esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Efficient LFSR Implementation  
The relationship between the \(m\)-sequences on \(GF(2^{n})\) and the \(m\)-sequences on \(GF(2)\), as described in the previous section, also allows us to establish an equivalence between the LFSRs that generate them. This section presents a practical and efficient method to obtain such LFSRs, i.e., to obtain the feedback polynomial \(p(x)\) and the seeds of each LFSR \(<Ln,p(x)>\) that allow us to generate the same sequence as the LFSR \(<L,f(x),n>\) from a given seed. Note that, according to the equivalent model, the \(n\) LFSRs in \(GF(2)\) have the same connection polynomial \(p(x)\) but different seeds, all of them related to the seed in \(GF(2^{n})\). Hence, in order to efficiently implement the LFSR \(<L,f(x),n>\) using the equivalent model, it is necessary to solve three main questions: the computation of \(p(x)\), the computation of the seeds, and how to speed up the performance of the binary LFSRs.  
Since the connection polynomial \(f(x)\) is primitive, the minimal polynomial \(p(x)\) of the decimated sequences \(D^{i}\) is also primitive and unique for \(0\leq i\leq n-1\). Hence, \(p(x)\) can be obtained analyzing a decimated sequence using the Massey-Berlekamp algorithm [18]. Consequently, the following Algorithm 1 is defined.  
```
input :LFSR \(<L,f(x),n>\) output :\(p(x)\) in LFSR \(<Ln,p(x)>\)
1 Implement the LFSR \(<L,f(x),n>\); Generate \(2 Ln+1\) elements, at least, using any nonzero seed; Obtain a decimated sequence \(D^{i}\) for any \(i,\quad 0\leq i\leq n-1\); Obtain the minimal polynomial \(p(x)\) of the \(D^{i}\) generated in step \(3\)
```  
**Algorithm 1**Computation of connection polynomial \(p(x)\).  
Once \(p(x)\) has been determined, we can construct the \(n\) LFSRs \(<Ln,p(x)>\), but we need their respective seeds in order to generate the same sequence as that generated from the initial seed \(s=(d_{0},d_{1},\ldots,d_{L-1})\) in the extended field. As it is derived from the equivalent model, the \(L\) known elements in \(GF(2^{n})\) that compose the initial seed only provide us with \(nL\) bits, while \(n^{2}L\) bits are needed to complete the \(n\) seeds in \(GF(2)\) (see Figure 3). Thus, the seed \(s^{l}\) of \(D^{i}\) can be partially written in terms of the seed \(s\) of \(D\) as:  
\[s^{j}=d_{0,j},d_{1,j},\ldots,d_{L-1,j},d_{L,j},\ldots,d_{n\cdot L-1,j} \tag{8}\]  
where \(d_{L,j},\ldots,d_{n\cdot L-1,j}\) are unknown for \(0\leq j\leq n-1\). However, the shifts \(\theta_{i}\) that relate the binary sequences to each other allow us to obtain the seeds completely. The following subsections describes the computation of \(\theta_{j}\) and \(s^{j}\).  
Figure 2: Relationship between \(m\)-sequences in the extended and base fields.
# Designing Audio Equalization Filters by Deep Neural Networks
## 3 Proposed Method
### Autoencoder  
An Autoencoder is a generative model [32] based on an encoder, a decoder and an internal representation that interconnects the two, often called latent space.  
In our case, the encoder is composed of convolutional and fully connected layers, similar to the CNN of Section 3.2. The decoder performs the inverse mapping, thus, it is based on fully connected layers and de-convolutional layers. Filters coefficients are sampled from the internal representation, that has, thus, a size of \(\mathcal{S}\times\mathcal{T}\). Impulse responses are used as input to the encoder, as shown in Figure 4.  
Figure 3: Scheme of the proposed method using a Convolutional Neural Network (CNN).  
Figure 2: Scheme of the proposed method using an Multilayer Perceptron (MLP). The impulse responses are all concatenated into a vector and fed to the first layer, which must have size \(\mathcal{S}\times\mathcal{M}\times L\).  
