esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### RNA Extraction, Library Preparation, and Sequencing  
RNA extraction, library preparation, and sequencing were carried out at Novogene (HK) Company Limited. Total RNA for individual time course and replicates was extracted using TiaGen extraction kit (Biotech Beijing Co., Ltd., Beijing, China) and treated with sigma DNase1 (D5025). RNA degradation and contamination was measured on 1% agarose gel while RNA purity was assessed using the NanoPhotometer(r) spectrophotometer (IMPLEN, Westlake Village, CA, USA). RNA concentration and integrity were assessed using Qubit(r) RNA Assay kit in Qubit(r) 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA) and RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA), respectively. Three micrograms of RNA samples were used as input for library construction. Libraries were constructed using NEBSnext(r) Ultra(tm) RNA Library Prep Kit for Illumina(r) (NEB, Ipswich, MA, USA) according to the manufacturer's instructions and index codes were added to attribute sequences to each sample. Finally, PCR products were purified using AMPure XP system and quality of the library assessed using the Agilent Bioanalyzer 2100 system. A cBot Cluster Generation System using HiSeq PE Cluster Kit cBot-HS (Illumina) was used to cluster the index-coded samples. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform 2500 generating 150-bp paired-end reads.
## 2 Theoretical Aspects
### Governing Motion Equation of a 3-DOF Model  
A 3-DOF mass stiffness model was employed to explain the theoretical aspects of this paper, and the nonlinear friction force was applied between \(m_{1}\) and \(m_{2}\), as shown in Figure 1.  
The governing equation of the above model is expressed as  
\[\left\{\begin{array}{cc}m_{1}\ddot{x_{1}}=(-k_{1}-k_{2})x_{1}+k_{2}x_{2}+f+f _{exc}+f_{c_{1}}\\ m_{2}\ddot{x_{2}}=k_{2}x_{1}+(-k_{2}-k_{3})x_{2}+k_{3}x_{3}-f+f_{c_{2}}\\ m_{3}\ddot{x_{3}}=k_{3}x_{2}+(-k_{3}-k_{4})x_{3}\end{array}\right.. \tag{1}\]  
In the matrix form, the governing motion equation is written as  
\[\left[\begin{array}{cc}m_{1}&0&0\\ 0&m_{2}&0\\ 0&0&m_{3}\end{array}\right]\left[\begin{array}{c}\ddot{x_{1}}\\ \ddot{x_{2}}\\ \ddot{x_{3}}\end{array}\right]=-\left[\begin{array}{cc}k_{1}+k_{2}&-k_{2}&0 \\ -k_{2}&k_{2}+k_{3}&-k_{3}\\ 0&-k_{3}&k_{3}+k_{4}\end{array}\right]\left[\begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\end{array}\right]+\left[\begin{array}{c}f\\ -f\\ 0\end{array}\right]+\left[\begin{array}{c}f_{exc}\\ 0\\ 0\end{array}\right]+\left[\begin{array}{c}f_{c_{1}}\\ f_{c_{2}}\\ 0\end{array}\right]. \tag{2}\]  
\[\text{where}\,\mathbf{M}=\left[\begin{array}{ccc}m_{1}&0&0\\ 0&m_{2}&0\\ 0&0&m_{3}\end{array}\right]\,\text{is the mass matrix of the system, }m_{1  
\[\mathbf{F_{c}}=\left[\begin{array}{c}f_{c_{1}}\\ f_{c_{2}}\\ 0\end{array}\right]\text{ is the active control force vector, and the corresponding matrix form is written as \[\mathbf{M}\ddot{\mathbf{X}}=-\mathbf{K}\mathbf{X}+\mathbf{F_{nl}}+\mathbf{F_{exc}}+\mathbf{F_{c}}, \tag{3}\]
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
###### Abstract
In response to the above problems, the main goals of this paper are to develop a deep learning model for eliminating the impact of land subsidence on GPS horizontal motion and apply it to the Tianjin land subsidence area to obtain surface horizontal motion information without the impact of land subsidence. Local deep learning models were constructed for the horizontal components of each GPS station in the Tianjin land subsidence area that needs to eliminate the impact of land subsidence; the corresponding dataset of each model was generated for model training and testing; the input of each model was obtained by using the time-series decomposition method; the output of the model can be considered GPS horizontal motion with the impact of land subsidence removed. Finally, the original horizontal velocity field and the predicted horizontal velocity field with the impact of land subsidence removed in the Tianjin land subsidence area are compared and analyzed.
