esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Preliminary Feasibility Studies
#### 3.2.2 Printability of Fine Structures  
Since the reactor concept presented in Section 3.1 has a completely closed design, it is only possible to check to a very limited extent, whether all structures have been implemented as desired on the final component. Particular attention has to be paid to the fine structures, such as the lattice structure and the transport structure inside the heat pipes. Figure 6 shows a metal print sample made for checking purpose. Both the transport structure of the heat pipes and the grid structure are imaged cleanly and uniformly. The transition between the lattice and the wall structures is given and stable everywhere, so that both heat conduction and strength stabilizing tasks can be performed by the lattice.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Ultimate** & **Technical** & **Uniform** & **Elongation** & **Elongation** \\  & & **Tensile Strength** & **Elastic Limit** & **Elongation** & **at Break** \\  & & **\(R_{m}\)/N/mm2 & **\(R_{p0.2}\)/N/mm2** & **\(A_{g}\)/\%** & **_A/\%_ \\ \hline \multirow{4}{*}{RT} & **Horizontal** & 749 \(\pm\) 11 & 621 \(\pm\) 5 & 23.7 \(\pm\) 1.3 & 45.1 \(\pm\) 2.0 \\  & **45\({}^{\circ}\)** & 757 \(\pm\) 7 & 647 \(\pm\) 10 & 26.1 \(\pm\) 0.2 & 56.7 \(\pm\) 2.7 \\  & **Vertical** & 700 \(\pm\) 4 & 623 \(\pm\) 13 & 29.0 \(\pm\) 2.9 & 64.6 \(\pm\) 0.0 \\ \hline \multirow{4}{*}{300 \({}^{\circ}\)C} & **Horizontal** & 561 \(\pm\) 0 & 505 \(\pm\) 16 & -* & -* \\  & **45\({}^{\circ}\)** & 556 \(\pm\) 7 & 521 \(\pm\) 14 & -* & -* \\  & **Vertical** & 523 \(\pm\) 5 & 489 \(\pm\) 12 & -* & -* \\ \hline \multirow{4}{*}{425 \({}^{\circ}\)C} & **Horizontal** & 541 \(\pm\) 5 & 450 \(\pm\) 22 & -* & -* \\  & **45\({}^{\circ}\)** & 533 \(\pm\) 2 & 453 \(\pm\) 16 & -* & -* \\ \cline{1-1}  & **Vertical** & 500 \(\pm\) 4 & 446 \(\pm\) 5 & -* & -* \\ \hline \hline \end{tabular}  
* not applicable for conductively heated tensile samples due to temperature gradient; see also Section 2.3.  
\end{table}
Table 1: Parameters determined during tensile tests of AM material 1.4404.  
Figure 5: Exemplary stress–strain curves (individual measurements) of tensile tests at room temperature for tensile samples additively manufactured in different spatial directions.
## 1 Introduction
To the best of our knowledge, this is the first time that the idea of the look-up table method has been used in the calculation of ultrasonic transducers. Moreover, based on the same amplitude of the source plane, we propose an acoustic field sub-region look-up table method which reduces the number of calculations by dividing the region into blocks. To demonstrate validity, the point source method based on the Rayleigh integral model is compared with the two proposed methods. Compared with the point source method, both the acoustic field look-up table method and the acoustic field sub-region look-up table method can obtain the same acoustic field. In the cost of time, the ALUT method takes only one third of the time of the point source method, and the ASLUT method takes only one eighteenth of the time of the point source method, with the possibility of further reduction. In practical applications, phased arrays in non-destructive testing (NDT), high-intensity focused ultrasound (HIFU), and ultrasound medical imaging require the fast and accurate calculation of the acoustic field to achieve safety and efficiency targets [30; 31; 32]. The study of the ALUT method and the ASLUT method has contributed to these performance indicators.
