esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Rotor Dynamic Analysis  
Simulations were made using XLHydroDynTM, a commercial software developed by Rotating Machinery Analysis, Inc. The software computes bearing static and dynamic characteristics and can deal with flexural pivots. The properties of the simulated bearing, schematically shown in Figure 4, are reported in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Time Step** & **Pressure [MPa]** & **Moment [N-mm]** \\ \hline
1 & 0.1 & 0 \\
2 & 0.2 & 0 \\... &... &... \\
10 & 1 & 0 \\
11 & 1 & 50 \\
12 & 1 & 100 \\... &... &... \\
30 & 1 & 1000 \\
31 & 1 & 950 \\... &... &... \\
50 & 1 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Example of loading history.  
Figure 4: Bearing used for simulations as represented by the software. The clearance is 30\(\times\) magnified to aid visualization. The pad pivots are numbered as shown. X and Y indicate the bearing horizontal and vertical axes respectively.  
Two configurations with different pivot curvature radii were compared. The pivot curvature radius and the housing radius were used to calculate \(K_{rot}\) as in Equation (8). Since \(K_{rot}\) depends on the pad normal load \(F_{b}\), the simulation was repeated iteratively until convergence on the value of \(K_{rot}\) was reached for all pads. The acceptable relative error was set to \(10^{-5}\) and quite few iterations were necessary. The convergence criterion on \(K_{rot}\) was not present in the software and was implemented by the authors through an outer loop. The software has its own convergence criterion consisting in a relative error criterion on journal eccentricity that was set to \(10^{-4}\). The generalized Reynolds equation is solved by discretizing the pad; 50 and 11 elements per pad were employed, respectively, in the circumferential and axial direction for pressure and temperature calculation. Pad and pivot mechanical and thermal deformations were not considered to isolate the contribution of the rolling motion. Turbulence coefficients were not considered as well.  
Once the static equilibrium was determined, the dynamic coefficients were calculated by the software by perturbing the static equilibrium condition. When the operating point is perturbed, \(K_{rot}\) is not updated, but its value, dependent on the static pad load, affects the perturbed equilibrium quantities because it is included in the software calculations as for the flexural pivot.
## 1 Introduction  
Superconducting Fault Current Limiters (SFCLs) are devices designed to limit short-circuit currents, most often based on high-temperature superconductors. They use the physical properties of a superconductor, which transition from a superconducting state to a resistive state as a result of exceeding one of the critical parameters. The SFCL is characterized by a fast operation, which is related to the very short transition time of the superconductor from the superconducting state to the resistive state [1; 2; 3; 4; 5; 6; 7]. In the resistive state, the SFCL can effectively limit short-circuit currents [1; 2; 3; 8; 9; 10].  
The superconducting element of the SFCL is constructed from high-temperature superconducting tapes made using thin-film technology [11]. Buffer layers and a superconductor are applied to the Hastelloy stainless steel substrate. The HTS tape is covered with silver layers on both sides, and can additionally be covered with a copper stabilizer layer. Hastelloy constitutes over 94% of the HTS tape components and is made of a non-magnetic alloy with a high resistivity with the following composition: 57% Ni, 15% Mo, 15.5% Cr, 5.5% Fe, 4% W, and 2.5% Co [12]. Hastelloy is characterized by good mechanical properties that improve both the elasticity of the superconductor and its thermal stabilization. Bufferlayers increase the adhesion of the superconductor to the Hastelloy substrate and also constitute a diffusion barrier [13].  
The 1 \(\upmu\)m thick superconductor layer is made of YBCO (sometimes rare earth elements, ((RE)BCO), are used instead of yttrium). As ceramic materials, YBCO contains certain imperfections in its structure in the form of grains separated by boundaries and scarring. This affects the transmission of currents with a high critical density [14].  
