esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this work, an analysis of the performance of an EDFA in a harsh environment, subjected to the presence of ionizing radiation, was carried out. From the literature [22; 23; 24; 25], the modification and worsening of the behavior of optical amplifiers under the effect of radiation was known. Our first objective was to understand what were the factors impacting on the degradation of the performance of the amplifiers, including the spectroscopic parameters and the RIA, and quantify their impact on the amplifier gain. It was observed that, for the sample studied, the variation in the spectroscopic parameters has an impact of less than 1% on the gain of the EDFA, and therefore, it was concluded that the RIA is the main factor that degrades the EDFA gain.  
The second goal was to compare the measured gain degradation, at two different dose rates, with the simulated one that considered the measured RIA, trying to obtain the best possible agreement between experiment and simulation.  
Specifically, for the simulation of the gain degradation relating to an RIA measurement at a dose rate of 0.093 Gy/s and a pump injected for 99% of the time slot, an almost perfect agreement was obtained between the experimental and the simulated curves with a maximum error of 2%.  
In conclusion, the software, combined with this RIA measurement setup, presented in Section 2.4, turns out to be a good solution for predicting the behavior of an optical amplifier subjected to the effect of ionizing radiation.  
Conceptualization, A.F., A.M., L.M., C.C., T.R., Y.O., A.B. and S.G.; methodology, A.F., A.M., C.C. and M.M.K.S.; software, A.F., A.M., L.M. and C.C.; formal analysis, A.F., A.M., L.M., C.C. and S.G.; data curation, A.F., A.M., C.C. and M.M.K.S.; resources, T.R.; writing--original draft preparation, A.F., A.M., L.M., Y.O. and S.G.; writing--review and editing, C.C., T.R., E.M. and A.B. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by a public grant from the French National Research Agency (ANR) under the \"France 2030\" investment plan, which has the reference EUR MANUTECH SLEIGHT-ANR-17-EURE-0026.  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author.  
Conflicts of Interest:Author [PERSON] was employed by the company Exail. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Results and Discussion  
To verify the performance of MADPSO in solving the SACOP problem, this section took a group of actual pallet-picking tasks' data from a shipyard in Southeast China as instances to conduct instance validations. Moreover, to further discuss the performance of MADPSO under different working conditions, we took the existing traditional pallet-picking mode (TPPM) in shipyards and other pallet-picking optimization algorithms as the reference object and carried out multiple groups of comparative experiments under different levels of experimental factors (including different task sizes, the number of available AGVS, the number of picking stations and so on).
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
## 3 Results and Discussion
### The Effect of Road Condition on the PSD  
To determine how the road surface affects vibration levels, PSD profiles were collected and analyzed for only two of the forklifts types (Figure 9). Because the reach truck is generally not driven outside, and thus was not part of this analysis. The comparison was conducted using vibration data measured at the fork time tips using the parameters that resulted in the highest vibration intensity, which was a speed of 4.82 km/h and a weight of 680 kg. The intensity of vibration increased with the increasing roughness of the road (Figures 4 and 5). The greatest change was observed for the first peak of the PSD observed at 3-4 Hz. The intensity of the peak increased and slightly shifted towards the lower frequency region when the forklift was driven on asphalt, compared to concrete.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 5 Results
### Static Scenario  
Figure 14 presents the tracking performance \(\mathrm{P_{Tracking}}\) and system performance \(\mathrm{P_{System}}\) of the implemented adaptive techniques in a stationary scenario. Four configurations of the \(C/N_{0}\)-based DSKF using different steady-state process noise variances (\(q=\{10,100,1000\}\)) and different correlation samples (\(N=\{100,500\}\)) are evaluated. Moreover, the LBCA-based DSKF, and the LBCA-based LUT-DSKF are analyzed using the weighting function defined in Equation (48). These adaptive tracking techniques are compared with the LBCA-based STL using the same weighting function.  
Figure 13(a) shows that all the presented configurations of the \(C/N_{0}\)-based DSKF do not lose the lock at any \(C/N_{0}\) level. The number of correlation samples \(N\) does not impact the static tracking performance. In contrast, \(q\) has an effect at low \(C/N_{0}\) levels. An intermediate value of \(q=100\) cycles\({}^{2}/s^{6}\) achieves the lowest results, meaning the best tracking performance. In the LBCA-based DSKF, \(q\) starts with an initial value of \(10\) cycles\({}^{2}/s^{6}\) and is adapted based on the LBCA's loop bandwidth update, while \(R\) is fixed to \(10^{-7}\)cycles\({}^{2}\). At high \(C/N_{0}\) levels, this method presents the same tracking performance as the other techniques. However, at low \(C/N_{0}\) levels, \(\mathrm{P_{Tracking}}\) increases significantly, until finally losing the lock at \(25\,\mathrm{dBHz}\). The LBCA-based LUT-DSKF does not lose the lock at low \(C/N_{0}\) levels, but it does not perform better than the \(C/N_{0}\)-based DSKF. The LBCA-based STL performs best at high \(C/N_{0}\) levels, but the performance deteriorates at low \(C/N_{0}\) levels. The weighting function of the LBCA is configured to be robust to signal dynamics, with the cost of a worse performance at low \(C/N_{0}\) levels.  