## 3 Results and Discussion
### Discussion of Parametric Analysis  
The general behavior of the system on the selected parameters may be summarized as follows:  
* There is a local optimum value in the exergy efficiency at the specific value of \(\text{TIP}=16.3\) bar and \(\text{T}_{\text{gen}}=51.5\)\({}^{\circ}\)C in the typical day of WWC. In the other typical day categories, the exergy efficiency has local optimum at the different values of TIP and \(\text{T}_{\text{gen}}\) in comparison to typical day of WWC.
* The total exergy destruction rate in minimized locally at the specific values of \(\text{TIP}=16.53\) bar in the typical day of WWC. This value of TIP is changed in the other typical day categories in order to minimize locally the total exergy destruction rate.
* The general behavior of operational parameters for all typical days categories is almost the same of the typical day of WWC as shown in Figures 5-7.
* In the hot days; TSF, TWF, SWX and SSX, with an increase in the \(\delta\)T\({}_{\text{CON1}}\) the exergy efficiency decreases which is in reverse for other category days.
* The values of \(\delta\)T\({}_{\text{CON1}}\) and \(\delta\)T\({}_{\text{CON1}}\) are limited by the ambient temperature and take different range in different typical day categories.  
Figure 5: The influence of the \(\text{T}_{\text{gen}}\) (**a**) on exergy efficiency and total exergy destruction (**b**) on the net produced power and input exergy to the system.  
Figure 6: The variation of exergy efficiency and the total exergy destruction rate with the temperature differences at the condensers exit.
# A Simple Approach to Dynamic Optimisation of Flexible Optical Networks with Practical Application
###### Abstract  
This paper provides an initial introduction to, and definition of, the 'Dynamically Powered Relays for a Flexible Optical Network' (DPR-FON) problem for opto-electro-optical (OEO) regenerators used in optical networks. In such networks, optical transmission parameters can be varied dynamically as traffic patterns change. This will provide different bandwidths, but also change the regeneration limits as a result. To support this flexibility, OEOs ('relays') may be switched on and off as required, thus saving power. DPR-FON is shown to be NP-complete; consequently, solving such a dynamic problem in real-time requires a fast heuristic capable of delivering an acceptable approximation to the optimal configuration with low complexity. In this paper, just such an algorithm is developed, implemented, and evaluated against more computationally-demanding alternatives for two known cases. A number of real-world extensions are considered as the paper develops, combining to produce the 'Generalised Dynamically Powered Relays for a Flexible Optical Network' (GDPR-FON) problem. This, too, is analysed and an associated fast heuristic proposed, along with an exploration of the further research that is required.  
flexible optical networks; FONs; opto-electro-optical regenerators; OEOs; optimisation problems; NP-complete; dynamic relay optimisation; DPR-FON; algorithmic complexity; heuristics; GDPR-FON +
Footnote †: journal:  
_Future Internet_**2017**, \(9\), 18; doi:10.3390/f9020018
## 2 Set-Up and Hardware Description  
Multipath propagation is one of the main limiting factors on the accuracy of GNSS for professional applications operating in harsh environments like dense urban areas or foliage zones [14; 15; 16; 17]. In particular, the case when the direct Line-of-Sight (LOS) signal is not present, known as NLOS signal reception, is very dangerous because these kind of signals can induce very large errors depending on the distance of the reflector. These effects can be sensed by using a dual-polarized antenna, as we show in this section by introducing the used hardware and the collection campaign carried out within the framework of the FANTASTIC project. This set-up is the cornerstone for the development of multipath mitigation techniques with dual-polarized antennas.
