esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 2 Methodology
### RUL Prediction
#### 2.4.2 AR-UKF Prediction Model  
To predict RUL of the rotating machinery, the state transition function, which can accurately describe the entire degradation process, should be set up in advance. In fact, the state of the rotating machinery behaves in temporal correlation; thus, the value of the health indicator of the current time depends on that of the previous time. Therefore, the Autoregressive (AR) model, which can predict of the future data of the random variable using its historical data, is adopted to set up the state transition function as follows.  
\[s_{k}=\sum_{j=1}^{p}a_{j}s_{k-j}+w_{k-1}, \tag{34}\]  
where \(a_{j}\left(j=1,2,...,p\right)\) is the coefficient which indicates the importance of each historical data on generating the prediction data. \(p\) is the order of the AR model, which can be achieved by applying AIC rule and Burg algorithm. \(w\) is the process noise.  
Furthermore, the measurement function is introduced as follows.  
\[o_{k}=s_{k}+v_{k} \tag{35}\]  
where \(v\) is the measurement noise. To predict RUL of the rotating machinery, it is necessary to calculate the actual state \(s\) by using the observation \(o\).  
In this paper, UKF is adopted to estimate the state of the rotating machinery with the proposed CEEMDAN-ApEn feature. The flow diagram of the proposed AR-UKF prediction model is shown in Figure 2, and its specific steps are as follows.  
1. Calculate the proposed CEEMDAN-ApEn feature according to the method proposed in Section 2.2. Obtain the prediction starting time (PST) and the threshold of the final failure using the method proposed in Section 2.3.
2. Start RUL prediction by setting \(T=\) PST.
3. At the time \(T\), select \(n\) data samples of the CEEMDAN-ApEn feature on time slots \(\left\{T-n+1,T-n+2,...,T\right\}\) to construct the AR model.
4. Use UKF to perform multiple steps prediction on time slots \(\left\{T+1,T+2,...\right\}\) until the predicted value is greater than the threshold of final failure \(\zeta\). Suppose the time corresponding to above predicted value is \(T+m\); thus, \(m\) is the predicted RUL at the time \(T\).
5. Repeat Equations (3) and (4) by setting \(T=T+1\).
6. Finally, the predicted RUL value obtained at each time is used to plot a RUL curve.
# Resource Allocation in 5G Cellular IoT Systems with Early Transmissions at the Random Access Phase
## 5 Numerical Analysis
### Optimal RA/DT Resource Allocation  
Having observed that the static allocation of resources to the RA and DT phases may lead to under-utilization of the DT phase, we now proceed to assess what would be the optimal division between them resulting in the maximal throughput.  
Figure 9 shows the optimal fraction of resources (in terms of the minimal mean uplink delay) that need to be allocated to the DT phase as a function of \(\lambda_{r}\) and two software update intensities, \(\lambda_{d}=1\) upd./s and \(\lambda_{d}=10\) upd./s. As one may observe, the optimal division of resources varies drastically depending on the arrival rate of packets in the uplink and downlink directions. The current allocation utilized in 4G/5G CIoT systems is closer to the case when the DT phase is highly loaded with software update traffic (\(\lambda_{d}=10\) upd./s, \(\approx\)30% of DT utilization). For more realistic loads in the downlink direction, the capacity of the DT phase must be kept smaller to accommodate more packets in the uplink direction as the RA phase becomes a bottleneck.
## 6 Evaluation
### Learning Performance and Computational Cost
#### 6.1.4 Computational Cost for IID and Non-IID Data  
Figure 9,b display the execution times of IID data federated learning using MNIST [29] and Fashion-MNIST [30], respectively. These times are mainly calculated from local training on the client and include the times for splitting gradients, sending them to the server, server aggregation, sending back to the client, and client updates. This represents the time for the complete process of federated learning. It can be clearly seen from the two figures that the larger the number of servers and clients, the longer the execution time. FedAvg [2] takes the least amount of time among the three methods because the steps for gradient splitting and distributing to multiple servers are omitted. The execution time of our proposed method is the longest of the three methods, because unlike Fedshare [8], we need to perform secret sharing multiple times based on the number of servers, and not just once for the model parameters of each client. Additionally, we need to select a combination of \(t\) servers that meet the requirements, which further increases our execution time, thus making it the slowest of the three methods.  
