esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation  
### Experimental Setup  
The efficiency of the proposed edge caching scheme has been rapidly evaluated using the Java-based simulator [48] under benchmarked simulation parameters as presented in Table 1. We created a cluster of five MEC access nodes with cell coverage size of 200 m. Each MEC access node has 20 channels with a channel bandwidth of 20 MHz and a transmitting power of 40 W. Xender is one of the world's leading applications for file transfers and sharing with the convenience to transfer files of different types and sizes without cellular internet connection, or cables. We use Xender's captured video tracking for the month of August in 2016 as recorded in [29], with 153,482 video deliveries out of 271,785,952 video requests from 450,786 mobile users [49]. Most of the video contents are small files under 200 MB and the tracking can be fitted with the Mandelbrot-Zipf (MZipf) distribution of platform factor \(-0.88\) and skewness factor of 0.35. The MZipf value gives an open relation of content popularity; however, this can be easily adjusted to realize a closer popularity among contents during simulation setup. When the Zipf distribution is set low, the cached contents are closely ranked in popularity making every one of them highly probable to be requested.  
To determine the superiority of the proposed caching scheme, different set of experiments have been performed. The first experiment is carried out by keeping all parameters constant and varying only the cache size of the MAN servers. A hundred iterations are run, and the results are averaged. The relationship between the varying cache sizes and key performance metrics are drawn with conclusions in the results and discussion section. Similarly, for the second to the fifth experiments, the file request rates, the number of users, the Zipf distribution, and the number of files are respectively varied while keeping all the other parameters set at the constant values depicted in Table 1. We evaluated the proposed scheme in comparison with four other baseline schemes with every experiment scenario set to run at a hundred iterations and averaging the results. With an exception to the LFRU and TLRU, the rest of the baseline algorithms have been natively implemented as part of the simulator. The LFRU and TLRU are modified versions of the LFU and LRU algorithms which have also been natively designed.  
Running a scenario begins with the simulator initializing the size and popularity of all contents. Time slices were created to balance cache replacements, file request arrivals, and results logging. After each complete run of a time slice, the selected algorithm adjusts the cache in the MAN servers. The requests in each time slice arrive and the cache hit rate, latency, and backhaul traffic are recorded.
## 2 Materials and Methods
### SPME GC-MS Analyses  
Volatile compounds were extracted by solid phase micro extraction (SPME-Supelco) coupled with gas chromatography (GC-PerkinElmer Clarus 580) and mass spectrometry (MS-PerkinElmer Clarus SQS8). A 50/30 \(\upmu\)m Divinylbenzene/Carboxen/Polydimethylsiloxane fiber (DVB/CAR/PDMS) with StableFlex core and a Zebron (Phenomenex, Bologna, Italy) capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) were used. Extraction conditions, GC-MS parameters, and VOCs identification were described in-depth in [PERSON] et al. (2010, 2016) for wheat kernels and wheat beers, respectively. GC-MS analyses were conducted in duplicate. Peak areas were calculated using TurboMass 6.1.0 software and expressed as total ion current (TIC) multiplied 10\({}^{-7}\).
# Temperature Condition Analysis for Freezing on Tunnel Lining Back Side According to Tunnel Length
## 3 Air Temperature Change inside the Tunnel in Winter
### DTR Correlation between outside and inside Air Temperature by the Tunnel Length
#### 3.2.2 Prediction of Diurnal Temperature Range (DTR) Change by Tunnel Length  
The measurement results of the correlation between the outside and inside air temperatures are shown in Figure 7. Assuming the diurnal temperature range (DTR) of the outside air temperature was 100%, the DTR decreased to 58.2% at 120 m from the tunnel entrance, 38.2% at 880 m, 26.6% at 1740 m, and 19.7% at 3270 m, respectively.  
