esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
## 2 System Model
For the imperfect CSI settings, we consider an actual system that faces difficulties because of the erroneous CSI assessment of the receiver. The imprecise calculation of CSI at the receiver leads to system performance degradation, especially in scenarios where accurate channel estimation is crucial for effective signal detection and decoding. The estimated channel \(h(\alpha)\) is modeled as follows:  
\[h(\alpha)=\hat{h}(\alpha)+E(\alpha), \tag{2}\]  
where \(E(\alpha)\sim\mathcal{CN}(0,\epsilon^{2})\) represents the channel estimation error, and \(\hat{h}(\alpha)\sim\mathcal{CN}\Big{(}0,\frac{1}{\epsilon^{2}}\Big{)}\) is the imperfect estimate of the channel. Here, \(\epsilon^{2}\) denotes the error variance in the CSI estimation. In this model, the channel estimation error variance \(\epsilon^{2}\) is dependent on the average SNR (\(\overline{\gamma}\)) and is defined as \(\epsilon^{2}=\frac{1}{1+\overline{\gamma}}\). Thus, as the SNR increases, the channel estimation error variance decreases, reflecting improved CSI accuracy at higher SNR values [12]. Figure 1: A general configuration of the MC-AE system.
# Dual-Frequency Linear-to-Circular Polarization Converter for Ka-Band Applications
## 4 Measured Results  
The proposed dual-band linear-to-circular polarization converter was fabricated using a standard PCB manufacturing process and it is shown in Figure 8. The six Rogers Ro3003 substrate layers are interleaved with five foils of Rohacell foam with the following thickness: _hf foam_1 = 3.5 mm and _hf foam_2 = 1.5 mm (Figure 1). 38 \(\upmu\)m of glue was necessary between the dielectric substrates and the foam. It should be noted that converter performances are affected by the glue layers. In particular, a small frequency shift and bandwidth reduction were observed in simulations. The characterization of the prototype was realized using a measurement setup similar to the one used in [13] and is shown in Figure 9.  
The prototype was placed between two standard horns used as transmitting and receiving antennas. The two antennas were connected to a vector analyser (VNA) for S-parameters measurement. The accuracy of the phase measurement can be severely limited by the test environment. For this reason, a method based on amplitude-only measurements was used to evaluate the Axial Ratio. During the test, the receiving antenna was rotated around its axis at \(\varphi=0^{\circ}\), \(90^{\circ}\), \(45^{\circ}\) and \(135^{\circ}\) (see Figure 9). The four linear polarization amplitudes \(E_{1}\), \(E_{2}\), \(E_{3}\), \(E_{4}\) acquired during the rotation were used in relations (11) and (12) of [36] to calculate the AR.  
Figure 8: Manufactured prototype: (**a**) Top side; (**b**) bottom side.  
Figure 7: HFSS simulated results of the elementary cell. (**a**) The module of the transmission coefficient: Continuous line, x-polarization; dashed line, y-polarization. (**b**) Transmission coefficient phase difference. (**c**) Axial Ratio.  
The measured axial ratios are compared with the simulations in Figure 10. The measurements show close agreement with the simulation values if the glue layers are included in the HFSS model. A bandwidth reduction and a limited frequency shift were observed in the experimental data and are attributed to fabrication tolerances. The AR remains below 3 dB in the bands 19.4-21.8 GHz (12.5%) and 27.9-30.5 GHz (8.7%). The maximum measured insertion loss is 0.5 dB and 0.4 dB at the lower and upper frequency respectively.  
Table 1 shows the performance of the dual-band converter proposed in this paper compared with other solutions presented in the recent literature. The proposed design compares well with other cases in terms of both bandwidth and insertion losses.  
Figure 10: Simulated (continuous line) and measured (dashed line) ARs of the proposed converter. Glue layers have been included in the simulation.  
Figure 9: Block diagram of the measurement setup.
## 3 Results
### Results of X-ray Diffraction  
Figure 2 shows X-ray diffraction (XRD) peaks for the samples before and after adding Fe\({}_{2}\)O\({}_{3}\) + Ag. The results of XRD analysis for the samples of Al reinforced by Fe\({}_{2}\)O\({}_{3}\) depict that increasing the wt.% of Fe\({}_{2}\)O\({}_{3}\) with a constant wt.% of Ag leads to an increase in the peaks of Fe\({}_{2}\)O\({}_{3}\) phase and Ag phase. In fact, the results of the XRD examination reveal that the increase in the wt.% of the reinforcement materials leads to an increase in the intensity of diffraction peaks. Moreover, there are no other possible intermetallic inclusions, most likely created between Al and Fe\({}_{2}\)O\({}_{3}\) + Ag in a thermo-dynamically steady state [17].  
