esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Stator-Side External Retrofit Techniques
### Fault Current Limiter  
Fault current limiters are now commonly used in large interconnected power systems to limit overcurrent during fault conditions. In this way, generators and transmission lines can be protected from being damaged. Furthermore, the FCL can maintain terminal voltage regulation by injecting reasonable resistance into a stator circuit and providing adaptive voltage compensation A superconducting fault current limiter (SFCL) is a typical configuration in the application of superconductors. The proposed SFCL consists of a coupling transformer (CT), a controlled switch, and a superconducting coil (SC), as shown in Figure 15. In normal operation conditions, the controlled switch is closed and the flux between the two CT windings can offset each other. Thus, only the CT operation impedance is implemented in the circuit. During transient periods, the controlled switch is open, which results in the destruction of non-inductive coupling. The superconducting coil is quenched to its high-resistance state to restrict fault current [51]. Based on the operating principle of the SFCL, this device is generally installed on the stator side of DFIG-WTs to provide high resistance during transient periods [52]. Therefore, the stator overcurrent can be restrained.  
The saturated-core fault current limiter (SCFCL) is another modified FCL topology which was proposed in Reference [8]. The SCFCL is mainly composed of a superconducting DC coil and a coupling transformer in which AC coils are embedded in a saturated iron core. The DC coil is located between two limbs of AC coils, as shown in Figure 16. In normal working conditions, the impedance of the SCFCL is negligible due to the saturated iron core. After a fault occurs, abruptly increased current flows through AC coils, resulting in the iron core becoming unsaturated. Therefore, the impedance of AC coils increases quickly, and the fault current can be restricted. The installation of an SCFCL on the stator side, as shown in Figure 17, provides flexibility in stator resistance. Due to the specific characteristics of the SCFCL, its external installation has no resistance under normal conditions but provides a high resistance during transient periods to restrain stator overcurrent and prevent voltage dips in a DFIG [53].  
Figure 16: Saturated-core fault current limiter (SCFCL).  
Figure 15: Superconducting fault current limiter.  
A circuit configuration combining an FCL with a superconducting magnetic energy storage (SMES) was proposed in Reference [54] to enhance the LVRT capability of DFIG-WTs. During normal operation, the SMES circuit is activated to smooth the output power of wind turbines. When a fault occurs, the superconducting coil is injected into the circuit to restrain stator overcurrent. In order to eliminate power fluctuations in power systems, the FCL resistance is reduced to its initial value during post-fault periods, and the SMES-FCL returns to operating as an SMES [54].
# A Single-Launch Deployment Strategy for Lunar Constellations
## 3 Design of the Deployment Strategy  
The constellation deployment strategy designed in this section is developed to allow the transfer of satellites from the same launch trajectory to different orbital planes about the Moon. The strategy is organized into two phases. First, the launch vehicle is transferred to a low-energy trajectory approaching the equilibrium region. Here the satellites are released from the launcher and, independently, are transferred to low-energy lunar captures with prescribed orbital elements. The design of the two phases is presented separately and the design parameters discussed hereafter are summarized in Tables 1 and 2. For the sake of clarity, the algorithm implementing the design of the deployment strategy, used for the numerical study in Section 4, is reported at the end of this section (see Algorithm 1).  
(1) Determine the orbital elements \(\Omega_{d}\), \(\omega_{d}\), \(i_{d}\) at the initial epoch \(t_{0}\) by solving a two-point boundary value problem (TPBVP) using the following sets of boundary conditions  
* \(a_{d}\), \(e_{d}\), \(\theta_{d}\) at \(t_{0}\)
* \(r_{p,a}\), \(y_{a}\), \(i_{a}\) at the time of periselenium passage (\(t_{a}\))  
(2) Using the orbital elements determined in Step 1, propagate the trajectory of the launch vehicle to the periselenium and compute the \(\Delta V_{a}\) to be provided for reducing the energy level to the desired value \(h_{\sigma}\)  
(3) After Step 2, the satellites are deployed from the launch vehicle and maneuvered independently; for each satellite  
* select a guess value for \(x_{\sigma}\) within a suitable interval
* propagate the trajectory from the periselenium until it reaches the coordinate \(x_{\sigma}\) and compute the remaining state variables \(y_{\sigma}\), \(z_{\sigma}\), \(v_{\sigma\sigma}\), \(v_{\sigma\sigma}\) and \(v_{\sigma\sigma}\)
* solve Equation (17) to determine the \(\Delta V_{\sigma\sigma}\) to be provided to establish the desired \(i_{c}\) and \(r_{p,c}\) at capture
* propagate the trajectory to the periselenium
* if the difference between the actual value of RAAN and its target value \(\Omega_{c}\) is larger than a prescribed error **then** repeat from Step 3.1 with a different value of \(x_{\sigma}\)**else** proceed to Step 4  
(4) Compute the \(\Delta V_{c}\) eventually required to correct \(e_{c}\)
## 4 Results and Discussion
### Results of Single Parameter Evaluation
#### 4.1.5 Topography  
Topographic slope plays a significant role in groundwater migration and subsequently affects groundwater vulnerability [42]. In this study, a digital elevation model (DEM) of the research area was acquired through a measurement system. The DEM had an accuracy of 12.5 m, which met the guidelines' requirements. Using the GIS's slope tool, the slope was calculated based on the projected DEM map. Then, according to the scoring standard, each zone's attributes were determined through reclassification (Figure 3e). Due to the high resolution of the data, fragmentation zones were formed considering the impact of rivers and dams in the plain area on slope classification. This evaluation utilized the geomorphic zones of the study area as a reference, and the average terrain slope within each zone was considered the representative slope value.  
