esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Accuracy Evaluation Metrics  
The selection of accuracy evaluation metrics should be tailored to specific application scenarios and analysis needs. To quantitatively assess the performance of the network model used for time-series predictions in the study area, five commonly used accuracy evaluation metrics (AEMs) in the field of time-series forecasting were selected based on the characteristics of the data: the mean absolute error (MAE), the root mean squared error (RMSE), the coefficient of determination (R2), the mean absolute percentage error (MAPE), and the symmetric mean absolute percentage error (SMAPE).  
The MAE is the average of the absolute errors, with a range of [0, +\(\infty\)). The larger the value, the greater the error; the smaller the value, the higher the accuracy of the predictive model. A value of 0 indicates a perfect model, where the predicted values completely match the actual values. The formula for the MAE is as follows:  
\[\text{MAE}=\frac{1}{n}{\sum_{i=1}^{n}}|y_{i}-\hat{y}_{i}| \tag{15}\]  
The RMSE is the square root of the average of the squared differences between the predicted and actual values. The range is [0, +\(\infty\)), where a larger value indicates greater error and a smaller value indicates less error. Unlike the MAE, the RMSE is more sensitive to outliers; a large discrepancy between one predicted value and its actual value can significantly inflate the RMSE. The formula for the RMSE is as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2}} \tag{16}\]  
R2 indicates how well the independent variables explain the variability in the dependent variable. The value typically ranges from 0 to 1, with values closer to 1 indicating a better fit and a higher prediction accuracy, and values closer to 0 indicating a poorer fit. The formula for R2 is as follows:  
\[\text{R}^{2}=1-\frac{\sum_{i=0}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=0}^{n}(y_{ i}-\overline{y})^{2}} \tag{17}\]  
The MAPE is the average of the absolute percentage errors between the predicted and actual values. The range is [0, +\(\infty\)), with a value of 0 indicating a perfect model, while a value greater than 100% indicates a poor model. The smaller the MAPE, the better the predictive performance of the model. The formula for the MAPE is as follows:  
\[\text{MAPE}=\frac{100\%}{n}{\sum_{i=1}^{n}}\left|\frac{\hat{y}_{i}-y_{i}}{y_ {i}}\right| \tag{18}\]The SMAPE is similar to the MAPE, but is a modified metric designed to address the problem of overly large values when the actual values are very small. The formula for the SMAPE is as follows:  
\[\text{SMAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\frac{|\dot{y}_{i}-y_{i}|}{\left(| \dot{y}_{i}|+|y_{i}|\right)/2} \tag{19}\]
###### Abstract  
Epstein-Barr virus-associated gastric carcinoma (EBVAGC) is a type of stomach cancer linked to EBV infection. This study explores how the male hormone dihydrotestosterone (DHT) affects the immune response against EBvAGC. Using the SNU719 cell line, we treated the cells with DHT and observed an increased expression of the androgen receptor (AR) and the activation of the NF-kB pathway. This led to higher levels of MICA, a protein that interacts with immune cells like natural killer (NK) and T cells, enhancing their ability to kill EBVAGC cells. Importantly, this immune boost occurred without increasing harmful inflammatory signals. Our findings suggest that DHT enhances the immune system's ability to target EBvAGC, providing a potential therapeutic approach by modulating androgen signaling to improve anti-tumor immunity.  
Epstein-Barr virus-associated gastric carcinoma (EBVAGC) is a subset of gastric cancers linked to EBV infection. While the role of male hormones in cancers such as prostate, breast, and ovarian cancers is well-studied, their impact on EBVAGC remains less understood. This study aims to examine the effect of dihydrotestosterone (DHT) on EBVAGC, particularly focusing on its influence on the immune response. Methods: The study utilized the SNU719 EBvAGC cell line. Cells were treated with DHT to assess androgen receptor (AR) expression and the activation of signaling pathways, including NF-kB. The expression of MHC class I polypeptide-related sequence A (MICA) and its interaction with the NKG2D receptor on NK and T cells was evaluated. Cytotoxicity assays were conducted to determine DHT's effect on NK and T cell-mediated cytotoxicity, and proinflammatory cytokine gene expression was analyzed. Results: DHT significantly increased AR expression in EBvAGC cells and activated the NF-kB pathway, which led to increased transcription of target genes such as MICA and EBNA1. These changes enhanced the interaction with receptors on NK and T cells, thereby boosting their cytotoxicity against EBvAGC cells. Importantly, DHT did not upregulate proinflammatory cytokine genes. Conclusion: DHT enhances the immune response against EBvAGC by upregulating MICA and activating NK and T cells. These findings suggest potential therapeutic strategies targeting androgen signaling to improve anti-tumor immunity in EBvAGC.  
