esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Image Preprocessing
#### 3.1.2 Data Compression and Data Filling  
In this part, ShareA is first divided into 8 \(\times\) 8 nonoverlapping blocks, and then the Huffman coding algorithm is used to compress each block. Finally, each block's compressed data are stored in \(C\), where the compressed data's length is \(n\), and then it is repeatedly filled in a Share image of \(512\times 512\) as the size of the original medical image, which needs to be filled repeatedly (\(512\times 512\))/\(n\) times in order to facilitate image recovery. The same data compression operations are performed for ShareB.  
We give a concrete example: if the original medical image size is \(512\times 512\), then it has 262,144 pixels. If the size of the extracted ROS is \(352\times 352\), it has 123,904 pixels, and the extracted ROS pixels are reduced by almost half; then, ShareA is compressed in blocks, and the number of pixels after compression is about 30,621. In order to obtain an image of size \(512\times 512\), the compressed data must be filled at least \([262,144/30,621]=8\) times. The processes of data compression and data filling are shown in Figure 3.  
Figure 2: The process of image segmentation and bit plane separation reorganization.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 3 Model Framework  
Figure 1 illustrates the workflow of cloud-edge collaboration. The edge-side model first performs object detection on the target image using a pre-trained edge model and uploads the detection results to the cloud. The cloud then fuses multi-view detection results from the edge. In response to inconsistencies between cloud-side and edge-side detection results, the edge employs a reinforcement-learning-based decision mechanism to determine whether to conduct localized classification head transfer training for specific categories. This enables low-overhead, highly adaptive model self-updating, ultimately forming a closed-loop cloud-edge collaborative system that integrates edge detection, cloud fusion, and policy optimization.  
To address the inconsistency between edge-side detection results and cloud-side fusion outcomes, a cloud-edge collaborative model adaptation method is proposed, which  
Figure 1: The workflow of cloud-edge collaboration.  
integrates DQN-based decision making with transfer feature extraction. The detailed model architecture is illustrated in Figure 2.
## 1 Introduction
### Model Construction  
The single-phase grounding detection model based on PSO-SVM is mainly divided into two parts, the PSO algorithm and the SVM. By improving the inertia weight value in the particle swarm algorithm, the value of the learning factors is accordingly enhanced. Furthermore, by introducing a flight-time factor to optimize the particle position update formula, the optimization speed is accelerated, thereby obtaining an improved PSO algorithm. The SVM parameters \(C\) and \(g\) are fine-tuned using the improved PSO algorithm to find the most suitable parameters. After preprocessing the data set of grounding faults in low-voltage systems, it is divided into a training set and a test set in a certain proportion. The features of the data set are used as inputs, and the fault detection model is employed for prediction to obtain the output results. The specific steps of Figure 4 are as follows:(1) Initialize the required parameters in the particle swarm optimization algorithm, including particle swarm size, maximum iteration count, learning factor, etc.  
(2) Update the velocity and position of particle according to Equation (5).  
(3) By using the pre-set fitness function, the fitness values of the particles appearing in the population are calculated.  
(4) Compare the calculated particle fitness value with the best value found in history. If it is better than the historical value, replace it, otherwise it remains unchanged.  
(5) Update inertia weights. Update the current inertia weight value based on the formula proposed in the previous text for improving weights.  
(6) When the particles iterate to the predetermined maximum number of iterations, output the best solution found by the particles during the iteration process and output it. If the set number of iterations is not reached, return to step two and iterate again.  
(7) Apply the optimized parameters (\(C\) and \(g\)) to the fault detection model.  
(8) Apply the data set to the improved new model for training and testing.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
###### Abstract  
Underwater Sensor Networks (UWSN) have attracted huge attention due to their significance in oceanic observation and exploration. They offer a vast number of applications, many of which require routing the sensed data to a centralized location. This makes routing an important part of the design of such applications. In this paper, we present a comprehensive survey of recently proposed routing protocols for UWSNs. We evaluate the proposed schemes through an extensive set of parameters that define the core characteristics of a routing protocol. Moreover, we present a summary of the methods used by each scheme to familiarize readers with the basic operations of the schemes. We also present our view of the strengths and weakness of each scheme. For ease of description, the addressed routing protocols are divided into two categories: localization-based, and localization-free routing schemes. Each of the two categories is further divided into the protocols that consider node mobility, and those that do not. Lastly, we present our view on open research topics.  