Figure 10,b display the execution times of non-IID data federated learning using MNIST [29] and Fashion-MNIST [30], respectively. There is little difference in the execution time compared to the IID data experiments (Figure 9). FedAvg has the shortest execution time because the gradient-splitting step is omitted. In contrast, our method requires the longest time due to more frequent gradient-splitting steps and additional server selection steps. In addition, similarly to the IID experiment, the execution time for aggregate updates of the three methods increases as the num  
Figure 8: Comparison of the accuracy of our proposed method with FedAvg [2] and FedShare [8] using the Non-IID MNIST [29] and Non-IID Fashion-MNIST [30] datasets with 5 servers and varying numbers of clients.  
Figure 9: Comparison of the average execution time using the IID MNIST [29] and IID Fashion-MNIST [30] datasets for different numbers of clients and servers with FedAvg [2], FedShare [8], and our method.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 4 Optical OFDM Signaling
### Classical O-OFDM Schemes  
Due to the use of IM/DD signaling, the transmitted signal must be strictly positive and real. Therefore, most proposed O-OFDM schemes impose the Hermitian symmetry constraint in the frequency domain to obtain a real signal in the time domain [26]. In order for DCO-OFDM to ensure unipolarity of the transmitted signal, a DC bias is added to the signal before upper and lower clipping, which results in a relatively high spectral efficiency at the cost of a lower power efficiency. In ACO-OFDM, on the other hand, only the odd sub-carriers are modulated, resulting in a time-domain signal with anti-symmetry property. After applying hard clipping to the negative part, the resulting clipping noise does not affect the modulated sub-carriers. Nevertheless, this incurs a spectral efficiency loss of factor 2, compared to DCO-OFDM, but provides a better power efficiency [8].  
An alternative technique to ACO-OFDM is the so-called flip-OFDM [27], also known as U-OFDM [28; 29; 30], which consists of transmitting the positive amplitude portions of the signal followed by the flipped negative amplitude portions, separated by a cyclic prefix (CP) to avoid interference between the negative and positive blocks. Compared to ACO, the effective Rx noise variance for signal detection is doubled, but the Rx has a lower computational complexity. Another proposed alternative to ACO-OFDM (with the same spectral efficiency), is the so-called pulse-amplitude-modulation discrete-multi-tone (PAM-DMT) [31], by which PAM symbols are converted to imaginary signals before applying the Hermitian symmetry.
# Lessons for Coastal Applications of IMTA as a Way towards Sustainable Development: A Review
## 5 Economic Value and Financial Viability of Coastal IMTA
### Region- and Country-Specific IMTA  
#### 5.2.1 China  
MTA application is moving forward in Canada, the USA, France, Spain, Italy, and Chile [52]. While most IMTA systems so far have remained experimental, cases of commercial implementation of IMTA exist in China. In China, The Zhangzidao Fishery Group Co., Ltd. has been granted permission to cultivate scallops (_Patinopecten yessoensis_), arkshell (_Sapharca broughtonii_), sea cucumber (_Apostichopus japonicus_), and abalone (_Haliotis discus hannai_) on up to 40,000 hectares. The business has operated for over a decade, with a reported harvest of 28,000 tonnes in 2005, which was valued at over USD 60M and rendered a reported USD 18M of net profit [24]. This company is now considering seaweed farming and the creation of artificial reefs in offshore areas to improve ecological conditions and the operation's long-term viability. In Sungo Bay (China), a company operates on an industrial scale, producing _Laminaria japonica_ kelp with _Chlamys farreri_, abalone (_H. discushannai_), and blue mussel (_Mytilus edulis_). Several studies evaluate the environmental costs and benefits of Chinese aquaculture. It was examined how IMTA systems affected four ecosystem services: food security, oxygen production, climate regulation, and water treatment [114]. Using a standard cost-benefit assessment (CBA) approach, the authors find that marculture in Sanggou Bay had a broadly positive impact on ecosystem outcomes, which account for the majority of the profits and costs associated with marculture activities (including both economic and ecological contribution and loss). For instance, increasing the value of food production shows economic benefit, while decreasing the value of some ecosystem services results in environmental degradation. Importantly, a comprehensive assessment shows that IMTA is more economically and environmentally sustainable than two monoculture techniques for the same region; hence, they recommend it for open-water systems in China [31].
