esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results & Discussion
### Comparison of Case 2
#### 4.2.2 Reflection on Velocity  
To explain the mechanism of enhancing heat transfer, analyzing the flow characteristics is essential on the thermal surface in a simplified two-chamber. Figure 6 provides information on detailed flow characteristics above and nearby the rib turbulator arrangement. The result on the section in the XZ plane (Y = 0 in Figure 2a) indicates that the fluid vector will be altered by the convex surface, which means that the distribution of fluid has been changed. Most researches point out that the vortex makes convective heat transfer improvement on the fluid-solid surface. Two vortex can be seen in the cooling chamber of Figure 6 which were caused by inducing of double-rib structures. One is generated above the rib, with the other being located between two ribs. The height of rib \(H\) is perceived to be the influence factor. When \(H\) = 5 mm, there is amounts of space over the rib remained for vortex growing.  
Figure 5: Comparisons of temperature distribution on the thermal surfaces.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & \(W\) **(mm)** & \(H\) **(mm)** & \(\lambda_{out}\) **(K-kg/s)** & \(T_{min}\) **(K)** & \(\eta\) \\ \hline
1 & 0 & 0 & \(-83.6\) & 783 & \\
2 & 5 & 5 & \(-105.7\) & 601 & 26.4\% \\
3 & 5 & 10 & \(-110.8\) & 532 & 32.5\% \\
4 & 5 & 15 & \(-102.0\) & 467 & 22.0\% \\
5 & 10 & 5 & \(-104.1\) & 610 & 24.5\% \\
6 & 10 & 10 & \(-110.6\) & 529 & 32.3\% \\
7 & 10 & 15 & \(-108.5\) & 479 & 29.8\% \\
8 & 15 & 5 & \(-107.4\) & 593 & 28.5\% \\
9 & 15 & 10 & \(-105.1\) & 566 & 25.7\% \\
10 & 15 & 15 & \(-108.3\) & 474 & 29.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data of flow rate temperature and cooling efficiency on thermal surface in case 2.  
However, when \(H\) increased, the scale of vortex is squeezed. The larger the \(H\) is, the flatter vortex become. Simultaneously, the change of \(W\) can also affect the velocity and the generation of vortex, which indirectly influenced the heat transfer enhancement of convex surface. Overlarge \(W\) would narrow the space between two ribs, which may bring about heat-obstruct effect as ball bearing or make vortexes hardly exist. Similarly, vortexes cannot effectively cool down the whole space if \(W\) is too small. It is worth noting that oversized \(H\) may cause no vortex be found between ribs. In Figure 6, the result of group 4 (10 \(\times\) 15) shows almost no vortex during the space of double-rib and the worst result, which is opposite to all the other results with non-smooth surface.
###### Abstract  
In most state-of-the-art Bridge Management Systems, structural condition is predicted by a homogeneous Markov chain model that uses condition ratings assigned during visual inspections. Although generally accepted, such an approach exhibits certain shortcomings, one of which is not considering the nature of actual physical phenomena that cause deterioration. To overcome this shortcoming, this article presents a framework that combines both information on condition ratings through the semi-Markov process and knowledge of bridge properties using analytical deterioration models. In this manner, and contrary to current practice, not only are the results of visual inspection taken into account, but also information such as environmental loading, as well as material and structural properties. The presented framework was implemented in the case study bridge, in which the deterioration caused by carbonation-induced corrosion was studied. Along with the implementation in the case study, the article contained a detailed overview of the subject of carbonation-induced corrosion and emphasized issues that require additional research in order to develop the framework into a comprehensive and fully applicable tool for condition prediction. Accounting for its adaptability to other material types and deterioration processes and its consideration of the historic deterioration path, the framework presents a suitable alternative to frameworks presently implemented for condition prediction.  
