esa-sceva/satcom-chunk-collection huggingface dataset:
# Ecotoxicological Assessment of Soils Reclaimed with Waste
## 3 Results
### Assessment of Ecotoxicity Using the MARA Test  
The multi-species MARA test was used to assess the biotoxicity of the samples. The assessment of the degree of ecotoxicity in the samples revealed that the soil reclaimed solely with sewage sludge exhibited the highest degree of toxicity, with MTC = 18 \(\pm\) 2.5%. In the case of this test, the most sensitive microorganisms were the bacteria _Kurthia gibsoni_ (MTC = 14 \(\pm\) 1.25%). For soils reclaimed with mineral wool (in the form of an insert), NPK, and sewage sludge (variants 1, 3, 5, 6), low selective toxicity was demonstrated (MTC from  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**No.** & **Reclamation Variant** & **A** & **B1** & **B2** & **B3** & **M1** & **M2** & **M3** & **Mean B** & **Mean M** \\ \hline
1. & Degraded soil & 10.00 & 5.00 & 6.00 & 4.00 & 200.00 & 166.67 & 250.00 & 5.00 & 205.56 \\ \hline
2. & Degraded soil + lime + NPK & 10.00 & 4.00 & 3.00 & 2.00 & 250.00 & 333.33 & 500.00 & 3.00 & 361.11 \\ \hline
3. &
\begin{tabular}{c} Degraded soil + lime + sewage \\ sludge 100 Mg:ha\({}^{-1}\) \\ \end{tabular} & 10.00 & 3.00 & 3.00 & 4.00 & 333.33 & 333.33 & 250.00 & 3.33 & 305.56 \\ \hline
4. &
\begin{tabular}{c} Degraded soil + sewage sludge \\ 100 Mg:ha\({}^{-1}\) \\ \end{tabular} & 10.00 & 2.00 & 1.00 & 3.00 & 500.00 & 1000.00 & 333.33 & 2.00 & 611.11 \\ \hline
5. &
\begin{tabular}{c} Degraded soil + wool 5 cm/40 cm \\ + lime + NPK \\ \end{tabular} & 10.00 & 2.00 & 2.00 & 1.00 & 500.00 & 500.00 & 1000.00 & 1.67 & 666.67 \\ \hline
6. &
\begin{tabular}{c} Degraded soil + wool 5 cm/40 cm \\ + lime + sewage sludge 100 Mg:ha\({}^{-1}\) \\ \end{tabular} & 10.00 & 3.00 & 2.00 & 3.00 & 333.33 & 500.00 & 333.33 & 2.67 & 388.89 \\ \hline
7. &
\begin{tabular}{c} Degraded soil + lime + NPK + wool \\ 400 m\({}^{3}\):ha\({}^{-1}\) \\ \end{tabular} & 10.00 & 2.00 & 1.00 & 1.00 & 500.00 & 1000.00 & 1000.00 & 1.33 & 833.33 \\ \hline
8. &
\begin{tabular}{c} Degraded soil + lime + wool \\ 400 m\({}^{3}\):ha\({}^{-1}\) + sewage sludge \\ 100 Mg:ha\({}^{-1}\) \\ \end{tabular} & 10.00 & 1.00 & 1.00 & 1000.00 & 1000.00 & 1000.00 & 1.00 & 1000.00 \\ \hline \multicolumn{2}{c}{Average between reclamation variants} & 2.75 & 2.38 & 2.38 & 452.08 & 604.17 & 583.33 & 2.50 & 546.53 \\ \hline \multicolumn{2}{c}{Standard deviation} & 1.20 & 1.58 & 1.22 & 234.44 & 322.08 & 330.72 & 1.21 & 259.82 \\ \hline \end{tabular}
\end{table}
Table 4: Phytotoxicity assessment of plants.  
32 \(\pm\) 21.1 to 42 \(\pm\) 2.5%). In the variants in which crushed mineral wool and NPK fertilizers were used (variants 7, 8, and 2), no toxicity was demonstrated in the screening test.