androgen receptor; dihydrotestosterone; Epstein-Barr virus; MICA; NF-kB
# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 1 Introduction  
Blockchain was originally proposed by [PERSON] [1] as the underlying technology for Bitcoin [2]. Blockchain 1.0 is represented by Bitcoin, which focuses on solving the problem of decentralization of currencies and payments. Blockchain 2.0 is represented by Ethereum [3], which uses smart contracts to solve the trust issues of decentralization in the financial sector. Blockchain transaction involve three main components: the sender, the receiver, and the transaction amount. But both Bitcoin and Ethereum have limitations in privacy protection [4]. The identities of the sender and receiver are realized through user public key addresses, which has a certain degree of anonymity [5]. However, it is still possible to obtain the real identities of the traders by mining their associated information through data analysis or machine learning techniques. Furthermore, since the transaction amount is completely exposed on the blockchain and anyone can access it by querying the blockchain full node, attackers can deduce information such as account balances and fund flows [6], thereby compromising transaction privacy. Current anonymous cryptocurrencies like Dash, Monero and later Beam/Grin [7] utilize ZKPs (zero-knowledge proofs), ring signatures, and cryptographic commitments to protect the privacy of transaction details and participant identities [8]. However, these methods lack regulatory functions and may be used for illegal trading activities.  
In order to address the issues mentioned above, we propose a scheme that balances privacy protection and regulatory functions. Our main contributions are summarized below:We propose a blockchain transaction scheme that integrates a variety of cryptographic technologies to balance privacy protection and regulatory functions. Specifically, it adopts probabilistic public-key encryption to protect the user's identity from being exposed.
2. To validate the basic legality of blockchain transactions, our scheme employs cryptographic commitment schemes and zero-knowledge proof technology. It further integrates graph neural networks (GNNs) technology for anomaly detection in blockchain transaction data, thus meeting the requirements for transaction privacy protection and regulatory compliance without disclosing sensitive transaction information.
3. Our scheme allows regulatory authorities to avoid storing users' real identities and key information, significantly reducing storage and computational burdens. Under the premise of ensuring transaction efficiency as much as possible, it balances the implementation of privacy protection and regulatory functions.  
This paper is organized as follows: Section 2 introduces the prerequisite knowledge necessary for constructing the scheme. Section 3 explores the blockchain transaction privacy protection model. Section 4 makes a comprehensive analysis of the scheme. Section 5 provides the conclusions and discussions of this study.
## 3 On-Body Communication and Connectivity
### Space-Time Varying Radio-Links and Signal Propagation  
Accurate channel modeling is a very active topic of research in both BAN and BBN. In particular, under dynamic and diverse mobility patterns, based on the positions of on-body sensors, space and time varying radio-links can severely affect the connectivity between sensors-coordinator communication.  
Such space-time varying characteristics often provide higher degree of correlation as identified in [26]. Further, both short-term and long-term channel fading can be modeled to precise the path-loss factors from signal propagation. Moreover, body shadowing also has to be taken into account for accurate modeling of on-body and body-to-body links [27].  
In the IEEE 802.15.6 standard, the proposed channel models are limited to stationary radio-links, which consequently requires space-time variations and accurate signal propagation enhancements. At the narrow band, both proposed channel models (i.e., CM3-A and CM3-B) are distance-dependent; the path-loss derived for those models from measurement campaigns was recently enhanced using bio-mechanical mobility and deterministic models [28]. As an example shown in Figure 2, for a space-time varying link such as \"wrist-chest\" the average peak-to-peak path-loss is 10-dBs higher than for IEEE 802.15.6 standard channel models and hence is more accurate. Realistic radio-link conditions and signal propagation are important to analyze the true connectivity between on-body sensors-coordinator communication.  
