esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Evaluation
### Experimental Results  
**(1) Single-value query**. Figure 4 illustrates the behavior of the SReB_GCA and DP_BASE methods as they handle single-value queries across various privacy parameter settings. Notably, our proposed SReB_GCA method demonstrates a more consistent mean relative error performance compared to the DP_BASE method across different bin sizes. Specifically, it significantly enhances the accuracy for smaller bins without imposing a substantial detriment to the error rates of larger bins. The experiments conducted on the four datasets, depicted in Figure 4a-d, collectively confirm the efficacy of the SReB_GCA method in achieving a balanced reduction in relative errors, particularly for bins with lower counts.  
**(2) Range query**. Figure 5 illustrates the performance of the SReB_GCA and DP_BASE methods in response to range queries for varying privacy parameters. It indicates that the SReB_GCA method achieves higher accuracy across different range queries compared to the DP_BASE method. Furthermore, the experimental results from the four datasets presented in Figure 5a-d reveal an improving trend in range query accuracy as the privacy parameter \(\epsilon\) increases.  
**(3) Data distribution**. Based on the KLD metric, Figure 6 illustrates that the distribution generated by the SReB_GCA method in this paper is closer to the true data distribution compared to the DP_BASE method. Moreover, Table 2 demonstrates that our SReB_GCA method achieves a smaller KLD value than the AHP method reported in study [22], indicative of a distribution that is closer to the genuine data. Study [22] is designed based on the absolute value error, and its optimization objective is different from the one we are considering, but it is comparable in terms of KLD for evaluating the performance of data distribution. Considering that study [22] reports smaller KLD values across a range of privacy parameters \(\epsilon\) compared to other studies [13; 15; 19], the SReB_GCA method introduced herein excels in KLD performance over these preceding works, thereby producing sanitized data of superior quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & \(\epsilon=1\) & \(\epsilon=0.1\) & \(\epsilon=0.01\) \\ \hline Waitakere & AHP & 0.018 & 0.203 & 0.467 \\  & SReB\_GCA & 0.0004 & 0.056 & 0.171 \\ Search Log & AHP & 0.054 & 0.103 & 0.189 \\  & SReB\_GCA & 0.0001 & 0.009 & 0.099 \\ NetTrace & AHP & 0.153 & 0.572 & 1.229 \\  & SReB\_GCA & 0.004 & 0.092 & 0.252 \\ Social Network & AHP & 0.071 & 0.309 & 0.825 \\  & SReB\_GCA & 0.0001 & 0.003 & 0.099 \\ \hline \hline \end{tabular}
\end{table}
Table 2: KLD with SReB_GCA and AHP.  
Figure 6: KLD on four datasets.
###### Abstract
However, these methods [3; 4; 5; 6; 7] require complex feature extraction and selection process and may be only able to achieve good results for specific tasks or data sets. The automatic detection algorithm for myocardial infarction has also taken advantage of the progress of deep learning. [PERSON] et al. [8] introduced the convolutional neural network to analyze ECG heartbeats for myocardial infarction. The accuracies of their method for heartbeats without noise and with noise were 95.22% and 93.53%, respectively. However, they only used single-lead heartbeats to verify their method, which was not present in a rigorous and practical manner. In clinical myocardial infarction diagnosis, the standard 12-lead ECG record is necessary [3] and the beat-wise annotations are expensive. Accordingly, we need to measure the difference between unlabeled heartbeats based  
Figure 1: Electrocardiogram (ECG) signal of myocardial infarction. on standard 12-lead ECG records. [PERSON] et al. [9] first applied Multiple Instance Learning (MIL) to the classification of myocardial infarction. They proposed a new MIL strategy, called latent topic MIL, to map polynomial-fitted heartbeats to subject spaces. It measured the difference between each unlabeled heartbeat based on 12-lead ECG records. Their best performance was obtained using the SVM classifier, with sensitivity and specificity reaching 92.6% and 82.4%, respectively. In summary, existing studies have the following three issues. First, deep learning networks tailored for the detection of myocardial infarction have not been well studied. In addition, only one or some specific leads ECG signal are unreliable for the detection of myocardial infarction [3] and the beat-wise annotations are of great cost. Finally, few studies have weighted the difference between unlabeled heartbeats based on standard 12-lead ECG records. In this paper, we propose a bidirectional, long short-term memory (Bi-LSTM) network with heartbeat-attention mechanism. First, Bi-LSTM networks can automatically extract and select features similar to convolutional neural networks. Furthermore, we propose a heartbeat-attention mechanism to automatically weigh the difference between heartbeats based on 12-lead ECG records. Finally, our method can automatically detect myocardial infarction without any labels of heartbeats. The structure of this paper is as follows: The second part introduces the methods and related theories proposed in this paper, including the system architecture, preprocessing and network framework of myocardial infarction detection algorithm. The third part presents the experimental condition and results of our proposed method. Finally, we conduct related discussion and present the conclusion in the fourth part and fifth part of this paper.
