esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Although wireless sensors can be found everywhere, ISAC technology has become a key technology for 6G development to reduce system consumption and spectrum resource shortage. In the early stage of ISAC technology development, the MWC system can be used as the initial application of ISAC technology. The MWC system can support the miniaturization and intelligence of wireless device systems. However, the traditional MWC system still has space for improvement in performance. Therefore, we optimize the MWC system based on spatial information entropy. We propose a dynamic flexible processing method based on spatial information entropy and a Genetic-OMP algorithm based on eigenvectors to optimize the MWC system. Our proposed scheme takes into account the need for subsequent reconstruction and the noise characteristics of the sampled signal in preprocessing. In the optimization of the reconstruction algorithm, the signal characteristics after pre-processing are combined to solve the characteristics of the OMP algorithm, which has low accuracy and is prone to fall into local optimal solutions. The experimental results show that our optimization is very helpful in improving communication and sensing accuracy. However, the precondition of this scheme is that the sampled signal has sparsity. Real-life signals with sparsity are limited, and in the next step, we will further study the limitations imposed by the sparsity of the signals.  
Figure 11: When \(SNR=-5\) and the number of sampling channels is 50, the spectrum of the reconstructed signal is as shown in this figure.  
Figure 12: BERs of recovered signals at different SNRs.  
**Author Contributions:** Conceptualization, X.W. and M.J.; Methodology, X.W.; Software, X.L.; Formal analysis, X.W.; Resources, M.J.; Writing--original draft, X.L.; Writing--review & editing, X.W. and X.L.; Visualization, X.L.; Supervision, X.W. and M.J.; Funding acquisition, X.W. and M.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Science Foundations of China No.62231012 and Hei-longjiang Postdoctoral Foundation, grant number LBH-Z19168.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Other Graphene ECG Electrodes  
In addition to the typical graphene ECG electrodes mentioned above, there are some other graphene electrodes with unique preparation methods or characteristics that will be introduced in this section.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 4 Experiments and Discussion
### Performance of Sensitivity to Tampering  
The authentication process of a multi-spectral image should be able to detect the local tampering of the bands, which means that the tampered and original images should have significantly different hashes. To test the performance of sensitivity with respect to tampering, we take several kinds of tampering operations, including removing, appending and changing the object, as shown in Figures 9-11. When the band of the image is tampered with, the regenerated perceptual hash values of the grid and the whole image would be changed, and the malicious tampering can be detected. Take the tampering of In\(R_{1}\) of the original image A as an example, as shown in Figure 9,b, the perceptual hash values of the image and grid cell would be changed tremendously, as shown in Table 5.  
For the above tampering example, the comparison of the hash values of each grid can be used to locate the tampering with respect to the corresponding geographic region, and the location granularity will depend on the resolution of the grid divisions. Figure 12 shows the tamper location of Test 1 as an example.
## 4 Results
### Received Optical Power  
In this subsection, the received optical power performance corresponding to the proposed V2I-VLC propagation model is discussed. Figure 8 gives the received optical power in different links under moderate fog conditions based on (16), (17), and (18). It can be seen that the received optical power is highly correlated with the average path loss, and they are directly proportional to each other. Thus, the trends of the received optical power curves shown in Figure 8 are consistent with those in Figure 7. Meanwhile, in the total received power at Rx, the received optical power from the LOS link is much larger than the received optical power from the two NLOS links. In addition, when \(V_{1}\) is at a close distance from Rx, under the same conditions, the NLOS power caused by the reflection from \(RS\) is larger than that caused by the reflection from \(M\). This is mainly due to the fact that the NLOS propagation distance of the rays reflected by the road surface (\(D_{NLOS,j}^{V_{1}-RS}\) and \(D_{NLOS,j}^{RS-PD}\)) is smaller than the NLOS propagation distance of the rays reflected  
Figure 6: Average path loss of (**a**) LOS link; (**b**) NLOS-M link; (**c**) NLOS-RS link under different weather conditions [(\(-\)) sign in the path loss value indicates that the path loss is in the form of a penalty].  
Figure 7: Average path loss corresponding to the proposed V2I-VLC propagation model under fog weather conditions.  
by the mountain (\(D_{NLOS,j}^{V_{1}-M}\) and \(D_{NLOS,j}^{M-PD}\)) during the light propagation process. Additionally, when \(V_{1}\) and Rx become gradually farther away from each other, the received optical power of the two NLOS links is also basically the same because the propagation distances of the rays are not very different.  
