esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionization of Xenon Clusters by a Hard X-ray Laser Pulse
###### Abstract
Similar NIR-driven ionization dynamics has been observed on the XFEL-ignited nanoplasma containing \(\sim\)6 trapped electrons on average [37]. Ref. [35] reported another NIR-driven process, namely, surface plasma resonance [38; 39] in high-density nanoplasma created by XUV-laser pulses. Surface plasma resonance causes an effective energy absorption of nanoplasma, which can be mathematically tantamount to a Mie resonance when the collective motion frequency of plasma electrons coincideswith the frequency of the NIR laser [40]. The surface plasma resonance effect has been also observed in the XFEL-ignited nanoplasma [31; 36]. Further probing with NIR pulses has been expected to track the time evolution of the electron density within the XFEL-ignited nanoplasma. Here, we report on electron and ion spectroscopies for xenon clusters exposed to X-ray laser pulses with the photon energy of 5.5 keV at the XFEL facility SACLA [3]. First of all, we focused on the dependence of ion yields produced from the XFEL-ignited nanoplasma on the XFEL intensity to obtain a deeper insight into the cluster dynamics with hard X-ray lasers of a moderate fluence. The XFEL fluence dependence indicates that the increase in the ionic mean charge state within the XFEL-ignited nanoplasma has hindered oligomer formation due to reorganization of the bonding. Moreover, to study the ionization dynamics of small-sized clusters, which has never been experimentally reported in the hard X-ray regime, we investigated the cluster-size dependence of ion data. The ion data for different-size clusters indicate that the oligomer formation is enhanced with the increase in cluster size. Furthermore, in order to deepen this insight, we performed time-resolved spectroscopy by using a probe NIR laser to track the expanding XFEL-ignited nanoplasma. The time-resolved data provide evidence that the trapped electrons may be inhomogeneously distributed in the XFEL-ignited nanoplasma produced from xenon clusters with an average size of \(\sim\)5000 atoms when the XFELs fulfill the moderate fluence condition.
## 3 Path Model Design
### R-Curve Path Model
#### 3.1.1 Stage 1: Experiment Preparation  
The target in this stage is to guarantee that all unknown sensors can determine their positions. First, we test the communication range of the mobile node at the environments where we execute the experiments. From [28], all the non-localized sensors can obtain their positions if:  
\[\forall\;\mathrm{U_{x}}\;(\mathrm{x=1,2,3...r})\;\exists\;\{\mathrm{M_{y(y=1,2,3 )}}\,|\mathrm{distance}\;\left(\mathrm{U_{x}},\;\mathrm{M_{y}}\right)\leq\mathrm{ CR} \tag{1}\]  
where \(\mathrm{U_{x}}\) signifies non-localized sensors, \(\mathrm{M_{y}}\) signifies the mobile node pausing points positions, distance (\(\mathrm{U_{x}},\;\mathrm{M_{y}}\)) is the distance between non-localized nodes and mobile node positions, and CR is the communication range of mobile node.  
We then adjust the model level based on the communication ratio. First, as we illustrated above, the network area in \(\mathrm{le}=1\), is split into four square sub-fields, defined as \(\mathrm{SF_{f}}\) (\(\mathrm{f=1,\ldots,4}\)). The mobile node pauses for one minute at pausing points \(\mathrm{M_{p(p=0,1,\ldots)}}\). We define the resolution of the introduced path model as the side length \(\mathrm{L}\) of a square sub-field. Each non-localized sensor node should receive messages from different mobile node positions to guarantee a successful positioning process.  