Figure 13(b) discloses each adaptive tracking technique's system performance (see Equation (54)). All the configurations show a good system performance above 0.8, except for the LBCA-based DSKF at \(25\,\mathrm{dBHz}\). While the system performance of this technique at \(25\,\mathrm{dBHz}\) is lower than 0.6, the PVT has not been lost.  
Figure 13: Sky-plot of simulated scenario. (**a**) Static Scenario. (**b**) Dynamic Scenario.
## 2 System and Channel Model  
### Model of Satellite Laser Communication System  
Figure 1 is a schematic diagram of a ground-satellite laser communication system, including the ground transmitter, HAP, and satellite terminal. The ground station is equipped with a single-antenna transmitter. The HAP is fitted with a multi-aperture receiver. It adopts the decode-and-forward relay protocol (DF), and the satellite station has a laser receiver. The laser beam is emitted from the ground transmitting end in the uplink. When transmitted upward through HAP, it is affected by atmospheric turbulence such as light intensity scintillation, beam drift, and the angle of arrival fluctuation. Finally, after transmission through the vacuum channel, it is received by the satellite terminal through the receiving antenna.  
Figure 1: Uplink system diagram of ground-to-satellite laser communication.
# Ten Years of TerraSAR-X Operations
## 7 Orbit Control
Safe mode drops, e.g., because of an on-board computer boot, changed the orbit and the maneuvers for the re-acquisition of the reference orbit became necessary. Fortunately, the on-board software was upgraded at the end of 2010 to feature a force-free safe mode based on magnetorquers, as described in Section 6. Besides the drag make-up maneuvers, three to five inclination control maneuvers are performed per year, with maneuver sizes between 10 and 30 cm/s. Furthermore, every year, one to two critical close approaches with man-made objects require active collision avoidance. In summary, the implemented reference orbit and the orbit control concept have proved to work remarkably well, and more than 99% of the time TSX has been staying inside the 250 m control tube.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 1 Introduction
### State of the Art
These advancements collectively signify a transition from static, traditional security measures toward dynamic, AI-driven solutions that enhance UAV security in complex and evolving threat landscapes. Standardization provides a critical framework for UAV safety and security worldwide. Currently, laws and regulations exist to support the healthy development and stable operation of the low-altitude economy. In 2019, the European Aviation Safety Agency (EASA) issued two European Union (EU) general regulations on UAVs, Commission Delegated Regulations 2019/945 [30] and 2019/947 [31]. These regulations categorize UAVs on the basis of operational risk and establish a registration system, a remote identification system, and a geo-awareness system to lay the foundation for U-Space. In 2023, the EU released a directive on the protection against unmanned aircraft systems [32] to address UAV-related threats and ensure that the increasing number of UAVs and rapid technological advancements do not lead to an uncontrolled surge in risk to civil airspace. The Unmanned Aircraft Systems Standardization Collaborative (UASSC) under the American National Standards Institute (ANSI) is responsible for the development of relevant standards, which are intended to promote the safe, reliable, and effective operation of UAV systems and to provide the UAV system industry with unified technical benchmarks and guidance [33]. Additionally, 3 GPP is the main organization for mobile communications standardization. Specification 3 GPP TS 22.125 defines the requirements for UAV system operations, including remote identification and tracking operational and security requirements [34]. China has released more than 150 policy documents related to the low-altitude economy. In 2024, the Safe Administration of Civil Unmanned Aerial Vehicle Operations and other regulations were enacted. To provide a comprehensive overview of the diverse applications and challenges in UAV safety and security, Figure 2 illustrates a panoramic view of UAV operations across multiple domains.
