esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
###### Abstract  
Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) proves to be a better choice for high speed underwater acoustic (UWA) communication as it increases the data rate and solves the bandwidth limitation issue; however, at the same time, it increases the design challenges and complexity of the receivers. Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI) are introduced in the received signal by the extended multipath and Doppler shifts along with different types of noises due to the noisy acoustic channel. Here we propose two iterative receivers: one is ICI unaware iterative MIMO-OFDM receiver, which uses a novel cost function threshold based soft information decision feedback method. The second one is ICI aware progressive iterative MIMO-OFDM receiver, which adapts and increases the progressions according to the level of ICI present in the received signal, while fully utilizing the soft information from the previous iterations, therefore reducing the complexity. Orthogonal Matching pursuit (OMP) channel estimation, low density parity check (LDPC) decoding and minimum mean square error (MMSE) equalization schemes are exploited by both the receivers. The proposed receivers are analyzed and compared with the standard Alamouti MIMO receiver as a reference and also compared with the non-iterative, basic turbo iterative and non-progressive iterative MIMO receivers. Simulations and experimental results prove the efficiency and effectiveness of the proposed receivers.  
MIMO; OFDM; channel estimation; equalization; iterative receiver; underwater acoustic (UWA) communication +
Footnote †: journal: Journal of the Acoustical Society of America
## 5 Simulation Results
The curves corresponding to \(\alpha_{\mathrm{r}}=0\) do not have normalized misalignment values lower than \(-10\) dB, while the least accurate WL-DR-RLS-DCD version having \(\alpha_{\mathrm{r}}=0.33\) generates performance levels with more than 5 dB better values. Similar to the previous cases, we can note the compromise between tracking speeds on one side, respectively performance at steady state and arithmetic complexity, on the other side. In addition, values of the parameter \(N_{it}\) situated above a certain threshold might generate limited progress with respect to the tracking capabilities, which would not justify some of the corresponding additional arithmetic costs. For example, the curve corresponding to \(N_{it}=2\), \(\alpha_{\mathrm{r}}=0.33\) has similar tracking speed when compared to \(N_{it}=3\), \(\alpha_{\mathrm{r}}=0.33\) and more than 3 dB better accuracy at steady state.
## 4 Discussion
### Study Limitations  
First, the present study is subject to the limitations of observational studies. Second, the data presented in this manuscript reflect the practice of two high-volume referral hospitals for peripheral interventions, where highly skilled operators performed the procedures; therefore, these data may not be wholly generalizable. Third, this study does not have core laboratory assessments of the patients' angiograms, CTO lesion characteristics, and a final procedural approach. Furthermore, there is no independent angiographic and clinical event adjudication, which might lead to an overestimation of technical success and, conversely, an underestimation of procedural complications. In addition, **the variation in lesion length represents a potential bias in the analysis of the outcome; moreover,** the selection of guidewires and equipment was left to the operator's discretion, and its impacts on success and complication rates were not assessed. Moreover, although the use of CPMD would be desirable for the treatment of severely calcite lesions, it was never used in our cohort of patients. **Furthermore, the small sample size remains a concern, reducing the power of the statistical analysis and limiting the generalizability of the data.** In addition, a one-year follow-up with DUS was available in only 82% patients; however, this was not the main object of the study. Finally, our results should be considered as hypothesis-generating and validated in larger studies.
## 3 The Proposed Algorithm of PQDs Detection and Classification Based on PSR and CNN
### PQ Disturbance Model  
To illustrate the use of the PSR-CNN method for PQDs classification, ten types of single and mixed voltage disturbance signals have been synthesized in MATLAB according to the IEEE-1159. The labels and numerical models are presented in Table 1. The amplitude of the simulated signals are normalized to 1 p.u. and the fundamental frequency is 50 Hz. These PQDs are all simulated over the defined parameter range as shown in Table 1.  