The study area exhibits significant variations in topographic slope, generally decreasing from north to south. High topographic slope values are concentrated in the northern mountainous area, while the intermountain basin, piedmont plain, and coastal plain exhibit relatively low slopes. The highest topographic slope values are found along the northern edge of Yutian County, the southern part of Zunhua City, the northern part of Fengrun District, the southern part of Qianxi County, the western part of Qian'an City, and the northwestern part of Luanzhou City. Conversely, the lowest slope values are primarily distributed in Caofeidian District along the southern coast.
# Strength Analysis of Real-Life Passwords Using Markov Models
## 1 Introduction
### Contributions  
We make three contributions in this area. The first, and also the novelty in this research field, lies in training the models on a variety of datasets (12 in total), each with different characteristics, and testing them on the same password dataset in order to investigate the effect of the dataset on the success of the model. In particular, we show that the dataset has a significant effect on the final scoring of the passwords. The fact that different training datasets can lead to statistically different password scores, leads us to the conclusion that it is very important what kind of dataset is used to train the Markov models. We argue that one universal dataset should not be used to train one Markov model if we want to have an effective password scoring tool. The second contribution is the confirmation of our previous statement: the use of multiple different Markov models is better for efficient estimation of password strength rather than using one universal model, which is trained on one big dataset that combines multiple different datasets. Finally, the third contribution is that we showed that the difference in outputs between two Markov models with different orders (1 st and 2 nd) is, in most cases, not statistically significantly different. Therefore, the general conclusion here is that, without a doubt, it is important to analyse the dataset before selecting the order of the model.  
Overall, we show that if we want to have an effective password scoring tool for calculating the strength of a wide range of passwords, it would be required to use multiple different Markov models, which should be constructed and trained on a particular dataset of passwords so that they can be more efficiently used on that particular password group.
## 2 Results
### RNAi Effect of DaAST and DaJHAMT
#### 2.5.2 Knockdown Effect of Injecting dsAST and dsJHAMT Separately  
In this study, we found that knockdown of _DaAST_ in larvae enhanced the expression of _DaHMG-R_, _DaPMK_ and _DaJHAMT_ (_DaHMG-R_: t = \(-\)4.191, df = 4, \(p\) = 0.014; _DaPMK_: t = \(-\)5.459, df = 4, \(p\) = 0.005; _DaJHAMT_: t = \(-\)9.118, df = 4, \(p\) = 0.001) (Figure 7A), with no significant expression of MVA pathway genes when knocking down _DaAST_ in pupae (Figure 7B). Knockdown of _DaAST_ in females enhanced the expression of _DaJHAMT_ (_DaJHAMT_: t = \(-\)4.035, df = 4, \(p\) = 0.016) (Figure 7C), whereas knockdown of _DaAST_ in males enhanced the expression of _DaHMG_ and _DaJHAMT_ (_DaHMG-R_: t = \(-\)3.574, df = 4, \(p\) = 0.023; _DaJHAMT_: t = \(-\)4.232, df = 4, \(p\) = 0.013) (Figure 7D).  