## 1 Introduction
### Problems Related to Spatial Perception  
In 3D VR applications, VR screens provide users with a unique visual experience. The use of small screens to display visuals enables users to generate their visual centers and intuitively operate the virtual interface by drawing from their personal experiences. Although users can turn, move, raise, or lower their heads to adjust their vision in the VR environment, a smaller field of view affects their spatial perception ability [5]. Therefore, studies have investigated users' spatial abilities and the visual perception problems caused by interacting with VR environments. Spatial orientation refers to users' ability to discern and maintain their positions relative to other positions in a spatial environment [6]. In 3D VR, a user's line of sight may be obstructed by signs, walls, or buildings. In such cases, users' spatial orientation determines the smoothness of their positioning and the navigation process.  
Spatial orientation determines one's ability to obtain and use spatial information. It includes spatial perception abilities and abilities related to operating and controlling space. In investigations of problems related to spatial perception, scholars have discovered that spatial perception ability determines users' ability to assess their position and direction and quickly arrive at their destination. When users lose their spatial perception ability, they may experience a loss of direction [7]. Cognitive psychology studies have revealed that the human field of vision is approximately 130\({}^{\circ}\) vertically and 180\({}^{\circ}\) horizontally, and the central 120\({}^{\circ}\) is where stereo vision lies. However, in a VR environment on a flat-panel display, the first-person view is only 30\({}^{\circ}\) horizontally [8]. Limitations in field of view are critical factors that influence the amount of information users can acquire from a given space [9]. Middle-aged and older consumers generally have weaker spatial orientation abilities [10]. Therefore, VR spaces without large-scale reference coordinates representing spatial directions and 3D virtual stores with complex spatial allocation and product displays may cause middle-aged and older consumers to experience problems related to spatial perception.
## 1 Introduction  
SBS is a nonlinear effect with the lowest threshold in narrow-linewidth CW fiber systems [1], which limits power scaling. At present, SBS suppression methods can be classified as follows: using large-mode fibers [2], using high-doped gain fibers [3], reducing the overlap between the light field and the sound field [4], broadening the SBS gain spectrum [5], and using phase modulation to broaden the signal light [6]. The phase broadening of the signal laser and a reduction in power spectral density are currently the most effective solutions to increase the SBS threshold [7, 8, 9]. At present, the mainstream phase modulation signals include the Sine signal, WNS (white noise source), and PRBS. Among these, Sine signal generation is the simplest of the several signals; however, it makes the average power of the seed source spectrum higher, which is due to its completely separated spectrum, resulting in its poor inhibition of SBS. The spectrum of WNS is inherently continuous, but the randomness of its time-domain sequence causes random spikes, which reduce the maximum output power of the laser. A PRBS is a digital signal that separates spectral lines and has a certain periodic nature, which produces an envelope distribution similar to a WNS, but the spectrum can be precisely controlled by adjusting the clock rate so that the SBS effect can be better suppressed. Therefore, the PRBS phase modulation scheme is one of the most effective schemes for suppressing SBS [10, 11, 12, 13, 14, 15, 16]. However, the PRBS phase modulation scheme is mainly focused on the selection of the pattern, the optimization of the ratio of the filter's cut-off frequency to the clock rate, and the exploration of the optimal value of the modulation depth, but the time-domain characteristics of the forward and backward output have not been studied enough. In 2013, [PERSON] et al. [17] reported that when a pulsed signal was amplified in a fiber amplifier, a very high peak power random pulse was generated backwards. These backward-generated high-peak-power (\(k\)W-level) pulses could damage the components in the upstream amplifier and main oscillator stage. In 2017, [PERSON] et al. [18] also discovered kW-level pulses in self-pulsed fiber lasers, which were all attributed to SBS in fiber lasers. In 2020, [PERSON] et al. [19] investigated the high-intensity pulses caused by SBS after WNS phase modulation in high-power fiber lasers and increased the SBS threshold by cascading the WNS. It could be seen that the instability of the output laser and the backward Stokes light caused by the SBS effect would have extreme consequences. Thus, it is important to study the time-domain characteristics of the output light in PRBS phase-modulated fiber lasers.