Figure 2: _Cont._As shown in Figure 2, for the XRD data for the pure Al sample, the Al phase formed at 2\(\theta\) (38.485\({}^{\circ}\), 44.735\({}^{\circ}\), 65.135\({}^{\circ}\) and 78.285\({}^{\circ}\)) for the planes at hkl (111, 200, 220 and 311), respectively. For the XRD data for the Al/3 Fe\({}_{2}\)O\({}_{3}\) + 1 Ag sample, in addition to the Al phase, the Ag phase formed at 2\(\theta\) (38.035\({}^{\circ}\), 44.185, 64.385\({}^{\circ}\) and 77.435\({}^{\circ}\)) for the planes at hkl (111, 200, 200 and 311), and the Fe\({}_{2}\)O\({}_{3}\) phase was created at 2\(\theta\) (33.185\({}^{\circ}\), 35.585\({}^{\circ}\), 49.485\({}^{\circ}\), 54.085\({}^{\circ}\) and 62.485\({}^{\circ}\)) for the planes at hkl (104, 110, 024, 116 and 214), respectively. For the XRD data for the Al/\(\theta\)Fe\({}_{2}\)O\({}_{3}\) + 1 Ag sample, in addition to the Al and Ag phases, the Fe\({}_{2}\)O\({}_{3}\) phase formed at 2\(\theta\) (33.185\({}^{\circ}\), 35.585\({}^{\circ}\), 40.885\({}^{\circ}\), 49.485\({}^{\circ}\), 54.085\({}^{\circ}\) and 62.485\({}^{\circ}\)) for the planes at hkl (104, 110, 113, 024, 116 and 214), respectively. In addition, the XRD data for the Al/12 Fe\({}_{3}\)O\({}_{3}\) + 1 Ag sample show that, in addition to Al and Ag phases, the Fe\({}_{2}\)O\({}_{3}\) phase was created at 2\(\theta\) (33.185\({}^{\circ}\), 35.585\({}^{\circ}\), 40.885\({}^{\circ}\), 49.485\({}^{\circ}\), 54.085\({}^{\circ}\), 62.485\({}^{\circ}\) and 71.935\({}^{\circ}\))Finally, micro-hardness testing is considered an important tool to define the microstructures and mechanical properties of nano-composites [18].  
As shown in Figure 3, the maximum micro-hardness of Al/12 Fe\({}_{2}\)O\({}_{3}\) + 1 Ag is most likely a result of the phase created especially for the Fe\({}_{2}\)O\({}_{3}\) phase created at 2\(\theta\) (71.935\({}^{\circ}\)) for the hexagonal plane at hkl (1010), which in turn increases the micro-hardness for this sample.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 7 Performance Analysis of the Proposed ECSSP-SC
### Analysis of Communication Cost  
Communication cost or overhead of ECSSP-SC has been calculated by computing the size of the messages transmitted between the \(USR\) and the \(SVR\) during the execution of the mutual authentication phase of the protocol. In the proposed ECSSP-SC, two messages are transmitted, \(\{c,T,w\}\) and \(\{A_{S},T_{S}\}\). Assuming that 160-bit ECC parameters have been used, SHA-1 has been applied for producing the hash code, and AES-128, along with a 128-bit key, has been applied for encryption and decryption.  
The communication overhead of the ECSSP-SC is computed as follows:  
Overhead for the \(USR\):  
\[=256+320+160=736\text{ (bits)} \tag{38}\]  
Overhead for the \(SVR\):  
\[=256+160=416\text{ (bits)} \tag{39}\]  
Thus, the total communication overhead of the ECSSP-SC:  
\[=736+416=1152\text{ (bits)} \tag{40}\]  
A comparative analysis of the communication overhead of the ECSSP-SC and related protocols was performed and the graphical analysis of the comparison is shown in Figure 10.
