esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The present work provides a new, miniaturized, and green sample preparation method for the solubilization of hydroxycinnamic acids in honey prior to chromatographic analysis. The proposed method combined the advantages of ultrasound irradiation and ecofriendly hydroethanolic mixture. More specifically, honey samples were mixed with 60.0% \(v/v\) aqueous ethanol at a solvent-to-sample ratio of 10.0 mL g\({}^{-1}\). The solubilization procedure took place in an ultrasonic bath for a period of 17.8 min at 54.6 \({}^{\circ}\)C. The proposed method significantly decreases the preparation time, the sample size, and annihilates the volumes of organic solvents. Furthermore, the optimized sample preparation method was applied successfully for the determination of five hydroxycinnamic acids in multi-floral honey, providing, for the first time, the hydroxycinnamic acid fingerprint of honey produced in Cyprus, a Mediterranean island with high biodiversity in flora.  
Conceptualization, V.G.; methodology, A.C. and V.G.; investigation, K.S.; resources, V.G.; writing--original draft preparation, K.S. and V.G.; writing--review and editing, A.C.; project administration, V.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Samples** & **Caffeic Acid** & **Chlorogenic Acid** & _p_**-Couramic Acid** & **Ferulic Acid** & **Rosmarinic Acid** & **Total** \\ \hline C1 & 0.84 \(\pm\) 0.10 & 0.46 \(\pm\) 0.03 & 0.56 \(\pm\) 0.03 & nd & 0.16 \(\pm\) 0.03 & 2.02 \\ C2 & 0.66 \(\pm\) 0.05 & 0.57 \(\pm\) 0.05 & 0.94 \(\pm\) 0.05 & 0.02 \(\pm\) 0.00 & 0.19 \(\pm\) 0.01 & 2.38 \\ C3 & 0.83 \(\pm\) 0.07 & 0.75 \(\pm\) 0.06 & 0.91 \(\pm\) 0.10 & 0.04 \(\pm\) 0.01 & 0.21 \(\pm\) 0.01 & 2.74 \\ C4 & 0.66 \(\pm\) 0.05 & 0.56 \(\pm\) 0.05 & 1.00 \(\pm\) 0.09 & 0.10 \(\pm\) 0.02 & 0.20 \(\pm\) 0.03 & 2.52 \\ C5 & 0.97 \(\pm\) 0.11 & 1.04 \(\pm\) 0.10 & 1.11 \(\pm\) 0.11 & nd & 0.23 \(\pm\) 0.02 & 3.35 \\ C6 & 1.22 \(\pm\) 0.09 & 22.14 \(\pm\) 1.32 & 1.64 \(\pm\) 0.08 & 0.05 \(\pm\) 0.01 & 0.45 \(\pm\) 0.03 & 25.5 \\ C7 & 0.46 \(\pm\) 0.03 & nd & 0.94 \(\pm\) 0.07 & nd & 0.16 \(\pm\) 0.02 & 1.56 \\ C8 & 0.72 \(\pm\) 0.04 & 0.69 \(\pm\) 0.04 & 2.39 \(\pm\) 0.18 & nd & 0.18 \(\pm\) 0.03 & 3.98 \\ C9 & 0.30 \(\pm\) 0.03 & 1.74 \(\pm\) 0.11 & 2.59 \(\pm\) 0.20 & 0.06 \(\pm\) 0.01 & 0.30 \(\pm\) 0.03 & 4.99 \\ C10 & 1.04 \(\pm\) 0.10 & nd & 0.35 \(\pm\) 0.01 & nd & 0.20 \(\pm\) 0.02 & 1.59 \\ \hline Range & 0.46–1.22 & nd–22.14 & 0.35–2.59 & nd–0.10 & 0.16–0.45 & 1.56–25.5 \\ \hline Median value & 0.78 & 0.63 & 0.97 & 0.01 & 0.20 & 2.63 \\ \hline \hline \end{tabular} nd: not detected.  