Next, a 2D image is generated by the PSR method. The delay time \(\tau\) is an important parameter of the PSR method. A good choice of \(\tau\) facilitates the performance analysis. If the value is low, the adjacent successive elements \(x_{i+\tau}\) and \(x_{i+2\tau}\) of the vectors are strongly correlated; if it is high, the adjacent elements are almost independent. The effect of \(\tau\) for the attractor construction is the expansion degree of the diagonal in the phase plane. The trajectory can be expanded at the utmost around the diagonal, which is a function of \(\tau\). To choose an appropriate delay time \(\tau\), a mutual information function of the system \([s,\ q]=[x(t),\ x(t+\tau)]\)[35] is used in this paper:  
\[I(S,\ Q)=\sum_{i}\sum_{j}P_{sq}(s_{i},\ q_{j})\log\big{[}\frac{P_{sq}(s_{i},\ q_{j})}{P_{s}(s_{i})P_{q}(q_{j})} \big{]} \tag{4}\]  
where \(P_{s}(s_{i})\), \(P_{q}(q_{j})\) and \(P_{sq}(s_{i},\ q_{j})\) are the probabilities and the joint probabilities of the events \(s_{i}\), \(q_{j}\)[39], respectively. A delay time \(\tau\) is determined by dropping the \(I(S,\ Q)\) value to a sufficient small value \(\frac{1}{\epsilon}\)[35].  
Specifically, the sample frequency \(f_{s}\) is 6.4 kHz, the number of sample points in one cycle is 200, and there are 10 cycles in every synthetic signal. In addition, the dimension parameter \(m\) of the PSR method is 2 and the delay time \(\tau\) is 20 according to (4). Then, the 2D images of the PQD signals are transformed by the PSR method and illustrated in Figure 2. The size of every image is \(200\times 200\) pixels.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 3 Proposed Scheme
### Authentication Phase
If this equation holds, it means that \(CS\) is legal; otherwise, this authentication process will be terminated. \(S_{j}\) continues to calculate \(A_{j}^{\prime}=D_{8}\oplus h(r_{s}||PSID_{j}^{\prime})\) and updates \(A_{j}\) and \(PSID_{j}\) as \(A_{j}^{\prime}\) and \(PSID_{j}^{\prime}\) in the memory. At the end of this step, \(S_{j}\) sends the message \(M_{4}\) = {\(D_{11}\), \(D_{12}\), \(D_{13}\), \(D_{14}\)} to \(U_{i}\). Step 4: Once the smart card receives \(M_{4}\), the smart card uses \(B_{i}\), \(r_{u}\), and \(ID_{i}\) to extract \(PID_{i}^{\prime}\) and \((r_{s}\oplus r_{cs})\) from \(D_{11}\) and \(D_{13}\), respectively, i.e., \(PID_{i}^{\prime}=B_{i}\oplus D_{11}\oplus h(r_{u}||ID_{i})\) and \((r_{s}\oplus r_{cs})=D_{13}\oplus h(PID_{i}^{\prime}||r_{u}||PID_{i})\). The smart card will check whether or not \(D_{14}?=h(SK_{u}||D_{12}||D_{13}||B_{i})\), where \(SK_{u}=h(r_{u}\oplus r_{s}\oplus r_{cs})\). If this equation holds, it means that \(CS\) is legal; otherwise, this authentication process will be terminated. The smart card uses the new pseudo-identity \(PID_{i}^{\prime}\) to calculate \(C_{1}^{\prime}=D_{12}\oplus h(r_{u}||PID_{i}^{\prime})\oplus h(ID_{i}||u_{i})\) and updates \(C_{1}\) and \(PID_{i}\) as \(C_{1}^{\prime}\) and \(PID_{i}^{\prime}\). Finally, the smart card sends \(h(SK_{u})\) to \(S_{j}\). Step 5: When \(S_{j}\) receives \(h(SK_{u})\), \(S_{j}\) will check \(h(SK_{u})?=h(SK_{s})\). If \(h(SK_{u})=h(SK_{s})\), this means that they already correctly negotiate the session key.