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 3 Coarse-Level Focusing and Classification of MEO SAR Ship Targets
### Ship Target Coarse-Focusing Based on Doppler Parameter Estimation
The optimization model is constructed as follows:  
\[\Delta K_{a}=\underset{\Delta K_{a}}{\text{argmin}}\ IE\left(\sum_{k=0}^{L-1} \epsilon^{j\pi}\frac{\Delta K_{a}PRF^{2}}{K_{a}^{2}}(\frac{k}{L}-\frac{1}{2}) ^{2}G(k,n)\epsilon^{j}\frac{2\pi kl}{L}\right) \tag{16}\]  
in which \(k\) denotes the azimuth frequency bin, ranging from 0 to \(L-1\),  
\[G(k,n)=\sum_{l=0}^{L-1}{I^{0}(l,n)}\epsilon^{-j\pi}\frac{kl}{L} \tag{17}\]  
is the range-Doppler domain SAR image, and \(I^{0}(l,n)\) is the original SAR image. This is a one-dimensional optimization problem, which can be solved using [PERSON]'s iteration method. Ignore the constant term. The gradient of the objective function is given by  
\[\frac{\partial IE}{\partial\Delta K_{a}}=-\sum_{l=0}^{L-1}\sum_{n=0}^{N-1} \frac{\partial{|I(l,n)|}^{2}}{\partial\Delta K_{a}}\Big{(}\ln{|I(l,n)|}^{2}+1 \Big{)} \tag{18}\]in which  
\[I(l,n)=\sum_{k=0}^{L-1}G(k,n)e^{j\pi\frac{\Delta K_{a}PRF^{2}}{K_{a}^{2}}(\frac{k}{ L}-\frac{1}{2})^{2}}e^{j\pi\frac{2\pi kl}{L}} \tag{19}\]  
Figure 7:0  
The gradient descent method is employed to iteratively solve the optimization problem above to obtain the azimuth FM rate error. After the quadratic phase errors are corrected, the coarsely focused SAR image is obtained, which can be utilized to conduct the subsequent coarse classification.
# Unexpected Ferromagnetism--A Review
## 2 Examples of \(d^{0}\) Ferromagnetism
### Non-Magnetic Oxides
#### 2.2.3 CeO\({}_{2}\)  
Ceria (CeO\({}_{2}\)) gained much interest due to its use in environmentally friendly applications for power generation because ceria stores and releases oxygen vacancies [35]. Another known fact about ceria is that stoichiometric CeO\({}_{2}\) is paramagnetic. In order to investigate the magnetic properties of CeO\({}_{2}\), studies were made on different degrees of oxygen-deficient CeO\({}_{2}\). It was observed that the ferromagnetic behavior of CeO\({}_{2}\) can be enhanced by increasing the oxygen vacancy concentration [36].  
Early studies on non-magnetic oxide nanoparticles, including CeO\({}_{2}\), showed that all investigated nanoparticles exhibited room-temperature ferromagnetism, while their corresponding bulk form did not. It was believed that the ferromagnetic behavior was due to surface oxygen vacancies that induced exchange interactions between unpaired electron spins and it was suggested that room-temperature ferromagnetism could be exhibited in all metal oxides in the form of nanoparticles [37; 38]. Other studies on the matter determined that a ferromagnetic behavior in CeO\({}_{2}\) nanostructures could be present only in structures that have less than 20 nm. Moreover, the study excluded the possibility of ferromagnetic behavior induced by impurities or oxygen vacancies [39]. [PERSON] et al. investigated the ferromagnetic behavior of CeO\({}_{2}\) films both experimentally and theoretically. They concluded that the exhibited magnetism is due to structural defects which are attributed to cerium and oxygen vacancies [40].  
Recent studies suggest that the structures of the vacancies that induce magnetic defects strongly depend on the crystallographic surface, as well as the temperature regime used on CeO\({}_{2}\) nanoparticles [41]. A combination between defects, disorder, and non-stoichiometry in nanostructured CeO\({}_{2}\) might be the main reason for the unexpected ferromagnetic behavior [42].  
CeO\({}_{2}\) transparent thin films in regard to their magnetic behavior were investigated at high temperatures. It was observed that the changes in the electronic structure cannot be the only reason for the unexpected exhibited ferromagnetism. The results indicated that the magnetic moments are strictly linked to oxygen vacancies [43].