underwater sensor networks; routing protocols; survey; void avoidance; mobility +
Footnote †: journal: Sensors  
1
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Personalized Treatment Plans  
Personalized treatment plans, also known as precision medicine, are defined by the National Institutes of Health as 'an emerging strategy for the prevention and treatment of disease that takes into account individual differences in genes, environment, and lifestyle for each person' [50]. The objective is to utilize individual biology rather than population biology at all stages of a patient's medical journey to enhance treatment effectiveness, reduce side effects, and prevent over-treatment [27]. AI applications can assist in personalized treatment plans by analyzing a patient's genomic data to identify genetic variations linked to specific diseases or treatment responses. For example, in oncology, AI can analyze the genomic profile of a tumor to identify mutations that may respond to specific targeted therapies. This allows oncologists to tailor treatment plans to the genetic makeup of the tumor, thereby increasing the likelihood of treatment success and minimizing side effects [51]. They can also analyze patient health data to uncover related patterns and associations, thereby developing personalized treatment plans based on patient preferences and values [52]. For instance, in diabetes management, AI can integrate data from continuous glucose monitors, dietary logs, and physical activity trackers to create personalized insulin dosing recommendations. This helps patients manage their blood sugar levels more effectively, reducing the risk of complications and improving their quality of life [53].  
Given that personalized treatment plans involve sensitive, user-specific information, ensuring data security and user privacy is paramount [54]. Techniques such as federated learning and differential privacy can be employed to protect patient data while still enabling the development of accurate and effective personalized treatment plans. Federated learning allows AI models to be trained across multiple decentralized devices or servers without sharing raw data, while differential privacy ensures that any data used in AI training cannot be traced back to an individual patient [55].
## 3 Methods and Model
### A Mixed Evaluation Model  
Figure 3 depicts the action selection procedure with several action-value functions. Each Q-Network is in charge of fitting the action-value functions of the corresponding objects. First off, each Q-Network receives an identical state \(s\) from the environment, and outputs to the Mixed Evaluation Model the corresponding action-value function vector \(\{Q(s,a_{1}),Q(s,a_{2}),\cdots,Q(s,a_{z})\}\), where \(z\) represents the number of actions and the Mixed Evaluation Model decides the final action to be executed.The two nonlinear ranking methods that are most widely used are TOPSIS and VIKOR. While VIKOR looks for the solution with the greatest group utility and the lowest individual regret value, TOPSIS seeks the compromise solution that is most closest to the ideal option. Given that both approaches have advantages, our model combines them.  
Figure 2: Architecture of MIER-MO-DQN.  
3.3.1. The Linear Weighted Sum Function  
The most popular multi-criteria decision-making approach is the linear weighted sum function [37; 38], and its formula \(f\) is displayed in (6), where \(m\) is the number of objectives, and \(w_{i}\) is the weight of objective \(o_{i}\). The sum of the weights is 1.  
\[f(\tilde{Q}(s,a))=\sum_{i=1}^{m}w_{i}\cdot Q(s,a,o_{i}),\sum_{i=1}^{m}w_{i}=1. \tag{6}\]  
Its simplicity of usage and the capacity to acquire convergence guarantees supported by mathematical theory are its main advantages, but it also has certain drawbacks, and in some complicated conditions, this decision-making process may not produce the best results [39; 40]. Because nonlinear functions are more capable than linear weighted sum functions to provide a higher quality solution, they are used to address multi-objective problems. However, nonlinear functions also have the obvious drawback of being susceptible to non-convergence. In this research, a novel multi-objective deep reinforcement learning method based on the integration notion is proposed to address the aforementioned problems. This method integrates linear weighted sum functions and other nonlinear functions.
# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
## 4 Experiments and Results
### Comparison Time Complexity  
In addition, we further compared the time complexity of the different methods, as shown in Table 5. It can be found that a more complex model corresponds to a higher time complexity. Although the proposed method has the lowest time efficiency, its reconstruction accuracy is relatively the best.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MSSR** & **FMNSSR** & **StiNet** & **HRNet** & **AWAN** & **Proposed** \\ \hline mPSNR & 27.9931 & 27.7670 & 27.8081 & 28.0959 & 28.8122 & **29.3163**1  \\ SAM & 4.3814 & 3.8177 & 3.9325 & 4.1560 & 3.70281 & **3.0861**1  \\ ERGAS & 15.0157 & 15.1137 & 15.2949 & 15.0880 & 13.1582 & **12.8030**1  \\ RMSE & 0.0189 & 0.0187 & 0.0192 & 0.0184 & 0.0167 & **0.0151**1  \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quality assessment comparison on GF5 data.  
Figure 10: Spectral curve comparison at selected points in the GF5 data, showing the results for the various methods.
# A Compact Ultra-Thin 4 \(\times\) 4 Multiple-Input Multiple-Output Antenna
## 4 Measured Results and Discussion
### Discussion  
An eight-port antenna structure is discussed in this paragraph. As presented in Figure 17, two shorter lateral frame boards were further constructed along two sharp edges of the system board described in Figure 1. Two ground clearances with a size of 71.4 \(\times\) 1.8 mm\({}^{2}\) were cut to obtain a uniform resonating condition as the other two existing APs. The simulated S-parameters of the eight-port AP structure are illustrated in Figure 18a. The \(-\)10 dB bandwidth of two existing APs still can cover the target band, and there was almost no deterioration in isolation performance compared with before. However, a resonating frequency shift of about 100 MHz occurred to AP.3 and AP.4, and the \(-\)10 dB operating band of AP.3 and AP.4 was 3.23-3.82 GHz, which can also wholly contain the desired band. A remarkable coherence between \(S_{12}\) and \(S_{56}\) was achieved, and the isolation performance between any two ports is still superior to the 15 dB level. The simulated ECCs of the discussed eight-port AP system is shown in Figure 18b. When Ant. 1 was excited at 3.5 GHz, the worst ECC within the essential band appeared in ECC\({}_{12}\) (0.035).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design** & **Operating Band** & **Total Size** & **Dimension of** & **Port Distance** & **Isolation (dB)** & **ECC** \\  & **(GHz)** & **(mm\({}^{3}\))** & **an AP (mm\({}^{3}\))** & **in an AP (mm)** & **Isolation (dB)** & **ECC** \\ \hline
[12] & 3.4–3.6 (\(-\)10 dB) & 150 \(\times\) 73 \(\times\) 7 & 12 \(\times\) 7 \(\times\) 0.8 & 17 & 0.07 \\
[15] & 3.4–3.6 (\(-\)10 dB) & 150
## 5 Discussion
Generally, simple ground scenes have higher accuracy, while complex ground scenes have some effects for different machine learning algorithms. As shown in Table 4, among all the machine learning algorithms, the accuracy of decision tree decreased the least in the three regions on average, and the AUC index decreased 30.43% on average, followed by XGBoost. In the threshold method, although the change of adaptive threshold is small, its accuracy is always very low, while the Otsu algorithm not only has a good accuracy, but also the average decline of the AUC index is small, which is 13.46%. The decision tree algorithm can still achieve better classification results, and the Otsu algorithm also performs well. Experiments show that it is not recommended to directly use the machine learning model to extract water from remote sensing images in different periods. The Otsu classification result can be used as a reference, so that training samples can be selected in other periods quickly and conveniently to extract water bodies using machine learning algorithms. In summary, for water extraction from remote sensing images, although various algorithms can achieve satisfactory results under certain conditions, none of them can be applied to all remote sensing image and scenes. The factors affecting the classification accuracy of remote sensing images mainly include the complexity of the field landscape, the availability of data, the effectiveness of the processing method, and the experience judgment of the processing personnel [5; 76]. Therefore, on the basis of this study, when extracting water from remote sensing images, the water index (MNDWI preferred) can be used first and combined with the Otsu algorithm to classify water bodies. This result is in agreement with the results obtained by [PERSON] et al. [38], who used the NDWI image to select water samples from the input image. However, if the accuracy does not meet the requirements of the application, on the basis of its classification, researchers can further select the number of samples that meet the requirements of various machine learning algorithms (Figure 2) and select the corresponding machine learning training model. Among the various machine learning algorithms, XGBoost, decision tree, and logistic regression algorithms are preferentially recommended.