Figure 3 shows the average packet delivery ratio (PDR) results of the on-body communication. Twelve sensors placed on various locations around the body include links which provide space-time variations, static as well as periodic line-of-sight (LOS) and non-line-of-sight (NLOS) links. The results are presented for walking, standing-sitting and running mobility conditions. In addition to transmitting power variations (from 0 dBm to \(-20\) dBm), various configurations of the physical layers of the IEEE 802.15.6 standards are exploited. Configuration 1 (C-1) is based on 900 MHz, 101.2 Kbps and 16 bytes of packet size. Configuration 2 (C-2) and configuration 3 (C-3) differ from C-1 only by the packet sizes which are 128 bytes and 256 bytes, respectively. The last configuration (C-4) is based on  
Figure 2: IEEE 802.15.6 enhanced path-loss models obtained from bio-mechanical deterministic channel model. For example, a link between left wrist and chest is shown. (**a**) Time-varying distances; (**b**) Enhanced pathloss model CM3-A and (**c**) enhanced pathloss model CM3-B.  
2450 MHz, 971:4 Kbps and 128 bytes of packet length. It can be seen that, at very low transmission power (such as \(-10\) dBm and \(-20\) dBm), the PDR starts decreasing sharply. Therefore, it is important that, while operating at very low transmission power and under realistic conditions, the packet reception performance can be significantly degraded. Consequently, such power optimizations and fine tunning have to be managed with care to ensure robust connectivity.
# SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios
## 3 Proposed Method
### Low-altitude Pedestrian Detection Network Architecture: SMYOLO
#### 3.1.3 Lightweight Activation Function  
In YOLOv4, the Mish activation function is one of the innovations of YOLOv4. The Mish activation function is shown in Equation (5). Compared with other commonly used functions such as ReLU and Swish, the Mish activation function is a smooth activation function, which can achieve better accuracy and generalization [19]. Still, Mish activation will generate many calculations, making YOLOv4 unable to complete real-time detection. Therefore, we use the lightweight activation function Hard-swish, which has similar characteristics with the Mish activation function and reduces the number of model calculations. The comparison between the Hard-swish and Mish activation functions is shown in Figure 6. Hard-swish is shown in Equation (6).  
\[Mish(x)=x\times tanh(ln(1+exp(x))) \tag{5}\]  
Figure 5: The basic structure of the deep separable convolutional network.  
\[Hard-swish[x]=x\frac{ReLU6(x+3)}{6} \tag{6}\] \[ReLU6=min(max(0,x),6) \tag{7}\]  
The overall network architecture of the SMYOLO model is shown in Figure 7, where Convolution represents conventional Convolution, Conv 1 \(\times\) 1 represents point convolution, and Depthwise Conv represents depth separable Convolution. More profound network architecture can obtain higher accuracy. In contrast, a narrower network architecture limits the complexity of the network. Due to the limited computing power of airborne drone equipment, to limit the complexity of YOLOv4, SMYOLO uses deep separable convolution and point convolution to build the network. In the main convolution operation of the network backbone structure, by adding point convolution, the number of networks increases the depth of the network, thereby reducing the complexity of the model. Compared with other detection networks, SMYOLO has fewer network layers and fewer calculations, enabling it to deploy airborne drone equipment.  
Figure 6: The comparison between the Hard-swish activation function and the Mish activation function.  
Figure 7: The overall network architecture of the SMYOLO model.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Qos-Based Routing  
Qos-based routing plays an important role in any application technology, especially in resource constrained WBAN, which is a huge challenge. The Qos that need to be considered in the WBAN are--data priority, energy efficiency, link reliability and data transmission reliability, low transmission delay, node temperature, data security, etc.
