the feature extraction method mainly relies on artificially designed feature representations, so it has a low recognition rate in an unconstrained environment. Many scholars have also made many other attempts at masked-face recognition [15; 16; 17; 18]. [PERSON] et al. [19] proposed the robust nuclear norm to characterize the structural error and a new robust matrix regression model composed of RMR and S-RMR. [PERSON] et al. [20] used PCA for feature extraction and dimensionality reduction. They calculated the average facial features of each identity and performed identity recognition. [PERSON] et al. [21] proposed a Siamese convolutional neural network for facial recognition based on the Siamese convolutional neural network and the Inception module. In the absence of masked-face datasets, most methods only simulate mask occlusion by adding random noise or black pixels, which makes their abilities with real mask occlusion questionable. For the above problems, this paper proposes a masked-face recognition algorithm based on large margin cosine loss (MFCosface). It uses an algorithm based on the detection of key facial features to generate masked-face images as a training set; then it uses the large margin cosine loss to train the model; and finally, it adds an attention mechanism to the model to optimize the representations of facial features, which effectively solves the problem of low recognition rates with mask occlusion. In summary, our contributions are as follows:  
* We designed a masked-face image generation algorithm based on the detection of key facial features, and generate masked-face images from face datasets and mask templates. The images were used construct a dataset for training, which alleviated the problem of insufficient data. * A masked-face recognition algorithm based on large margin cosine loss is proposed. We analyzed the characteristics of the masked-face dataset, and proved the rationality of using the large margin cosine loss function. Experiments on our artificial masked-face dataset and a real masked-face image dataset proved that our algorithm greatly improves the accuracy of masked-face recognition, and can accurately perform facial recognition in spite of mask occlusion.