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 4 Protocol Evaluation
### Simulation Parameterization
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Number** \\ **Nodes** \\ \end{tabular} & \begin{tabular}{c} **Time** \\ **Slots** \\ \end{tabular} & \begin{tabular}{c} **Radius of** \\ **Proximity** \\ \end{tabular} & \begin{tabular}{c} **Bound** \\ **of AoI** \\ \end{tabular} & \begin{tabular}{c} **Produced** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Dropped** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Successful** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Remaining** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Execution** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Throughput** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **AoI** \\ **Time (Time** \\ \end{tabular} \\ \hline
50 * & 500 & 20 & 30 & 998 & 686 & 257 & 55 & 3.564 & 0.257 & 25.07 & 30 \\ \hline
50 * & 1000 & 15 & 70 & 1997 & 105 & 1838 & 54 & 10.398 & 0.92 & 28.87 & 70 \\ \hline
100 * & 1000 & 15 & 70 & 1963 & 793 & 1083 & 87 & 25.2 & 0.551 & 47.83 & 70 \\ \hline
100 & 1000 & 149 & 70 & 2004 & 848 & 1048 & 108 & 32.78 & 0.522 & 51.08 & 70 \\ \hline
100 & 2000 & 149 & 140 & 4029 & 1548 & 2252 & 229 & 97.76 & 0.558 & 102.08 & 140 \\ \hline
100 & 5000 & 149 & 200 & 10,014 & 4145 & 5558 & 311 & 457.35 & 0.555 & 150.45 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initial results for Modified-near-v1.
However, there was a chance that Cyt \(c_{\text{M}}\) participated in respiration or photosynthesis as a secondary element, i.e., as a backup piece that is only expressed under certain conditions to substitute or complement a major constituent. Interestingly, the expression levels of Cyt \(c_{\text{M}}\) sharply increase in stress conditions such as low temperature  
Figure 1: Cyt \(c_{\text{M}}\) protein and gene sequences from _Synechocystis_ sp. PCC 6803. The two potential translation initiation sites are underlined. The black triangle separates the hydrophobic region (orange) from the hydrophilic one (blue) and marks the point at which the signal peptide was presumed to finish, i.e., right after the VLA motif (boxed). The characteristic CXXCH heme binding motif (boxed) can also be observed. and high-intensity light whereas those of the two main electron carriers, plastocyanin (Pc) and Cyt \(c_{6}\), decrease [9]. Despite some further evidence indicating a \"spare component\" role for Cyt \(c_{\text{M}}\) in photosynthesis [10; 11], we ruled this out by showing that the surface electrostatic potential of Cyt \(c_{\text{M}}\) is too different from those of Pc and Cyt \(c_{6}\) and by proving that the kinetic interaction constant between Cyt \(c_{\text{M}}\) and PS I, to which Pc and Cyt \(c_{6}\) donate electrons, is negligible [6]. Moreover, the low redox potential of Cyt \(c_{\text{M}}\) (+150 mV) makes it thermodynamically unlikely that it can be reduced by Cyt \(f\) (with a redox potential of +320 mV), as is the case with Pc and Cyt \(c_{6}\)[6; 12]. Consequently, only respiration remained as a real possibility. Initially, it was suggested that Cyt \(c_{\text{M}}\) could reduce respiratory terminal oxidases (COX) since it was kinetically plausible [3] and it was impossible to create a \(\Delta\)cytM/PSI double mutant [10]. However, the hypothesis of Cyt \(c_{\text{M}}\) working as an electron donor in respiration was dismissed once it was observed that \(\Delta\)cytM mutants have in fact a higher respiration rate under dark heterotrophic conditions [13], with the lack of COX not affecting this whatsoever [8]. In conclusion, none of the hypotheses formulated to this day regarding the physiological role or the cellular location of Cyt \(c_{\text{M}}\) has proved solid, and this matter remains altogether an open debate. Since most attempts to determine any of these have been based on the assumption that it is a small soluble protein located in the lumen of the thylakoid, we have reevaluated the sequence and structure of Cyt \(c_{\text{M}}\) in the present work. Our results suggest that, contrarily to what was previously thought, Cyt \(c_{\text{M}}\) could be a membrane protein with a structure similar to that of the C subunit of bacterial nitric oxide reductase.