## 3 Method of Constructing Global POD ROM  
### Initial POD Reduced Basis Matrix  
In this section, mode shapes of a few configurations are used to construct the initial reduced basis matrix using POD technology. The implementation steps are as follows:  
Step 1: The first \(l\) mode shapes of any configuration \(\mathbf{p}_{l}\) are used to assemble the mode matrix \(\mathbf{\Phi}_{t}\):  
\[\mathbf{\Phi}_{t}=[\mathbf{\varphi}_{1}(\mathbf{p}_{t}),\mathbf{\varphi}_{2}(\mathbf{p}_{t}),\dots,\mathbf{\varphi}_{l}(\mathbf{p}_{t})], \tag{7}\]  
where \(\mathbf{\varphi}_{i},i=1,2,\dots,l\) is the mode shape, and any mode shape satisfies the following characteristic equations and normalization conditions:  
\[\begin{array}{c}\mathbf{K}(\mathbf{p}_{t})\mathbf{\varphi}_{l}(\mathbf{p}_{t})=\omega_{l}^{2 }(\mathbf{p}_{t})\mathbf{M}(\mathbf{p}_{t})\mathbf{\varphi}_{l}(\mathbf{p}_{t}),\\ \mathbf{\varphi}_{l}^{T}(\mathbf{p}_{t})\mathbf{\varphi}_{l}(\mathbf{p}_{t})=1,i=1,2,\cdots,l \end{array}, \tag{8}\]  
where \(\omega_{l}\) is the \(i\)th natural frequency of \(\mathbf{p}_{t}\).  
Step 2: The Latin Hypercube Sampling (LHS) method is used to obtain \(k\) sample configurations \(\mathbf{p}_{i},i=1,2,\cdots,k\) in the sample space, and then solve the corresponding mode matrix of them, assembling the snapshot matrix \(\mathbf{A}\):  
\[\mathbf{A}=[\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\ldots,\mathbf{\Phi}_{k}]. \tag{9}\]  
Step 3: The correlation matrix \(\mathbf{D}\) is calculated based on snapshot matrix \(\mathbf{A}\):  
\[\mathbf{D}=\mathbf{A}^{T}\mathbf{A}. \tag{10}\]  
Step 4: Then the eigenvalues \(\lambda\) and eigenvectors \(\chi\) of \(\mathbf{D}\) are solved, and the eigenvalues are arranged from the largest to the smallest:  
\[\begin{array}{c}\mathbf{D}\mathbf{\chi}_{i}=\lambda_{i}\mathbf{\chi}_{i},i=1,2,\cdots,p \\ \lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{p}\end{array} \tag{11}\]  
Step 5: The initial reduced basis matrix \(\mathbf{T}\) is constructed by the first \(m\) eigenvectors:  
\[\mathbf{T} =[\mathbf{v}_{1},\mathbf{v}_{2},\cdots,\mathbf{v}_{m}],\mathbf{v}_{i}=\frac{1}{ \sqrt{\lambda_{i}}}\mathbf{A}\chi_{i},i=1,2,\cdots,m\] \[m =\text{argmin}\{G(m)\leq\delta\},G(m)=1-\sum\limits_{i=1}^{m} \lambda_{i}/\sum\limits_{i=1}^{p}\lambda_{i}\prime \tag{12}\]  
where argmin represents finding the minimum \(m\) value satisfying \(G(m)\leq\delta\), \(\delta\) is a truncation threshold that is set as \(1\times 10^{-6}\) in this paper, and \(\mathbf{v}_{i}\) is the reduced basis vector that forms \(\mathbf{T}\).  
Due to the lack of knowledge of the design space in advance, it is difficult to determine the value of \(k\). Too few samples would lead to the difficulty of the ROM based on the initial reduced basis to meet the accuracy requirements, and the redundant samples would increase the computation cost. To solve this problem, the CV-Voronoi sequence sampling method is used to adaptively increase the sample configurations and update the reduced basis matrix, aiming to ensure that the prediction accuracy of the ROM satisfies the requirements through adding the minimum number of sample configurations.
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 5 Discussion  
Figure 8 shows the relationship between the reconstruction accuracy of different models and the model parameters. We compared the LNLCA model with 10 state-of-the-art SISR models: VDSR [14], DRCN [26], CARN [55], DBPN [49], RDN [50], EDSR [28], RCAN [15], SAN [20], HAN [52], and NLSN [31]. Among these methods, we observed that some methods, such as DRCN, VDSR, and CARN, contain less computational overhead, but at the cost of reduced reconstruction accuracy. Although some large-scale super-resolution models, such as EDSR and HAN, achieved good results in improving super-resolution performance, the large number of parameters of these models also hinders their use in lightweight applications. In contrast, our proposed LNLCN model has fewer parameters than RDN but achieves higher performance metrics, which means our LNLCN model can achieve good reconstruction results with moderate computational complexity. This is mainly due to the use of Deep Feature Fusion Attention Group (DFFAG) in our LNLCN model, which enables the model to fully utilize the limited LR sample information to achieve more powerful feature representations and thus improve super-resolution performance. Furthermore, our adopted Adaptive Target Generator (ATG) model helps the network explore potential solutions, resulting in sharp outputs. This strategy helps alleviate the ill-posedness of the super-resolution task, enabling the network to generate slightly different outputs, leading to realistically and perceptually pleasing super-resolution results.  