# Improving Adversarial Robustness of CNNs via Maximum Margin
## 4 Experiments
### Experimental Setup  
We use ResNet-18 [42] as the basic model on CIFAR-10 dataset [43]. The basic models are trained using SGD with momentum 0.9, weight decay \(5\times 10^{-4}\), and an initial learning rate of 0.1, which is divided by 10 at the 75 th and 90 th epochs. All clean images are normalized into [0, 1], and simple data augmentations are included in training such as 4-pixel padding with \(32\times 32\) random crop and random horizontal flip. As for adversarial training, the training attack is PGD-10 (10 step PGD) with random start and step size \(2/255\) and \(l_{\infty}\) maximum perturbation \(\epsilon=8/255\).
## 3 Result
### Datasets
Other datasets that are rarely used in the primary studies are CICIDS2017, RPL-NIDDS17, BoT-IoT, intelloT, MedBIoT, CAIDA, CONFICKER Worm, UNINA traffic traces, and DS2 OS. CICIDS2017, used in four primary studies, was generated by the Cyber Range Lab of the center of UNSW Canberra Cyber. These datasets have 78 features and eight attacks. These attacks include SQL injection, brute force secure shell protocol (SSH), heartbleed, brute force file transfer protocol (FTP), web attack, DDoS, DoS, botnet, and infiltration, which are not found in other datasets, such as KDDCUP99 and NSL-KDD. RPL-NIDDS17, used in two primary studies, was generated using the NetSim tool. These datasets have 20 features, 2 additional labeling attributes and 7 attacks. These attacks include blackhole, sinkhole, sybil, clone ID, selective forwarding, hello flooding and local repair attacks. BoT-IoT, used in two primary studies, was generated by the Cyber Range Lab of the center of UNSW Canberra Cyber. This dataset contains around 72 million records of network traffic captured from the IoT environment. The BoT-IoT datasets have 32 features and five attacks. These attacks include DoS, DDoS, keylogging, data exfiltration, and service scan. IntelloT, used in one primary study, was generated by [PERSON] at the intel research laboratory. This dataset contains around 2 million records captured from 54 sensors spread around the laboratory. For the intelloT, 30% of all of records became abnormal and the rest of them (70%) became normal. CAIDA, used in one primary study, was generated by the Center for Applied Internet Data Analysis institute. The CAIDA datasets contain unusual traffic traces from DDoS attacks. CONFICKER Worm, used in one primary study, was generated by Center for Applied Internet Data Analysis institute. This dataset was collected from the UCSD Network Telescope after 3 days of network study. DS2 OS, used in one primary study, was generated by Kaggle. This dataset contains attacks on sensors and applications; therefore, it consists of 357,952 records, 13 features, and 8 attacks. These attacks include DoS, malicious control, probing, scan, wrong setup, spying,  
Figure 2: Distribution of IoT attack datasets. and normal and malicious operation. The UNINA dataset contains traffic of WAN access router at the University of Napoli Federico.
## 2 Protocol Description
### Design of the Technical and Clinical Validation Processes  
The final system will undergo extensive technical and clinical validation to ensure its accuracy, reliability, and clinical utility. This phase is critical for establishing the system's effectiveness as a tool for clinical assessment and research (its flowchart is shown in Figure 2):  
* Technical validation: A fully detailed technical validation will be designed, in which the performance and correct functioning of the systems related to both the characterization of subjects' activity and the estimation of evaluation values will be assessed. A  
Figure 2: Protocol flowchart: definition of quantitative metrics (Section 2.4), definition of inferences (Section 2.5), and definition of the technical and clinical validation processes (Section 2.6).  
population over 18 years old will be selected. For the initial evaluations, the subjects will not have any pathology or functional limitation, fulfilling the strict requirements of data privacy and informed consent. These subjects will perform the selected activities for the automatic systems training, verification, and analytical validation of EYEFUL [30].
* Clinical validation: A comprehensive clinical validation will be designed with a focus on determining if the final system meets sensitivity and specificity criteria to detect the necessary functional limitations to discriminate a situation of dependence [31; 32; 30]. As a guide to the clinical validation of a system such as EYEFUL, the same processes and clinical validation of traditional tests, tools, and measurement instruments will be followed [30]. The steps involved in the clinical validation process are reproducibility analysis, content validity analysis, concurrent validity analysis, and discriminant validity analysis [31; 32; 30].  