The DTR reduction rate by the tunnel length is shown in Figure 8. The reduction rate of the outside air temperature rapidly and nonlinearly decreased to 60% within 90 m from the tunnel entrance. The graph decreased gradually from 90 m to 3270 m. The DTR reduction rate of the tunnel length over 3270 m could be estimated from the trend line expressed in Figure 8.  
The air temperature inside the tunnel changes according to the outside air temperature and has its own DTR with each tunnel length. The cosine function was derived to express the air temperature variation inside a tunnel, as shown in Figure 9. The periods of the daily minimum and maximum temperatures vary depending on the region. A day (24 h)  
Figure 8: DTR reduction rate (_a_) by tunnel length.  
Figure 7: Variation of the inside air temperature by the tunnel length.  
was divided into the increasing section (\(w_{i}\)) and the decreasing section (\(w_{d}\)), as shown in Equation (2). For example, in Korea, the occurrence time for the daily minimum air temperature in the winter is 7:00, and the occurrence time for the maximum air temperature is 15:00. Therefore, \(w_{i}\) is 8 h, \(w_{d}\) is 16 h, the phase shift (\(x_{c,i}\)) of the increasing section is \(-1\), and the phase shift (\(x_{c,d}\)) of the decreasing section is 15. As the tunnel length becomes longer, the inside air temperature decreases by the DTR reduction rate (\(\alpha\)). The inside air temperature variation over time can be simplified through Equation (2).  
\[T_{T,L.}=T_{T.L.\ (avg)}+\alpha DTR_{out}(Cos(\pi(x-x_{c})/w) \tag{2}\]  
Here, \(T_{T.L.}\): Air temperature variation inside a tunnel; \(T_{T.L.\ (avg)}\): Average air temperature inside a tunnel; \(\alpha\): DTR reduction rate by each tunnel length; \(DTR_{out}\): Daily outside air temperature difference; \(x\): Time (Hours); \(w\): Period (\(w_{i}\) for increasing graph (\(T_{T.L.(min)}\to T_{T.L.(max)}\)), \(w_{d}\) for decreasing graph (\(T_{T.L.(max)}\to T_{T.L.(min)}\)); \(x_{c}\): Phase shift when the \(w_{i}\) period \(x_{c,i}\) = (Min temp. Occurrence time--\(w_{i}\)), when the \(w_{d}\) period \(x_{c,d}\) = (Max temp. Occurrence time).  
Based on an average inside air temperature of \(-4.75\ ^{\circ}\)C, the temperature variation can be simplified using the DTR inside each tunnel length, as shown in Figure 10.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**Training Set (5-CV)**} & \multicolumn{4}{c}{**Testing Set**} \\ \cline{3-8}  & & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline \multirow{3}{*}{\(L_{1}\)} & GSE10072 & 1.00 & 1.00 & 1.00 & 0.92 & 0.98 & 0.95 \\  & GSE19084 & 1.00 & 0.98 & 0.99 & 0.87 & 0.72 & 0.81 \\  & GSE4115 & 0.83 & 0.97 & 0.91 & 0.77 & 0.74 & 0.73 \\ \hline \multirow{3}{*}{\(L_{EN}\)} & **Mean** & 0.94 & 0.98 & 0.97 & 0.85 & 0.81 & 0.83 \\  & GSE10072 & 0.98 & 1.00 & 0.99 & 0.93 & 0.94 & 0.94 \\  & GSE19084 & 1.00 & 0.98 & 0.99 & 0.90 & 0.68 & 0.81 \\  & GSE4115 & 0.94 & 0.98 & 0.96 & 0.78 & 0.85 & 0.78 \\ \hline \multirow{3}{*}{\(L_{1/2}\)} & **Mean** & 0.97 & 0.99 & 0.98 & 0.87 & 0.82 & 0.84 \\ \cline{1-1}  & GSE10072 & **1.00** & **1.00** & **1.00** & **0.94** & **1.00** & **0.97** \\ \cline{1-1}  & GSE19084 & **1.00** & **1.00** & **1.00** & **0.92** & **0.75** & **0.87** \\ \cline{1-1}  & GSE4115 & **0.98** & **0.99** & **0.98** & **0.78** & **0.93** & **0.83** \\ \hline \multicolumn{3}{c}{**Mean**} & **0.99** & **1.00** & **0.99** & **0.88** & **0.89** & **0.