## 3 Results and Discussion
### N-NO\({}_{3}\),N-NH\({}_{4}\), and P Contents in the Soil
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **NPK** & **NPK + PA \({}^{*}\)** & **NPK + BM \({}^{**}\)** & **NPK + BS \({}^{***}\)** & **NPK + PA + BM + BS** \\ \hline \multicolumn{5}{c}{**N-NO\({}_{3}\)**} \\
2017 & 2.32 \(\pm\) 0.52 \({}^{\text{bc}}\) & 3.79 \(\pm\)0.45 \({}^{\text{b}}\) & 2.77 \(\pm\) 0.43 \({}^{\text{bc}}\) & 3.30 \(\pm\) 0.57 \({}^{\text{bc}}\) & 6.05 \(\pm\) 0.73 \({}^{\text{a}}\) \\
2018 & 1.82 \(\pm\) 0.48 \({}^{\text{c}}\) & 3.25 \(\pm\) 0.81 \({}^{\text{bc}}\) & 2.82 \(\pm\) 0.45 \({}^{\text{bc}}\) & 2.60 \(\pm\) 1.17 \({}^{\text{bc}}\) & 2.80 \(\pm\) 0.45 \({}^{\text{bc}}\) \\
2019 & 2.25 \(\pm\) 0.18 \({}^{\text{bc}}\) & 2.75 \(\pm\) 0.51 \({}^{\text{bc}}\) & 2.22 \(\pm\) 0.42 \({}^{\text{c}}\) & 1.75 \(\pm\) 0.33 \({}^{\text{c}}\) & 3.05 \(\pm\) 0.96 \({}^{\text{bc}}\) \\ \hline \multicolumn{5}{c}{**N-NH\({}_{4}\)**} \\
2017 & 1.12 \(\pm\) 0.11 \({}^{\text{d}}\) & 2.97 \(\pm\) 0.81 \({}^{\text{a}}\) & 2.04 \(\pm\) 0.14 \({}^{\text{a}-\text{d}}\) & 3.03 \(\pm\) 0.52 \({}^{\text{a}}\) & 2.13 \(\pm\) 0.78 \({}^{\text{a}-\text{d}}\) \\
2018 & 1.48b \(\pm\) 0.50 \({}^{\text{cd}}\) & 1.46 \(\pm\) 0.07 \({}^{\text{bed}}\) & 2.15 \(\pm\) 0.01 \({}^{\text{abc}}\) & 1.88 \(\pm\) 0.02 \({}^{\text{bed}}\) & 2.99 \(\pm\) 0.58 \({}^{\text{a}}\) \\
2019 & 1.15 \(\pm\) 0.07 \({}^{\text{cd}}\) & 1.29 \(\pm\) 0.21 \({}^{\text{bed}}\) & 1.56 \(\pm\) 0.08 \({}^{\text{bed}}\) & 1.38 \(\pm\) 0.33 \({}^{\text{bed}}\) & 2.27 \(\pm\) 0.32 \({}^{\text{ab}}\) \\ \hline \hline \end{tabular} \({}^{*}\)PA—_Pranihucillus azotofixanus; \({}^{**}\)_BM—_Bacillus megaterium; \({}^{**}\)_BS—_Bacillus subtilis_. Values followed by the same letters do not differ significantly in [PERSON]’s (HSD) test (_p_ < 0.05).  
\end{table} 
Table 7: N-NO\({}_{3}\) and N-NH\({}_{4}\) contents in the soil after application of microbial preparations (mean value and standard deviation), interaction between the years and treatments. Figure 6: The N-NO\({}_{3}\) and N-NH\({}_{4}\) contents in the soil after application of microbial preparations (mean value and standard deviation), (**a**) average for the years, (**b**) average for treatment. Values followed by the same letters do not differ significantly in [PERSON]’s (HSD) test (_p_ < 0.05). PA—_Paenibacillus azotofixanus;_BM—_Bacillus megaterium;_ BS—_Bacillus subtilis_. \({}^{\text{a,b,c}}\)—statistically homogenous groups, _p_\(\leq\) 0.05.