concrete bridges; condition prediction; bridge management system; semi-Markov process; Markov chain; carbonation-induced corrosion; structural performance; deterioration
## 2 Materials and Methods
### Reactors Setup
#### 2.2.2 Batch Assays  
The experimental setup consisted of lab-scale reactors with a working volume of 500 mL, operated under anaerobic conditions for 7 days. Each reactor was inoculated with anaerobic granules from a wastewater treatment plant of an industrial Portuguese brewery (Super Bock Bebidas S.A., Matosinhos, Portugal) at an initial biomass concentration of 20.0 \(\pm\) 3.5 gVSS L\({}^{-1}\) (20-30% \(v/v\)). The reactors were acclimatized overnight to temperature and pH with concomitant redox potential decrease to values of \(-\)400 mV, indicating that anaerobic conditions were achieved. Subsequently, the reactors were fed with BSG hydrolysate. Four different organic loads (10, 20, 30 and 40 gCOD L\({}^{-1}\)) and three different pH values (5.0, 5.5 and 6.0) were tested, in triplicate experiments. The temperature was kept at 30.0 \(\pm\) 0.1 \({}^{\circ}\)C and magnetic stirring provided at 300 rpm. pH was controlled by the automatic addition of 1 M NaOH and 1 M HCl solutions. Redox potential was continuously monitored. No extra nutrients were fed to the batch assays, since BSG contains proteins and other nutrients necessary for the culture metabolism. Samples from the reactor and BSG hydrolysate used as feed were frequently collected for further analyses.
###### Abstract  
In carbon capture utilization and storage (CCUS) projects, the transportation of CO\({}_{2}\) by ship can be an attractive alternative to transportation using a pipeline, particularly when the distance between the source and usage or storage location is large. However, a challenge associated with this approach is that the energy consumption of the liquefaction process can be significant, which makes the selection of an energy-efficient design an important factor in the minimization of operating costs. Since the liquefaction process operates at low temperature, its energy consumption varies with ambient temperature, which influences the trade-off point between different liquefaction process designs. A consistent set of data showing the relationship between energy consumption and cooling temperature is therefore useful in the CCUS system modelling. This study addresses this issue by modelling the performance of a variety of CO\({}_{2}\) liquefaction processes across a range of ambient temperatures applying a methodical approach for the optimization of process operating parameters. The findings comprise a set of data for the minimum energy consumption cases. The main conclusions of this study are that an open-cycle CO\({}_{2}\) process will offer lowest energy consumption below 20 \({}^{\circ}\)C cooling temperature and that over the cooling temperature range 15 to 50 \({}^{\circ}\)C, the minimum energy consumption for all liquefaction process rises by around 40%.  
CO\({}_{2}\); liquefaction; CCUS; optimization; ambient temperature +
Footnote †: journal: Applied Sciences
## 3 Excitation Characteristics of Tuned Stators and Blade Period Design
### Effect of Stator Blade Number on Excitation towards the Bladed Disk
Therefore, when analyzing the circumferential wave components of the excitation, according to the resonance conditions, the cycle wave number can be set to 11. Figure 8 shows the circumferential wave spectrum distribution pressure at the monitoring points at the leading edge, basin, back, and trailing edges. The excitation components at the root, middle, and tip of the blade are distinguished by different colors. It can be seen from Figure 8 that harmonic excitation from 1 to 11 is reflected in the pressure circumferential wave spectrum distribution, and the components corresponding to 11 (\(N_{s}=11\)) are dominant, except at the trailing edge which also exhibits the smallest amplitude. The circumferential wave spectrum at the trailing edge is complicated. The reason for this phenomenon is that the excitation at the trailing edge is more sensitive to the number of rotor blades (\(N_{\rm r}=22\)). However, its overall amplitude is much smaller than that of the leading edge and the basin, and the excitation components here have little contribution to the pressure pulsation excitation components of the blade disk system. Figure 9 shows the wave spectra of pressure amplitudes for four monitoring points at the middle height of the rotor blade corresponding to three working conditions with different stator numbers (\(N_{s}=10,12,13\)). Figure 7: Frequency spectrum of monitoring point pressure at the leading edge of the rotor. By analyzing the excitation spectrum generated by different stator blade numbers (Figures 8 and 9), the following conclusions can be obtained:  
(1) The circumferential wave distribution of the excitation generated by the stator structure with the set period number on the downstream rotor structure contains various harmonic components; the main harmonic components are:\(N_{s}\), \(|N_{s}-N_{r}|\), \(2N_{s}-N_{r}\), \(3N_{s}-N_{r}\), and \(3N_{s}-2N_{r}\). It is not difficult to find that the expression of harmonic excitation above is consistent with the expression of nodal diameter numbers derived from Equation (3), namely:  
\[n=\pm(k_{s}N_{s}-kN_{r}), \tag{10}\]  
where \(k=0,\ 1,\ 2,\ \ldots\); \(k_{s}=1,\ 2,\ \ldots\) Thus, the main harmonic component of the circumferential wave distribution of the excitation generated by the stator (blade number \(N_{s}\)) is given by Equation (10);  
(2) In the excitation spectrum, the amplitude of harmonic excitation corresponding to \(k_{s}=1,k=0\) is the largest, and other components' amplitudes decrease with the increased order of the excitation spectrum. This is consistent with the relative magnitude of the frequency component. Figure 8: Circumferential wave spectrum of monitoring point pressure (\(N_{s}=11\)) at: (**a**) the leading edge; (**b**) the basin; (**c**) the back; and (**d**) the trailing edge.