## 4 Signal Combining Schemes for ADR Solutions
### Receiver Scenario with a Single PD (\(N_{pd}\) = 1)
#### 4.2.3 Maximum Ratio Combining Scheme  
This technique, proposed in [44], improves the EGC scheme due to the higher priority it gives to the PD signal that reaches the highest SINR. Therefore, like EGC, it assigns weights to the signals received by the ADR PDs, with the difference that the assigned weights will be proportional to the SINR that each signal obtains. For this scheme, like EGC, a \(T_{x_{top}}\) is selected to maximize the SINR by following the expression (33). Therefore, the SINR is presented as follows [22; 23; 44],  
\[\gamma_{T_{x_{top}},R_{x}}=\frac{\tau\sigma\xi\left(\sum\limits_{i=1}^{N_{PD}} P_{a_{top}}w_{T_{x_{top}},PD_{i}}H_{T_{x_{top}},PD_{i}}\right)^{2}}{\tau \sigma\xi\sum\limits_{i=1}^{N_{PD}}\sum\limits_{a=1,A\
eq a_{top}}^{N_{T_{x}}} \left(P_{a}w_{T_{x_{top}},PD_{i}}H_{T_{x_{top}},PD_{i}}\right)^{2}+\sum\limits _{i=1}^{N_{PD}}\sum\limits_{a=1,A\
eq a_{top}}^{N_{T_{x}}}\left(w_{T_{x_{top}},PD_{i}}n_{PD_{i}}\right)^{2}}, \tag{38}\]  
where \(w\) is the weight factor assigned to the signal received by the PDs, which can be calculated as [22; 23; 44]  
\[w_{T_{x_{top}},PD_{i}}=\frac{\left(\tau\sigma\xi P_{a_{top}}H_{T_{x_{top}},PD_ {i}}\right)^{2}}{\sum\limits_{a=1,A\
eq a_{top}}^{N_{T_{x}}}\left(\tau\sigma \xi P_{a}H_{T_{x_{top}},PD_{i}}\right)^{2}+\left(n_{PD_{i}}\right)^{2}}. \tag{39}\]  
By assigning proportional weights to the SINR detected in each PD, this scheme enhances the optimal signal and attenuates the noise and interference components, thus reaching a high SINR. However, for the physical implementation in the studied ADRs, complex circuits are needed. In addition to monitoring the SINR in each PD, adding and multiplying the signals is required to achieve the combination proposed in this technique.
## 3 Experiment and Results
### Determine the Atmospheric Light Intensity Value at Infinity  
Accurately determining the atmospheric light intensity at infinity is a critical step in transmittance estimation and dehazing. Conventional methods typically select the maximum intensity value in the image as the atmospheric light, but this approach is prone to significant errors. To achieve more precise localization of sky regions, this paper proposes a multi-scale threshold fusion method to robustly estimate the atmospheric light intensity at infinity.  
As shown in Figure 8, under foggy conditions, the gray-level distribution of light intensity information typically does not exhibit a bimodal histogram. In such similar scenarios, the background and objects are challenging to segment. However, since polarization characteristics are determined by the intrinsic properties of objects themselves, polarization information enables clear distinction between background and objects, as illustrated in Figure 9. Therefore, we adopt the OTSU method--a maximum inter-class variance threshold segmentation technique. This approach divides the image into background and object regions based on gray-level features. A larger inter-class variance between the gray values of these two regions indicates greater dissimilarity in composition and lower misclassification probability.  
As shown in Figure 10, within the polarization domain, atmospheric regions exhibit comparatively low DoP relative to target regions, demonstrating distinct segmentation characteristics. Concurrently, according to the atmospheric scattering model, in the intensity domain, atmospheric regions typically display higher intensity values than target regions. To leverage these properties, we perform threshold-based binary segmentation on the polarization and intensity images, yielding distinct regional classifications as illustrated in Figure 10: Figure 10a: Intensity Threshold Segmentation Map; Figure 10b: DoP Threshold Segmentation Map.  