## 2 Materials and Methods
### Statistical Analysis  
To calculate a sample size, we focused on our primary hypothesis that perioperative analgesia is improved with the parasternal block. We estimated the density of pain scores (mean 2; SD 1.5) based on published data regarding the use of parasternal block for cardiac surgery [16]. To simulate power, we used the truncated Gaussian distribution with range  
Figure 1: Parasternal Block Execution. _Legend_: A high frequency ultrasound probe was positioned immediately lateral to the sternum identifying the second and the fourth intercostal spaces (**A**). Then, an echogenic 100 mm needle (Stimuplex ultra-360, BBraun Deutschland GmbH & Co., Melsugen, Germany) was advanced through the skin with an in-plane approach and an injection of 10 mL 0.5% ropivacaine was performed between pectoral major and intercostal muscles for every intercostal space, bilaterally, with a total dose of 200 mg of ropivacaine (**B**). PMM: pectoral major muscle; IM: intercostal muscles; TTM: transversus thoracis muscle; PL: pleura; *: point of injection.  
0-10; standard deviation = 1.5; PARASTERNAL group mean = 2. Under these assumptions and 2-sided \(\alpha\) = 5%, we simulated 10,000 trials with sample size of 63 per group. With an overall sample size of 126 subjects, we have at least 90% power to detect group differences in pain as small as approximately 1.  
Statistical analysis and graphical presentation were obtained thanks to the use of the GraphPad Prism 8 software (GraphPad Software Inc., San Diego, CA, USA).  
The values of continuous quantitative variables are expressed as mean \(\pm\) standard deviation (SD); the values of discrete variables are expressed as median and interquartile range (IQR). Qualitative variables are expressed as number of observations and percentage of distribution.  
The parametric distribution of numerical variables was evaluated using the Shapiro-Wilk normality test. Difference between groups was assessed by T-Student test for continuous parametric variables, while Wilcoxon-Mann-Whitney U test was used when appropriate. Bonferroni-Dunn correction has been applied to multiple repeated measures in order to reduce the risk of type 1 error.  
Categorical variables were compared with Pearson's \(\chi^{2}\) test. The level of statistical significance was set for \(p\) value < 0.05.
# A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
## 4 Numerical Simulation  
The main purpose of attitude controller design is to track the roll speed instruction quickly and accurately, and control the roll speed to achieve the expected roll angle. This section uses Matlab software platform to simulate the missile roll state control and verify the effectiveness of the proposed control method.  
In this section, the mass and inertia loss of the aircraft, the deviation of the aircraft structure from the ideal situation, the absolute plus relative deviation of aerodynamic and aerodynamic moment coefficients, and wind disturbance are considered, respectively. In order to verify the adaptive ability of ADRC, a failure mode, namely rudder damage, is also considered.
# Detect Overlapping Community Based on the Combination of Local Expansion and Label Propagation
## 4 Experimental Result
### Synthetic Networks
Figure 2: Sub figure (**a-j**) represent the NMI of different algorithms in the \(LFR\) 1 to \(LFR\) 4, where there are different mixing parameter \(\mu\) and network size \(N\). The \(NMI\) of algorithms on each \(LFR\) are compared in Figure 4. It can be seen that \(LELP\) is better in most cases (the blue curve is usually at the top, except Figure 4a,c). However, in the networks with \(\mu=0.1\) the two label propagation algorithms, \(COPRA\) and \(SLPA\), obtain higher \(NMI\) as a result of the rapid stability of the labels during early propagation. \(IDEC\) also obtains a better result, which may be because the division of overlapping and non overlapping regions is clear in this case. For the networks where there are many overlapping nodes, \(LELP\) significantly outperforms other algorithms, excluding \(COPRA\). This is because the _closeness_ can more accurately reflect the distance between the nodes than the edge, so the impact of complex memberships is not serious. The performance of \(Game\), \(EAGLE\) and \(CFinder\) are worse than other algorithms in most cases. This shows that algorithms based on cliques are not suitable for large networks where the clique is much smaller than the community. \(NMI\) focus only on providing the measure of overlapping size in each community. It might not be sensitive enough to depict what is going on regarding nodes. It is possible that the overlapping