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 4 Experiments  
### Data Preparation  
In accordance with [21], the test set is a set of real-world satellite broadband power spectrum, consisting of 9581 subcarriers whose carrier-to-noise ratio (CNR) are all greater than 4 dB and BW regressions are greater than 10 kHz. The satellite power spectrum bandwidth is 36 MHz, and the raw coordinate length is 32,768.  
The training set produced by the MATLAB simulation signal power spectra is estimated by the Welch method with a simulation of complex time-domain signals. This produces 100,000 simulation samples for training the network. Additionally, in all the training processes, the ratio of the number of training sets between the validation sets is 4:1.
# Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising
## 7 Conclusions  
In this paper, we proposed a self-adaptive alternating direction method of multipliers for image denoising. By employing collaborative sparse regression, a common sparse representation can be discovered, maintaining structured sparsity within the image and facilitating a more profound exploration of spatial interrelations among pixels. To mitigate the impact of penalty parameters on convergence rates, an adaptive approach based on a balance principle for selecting appropriate penalty parameters is introduced. The experimental results demonstrate that the proposed S-ADMM algorithm yields significantly superior image quality. Other potential enhancements, such as further increasing denoising effectiveness, will be subject to further investigation. Future work could involve integrating deep learning techniques to enhance the performance of the algorithm on complex image data. Additionally, further analysis on multimodal data processing could be conducted to extend the algorithm's applications to a wider range of scenarios.  
Conceptualization, H.G.; Data curation, M.X.; Methodology, M.X.; Validation, M.X.; Visualization, M.X.; Writing--original draft, M.X.; Writing--review and editing, H.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MDPI & Multidisciplinary Digital Publishing Institute \\ DOAJ & Directory of Open-Access Journals \\ TLA & Three-letter acronym \\ LD & Linear dichroism \\ \end{tabular}
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 5 Experimental Design and Analysis
### Experimental Evaluation
#### 5.2.2 The Impact of Dynamic Environments  
In a natural environment, the indoor area will inevitably introduce dynamic changes in the environment, such as changes in the positions of tables and chairs and the addition of objects. These changes can cause the trained model to fail to recognize gestures because WiFi signals are particularly sensitive to environmental changes. We designed two additional sets of experiments to examine the robustness of our Wi-GC method. We designed two environments based on the original layout of the meeting room. Program 1: we changed the structure of the conference room environment, as shown in Figure 17a, and added some static obstacles, including coffee tables, plants and chairs, to the test area. Program 2: when laboratory personnel are doing experiments at the designated location, we arranged for another experimental user to walk back and forth on the other side of the conference table, as shown in Figure 17b. The experimental results of the two sets of environments are shown in Figure 18.  
Figure 17: Environmental design: (**a**) Static environment. (**b**) Dynamic environment.  
As seen from the results in Figure 18a,b, the accuracy of both schemes decreases as the environment changes. In program 1:, the accuracy of Wi-GC only drops by about 2%, indicating that static environments have little impact on the Wi-GC method. The second solution is considerably less accurate, as environmental changes significantly impact the Wi-Fi signal, but the accuracy of each gesture is above 60%. The results show that the Wi-GC method is robust and can adapt to static environmental disturbances.
## 4 Results
### Potential Business Transformations
#### 4.3.3 B3--Highway 4.0 Operator  
In addition to B1 and B2, the highway operator is expected to actively control the traffic e.g., by organizing trucks and cars platooning on the highway, organizing distribution slots for traveling and parking, and operating hubs where goods can be transferred from larger carriers to smaller ones, etc. In general, this model allows highway operators to take control of end-to-end highway activities, resulting in acquiring all the skills needed for such operations in house instead of outsourcing. It is perceived that in the future, data collected from several data points would also be considered as an asset and therefore, it will add much value to an organization's operations.  
Figure 4 below reflects the opinion of respondents on business model selection and paradigm shift of highway operators as future dynamics of highway operations change.  
The respondents seem to be inclined to adapt to the B2 business model as autonomous vehicles reach to the level of 30%, which is highly likely to happen by 2030 as discussed in the preceding subheading. However, approximately 31% of respondents voted to adhere to the orthodox business model (B1) and approximately 20% stated they do not know which model they should choose. Approximately 53% would still like to stay as the infrastructure provider with highly developed information skills as the percentages of autonomous cars on highways fluctuates between 30% and 70%. Approximately 22% of respondents would like to adapt to the B3 business model at this stage, and approximately 20% were not sure which model to choose. Approximately 53% are likely to choose the B3 business model and approximately 22% would stay with the B2 business model. A slight percentage of approximately 4% would still like to stay at B1, whereas, approximately 22% are not sure to what they would choose.  