\end{table}
Table 5: Contents of hydroxycinnamic acids in multi-floral honey. The results are expressed as mg hydroxycinnamic acid per kg of honey.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 2 Materials and Methods
### Effect of Dosage on Adsorption  
The same samples were prepared and oscillated at room temperature (25 \(\pm\) 1 \({}^{\circ}\)C). The concentration of residual copper ions in the supernatant of each sample was measured when the adsorption time reached 48 h.
## 4 Experiments
### Results  
#### 4.3.1 Quantitative Results  
In this section, we assessed our model's learning capacity using the Kvasir-SEG and ClinicDB datasets. To evaluate the model's generalization ability, we conducted evaluations on three additional datasets: ColonDB, CVC-300, and ETIS. We conducted a comparison between our model and the current mainstream fully supervised polyp segmentation networks.  
Table 1 presents the comparative findings of our method with fully supervised networks (including UNet [15], UNet++ [16], SFA [22], PraNet [51], C2 FNet [53], Polyp-PVT [52], and HSNet [43]). From the comparison in Table 1, WSPolyp-SAM exhibits superior performance over existing mainstream baseline networks across the Kvasir-SEG, ColonDB, CVC-300, and ETIS datasets. Specifically, our approach demonstrates a 0.7% enhancement in mDice score and a 0.2% boost in mIoU score compared to HSNet on the Kvasir-SEG dataset. In ColonDB, it achieves a notable 9.4% improvement in mDice score and a substantial 9.6% increase in mIoU score relative to HSNet. Furthermore, on the CVC-300 dataset, it delivers a 3% increase in mDice score and a 3.9% elevation in mIoU score compared to HSNet. On ETIS, it achieves an 8% improvement in mDice score and a 7.9% improvement in mIoU score compared to HSNet. Compared to the original SAM, our method demonstrates significant improvements after fine-tuning on all five datasets, demonstrating that SAM is more adaptable to specific domains after fine-tuning.
## 6 Conclusions  
In response to the security shortcomings of the DCSK system, we have introduced an innovative communication method called reference signal scrambling transformation short reference differential chaos shift keying (RSST-SR-DCSK). This approach integrates short reference differential chaos shift keying technology with reference signal scrambling transformation to enhance transmission speed, strengthen system security, and improve the error rate performance of the communication system.  
In RSST-SR-DCSK, we utilize the characteristics of chaotic sequences to eliminate the correlation between the reference signal and the information signal through the reference signal scrambling transformation. This transformation makes it more difficult for the reference signal to be intercepted or deciphered during transmission, thereby enhancing the overall security of the communication system. Additionally, RSST-SR-DCSK has achieved significant improvements in transmission speed and error rate performance. By carefully designing the scrambling transformation of the reference signal, we have successfully optimized the system's error rate, improving the reliability and stability of the communication system.  
Currently, we have studied the software simulation of RSST-SR-DCSK scheme, and in the future, we hope to implement the work of chaotic communication signal generation and the modulation scheme using USRP hardware platform. The RSST-SR-DCSK system combines short reference differential chaos shift keying technology with the scrambling transformation of the reference signal to comprehensively enhance the security, transmission speed, and error rate performance of the DCSK system.  
**Author Contributions:** Conceptualization, Y.F.; data curation, Q.Y.; investigation, F.L.; methodology, T.S.; writing--original draft, B.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported in part by the National Natural Science Foundation of China (Grant No. 61971291), the science and technology funds from Liaoning Education Department (serial number: JYTMS20230199), and the science and technology funds from Liaoning Education Department (serial number: LJKZ042).  
**Data Availability Statement:** Data is contained within the article.  
Figure 14: Comparison of optimal spreading factors for different systems.