# Application of Laser-Ultrasonics for Evaluating Textures and Anisotropy
## 3 Results and Discussion  
### Stainless Steel-Cylindrical Specimens  
Plots of the longitudinal wave velocity are shown in Figure 8 for all four material conditions in both rotation series, i.e., with the rotations around RD and around TD, corresponding to azimuthal angles of 0\({}^{\circ}\) and 90\({}^{\circ}\). In fact, the type and degree of anisotropy is rather similar in each material. There is a two-fold symmetry for rotations around the TD axis and four-fold symmetry for RD axis rotations.  
Also shown in Figure 8 are model calculations based on the textures measured by EBSD when enforcing symmetry on the calculated stiffness matrix. These are, in general, very close to the LUS measurements. Two sets of model results are included in each case. These refer to the Hill approximation used. It is universally assumed in the published literature that the Hill average is a simple arithmetic average of the Voigt and [PERSON] limit values. However, in his paper [19], [PERSON] points out that a geometric average should be equally applicable, so this is also included in Figure 8. In fact, the differences are very small, although the geometric average appears to agree slightly better with the measurements than does the arithmetic one. This outcome is, however, sensitive to the crystal constants that are assumed (from literature sources), so a general conclusion on the relative merits is probably preliminary at the present time.  
Figure 7: Photograph of the martensitic steel samples after the annealing (at the indicated temperatures).  
Figure 8: LUS measured velocity versus the polar angle for all the cylindrical samples where (**a**,**b**) represents the as-received 50% reduced, (**c**,**d**) the heat treated 50% reduced, (**e**,**f**) the as-received 70% reduced, and (**g**,**h**) the heat treated 70% reduced sample. The graphs also display the EBSD calculated velocity using [PERSON] arithmetic and the geometric approximation.  
In all cases, the calculated velocities lie above the experimental values, which may also relate to uncertainty in the crystal coefficients applied in the models or may reflect a fundamental inadequacy of the [PERSON] models. It may be added, however, that determinations of [PERSON]'s modulus on 316 stainless steel [23] showed a very similar deviation of the [PERSON] calculations to higher values than the experimental ones. [PERSON]'s model is not based on any strict physical principle, so it may not be correct in the limit. More fundamental approaches are possible, see, e.g., [27], but have not been attempted here.
## 3 Results and Discussion
### Evaluation of R. toruloides Growth on Different Concentrations of VFAs
_toruloides_ on synthetic VFAs and glucose (GC) with (**A**) 10 g/L and C/N 50, (**B**) 20 g/L and C/N 50, and (**C**) 30 g/L with C/N 50 and C/N 100.  
accumulation reached 5.2 g/L for glucose and 3.5 g/L for acetic acid, but the lipid content was still higher with acetic acid (49.3% \(w/w\) for C2 and 41.6% \(w/w\) for GC). The role of the C/N ratio in cell growth and microbial lipids production has already been observed. An enhancement in the production of the lipids has been associated with an increase in the C/N ratio (nitrogen limiting conditions). In order to increase lipid accumulation, _R. toruloides_ was cultivated using a C/N ratio of 100 at 30 g/L (Figure 2C). In that case, despite substrate consumption proceeded at a slower rate for both glucose and acetic acid, as compared with C/N 50, it reached 95.2% for glucose, while all acetic acid was consumed. The DCW for glucose did not change significantly (12.2 g/L) and for acetic acid, it increased slightly to 7.5 g/L. On the other hand, the lipid accumulation and content showed an important increase for acetic acid (4.2 g/L and 56.4%) with C/N 100 compared to C/N 50. For glucose, there was also a slight increase in the lipids accumulation and content (5.6 g/L and 45.8% \(w/w\)). This is also in line with the results reported by [PERSON] et al. (2016), where an increase in the C/N ratio for the cultivation of _R. toruloides_ with 20 g/L acetic acid did not lead to a further increase in the biomass formation, in contrast to the lipid content. The biomass yields decreased when increasing the substrate concentration (Figure 3A). On the contrary, the lipid yield (Figure 3B) of glucose and acetic acid shows an increase, with the highest lipid yield to be reached at 30 g/L and C/N 100 for glucose (0.19 g/gsubstrate) and for acetic acid (0.14 g/gsubstrate). However, with propionic and butyric acid, the lipid yield decreases with an increase in the initial concentration. The maximum lipid yield for propionic acid and butyric acid is observed at 10 g/L with a value of 0.08 g/gsubstrate for both VFAs. Among all substrates tested, the highest biomass and lipid yields are reached with glucose. However, among the VFAs, the highest biomass yield is reached with butyric acid, but this is not the case for the lipid yields which are significantly higher with acetic acid. This indicates _R. toruloides_ preference of acetic acid toward lipids, while butyric acid is favoring cell biomass formation.
