esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrasonic VMCF Tests on Very Large Specimens with Risk-Volume Up to 5000 mm+
## 3 Results
### Experimental Results  
Hourglass and Gaussian specimens were tested in a stress-amplitude range, measured at the specimen half length, \(s_{\text{contr}}\), equal to [560:950] MPa. Failures occurred in a range of number of cycles to failure, \(N_{f}\), equal to [3.13-10\({}^{6}\):7.08-10\({}^{8}\)]. The fatigue failures originated from a defect, which was experimentally observed by the Scanning Electron Microscope (SEM, Tescan Mira3 FE SEM). As in [9], the stress amplitude close to the defect location, \(s_{\text{local}}\), was considered as the applied stress amplitude during the tests, in order to take into account the real stress originating the fatigue failure. Figure 4 shows the S-N plot of the experimental datasets related to the hourglass specimens (blue square marker) and to the Gaussian specimens (green diamond marker). As for the hourglass specimens,  
Figure 3: Validation of the stress amplitude in Gaussian specimens with very large risk-volume: (**a**) normalized stress amplitude acquired by Gages A and B and computed through FEA; (**b**) stress amplitude acquired by Gages A and C.  
in total, 16 tests were carried out, with 13 failures and 3 runouts. In one case, however, the stress amplitude was not properly controlled during the tests, and it is not considered in the following analysis. Nonetheless, the defect originating the fatigue failure was clearly visible through a SEM investigation, and it was thus considered for the defect analysis. As for the Gaussian specimens, 20 tests were carried out, with 18 failures and 2 runouts. One specimen out of 20 failed before \(10^{5}\) cycles, due to a large surface scratch, and it is not considered in the following analysis.  
As expected, Figure 4 shows that the dataset related to hourglass specimens is above that related to Gaussian specimens. As for the hourglass specimens, no failure occurred below 730 MPa; however, when the Gaussian specimens are considered, no failure occurred below 605 MPa. These different responses offer clear evidence of a significant size-effect. Further discussions are carried out in Section 3.5.
## 1 Introduction
This method offers two main benefits: the exponential decay of the switching gain reduces chattering, and a disturbance-dependent function which is the switching gain coefficient maintains nominal system performance [26]. Moreover, paper [27] proposes a novel sliding mode control strategy combined with a fast-terminal sliding mode disturbance observer (FTSMDO) to address parameter variations in permanent magnet in-wheel motors. The sliding mode power converging law enhances the controller's response speed, while the FTSMDO compensates for parameter variations to improve robustness. To enhance PMSM control and improve motor performance, this paper proposes a composite control strategy incorporating extended state disturbance observer (ESDO) and fast-terminal super-twisting sliding mode control (FTSTSMC) algorithms for superior speed control in PMSM systems. Additionally, a new fast-terminal super-twisting reaching law (FISTRL) is integrated into FTSSMC to reduce reaching time. The main contributions of this paper are summarized as follows:  
* An FTSTRL is built to shorten the reaching time from the initial value to the sliding surface to enhance the robustness of sliding mode control. * In terms of anti-interference, the FTSTSMC demonstrates strong anti-interference capabilities due to the application of the FTSTRL. * There was coordination between the ESDO and the FTSTSMC to achieve feedforward compensation. * This was validated by comparing the FTSTRL with the CSTRL and NSMRL, using metrics like integrated square error (ISE), integrated time square error (ITSE), integrated absolute error (IAE), and integrated time absolute error (ITAE) for detailed comparative and quantitative analyses. The structure of the remaining sections is as follows: Section 2 introduces the mathematical model of the three-phase PMSM. Section 3 covers the controller design. Section 4 details the ESDO for estimating and feeding back disturbances to the FTSTSMC. Section 5 presents a simulation study validating the proposed approaches. Finally, Section 6 summarizes our conclusions.
# Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks
## 2 System Model  
Similarly to the network setup of [43; 44], we consider the relay-aided HARQ network shown in Figure 1, in which there are two transmission phases. In Phase 1, the source node broadcasts its packet to both destination and relay nodes. In Phase 2 of this HARQ setup, upon packet loss notification, both relay and source nodes cooperatively retransmit the lost packet information to the destination. Our goal is to optimize HARQ constellation mappings to improve modulation diversity so as to minimize the packet BER at the receiver.  