## 3 Database Collection and Processing
### Preparation of the Data for AI Modeling
#### 3.2.3 Statistical Parameters  
The performance of a network can be evaluated through a variety of parameters, such as coefficient of determination (\(R^{2}\)) and mean absolute error (MAE). Equations (7)-(12) show the definitions of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean squared logarithmic error (MSLE), root mean squared logarithmic error (RMSLE), and coefficient of determination (\(R^{2}\)).  
\[\text{MAE}=\frac{\sum_{N}\bigl{(}X_{m}-X_{p}\bigr{)}}{N} \tag{7}\]  
\[\text{MSE}=\frac{\sum_{N}\bigl{(}X_{m}-X_{p}\bigr{)}^{2}}{N} \tag{8}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{N}\bigl{(}X_{m}-X_{p}\bigr{)}^{2}}{N}} \tag{9}\]  
\[\text{MSLE}=\frac{\sum_{N}\bigl{(}\log\left(X_{m}+1\right)-\log\bigl{(}X_{p}+1 \bigr{)}\bigr{)}^{2}}{N} \tag{10}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variable** & **Observations** & **Minimum** & **Maximum** & **Mean** & **Std. Deviation** \\ \hline CBR & 21 & 0.900 & 16.700 & 6.883 & 4.379 \\ LL (soil) & 21 & 26.120 & 55.000 & 45.252 & 13.046 \\ PI (soil) & 21 & 8.830 & 34.000 & 25.220 & 11.731 \\ \% Sludge & 21 & 0.000 & 100.000 & 13.095 & 22.784 \\ Compaction & 21 & 10.000 & 65.000 & 30.000 & 18.841 \\ (number of blows) & 21 & 18.000 & 22.500 & 20.929 & 2.075 \\ OMC (soil) & 21 & 1.560 & 1.725 & 1.600 & 0.054 \\ OMC (mixture) & 21 & 18.000 & 41.500 & 21.357 & 5.094 \\ MDD (mixture) & 21 & 1.060 & 1.746 & 1.573 & 0.161 \\ G\({}_{\text{s}}\) (soil) & 21 & 2.170 & 2.750 & 2.605 & 0.250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistical information of the training database.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variable** & **Observations** & **Minimum** & **Maximum** & **Mean** & **Std. Deviation** \\ \hline CBR & 6 & 3.000 & 9.700 & 6.763 & 2.654 \\ LL (soil) & 6 & 27.280 & 55.000 & 50.380 & 11.317 \\ PI (soil) & 6 & 8.830 & 34.000 & 29.805 & 10.276 \\ \% Sludge & 6 & 0.000 & 20.000 & 6.667 & 7.554 \\ Compaction & 6 & 10.000 & 65.000 & 37.500 & 22.528 \\ (number of blows) & 6 & 18.000 & 22.500 & 21.750 & 1.837 \\ OMC (soil) & 6 & 1.560 & 1.586 & 1.582 & 0.011 \\ OMC (mixture) & 6 & 18.500 & 22.500 & 21.167 & 1.889 \\ MDD (mixture) & 6 & 1.450 & 1.682 & 1.590 & 0.083 \\ G\({}_{\text{s}}\) (soil) & 6 & 2.170 & 2.750 & 2.653 & 0.237 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The statistical information of the testing database.  
\[\text{RMSLE}=\sqrt{\frac{\sum_{N}\left(\log\left(X_{m}+1\right)-\log\left(X_{p}+1 \right)\right)^{2}}{N}} \tag{11}\]  
\[\text{R}^{2}=\left[\frac{\sum_{i=1}^{N}\left(X_{m}-\bar{X_{m}}\right)\left(X_{p} -\bar{X_{p}}\right)}{\sum_{i=1}^{N}\left(X_{m}-\bar{X_{m}}\right)^{2}\sum_{i=1} ^{n}\left(X_{p}-\bar{X_{p}}\right)^{2}}\right]^{2} \tag{12}\]  
where \(N\) is the number of datasets, \(X_{m}\) and \(X_{p}\) are the actual and predicted values, respectively, and \(\bar{X_{\text{m}}}\) and \(\bar{X_{\text{p}}}\) are the averages of the actual and predicted values, respectively. Ideally, the model should have an \(R^{2}\) value of 1 and MAE, MSE, RMSE, MSE, and RMSLE values of 0.