## 6 Results Analysis and Discussion
### Performance of Congestion Window across Varying AQM Algorithms  
**Scenario 1 (Bandwidth = 10 Mbps, Delay = 20 ms)**: We evaluated the performance of FQ-CoDel, FQ-PIE and L4S in two cases: Case 1 with enabled ECN and Case 2 with ECN disabled, for comparison. Figures 36 and 37 present the results for the two cases.  
In the FQ-CoDel algorithm with ECN enabled (Figure 36a), the _cwnd_ for some TCP connections steadily increases over time. There seems to be extreme unfairness in its _cwnd_ sizes across connections in the bottleneck, predominately showing a preference for TCP connections that started later. When ECN is disabled (Figure 37a), the _cwnd_ exhibits periodic oscillation due to frequent RTO and NewReno's response to congestion, and the _cwnd_ has a lower maximum _cwnd_ size than other algorithms. This directly influences the total data transferred throughout the process across all connections, as seen in Table 2, significantly lower than its L4S and FQ-PIE competitors when ECN is disabled.  
In the FQ-PIE and L4S algorithms, regardless of ECN, as illustrated in (Figure 36b,c when ECN is enabled, and Figure 37b,c when ECN is disabled), the _cwnd_ displays a sawtooth pattern characterized by sharp increases in _cwnd_ followed by rapid decreases upon detecting packet loss. This reflects NewReno's adaptive behavior to maximize throughput while avoiding congestion.  
**Scenario 2 (Bandwidth = 1 Mbps, Delay = 20 ms)**:  
Evaluation is conducted in two cases: Case 1 with ECN enabled and Case 2 with ECN disabled for comparison. Figures 38 and 39 present the results for these two cases.  
Similar to Scenario 1, FQ-CoDel's _cwnd_ with ECN enabled (Figure 38a steadily increases. Its _cwnd_ values are significantly higher during upwards trends of the staircase pattern, though its frequency of rapid change is lower compared to FQ-PIE and L4S. This behavior is brought about by FQ-CoDel's behavior of only using ECN notification instead of dropping packets in extreme congestion conditions. When ECN is disabled in Figure 39a, FQ-CoDel shows a sawtooth pattern in its _cwnd_ with a higher frequency of dips and rises compared to FQ-PIE and L4S, regardless of ECN with a lower maximum _cwnd_ size. Like Scenario 1, it directly influences the total data transferred throughout the multiple connections, as seen in Table 2, which is lower than its L4S and FQ-PIE competitors when ECN is disabled but not as exaggerated as in Scenario 1 because of its low bandwidth.  
Figure 37: Case 2: (ECN disabled) Congestion Window Scenario 1 with Bandwidth = 10 Mbps, Delay = 20 ms. (**a**) FQ-CoDel (_cwnd_); (**b**) FQ-PIE (_cwnd_); (**c**) L4S (_cwnd_).  
Similar to Scenario 1, In the FQ-PIE and L4S algorithm, regardless of ECN (Figures 38b,c and 39b,c), the _cvnd_ displays a sawtooth pattern demonstrating NewReno's aggressive behavior in adapting optimal _cvnd_ values.
# Guidelines for Experimental Algorithmics:
## 4 Guidelines for the Experimental Design
### Determining Your Hypotheses  
Experiments are mainly used for two reasons; as an exploratory tool to reveal unknown properties and/or as a means to answer specific questions regarding the proposed algorithm. The latter suggests the development of certain hypotheses on the behavior of our algorithm that can be confirmed or contradicted by experiments. In the following, we group common hypotheses of algorithm engineering papers in network analysis into two categories:  
1. Hypotheses on how our algorithm performs compared to the state of the art in one of the following metrics: running time, solution quality or (less often) memory usage. Ideally, an algorithm is deemed successful when it dominates existing algorithms in terms of these three metrics. However, in practice, we are usually content with algorithms that exhibit a good tradeoff between two metrics, often running time performance and solution quality. As an example, in real-time applications, we may be willing to sacrifice the solution quality (up to a certain threshold), in order to meet running time bounds crucial for the application.