In Figure 3b, \(\mathrm{U_{1}}\) is an unknown sensor which is located in the farthest point from \(\mathrm{M_{5}}\). If \(\mathrm{U_{1}}\) receives a message from \(\mathrm{M_{5}}\), it confirms that all unknown nodes in \(\mathrm{F_{4}}\) can receive messages from \(\mathrm{M_{5}}\). As has been recognized from Figure 3b:  
\[\mathrm{distance}\;(\mathrm{M_{5}}\;,\;\mathrm{U_{1}})=\sqrt{5}\;\mathrm{L}\; \mathrm{\leq CR} \tag{2}\]  
Figure 4: (a) R-Curve path model (\(\mathrm{le}=2\)); (b) R-Curve path model (\(\mathrm{le}=3\)).  
Figure 3: (a) Applied R-Curve path model; (b) R-Curve path model (\(\mathrm{le}=1\)).  
where distance (M5, U1) is the distance between unknown sensor U1 and the mobile node position at M5. In this way, all the non-localized sensors can receive three different messages and achieve full coverage area.
## 3 Discussion  
Numerous bone-graffing options exist to match various needs related to specificity of defects and patient's initial clinical conditions, from autografts to alloplasts, including xenografts and composite biomaterials [6; 12; 23; 24]. The ideal graft material is described as a substance that will change into regular bone under functional loading without resorption and offers the ability to form new bone, either osteoconductively or osteoinductively, to enable the support of dental implants [5; 6]. Therefore, autologous bone implantation has been widely adopted for decades because of the superior osteogenic properties of native bone grafts [13]. Autologous grafts are considered a gold standard for reconstruction because of their bioactivity, mechanical competence and immediate cellular function. However, the restricted volume of bone accessible to harvest intra-orally along with donor site morbidity and other known drawbacks have been widely calling for alternative methods in the last decades that range from xenografts to synthetic biomaterials [6; 12; 23; 24]. Growing interest is given to hybrid biomaterials as their composite nature allows them to offer better performances from both the biomechanical perspective and the clinical outcome [6]. Similarly, custom-made solutions are now entering the market area as the long-term tendency is to drift towards patient-specific approaches. In this complex and wide framework, the here presented case highlights both the use of a xeno-hybrid material and a custom-made bone block. Indeed, SB(r) mimics the characteristics of healthy human bone, including very high biocompatibility, adequate open and interconnected porosity, high mechanical performance and hydrophilicity. In particular, thanks to the strong performance, high workability, resistance and shape retention of SB(r), it is possible to obtain SmartBone(r) on Demand(tm), a custom-made bone graft uniquely shaped exactly to patient specifications. A custom-made bone graft for the reconstruction of a bone defect is a modern option that currently needs a preoperative stage of studying the bone defect and designing the implant [15], overall, offering interesting and clinically very positive options in lieu of using standard techniques for high-volume ridge augmentations. SmartBone(r) on Demand(tm) is indeed produced by following the data precisely, and it is contoured to fit the bone defect site. Moreover, surgery is very precise, and the operation time is significantly shorter than that required for a conventional graft, overall, minimizing surgical risks in favor of patients benefit. In addition, also noteworthy, the composite nature of SB(r) allows a complete remodeling over time, which, for example, is not recorded while just using simply bovine bone grafts [25]. Finally, a seven-year detailed follow-up is extremely rare to have [6; 25].
## 4 Conclusions and Future Work
### Further Work  
The proposed symbolic framework establishes rapid system development, from mathematical concepts applied on samples in the manner that are processed in SDR. Observing various constellation configurations, one can obtain useful conclusions on system behavior and elaborate data-driven optimization enhancements. Future work will consider real system SDR implementation for 6G NOMA schemes in multi-user NOMA scenarios. Such scenarios are very difficult to simulate and implement. Using the proposed symbolic framework, we will test the implementation of optimization via deep unfolded neural network for synchronization and decoding tasks and evaluation of system performance indicators, such as bit error rate and outage probability. Further work will also incorporate dynamically varying user deployments and channel conditions into the simulation and design adaptive algorithms for handling such dynamics.  