## 3 Probability of Chip Error in Coherent O-QPSK
### Without Pulse Shaping
#### 3.1.1 Case (a): \(\sqrt{I}<\sqrt{C/2}\)  
When \(\sqrt{I}\) is smaller than \(\sqrt{C/2}\), as shown in Figure 2a, the received signal never crosses other symbol regions. As a consequence, the demodulator will never make an error, and therefore:  
\[P_{c} =0\] \[P_{s} =0\]
## 5 Experimental Simulation Analysis
### The Impact of Different Replication Times P on the System  
In order to investigate the effect of different numbers of replications \(P\) on the BER performance of the NR-ODBR-DCSK system, two sets of comparative experiments are performed in this section, one for the comparison of the system performance simulation when the number of replications is taken as 1, 5, 10, and 20 and beta is 300, as shown in Figure 13a below, and the other for the comparison of the system performance simulation when the number of replications is taken as 1, 5, 10, and 20 and beta is 100, as shown in Figure 13b below.  
Figure 12: Comparison of BER performance of different DCSK systems.  
The figure shows that the bit error rate (BER) of the NR-ODBR-DCSK system decreases significantly as the SNR increases. The curves at different \(P\) values show that increasing the \(P\) value can effectively reduce the BER, and when \(P=20\), the system has the best BER, the lowest BER and the best performance at high SNR. When \(P=1\), the system is equivalent to a DCSK system, with significantly higher BER and relatively poor system performance. Therefore, increasing the \(P\) value can improve the noise immunity of the NR-ODBR-DCSK system and enhance the reliability of the communication system. Specifically, when \(P\) is increased from 1 to 10, the BER decreases significantly; however, when \(p\) is increased to 20, the magnitude of the performance improvement becomes smaller, indicating that \(P=10\) is the most effective choice to improve the system performance, and further increases in \(P\) have limited effect on the improvement in BER. Therefore, around \(P=10\) is the best balance point for system performance optimization, and further increasing \(p\) on this basis has less effect on BER.
## 6 Experimental Results
### Accelerator Core Function Verifications  
All cryptographic accelerator cores connected to the SoC through the peripheral bus were evaluated using custom firmware, developed and compiled using the standard and open-source GNU Compiler Collection (GCC) RISC-V toolchain.  
The firmware/software interface for each cryptographic component was developed using the mapping address information from Tables 1-4. The complete cipher functions and verification test cases for the PRINCE, PRESENT-80, ChaCha, and SHA3-512 algorithms are also included. The verification results of the PRINCE, PRESENT-80, ChaCha, and SHA-3 cores are displayed in Figures 12-16, respectively. This evaluation affirmed that all functional components were in the proper operation condition.  
Figure 14: Verification result for the PRESENT-80 block cipher. Two simple test cases verified the encryption and decryption function of the accelerator core and correctly returned the expected data.  
Figure 15: Verification result for the stream cipher ChaCha with 8 and 20 rounds of configuration, using the same 256-bit key and 64-bit initial vector, outputting 512-bit keystreams (blocks), which met the expected results.
## 4 Localized Query Loss Discovery with Distributed Query Resolution
### Distributed Query-Address Resolution (QAR)  
To efficiently detect and recover the query losses, each detector should judge whether a query is associated with the object node that the detector is monitoring. If an object node is associated with the query, the detector is activated to monitor the query losses at the object. On the other hand, if the detector finds that a query is not associated with the objects it is in charge of, the detector can go to inactive mode (such as sleep mode) to save energy.  