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 4 QAOA Mappings: Strings
### Example: MaxColorableInducedSubgraph
#### 4.3.1 Controlled Null-Swap Mixer at a Vertex  
The controlled null-swap partial mixer we defined has elements of the mixers we saw for the previous two problems, combining the control by vertex neighbors from MaxIndependentSet and the color swap from MaxColorableSubgraph. Here, however, we made substantial use of the uncolored state, and at each vertex only considered swapping a color with uncolored status. An uncolored vertex can be assigned color \(c\), maintaining feasibility, as long as none of its neighbors are colored \(c\), whereas uncoloring a vertex always preserves feasibility. This suggests a mixer may be obtained by swaps between each color and the uncolored state, controlled for each vertex by the colors of its neighboring vertices to ensure feasibility. This reasoning in terms of classical moves inspires, for problems containing \(\mathsf{NEQ}\) constraints, the controlled null-swap partial mixing Hamiltonian:  
\[H_{\mathsf{NS},v,a} =(\ket{a}\bra{0}_{v}+\ket{0}\bra{a}_{v})\,H_{\mathsf{NONE}( \mathbf{x}_{\mathsf{hold}(v)},a)} \tag{27}\] \[=(\ket{a}\bra{0}_{v}+\ket{0}\bra{a}_{v})\prod_{w\in\mathsf{nbnd} (v)}(I_{w}-\ket{a}\bra{a}_{w})\,,\]  
with corresponding controlled null-swap-rotation mixing unitary:  
\[U_{\mathsf{NS},v,a}(\beta)=\Lambda_{\mathsf{NONE}(\mathbf{x}_{\mathsf{nbnd}(v) },a)}\left(e^{-i\beta(\ket{a}\bra{0}_{v}+\ket{0}\bra{a}_{v})}+\sum_{b\
otin\{ 0,a\}}\ket{b}\bra{b}_{v}\right), \tag{28}\]  
where:  
\[\mathsf{NONE}(\mathbf{y},A)=\bigwedge_{\begin{subarray}{c}y\in\mathbf{y}\\ a\in A\end{subarray}}\mathsf{NEQ}(y,a)=\bigwedge_{a\in A}\bigwedge_{i=1}^{\ket{ \mathbf{y}}}\mathsf{NEQ}(y,a)=\bigwedge_{a\in A}\bigwedge_{i\in[\ket{\mathbf{ y}}]}\mathsf{NEQ}(y,a) \tag{29}\]  
is shorthand for none of the variables in \(\mathbf{y}\) having value any of the values in \(A\); when \(A=\{a\}\) is a singleton set, we write simply \(\mathsf{NONE}(\mathbf{y},a)=\mathsf{NONE}(\mathbf{y},\{a\})\).
## 3 Results and Discussion
### Effect of HPP on the Free Amino Acids Profile of Beef During Storage
[44] observed a low tryptophan content in beef. The tryptophan content also increased with the increase in storage time (Table 2). As in the case of most of the free amino acids described above, the dynamics of the increase in the tryptophan content was the highest in the control meat (from 12.64 to 47.68 mg/kg; approximately a 4-fold increase) and depended on the applied pressure. Compared with day 1, on the last day of storage of the HPP300 and HPP400 meat, an increase of approximately 3.5-fold in the tryptophan content, and in case of the HPP500 meat, 2.7-fold, was observed. In contrast to our study, [PERSON] et al. [44] found that the tryptophan content increased significantly only after 14 days of beef storage. The performed PCA analysis allowed us to reduce the multidimensional data set (amino acids) to two main components that best describe the variability in the data (Figure 3). The first two principal components (PC1 and PC2) explain 90.43% of the total variability in the data, with the first component (PC1) at 78.33% and the second component (PC2) at 12.10%. This means that most of the information contained in the original data set can be well interpreted using these two principal components. Points with similar colors (same HPP level) and shapes (same storage time) cluster in specific areas of the graph (Figure 3). This indicates that both the storage time and HPP level clearly affect the amino acid composition. Samples exposed to a higher pressure (lighter colors in the Figure 3) differ significantly from the control sample or samples treated by lower pressures. The different shapes of points suggest that the storage time (storage length) affects the amino acid composition. In some cases, samples stored for a longer time shifted in the PCA space, indicating chemical or biological changes over time. Higher levels of HPP may cause changes in the structure of the material, proteins, and amino acid interactions, which explains the changes in the amino acid composition. Long storage times may result in the degradation of certain amino acids or other chemical reactions that differentiate samples in the PCA space. The combination of a higher HPP level and long storage seems to have a particularly significant effect on samples, which is visible as a larger scatter of points. Moreover, the obtained results indicate that lysine has a very large negative correlation with the first component, which means that changes in lysine are strongly inversely related to this component (Figure 4). The remaining amino acids have a large positive correlation with the first component. Lysine clearly differentiates samples on the axis of the first component, which may be related to biological or chemical processes during storage. Other amino acids are more correlated with the opposite side of the first component, which may indicate different mechanisms of changes in the samples. Figure 3: The principal component analysis of cases by HPP level and storage time shapes.