Based on this, we define atmospheric regions as areas where the intensity segmentation map is white and the polarization degree segmentation map is black, with other regions classified as non-atmospheric. The final result is shown in Figure 10c. By selecting the maximum window-averaged intensity of the white regions in Figure 10c as the light  
Figure 9: DoP maps in different scenarios.  
intensity value at infinity \(A^{\prime}_{\infty}\), and further ensuring robustness, we introduce a coefficient \(A_{\infty}=\sigma A^{\prime}_{\infty}\). The finalized light intensity at infinity is then determined as \(A_{\infty}\).
# Development of Assessment Criteria for Managing the Quality of Taishan Black Tea
## 3 Results
### Aroma Analysis of Taishan Black Tea  
#### 3.3.1 Volatile Components of Taishan Black Tea  
Our analysis of 45 black tea samples from 15 companies detected 231 volatile substances, including 40 alcohols, 38 aldehydes, 28 esters, 29 olefins, 24 ketones, 21 alkanes, 18 aromatic hydrocarbons, 18 heterocycles, 10 terpen, 2 ethers, 2 acids, and 1 alkyne. As shown in Table S3, the black teas produced by various companies predominantly featured alcohols, aldehydes, esters, ketones, aromatic hydrocarbons, and heterocyclic components among their detected volatile components.
## 4 Results and Discussion
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
We employed standard information retrieval and classification metrics including Precision@k (P@k), Recall@k (R@k), mean average precision (MAP), normalized discounted cumulative gain (NDCG@k), accuracy, F1-score, and macro-averaged metrics for multi-class classification tasks.
## 2 Theoretical Background
### EMD-Based Methods  
#### 2.2.1 EMD Method  
Introduced by [PERSON] et al. [22], EMD is an adaptive method capable of analyzing or examining stationary and non-stationary time or 1D signals. In this regard, EMD is characterized by decomposing a 1D signal in diverse intrinsic mode functions (IMFs) or frequency bands according to its frequential information. For obtaining or considering an IMF, it has to fulfill the subsequent conditions: (1) the number of zero crossings as well as extrema in the 1D signal must be either different or equal only by one, and (2) the lower  
Figure 1: Acquired ECG signals for (**a**) first and second \(1\) min intervals prior to an SCD event and (**b**) normal cardiac rhythm.  
and upper envelopes have to be symmetrical, indicating that a mean value equal to zero is estimated.  
For estimating each IMF, a process called \"sifting process\" is performed. In general, it is based on five steps defined as follows:  
Step 1. Estimate the local maximum and minimum points of the 1D signal (\(x(t)\)).  
Step 2. Connect the local maximum and minimum points identified in step 1 in order to estimate a lower and upper envelope utilizing cubic-splines. The mean of both envelopes, \(m_{1}(t)\), is subtracted from the original 1D signal, \(x(t)\), for obtaining a new 1D signal, \(h_{1}(t)\), thus:  
\[h_{1}(t)=x(t)-m_{1}(t) \tag{1}\]  
If the new 1D signal, \(h_{1}(t)\), does not fulfill the conditions (1) and (2), the steps (1 and 2) must be repeated until \(h_{k}(t)\) fulfills both conditions; so, \(h_{k}(t)\) is deemed as the first \(IMF_{1}\) and represented by:  
\[c_{1}(t)=h_{k}(t)=IMF_{1} \tag{2}\]  
Step 3. Once the \(IMF_{1}\) is estimated, it is subtracted from the initial 1D signal for calculating a residue signal, \(r_{1}\)(t), denoted by:  
\[r_{1}(t)=x(t)-IMF_{1} \tag{3}\]  
Step 4. If the obtained residue signal, \(r_{1}(t)\), is deemed a monotonic function, the process is stopped, indicating that no more IMFs are estimated. On the other hand, if it is not considered a monotonic function, the residual signal is now considered the original 1D signal, which is again evaluated by steps (1 to 3) for estimating the other IMFs.  