The total received optical power of the V2I-VLC system, based on (19) as well as the simulation curves under different weather conditions, are presented in Figure 9. It can be seen that the total received optical power is the largest in clear weather conditions and the smallest in dense fog conditions. Moreover, the further the distance between \(V_{1}\) and Rx, the larger the difference in total received optical power. For example, when \(I_{V_{1}-PD}=10\) m, the total received optical power in dense fog conditions is only about 12.5% lower than it is in clear weather conditions. However, when \(I_{V_{1}-PD}=100\) m, the ratio reaches 76.2%. This is due to the fact that, the further away \(V_{1}\) is from Rx and the worse the weather conditions are, the more the light from the headlights is affected, making the performance of the V2I-VLC worse as well.
## 1 Introduction
### Zeus Malware Architecture  
An important feature of the Zeus malware is the way that it communicates, as it uses command and control channels (C&C) for this purpose. The author of [14] has discussed the various phases of the C&C communication, which can be seen in Figure 1. This communication can occur using either a centralised or a peer-to-peer architecture, with the peer-to-peer architecture being more robust and resilient [15]. This is because if the central C&C server becomes unreachable or is taken down, the Zeus bots will not be able to communicate with the C&C server, preventing the bots from receiving commands, updating themselves and downloading new configuration files [16]. Newer variants of Zeus use the P2P C&C architecture. These are more resilient to takedown efforts because the configuration file does not point to a static C&C server [17]. Instead, the C&C server information is obtained from a peer (proxy bot), which can be updated if the C&C server is taken down or becomes unreachable [18]. Stolen data is routed through the C&C network to the malware authors' C&C server, where the stolen data is decrypted and saved to a database [19].  
Figure 1: C&C Communication phases.  
As discussed by [20], Zeus propagates like a virus, mainly infecting Windows systems and predominantly, the infection vector occurs via phishing emails, which is a significant distribution mechanism for malware. Research by [21] has discussed this in detail, and states that around 90 percent of data breaches are caused by phishing. Once the Zeus binary executes on a Windows system, it performs several actions. One of these is to create two files called local.ds and user.ds. Local.ds is the dynamic configuration of the file downloaded from the command and control (C&C) server, while the user.ds stores stolen credentials and other information that needs to be transmitted back to the C&C server [22]. Additional code is injected into svchost and is responsible for network communications. Svchost is also responsible for injecting malicious code into many Windows processes, which provide Zeus with the ability to steal credentials and launch financial attacks.
## 3 The Data Filtering Based Recursive Least Squares Algorithm  
Define a unit backward shift operator \(q^{-1}\) as \(q^{-1}u(t):=u(t-1)\) and a rational function \(C(q):=1+c_{1}q^{-1}+c_{2}q^{-2}+\cdots+c_{n_{c}}q^{-n_{c}}\). In this section, we use the linear filter \(C(q)\) to filter the input-output data and derive an F-RGLS algorithm. For the multi-input OEAR system in Equations (1)-(4), we define the filtered input-output data \(u_{i\mathrm{f}}(t)\) and \(y_{\mathrm{f}}(t)\) as  
\[u_{i\mathrm{f}}(t) := C(q)u_{j}(t)=u_{j}(t)+c_{1}u_{j}(t-1)+c_{2}u_{j}(t-2)+\cdots+c_ {n_{c}}u_{j}(t-n_{c}),\] (8) \[y_{\mathrm{f}}(t) := C(q)y(t)=y(t)+c_{1}y(t-1)+c_{2}y(t-2)+\cdots+c_{n_{c}  
\[= \boldsymbol{\phi}_{\mathrm{f}}^{\mathrm{\tau}}(t)\boldsymbol{\phi}+v(t). \tag{13}\]  