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 3 SCAR-Phase of Spectrum Sensing
### First Sample Processing Period
#### 3.1.2 Energy Detection  
At the same time the synchronization is performed, a first decision about the presence or absence of the PU's signal can be made by means of an energy detector, since it does not require any prior information about the PU's signal neither require synchronization. This allows the proposed approach to work according to the SNR level. That is, in the high SNR the operation of the energy detector might satisfy the minimum requirements of the PUs and SUs with a reasonable number of samples. Whereas in the low SNR, where the energy detector might exhibit a low detection performance and therefore require a very large number of samples, an enhanced ST-based detection can be implemented to improve the overall performance using a reduced number of samples once the synchronization has been realized.  
The energy detector test statistic decides \(\mathcal{H}_{1}\) if  
\[\Phi_{ed}(\mathbf{x})=\sum_{n=0}^{N-1}|x[n]|^{2}>\lambda_{ed}, \tag{9}\]  
where \(\lambda_{ed}\) is the threshold used to compared the test statistic. The probability of a false alarm of the energy detector is defined as \(P_{\text{fa}}^{ed}\triangleq\Pr\{\Phi_{ed}(\mathbf{x})>\lambda_{ed}|\mathcal{H }_{0}\}\), where \(\Pr\{\cdot\}\) denotes the probability operator. Hence, under the received signal model in (2) and for large \(N\) it is given by [20]  
\[P_{\text{fa}}^{ed}=\mathcal{Q}\left(\frac{\lambda_{ed}-N\sigma^{2}}{\sqrt{N \sigma^{4}}}\right), \tag{10}\]  
where \(\mathcal{Q}(\cdot)\) is the Q-function or complementary cumulative distribution function of a standard Gaussian distribution [21]. Similarly, the probability of detection is defined as \(P_{\text{d}}^{ed}\triangleq\Pr\{\Phi_{ed}(\mathbf{x})>\lambda_{ed}|\mathcal{H }_{1}\}\), which is given by  
\[P_{\text{d}}^{ed}=\mathcal{Q}\left(\frac{\lambda_{ed}-N\theta^{2}(\sigma_{d}^{ 2}+\sigma_{t}^{2})-N\sigma^{2}}{\sqrt{2N\theta^{2}\sigma_{t}^{2}(\theta^{2} \sigma_{d}^{2}+\sigma^{2})+N(\theta^{2}\sigma_{d}^{2}+\sigma^{2})^{2}}}\right). \tag{11}\]  
Then, for a specific value of false alarm \(P_{\text{fa}}^{ed}=\beta\), the threshold can be obtained from (10) as:  
\[\lambda_{ed}=\sqrt{N\sigma^{4}}\mathcal{Q}^{-1}\left(\beta\right)+N\sigma^{2}, \tag{12}\]  
which after its substitution in (11), gives the attained probability of detection for the \(\beta\) value.
## 1 Introduction
The bottom-up approach focuses on automatically extracting knowledge from various types of data, emphasizing the discovery of entities and relationships, while the top-down approach concentrates on modeling and standardizing knowledge, that is, defining the ontology and data patterns ahead of time, and then adding entities and relationships to the database. In response to the dynamic sensing characteristics of LEO satellites and the structural characteristics of spectrum sensing data, it is necessary to be able to extract concepts, conceptual hierarchies, and inter-concept relationships from heterogeneous spectrum sensing data. At the same time, as the spectrum knowledge graph needs to satisfy sufficient accuracy, the schema-layer ontology needs to be constructed to constrain the framework and rules of the knowledge graph. Accordingly, a knowledge graph construction idea applicable to the field of spectrum sensing of LEO satellite is designed, which blends the bottom-up approach and top-down approach. The idea includes knowledge extraction, ontology construction, knowledge fusion, and knowledge visualization processes. Moreover, the specific implementation of each process is described in detail. Based on the proposed research methodology, we finally complete the construction of the spectrum knowledge graph and realize the visualization display. According to the characteristics of the constructed spectrum knowledge graph, its application trends and prospects are illustrated. It is shown that the introduction of the designed spectrum knowledge graph can carry out efficient spectrum knowledge utilization and frequency-usage posture generation.