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 4 Results
### Throughput Analysis  
Bandwidths defined by [26] for 5G were 100, 200, and 400 MHz, which were chosen for the analysis. Simulations considered a 36 \(\times\) 5 mMIMO system, and a subcarrier spacing of 120 kHz according also to the 5G recommendations. Tables 4 and 5 show the MCSs and, for each bandwidth, the number of subcarriers chosen. In the results, the throughput for each Tx-Rx distance was calculated by applying the SNR shown in Figure 6 at each point. The parameters associated with the algorithms _Ns_ and _NRF_ were fixed at 2 and 4, respectively, assuming the condition _min_(_Nt_,_Nr_) \(\geq\)\(N_{RF}\)\(\geq\) 2_Ns_ for an optimum performance [10].  
For statistical purposes, and in order to calculate the Packet Error Rate (PER), 100,000 transport blocks per position were first simulated, achieving a PER \(\geq\) 10\({}^{-3}\), which was low enough to allow the comparison of the different algorithms. Finally, the throughput (_Th_) was calculated from the PER as _Rb_(1-PER), where _Rb_ was calculated as in [32].
## 1 Introduction
As network traffic volumes grow and the complexity of cyber threats evolves, it is essential to ensure that intrusion detection systems can effectively process and analyze large-scale data streams in a timely manner. This study will explore techniques for optimizing the computational efficiency and resource utilization of deep learning models, such as parallel processing, distributed training, and model compression. The key contributions of this research are outlined as follows:  
1. The study explores the application of deep learning models, such as CNN and LSTM, for detecting network intrusions, aiming to enhance the accuracy and effectiveness of IDS in large-scale network environments. 2. The research compares the performance of deep learning models to traditional machine learning techniques (e.g., logistic regression, naive Bayes, and random forest), providing a comprehensive evaluation of their effectiveness in threat detection. 3. The paper highlights the potential of deep learning-based IDS to improve cybersecurity practices by offering more advanced, accurate, and efficient threat detection solutions, with insights that could inform the development of IDS for other applications, such as malware detection and vulnerability assessment. 4. The research discusses the trade-offs between accuracy and computational overhead, emphasizing the importance of scalability and resource utilization for deep learning-based IDS in real-world environments. The remainder of the paper is structured as follows: Section 2 offers a comprehensive literature review on network intrusion detection. Section 3 outlines the methodology, including the overall design, dataset, data cleaning, and feature engineering procedures. Section 4 presents the results and analysis, which are followed by a discussion in Section 5. Lastly, Section 6 concludes the paper and highlights future research directions.
## 2 Non-Orthogonally Multiplexed Cognitive Radio (NOMCR) System
### Fast Convolution Filtered OFDM Based-NOMCR System
#### 2.2.1 Fast Convolution Filtered OFDM (FC-F-OFDM) Transceiver
)) \tag{3}\]  