It can be seen that as the number of autonomous vehicles increases on the highways, it is highly likely that highways and toll operators would switch from existing traditional business models to the newer ones in order stay competitive. It will not only allow them to stay in competition, but also to earn extra revenues by facilitating travelers in several different ways and charge for the additional services provided.  
Figure 4: Potential business transformations.
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 7 Performance Evaluation
### Experimental Results  
We have implemented SACAR-IR, SACAR-OCVN, SACAR-HC, and SACAR-Hybrid in The One Simulator [34]. The benchmark algorithms except GSaR are already included in the simulator; therefore, we decided to also implement the GSaR algorithm in order to compare the proposed routing solutions with another approach following a similar philosophy. Each algorithm was run on a laptop with 2.3 GHz 2-core Intel i5, 16 GB of RAM, and Intel Iris Plus Graphics 640.
## 2 Materials and Methods
### Sample Preparation and Analysis of Metabolism
#### 2.4.2 Liquid Chromatography-Mass Spectrometry Analysis  
The UPLC-MS/MS analysis was performed using an Agilent SB-C18 column (1.8 \(\upmu\)m, 2.1 \(\times\) 100 mm\({}^{2}\)) with ultrapure water (0.1% formic acid added) as phase A and acetonitrile (0.1% formic acid added) as phase B. The elution gradient was as follows: 5% phase B at 0.00 min, linear increase to 95% phase B at 9.00 min, maintained at 95% for 1 min (10.00-11.10 min), decreased to 5% phase B, and equilibrated with 5% for 14 min with a flow velocity of 0.35 mL/min, column oven at 40 \({}^{\circ}\)C, and infusion volume of 4 \(\upmu\)L. The mass spectrometry was performed using linear ion trap (LIT) and triple quadrupole (QXQ) scans obtained on a Q TRAP triple quadrupole linear ion trap mass spectrometer. The AB4500 Q TRAP UPLC/MS/MS system was equipped with an electrospray ionization (ESI) turbo-ion atomized interface and controlled by Analyst 1.6.3 software (AB Sciex) to run in both positive and negative modes. The ESI source operating parameters were as follows: ion source, turbo spray; source temperature 550 \({}^{\circ}\)C; ion spray voltage (IS) 5500 V (positive-ion mode)/\(-\)4500 V (negative-ion mode); and ion source gas I (GSI), ion source gas II (GSI), and curtain gas set to 50, 60, and 25.0 psi, respectively. The collision-induced ionization parameters were set high. Instrument tuning and mass calibration were performed in QQQ and LIT modes using 10 and 100 \(\upmu\)mol/L polypropylene glycol solutions. The QQQ scans were performed under the multiple reaction monitoring (MRM) mode with the inert gas (nitrogen) postulate medium. Further declustering potential (DP) and collision energy (CE)optimization were accomplished for individual MRM ion pairs, and a particular set of MRM ion pairs was checked in every stage based on the eluted metabolites in each stage.
## 4 Next Forwarding Node Selection in UWSNs
### Comparative Analysis and Discussion
In other words, all neighbor nodes above the level of the sender are eligible to forward the data packets. Each protocol in this subcategory has different ways of selecting the next forwarding nodes. For instance, DBR selects the next forwarding nodes based on depth only. DBMR calculates a weight based on depth and residual energy and the node with the highest weight can be selected to forward the data packets. EEDBR uses depth and residual energy for selecting the best node by calculating the holding time based on residual energy. HydroCast employs depth and link quality for selecting the next forwarding nodes. EEF selects the next forwarding nodes by calculating a fitness value based on depth, distance, and residual energy, then choosing the highest fitness as the best forwarder. VAPR utilizes the hop count to the sink for selecting the best candidate. AMCTD calculates a weight for all neighbors based on depth and residual energy to select the best candidate. Finally, IVAR selects the best candidate based on the fitness value. Choosing the best next forwarding nodes has a significant impact on overall network performance and prevents the lower nodes' performance to forward the packets. However, existing opportunistic energy-efficient and reliable pressure-based routing protocols in UWSNs suffer from a poor forwarding nodes selection algorithm based on multi-metrics. Therefore, it is essential to design a forwarding nodes selection algorithm based on multi-metrics, which selects energy-efficient and reliable forwarding nodes for non-localization pressure-based routing to reduce the energy consumption, reduce network traffic, and ensure data delivery.
# A Performance Evaluation for Software Defined Networks with P4
## 2 Related Work  
### Applications and Network Performance Enhancements Using P4  
Respective research by [PERSON] et al. [16] and [PERSON] et al. [17] have highlighted technological enhancements for networks, such as OpenFlow, P4, the Data Plane Development Kit and Click-based solutions. It has been established that the protocol independent nature of the high-level language P4 [6], together with independence from the underlying hardware and header limitation, benefits the network, increasing performance through the faster processing of packets.  