We found that _DaJHAMT_ deletion in larvae significantly downregulated the expression of the MVA pathway gene _DaAACT_ and _DaHMG-R_ (_DaAACT_: t = 5.406, df = 4, \(p\) = 0.006; _DaHMG-R_: t = 3.407, df = 4, \(p\) = 0.027) (Figure 7E). We found that knockdown of _DaJHAMT_ in females significantly downregulated the expression of _DaAACT_, _DaPMK_, _DaMPDC_ and _DaFPPS_ (_DaAACT_: t = 5.134, df = 4, \(p\) = 0.007; _DaPMK_: t = 8.983, df = 4, \(p\) = 0.001; _DaMPDC_: t = 6.999, df = 4, \(p\) = 0.002; _DaFPPS_: t = 4.921, df = 4, \(p\) = 0.008) (Figure 7F, but significantly upregulated the expression of _DaHMG-S_, _DaHMG-R_ and _DaMK_ (_DaHMG-S_: t = \(-\)4.416, df = 4, \(p\) = 0.012; _DaHMG-R_: t = \(-\)10.053, df = 4, \(p\) = 0.001; _DaMK_: t = \(-\)4.682, df = 4, \(p\) = 0.009) (Figure 7G). Knockdown of _DaJHAMT_ in males significantly downregulated the expression of _HMGR_ (_DaHMG-R_: t = 3.211, df = 4, \(p\) = 0.033) (Figure 7H).
# Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence
## 2 Materials and Methods
### Inspected Object
The main aim is to adapt the lighting system to highlight edges defined by texture in the form of describing the tire and uneven small parts of the area. Inspection systems have to adapt to search of specific defects. In the case of production, it is necessary to define mainly types defects that possibly occur on final products [21]. In real conditions of production it is possible to capture defects, which are different in terms of position, shape, size. Other differences are based on excess materials, missing materials, cracks or otherwise damage the area. For inspection systems it is necessary to choose appropriate candidates of defects with representative types of defects mentioned above. Within the tests, specific types of elements were selected on the inspected object, which represent possible types of defects. Thus, these are required to be detected using the proposed methodology of automated visual inspection. These elements were chosen so that it was possible to test the suitability of the proposed methodology and also to cover as objectively as possible all the recorded types of production defects occurring on tires in a real operation. During the direct visual inspection carried out in factories, during the inspection of the final tire and both the sidewalls, six representative types of defect mainly occurring in production were chosen and these are displayed in Figure 2:  
* occurrence of impurities from the same material as the tire (CH01),
* occurrence of impurities from other materials (CH02),
* damage of the material by the action of temperature and pressure (CH03),
* crack/tear (CH04),
* mechanical integrity damage (CH05),
* material etching (CH06).
# Circularly Polarized High-Gain Fabry-Perot Cavity Antenna with High Sidelobe Suppression
###### Abstract  
The proposed design approach improves the circularly polarized Fabry-Perot cavity antenna (CP-FPCA) by increasing gain and sidelobe suppression (SLS) while reducing the axial ratio (AR) and cross-polarization levels. Conventional CP-FPC antennas have a high AR due to the lack of independent control over circular polarization conditions. The solution proposes a double-layered circularly polarized partially reflecting surface (CP-PRS) that independently controls the circular polarization conditions at the design frequency \(f_{0}\) (10 GHz) for equal magnitudes and at a \(\pm\)90\({}^{\circ}\) phase difference between orthogonal components of the transmitted waves. The PRS and artificial magnetic conductor (AMC) unit cells are employed to satisfy [PERSON]'s beamforming condition, leading to increased gain and SLS and lowered AR and cross-polarization levels. Consequently, the proposed CP-FPCA provides a 15.4 dB high gain with 25.3% aperture efficiency and more than 23.5 dB high SLS in each plane. Moreover, it achieves an AR lowered by 0.12 dB and a cross-polarization level below \(-\)42 dB. A strong correlation between the simulations and experiments proves the practicality of our proposal.  
Keywords:axial ratio (AR); circularly polarized Fabry-Perot cavity antenna (CP-FPCA); linear arrays; circularly polarized partially reflective surface (CP-PRS); peak realized gain; sidelobe suppression (SLS) +
Footnote †: journal: Applied
# A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering
## 3 Experimental Results and Analysis
### Passage Retrieval with HTGQE  
This section focuses on highlighting the effectiveness of the HTGQE. In addition to the BM25 and the BM25+RM3 [10], other comparisons are carried out between GAR [10] and HTGQE. As shown in Table 2, we examine the Top-k retrieval accuracy of both sparse and hybrid retrieval methods. In sparse retrieval, The result of GAR is a combination of GAR(\(ctx(A)\)), GAR(\(ctx(S)\)), and GAR(\(ctx(T)\)); the specific fusion method can be found in Section 3.3. HTGQE follows the same principle.  