It is known from literature studies that mechanical damage (deformation) may occur in HTS tapes as a result of currents higher than critical currents [15; 16], the separation of the stabilizer layer [17; 18], damage at the soldering points [16; 19], as well as changes in the critical current value [16; 18; 20; 21; 22; 23]. In [23], it was suggested that the cause of the degradation of the critical current value may be perforations in the superconducting layer. The research results described in [15; 16; 17; 18; 19; 20; 21; 22; 23] indicate that macroscopic changes in the tapes were responsible for the \(I_{C}\) degradation. In [24], the influence of prospective short-circuit currents on the change in the HTS tape parameters was studied. They confirmed that changes in the I\({}_{C}\) values can occur despite the lack of macroscopic changes in the tapes. Multiple SFCL activations may cause micro-damages in the superconductor layer, which are responsible for changing the critical current of the tape. The main objective of this paper is to determine the causes of the changes in the critical current value due to the action of surge currents based on comparative microstructural studies of new (unused) samples and samples with reduced \(I_{C}\) values using a JEOL 7600F field emission scanning electron microscope (SEM).
# Optical Technology for NFV Converged Networks
## 3 Silicon Photonics Devices in Optical Transport: Challenges and Opportunities
### System Applications Enabled by SOAs Integrated in Silicon Photonics Devices
The ROADMs are silicon photonics chipsets that use silicon micro-ring resonators as wavelength switching elements, controlled Mach-Zehnder interferometers for protection switching and silicon variable optical attenuators for loss equalization. Input SOAs are flip chip bonded to the silicon photonic chipset to compensate for device and fiber loss. The total power at SOA input was \(-1.2\) dBm, corresponding to \(-12\) dBm of channel power. The results in [27] show that it possible to use SOAs as integrated line amplifiers, but with limitations in terms of number of SOAs in the network (up to 5, in the example in [27]) and their gain (about 10 dB in the same example). This reduces maximum number and length of the fiber spans, but these limitations remain compatible with therequirements discussed in Section 2. For example, a SOA gain of 10 dB would allow to allocate 5 dB for the ROADM insertion loss of 5 dB and further 5 dB of fiber span loss, corresponding to a fiber length 20 km, with an attenuation coefficient of 0.25 dB/km. With 5 ROADM nodes, the total length of the ring would be 100 km, well beyond the typical distance of a metro access network [30].
An early assessment of the newly available signal's quality of the multi-GNSS (GPS + GLONASS + BDS + QZSS) was provided by computing zero-baseline double-differenced carrier phase residuals and it was found that all the available constellations can improve the precision and accuracy in both the horizontal and vertical components [14]. QZSS is able to decrease the position error and increase the success rate of GPS resolution while offering more available and reliable GPS in Japan and most East Asian regions for a single receiver [15]. The J01 (QZS numbered 01) was launched in 2010 and the J02 (QZS numbered 02) was fixed in orbit in June 2017. The number of QZS increased to four in March 2018 and three are in normal service. Some literature only analyzed the QZSS positioning performance based on simulation results while other work examined the measured data of only one QZS. These results could not comprehensively reflect the current features of the QZSS constellation and its enhancement effect on GPS. The SFSE double-differenced (DD) baseline resolution is the original and the most classical solution of high accuracy carrier phase positioning, and is also the most essential approach to reflect the reasonableness of the constellation structure and the quality of the measurement data [16; 17]. Accordingly, the performance of the SFSE carrier phase DD baseline resolution for the integrated GPS/QZSS and BDS/QZSS in the Asia-Pacific region needs more attention. In this paper, the availability, PDOP, ADOP, success rate, and accuracy of GPS/QZSS and B DS/QZSS were analyzed. The auxiliary and enhancement effects of QZSS were studied and analyzed. This paper is arranged as follows: Section 1 is the introduction. The basic state of QZSS is introduced in Section 2. In Section 3, the SFSE code and carrier phase DD model for the integrated BDS/QZSS and GPS/QZSS are deduced and the corresponding forms of the weighting model are described. Section 4 presents the promotion of availability and PDOP of BDS and GPS when assisted by QZSS and introduces 3 indicators of ambiguity estimation. Section 5 analyses the improvement of PDOP, ADOP, actual success rate, and baseline precision of GPS/QZSS and BDS/QZSS with two real-data experiments. Finally, the main conclusions are drawn in Section 6.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 4 Results and Discussion
### Potential Limitations  
The proposed mitigation strategy was primarily evaluated under reactive and constant jamming conditions, which are among the most common threats in WSNs. However, other sophisticated forms of jamming, such as random and deceptive jamming, could pose additional challenges to network resilience. Random jamming, characterized by alternating interference periods, may result in intermittent network disruptions that could delay the cluster-based adaptation process. Similarly, deceptive jamming, where malicious packets are injected to confuse network protocols, might undermine routing decisions and lead to inefficient energy utilization. While our current strategy relies on adaptive cluster selection and energy-aware protocol switching, its effectiveness against these advanced jamming tactics remains an open research question. Furthermore, the impact of adversarial machine learning techniques on jamming strategies is an emerging threat. Attackers could potentially use reinforcement learning to optimize jamming patterns, making them more difficult to detect and mitigate. Although our algorithm dynamically reconfigures network topology based on real-time performance metrics, future work should explore how intelligent adversarial attacks could adapt to and counteract mitigation efforts. In particular, the integration of AI-driven anomaly detection and predictive analytics could enhance the robustness of our approach by proactively identifying evolving interference patterns and adjusting mitigation strategies accordingly.  