The loss function for the autoencoder is defined as the sum of the Euclidean distance of Equation (3), and the reconstruction loss. The latter is expressed as the Euclidean distance between the input impulse response and the reconstructed one. Overall the loss for the autoencoder is:  
\[J_{AE}\quad=\quad\Bigg{(}\alpha\ \cdot\ \sum_{m=1}^{M}\big{\|}|\widehat{Y}_{m}( \omega)|-|Y_{des}(\omega)|\big{\|}_{2}\ +\ (1\ -\ \alpha)\ \cdot\ \sum_{m=1}^{M}\sum_{s=1}^{S}\big{\|}\hat{h}_{m,s}(n)-h_{m,s}(n)\big{\|}_{2 }\Bigg{)}. \tag{4}\]  
The term \(\alpha\) allows to weight the two losses, but for the rest of the paper it is kept equal to 0.5.
## 2 Materials and Methods
### Approaches  
After applying the 1B11 V8 algorithm described in Section 2.1, the brightness temperatures have been validated by two methods. First, the simulated (modeled) brightness temperatures were used to calculate the SD between the observed and the modeled, which is a good indication of the stability of the Tb measurements. For example, SD's that do not change in time are sufficient to assure a stable radiometric calibration. However, the contrary may not necessarily indicate calibration instability because this SD method is sensitive to uncertainty in the input environmental parameters for atmosphere and surface, as well as issues with the physics of the RTM used to produce the modeled Tb's. Another method, which is more robust, was to use inter-satellite radiometric calibration to calculate the double difference (DD) between TMI and another well calibrated instrument (GMI). This DD method dramatically reduces the issues associated with the SD technique; however, it uses less points, because it depends on the collocation between two instruments.
## 5 Internet of Medical Things
### IoMT Devices  
The IoMT is a network of medical devices, sensors, and other healthcare-related equipment connected to the internet to collect, process, and transmit data for healthcare purposes. It refers to a wide range of connected medical devices and applications that collect and transmit patient data for analysis and monitoring. There are several types of IoMT devices, including:  
* Wearables: These medical devices, including smartwatches, fitness trackers, and health monitors, are worn on the body and track various health parameters such as heart rate, blood pressure, and sleep patterns.
* Implantable: These medical devices, such as pacemakers and insulin pumps, are implanted inside the body to provide continuous monitoring and treatment for chronic conditions.
* Remote monitoring devices: These medical devices, such as blood glucose monitors and blood pressure monitors, remotely monitor patients and transmit real-time data to healthcare professionals for informed decision-making in patient care.
* Telemedicine applications: These applications, such as video conferencing and messaging apps, enable remote communication between patients and healthcare professionals, particularly benefiting those in remote or underserved areas.
* Medical imaging devices: These devices, such as X-rays, CT scans, and MRIs, capture medical images that are transmitted to healthcare professionals for analysis and diagnosis.
* Smart hospital equipment: Connected medical devices used in hospital settings, such as patient monitoring systems and infusion pumps, provide real-time data on patient health, empowering healthcare professionals to make well-informed decisions regarding patient care.  
Here is a classification in Table 2 for IoMT, categorized by the type of devices and their application areas, with examples. These categories are not mutually exclusive, and there may be some overlap in the application areas for each type of device.  
Figure 6: IoT concept in Healthcare Domain.
## 7 Summary and Conclusions  
In this paper, based on the J-C constitutive and damage models, a finite element calculation model for collision is built, and the parameters of the J-C model are verified using different materials. After verification using the test of bullet impacting a PMMA plate, a simplified model was constructed for the dynamic analysis of a spherical hull under concentrated impact loadings in the underwater state, and series of numerical calculations were carried out accordingly. The main findings of the article are as follows:  
(1) The least squares method (LSM) is developed to determinate the parameters of the J-C constitutive and damage models. After comparing with test data and reference [8] results, respectively, the presented LSM was confirmed and also applied to determinate PMMA materials in the J-C model at different strain rates.  