## 2 Basic Concept
### Fundamental Principles of the Utilized Cryptographic Framework
#### 2.1.3 Theoretical Perspectives on Diffie-Hellman and Elliptic Curve Diffie-Hellman (ECDH)  
Elliptic-curve Diffie-Hellman (ECDH), is a mechanism in the public key system that allows communication parties to agree on a key to encrypt confidential information. Communication parties need a pair of elliptic curve public-private keys to generate keys, which gives the ability to generate keys directly or to generate a set of keys for each connection.  
As part of ECC theory, another theoretical model for key agreement has been proposed within its range of suggested sub-theories, highlighting the characteristic of anonymity. This subcategory is commonly referred to as the Elliptic Curve Diffie-Hellman Key Exchange (ECDH). The process of generating public and private keys based on the elliptic curve by the communication parties can be considered as one of the essential aspects of this mechanism, which gives assurance to the communication parties that the joint session key generated for their communication remains completely confidential. Following the description, envision an elliptic curve where two specific points, _p._ ai and _p._ bi, are situated. The ECDH asserts that even with access to the specified points, it becomes impossible for the adversary in the network to calculate or attain the points bi (ai. p) and ai (bi. p).
# Wheeled Permanent Magnet Climbing Robot for Weld Defect Detection on Hydraulic Steel Gates
## 3 Computer Vision Guided Autonomous Mobility
### Weld Image Process  
After image acquisition, further processing is required to extract the corresponding feature points and feature lines. The raw image in grey-scale pixels was processed by a series of computer vision algorithms using the OpenCV library [25]. The image processing procedure is mainly divided into two stages. First, the weld region is segmented to distinguish the weld area from the non-weld background. Second, the centerline of the weld is extracted from the segmented weld region.  
Threshold segmentation is a region-based image segmentation technique that is widely applied in image processing. Given the significant visual differences between the weld and non-weld regions, threshold segmentation can be directly utilized for this task. By selecting an appropriate threshold value, the weld region can be effectively separated from the background, as illustrated in Figure 7. This segmentation provides a foundation for subsequent image processing steps.  
Based on the previously segmented image regions, the features of the weld area have been obtained. The next step involves extracting the edges of the weld and overlaying them onto the original image to facilitate equipment debugging, as shown in Figure 8. Utilizing the extracted weld edges, the centerline of the weld is subsequently calculated and used as the tracking trajectory for the robot.  
The procedure from initial image acquisition to final deviation calculation is summarized as follows:  
1. Process the mask image obtained from weld area segmentation to generate a single-channel binary image;
2. Extract the weld skeleton by refining the binary weld mask, reducing the weld edge region to a skeleton with a width of several pixels;
3. Detect straight lines within the weld skeleton image using the Hough transform;
4. Compare the lengths of the detected straight lines and select the best one as the weld edge line;
5. Calculate the deviation of the robot relative to the weld path, where the deviation includes both lateral displacement and angular deviation.  
Figure 8: Weld edge extraction.  
Figure 7: Threshold segmentation.
## 3 Requirements  
There are several requirements for our system. First, the system should not miss important events. This means that images should be taken at a relatively high rate, high enough to detect events, such as trapped animals or bird presence/absence, with high accuracy. Second, the system must be scalable enough to monitor a sufficient number of traps and nests to derive statistically robust results. This means that the system should be capable of acquiring and managing images from a large number of sensor nodes spread over a large area. Third is the ease of use by non-computer scientists (e.g., biologists), both in set-up and the operation and maintenance of the deployment, with minimal dependence on computer programmers. The system should allow the user to easily start, stop, modify and reconfigure the application. Furthermore, we need an end-to-end system to allow the operator to monitor incoming images and data in real time. This will allow the personnel who are maintaining the system to detect problems as they happen and to alter various settings to maximize the data return.  
