esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Knowledge Representation for VSP Wavefield Separation
### Morphological Knowledge Representation
The gradient along the axial direction is calculated as follows:  
\[G\left(\alpha,\widetilde{U}\right)=min\bigg{\{}\
abla_{\alpha_{i}}*\widetilde{U} \Big{|}1\leq i\leq p,p\geq 1\bigg{\}}G\left(\beta,\widetilde{D}\right)=min \bigg{\{}\
abla_{\beta_{i}}*\widetilde{D}\Big{|}1\leq i\leq q,q\geq 1\bigg{\}}, \tag{8}\]  
where \(*\) represents convolution operation; min represents element-wise minimum operation; \(\alpha=\{\alpha_{i}|1\leq i\leq p,\alpha_{i}\in R\}\), \(\beta=\{\beta_{i}|1\leq i\leq q,\beta_{i}\in R\}\) represents the axial dip angles of upgoing and downgoing wavefields in the depth-time domain; \(\
abla_{\alpha_{i}}\) and \(\
abla_{\beta_{i}}\) represent the modified Sobel operators that are constructed in accordance with the axial-dip angle sets of upgoing and downgoing waves; and \(G\left(\alpha,\widetilde{U}\right)\) and \(G\left(\beta,\widetilde{D}\right)\) are the calculated gradient information matrices. As upgoing and downgoing waves have characteristics of axial slope differences and axial continuity, the wavefields should be smooth along their axial directions (which imply small variations in their axial directions). This knowledge representation can be used to construct loss functions based on \(G\left(\alpha,\widetilde{U}\right)\) and \(G\left(\beta,\widetilde{D}\right)\), guiding the network to optimize axial continuity during training, thus achieving morphological constraints on upgoing and downgoing waves. Figure 3 shows the basic horizontal and vertical Sobel operators (Figure 3a,b) and an example of a Sobel operator rotated by 45\({}^{\circ}\) (Figure 3c), which form the basis for constructing directionally sensitive gradient detectors. Figure 4 illustrates the process of extracting the morphological prior-knowledge representation. First, the axial dip angle sets, \(\alpha\) and \(\beta\), of the upgoing and downgoing waves are manually picked from the original VSP data. Then, modified Sobel operators are constructed based on the dip angle sets. Finally, the axial gradient information is extracted from the predicted upgoing and downgoing wavefields, \(\widetilde{U}\) and \(\widetilde{D}\), using their corresponding modified Sobel operators. Figure 4: Flowchart presenting morphological knowledge extraction. (**a**) Morphological knowledge representation of upgoing waves; (**b**) morphological knowledge representation of downgoing waves; (**c**) Sobel operator for upgoing waves; (**d**) Sobel operator for downgoing waves.
## 3 Proposed Method
### Backbone Feature Extraction  
In the context of image restoration tasks, the goal of designing the backbone feature extraction network is to ensure that the model can effectively capture and represent the crucial information in the images, enabling high-quality restoration in subsequent tasks. In this regard, we meticulously crafted the arrangement sequence of various modules.  
It is noteworthy that in the encoder part, a deliberate decision was made to abstain from adopting the FSGS-ViT module. This decision was influenced by the perspective presented in [44], which posits that shallow-level features extracted by the encoder tend to be relatively ambiguous compared to the deep-level features extracted by the decoder. Given the critical importance of accurate similarity estimation in image restoration tasks, the inclusion of ambiguous features in the encoder could potentially lead to inaccurate restoration results. Hence, we chose to avoid integrating the FSGS-ViT module in the encoder, ensuring that the features we extract possess clarity and precision.  
Contrarily, the FSGS-ViT module was exclusively employed in the decoder part. By introducing this module in the decoder, we could leverage deep-level clear features for more precise similarity estimation. This asymmetric encoder-decoder structure was intentionally designed to meet the specific requirements of image restoration tasks.  
The objective of this design decision is to optimize the network architecture to better suit the demands of image restoration tasks. By emphasizing the critical role of deep-level features in similarity computation, we aim to enhance the model's understanding of the internal structure and context of images, thereby achieving superior performance in image restoration tasks. This clever structural arrangement contributes to achieving more accurate and reliable image restoration outcomes.