Step 5. The initial 1D signal, \(x(t)\), can be reconstructed by summing the obtained IMFs and the final residue signal in this way:  
\[x(t)=\sum_{i=1}^{N}IMF_{i}+r_{N}(t) \tag{4}\]  
Figure 2 shows the flowchart to compute the EMD method which is also the basis for the EEMD and CEEMD methods.
## 4 Discussion
### The Discussion of the Algorithm Robustness  
The dual receivers tracking system and the corresponding MPSI algorithm proposed in this paper not only have the characteristics of high accuracy, but also strong robustness. As the ground soil and vegetation change over time, the pipeline may no longer be parallel to the ground. In addition, considering the invisibility of the underground pipeline, the receivers may be offset or tilted relative to the pipeline when it is installed. MPSI algorithm cannot only realize the tracking of four DOFs of a PIG but can also deal with these various unexpected cases.
## 4 Analysis
### Security Analysis
#### 4.2.2 Relay Nodes False Reports on the Time Difference  
The adversary may attempt to exploit compromised nodes to disrupt the delay estimation process. Nonetheless, the time differences reported by relay nodes should be a sequence, which is listed as follows:  
\[\Delta T_{1},\Delta T_{2},\ldots,\Delta T_{n-1} \tag{42}\]  
where \(\Delta T_{i}\) is the time difference between the challenge and the response forwarded on the relay node \(i\).  
Then, the delay between two adjacent nodes is:  
\[\left\{\begin{array}{l}D_{1}=\frac{\Delta T_{1}-\Delta T_{verifier}}{2}\\ D_{i}=\frac{\Delta T_{i}-\Delta T_{i-1}}{2}\qquad(1<i\leq n-1)\end{array}\right. \tag{43}\]  
According to the central limit theorem, the delay approximates a normal distribution, and we have:  
\[D_{i}\sim N(\upmu,\sigma^{2}) \tag{44}\]  
where expectation \(\upmu\) is estimated by the sample mean \(\bar{D}\) and variance \(\sigma^{2}\) is estimated by the sample variance \(s^{2}\).  
If the relay node \(i\) sends false time difference data, the time difference between adjacent nodes will significantly deviate from the sample mean. This means that the time difference \(D_{i}\) will satisfy the following inequalities:  
\[\mid D_{i}-\bar{D}\mid>3\sigma,\mbox{ or }\mid D_{i+1}-\bar{D}\mid>3\sigma \tag{45}\]  
Therefore, the verifier could remove the false data by the Pauta criterion [28].
## 3 Proposed Method
### System Architecture
#### 3.1.2 Learnings Distribution  
With the aim of integrating the models and developing a better intrusion detection system with optimum parameters, the clients exchange their trained learning with a server-based system for aggregation. The intelligent communication administrator (e.g., security gateway) is in charge of all interactions between clients and the aggregation server.
## 5 Simulation
### Results
Because the proposed scheme has screened out a number of MNs or defective nodes with very poor communication environments during the trust calculation, it prioritizes the total cabling distance in data transmission and avoids the void areas. This makes the proposed scheme more energy-efficient. As shown in Figure 6, as the number of simulation rounds increases, the AE2 ED also increases. This is mainly due to the fact that, as the UWSN operates, some of the UW-sensors consume energy too quickly, resulting in data forwarding paths having to choose nodes with longer total cabling distances. The AE2 ED of the scheme is always lower than the other three schemes, while HH-VBF and GEDAR have the worst performance because these two algorithms are limited to nodes within the radius of the pipeline or nodes shallower than the current node, which is not flexible enough in forwarding node selection, resulting in their higher AE2 EDs. Figure 5: (**a**) Residual energy vs. number of turns; (**b**) AEC vs. number of nodes. Figure 6: AE2 ED vs. rounds. As shown in Figure 7a, 20 malicious nodes were randomly distributed in the setting node of the simulation experiment, and the simulation results of the average energy consumption (AEC) per round of the network and the number of nodes were obtained. As can be seen from the figure, the energy consumption of the target scheme was relatively low compared with other schemes. Figure 7b shows the random distribution of 20 malicious nodes in the setting node of the simulation, and the results of the AE2 ED and simulation rounds. By combining Figure 7a,b, compared with the other three protocols, when there are malicious nodes in the network, the network performance of the target scheme is better than that of the other three protocols, and the impact of malicious nodes on the network is less than that of the other two protocols: HH-VBF and GEDAR perform the worst. This is because they lack security mechanisms to respond effectively to malicious nodes. This proves that the trust model can effectively identify malicious nodes, and the target scheme is robust enough to detect malicious nodes.