In addition to the above requirements, the incremental cost of adding more nodes to the system should not be too high. It should be possible to place sensor nodes at places where direct radio communication to the server is not possible. Furthermore, each camera must be low power to reduce maintenance costs or, with the help of solar panels, eliminate those costs during the monitoring period. All of these requirements have implications for multihop communication, packet reliability, congestion control, more stability, power consumption, and image processing algorithms.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 3 Overall Architecture
### Overall Architecture of the LLIE Method  
For a low-light image I with width W and height H, the entire process can be described as follows:  
\[I_{\text{out}}=E(I_{low},\theta) \tag{1}\]  
where \(I_{low}\) represents the low-light image, \(I_{\text{out}}\) denotes the restored image, and F is a framework with trainable parameters. The goal of deep learning is to find the optimal \(\theta\) that minimizes the discrepancy between the restored image and the ground truth (gt) image captured under normal lighting conditions:  
\[\theta^{\prime}=argminL(I_{\text{gt}},I_{\text{out}}) \tag{2}\]  
where \(I_{\text{gt}}\) is the normally illuminated image, and \(I_{\text{out}}\) is the enhanced image. The loss function L governs the optimization of the network.  
For the semantic-guided extreme low-light image enhancement network, the process can be divided into two steps. First,  
\[M=NN_{segment}(I_{low},\theta_{s}) \tag{3}\]  
where M represents the semantic prior, including segmentation results and multi-scale intermediate features. \(F_{segment}\) is a pre-trained semantic segmentation network that serves as the Semantic Knowledge Base (SKB), and its parameters \(\theta_{s}\) are frozen during training. Then, M is used as an input to the enhancement stage:  
\[I_{out}=NN_{enhancement}(I_{low},M;\theta_{e}) \tag{4}\]where \(I_{out}\in R^{\rm{W}\times H\times 3}\), and \(F_{enhancement}\) represents the enhancement network. During training, the optimization objective is minimized while keeping \(\theta_{\rm{s}}\) fixed, allowing updates to be guided by M:  
\[\theta_{e}=argminL(I_{\rm{out}},I_{\rm{gt}},M) \tag{5}\]
# Performance Assessment of Predictive Control--A Survey
## 2 Model Predictive Control  
Model Predictive Control [15] significantly contributes to the frequent usage of the APC in process industry. When regulatory control utilizing PID algorithm is not sufficient, there is an opportunity for a predictive control strategy. MPC history starts with [PERSON] research [16] on the Linear Quadratic Regulator (LQR) in the early 1960s. Consecutive research brought an introduction to the Model Predictive Heuristic Control [17] (now known as Model Algorithmic Control, MAC) and Dynamic Matrix Control (DMC) [18] in the late 1970s. Generalized Predictive Control (GPC) was introduced in the 1980s [19; 20]. MPC is characterized by a fact that process mathematical model is continually used to perform a prediction of the future future and find the optimal control strategy [21]. The optimization procedure is repeated at each sampling interval.  
Predictive control is renowned for its high accuracy and an ability to embed process limitations into the algorithm. However, the need to have precise model process simultaneously constitutes its main shortcoming. Such algorithms have been mainly utilized in process industry [22; 23; 24; 25], such as paper machines, petrochemical and chemical installations, reactors, turbines, etc. Nowadays, MPC solutions are developed for nonlinear [26; 27] fast embedded systems, thanks to the micro-controllers and applications such as unmanned vehicles [28], cars [29], vehicles' anti-lock brake systems [30], active vibration suppression [31], combustion engines [32], and unmanned aerial vehicles [33].