## 2 Fault Diagnosis Method of Scraper Conveyor Gear
### Current Preprocessing Method Based on WPMD and HT  
The current of the scraper conveyor is affected by the impact of coal flow load and scraper chain, and has the strong interference of fundamental frequency. These two factors lead to the weakening of fault characteristics in the current. Therefore, a current preprocessing method, which is based on WPMD and HT, is proposed to enhance the fault characteristics in the current.  
WPMD is an effective signal-processing method [29; 30]. Compared with wavelet analysis, WPMD is more precise in signal decomposition, avoiding the shortcomings of low high-frequency resolution. Once the orthogonal scale function \(\varphi(x)\) and the wavelet function \(\psi(x)\) are determined, the dual-scale equation can be derived:  
\[\left\{\begin{array}{l}\varphi(x)=\sqrt{2}\sum\limits_{k}h_{k}\varphi(2x-k) \\ \psi(x)=\sqrt{2}\sum\limits_{k}g_{k}\varphi(2x-k)\end{array}\right., \tag{10}\]  
where \(h_{k}\) and \(g_{k}\) represent a low-pass decomposition filter and a high-pass decomposition filter, respectively. A high frequency coefficient and a low frequency coefficient can be obtained by using the dual-scale equation. The essence of WPMD is to calculate the next layer decomposition coefficient by the current decomposition coefficient, which can be expressed as  
\[\left\{\begin{array}{l}d_{l}^{2i-1}=\sum\limits_{k}h_{k-2n}d_{l-1}^{i}\\ d_{l}^{2i}=\sum\limits_{k}g_{k-2n}d_{l-1}^{i}\end{array}\right., \tag{11}\]  
where \(d_{l}^{i}\) represents the \(i-th\) WPMD coefficient in the \(j-th\) layer. The \(2^{N}\) frequency bands can be obtained by the N-layer decomposition of the current. If the sampling frequency is \(f_{s}\) and a subspace of the \(j-th\) layer is \((i,j)\), then the frequency range of the subspace can be expressed as \(\left[\frac{f_{s}}{2^{N-i+1}}\times j,\frac{f_{s}}{2^{N-i+1}}\times(j+1)\right]\). Assuming that the subspace is a gear fault frequency band \(x(t)\), it can be extracted by filtering out the impulse signal irrelevant to the fault from the full frequency band signal. After the amplitude demodulation of \(x(t)\) by HT, the signal \(y(t)\) is obtained. \(y(t)\) can be expressed as  
\[y(t)=x(t)\star\frac{1}{\pi}=\frac{1}{\pi}\int_{-\infty}^{+\infty}\frac{x(\tau)}{t -\tau}d\tau, \tag{12}\]  
After the HT of the fault frequency band signal \(x(t)\), the positive frequency component will undergo a \(-90^{\circ}\) phase shift, while the negative frequency component will undergo a \(+90^{\circ}\) phase shift. Taking \(x(t)\) as the real part and \(y(t)\) as the imaginary part, the analytical signal of the fault frequency band \(z(t)\) can be constructed jointly, which can be expressed as  
\[z(t)=x(t)+jy(t), \tag{13}\]  
The envelope \(e(t)\) can be obtained by taking the absolute value of the analytical signal, which can be expressed as  
\[e(t)=\left|x(t)+jy(t)\right|=\sqrt{x^{2}(t)+y^{2}(t)}, \tag{14}\]  
The significance of the amplitude demodulation for the fault frequency band is to eliminate the interference of the current fundamental frequency.