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean results of empirical datasets. The results of our proposed method are given in bold. Figure 1: The number of genes selected by \(L_{1}\), \(L_{EN}\), and \(L_{1/2}\). Table 4 shows that the genes were selected by \(L_{1/2}\). At the beginning of the experiments, the attribute of genes was prob set ID. Thus, we could transform prob set ID to gene symbol by using the software DAVID 6.8 [34]. The data distribution for the selected genes is displayed in Figures 3-5. From inspecting the figures, we can find that some genes facilitated the classification of lung tumor and normal lung, such as FAM107A, KDELR2, AASS, and SFRP1 for dataset GSE10072; and SOCS2 and EHD2 for dataset GSE19804. In addition, we found that a common gene in the three different datasets using \(L_{1/2}\) was EGFR [35, 36]. However, due to the distribution of the data of different datasets, we cannot use gene EFGR to classify different types of cancer and improve the prediction accuracy. Furthermore, the literature indicates that never-smokers with adenocarcinoma have the highest incidence of EGFR, HER2, ALK, RET, and ROS1 mutations [37]. Therefore, our proposed \(L_{1/2}\) is an effective technique in gene selection and classification. Figure 2: Venn diagram analysis of the results of \(L_{1}\), \(L_{EN}\), and \(L_{1/2}\).
# Performance Analysis of Interferometer Algorithm under Phase Measurement Error
## 5 Equivalent Derivation of the MSE of Azimuth and the MSE of Elevation
\right. \tag{32}\]  
where \(E[\Delta\phi_{m}]=0\) and \(E[\Delta\phi_{n}]=0\) are used both for \(m=n\) and for \(m\
eq n\). \(E[\Delta\phi_{m}]=0\) and \(E[\Delta\phi_{n}]=0\) are valid when \(\Delta\phi_{m}\) and \(\Delta\phi_{n}\) are zero-mean random variables. From (26), \(\mathbf{b}\mathbf{b}^{T}\) can be expressed as  
\[\sum_{m=0}^{M-1}\sin\biggl{(}2\theta-\frac{4\pi m}{M}\biggr{)}=0. \tag{42}\]  
By substituting (40)-(42) in (39), \(\mathbf{A}^{T}\mathbf{A}\) can be reduced to  
\[\mathbf{A}^{T}\mathbf{A}=\left(\frac{2\pi r}{\lambda}\right)^{2}\left[ \begin{array} []{cc}\frac{M}{2}\{\cos(\psi)\}^{2}&0\\ 0&\frac{M}{2}\{\sin(\psi)\}^{2}\end{array} \right]=\frac{M}{2}\biggl{(}\frac{2 \pi r}{\lambda}\biggr{)}^{2}\left[ \begin{array}{cc}\{\cos(\psi)\}^{2}&0\\ 0&\{\sin(\psi)\}^{2}\end{array} \right]. \tag{43}\]  
Since \(\mathbf{A}^{T}\mathbf{A}\) is a diagonal matrix, \(\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}\) is also a diagonal matrix. The diagonal entries of \(\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}\) are given by the reciprocals of the diagonal entries of \(\mathbf{A}^{T}\mathbf{A}\):  
\[\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}=\frac{1}{\frac{M}{2}\left(\frac{2 \pi r}{\lambda}\right)^{2}}\left[ \begin{array}{cc}\frac{1}{\{\cos(\psi)\}^{ 2}}&0\\ 0&\frac{1}{\{\sin(\psi)\}^{2}}\end{array} \right]. \tag{44}\]  
By substituting (44) in (38), the covariance matrix can be expressed as :  
\[\left[ \begin{array}{cc}E\left[\Delta\theta^{2}\right]&E\left[\Delta\theta \Delta\psi\right]\\ E\left[\Delta\theta\Delta\psi\right]&E\left[\Delta\psi^{2}\right]\end{array}  \right]=\frac{\sigma_{\phi}^{2}}{\frac{M}{2}\left(\frac{2\pi r}{\lambda} \right)^{2}}\left[ \begin{array}{cc}\frac{1}{\{\cos(\psi)\}^{2}}&0\\ 0&\frac{1}{\{\sin(\psi)\}^{2}}\end{array} \right]. \tag{45}\]
# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 2 Related Work