(2) Based on the J-C constitutive and damage models, a numerical model of plate with bullet impacting is obtained on Abaqus software. Moreover, for different thicknesses of plates and initial velocities of bullets, the presented numerical results with the J-C model could have a good agreement with test data and even with numerical results from LS-DYNA.  
(3) From the theory of plate and shell, a plate with bullet impact is simplified to simulate the spherical hull under impact loading. The numerical results show that the residual velocity with pressure is larger than that without pressure, and it also raises when the pressure increases. Deformation of the impacted block is larger and more serious too. Such a pressure effect will be more obvious for the thick plate.  
Conceptualization, methodology, writing-review and editing, resources, supervision, project administration, funding acquisition, Q.D.; software, validation, formal analysis, investigation, writing--original draft preparation, F.L.; data curation and visualization, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program-Special Deep Sea Key Technology and Equipment 'Damage Detection and Prediction of Glass Ball Cabin' (grant number 2018 YFC0308004), and Program of National Natural Science Foundation of China 'Theoretical and Experimental Research about Structure of Pressure Toroidal Shell in Underwater Engineering' (grant number 51109190).  
Not applicable.  
The authors declare no conflict of interest.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 4 Sea Ice Extent Mapping and Validation
### Sea Ice Mapping and Validation  
Using the sea water and ice discrimination method presented above, we processed the daily Arctic and Antarctic daily ice maps from 1 January 2019 to 31 December 2021. Figure 8 shows the geographical distribution of sea ice results derived from a multisource  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Classification** & **Precision** & **Recall** & **F1 Score** & **Overall Accuracy** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{3}{*}{Arctic} & Open water & 98.79 & 97.31 & 98.04 & \\ \cline{2-6}  & Ice & 91.89 & 95.77 & 93.76 & \\ \hline \multirow{3}{*}{Antarctic} & Open water & 99.09 & 98.66 & 98.97 & \\ \cline{2-6}  & Ice & 93.21 & 96.69 & 94.82 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of averaged classification accuracies obtained using an SVM classifier in the Arctic and Antarctic from 2019 to 2021.  
Figure 7: Antarctic Sea ice maps on 26 September 2020 (gray parts in center represent land): (**a**) SVM model outputs; (**b**) after misclassification reduction processing; (**c**) NSIDC sea ice concentration.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 7 Conclusions  
In this study, the detection accuracy was improved by utilizing the advantages of dynamic-analysis-based malicious file detection. In addition, it was possible to detect and analyze unknown files, which is expected to increase the analysis reliability and secure excellent field connectivity.  
This was accomplished by deriving high accuracy standards for the Random Forest, AdaBoost, and Gradient algorithms, and synthesizing the formulas of research cases. To verify the results, the PE malicious file dataset was analyzed, experimental data were generated, and an experiment was conducted.  
In addition, to perform dynamic analysis, normal, suspicious, and malicious codes were analyzed using the app.any.run program, and the results were mapped to the MITRE ATT&CK framework to visualize the attacker's form and characteristics, easily identifying their attack intention. Therefore, users could easily identify malicious and normal files and respond quickly.  
Future studies plan to use the introduced hybrid approach to overcome the limitations of dynamic and static analysis techniques. The hybrid method can overcome the disadvantages of both static analysis and dynamic analysis, and the ability to accurately detect malicious code and the speed at which this is carried out are improved. At the same time, it has the great advantage of being able to efficiently analyze suspicious files and having a low false-positive rate upon detection. In addition, we will continuously collect and analyze attack datasets, and we will also experiment with ways to apply linear regression, GLM, SVR, and GPR algorithms. Additionally, deep learning algorithms will explore ways to apply deep neural networks. In the future, we plan to conduct research to improve the speed of numerical accuracy by experimenting with a hybrid method.  