The utilisation of the language P4 has been presented for many applications [7; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. [PERSON] et al. in [7] applied the programmability of P4 to create an application capable of handling data centre traffic. [PERSON] et al. [18], presented a congestion control mechanism together with P4, and the research by [PERSON] et al. [19] employed mechanisms for packet processing capabilities through P4 utilised with a Robust Header Compression (ROHC) scheme for improving performance. Further examples include a tool for developing and evaluating data plane applications [20], Service Function Chaining on P4 devices in [21], and an extension to the application of Open vSwitch, to act as a hypervisor switch presented by [PERSON] et al. [22]. A recent study by [PERSON] et al. [24] promoted a classification approach employing data plane programming to improve QoS by embedding Service Level Objectives into packets. Another recent study by [PERSON] et al. [25] presented a design, implementation, and evaluation of a high-performance, high-precision NetFlow/IPFIX flow-monitoring system on a P4 hardware switch. Designed using the same P4 architecture as employed in our research they focused on traffic monitoring, highlighting the broader applicability of P4 in enhancing data-plane operations and high performance. Similar to the work by [PERSON] et al. [25], [PERSON] et al. [26] employed the same P4 architecture to show the flexibility of packet processing, which allows efficient and comprehensive flow tracking to facilitate improved security and performance. [PERSON] et al. [27] introduced a load balancer implemented using P4. The study confirmed that the employment of P4 resulted in a higher throughput, reduced latency, and a higher Quality of Service. [PERSON] et al. [28] suggested a hybrid approach of control plane and data plane programmability, which was aimed at enhancing resilience and fault tolerance whilst reducing processing overhead.
## 3 Experimental Results and Comparison
PSNR higher than 40 dB means that the quality of the marked decrypted image is very close to the original image. PSNR value is calculated by  
\[\text{PSNR}=10\times\log_{10}\frac{\left(2^{n}-1\right)^{2}}{MSE} \tag{8}\]  
where \(MSE\) is the mean square error between all pixel values of the original image and the marked decrypted image. [PERSON] et al. 's method [15] is not included in the comparison because, according to its original publication, the method directly embeds secret data into MSB planes. After image decryption, the method cannot generate a marked decrypted image. As shown in the figure, the proposed method can achieve a relatively high visual quality at high embedding rates. The average PSNR of the proposed method was 51 dB/44 dB when the embedding rate was 1 bpp/2 bpp. For different embedding rates, the PSNRs of the proposed method are higher than those of the methods in [21; 34; 41], and they are almost the same as the PSNRs of BBE [15] and [PERSON] et al. 's method [17]. This is because the proposed method embeds the secret bits in the LSB planes by bit substitution, which is the same as BBE [15] and [PERSON] et al. 's method [17]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Images** & **Airplane** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline [PERSON] [34] & 0.034 & 0.005 & 0.017 & 0.017 & 0.029 & 0.030 & 0.014 & 0.019 \\ [PERSON] and [PERSON] [41] & 0.070 & 0.066 & 0.070 & 0.070 & 0.070 & 0.070 & 0.070 & 0.070 \\ [PERSON] et al. [21] & 0.698 & 0.223 & 0.404 & 0.732 & 0.761 & 0.770 & 0.558 & 0.741 \\ BBE [15] & 2.204 & 0.568 & 1.319 & 1.287 & 1.735 & 1.819 & 1.607 & 1.820 \\ [PERSON] et al. [17] & 2.340 & 0.535 & 1.409 & 1.398 & 1.947 & 1.944 & 1.678 & 1.879 \\ [PERSON] et al. [18] & 3.092 & 1.098 & 1.902 & 2.385 & 3.004 & 2.614 & 2.175 & 2.299 \\ Proposed & 3.711 & 1.745 & 2.408 & 3.021 & 3.545 & 3.413 & 2.733 & 3.088 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of embedding rates for the eight test images. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Images** & **Airplane** & **[PERSON]** & **[PERSON]** & **Couple** & **Crowd** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline Linear regression & 0.063 & 0.065 & 0.067 & 0.066 & 0.065 & 0.065 & 0.066 & 0.066 \\ Vacating room & 3.168 & 4.266 & 4.392 & 3.787 & 3.140 & 3.390 & 4.420 & 3.473 \\ Image encryption & 0.008 & 0.008 & 0.008 & 0.008 & 0.008 & 0.008 & 0.008 & 0.008 \\ Data hiding(1 bpp) & 0.117 & 0.117 & 0.117 & 0.117 & 0.117 & 0.117 & 0.117 & 0.117 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of embedding rates for the eight test images (second). Figure 8: Comparison of the marked decrypted image quality (_Airplane_, _Baboon_, _[PERSON]_, _Couple_). Figure 9: _Cont._