## 4 Discussion  
The HABS-HD project [9; 14] collected data with unchanged prevalence of AD and MCI in the population. About 1 in 9 of those aged 65 and older (11%) in the United States has AD [3]. A study of a nationally representative sample of people aged > 65 years from the United States yielded a prevalence for MCI of approximately 12% to 18% [24]. The HABS-HD dataset contains 1328 normal controls and 377 MCIs and ADs (prevalence rate = 22.1% and class imbalance index = 0.31). A previous workflow [1] for hyperparameter tuning with HPC could have encountered an out-of-memory problem for the big imbalanced HABS-HD data. This study aimed to (1) solve the out-of-memory problem, (2) improve the model performance for imbalanced data, and (3) increase computation efficiency. We achieved the three goals by incorporating the _bigmemory_, _foreach_, and _doParallel_ packages together with a single-node multicore parallel setup in the Lonestar6 HPC system. By switching to Lonestar6, we improved computation efficiency, which was up to four times  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Actual** & \\ \hline Predicted & ADMCI & NC \\ ADMCI & 53 & 130 \\ NC & 22 & 135 \\ Precision/PPV & 28.96\% & \\ Accuracy & 55.29\% & \\ Sensitivity & 70.67\% & \\ Specificity & 50.94\% & \\ NPV & 85.99\% & \\ AUC & 64.73\% & \\ PPV12 & 16.42\% & \\ NPV12 & 92.72\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance for testing set in 10 times repeated fivefold cross-validation after hyperparameter tuning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Actual** & \\ \hline Predicted & ADMCI & NC \\ ADMCI & 0 & 0 \\ NC & 75 & 265 \\ Precision/PPV & NaN\% & \\ Accuracy & 77.94\% & \\ Sensitivity & 0.00\% & \\ Specificity & 100.00\% & \\ NPV & 77.94\% & \\ AUC & 59.21\% & \\ PPV12 & NaN\% & \\ NPV12 & 88.00\% & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance for testing set in 10 times repeated fivefold cross-validation without hyperparameter tuning.  
faster than that in Talon3 [1]. Each compute node in Lonestaro has two AMD EPYC 7763 64-core processors (Milan) and 256 GB of DDR4 memory. In contrast, each node in Talon3 has only two 2.4 GHz Intel Xeon E5-2680 v4 14-core processors and 64 GB memory. As an example, after loading into Lonestaro, the oasis longitudinal dataset from [1] only took 23.298 s and 12.119 s to complete for 28 cores and 128 cores, respectively, which was four times faster than Talon3 (which took 40 s).
## 5 Discussion
### Quantitative Analysis of Liver Segmentation
To provide further insights into the test results of each model on the LiTS dataset and to assess the distinctions between the predictions of different models and the ground-truth labels, we utilized the confusion matrix. The results are presented in Figure 11, revealing that U-Net, U-Net++, and ResUNet exhibited difficulties in accurately recognizing the liver region, often misclassifying it as background. In contrast, MDAU-Net demonstrated a more balanced discrimination between the liver and background compared to other methods, with an extremely low probability of mis-segmentation and superior overall segmentation quality. Quantitative Analysis of Liver Segmentation on SLiver07. We opted to retrain MDAU-Net using the SLiver07 dataset to further assess its model performance. The experimental outcomes are detailed in Table 5, while Figure 9 presents corresponding radar plots of the experimental data. As indicated in the table, MDAU-Net achieved evaluation scores of 0.9706, 0.9743, 0.9757, 0.0569, and \(-\)0.0095 for various metrics. These scores represent improvements of 0.1138, 0.0137, 0.0169, 0.0932, and 0.1524 compared to the baseline U-Net, and they surpassed the performance of other methods to varying degrees. In the radar plot, MDAU-Net is depicted by a red outline, clearly demonstrating that it covers a wider area, indicative of its overall superiority compared to other examined methods. Figure 9: This image shows the radar chart results from the liver segmentation in Section 5.1 on LiTS/Sliver07: (**a**) radar chart from LiTS, (**b**) radar chart from Sliver07. The visualized segmentation results for this set of experiments are presented in Figure 12. In these figures, the green lines represent the true labels, while the red lines depict the predicted results. To highlight the differences in segmentation outcomes, we magnified specific local areas. It is evident that U-Net and UNet++ exhibited more pronounced instances of mis-segmentation in the liver slices, with significant disparities between the segmentation results and the real labels in other slices. While ResUNet and SAR-U-Net  
Figure 11: Confusion matrix from liver segmentation in Section 5.1 on LiTS. Figure 10: Visualization of results of liver segmentation using different methods on LiTS. produced improved segmentation results in the liver region compared to the former two methods, they still missed some detailed information in challenging segmentation areas. Conversely, MDAU-Net demonstrated the most complete overlap between the segmentation curves and the real labels, while also processing details such as the liver contour edge more comprehensively. This resulted in improved segmentation outcomes for liver slices of varying shapes and sizes compared to other methods. Figure 13 presents the confusion matrix illustrating the segmentation results of each model on the SLiver07 dataset. It is evident that MDAU-Net exhibited a more balanced segmentation ability for both the liver and background regions, achieving superior segmentation results compared to other methods.