## 3 Discussion
Although the coding gene of the KpeA homolog did not show significant transcriptional differences as described above, it was found that regulatorsReg2 and Reg19 (which also belong to different groups of Zn(II)2 Cys6 transcription factors) probably perform distinct functions at various stages of Okaramine B production. Cluster-specific Zn(II)2 Cys6 transcription factors inside the BGCs, such as Mlck in _P. citrium_[41] and CgcheR in _Chuetonium globosum_[42], usually act as transcriptional activators in fungi. Investigating whether Reg2 and Reg19 exhibit regulatory effects comparable to those of similar transcriptional regulators inside BGCs will be a crucial focus for future research. Moreover, a previous study showed that the biosynthesis of pentacyclic triterpenes in _Saccharomyces cerevisiae_ was significantly increased by up to 127-fold through the over-expression of the mevalonate pathway and suppression of a competing pathway (sterol synthesis) [43]. This demonstrates that the regulation of metabolic flux direction is another crucial determinant of secondary metabolite production. Tryptophan serves as a building block for several signature indole alkaloids present in fungi. Okaramine B is derived from a combination of two L-tryptophan molecules [21], indicating that tryptophan metabolism likely has a significant influence on the regulation of Okaramine B biosynthesis either directly (e.g., precursor availability) or indirectly (e.g., by affecting gene expression in _oka_ cluster). During the semi-solid fermentation process, the conversion of tryptophan to formyl-anthranilate appeared to be inhibited compared to during the submerged fermentation, resulting in different Okaramine B yields. In both processes, the genes responsible for the conversion between tryptophan and anthranilate were down-regulated (e.g., genes 2.442, 4.1036, 2.670, 2.96, and 3.210 in Table S5), along with the Okaramine B biosynthesis. Based on the above results, it is hypothesized that Okaramine B biosynthesis is intricately linked to tryptophan flux in alternative metabolic pathways. In addition, effective transportation of synthetic products is essential for maintaining the continuous and beneficial flow of the biosynthesis pathway [30]. Transporter genes that were up-regulated during Okaramine B production were identified in both fermentation processes. Disruption/deletion mutants of these genes could provide more detailed insights into their roles in the regulation of Okaramine B biosynthesis.
## 3 Effective Thermal Conductivity Analysis
### Solid Conduction + Contact Area Conduction + Solid Conduction
The first and third type of effective thermal conductivities are the same under the two conditions, which are only related to the material and structure of the pebble bed. The TF-PBEC conducted by Tsinghua University aims to cover the whole temperature range of the safety analysis of HTR-PM (0\(\sim\)1600 \({}^{\circ}\)C). Figure 8 gives the predicted total effective thermal conductivity of the pebble bed under helium conditions over the whole temperature range, which is useful for the design of insulating layers and heating power of the facility. Relevant parameters are used following the SANA experiment due to the similarity of the pebble bed. Figure 8: Total effective thermal conductivity of the pebble bed for helium conditions over the whole temperature range. Figure 7: Total effective thermal conductivity and the experimental data of the pebble bed in SANA experiment under nitrogen conditions. However, it should be noted that we use a new fitting formula to describe the temperature-dependent heat conductivity of the pebble materials, as listed below. \[\lambda_{\mathrm{s}}=141.10858\times\mathrm{e}^{(-t/382.46023)}+44.0461 \tag{5}\]  
Figure 9 gives the comparison showing the old and new formula of the heat conductivity of the pebble material over the whole temperature range. As we can see in the picture, the star shows the experimental data of the heat conductivity of the pebble material. The circle shows the heat conductivity trend according to the recommended formula in the SANA report, while the inverted triangle shows the heat conductivity trend according to the new fitting formula. As we can see, the recommended quartic polynomial in the SANA report [12] gives a reasonable result below 1000 \({}^{\circ}\)C. However, it gives an illogical result in the high temperature region.
## 5 Fingerprinting
### Online Phase
#### 5.2.1 Classical Machine Learning  
K-Nearest Neighbor (KNN) is the first simple candidate to select K points in the dataset based on similarity. Considering the computation complexity, the achieved performance for this algorithm is acceptable. In a WKNN, different weights are assigned to measurements based on criteria such as the distance to the AP. For example, in [63], WKNN is employed where, based on the inverse of RSS distance, weights are assigned to the APs. This criterion, however, is not consistent with position distance due to the nature of path loss, which decays logarithmically based on distance (the reader is referred to [64] for more details). To address this issue, [64] introduces a new weighting process and a new distance measure based on the RSS similarity and spatial position.  
One disadvantage of traditional KNN is that it is implemented with a fixed number of K. A Weighted Adaptive KNN Algorithm is adopted in [65] to settle this problem. It can choose a variable number of RPs according to both the improved RSS similarity and position proximity. Density-based spatial clustering of applications with noise (DBSCAN) method and Affinity Propagation Clustering (APC) does not require the number of clusters to be pre-determined and is suitable for handling large databases. The authors in [66] show that clustering could reduce noise's impact in large datasets. Hence, the DBSCAN method is proposed for localisation, where the fingerprints are divided around a centre point based on the density. [PERSON] et al. [67] apply APC offline until the algorithm converges to a final clustering after several iterations and message passing among the points (attraction and attribution messages).  