## 5 Conclusions  
In this paper, we performed a quantitative assessment of AE signals acquired from a spherical storage tank. The main objective of this work was to perform data-driven structural integrity assessment of a spherical storage tank. We recorded one-second-long AE signals from the test bed under normal (no crack) and cracked (3-mm pinhole crack) conditions. Then, we extracted time-domain statistical features from these AE signals. These features are a compressed representation of the original signals used to differentiate instances into the two classes, as well as to avoid the high computational complexity of the original time-domain signals. The extracted features were then provided to the support vector machine (SVM) classifier. The results showed that the proposed method provides an average accuracy of 90%, making it an effective data classifier. We conclude that the proposed method can be deployed to effectively detect leakage in spherical storage tanks.  
Conceptualization, M.S., D.-C.J., J.-M.K.; Methodology, M.S., M.I., J.K.; Software, M.S.; Validation, M.S. and J.-M.K.; Formal Analysis, M.S. and M.I.; Investigation, M.S., D.-C.J. J.-M.K.; Resources, M.S., M.I., J.K.; Data Curation, M.S., M.I., J.K.; Writing-Original Draft Preparation, M.S.; Writing-Review & Editing, M.I., J.K., D.-C.J., and J.-M.K.; Supervision, J.-M.K.; Funding Acquisition, J.-M.K.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20162220100050). This research was also financially supported by the Ministry of SMEs and Startups (MSS), Korea, under the \"Regional Specialized Industry Development Program (R&D or non-R&D, P0002964)\" supervised by the Korea Institute for Advancement of Technology (KIAT).  
The authors declare no conflicts of interest.
## 4 Performance Evaluation in Edge Scenarios
### System Call Overhead
#### 4.5.2 Inter-Process Communication Performance  
Considering the different process execution models, in addition to process creation, inter-process communication is also a common and important scenario. First, we used the process creation methods mentioned earlier, whereas Gramine and native Linux used fork, and Occlum used spawn to create a child process. Communication between the parent and child processes was then established through the pipe system call. The parent process is responsible for writing data into the pipe, while the child process reads data from it. The throughput under different buffer sizes was used to reflect the efficiency of inter-process communication. In Figure 9, it can be observed that Occlum demonstrates high efficiency in inter-process communication, with almost no difference compared to native Linux. As the buffer size increases, the throughput of the pipe gradually increases. However, Gramine shows very low efficiency in inter-process communication. Through testing process-related system calls, although Gramine supports multi-process mode, its performance significantly declines. In contrast, Occlum achieves native level performance in both process creation and inter-process communication system calls.  
Figure 8: Process creation latency.  
Figure 9: Inter-process communication.
## 3 Simulation Results
Compared with the filters in [14; 31; 32; 42; 43; 46], the proposed mixed-mode universal filter can offer a maximum number of transfer functions, thanks to the multiple-input, multiple-output of the DDCCTA, both non-inverting and inverting transfer functions of five standard filter responses of four modes can also be obtained. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Factor** & **Proposed** & [14] **2016** & [31] **2022** & [32] **2024** & [42] **2021** & [44] **2022** & [47] **2022** \\ \hline Number of active devices & 3-DDCCTA & 1-FDCCII, 1-DDCC & 8-OTA & 4-OTA & 2-VDBA & 2-VD-DVCC & 1-VGA \\ \hline Realization & 0.18 \(\upmu\) CMOS & 0.18 \(\upmu\) CMOS & 0.18 \(\upmu\) CMOS & 0.18 \(\upmu\) CMOS & 0.18 \(\upmu\) CMOS & 0.18 \(\upmu\) CMOS & 0.18 \(\upmu\) CMOS \\ \hline Number of passive devices & 2-C, 3-R & 2-C, 6-R & 2-C & 2-C, 1-R & 2-C, 2-R & 2-C, 3-R & 2-C, 1-R \\ \hline Total number of offered responses & 179 & 80 & 20 & 41 & 15 & 20 & 22 \\ \hline Electronic control of \(\omega_{u}\) & Yes & No & Yes & Yes & Yes & Yes & Yes \\ \hline Orthogonal control & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline All passive devices grounded & Yes & No & Yes & Yes & No & No & No \\ \hline High-input impedance (VM) & Yes & Yes & Yes & Yes & No & Yes & No \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed mixed-mode universal filter’s properties with those of some previous filters. Figure 17: The equivalent output noises of LPF: (**a**) VM, (**b**) CM.