### Transformer-Based Underwater Images  
In contrast to CNN-based methods, [PERSON] et al. [44] suggested that the direct application of a pure transformer to a sequence of patches in a visual transformer (ViT) could perform the task of image classification well. [PERSON] et al. [18] put forth a shifted windows transformer (swin transformer) to address the difficulty of matching transformers from the verbal domain to the visual domain due to the differences between the two domains. [PERSON] et al. [45] put forth an architecture that includes a novel block of locally enhanced window transformers for stripping, denoising, and deblurring. Recently, transformers have gradually been increasingly utilized in underwater imaging with notable success. For example, [PERSON] et al. [19] developed a U-shaped transformer, integrating a channel-level multi-scale feature fusion transformer block and a spatial-level multiscale feature fusion transformer block to model global features so as to enhance underwater images. [PERSON] et al. [46] presented a dual transformer structure based on a U-shaped structure for super-resolution reconstruction of underwater images and achieved exciting results. [PERSON] et al. [47] developed the WaterFormer, a two-stage network that combines deep learning and an underwater physical model to tackle the numerous distortions found in underwater images as a result of water's absorption and scattering properties. [PERSON] et al. [48] developed a model comprising a strengthened LeWin transformer block-based encoder and decoder to enhance color accuracy. The authors of [49] constructed a model consisting of a grey-scale attention and phase transformer block for underwater enhancement. [PERSON] et al. [50] constructed an underwater image enhancement network, known as SGUIENet, utilizing semantic information as high-level guidance to improve the acquisition of locally enhanced features. Although transformers perform well in terms of capturing global dependencies, they are relatively weak in terms of local details. This is a challenge for image SR tasks because local features are critical for understanding details in an image. In this paper, we propose a module, called the DAEM, which efficiently models both global and local information and facilitates high-quality underwater image reconstruction.
## 3 Smart and Secure HIE Systems: SSHES
### Integrating AI and Blockchain into HIE Systems: Practical Challenges and Solutions
#### 3.6.1 Applicability  
Deploying blockchain- and AI-based frameworks is one of the major challenges facing the real implementation of such systems. The practical challenges are related to the following factors:  
* Implementation strategies: In the literature, one can distinguish between decentralized and centralized [39] implementation strategies. In decentralized strategies, patient data are stored locally on premises or at each health entity and shared upon request from other entities. Such a strategy gives a health entity full control and management of its infrastructure, servers, security policy as well as it reduces latency for data access. However, decentralized strategies require significant upfront investment, complex scaling, and greater responsibility for maintenance. In contrast, centralized strategy patient data are stored on data centers managed by cloud service providers. This strategy is very powerful in terms of adding blockchain nodes for additional entities and providing a huge computational process to train and test complex AI models. In addition, centralized strategies offer a more practical solution for integrating blockchain and AI technologies into health systems where the cloud providers handle hardware maintenance, software updates, and other IT management tasks. However, such a strategy raises security and privacy issues and relies on the provider policies.