Another potential limitation lies in the scalability of the mitigation strategy. While our experiments were conducted in a controlled university campus environment, large-scale deployments in industrial IoT or smart city applications might face additional challenges. The increased number of nodes and varying interference sources could lead to higher computational overhead for cluster selection and protocol switching, potentially affecting response  
Figure 12: Histogram and probability density function of energy consumption for BLE, LoRa, and BLE-LoRa under jamming conditions.  
times. Future research should investigate distributed decision-making mechanisms that allow for real-time adaptation with minimal processing requirements.  
These considerations underscore the need for further studies to validate the proposed mitigation framework under diverse jamming conditions and larger-scale network deployments. By addressing these aspects, we can enhance the adaptability and robustness of our approach, ensuring its effectiveness across a broader range of real-world scenarios.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 3 Results
### Peyg and rRNA Promoter Affinities for RNAP Change with DNA Relaxation In Vitro  
We tested the relative affinity of RNAP for promoter DNA by performing in vitro transcriptions as a function of increasing promoter DNA concentration. We used the tandem _rrn_B P1+P2 DNA fragment and _Pey_. The GTP concentration was set to 1 mM to ensure high efficiency of open complex formation for the tested promoters. Affinity for RNAP of both rRNA promoters was unchanged or slightly decreased on relaxed templates, but this effect was not statistically significant (Figure 4). Therefore, it is possible that the observed decrease in bulk transcription from _rrn_B P1 (SC vs LIN) in vitro could be due to yet another factor (e.g., promoter escape).  
Figure 4: The affinity of RNAP for promoter DNA. Multiple-round transcriptions were carried as a function of the increasing DNA/RNAP ratio. The tested promoters were _rrn_B P1+P2 (**A**) and _Pey_ (**B**). Primary data are shown above the graphs. The maximum signal in the plateau phase was set as 1. SC—supercoiled and LIN—linear DNA templates. The experiments were conducted at least four times with similar results. Representative primary data are shown. (**C**) Graphical comparison of relative affinities of RNAP for Peyg and _rrn_B P1+P2 promoters. The bars show relative concentrations of promoter DNA at which the activity of RNAP was 50%. The affinity of RNAP for SC promoter DNA was set as 1 for each promoter.  
The opposite trend was observed with _Pegg_: a relatively low level of the relaxed promoter DNA was able to saturate RNAP compared to the supercoiled template. This behavior could then explain why the activity of _Pegg_ decreased less than the activity of _rmt_B P1 both in vitro and in vivo. Importantly, it was previously reported that the levels of RNAP subunits decrease from exponential to stationary phase [50; 51] and we also observed this trend (Figure 5).