Denote \(\mathcal{C}\) as the primary constellation used by this relay network whose cardinality equals \(Q=|\mathcal{C}|\). In the first phase, the source converts a bit sequence of length \(\log_{2}Q\) into symbols with Gray mapping \(\lambda_{0}:\{0,\ldots,Q-1\}\rightarrow\mathcal{C}\). The bit sequence can be indexed by its decimal equivalence \(p\in\{0,\ldots,Q-1\}\). The source transmits \(\lambda_{0}[p]\) to the destination via channel \(h_{0}\), though \(\lambda_{0}[p]\) is simultaneously received by the decode-and-forward (DF) relay. Following the approach of [20; 45], we consider that the relay is placed strategically such that it has a negligible decoding error rate. Upon receiving a request for retransmission, the second phase begins with the source and the relay re-mapping \(p\) into \(\lambda_{1}[p]\)and \(\lambda_{2}[p]\), respectively. Specifically, we note that in the second phase, the source retransmits the same message again towards the destination. In the meantime, the relay also forwards the same message to the destination. Therefore, the messages from source and relay are remapped in the form of \(\lambda_{1}[p]\) and \(\lambda_{2}[p]\). In general, we have three distinct mappings--\(\lambda_{0},\lambda_{1}\) and \(\lambda_{1}\), such that \(\lambda_{1}\
eq\lambda_{0}\) and \(\lambda_{2}\
eq\lambda_{0}\). The remapped symbols are transmitted simultaneously on the same channel band to the destination via channels \(h_{1}\) (from source to destination) and \(h_{2}\) (from the relay to destination). In summary, the signals received at the destination during the two phases are, respectively,  
\[y_{1} =h_{0}\lambda_{0}[p]+v_{1}, \tag{1a}\] \[y_{2} =h_{1}\lambda_{1}[p]+h_{2}\lambda_{2}[p]+v_{2}, \tag{1b}\]  
where \(v_{1},v_{2}\sim\mathcal{CN}(0,\sigma_{v}^{2})\) are additive channel noises. Throughout this work, we assume fading wireless channels \(h_{0}\), \(h_{1}\) and \(h_{2}\) to follow independent Rician distribution.  
Assume that the destination acquires perfect channel state information (CSI). Based on the received symbols \(y_{1}\) and \(y_{2}\), the destination decodes the data by identifying index \(p\) via maximum likelihood (ML) detection:  
\[\min_{\hat{p}}|y_{1}-h_{0}\lambda_{0}[\hat{p}]|^{2}+|y_{2}-h_{1} \lambda_{1}[\hat{p}]-h_{2}\lambda_{2}[\hat{p}]|^{2}. \tag{2}\]
## 2 Datasets
### Traditional Altimeters  
Traditional altimeters have been widely used for accurate SWH measurements based on the waveform of radar returns. Owing to their high accuracy, SWH data from traditional altimeters are commonly used for validating retrievals from SARs. In this study, these traditional altimeter SWH measurements were also used to validate InIRA SWH retrievals. Data from four types of traditional altimeters (JASON2, JASON3, SARAL, and HY2A) were used and collocated with the InIRA image center using the criteria of 3 h and 20 km. For the SARAL altimeter, Ka-band SWH data were used, while Ku-band SWH data were used for the rest. These traditional altimeter SWH data were downloaded with Geophysical Data Record (GDR) standard products. We directly used these data without any correction, assuming that these products have good quality.