# An Ensemble Transfer Learning Model for Detecting Stego Images
## 2 Literature Review
Recently, studies such as the one conducted by [PERSON] et al. have shown that well-designed convolutional neural networks (CNN) can perform similarly to two-step machine learning algorithms. This research proposed a CNN that outperformed the state of the art in error probability. The proposal was a creative combination of essential bricks from several studies and followed prior proposals. The CNN used a pre-processing filter bank, a Truncation activation function, five convolutional layers with batch normalization and a scale layer, and an adequately large, fully connected section. The CNN was trained using an enhanced database. Our CNN was tested against the S-UNIWARD and WOW embedding algorithms, and it was compared to an ensemble classifier, a rich model, and two other CNN steganalysis methods. An ensemble classifier trained with rich features was used for approximately 10 years to detect a concealed message in an image, and it showed that well-designed convolutional neural networks (CNN) can perform as well as two-step machine learning techniques can. This research proposed a CNN with a lower error of probability than that of the current state of the art. The proposal continued what had been offered recently and cleverly combined essential bricks from many articles. The CNN used a pre-processing filter bank, a truncation activation function, five convolutional layers with a batch normalization associated with a scale layer, and an appropriately large, fully connected section. An augmented database had also been utilized to train a CNN. The proposed CNN was experimentally evaluated against the S-UNIWARD and WOW embedding techniques, and it was compared to three additional methods: an ensemble classifier, a rich model, and two CNN steganalysis methods [16]. In [17], the authors examined how deep learning could improve web image prediction accuracy and performance. The researchers trained 36 CNN models on the same dataset using convolutional neural networks (e.g., ImageNet). Using a \"real-world\" binary image categorization application, they evaluated the pre-trained models. Eurasian lynx (_Lynx lynx_) camera trap images from Croatia were used to classify wildlife photos. According to their analysis, the dataset was extremely uneven in terms of the percentage of shots that showed lynxes, and image quality varied greatly. Several steganography techniques can hide information in JPEG images by altering the discrete cosine transform (DCT) coefficients, even though most of these algorithms work in the spatial domain. To further reduce the likelihood of discovery, some algorithms employ content addictiveness to primarily embed data in less predictable places, making it harder to notice changes. We focused on these alterations since they are the most difficult to identify. Authors of past studies have selected nsF5 [18], JPEG universal wavelet relative distortion (J-Uniward) [19], and uniform embedding revisited distortion (UERD) [20] for their analyses.
# Metadata Quality for Biobanks
## 5 Metadata Quality in Biobanks
### Metadata Quality on Different Levels of Aggregation
#### 5.4.1 Aggregated Data Item Quality Values as Quality Metadata  
We start with possible aggregations while calculating data item quality metric values which form quality metadata. There exist three aggregation approaches to calculating such quality metadata (data item quality metric values):  
1. The first and the simplest approach is the calculation of quality metadata as scalar metric values separately for each data item attribute value without aggregation. This can be done with formulas performing calculations directly on specific values. An example could be _the accuracy or reliability for a body mass index (BMI) attribute value defined for a specific sample_.
2. The second approach is the calculation of such metadata values directly on higher levels of aggregation. This can be done through formulas calculating quality values for collections or biobanks directly based on corresponding sets of data item attribute values. An example could be _the completeness of the BMI attribute values for a collection which requires the whole set of BMI values defined for all the samples for its calculation_.
3. The last approach is the aggregation of other quality metric values (e.g., calculated for separate attribute values or samples) to obtain values on higher levels of aggregation (e.g., for collections or biobanks). This can be done through formulas aggregating quality metric values to form other quality metric values. An example could be _theaverage accuracy of a BMI attribute for a collection calculated based on a set of the BMI accuracy values for the samples._  
Detailed treatment of the calculation of such metric values with examples involving different aggregations is provided in [10].