where \(\mathrm{L}_{\mathrm{ACT},\mathrm{m}}\) denotes the number of active subcarriers for the \(\mathrm{mth}\) sub-band of the SU- FC-F-OFDM, \(\mathrm{L}_{\mathrm{TBW},\mathrm{m}}\) defines the transition bandwidth (TBW) of \(\mathrm{mth}\) sub-band and \(\mathrm{d}^{\mathrm{su}}_{\mathrm{i},\mathrm{m}}\), i = 0, 1, \(\ldots,\mathrm{L}_{\mathrm{TBW}}-1\), are the non-trivial frequency domain filter weights in the transition band. The \(\mathrm{mth}\) sub-band pass-band weights are represented by the \(\mathrm{L}_{\mathrm{ACT},\mathrm{m}}\)-by-1 column vector of ones, denoted by boldface \(\mathbf{1}_{\mathrm{L}_{\mathrm{ACT},\mathrm{m}}-\mathrm{by}-1}\). On the other hand, stop-band weights of the \(\mathrm{mth}\) sub-band are denoted by the \(\left(\left\lfloor\frac{|\mathrm{L}_{\mathrm{m}-\mathrm{Lat},\mathrm{m}}|}{2} \right\rfloor-\mathrm{L}_{\mathrm{TBW},\mathrm{m}}\right)\)-by-1 column vector of zeros, denoted by the boldface \(\mathbf{0}^{\mathrm{su}}_{([\frac{|\mathrm{L}_{\mathrm{m}-\mathrm{Lat},\mathrm{m}}|}{2} |-\mathrm{L}_{\mathrm{TBW},\mathrm{m}})-\mathrm{by}-1}\). The receiver side AFB processing operations for the rth sub-block of the mth sub-band can be expressed as  
\[\mathbf{G}_{\mathrm{m},r}^{\mathrm{su}}=\mathbf{S}_{\mathrm{L}_{m}}^{\mathrm{su}} \cdot(\mathbf{W}_{\mathrm{L}_{\mathrm{m}}}^{\mathrm{su}})^{-1}\cdot(\mathbf{P}_{ \mathrm{N}_{\mathrm{sym}}^{\mathrm{su}}}^{\mathrm{su}})^{\frac{N_{\mathrm{sym}} ^{\mathrm{su}}}{2}}\cdot\mathbf{D}_{\mathrm{m}}^{\mathrm{su}}\cdot(\mathbf{M}_{ \mathrm{m},r}^{\mathrm{su}})^{\mathrm{T}}\cdot\mathbf{W}_{\mathrm{N}_{\mathrm{ sym}}^{\mathrm{su}}}^{\mathrm{su}} \tag{4}\]  
The receiver side sub-band processing operation, \(\mathbf{G}_{\mathrm{m},r}^{\mathrm{su}}\), acts as a matched filter on the received signal to recover the CP-OFDM for the mth sub-band. As evident from the above discussion, different sub-bands can have a different number of sub-carriers.
## 3 Results and Discussion
### EIS Measurements
Therefore, the calculation of the corrosion inhibition efficiency mainly takes into account the change in the charge transfer resistance value. The significant increase in \(Rf\) indicates that the adsorption of the PCA-GLU on the electrode surface forms an efficient shielded effect. The values of \(Cdl\) decline with an inhibitor concentration increase. From Table 2, it can be observed that as the concentration of PCA-GLU in the system rises, the \(CPE\) value reduces. The \(CPE\) value of the system can be described according to Equation (3). \[CPE=\frac{\varepsilon^{0}\cdot\varepsilon}{d}.S \tag{3}\]  
In the formula, \(d\) is the thickness of the double layer, \(S\) is the surface area of the carbon steel electrode exposed to the corrosive solution, and \(\varepsilon^{0}\) and \(\varepsilon\) are the dielectric constant and local dielectric constant of air, respectively. Therefore, we found that at higher concentrations of inhibitors, the surface area of the electrode reveals that the solutions are degraded, effectively inhibiting the corrosion of Q235. In addition, the water on the electrode surface will gradually be replaced by PCA-GLU molecules, reducing the local dielectric constant and thickening the electrical bilayer. These are all reasons for the decrease in \(CPE_{all}\) values. The electrochemical impedance spectroscopy results confirm that PCA-GLU has a good corrosion inhibition effect on carbon Q235 in a simulated seawater system.
# Crosslinked Bifunctional Particles for the Removal of Bilirubin in Hyperbilirubin Cases
## 2 Materials and Methods
### Methods
#### 2.2.3 Bilirubin Removal  
To study the bilirubin removal kinetic and capacity, the desired amount of bilirubin was dissolved in 0.06 M sodium hydroxide and added to either PBS or plasma. The resulting mixture was then passed through a bed of particles at a flow rate of 120 mL/min for a period of 2 h. The conditions were selected according to conventional protocols for extracorporeal depturation treatments [15]. The experiments were conducted at room temperature and protected from light to prevent degradation of bilirubin.