# Multi-Body Model of Agricultural Tractor for Vibration Transmission Investigation
## 3 Results and Discussion
### Model Validation
The most crucial deviation while passing the big bump is 1.93 m\(\cdot\)s\({}^{-2}\) at 14.5 s (see Figure 9--right). On the other side of the tractor body, at the right cabin bushing location, the correlation for the whole curve is clearly worse. Although the number of amplitudes is still identical for simulation and experiment, the values of the amplitudes are significantly different. The most significant deviation between the simulation and the experiment can be observed when the front axle crosses the bump (see marked area in Figure 10). The measured data show a difference in acceleration amplitudes between the tractor's left and right sides. This phenomenon could be attributed to the fuel tank near the bushing mount on the right side of the tractor body. The fuel in the tank may act as a damper for these vibrations; however, confirming this hypothesis needs further investigation. On the contrary, the simulation model has almost identical responses on both sides. In this regard, it should be emphasised that the tractor body is modelled as a rigid body. Future work will include an evaluation of the influence of flexible tractor bodies on low-frequency vibration response. At the right and left spring-damper locations, the number of amplitudes is the same when passing the small bump. When passing the big bump, one amplitude is missing in the simulation at 15.45 s (see Figures 11 and 12--right). The left and right sides are relatively similar compared to the accelerations in the locations of bushings, which means that the rear part of the tractor body is not affected by the fuel movement. On both sides, the most significant difference in values of acceleration is at time 14.01 s: on the left side, it is 2.9 m\(\cdot\)s\({}^{-2}\) (see Figure 11--right), and on the right side, the difference is 2.48 m\(\cdot\)s\({}^{-2}\) (see Figure 12--right). Correlation, in general, is highly dependent on the vertical stiffness and damping of tyres and the mass properties of the tractor body and cabin. Also, the tractor's cruise speed plays a huge role and is not constant during bump passing. It is also essential to remember that the processing and filtering of measured signals affect the amplitude values. The validated dynamic model can predict ride behaviour regarding driving safety, evaluate dynamical forces acting on various parts of the tractor, and study the properties of various suspension configurations. The goal of the model was to evaluate driving comfort. However, the bumps used for model validation do not represent typical road profiles during the operation of agricultural tractors. Real road excitations randomly vary in frequency and amplitude and, furthermore, contain phase shifts between the left and right sides.
## 3 Research and Development Methodology
### Dapper ORM  
Dapper is a high-performance Object-Relational Mapper (ORM) designed for.NET, as depicted in Figure 7, optimized for scenarios where both speed and simplicity are essential. Unlike traditional ORMs, Dapper minimizes overhead by executing SQL queries [40] directly and mapping the results to.NET objects with minimal performance loss. It is developed by the Stack Overflow team and is widely adopted in performance-critical applications.  
In the context of Digital Twin technology for monitoring manufacturing processes, Dapper can be a crucial tool for efficiently managing large datasets generated by IoT sensors, machines, and real-time monitoring systems. Here is how it can be used:  
* Real-Time Data Querying: Dapper can fetch real-time sensor data from SQL-based databases efficiently. This is particularly important in Digital Twin environments where up-to-the-second data accuracy is necessary for simulating the physical manufacturing environment.
* High-Performance Data Operations: the performance advantages of Dapper make it ideal for high-frequency data operations in Digital Twin systems, where large volumes of machine and sensor data must be processed quickly.
* Batch Updates for Monitoring States: Manufacturing systems may need to frequently update machine states, sensor readings, or production statuses. Dapper can handle batch updates, ensuring that the digital twin stays in sync with real-world conditions without lag.
* Compatibility with Various Databases: whether your manufacturing system stores data in SQL Server, PostgreSQL, or other relational databases, Dapper can easily integrate, allowing for flexible data management across different platforms.
* Efficient Resource Management: with minimal overhead, Dapper can reduce the computational resource consumption of your monitoring system, which is critical when scaling a Digital Twin to monitor multiple production lines or machines.
* By using Dapper in your Digital Twin system for manufacturing, you ensure that data flow efficiently between the physical environment and the virtual model, enabling real-time decision-making and optimization of production processes.
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 7 Conclusions  
In this study, we analyzed the performance of a cooperative communication system within a heterogeneous network that includes an FSO-RF link for enhanced reliability. To achieve this, we compared cooperative and economic models regarding the method of frequency resource sharing. We also evaluated the SAS method in conjunction with the SMT method. The low-cost economic model demonstrates performance levels that are comparable to the reference model when \(\lambda_{\text{HF}}\) is low; however, it exhibits the poorest  
Figure 8: Comparison of stable regions for simultaneous multipacket transmission with \(z=z^{I}=3000\) m.  