This phase will include deciding which of the available standardized evaluation tools will be used to compare their results with those of the developed system for criterion validity. These tools should be the gold standard for the concepts being measured.
## 5 Discussion  
The accuracy of the INV is susceptible to multiple factors, including environmental noise in data acquisition and localized collapses near monitoring points [7]. The proposed multi-point group joint prediction method aims to mitigate the impact of such stochastic disturbances on single-point prediction accuracy by leveraging the enhanced robustness derived from integrated multi-point data analysis.  
The multi-point group joint prediction method for landslide timing proposed in this study is not only applicable to scenarios where parameter \(\alpha\) in the inverse velocity method equals 2 [13]. Theoretically, it can also be extended to cases where \(\alpha\) is greater or smaller  
Figure 12: Stability test results of multi-point joint prediction.  
than 2 [6], though the selection and application should be determined based on the pre-failure velocity distribution patterns.  
The temporal velocity datasets employed in this work may not represent the optimal choice. Alternative temporal resolutions, such as single-acquisition velocity, half-hourly velocity, or three-hourly velocity, could also be considered. However, excessively dense data may introduce significant noise interference, while overly sparse data may result in insufficient data points to ensure the stability of prediction outcomes.  
This study utilized 120 spatial monitoring point groups within the landslide area. In practical engineering applications, S-SAR monitoring systems can cover millions of point groups [20], allowing for a substantially larger number of groups to be incorporated into the prediction framework. Following the methodology proposed here, real-time landslide monitoring systems could integrate data from thousands or even tens of thousands of point groups to generate highly robust time-of-failure predictions. Furthermore, for landslides monitored via alternative displacement measurement techniques, such as GNSS [23], multi-point displacement datasets could similarly be leveraged for joint prediction studies under the proposed framework.
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 3 Conventional Relay Selection Scheme
### Best Harmonic Mean (BHM) Relay Selection Scheme  
The best harmonic mean (BHM) relay selection scheme is a method for the selection of the optimal relay. The BHM relay selection scheme uses the harmonic mean of the channel amplitude between the BS and relays and channel amplitude between the relays and UEs. The harmonic mean of the two channel amplitudes is as follows:  
\[h_{BHM}=\frac{1}{\left(\left|\mathbf{H}_{S,i}\right|^{-2}+\left|\mathbf{H}_{Ri,j}\right|^{-2}\right)}, \tag{7}\]  
where the \(\mathbf{H}_{S,i}\) denotes the channel between the BS and relays, and the \(\mathbf{H}_{Ri,j}\) refers to the channel between the relays and UEs. The \(\mid\bullet\mid\) refers to the 2-norm (amplitude) of the channel matrix. The BS selects the relay with largest harmonic mean of the two channel amplitudes. Therefore, the BHM relay selection scheme can select the optimal relay. However, in the BHM relay selection scheme, the BS must know all channel information. Therefore, the BHM relay selection scheme is very complicated compared to other relay selection schemes, thus it is difficult to apply the scheme to the actual multi-hop transmission system.
## 3 Data Collection and Experiment Analysis
### Outliers Detection for the Offline Data Based on K-Means
#### 3.2.1 Cluster Analysis
It is worth noting that here for better visualization, the probability-density curves of each cluster are independently normalized so that their respective acreage under each curve is 1. According to the probability distribution, it can be inferred that cluster 1 in Figure 8 is the LOS observation sample. It can be seen that its main distribution area is concentrated in the space with the maximum satellite elevation angle and carrier-to-noise ratio, the smallest pseudorange residual, and the consistency of the pseudorange rate, in which the elevation angle of the satellites is basically above 25\({}^{\circ}\). This is highly similar to the nature of LOS observations in urban environments. The remaining four clusters can be considered to be different distribution patterns of the sample set composed of observations contaminated by multipath or NLOS signals.