Conceptualization, G.A., W.P. and D.S.; methodology, G.A., W.P. and D.S.; software, G.A. and W.P.; validation, K.K., W.P. and D.S.; formal analysis, G.A.; investigation, G.A.; resources, G.A. and W.P; data curation, G.A., K.K., W.P. and D.S.; writing--original draft preparation, G.A.; writing--review and editing, K.K., W.P. and D.S.; visualization, G.A.; supervision, K.K., W.P. and D.S.; project administration, D.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Future Challenge Defense Technology Research and Development Project (9129156) hosted by the Agency for Defense Development Institute in 2020.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Discussion  
The simulation and analysis above show the following:  
1. The composite BOC signal can perform Doppler frequency measurement and high-speed communication at the same frequency. In addition, the communication signal and BOC signal are orthogonal in the time domain, which minimizes the influence of the BOC signal on the communication signal.
2. The paper gives the receiving algorithm of the composite signal. The algorithm includes the acquisition and tracking of the BOC signal and demodulation of the BPSK signal. By using the synchronization clock of the BOC signal for BPSK demodulation, the tolerance of the BPSK signal to a highly dynamic environment is increased, and the receiver design is simplified.
3. We discuss the fast-switching algorithm used to make the composite signal have better performance in spacecraft formation or UAVs. The reacquisition time after switching is reduced to one clock cycle.
4. We simulate and analyze the Doppler measurement and BER performance under different BOC and BPSK signal power ratios, as well as under different \(C/N_{0}\)s. This shows that the BPSK signal will interfere with the BOC signal at low \(C/N_{0}\)s. In this situation, the power ratio of the BOC signal needs to be improved. However, the performance of the BPSK signal is influenced only by its own power in the composite signal.  
Figure 18: The dual one way ranging.  
(5) The fast-switching algorithm can operate at different \(C/N_{0}\)s and signal power ratios in the case of the receiver regenerating local BOC signal to cancel the cross-correlation interference or the different nodes at similar distances. (6) The hardware platform can achieve all the functions of the composite BOC signal, which shows that the platform is feasible in the physical environment.
## 3 Why Palmprint Recognition?  
In the field of biometric recognition, facial identification still has limitations due to persistent challenges, such as pose, lighting, and orientation variations [11]. Conversely, fingerprints have been widely adopted due to their efficiency, although certain populations, such as manual workers and the elderly, may have difficulty with capturing fingerprints. In a networked society, reliable personal authentication remains critical for security [12]. Compared to other biometric modalities, palmprints have proven to be more effective and acceptable. The palmprint biometric system offers higher accuracy than fingerprints  
Figure 1: Palmprint characteristics (**a**) at low resolution and (**b**) at high resolution.  
and higher acceptance than facial recognition. With characteristics such as uniqueness, reliability, and security, palmprints have been widely adopted by security agencies, providing a cost-effective and non-intrusive option for developing accurate and efficient biometric systems.  
Advanced research in palmprint feature extraction [13; 14] has been conducted for contactless systems. Contactless palmprint recognition aims to improve usability and privacy. However, the lack of a knuckle guide can lead to variations in palmprint images due to hand movements. Various methods, such as the utilization of texture operators like local binary pattern (LBP) [15] and Gabor filters [16], were proposed to overcome these challenges.  
Palmprint has advantages over other biometric methods, including iris and fingerprint, in terms of identity matching. Palmprints offer the advantage of easy capture with low-resolution devices, mitigating the high costs associated with other modalities. Moreover, law enforcement agencies have extensively employed palmprints for criminal identification, leveraging their unique and stable characteristics [17; 18]. These prints encapsulate diverse features like primary lines, minutiae points, ridges, and overall texture. Each feature class contributes significantly to the individuality and discriminative power of a palmprint. This flexibility permits adaptation to the specific security requirements of individuals and organizations.