For sparse retrieval, GAR and HTGQE significantly outperform the initial BM25 and BM25+RM3, demonstrating the effectiveness of query expansion based on generative models. Under various conditions, HTGQE's Top-k retrieval accuracy surpasses that of GAR, indicating that with proper \"reference materials,\" the accuracy of open-domain generative models can be significantly improved. HTGQE uses the top three Pseudo-Relevance passages of the initial query retrieval results as these reference materials. In the NQ/Trivia dataset, GAR's Top-5 and Top-20 accuracy are 14.7%/2.4% and 9.7%/1.3% higher than BM25, respectively, while HTGQE's Top-5 and Top-20 retrieval accuracy are 8.8%/5.5% and 3.6%/1.9% higher than GAR, respectively. Furthermore, regardless of how many passages are retrieved, HTGQE consistently outperforms GAR, and the gap between GAR and HTGQE narrows as the value of \(k\) increases. In contrast, the classic QE method RM3 slightly improves over regular BM25 but does not perform comparably to HTGQE or GAR. According to Table 2, the effectiveness of different query contexts varies, with \(ctx(A)\) and \(ctx(S)\) being more effective than \(ctx(T)\). However, the hybrid results are higher than any individual retrieval performance, suggesting that merging each query context will yield better retrieval results. The complementary nature of different generative-enhanced queries is also mentioned in Section 3.4.  
For hybrid retrieval, as seen in Table 2, the retrieval accuracy of HTGQE+DPR is higher than that of GAR+DPR and BM25+DPR. On NQ, HTGQE+DPR's Top-5/Top-20 accuracy are 9.4% 4.5% and 3.5%/1.5% higher than BM25+DPR and GAR+DPR, respectively. On Trivia, HTGQE+DPR's Top-5/Top-20 accuracy are 3.1%/2.0% and 0.9%/0.3% higher than BM25+DPR and GAR+DPR, respectively. The specific fusion method is described in Section 3.3.
# The Development of an OpenAI-Based Solution for Decision-Making
## 5 Discussion and Implications
### Comparison with Traditional Decision-Making Approaches
The weakness of an expert-driven method is that it becomes a single-point-of-decision system--it lacks the diversity of input and can suffer from the expert's personal biases or limited perspective. Even seasoned experts can disagree or make errors, and their decisions might not always be transparent to others. Additionally, an expert's availability can become a bottleneck. Our AI-blockchain system can be seen as \"consulting a virtual expert\" in the loop: the AI analyzes large amounts of data and past cases (something a human expert does with experience) and provides a recommendation. AI systems excel in consistency and analytical breadth, often detecting patterns across Big Data that humans might miss, and they do not get fatigued or distracted. However, human experts are still crucial for contextual understanding and ethical judgment, as they can interpret nuances and ethical implications better than a machine. In practice, our framework augments human expertise rather than replacing it: the AI system offers a data-driven opinion, and human decision-makers (the participants) incorporate that alongside their own domain knowledge when voting. Compared to a scenario of a lone expert, our approach adds transparency (everyone sees the rationale and contributes to the final call) and guards against individual bias while still benefiting from expertise (both human and AI). It is effectively a balance between expert insight and collective agreement, recorded transparently. * Strengths of AI-Blockchain Framework: By synthesizing elements of these approaches, the proposed system has several advantages. It accelerates decision speed (addressing a major drawback of traditional group decisions) through automation and parallel participation. It improves decision quality by combining collective input with AI analytical support, which can reduce human error and bias. It also ensures transparency and trust via blockchain--every vote or action is logged and verifiable, which is rarely the case in conventional methods. Unlike purely hierarchical decisions, it distributes power and information, likely leading to higher acceptance among stakeholders because they had a say in the outcome. Of course, this framework is not without trade-offs: it introduces technical complexity and requires user training, and the quality of AI recommendations depends on data quality and robust algorithms. But overall, when contrasted with traditional decision-making, the AI-blockchain approach shows a compelling blend of objectivity, inclusivity, and efficiency, making it a strong alternative or complement to existing decision processes in organizations.