## 3 Efficiency and Secure Key Rates
### Finite Key Analysis for SDC  
We let a total of \(2M(n,k)\) pairs of qubits travel to and fro between [PERSON] and [PERSON] with the latter committing to encoding with probability \(c\). Thus one gets \(n\) amount of quaternary digits from the EM. Following earlier notation, the \(n\) quaternary strings of [PERSON] and [PERSON] are \(S_{A}^{\text{EM}}\) and \(S_{B}^{\text{EM}}\) respectively while the \(k\) quaternary strings derived from the CM are \(S_{A}^{\text{CM}}\) and \(S_{B}^{\text{CM}}\) respectively. Considering a _gedankenexperiment_ where all runs are CM lets us bound [PERSON]'s information on the \(n\) quaternary string using Equation (11) as  
\[H_{min}^{\epsilon}(S_{B}^{\text{EM}}|E)+H_{max}^{\epsilon}(S_{B}^{\text{CM}}| S_{A}^{\text{CM}})\geq 2n. \tag{16}\]  
The term \(H_{min}^{\epsilon}(S_{B}^{\text{EM}}|E)\) is the smooth min-entropy of \(S_{B}^{\text{EM}}\) conditioned on \(E\). It reflects [PERSON]'s correlation with [PERSON]'s string in the EM giving the number of bits contained in \(S_{B}^{\text{EM}}\) that are \(\epsilon\)-close to a uniform distribution and independent of \(E\). In ascertaining the term \(H_{max}^{\epsilon}(S_{B}^{\text{CM}}|S_{A}^{\text{CM}})\) reflecting the correlation between [PERSON]'s and [PERSON]'s strings, the case for two way channels with depolarization \(q\) each allows us to consider bit error rate in qubit measurements as \(q/2\) for each channel instead of a channel transmitting quaternary digits instead. Thus in this case,  
\[h_{4}(\overline{q})=2h_{2}(q/2), \tag{17}\]  
for \(\overline{q}=((1-q/2)q/2,(q/2)(1-q/2),q^{2}/4)\). The number of bits required to reconstruct \(S_{B}^{\text{CM}}\) from \(S_{A}^{\text{CM}}\) (as two \(n\) bit length strings) up to a probability of failure \(\epsilon\),  
\[H_{max}^{\epsilon}(S_{B}^{\text{CM}}|S_{A}^{\text{CM}})\leq 2 nh_{2}\left( \frac{q}{2}+\mu(n,k)\right), \tag{18}\]  
where \(q/2\) is the error rate for each independent depolarizing channel in the CM. If we write the smooth max-entropy of [PERSON]'s string conditioned on [PERSON]'s in EM as \(H_{max}^{\epsilon}(S_{B}^{\text{EM}}|S_{A}^{\text{EM}})\leq nh_{4}(\mathcal{ Q}_{E})\) with \(\mathcal{Q}_{E}\) as errors in the EM, then Equations (14), (16) and (18) gives the key length after error correction and privacy amplification as  
\[L_{SDC} \leq n\left[2-2h_{2}\left(\frac{q}{2}+\mu(n,k)\right)-h_{4}(\mathcal{ Q}_{E})\right]-\log\frac{2}{\epsilon_{S}^{2}}, \tag{19}\]  
where from [9], we have the term \(nh_{4}(\mathcal{Q}_{E})\) as the amount of information leaked to [PERSON] due to error correction [30] of the string \(S_{A}^{\text{EM}}\) and \(\log{(\epsilon_{S}^{2})}\) results from the quantum Leftover Hashing Lemma [27].
## 2 Proposed Bearing Fault Diagnosis Method
### Signal Preprocessing
#### 2.1.2 Implementing STFT Analysis for Spectrograms  
With the advantages of frequency-domain analysis for nonstationary signals, STFT can be a good approach for analyzing the bearing signals under complex conditions or with background noise. To efficiently apply STFT to bearing fault signals, the STFT matrix can be determined by a new routine with the MATLAB tool to achieve high accuracy and computational efficiency [12]. After analyzing which segment duration of the signal has essential features (such as _mean_, _RMS_, _standard deviation_, _variance_, _kurtosis_, _skewness_, _crest factor_, and _form factor_ that do not change with time), the window length (_wlen_) is set as 1024. The accuracy is prioritized to _wlen_, although the shorter windows affect the calculation volume in a negative manner. The hop size is set to _wlen_/4 based on various experiments [12]. Figure 2 shows the spectrograms produced by applying STFT to vibrations signals with four different shaft speeds (1730, 1750, 1772, and 1797 RPM) with three kinds of faults (inner race fault, outer race fault, and roller fault).  