The basic function of _Query-Address Resolution_ (QAR) is in some way analogous to that of judgment of \"pathogen harmfulness\" in immune systems. It enables each detector node in sensor networks to make a correct decision of whether to \"remove\" a query loss by initiating a query loss discovery. QAR resolves the query at each individual sensor node to the corresponding addresses of sensor nodes and it lets each loss detector judge whether a query is correlated with the object node of a detector. Each node registers its ID, location and attributes, such as temperature or humidity, to its detectors. A query message can be resolved to the corresponding sensor addresses by the QAR.  
An example of the information maintained at QAR of a node is illustrated in Figure 3, where Node 3 registers its ID, location and attributes to its loss detectors Nodes 2 and 7. Upon receiving a query message _\"what is the brightness in the region 1?\"_, the detectors are able to judge that node with ID 3 in the resolution table should give a response. The detectors will initiate a loss recovery if they detect that the node with ID 3, rather than other nodes, has not replied to the query.  
The query-address resolution enables each detector node to have a selective and localized response to each query, which is correlated with a certain type of sensor nodes rather than all sensor nodes [1,2,5]. That is, each detector adopts an activation/response manner to start working only when a query loss is found to be correlated with its object sensor nodes. Figure 4 shows the state transitions at a detector node. The detector node is in its activated state when it receives a query message requesting data from one of the objects that it is monitoring. The detector node then decides whether it should respond by checking the query loss of its monitored objects. This is realized by overhearing packet replies from the object nodes. If there is packet loss, the detector initiates the recovery process in which each detector of the object node sends a recovery packet in turn to recover from packet loss at the object node. When the detector node receives a confirmation of recovery or the time of the recovery process expires, the detector returns to its initially inactive state.
## 4 Discussion
### Atmosphere  
The signal emitted by radar is electromagnetic waves. During the propagation process in atmospheric media, uneven media can cause disturbances to the propagation path, causing changes in the direction and path of signal propagation and generating an atmospheric additional phase. This phase is affected by atmospheric parameters such as temperature, humidity, and atmospheric pressure. If there is a significant change in atmospheric parameters during the two measurements, it will have a significant impact on the inversion of the true deformation value, thereby affecting the measurement results of glacier movement speed. The atmospheric parameters in the Sichuan Tibet region change rapidly, and the runoff after glacier meltwater in the experimental environment is relatively large, which has a certain degree of interference with data collection. The normal methods include atmospheric parameter correction [58; 59] and Permanent Scatter (PS) technology [60; 61]. Atmospheric parameter correction requires measuring the dry temperature, wet temperature, air pressure, and absolute temperature of the atmosphere to establish an atmospheric refraction model and solve the atmospheric refractive index. However, obtaining parameters on the TP is very difficult. It is not easy to determine whether there is a stable atmospheric region around it, and isolated coherent regions cannot achieve atmospheric phase estimation, which is also a key aspect that needs to be focused on in the processing of micro deformation measurement results.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### Scattering Parameters (S-Parameters)  
Scattering parameters (S-parameters) characterise how RF signals are transmitted and reflected between ports in a multi-port network. For a three-port device such as a single-pole double-throw (SPDT) switch, the S-parameter matrix, denoted by the \(3\times 3\) matrix \(\mathbf{S}\), is given by  
\[\mathbf{S}\ =\ \begin{bmatrix}S_{11}&S_{12}&S_{13}\\ S_{21}&S_{22}&S_{23}\\ S_{31}&S_{32}&S_{33}\end{bmatrix}, \tag{41}\]  
where each element \(S_{ij}\) represents the ratio of the reflected wave at port \(i\) to the incident wave at port \(j\).  
* \(S_{11}\) is the reflection coefficient at port 1.
* \(S_{21}\), \(S_{31}\) are the transmission coefficients from port 1 to ports 2 and 3, respectively.
* \(S_{12}\), \(S_{13}\) represent the transmission from ports 2 and 3 back to port 1, respectively.
* \(S_{22}\) is the reflection coefficient at port 2.
* \(S_{33}\) is the reflection coefficient at port 3.
* \(S_{23}\), \(S_{32}\) indicate the transmission between ports 2 and 3.