## 3 Results
### GNSS Positioning Test Results
The \(\mu\)2D and \(\mu\)3D values report the mean distance of the set of surveyed points in relation to the trajectory travelled in planimetric and 3D, \(\sigma\)2D and \(\sigma\)3D are the respective standard deviations in planimetric and 3D. In order to evaluate the dispersion of the points surveyed in the pool test, Figure 14 shows a comparison between the G \(>\) S \(>\) P Sys positioning data and the real trajectory travelled. In these figures the real trajectory is shown in green; in particular, Figure 14a,d show the SPP data in planimetric and 3D, respectively, Figure 14b,e show the PPP-kinematic positioning data in planimetric and 3D, respectively and Figure 14c,f show the PPK positioning data in planimetric and 3D, respectively. Figure 13: Accuracy in static planimetric post-processing positioning in static mode (grey) and in PPP mode (yellow) of the G \(>\) S \(>\) P Sys. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Device** & **Positioning** & **Obs. Time (s)** & **\(\Delta\)N (m)** & **\(\Delta\)E (m)** & **\(\Delta\)U (m)** & **\(\sigma\)N (m)** & **\(\sigma\)E (m)** & **\(\sigma\)U (m)** & **THU (m)** \\ \hline \multirow{8}{*}{G \(>\) S \(>\) P Sys} & & 300 & 0.001 & 0.006 & 0.031 & 0.002 & 0.002 & 0.004 & 0.005 \\  & Post-Processing & 900 & 0.001 & 0.006 & 0.030 & 0.002 & 0.002 & 0.005 & 0.005 \\  & Static & 1800 & 0.001 & 0.006 & 0.031 & 0.001 & 0.002 & 0.006 & 0.004 \\  & & 3600 & 0.001 & 0.006 & 0.031 & 0.001 & 0.001 & 0.006 & 0.003 \\ \cline{2-10}  & & 300 & 0.667 & 0.632 & 0.249 & 0.474 & 0.906 & 1.107 & 2.220 \\ \cline{2-10}  & & 900 & 0.639 & 0.712 & 0.074 & 0.134 & 0.317 & 0.320 & 0.778 \\ \cline{2-10}  & PPP & 1800 & 0.636 & 0.765 & 0.043 & 0.058 & 0.143 & 0.148 & 0.352 \\  & & 3600 & 0.646 & 0.750 & 0.026 & 0.010 & 0.022 & 0.033 & 0.053 \\ \hline \end{tabular}
\end{table} 
Table 10: Performance in static positioning post-processed in static mode and PPP mode of the G \(>\) S \(>\) P Sys.
## 6 Algorithm to Arrange the Available Paths  
In this section we present our proposal of algorithm to arrange the available paths found from source to destination. Finally, our _QoS-aware social-aware routing protocol to provide video-streaming services over MANETs_ (QSMVM), will select the forwarding path with the highest multimetric score, see Equation (9).  
Once the source has filtered out the set of paths that meets the user's QoS requirements (see Equation (1)), a multimetric score is computed for every path, taking into account the qualifications of the QoS parameters as well as the tie strength (TS) qualification. This multimetric score is computed as follows, for each path \(k\) at each iteration \(i\) of the algorithm:  
\[MScore_{k}^{i}=w_{QoS}\cdot(BW+L+D+J+H+RM+MM)_{k}^{i}+w_{TS}\cdot\overline{TS_ {k}^{i}} \tag{9}\]  
where \(w_{QoS}\) and \(w_{TS}\) are weights that sum one, see Equation (10). In Equation (9), \(w_{QoS}\) is the weight given to the set of QoS parameters. In this work we give the same weight to all QoS metrics. Alternatively, we could also assign different weights if we want some parameter to have more importance than the others in the \(MScore_{k}^{i}\) calculation. Besides, in Equation (9) \(w_{TS}\) is the weight given to the TS of path \(k\). The computation of the TS associated to each path is computed with Equation (8).  
Figure 3: General mobile ad hoc network (MANET) scheme to transmit video frames from a source \(S\) to its destination \(D\). Paths are arranged according to a combined QoS and social multimetric score, see Equation (9).  
\[w_{QoS}=1-w_{TS} \tag{10}\]  
Once computed the \(MScore_{k}^{i}\) for each available path \(k\) in iteration \(i\), the source arranges them from the highest-quality path to the worse-quality path. Finally, the source selects the path with the highest \(MScore_{k}^{i}\) score. Notice that as the \(w_{TS}\) increase, the average level of confidence among nodes forming a particular path becomes more decisive in the \(MScore\) and therefore, in the selection of the best path. Nonetheless, there will be a trade-off between the confidence level among the forwarding nodes that compose the selected path and the QoS provided by that path.  
In the next section we will carry out a performance evaluation to asses the benefits of our proposal regarding the QoS in terms of packet losses and packet delay, and also in terms of confidence level of the relying nodes. Additionally, we will analyse the trade-off between QoS and confidence level of the forwarding path.