Figure 9: Stable regions as a function of the detection probability with \(z=z^{I}=3000\) m.  
performance as \(\lambda_{\mathtt{HY}}\) increases. While the cooperative model generally outperforms the reference model, the reference model performs better in scenarios where \(\lambda_{\mathtt{HY}}\) is quite high. Additionally, the simultaneous multipacket transmission method consistently outperforms the switch-and-stay method. The hybrid SAS-SMP method, which combines both approaches, performs better than the simultaneous multipacket transmission in moderately rainy environments where establishing the FSO link is challenging. However, this trend reverses as \(\lambda_{\mathtt{HY}}\) increases.  
Finally, the proposed cognitive RF/FSO systems are currently not being implemented and the performance should be evaluated with more real-world applications. When practically applying the proposed systems, each transmitter-receiver pair does not require a separate control device because it does not share information. In the cognitive mode, the hybrid transmitter can transmit up to three packets to one FSO channel and two FSO channels, and the RF-only transmitter is capable of transmitting up to two packets to RF channels, which may be a manageable requirement in implementation. In the stability analysis, the buffer size is usually assumed to be infinite, but in practical systems, certain packets could be lost if the buffer is full. This induces some limitation in applying stability analysis, which is left for future study.  
**Author Contributions:** Original draft preparation, analysis, Y.C.; and organization, review and editing, supervision, D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the research fund of Hanyang University (HY-2020).  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 4 Asymptotic Analysis  
The analytical expression for the outage probability of the considered system model is quite complex. Therefore, asymptotic outage probability provides more understanding on the impact of the system's channel parameters on the system's outage probability. By assuming an independent and identically distributed case, that is, \(\tilde{\gamma}_{RF}=\tilde{\gamma}_{FSO}=\gamma_{UW}\), the overall asymptotic outage probability can be written as the sum of the individual asymptotic CDF of the each channel SNR. In the high-SNR region, the overall asymptotic outage performance of the triple-hop mixed communication system can be approximated as  
\[P_{out}^{\infty}\cong F_{\gamma DF}^{\infty}(\gamma_{th})\cong F_{\gamma RF}^ {\infty}(\gamma_{th})+F_{\gamma FSO}^{\infty}(\gamma_{th})+F_{\gamma UW}^{ \infty}(\gamma_{th}), \tag{8}\]  
where \(F_{\gamma RF}^{\infty}(\gamma_{th})\), \(F_{\gamma FSO}^{\infty}(\gamma_{th})\), and \(F_{\gamma UW}^{\infty}(\gamma_{th})\) are CDF's of \(S\to R_{1}\), \(R_{1}\to R_{2}\), and \(R_{2}\to D\) links at high-SNR regimes, respectively.  
The coding gain and the diversity order can be defined as \((G_{C}^{xy}\gamma)^{-C_{D}^{xy}}\), where \(xy\in\{SR_{1},R_{1}R_{2},R_{1}D\}\), \(G_{C}^{xy}\) is the coding gain and \(G_{D}^{xy}\) is the diversity order of the link [36]. We need to find out \(F_{\gamma RF}^{\infty}(\gamma_{th})\), \(F_{\gamma FSO}^{\infty}(\gamma_{th})\), and \(F_{\gamma UW}^{\infty}(\gamma_{th})\) one by one as shown below.
# Watermarking Protocols: A Short Guide for Beginners
## 2 Requirements
### Security Requirements
#### 2.2.6 Ambiguity Problem  
To solve the problems reported above, a number of watermarking protocols need a double watermark insertion carried out by distinct web entities, such as, for example, content providers and WCAs. Such solutions, however, may give rise to a further problem caused by the fact that, when embedded independently, the second watermark insertion can impair the previously inserted watermark, thus discrediting its authority. In addition to confusing the copyright information embedded in digital contents, a double watermark insertion may also impair the final quality of the protected contents, thus reducing their commercial value. As a consequence, a double watermark insertion ends up behaving as an \"ambiguity attack\" on watermarks, and this strongly reduces the security performance of protocols [18; 19].
# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
## 2 Microservices and Grayscale-Based Image Transform Features  
### File Type Microservices  
#### 2.1.1 Portable Executable (PE) Files  
The majority of Windows OS malware are presented in the Portable Executable (PE) format, which is.exe [21]. Figure 1 illustrates the main components of the PE file format.  