# A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem
## 4 MDP Framework for HFSP
### Exploration and Exploitation  
The agent needs to continuously interact with the environment in Q-learning. The agent selecting the correct action based on the perceived external information determines whether the interaction is effective or not. When selecting an action, the action maximizing the Q-value function should be selected in each state to obtain as many rewards as possible; this is exploitation. On the other hand, the agent should explore the better actions to obtain the optimal Q-value so as not to fall into the local optimal value. To solve the problem, \(\varepsilon\)-greedy policy is often adopted, where the agent, given a coefficient \(\varepsilon\in[0,1]\), with the probability \(1-\varepsilon\), selects the action with the largest Q-value and randomly selects an action to execute with the left \(\varepsilon\) probability. Undoubtedly, \(\varepsilon\)-greedy policy increases the probability to choose the better action at the beginning of learning. However, it affects the learning efficiency with an array of explorations when the policy is close to the optimal one in the later stage of study. Two solutions are usually available to solve the flaw of \(\varepsilon\)-greedy policy.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
###### Abstract  
The increase of the global population and shortage of renewable water resources urges the development of possible remedies to improve the quality and reusability of waste and contaminated water supplies. Different water pollutants, such as heavy metals, dyes, pesticides, endocrine disrupting compounds (EDCs), and pharmaceuticals, are produced through continuous technical and industrial developments that are emerging with the increasing population. Molecularly imprinted polymers (MIPs) represent a class of synthetic receptors that can be produced from different types of polymerization reactions between a target template and functional monomer(s), having functional groups specifically interacting with the template; such interactions can be tailored according to the purpose of designing the polymer and based on the nature of the target compounds. The removal of the template using suitable knocking out agents renders a recognition cavity that can specifically rebind to the target template which is the main mechanism of the applicability of MIPs in electrochemical sensors and as solid phase extraction sorbents. MIPs have unique properties in terms of stability, selectivity, and resistance to acids and bases besides being of low cost and simple to prepare; thus, they are excellent materials to be used for water analysis. The current review represents the different applications of MIPs in the past five years for the detection of different classes of water and wastewater contaminants and possible approaches for future applications.  
molecularly imprinted polymers; solid phase extraction; heavy metals; synthetic dyes; bisphenols; personal care products; pharmaceutical compounds 202126615 2 October 202  
creates a need for more efficient adsorption and preconcentration approaches. Molecularly imprinted polymers (MIPs) represent a class of promising materials that can be used in environmental sciences due to their different synthesis approaches that can be tailored according to the required target. MIPs are reported to have superior properties being easy to prepare, ecofriendly, cost-effective, and sensitive to different classes of pollutants that are expected to be found in wastewater [1; 3].
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 2 JRC-CWG Model  
### Transmit JRC-CWG Model  
In this paper, we consider an ITS as shown in Figure 1. In Figure 1, the yellow car that is equipped with an automotive JRC system transmits the JRC waveforms to detect the white bus and simultaneously sends communications information to it.  
In this paper, we consider that the transmit JRC waveform is the JRC-CWG, as shown in Figure 2. There are \(G\) groups of JRC-CWGs transmitted in each coherent processing interval (CPI) to perform radar and communications jointly. Each JRC-CWG contains \(M\) waveforms, and, hence, there are \(GM\) waveforms that will successively be transmitted with a fixed PRI, denoted as \(T\) in Figure 2. Let \(\mathbf{S}_{g}=\left[\mathbf{s}_{g,1},\mathbf{s}_{g,2},\ldots,\mathbf{s}_{g,M} \right]\in\mathbb{C}^{N\times M}\) be the matrix formed by the waveforms of the \(g\)-th JRC-CWG. \(\mathbf{s}_{g,m}\) is the \(m\)-th transmit waveform in the \(g\)-th JRC-CWG, which is denoted as  
\[\mathbf{s}_{g,m}=\left[s_{g,m}(1),s_{g,m}(2),\ldots,s_{g,m}(N)\right]^{T} \tag{1}\]  
In \((1),s_{g,m}(n)=\frac{1}{\sqrt{N}}e^{\mathrm{i}\theta_{g,m}(n)}\) denotes the \(n\)-th entry of \(\mathbf{s}_{g,m};\theta_{g,m}(n)\) represents the phase of \(s_{g,m}(n)\); and \(N\) is the length of \(\mathbf{s}_{g,m}\).  