Random Forest is a classifier and regression method based on multiple decision trees that enjoy fast training and prediction, which works well with high dimensional data. Thus, it is a suitable candidate for handling large data-set [52; 68]. SVM is another classifier engine that can resolve the regression problem. For localisation, examples of multi-class SVM can be seen in [69; 70]. To make the fingerprints more separable, [69] exploits a fuzzy kernel that maps the data to the higher dimension space.
# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
## 2 PLC Technologies  
NB-PLC technology is very suitable for Smart Grids and AMI applications, as reflected in different studies [10; 11] and pilot projects developed by main DSOs, such as, e.g., STAR (Iberdrola) [12] or SmartCity Malaga (ENEL) [13]. Nevertheless, BPL is gaining momentum in Smart Grid and AMI scenarios as a solution not only for transmitting aggregated traffic through long distances (e.g., MV-BPL cells) [14] but also for smart meter communications in last-mile [15] and other related applications [16].  
For the sake of completeness, this section provides a summary of available PLC technologies, focusing on PRIME technology and IEEE 1901, since the toolkit presented in this paper is focused on them.  
Regarding NB-PLC technologies, the most widely used ones are as follows.
## 3 Results  
The mean and SD values for time to failure (in seconds) and the mean length of the fractured file tip (mm) for each of the study groups are displayed in Table 1 and Figure 3.  
The ANOVA analysis showed statistically significant differences between time to failure and the time of use of NiTi endodontic reciprocating files (\(p<0.001\)) (Figure 3). However, no statistically significant differences were observed between time to failure and the number of sterilization cycles \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time of Use** & **Steerilization Cycles** & \(n\) & **Mean** & **SD** & **Minimum** & **Maximum** & **Fracture Length** \\ \hline \multirow{4}{*}{0 s} & 0 a & 10 & 235.29 & 45.07 & 153.20 & 282.83 & 3.06 \\  & 1 a & 10 & 239.55 & 35.54 & 176.75 & 276.66 & 3.10 \\  & 5 a & 10 & 225.26 & 32.04 & 164.10 & 265.31 & 3.17 \\  & 10 a & 10 & 235.30 & 32.83 & 182.28 & 276.91 & 3.04 \\  & 0 b & 10 & 195.59 & 45.56 & 112.84 & 242.92 & 3.13 \\  & 1 b & 10 & 191.45 & 31.82 & 136.77 & 234.89 & 3.06 \\  & 5 b & 10 & 182.64 & 28.97 & 132.47 & 216.00 & 3.01 \\  & 10 b & 10 & 191.18 & 29.71 & 145.94 & 234.33 & 3.04 \\  & 0 c & 10 & 88.58 & 12.39 & 61.82 & 102.35 & 3.11 \\  & 1 c & 10 & 80.85 & 9.70 & 61.91 & 92.87 & 3.08 \\  & 5 c & 10 & 86.90 & 9.56 & 72.16 & 102.01 & 2.99 \\  & 10 c & 10 & 97.62 & 10.40 & 82.09 & 121.11 & 3.02 \\ \hline \hline \end{tabular} a,b,c Statistically significant differences between groups (\(p<0.05\)).  
\end{table} 
Table 1: Descriptive analysis of the time to failure in relation to time of use and number of sterilization cycles. applied to NiTi endodontic reciprocating files (\(p\) = 0.848) (Figure 3). The mean length of the fractured file tip did not show statistically significant differences between time to failure of the time of use (\(p\) > 0.05) and the sterilization cycles (\(p\) > 0.05) of the NiTi endodontic reciprocating files (Figure 3). The scale distribution parameter (\(\eta\)) of Weibull statistics found statistically significant differences between time to failure and the time of use of NiTi endodontic reciprocating files (\(p\) > 0.001) (Table 2, Figure 4A); however, there were no statistically significant differences in time to failure between the number of sterilization cycles applied to NiTi endodontic reciprocating files (\(p\) > 0.05) (Table 2, Figure 4B). The shape distribution parameter (\(\beta\)) of Weibull statistics found no statistically significant differences between time to failure in relation to time of use (\(p\) > 0.05) (Table 2, Figure 4A), and the sterilization cycles applied to NiTi endodontic reciprocating files (\(p\) > 0.05) (Table 2, Figure 4B). Figure 4: **(A)** Weibull probability plot of time to failure for the time of use and **(B)** sterilization cycles study groups. Figure 3: Box plot of the time to failure for the time of use and the sterilization cycles study groups. The horizontal line in each box represents the respective median value. The mean and SD values for number of cycles to failure and the length of the fractured file tip (mm) of the study groups are displayed in Table 3 and Figure 5.