Table 5 shows the relationship between inference speed and computational cost of different models. We compared the LNLCA model with five state-of-the-art SISR models: VDSR [14], CARN [55], RDN [50], NLSN [31], and HAN [52]. Among these methods, we observed that our proposed LNLCN model performs better in terms of number of parameters, FLOPs, and running time. Compared with other models, the LNLCN model has higher inference speed and lower computational cost.  
However, our work also has some limitations. Although the performance on the standard dataset reached a good performance index, the real image has the problem of sparse data samples and unknown degradation kernel. In actual application, the algorithm may not achieve the expected effect. Determining how to simulate the actual image degradation kernel is the key to improving the robustness of the algorithm.  
In summary, in future work, we will focus on reconstructing natural and realistic SR images under different degradation conditions. We will use image prior knowledge to improve the accuracy of super-resolution reconstruction in real complex scenes. Specifically, we will use the parser to deal with the degradation factors of different spatial variations,  
Figure 8: Performance versus model parameters for different models on Set5 (\(\times\)2).  
simulating the situation of super-resolution tasks in real situations, making the model more applicable.
## 3 Results and Discussion
### Optimization of the 2 nd Step  
Successively, we faced off the indolization of tautomers **4a-a\"** into the alkyl indole-2-carboxylate **5a**. In this context, we first performed the addition/elimination one-pot process, then the TMG was removed by fast filtration through a short pad of Silica, and finally, after the removal of the solvent under a vacuum, the crude **4a-a\"** were subjected to a variety of acidic conditions. Initially, inspired by the literature, we tested PPA, ZnCl\({}_{2}\)/AcOH, SOCl\({}_{2}\)/EtOH, and \(p\)-toluenesulffonic acid/EtOH [26; 27; 28; 29]. Only the latter system was efficient for the indolization reaction (3 eq. \(p\)-ToOH, EtOH, reflux, 8 h, 32%). Based on this result and following our studies concerning the use of heterogeneous systems, we repeated the reaction using 1g/mmol of Amberlyst 15, a sulfonic acid type based upon a styrene-DVB copolymer [30]. Under these conditions, **5a** was recovered in a similar yield (30%), but with the remarkable advantage of minimizing the work-up step to an easy filtration, thus avoiding the classic tedious and wastefully aqueous work-up. Further tests permitted the optimization of reaction conditions (Table 2), and the best yield of **5a** (over the two steps) was recorded, under microwave irradiation at 90 \({}^{\circ}\)C for 4 h, using 2 g/mmol of Amberlyst 15 (_Entry_\(k\), 54%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Entry** & **Base** & **Solvent** & **Temp.** & **Time (h)** & **Yield (\%) of 4a-a”** \\ \hline A & DBU (1 eq.) & MeCN & rt & 2 & 23 \\ B & TMG (1 eq.) & MeCN & rt & 4 & 55 \\ C & KF/Al\({}_{2}\)O\({}_{3}\) (1 eq.) & MeCN & rt & 24 & — \({}^{1}\) \\ D & PS-Carbonate (1 eq.) & MeCN & rt & 24 & — \({}^{1}\) \\ E & PS-TBD (1 eq.) & MeCN & rt & 4 & 35 \\ F & TMG (2 eq.) & MeCN & rt & 4 & 75 \\ G & TMG (2 eq.) & MeCN & 50 \({}^{\circ}\)C & 1 & 60 \\ H & TMG (3 eq.) & MeCN & rt & 4 & 71 \\ I & TMG (2 eq.) & THF & rt & 4 & 35 \\ J & TMG (2 eq.) & EtOAc & rt & 4 & 49 \\ K & TMG (2 eq.) & DCM & rt & 4 & 21 \\ \hline \end{tabular} \({}^{1}\) No reaction occurs.  
\end{table}
Table 1: Optimization studies concerning the addition/elimination step.