* Resources requirements: Implementing AI and blockchain technologies in healthcare systems requires considerable resources across multiple areas [40]. Significant financial investment is needed to cover the costs of advanced hardware, software, and ongoing maintenance. Equally important are human resources, with skilled professionals such as data scientists, AI specialists, blockchain developers, and healthcare IT experts being essential. Training programs are also necessary to upskill existing staff and ensure smooth adoption of these new technologies. A robust data infrastructure is crucial, including secure data storage solutions and high-speed network capabilities to manage large volumes of healthcare data. Compliance with regulatory requirements necessitates legal expertise to navigate and adhere to healthcare data privacy laws. Additionally, partnering with technology vendors and consultants can provide the specialized knowledge required for successful implementation. Pilot projects and continuous monitoring are vital for evaluating performance, addressing issues, and refining systems, ensuring that these technologies effectively meet clinical needs and regulatory standards [41; 42].
# The Role of Buoyancy Induced Instability in Transpirational Cooling Applications
## 4 Solution Techniques  
### Power Series  
The system in Equation (10) will be solved in three steps. First, by considering the case of \(\lambda=0\) and \(g=0\). Second, \(\lambda=0\) and \(g\
eq 0\). Lastly, a non-zero \(\lambda\) and \(g\). For the first two cases where \(\lambda=0\), the solution can be obtained using a power series expansion. In order to do this, the system is simplified to one equation in terms of \(\Theta\).  
\[\begin{cases}[D^{2}-\alpha^{2}]^{2}\Theta=\text{\rm{Fa}}^{2}(1+g)\Theta\\ z=0:\quad\Theta=D^{2}\Theta=0\\ z=1:\quad\Theta=D^{2}\Theta=0.\end{cases} \tag{11}\]Since \(g\) is a polynomial, the power series method is well-suited where \(\Theta\) is in the following form:  
\[\Theta=\sum_{i=1}^{n}a_{n}z^{n}.\]  
The coefficients can be obtained using the resultant boundary conditions at \(z=0\) and plugging the power series into Equation (11), collecting like powers of \(z\), and re-normalize everything by with respect to \(a_{1}\). This process yields the following coefficients:  
\[a_{0}=a_{2}=a_{4}=0,\quad a_{5}=\frac{a^{2}}{120}\big{(}12a_{3}- \alpha^{2}+\Gamma(1+2c_{1}+3c_{2})\big{)}\] \[a_{n+4}=\frac{a^{2}}{(n+4)(n+3)(n+2)(n+1)}\big{[}2(n+2)(n+1)a_{n+2 }-\alpha^{2}a_{n}+\] \[\Gamma\big{(}a_{n}(1+2c_{1}+3c_{2})-a_{n-1}(2c_{1}+6c_{2})+3c_{2} a_{n-2}\big{)}\big{]}.\]  
Note that the recursive formula for \(a_{n+4}\) is a function of \(a_{3}\). In order to determine \(a_{3}\), the boundary condition \(D^{2}\Theta=0\) at \(z=1\) is used to solve for \(a_{3}\). The truncated power series for \(D^{2}\Theta\) will take on the form:  
\[D^{2}\Theta=\phi_{1}+a_{3}\phi_{2}=0\to a_{3}=-\frac{\phi_{1}}{\phi_{2}}\]  
where \(a_{3}\) can be factored out of \(\phi_{2}\) and explicitly solved. The boundary condition for \(\Theta=0\) at \(z=1\) is then used to numerically solve for \(\Gamma\) as a function of \(\alpha\). Due to expanding the power series at \(z=0\), high accuracy requires more terms. Truncating the power series to 30 terms results in a maximum percent error of \(1.92\times 10^{-13}\) as compared to the analytical solution for Case 1 for wavenumbers from 1 to 6.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 2 Basic Principles
### Bidirectional Long Short-Term Memory  
The long short-term memory (_LSTM_) network, a recurrent neural network proposed by [PERSON], overcomes the common gradient vanishing and explosion issues in recurrent neural networks (RNNs) [PERSON] (1996). An LSTM network can learn and retain sequential and temporal patterns in long sequences. Compared with traditional RNNs, LSTM networks depend on the gate mechanism and introduce forgetting, input, and output gates based on an RNN, controlling the information flow through the gate mechanism [PERSON] (1996).  