## 2 Results and Discussion
### Adsorption Experiment
#### 2.9.4 Reusability of NR/WMS-NH\({}_{2}\)  
The reusability test of NR/WMS-0.05 as the suitable adsorbent was carried out using the CFA concentration of 5 mg L\({}^{-1}\) and the adsorbent to aqueous solution ratio was fixed at 1 g L\({}^{-1}\). The spent adsorbent was separated from the mixture by filtration, followed by thoroughly washing with ethanol and drying at 90 \({}^{\circ}\)C for 12 h. As shown in Figure 11, the adsorbent could be reused at least three times, during which the adsorption capacity was slightly decreased from 0.93 to 0.84 mg g\({}^{-1}\). The physicochemical properties of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (K)** & \(\Delta G^{\circ}\) **(kJ mol\({}^{-1}\))** & \(\Delta\)H\({}^{\circ}\) **(kJ mol\({}^{-1}\))** & \(\Delta\)S\({}^{\circ}\) **(kJ mol\({}^{-1}\) K\({}^{-1}\))** \\ \hline
298 & \(-\)13.18 & 8.08 & 71.36 \\
308 & \(-\)13.90 & & \\
318 & \(-\)14.61 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermodynamic parameters for CFA adsorption onto NR/WMS-0.05 at different temperatures.  
adsorbent spent after the third cycle was characterized using XRD, TGA, and CHNS analyses. The XRD pattern of the reused adsorbent still showed a single reflection at 2\(\theta\) around 1.5\({}^{\circ}\)-3.0\({}^{\circ}\), although its intensity slightly decreased (Figure S4), indicating that the mesostructure of the NR/WMS-0.05 was not significantly changed by the repetitive uses and regeneration process. Similarly, the TGA analysis revealed no difference in the temperature-dependent weight loss behavior of the fresh and reused adsorbents (Figure S5). This implied that the rubber phase was retained in the mesostructured framework of the NR/WMS nanocomposite. However, the amount of nitrogen dropped around 16% (Table S1). These results indicate that the leaching of anchored amine groups from the adsorbent surface during their repetitive use in the adsorption was a major cause of their decreased capacity for CFA removal. Nevertheless, since the reused material still has a well-ordered mesostructured framework, it is possible to reactivate the spent adsorbent by grafting the APS to compensate for the amount of lost amine groups.
## 3 Signal Level Compatibility Analysis
### Analysis of the Effect of Interference on the Capture Performance of BeiDou
According to the relationship between detection probability and equivalent carrier-to-noise ratio from Equation (12), the effect of 5G signal transmit power on detection probability is obtained by simulation, as shown in Figure 9 below. As can be seen from the simulation results in Figure 9, when the 5G signal power is \(-50.54\) dBw, the signal detection probability is less than 1, which indicates that, in this case, the signal may not be fully captured or detection failure may occur, and when the 5G signal power is lower than \(-50.54\) dBw, it will not have an impact on the BeiDou signal capture performance. According to the research scenario of this paper, the safety distance analysis is performed from the signaling perspective, and the transmit power of the 5G base station is determined. According to the regulations in 3 GPP TS38.104, 5G base stations can be categorized into Type 1-C, Type 1-H, etc., based on the operating frequency range and the type of reference port connection. Since the research object of this paper is the FR1 band, a 5G base station of type 1-C is selected as the research object. Based on the 3 GPP document, the transmit power of this type of 5G base station is 8 dBw, so from the simulation results, the propagation loss of the 5G signals is about 58 dB when there is an incomplete capture situation. If the 5G signal propagation environment is regarded as free space propagation, the relationship between the propagation distance and the loss value is shown in Table 3
## 3 Results and Discussion
### Stress Path  
Figure 6 shows the stress path in the \(p\)-\(q\) space for the non-stabilised and stabilised soils. The behaviour of the soils as described above was mostly confirmed from these stress-path diagrams. The non-stabilised sulphate soils all tended to be lightly over-consolidated with more dilation of the sulphate soils containing higher percentages of gypsum (Figure 6a). Meanwhile, mixed behaviours of the stabilised samples were observed as indicated by the stress paths of Figure 6b. A detailed examination of Figure 6d,e indicates that the stabilised soils possessing higher quantities (20% and 30%) of gypsum tended to be normally consolidated when in the shearing stage are subjected to higher constant effective pressures. On the other hand, the stabilised sulphate soil with 10% gypsum tended to be over-consolidated (Figure 6c). In general, it can also be appreciated from the stress-path diagrams that the attainment of a higher failure stress (upper failure envelope or CSL) occurred with stabilisation of the sulphate soils.  