In each transmit JRC-CWG, the first \(M_{r}\) transmit waveforms are WTOs, which are represented by solid bars, and the last \(M_{c}\) transmit waveforms are WCIs, which are represented by shadowed bars in Figure 2. Clearly, it can be seen that \(M=M_{r}+M_{c}\). That is to say, the whole JRC-CWG consists of WTOs and WCIs. In order to perform the communications function, the traditional unimodular communications waveforms, such as BPSK waveforms, will be employed as the WCIs. The WCIs are determined by the transmitted communications information. In order to improve the radar performance, the WTOs are optimized to suppress the sidelobe level of the whole JRC-CWG.  
In this paper, we consider a radar and communication system with a single transmit antenna. It is worth noting that the proposed JRC-CWG is also applicable for multiple transmit antennas and receive antennas. This will be discussed in our future work.
## 2 Mobile Social Networking
### Attacks and Defenses in CAMSN services
Like many networked systems, mobile social networking services are subject to denial of service, eavesdropping, spoofing, and replay attacks. A _denial of service attack_ consists of an attacker consuming excessive resources on a mobile social networking service's infrastructure. Twitter, for example, was brought down for days in August 2009 by a denial of service attack against its web servers [28]. An _eavesdropping attack_ consists of a malicious user listening in on wireless communications, and is preventable via end-to-end encryption. A _replay attack_ involves eavesdropping, recording, and replaying some critical pieces of information, such as a session authentication or other identifying message. _Spoofing attacks_ occur when an attacker pretends to be an identity (or authority) which he is not. Replay attacks are often used in carrying out a spoofing attack. The link layer Address Resolution Protocol (ARP) is notoriously susceptible to spoofing attacks which allow an attacker to take control of the IP address of a legitimate network device [29]. The anonymous identifiers system proposed in [26] prevents eavesdropping, replay, and spoofing attacks. Services can use location verification (see section 5) to aid in the prevention of replay and spoofing attacks. Other methods of preventing replay attacks are summarized in [30]. A _wormhole attack_ is a variant of the replay attack in which recorded data from a physical location \(A\) is replayed at a different physical location \(B\), with the intent of making a user or device at location \(A\) appear to be at location \(B\). Malicious entities can use wormhole attacks in mobile social networking services to create confusion amongst users or to alter network topology with the goal of maliciously intercepting (and replaying) traffic. Differing network speeds make wormhole attacks possible: in a peer-to-peer MSN, if device \(X\) sends a message \(m\) to device \(Y\), \(m\) will follow an expected route (e.g. the fastest route) across peer devices. However, if a wormhole is introduced as the shortest path between \(X\) and \(Y\), the message \(m\) might flow along the wormhole, thus allowing the wormhole's controller to eavesdrop on and manipulate the message \(m\) (assuming \(m\) is not encrypted). One solution to wormhole attacks involves the use of Packet Leashes, or limitations on a packet's lifetime and traveled physical distance [31]. A routing protocol which attempts to identify the effects of a persistent wormhole has been proposed [32]. Algorithms utilizing nodes dedicated to the task of monitoring network topology have been proposed [33]. Many other strategies to aid in the prevention and detection of wormhole attacks have been proposed and we refer the reader to the following literature for further reading [34,35].
## 2 Vehicle Model Used in Agricultural Vehicle Path Tracking Control
### Summary of Vehicle Models  
Several vehicle models frequently used in path-tracking control of agricultural machinery have been introduced. The summary of agricultural vehicle models in path tracking control is shown in Table 4. The advantage of model control methods is that when accurate models can be obtained, the control performance is better; that is, the accuracy of the model is required to be higher. The method based on the kinematic model mainly adopts a small-angle linearization approximation model, and assumes that the agricultural machinery is low-speed and constant, but it will introduce linearization errors and increase the impact of speed changes on the performance of the controller. The control method based on the dynamic model can take into account the dynamic characteristics of agricultural machinery well, but the parameters are difficult to obtain online in real time. Moreover, the accurate establishment of the vehicle dynamic model becomes more difficult due to factors such as wind force, changes in the unit's own mass, and changes in the adhesion between the track and the ground. Existing studies have confirmed that when an agricultural vehicle with low speed, the path tracking control method based on a kinematic model can often achieve acceptable tracking accuracy and stability. Therefore, from a general point of view, the path-tracking control algorithm for agricultural vehicles based on the kinematic model is still worthy of further research and exploration.  
Figure 5: Tracked robot dynamic model.