Taking advantage of the idea in [28] for the filtered identification model in Equation (13), we can get  
\[\boldsymbol{\hat{\vartheta}}(t)=\left[\sum_{i=1}^{t}\boldsymbol{\phi}_{ \mathrm{f}}(i)\boldsymbol{\phi}_{\mathrm{f}}^{\mathrm{\tau}}(i)\right]^{-1} \sum_{i=1}^{t}\boldsymbol{\phi}(i)y_{\mathrm{f}}(i). \tag{14}\]  
Since the information vector \(\boldsymbol{\phi}_{\mathrm{f}}(t)\) contains the unknown variables \(u_{\mathrm{f}}(t)\) and \(x_{\mathrm{f}}(t)\), the algorithm in Equation (14) cannot be applied to estimate \(\boldsymbol{\hat{\vartheta}}(t)\) directly. According to the idea in [29], the unknown variables \(x_{\mathrm{f}\mathrm{f}}(t)\) are replaced with the outputs of relevant auxiliary model, and the unmeasurable terms \(u_{\mathrm{f}}(t)\) and \(y_{\mathrm{f}}(t)\) are replaced with their estimates \(\hat{u}_{\mathrm{f}\mathrm{f}}(t)\) and \(\hat{y}_{\mathrm{f}}(t)\), respectively. The derivation process is as follows. Let \  
Applying the least squares principle to the noise model in Equation (6), we can obtain the following algorithm to estimate the parameter vector \(\mathbf{c}\):  
\[\hat{\mathbf{c}}(t) = \hat{\mathbf{c}}(t-1)+\mathbf{L}_{\mathrm{n}}(t)[w(t)-\mathbf{\psi}^{\mathrm{ T}}(t)\mathbf{c}(t-1)], \tag{19}\] \[\mathbf{L}_{\mathrm{n}}(t) = \mathbf{P}_{\mathrm{n}}(t-1)\mathbf{\psi}(t)[1+\mathbf{\psi}^{\mathrm{ T}}(t)\mathbf{P}_{\mathrm{n}}(t-1)\mathbf{\psi}(t)]^{-1},\] (20) \[\mathbf{P}_{\mathrm{n}}(t) = [\mathbf{I}-\mathbf{L}_{\mathrm{n}}(t)\mathbf{\psi}^{\mathrm{ T}}(t)]\mathbf{P}_{\mathrm{n}}(t-1). \tag{21}\]  
The noise information vector \(\mathbf{\psi}(t)\) involves the unknown term \(w(t-i)\).
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle
### Planning Phase
#### 4.2.2 Simulation Tool  
A simulator tool was developed to aid during the planning phase. The software tool has been fully implemented in MATLAB [58] and basically can emulate the whole system, positioning the SR antennas, generating a radio map from simulated RSS fingerprints and training the ANN. Then, the performance can be approximately predicted, emulating the online stage and analyzing the performance. In Figure 7 is illustrated the interface of the simulator tool, where the main interface shows the digital map of a test scenario, and the RSS intensity is overlapped (in a range of red to white colors) for a selected SR (green triangles), visualizing the RSS distribution along the scenario.  
To this end, a full model of the scenario has been developed, including walls, rooms/areas and hardware characteristics, such as the tag transmission power (\(P_{TX}\)) or the antennas' radiation pattern.  
The RSS distribution is obtained by means of an IEEE 802.11 channel model found in [59]. This model is based on the double-slope path-loss model.  
\[L\left(d\right)=L_{0}+\sum_{w=1}^{W}L_{w}+10\alpha\log\left(d\right)+X \tag{1}\]  
Equation (1) includes four terms: \(L_{0}\) (dB), which represents the signal losses at a reference distance of 1 m, \(L_{w}\) (dB), which is the contribution due to walls (and depends on the wall material [59]), the path-loss term, which depends on \(\alpha\) and \(d\) (the Euclidean distance between the tag and the SR, respectively) and \(X\), a random variable related to the shadow fading effect, modelled by a Gaussian-type random variable with zero mean (\(\mu_{x}=0\)) and variance \(\sigma_{x}\).  
\[X=\frac{1}{\sqrt{2\pi}}e^{\frac{-x^{2}}{2\sigma_{x}^{2}}} \tag{2}\]  
Furthermore, notice that \(\alpha\) and \(\sigma_{x}\) will be defined as a function of the type of room (office, large office, _etc._). The path-loss coefficient depends on distance, changing from a break point distance (\(d_{BP}\)), as explained in [59].  
Regarding the system performance evaluation for the planning phase, the figures of merit to assess the system accuracy are introduced in the next subsection.  
Figure 7: Interface of the ILS simulator.