## 5 Experiments
### Effect of Coil Arrays  
As shown in Figures 26 and 27, the running speed of the wire rope is 5 m/s, and the lift-off distance is 15 mm. We analyzed the data collected from three types of coil arrays with the magnetizer mentioned above for this study. Comparing the signal waveforms, the results show that the coil arrays arranged side by side acquire much noise. Comparatively, the stacked and the concentric can collect the single broken wire defect, represented by the minor spike. More specifically, the SNR of the signal collected by the concentric coil array is better than the stacked, and the defect signal cannot be distinguished from the side-by-side sensor.  
Meanwhile, all the coil arrays in the experiment are connected differentially. To prove the effectiveness of the differential connection, under the situation that the running speed is 10 m/s, Figure 28 demonstrates that the differential connection can improve SNR and  
Figure 27: (**a**) Concentric; (**b**) Stacked; (**c**) Side-by-side.  
Figure 26: (**a**) Concentric; (**b**) Stacked; (**c**) Side-by-side.  
Figure 24: (**a**) Untwine of wires in strand; (**b**) Irregular joining of wires; (**c**) Binding single broken wire.  
reduce noise, which is caused by the swing signal and strand signal. It can be concluded that the differential connection can reduce noise because the single broken wire defect signal is easily distinguished. In all of the experiment signal figures, the enormous amplitude and the smallest amplitude, respectively represent untwine of wires in a strand and the single broken wire. The defect of irregular joining of wires is located between the two defects.
# Design Optimization of the Magnet-Free Synchronous Homopolar Motor of a Subway Train
## 2 Main Design Parameters of the SHM  
Figure 1 shows the considered SHM design. The SHM has two stacks on the stator and two stacks on the rotor made of laminated steel. The stator stack and the rotor stack located in the front of the stator stack form the stator-rotor stack combination (SRSC). The stator lamination has 60 slots and 8-pole winding with a number of slots per pole and phase \(q=60/(8\cdot 3)=2.5\). The laminations are assembled from sheets of electrical steel 0.5 mm thick.  
An excitation winding is located between the SRSCs. The rotor has no windings. Each rotor stack has 4 teeth, and the teeth of the rotor stacks are shifted by 45 mechanical degrees with respect to each other. The SHM housing and the rotor sleeve carry the stator lamination and the rotor lamination, respectively. They are made of a solid (not laminated) steel and provide the link to the flux produced by the excitation winding. No standard regulates the magnetic properties of solid steels, and, therefore, they can have significant dispersion. To guarantee that the housing and the sleeve are able to conduct the required flux, the flux density in these parts is restricted with the value of 1.6 T.  
Figure 2 shows the inverter circuit diagram for powering the considered traction motor. To control the three-phase winding, field-oriented control is used, in which the stator current vector is formed in a coordinate system rotating synchronously with the rotor. The excitation winding current is set using PWM proportional to the armature current. The offset angle of the armature current vector from the direction of the greatest magnetic conductivity of the rotor pole for various operating points and the ratio between the armature current and the excitation current are reported in Section 6.
## 6 Results and Discussion
### Classification Performance Based on the Real Dataset  
To validate the accuracy of the proposed classification system, it was tested using the real dataset of the practical distribution networkThe dataset was adopted from the PQube equipment, which is a tool used for real-time monitoring the power quality and electrical signal phenomena [71]. We focused on a case study of the distribution system located in Singapore, China, Sweden, and South Africa. Table 9 displays the classification system's accuracy when using adaptive SSA as the optimal feature selection. It was found that the classification system proposed in this work provides 98.5% accuracy for identifying 5 types of power quality signals, which is consistent with that obtained by using the synthetic waveforms (98.8%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Selection Algorithms** & **Overall Accuracy (\%)** & **Required Iterations** \\ \hline PSO & 98.46 & 80 \\ GA & 97.85 & 106 \\ SSA & 96.85 & 105 \\ Adaptive SSA & 98.77 & 79 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Detail of classification performance using different feature selection algorithms.  
Figure 4: Convergence rate of classification system using different feature selection algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Signal Type** & **Classification Accuracy (\%)** \\ \hline Pure sine & 97.5 \\ Sag & 100 \\ Swell & 100 \\ Interruption & 95 \\ Harmonic & 100 \\ \hline Overall accuracy & 98.5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification accuracy using the real dataset.