2. Hypotheses on how the input instances or a problem-/algorithm-specific parameter affects our algorithm in terms of the aforementioned metrics. For example, a new algorithm might only outperform the existing algorithms on a certain type of graphs or an approximation algorithm might only be fast(er) if the desired approximation quality is within a certain range. If a hypothesis involves such restrictions, it should still be general enough for the algorithmic result to be relevant--overtuning of algorithms to uninteresting classes of inputs should be avoided. Other aspects of investigation may be decoupled from the state of the art to some extent and explore the connection between instance and algorithm properties: for instance, a hypothesis on how the empirical approximation error behaves over a range of input sizes.  
KADABRA Example  
In the context of our betweenness approximation study, we formulate three basic hypotheses:  
1. KADABRA has a better running time and scalability (with respect to the graph size) than other algorithms, specifically the main competitor RK [23].
2. There is a significant difference between the solution quality of KADABRA and that of RK. (In Section 4.6, we explain how to evaluate the solution quality for approximation algorithms in more detail).
3. The diameter of input graphs has an effect on the running time of KADABRA: The KADABRA algorithm computes the betweenness values faster for graphs with low diameter than for graphs with large diameter.  
The first two hypotheses belong to the first category, since we compare our implementation of the KADABRA algorithm to related algorithms. The other hypothesis belongs to the second category and is related to the performance of the KADABRA algorithm itself. Namely, we test how a data-specific parameter may influence the running time in practice. To evaluate these hypotheses experimentally, we need to select instances with both low and high diameter values, of course.
## 2 Design and Simulations
### Mathematical Models  
The commercial Computational Fluid Dynamics (CFD) software (Ansys FLUENT 19.0) is adopted to perform the numerical simulations. In the simulation, the flow and heat transfer in the electric heater are governed by conversation, momentum, and energy-conversation equations. In view of the long heating pipe (\(>\)5 mm) with a small inner diameter (\(<\)10 mm), the entry region can be ignored. For the fully developed turbulent flow, the standard k-\(\epsilon\) model is sufficient with reasonable accuracy, and the similar results are expected to be obtained from different turbulent models. The Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm is employed to address the coupling of velocity and pressure. For the fully developed turbulent region, the boundary-layer profile can be divided into the viscous sublayer, buffer layer, log-law region inner layer, and outer region. The sublayer region can be up to y\({}^{*}<\) 10, and the log-law region is applied for y\({}^{*}>\) 30. The buffer layer in between cannot be precisely resolved. In our calculation, the inner-tube diameter ranges from 5 mm to 7 mm. If there are 24 heating pipes, for the largest flow rate \(G\) = 10 Nm\({}^{3}\)/min, the nitrogen velocity is calculated to be smaller than 5 m/s with a downhole pressure of 10 Mpa and temperature of 50 \({}^{\circ}\)C, the corresponding Reynolds number is about 10\({}^{5}\). Considering the large Reynolds number, the turbulent boundary layer can be in a small impact on the flow velocity. Therefore, the standard wall functions are adopted. The benefit of the wall-function approach lies in a significant reduction in mesh resolution. The governing equations for turbulence flow can be stated as follows [20]:  
Continuity equation:  
\[\frac{\partial}{\partial x_{j}}\big{(}\rho u_{j}\big{)}=0 \tag{1}\]  
Momentum equation:  
\[\frac{\partial}{\partial x_{j}}\big{(}\rho u_{i}u_{j}\big{)}=-\frac{\partial p }{\partial x_{i}}+\frac{\partial}{\partial x_{j}}\Bigg{[}\mu\Bigg{(}\frac{ \partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\Bigg{)}- \rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\Bigg{]} \tag{2}\]  
Figure 1: Geometric model of electric heater: (**a**) front view of electric heater, (**b**) left view, and (**c**) right view of layout schemes of heating tubes. Energy equation for fluid flow:  
\[\frac{\partial\left(\rho u_{j}T\right)}{\partial x_{j}}=\frac{\partial}{\partial x _{j}}\left(\frac{\mu}{Pr}\frac{\partial T}{\partial x_{j}}-\rho\overline{T^{ \prime}u_{j}^{\prime}}\right) \tag{3}\]  
Energy equation for solid:  
\[\frac{\partial}{\partial x_{j}}\left(k\frac{\partial T}{\partial x_{j}}\right)=0 \tag{4}\]  
where \(Pr\) is the Prandtl number, \(k\) is the turbulent kinetic energy, \(u_{i}\) is the average velocity, \(u_{i}^{\prime}\) is the fluctuating velocity, and \(T^{\prime}\) is the fluctuating temperature.