###### Abstract  
Single-longitudinal mode lasers are widely used as light sources in coherent lidars due to their narrow linewidth and long coherence length. However, we observed spontaneous frequency drift in a single-longitudinal mode laser, accompanied by the oscillation of a second mode, which could compromise the laser's performance. In this study, we recorded the frequency drift and the resulting power fluctuations. By analyzing the centroid positions of the laser spot, we confirmed that the drift originated from changes in the optical path length. To address this issue, we designed an electro-optic Fabry-Perot etalon. When a 400 V voltage was applied, the laser returned to single-longitudinal mode operation within 15 s. The electro-optic etalon induced an optical path variation of 0.05 \(\upmu\)m. This work provides new insights into the application of electro-optic crystals for stabilizing laser frequencies.  
single-longitudinal mode laser; optical lightpath length change; spontaneous drift; electro-optic Fabry-Perot etalon; LiTaO\({}_{3}\) crystal Article
###### Abstract  
Detecting abnormal ECG patterns is a crucial area of study aimed at enhancing diagnostic accuracy and enabling early identification of Chronic Kidney Disease (CKD)-related abnormalities. This study compares a unique strategy for abnormal ECG patterns using the LADTree model to standard machine learning (ML) models. The study design includes data collection from the MIT-BIH Arrhythmia dataset, preprocessing to address missing values, and feature selection using the CfsSubsetEval method using Best First Search, Harmony Search, and Particle Swarm Optimization Search approaches. The performance assessment consists of two scenarios: percentage splitting and K-fold cross-validation, with several evaluation measures such as Kappa statistic (KS), Best First Search, recall, precision-recall curve (PRC) area, receiver operating characteristic (ROC) area, and accuracy. In scenario 1, LADTree outperforms other ML models in terms of mean absolute error (MAE), KS, recall, ROC area, and PRC. Notably, the Naive Bayes (NB) model has the lowest MAE, but the Support Vector Machine (SVM) performs badly. In scenario 2, NB has the lowest MAE but the highest KS, recall, ROC area, and PRC area, closely followed by LADTree. Overall, the findings indicate that the LADTree model, when optimized for ECG signal data, delivers promising results in detecting abnormal ECG patterns potentially related with CKD. This study advances predictive modeling tools for identifying abnormal ECG patterns, which could enhance early detection and management of CKD, potentially leading to improved patient outcomes and healthcare practices.  
Chronic Kidney Disease (CKD); machine learning (ML); ECG signal data; LADTree model; feature selection
# Key Roles of Plasmonics in Wireless THz Nanocommunications--A Survey
## 5 Plasmonic THz Wireless Nanoscale Link Components
### THz Band Nanotransceivers  
In general, as known, a transceiver is a set of components which is responsible for generating and modulating the outgoing information to the antenna, via appropriate transmitters at the front end of the nanolink, and also responsible for detecting the incoming from the antenna information via appropriate receivers at the back end of the nanolink. A block diagram of a typical plasmonic transceiver architecture is seen in Figure 16[102]. Apart from the basic transceiver components, namely, the signal generator, the transmitter which is normally a modulator source, and the detector-receiver, there may be other components acting complementary, in order to propagate the information signal across the link with the minimum loss, such as interconnects, switches, filters, high tunability phase shifters, mixers, frequency multipliers, and impedance matched elements, placed between the antenna and the waveguide of the link. As can be seen from Figure 16, at the front end, there is an electric signal generator that generates the electric signal that will later on, feed the transmitter source. Then the outgoing signal, which in our case of plasmonic THz sources is normally a modulated SPP wave, is feeding the plasmonic nanoantenna, which converts the SPP wave into an EM wave. At the receiver end, there is another plasmonic nanoantenna with a similar role, that inversely converts the EM wave again into an SPP wave. Finally, a plasmonic nanoreciver converts the SPP wave into an electric signal, which will be demodulated to the original information data by the signal detector[102].  
Figure 15: Hybrid metal insulator metal (HMIM) multilayer hybrid plasmonic nano patch antenna.