Figure 6: _Cont._  
Figure 7 indicates the behaviour of the samples at failure. The pattern of failure for the unstabilised soils was almost similar (with failure indicated as a bartrelling of the specimens) from close observations irrespective of the quantity of gypsum content and the level of cell pressure under which they were subjected. Hence, these are represented by few general illustrations for brevity (Figure 7a) These illustrations confirm the general ductile behaviour of the unstabilised soils with subsequent yielding or failure occurring at relatively larger strains. However, for the stabilised soils, shearing or failure indicated as cracking or faults across most of the cylindrical samples was clearly noticeable. As already observed from the stress-strain curves shown previously, the stabilised soils were initially ductile (under very small strains) with the yielding point followed by an almost perfectly plastic behaviour up to about 6-8% of the strain before finally undergoing small amounts of strain softening. Stabilised soils with a higher content of gypsum (particularly 30% gypsum) seemed to be the most brittle and, hence, possessed the lowest yielding or failure stress under the two cell pressures, as noticed previously.  
Figure 6: Stress path in \(p\)–\(q\) space: (**a**) unstabilised soils; (**b**) stabilised soils; (**c**) 10% gypsum content for unstabilised and stabilised soils; (**d**) 20% gypsum content for unstabilised and stabilised soils; (**e**) 30% gypsum content for unstabilised and stabilised soils.
## 4 Statistical Evaluation of Results
### Correlation between Tensile and Compressive Strengths
\[f_{sp} =0.20\,{f_{cm}}^{0.7} \tag{10}\] \[f_{sp} =0.55\,{f_{cm}}^{0.5}\] (11) \[f_{sp} =0.067\,{f_{cm}}^{1.0889}\] (12) \[f_{sp} =0.55\,{f_{cm}}^{0.5}\] (13) Figure 17.0  
From Equations (9)-(14), Equation (9) predicted the splitting-test tensile strength from the compressive strength, values that were close to the results of this study, with an average of 2.11% error in overestimation. Equation (10) also predicted the results with a 6.27% underestimation error. Equations (11)-(14) predicted the results with 22.09%, 34.29%, 22.09% and 15.43% overestimation errors, respectively. Figure 19 denotes the relationship between the compressive strength, \(f_{cm}\), and modulus of rupture, \(f_{r}\), of PPFRC. As observed, the modulus of rupture remained constant as the compressive strength increased. After 28 days of measurements, the modulus of rupture ranged from 5.23 to 6.49 MPa. Previous studies (2015, 2015,  
According to [PERSON], the tensile strength by the modulus of rupture is estimated as 0.7 to 0.8 times the square root of the compressive strength [69]. The proposed Equation (15), which was based on the results of this experimental study, indicates a nonlinear exponential relationship between the modulus of rupture and compressive strength, as shown below:  
Figure 17.1  
where \(f_{r}\) is the modulus of rupture, and \(f_{cm}\) is the uniaxial compressive strength, both in MPa. An extremely low value of determination ratio was observed because, in the results from this study, no correlation existed between the compressive strength and modulus of rupture. Figure 21 denotes the comparison of the experimental values of the modulus of rupture calculated using several equations given by [PERSON] et al. [77] for palm-shell reinforced concretes with PP fibers in Equation (16), by [PERSON] and [PERSON] [82] for self-compacting concretes with PP macro fibers between 45 and 50 MPa in Equation (17), and by [PERSON] et al. [76] for high-strength lightweight concretes with PP fibers between 40 and 46 MPa in Equation (18). Figure 17.2  
Figure 17.3  
Figure 17.4  
Figure 17.5 Modulus of rupture versus compressive strength of PPFRC. Figure 17.6 The ratio of compressive strength to the modulus of rupture (\(y\)-axis) versus compressive strength (\(x\)-axis) of PPFRC. From Equations (15)-(18), Equation (15) predicted the modulus of rupture from the compressive strength, values close to the results of this study, with an average of 0.39% error in overestimation. Equation (17) also predicted the outcomes with a 1.81% error in underestimation. Finally, Equations (16) and (18) predicted the results with underestimation and overestimation errors of 22.50% and 6.69%, respectively.