# A Visual User Interfaces for Constant Checking of Non-Invasive Physiological Parameters
## 3 Results
### Assessment Framework according to ISO/IEC 25012
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multirow{2}{*}{**Aspects to Evaluate**} & \multicolumn{2}{c}{**Decision Criteria**} \\ \cline{3-4}  & & **Unacceptable** & **Minimally Acceptable** & **Target Range** \\ \hline \multirow{4}{*}{Inherent data quality} & Accuracy & & \(\surd\) \\ \cline{2-3}  & Consistency & & \(\surd\) \\ \cline{2-3}  & Credibility & \(\surd\) & \\ \hline \multirow{4}{*}{System-dependent data quality} & Accessibility & & \(\surd\) \\ \cline{2-3}  & Efficiency & & \(\surd\) \\ \cline{1-1} \cline{2-3}  & Precision & & \(\surd\) \\ \cline{1-1} \cline{2-3}  & understandability & & \(\surd\) \\ \cline{1-1} \cline{2-3}  & Availability & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results for the temperature parameter, heart rate and oxygen saturation tests (Assessment according to ISO/IEC 25012). dealt with the inherent data quality. When the final tests were tabulated, the heart rate was off by 1%, and the oxygen saturation was off by 0.8%, demonstrating that the data was nearly accurate when compared to a doctor, so the first parameter, accuracy, was within the desired range. The second parameter, consistency, was within the target range because the patient could reasonably visualize their data and determine whether his health was good or if there was an anomaly in his body. The prototype became at least minimally acceptable as a third parameter for credibility because of the prototype's small margin of error and the accuracy of the data. According to the system-dependent data quality, five factors were evaluated in the second section: accessibility, Efficiency, accuracy, comprehension, and availability. The first accessibility parameter was met because users can always access their physiological parameter data whether or not they have Internet access. Efficiency, the second parameter, is effectively maximized because it falls within the target range, given that it only takes a few seconds to display the data. The objective range was finally attained with the Precision, Comprehension, and Availability parameter because the data it provides is sometimes accurate and other times close to the true value. Additionally, this research employs a mobile application that the user can use to dispose of their information and permit it to be tracked for however long they require.
# Failure Detection within Composite Materials in System Engineering Applications
###### Abstract  
This paper introduces essential key attributes of composite materials with a focus on carbon fibre (CF), followed by a description of common failure modes and proceeds to an investigation of stiffness of continuous CF laminates of 4-ply and 7-ply epoxy resin in pre-preg and wet layup. The three-point flexural test was performed with a Zwick Z010 machine, and the findings are presented. Continuing to real world failure scenarios and moving onto novel concept methods of live failure detection including scope for wood composites. Showing that early design considerations and further research can lead to advantages for system engineering.  
composite; fracture point; flexural test; carbon fibre; failure detection +
Footnote †: journal: Applied sciences  
-rep additional benefits currently not taken to date. Some benefits include, for example: (1) early detection of fracture failures and wear of carbon fibre based machinery with the intention to minimize downtimes and operational costs; (2) probabilistic forecast of the future conditions or the remaining useful life of the carbon fibre based equipment [1].  
It has been suggested that live failure detection techniques may be useful in the field of carbon fiber composites and other frequently used materials [2]. These approaches offer an early indicator and potential warning of a material's impending fracture point. In the subsequent sections, we provide a quick overview of composite materials' properties and behaviour, as well as a list of the most prevalent faults.
###### Abstract  
By developing new wave structure function of a beam waves, we derive the transmitting probability of signal vortex modes in oceanic turbulence based on Rytov approximation theory. Applying this transmitting probability of signal vortex modes, we study the influence of oceanic turbulence on the transmittance of the vortex modes carried by Mathieu-Gaussian beam. This model shows the transmitting probability of Mathieu-Gaussian beam with narrow initial beam width, long wavelength, and small ellipticity parameter is higher than the transmitting probability of the signal vortex modes in case of the beam with wide initial beam width, short wavelength, and great ellipticity parameter. Furthermore, when Mathieu-Gaussian beam has a suitable semi-cone angle, the effect of weak-turbulence channel on the transmitting probability of signal vortex modes with different topological charge can be ignored. Mathieu-Gaussian beam is a more suitable carrier for high information channel of underwater wireless optical communication than Laguerre-Gaussian beam.  
oeacnic turbulence; Mathieu-Gaussian beam; quasi-diffracting free finite energy beam; vortex topological charge; transmitting probability