Bidirectional long short-term memory (BiLSTM) is an extension of the traditional long short-term memory (_LSTM_) network that improves the performance of the model in handling sequential problems. A BILSTM network structurally combines an LSTM network in both directions. Through its bidirectional structure, the output layer of the network can efficiently utilize the state information of the previous moment and that of the subsequent moment to make full use of the data for prediction. The structure of the model is shown in Figure 3. The BiLSTM network's forward and backward layers process the sequences separately, and then the two outputs before and after are spliced to obtain the final output result. BiLSTM can be implemented using the following formulas:  
\[\begin{array}{l}\overset{\rightarrow}{h}_{t}=LSTM\Big{(}\overset{ \rightarrow}{h}_{t-1},x_{t}\Big{)}\\ \overset{\leftarrow}{h}_{t}=LSTM\Big{(}\overset{\rightarrow}{h}_{t+1},x_{t} \Big{)}\\ \qquad\qquad h_{t}=\Big{[}\overset{\rightarrow}{h}_{t},\overset{ \leftarrow}{h}_{t}\Big{]}\end{array} \tag{4}\]where \(h_{t}\) represents the output of the output layer at time \(t\), \(\overset{\rightarrow}{h_{t}}\) represents the output of the forward layer at time \(t\), \(\overset{\leftarrow}{h_{t}}\) represents the output of the backward layer at time \(t\), and \(x_{t}\) represents the input at time \(t\).
## 5 Conclusions  
The NPP in various land cover types varies by environmental influencing factors. The study concluded that (1) NPP in the Songnen Plain increased at a rate of 19.87 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) from 2001 to 2020, and in terms of spatial distribution, the NPP values were generally higher in the northeast region and lower in the southwest region. The increase in NPP in FOR was the most significant among all land use classes from 2001-2020. (2) Although the correlation between NPP and influence factors varied with land cover type, the cumulative contribution of PRE to NPP ranks first in all land types and is the most vital influencing factor of NPP in the Songnen Plain. SM as an important influence, but the contribution of NPP was greater in land classes with shallow root systems. (3) Land type transformation also impacts NPP. The most significant growth of NPP per unit area was found in the GRA maintenance area, which was 154.33 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). Preserving the continuity of land use patterns such as FOR, GRA, and PAD has a more positive effect on the NPP growth in the Songnen Plain than transforming them to other patterns. This was followed by 148.87 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) in the GRA to SWA conversion area, the lowest growth occurred in the SWA to FOR conversion area, and perennials led to a more significant degree of NPP enhancement.  
Decision-makers should adopt targeted ecological restoration strategies for each land cover type. Despite the findings of this study, which underscore the significance of comprehensively considering the variations in meteorological and topographical factors among different land cover types when assessing the contribution of influencing factors to NPP, certain limitations arise due to the diverse ecological and climatic conditions in different regions. Moreover, the study lacks consideration of time lag issues. Future research will thus delve into further studying NPP changes in the Songnen Plain, focusing on the perspective of water stress.  
Conceptualization, N.L. and J.L.; methodology, R.J.; validation, S.L.; investigation X.L.; writing--original draft preparation, N.L. and J.L.; writing--review and editing, N.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Development Project of Jilin Province (20210203016 SF), the Natural Science Foundation of Jilin Province (20230101373 JC), and the Science and Technology Research Planning Project of Education Department of Jilin Province (JJKH20220290 KJ and JJKH20210269 KJ).  
Data Availability Statement: Data are available upon reasonable request from the corresponding author.  
We are most grateful to the anonymous reviewers and the editors for their critical and constructive reviews of this manuscript.  
The authors declare no conflict of interest.