## 5 Engineering Application
### Reassessment of IES Reliability After Supplementary Life Testing  
Analyzing Equation (28), it is evident that among the three components, the rotating device provides the least life information, significantly impacting the LCL of the system's reliability. The historical data of the rotating device alone are insufficient to support a  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Lifetime Distribution** & **Shape Parameter \(\mathbf{\alpha}_{i}\)** & **GTT \(T_{i}\)** \\ \hline Electronic circuit & exponential & 1.0 & 395.10 \\ Rotating device & Weibull & 1.5 * & 677.29 \\ Infrared detector & exponential & 1.0 & 2615.91 \\ \hline \end{tabular}  
* Considering historical data, product maturity, and engineering experience, the shape parameter of the rotating device is set to 1.5.  
\end{table}
Table 4: GTTs of the three components.  
\begin{table}
\begin{tabular}{c c c} \hline
**Required Indicator** & **LM Method** & **Proposed Method** \\ \hline
0.93 & 0.9048 & 0.9260 \\ \hline \end{tabular}
\end{table}
Table 5: LCL of the IES reliability for preliminary assessment.  
high-precision reliability assessment of the IES. To address this issue, eight rotating devices were put into service for life testing: three samples underwent a 5-fold accelerated life test, three samples underwent a 3-fold accelerated life test, and two samples underwent a standard 1:1 life test. The test results are presented in Table 6.  
Combining the historical flight data in Table 2 with the supplementary life test data in Table 6, the GTT for the rotating device is recalculated to \(T_{2}\) = 780.77, while the life information of the other components remains unchanged. The LCL for the reliability of the IES is then reassessed using both the LM and the proposed methods. Table 7 shows that after supplemental life testing, the LCL of the IES reliability estimated by the LM method is only 0.9142, which falls short of the reliability index requirement of 0.93. Continuing with the LM method would necessitate additional component data, making the engineering effort unaffordable. In contrast, the proposed method demonstrates higher accuracy, confirming that the IES meets the reliability index requirement of 0.93 at a confidence level of \(\gamma=0.9\).  
In summary, the proposed method exhibits a clear accuracy advantage over the LM method, fulfilling the requirements of high-precision reliability assessment in engineering while saving tests and resources. This conclusion aligns with the simulation results in Section 4, further validating the accuracy and applicability of the new method.
## 4 Simulations and Results Analysis
### Resilience Probability (\(P_{r}\))  
In this section, we first present the behavior of the metric \(P_{R}\) according to Equation (7). Then, we present the accuracy achieved by \(P_{R}\) in the simulations.
## 3 Experimental Verification
### Experimental Results and Analysis
#### 3.2.4 Network Middle Layer Visualization  
To further validate SRMANet's excellent ability to recognize signals containing complex components and to explain SRMANet's learning process, this subsection presents the visualization of SRMANet's middle layer. Figure 12 shows the distribution of features in two dimensions for the different intermediate layers. The tensor features of the interlayer output are projected onto the low-dimensional space via t-distributed random neighborhood embedding (t-SNE). The figure contains the numbers from 0 to 11, corresponding to the different operating conditions from C0 to C11 of the planetary gearbox.  
Obviously, the distribution of features of the original signal is highly aggregated. As the SRMANet network deepens, the samples of different working conditions are gradually separated and the distance of samples of the same working condition keeps decreasing. When the final layer of SRMANet is reached, the twelve types of operating conditions of the planetary gearbox can be precisely identified.
# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 3 Future Research Directions
### Dynamic Model-Driven RUL Prediction Methods
#### 3.1.3 RUL Prediction Model under Multiple Working Conditions  
For special power plants, one tends to constantly adjust the working conditions according to the task profile, and the dynamic parameters of the internal reciprocating motion mechanism will also change under different working conditions (mean, variance, etc.). A dynamic performance degradation model obtained under a single working condition cannot fully describe the performance degradation law under all working conditions. Therefore, the similar trajectory method (STM) can be combined for the prediction of RUL under multiple operating conditions to achieve RUL prediction for special power plants under multiple operating conditions.  
First, according to the dynamic performance degradation model obtained under multi-factor coupling excitation, the dynamic performance degradation trajectory under different conditions can be established. Then, the similarity of the trajectory can be evaluated, Euclidean distance similarity evaluation criteria can be obtained, the performance degradation trajectory can be integrated under different working conditions, and the RUL prediction model can be formed under multiple working conditions.