## Abbreviations  
AE Autoencoder AI Artificial Intelligence ANN Artificial Neural Network ARGUS Audit Record Generation and Utilization System BA Bat Algorithm CFBPNN Cascade Forward Back Propagation Neural Network CLA Classical Learning Algorithm CMAF-IIoT Chaotic Map and resource-efficient AE scheme-based Authentication Framework for IIoT CNN Convolutional Neural Network CPS Cyber-Physical Systems DBN Deep Belief Network DEMA Data Extraction and Monitoring Activity DH Diffie-Hellman DL Deep Learning DNS Domain Name System
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 4 Design of the Framework
### Damage Mechanism  
The damage mechanism describes the method used by the attacker to harm the target. From the case studies, several key damage mechanisms have been identified:  
* Hardware Damage: This involves direct physical harm to hardware components. An example is the Commodore PET's \"Killer Poke\", which can cause monitor damage, and USB Killer.
* Bootloader Corruption: This targets the essential code required to launch the operating system. Malware like CIH/Chernobyl Virus and MBRLock exemplify this, potentially rendering devices inoperable.
* Data-flow Manipulation: This alters the way data are processed or transmitted within a system, leading to disrupted operations. Stuxnet and TDL4 are notable examples, demonstrating sophisticated interference with system processes.
* Data Encryption/Deletion: This involves rendering data inaccessible or permanently removing them. This can be seen in attacks like Mamba, where data encryption locks out legitimate users, or in instances of destructive malware like StoneDrill, which deletes critical files.
## 4 Results
### Fire Identification and Control  
After the fire points are located by the cluster head UAV (hired bee), the other member UAVs (observation bees) start to assess the specific fire situation in the ABC algorithm-based mechanism for identifying and controlling mountain forest fire points. In this mechanism, the member UAVs (observation bees) are employed to explore the area and severity of the fire point. Typically, the member UAVs (observation bees) are equipped with multi-function sensors, enabling accurate identification of fire severity through image recognition algorithms. To quantify the process of fire identification and control, a series of height projections on the horizontal plane of the simulated mountain forest is used to indicate the fire area, with the highest point representing the severity of the fire and the product of the fire area and severity representing the fire control function. The forest fire identification and control mechanism can be mathematically expressed as follows:  
\[A_{f}=\sum_{i}^{R}Z(x_{i},y_{i}) \tag{14}\]  
\[S_{f}=\max(Z(x_{i},y_{i})) \tag{15}\]  
\[F_{control}=A_{f}\times S_{f} \tag{16}\]  
where \(A_{f}\) represents the fire area, \(S_{f}\) represents the fire severity, \(F_{control}\) represents the fire control function, \(Z(x_{i},y_{i})\) represents the height of the mountain forest under the \((x_{i},y_{i})\) coordinate according to Equation (1), and \(R\) represents the max value of \(x_{i}\) and \(y_{i}\).  
According to the above-modeled mountain forests and fire points, the characteristics of the fire point situations are shown in Table 3. In order to demonstrate the mechanism of fire identification and control visually, the representational model is depicted in Figure 11. It is evident that Fire Point F5 has a larger area and more severe fire conditions, resulting in a higher \(F_{control}\) value and requiring more UAVs to extinguish the fire. It should be noted that the quantity of UAVs at various fire points in Figure 10 only signifies an estimated amount and not a precise one.  
Upon completion of the fire identification and control at the current fire point, the cluster head UAV (hired bee) transitions into a scout bee to locate the next fire point, marking the completion of the closed loop of the ABC algorithm. Building on the above results and analysis, the mechanism of identifying and controlling mountain forest fire points based on the ABC algorithm is established, offering a new research foundation for the precise management of mountain forest fires in the real world.  
Figure 10: Process for optimal fitness change.