scale detected by algorithms is close to the ground truth, but the overlapping nodes detected are not true. For this reason, measuring the overlapping nodes detected is essential for evaluating the accuracy of detection algorithms. We measure the F-score of algorithms in Figure 5. It can be seen that the F-score of \(LELP\) is much higher than that of other algorithms when \(O_{m}\) is small. In particular, in \(LFR\) 4 and \(LFR\) 5, all algorithms are far behind \(LELP\), except for \(SLPA\) in the case of large \(O_{m}\). In \(LFR\) 6 and \(LFR\) 7, \(LELP\) declines sharply with the increase in \(O_{m}\), but it is still higher than most algorithms. This trend of the F-score is due to the sharp decrease of the _precision_ as \(O_{m}\) increases. The reason is that the _precision_ of \(LELP\) is sensitive to \(O_{m}\), which has little influence on \(recall\). The \(recall\) of \(LELP\) is relatively stable in the face of different \(O_{m}\), and even ascend slightly with the rise in \(O_{m}\). This shows that \(LELP\) can correctly detect most of the true overlapping nodes, but some non-overlapping nodes are wrongly detected as overlapping nodes. Meanwhile, almost all the non-overlapping nodes are detected correctly. Figure 4: Sub figure (**a**–**g**) represent the NMI of different algorithms in the same \(LFR\).
# Quality Index of Supervised Data for Convolutional Neural Network-Based Localization
## 3 Approach  
### Small Imaging Sensor: SPAD LiDAR  
For localization of AGVs, we use a small imaging sensor named SPAD LiDAR [14]. Figure 3 and Table 1 show the configuration of the prototype. A laser diode (LD) with its collimator lens is installed behind an imaging mirror. The laser beam is emitted through a hole in the imaging lens and directed toward the targets after reflecting from a polygon mirror. Backscattered light returns through the same optical path and is focused on the photodetector, consisting of an array of 16 pixels. Since each facet of the hexagonal polygon mirror has a slightly different tilting angle, a horizontal scan, measuring 16 lines, can be achieved by rotating the polygon mirror and six vertical scans are achieved in one revolution. The 16 lines per vertical scan for the six vertical scans form a total of 96 lines of depth imaging, while 202 points are measured in the horizontal scan. The photodetector is a SPAD fabricated using the complementary metal oxide semiconductor (CMOS) process. The time-of-flight (TOF) is estimated based on time-correlated single photon counting (TCSPC), wherein the histogram of photon incident timing is generated and the peak is extracted to distinguish the back-scattered light from ambient light. The peak value is regarded as the reliability, while the peak position is the TOF in the histogram. Ambient light, regarded as the intensity, is measured with another 1D array of pixels and placed next to the pixels for TOF measurement where the laser beam has not fallen. As a result, three kinds of information--TOF, reliability, and intensity--are acquired with each pixel. An example of the acquired data as well as a reference image captured with a camera is shown in Figure 4.
## 7 Discussion
### Future Directions  
Future research must address these challenges by emphasizing multimodal feature fusion, model generalization, and explainability. Integrating temporal and spectral features into unified architectures will likely yield models that are both accurate and resilient to signal variability. Techniques such as transfer learning, federated learning, and domain adaptation hold promise for developing generalized models that can operate effectively across heterogeneous EEG datasets and clinical environments.  
The standardization of EEG datasets is also essential. Harmonizing sampling rates, electrode configurations, and annotation protocols across public databases would enable fairer benchmarking and facilitate model replication. Initiatives aimed at curating large-scale, labeled, and demographically diverse EEG datasets will be crucial in advancing the field.  
Ultimately, the performance of DL-based seizure prediction systems is shaped by the feature domain from which models learn. Spectral-domain features, particularly when combined with advanced architectures such as Transformers and hybrid CNN-LSTM models, substantially improve the detection of ictal states. However, continued progress depends on addressing unresolved issues related to generalization, interpretability, and deployment feasibility. The future of seizure prediction lies in the development of adaptive, transparent, and clinically reliable models that can accurately forecast seizures and support real-time interventions.