Figure 2: _Cont._
## 3 Results
### Effect of Hfq-CTR on RNA I-RNA II Annealing  
#### 3.2.1 Hfq-CTR Accelerates RNA I-RNA II Annealing  
The effect of Hfq-CTR on RNA annealing was analyzed. Indeed, the best-known Hfq function is to promote RNA annealing [47], and Hfq was strongly suspected to play a role in RNA I-RNA II complex formation during CoE1-type plasmid DNA replication [17]. However, due to the high molecular weight of the complex with the amyloid form of CTR that stays on the top of the gel, it is difficult to analyse this process solely with EMSA and even more difficult to make precise kinetics measurements. As explained previously, an increase of the amplitudes of the SRCD peaks at 262 and 183 nm would signify the formation of base pairs and of right-handed double-stranded RNA, respectively. Thus, SRCD analysis of Hfq-CTR allows measurement at 262 nm of an apparent catalytic kinetic constant \(\mathrm{k_{cat}}^{\mathrm{APP}}\) of 0.015 mdeg.M\({}^{-1}\).min\({}^{-1}\) and, at 183 nm, an apparent catalytic kinetic constant \(\mathrm{k_{cat}}^{\mathrm{APP}}\) of 0.088 mdeg.M\({}^{-1}\).min\({}^{-1}\) (Figure 6). This activity is similar to that reported previously for DsrA small RNA annealing to _rpoS_ mRNA [24].  
Figure 3: Analysis of dsDNA CoE1 origin melting by Hfq-CTR using SRCD. Spectra of the kinetics are from blue (taken 2 min after the addition of the CTR) to red (80 min after the addition of the CTR). Inset: control with full-length Hfq protein. As shown, the structure does not change, indicating that neither Hfq nor Hfq-CTR can melt dsDNA CoE1 origin.
## 5 Discussion
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & **RMSE** & & & & \\ \hline  & **Minimum** & **Maximum** & **Mean** & **STD** & **CPU Time (s)** \\ \hline ISSAEDO & **9.8248485E-04** & **9.8602238E-04** & **9.8408156E-04** & **1.4185568E-06** & \\ ISGK & **9.8248485E-04** & 9.86021880E-04 & 9.8272774E-04 & 8.9578942E-06 & 14.08 \\ MTLBO & 9.8250260E-04 & **9.8248490E-04** & **9.8248550E-04** & **3.3000000E-09** & — \\ MIGTO & 9.8266170E-04 & 9.8602187E-04 & 9.8266170E-04 & 7.7100000E-06 & 5.32 \\ DERL & 9.8249600E-04 & 1.0091744E-03 & 9.8634733E-04 & 4.4372558E-06 & 3.82 \\ SEDE & 9.8262739E-04 & 1.1069935E-03 & 1.0092765E-03 & 3.9080194E-05 & 0.22 \\ EJYA & 9.8249988E-04 & 9.8613006E-04 & 9.9057738E-04 & 1.7720444E-06 & 12.02 \\ ATLDE & 3.2186265E-03 & 1.4438034E-02 & 7.9795069E-03 & 2.9918516E-03 & 1.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Statistical outcomes achieved by ISSAEDO and recent methods for the DDM. of these parameters, and finding the right combination of parameter values may require additional effort. Also, the complexity of combining these algorithms can affect the efficiency of ISSAEDO in terms of computational resources and time. There is a possibility that the hybrid algorithm will have difficulty maintaining diversity in the population. It is important to know that ISSAEDO represents a new hybridization of optimization techniques. Users should be aware of potential limitations and carefully consider its applicability to specific problem areas by performing comprehensive experiments and sensitivity analysis in understanding the strengths and weaknesses of the algorithm in different scenarios. We can address it in the future in different research to develop this research with different modifications.