## 3 Results
### Machine Learning (ML) Analysis  
The 41 genes that met a lenient criterion of adjusted _p_-value < 0.05 and absolute log\({}_{2}\) fold change \(\geq\) 0.85 from the meta-analysis were input into the ML models for training. A total of 426 samples, containing 238 normal and 186 HD, were randomly divided into training sets (N = 339) and testing sets (N = 87) (Figure S2). We then assessed the classification performance of these ML models on the testing sets (Figure 4a). We chose AUC as the primary evaluation metric for selecting optimal models because it can provide a threshold-independent measure of classification quality, particularly in class imbalance, by considering true and false positives and negatives [35], making it especially suitable for biomarker identification when the optimal threshold is unknown and explains its widespread use in biomarker research [14; 36; 37; 38]. The ET model performed very well in categorizing HD and control, obtaining the highest AUC score of 0.867 (Figure 4c) in training and 0.853 (Figure 4d) in testing. Moreover, the MCC of 0.607 in the train set and 0.615 in the test set demonstrates its outstanding performance with minimal difference between the two sets. Besides ET, two other models, CB and XGB, also performed well in classifying the disease from the control. The CB model achieved an AUC of 0.862 (Figure 4c) on the training set and an MCC of 0.587, while on the testing set, it displayed strong discrimination ability with an AUC of 0.854 (Figure 4d). The XGB model attained an AUC of 0.858 (Figure 4d) and an MCC of 0.583 on the test data, while achieving an AUC of 0.870 (Figure 4c) and an MCC of 0.576 during training. We selected these three models to identify significant biomarkers through SHAP analysis, as they demonstrated consistent performance across training and testing, with high AUCs and minimal difference, indicating strong generalization.  
Figure 3: Gene Ontology (GO) enrichment analysis of 41 differentially expressed genes (DEGs). (**a**) Gene enrichment analysis of the 41 genes identified the top enriched gene ontology terms, biological process (BP), cellular components (CC), and molecular function (MF). (**b**) Enrichment map (also called Emap) displaying the top enriched GO terms of Biological processes, where each node represents the GO terms with node color scale denoting the different fold change thresholds and node size demonstrating the number of genes associated with each term. Edges visualize the degree of gene overlap in each term, indicating functional similarity.
## 2 Experimental Investigation
### Signal Analysis
#### 2.4.3 Amplitude Demodulation Analysis  
The demodulation procedure starts filtering the time domain signal in the frequency range studied to remove the low-frequency and high-frequency content outside the frequency range. Then the envelope of the filtered signal is computed using an algorithm based on the Hilbert transform (\(Hi\)) [9]. Mathematically, the Hilbert transform of a time signal \(x(t)\) is defined as in Equation (5):  
\[Hi(x(t))\ =\ \frac{1}{\pi}\int_{-\infty}^{\infty}x(\tau)\frac{1}{t-\tau}d\tau \tag{5}\]  
The envelope is obtained by forming the complex analytical signal \(\widetilde{x}(t)\), which is the sum of the time signal \(x(t)\) (real part) and the Hilbert transform of \(x(t)\) (Equation (5)) (imaginary part):  
\[\widetilde{x}(t)\ =\ x(t)+jHi(x(t)) \tag{6}\]  
The analysis of the resulting envelope in Equation (6) in the frequency domain permits identifying the modulating frequencies inside the high-frequency band selected for the study. In this case,after applying this analysis to the signals acquired by every sensor, it is desired to obtain the rotating frequency (\(f_{i}\)) or the modulating frequencies \(f_{1}=22.1\) Hz and \(f_{2}=1.4\) Hz depending on the case. The amplitude of the frequencies obtained after applying this analysis can be compared for all the sensors to determine the best sensor to detect erosive cavitation.
# Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences
## 4 Numerical Example Analysis
### Robustness Research
From the figure, it can be easily seen that the total required resources for the service subset \(\{3,4,7,8,9,15,25,28\}\) are 12. Among them, Service 28 is the departure service, ending at 599. Service 4 is for inbound service, and the required arrival time is 608. The transfer time between the two services will be at least 16.6 min, so these two services cannot be executed by the same shuttle bus. When considering the transfer time, the probability of conflicts is approximately higher when the number of resources is less than 12. From the above results, when the quantity is 11, the confidence level is approximately 89.94%. When the quantity is 9, the confidence level is only 3.17%. From the results, it can be seen that for the experimental cases, the number of resource allocations is relatively reasonable between 11 and 12. This article proposes a GA solution method for the dynamic problem of ferry scheduling. This method can quickly solve various subproblems. The scheduling plan obtained from the operation is also relatively reasonable. To analyze the rationality of resource allocation in this case, the overall pattern of the sampled samples was analyzed through multiple experiments. After analysis and induction, the confidence level at which different resource configurations can meet the scheduling plan of the ferry is obtained. Figure 11: Scheduling plan for ferries.