Extracting static features from PE files involves understanding the file structure. These features can include things like opcodes (instructions), n-grams (sequences of instructions), hashes, and information about the PE file itself. Malware often hides within Windows software, so analyzing PE files is crucial to cybersecurity. Static analysis involves disas  
Figure 1: PE file format [2].  
sembling the software, checking the PE header and sections, and extracting information like text strings and dynamic linked libraries (DLLs) [2; 21]. Dynamic analysis, on the other hand, requires running the software in a controlled environment to observe its behavior. Researchers often use a combination of these approaches based on their needs.  
Static analysis mainly focuses on examining the assembly code of PE files, but it can be challenging to extract meaningful information, especially from obfuscated malware that intentionally tries to hide its true purpose [22]. To tackle these challenges, recent research has explored analyzing the binary representation of the code using techniques like image processing [9; 23; 24; 25] and signal processing [26]. These methods convert raw executable binaries into grayscale images with different visual patterns and textures. Neural networks are then used to learn features from these images and classify them to detect malware and identify their specific families [9; 23; 24; 25]. This approach enhances the malware detection process by making it more effective and resilient against obfuscation techniques.
# Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI
## Appendix B The mathematical formulas for the 3 graph-theory methods are presented below.  
**Pearson's Correlation**  
The Pearson's correlation coefficient estimates the linear relationship between two variables. In the fMRI connectivity, [PERSON]'s correlation quantifies the linear relationship between the signals of the ROIs i and j:  
\[\rho_{\rm ij}=\frac{\rm cov_{\rm ij}(t)}{\sqrt{\rm var_{i}(t)var_{j}(t)}}\] (A1)  
The value of \(\rho\_{\rm ij}\) will vary from \(-1\) to \(1\). When the absolute value of the correlation is equal to \(1\), there is a perfect correlation between the two ROIs, while \(0\) indicates that there is no relationship between the signals.  
**Mutual Information (MI)**  
Mutual information is a statistical measure that quantifies the amount of shared information between two signals. It is a non-linear measure to evaluate the functional connectivity between the signals of two ROIs. If X_i (t) is the signal of ROI i, then the information of the time series is defined from the Shannon entropy as follows:  
\[\rm I(X_{i})=-\sum_{k=1}^{K}P_{k}Inp_{k}\] (A2)  
where the X\({}_{i}\) signal has been discretized into K bins, and p\({}_{k}\) is the probability of the k = 1, 2... K bin. The number of bins was defined according to the Freedman--Diaconis rule [87]. According to this definition, we can define the joint entropy of two signals X and Y:  
\[\rm I(X,Y)=-\sum_{ij}P_{ij}Inp_{ij}\] (A3)  
where p_ij is the joint probability of the two signals. The mutual information between the two signals can be calculated as:  
\[\rm MI(X,Y)=I(X)+I(Y)-I(X,Y)\] (A4)  
**Granger Causality (GC)**  
Granger causality (GC) estimates the effective connectivity between two ROIs by quantifying the improvement in predictability of one random variable X, if we use not only its past values but also the information of another random variable Y. The GC is estimatedusing linear autoregressive models. If we have the time series of two ROIs \((X_{t},Y_{t})\), then the estimated model of X_t using the information of Y_t is defined as follows:  
\[X_{t}=a_{1}X_{t-1}+\cdots+a_{p}X_{t-p}+b_{1}Y_{t-1}+\cdots+b_{p}Y_{t-p}+e_{t} \tag{11}\]  
While the autoregressive model for \(X_{t}\) is:  
\[X_{t}=a_{1}^{\prime}X_{t-1}+\ldots+a_{p}^{\prime}X_{t-p}+e_{t}^{\prime} \tag{12}\]  
In these models, p is the order of the autoregressive models and the residuals \(e_{t},e_{t}^{\prime}\) must be independent and identically distributed. If \(\Sigma\), \(\Sigma^{\prime}\) are the estimators of the residuals' covariance matrices, then the GC measure can be expressed as:  
\[GC_{Y\to X}=\ln\left(\frac{\det(\Sigma)}{\det(\Sigma^{\prime})}\right) \tag{13}\]  
where \(\det(\Sigma),\det(\Sigma^{\prime})\) are the generalized variances that quantify the prediction errors. The appropriate order is defined after comparisons between the models, according to the Chi-square test [88].