## 3 Materials and Methods
### CT Scanning/Thin Sections  
Medical computed tomography was conducted for each core segment (1 m) separately, using a Toshiba Aquilion Prime CT scanner, at the University of Patras, Greece. Acquisition parameters were set as follows: 0.5 mm slice thickness, 0.3 mm slice interval, helical rotation with pitch factor 0.637, 120 kV and 350 mA. For enhancement of the output data, during the rendering process, the soft tissue and bone algorithms were used. Each algorithm provides a different setting; thus, both approaches were examined and compared for better visualization of the sediment structures. Each core segment scanned (\(\sim\)1 m) produced 3500-4100 DICOM files when the scanner was set at the highest possible resolution.  
Micro-CT scanning was performed at the Hellenic Centre for Marine Research (HCMR) using a Skyscan 1172 micro-tomograph (Bruker, Kontich, Belgium). The scanner uses a tungsten X-ray source which is equipped with an 11 PM CCD camera (4000 \(\times\) 2672 pixel). The sample was scanned at a voltage of 100 kV and a current of 100 \(\upmu\)A with a combination of aluminum and copper filter and a pixel size of 13.79 \(\upmu\)m for a half rotation of 180\({}^{\circ}\). Projection images were reconstructed into cross-section images using the SkyScan's NRecon software (NRecon, Bruker, Kontich, Belgium), which implements a modified Feldkamp's back-projection algorithm. Subsequently, the reconstructed images were loaded into the software CT Analyserv v.1.18.4.0 + (CTAn, Bruker, Kontich, Belgium) to calculate the mean grayscale value, which represents the relative density of the sample. Furthermore, 3D analysis of the scanned sample was performed by using the custom processing plugin of CTAn software to calculate the 3D thickness of the sample. For optimization of the exported data, all samples during both CT scanning methods were scanned \"wet\" [45].  
Three representative thin sections (4.3 \(\times\) 2.3 cm) were prepared using standard techniques, including embedding in epoxy resin, and grinding, aiming to underline potential variations and advantages/disadvantages between CT scanning and microscopic analysis. Microscopic examination was performed through a petrographic microscope at 100-400\(\times\) magnification.
## 5 Conclusions  
The effect of uniform, grooving and non-uniform random corrosion on the hot-spot stress of a T-shaped tubular joint was investigated using the FE method. The following conclusions can be established:  
The FE model takes the weld geometry into account. The FE results are in good agreement with the experimental results and results from empirical equations. The uniform corrosion increases the SCFs, and the influence depends on the loading condition and the location. The SCF variations due to uniform corrosion predicted by the FE method are slightly different from those predicted by empirical equations.  
The grooving corrosion is modelled such that the corrosion rate of the weld metal is 1.4 times that of the base metal. For the grooving corrosion conditions, the SCFs obtained by the surface stress extrapolation method decrease with the increase in corrosion depth due to the complex stress distribution when approaching the weld toe, making the method questionable. For the grooving corrosion conditions, the through-thickness linearization method is more appropriate. The SCFs of grooving corroded tubular joints are higher than the corresponding uniform corroded tubular joints. The grooving corrosion is more detrimental than uniform corrosion for the tubular joint.  
The non-uniform corrosion is modelled based on the roughness parameter which increases with exposure time. The SCF of the non-uniformly corroded tubular joint fluctuated around the SCF of the uniformly corroded one. The SCF deviation at the critical region can be described by a normal distributed random variable. The standard deviation of the SCF deviation has a linear relationship with the roughness parameter. A simple method is proposed to account for the effect of non-uniform corrosion on the maximum SCF of the tubular joint.  
Figure 16: Relationship between standard deviation of SCF deviation and roughness.  
Note that the modelling of the grooving and non-uniform corrosion degradation is an idealization of the real condition. The deviation of the results due to the idealization should be investigated in the future.  
Conceptualization, Y.D.; methodology, Y.D., L.L., H.Y., X.L. and L.Z.; validation, L.L. and H.Y.; formal analysis, L.L. and M.X.; investigation, L.L., H.Y. and M.X.; writing--original draft preparation, L.L., Y.D., X.L. and L.Z.; writing--review and editing, Y.G. All authors have read and agreed to the published version of the manuscript.  
The present work is supported by the National Natural Science Foundation of China (Grant No. 52101350).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.