[PERSON] proposed the method, collected the data, and conducted the experiments. [PERSON] designed the experiments and supervised the project. [PERSON] revised the manuscript presentation and evaluated the experiments. [PERSON] provided theoretical support and guidance for the analysis of NOMA signals. [PERSON] contributed to the development of the symbolic framework and the adaptation of the SDR implementation. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Ministry of Science, Technological Development, and Innovation of the Republic of Serbia, and these results are parts of the Grant No. 451-03-66/2024-03/200132 with University of Kragujevac--Faculty of Technical Sciences C
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
## 3 Proposed GRU-MC-AE Network Architecture
#### Training and Testing Procedure
Training the GRU-MC-AE model requires the careful selection of the SNR level, referred to as \(\overline{\gamma}_{t}\), due to its critical impact on the model's performance. Specifically, it is essential to choose the optimal \(\overline{\gamma}_{t}\) to ensure that the model trained at this SNR level maintains effective performance across a range of relevant SNRs. If \(\overline{\gamma}_{t}\) is set too low, the effects of noise may not be adequately represented during training, leading to a poorly generalized model [46]. The subsequent simulation results will detail the specific training SNR levels used for each experimental setup. All other training parameters, such as learning rate, batch size, and the number of epochs, which have a significant impact on model training, are chosen very carefully. The testing procedure for the MC-AE system evaluates its performance by transmitting encoded test data through a simulated wireless channel and measuring the error rates. The testing procedure is performed using 30,000 samples. BER and BLER are calculated by comparing the decoded output with the original messages. The performance is tested over a range of SNR values, and the results are plotted to compare BLER and BER under perfect and imperfect CSI conditions. At high SNRs, the diversity gain \(g_{d}\) and coding gain \(g_{c}\) can be used to approximate the BLER as follows [47]:  
\[BLER=\left(g_{c}\gamma\right)^{-g_{d}}+o\left(\tilde{\gamma}^{-g_{d}}\right). \tag{12}\]
# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 3 Methodology
### GlobalPointer Joint Decoder  
At first glance, joint extraction appears to be the extraction of triples (\(s\), \(p\), \(o\)) (i.e., subject, predicate, object), but it really is the extraction of quintuples (\(sh\), \(st\), \(p\), \(oh\), \(ot\)), where \(sh\) and \(st\) are the head and tail positions of subject entity, respectively, and \(oh\) and \(ot\) are the head and tail positions of object entity, respectively. Here, we can convert the triple in Figure 1 into a quintuple, as shown in Figure 4. From the perspective of the probability map, it is only necessary to design a quintet scoring function. However, if we count all quintuples one by one, the total is far too enormous. Suppose the sentence length is \(m\) and there are \(n\) relations, then the number of all quintuples is  
\[n\times\frac{m(m+1)}{2}\times\frac{m(m+1)}{2}=\frac{1}{4}nm^{2}(m+1)^{2} \tag{9}\]  
Figure 4: Examples of overlapping quintuples are divided into normal, single entity overlap (SEO), and entity pair overlap (EPO).  
It is tough for us to perform the aforementioned equation. As a result, a simplification is required. We may use the following decomposition strategy for this:  
\[S(sh,\;st,\;p,\;oh,\;ot)=S(sh,\;st)+S(oh,\;ot)+S(sh,oh|p)+S(st,ot|p) \tag{10}\]  
where \(S(sh,\;st)\) and \(S(oh,\;ot)\) denote the head and tail scores of the subject entity and object entity, respectively, and the subject entity and object entity are parsed out by \(S(sh,st)\) > 0 and \(S(oh,\;ot)\) > 0. \(S(sh,\;oh|\;p)\) means that the head features of subject and object are used as their representations to conduct one matching. The relation can be resolved when \(S(sh,\;oh|\;p)>0\). If there is entity nesting, it is necessary to consider \(S(st,\;ot|\;p)\).  
\(S(sh,\;st)\), \(S(oh,\;ot)\) is used to identify the entity corresponding to the subject and object, and it is equivalent to having a named entity recognition (NER) task, so we can use a GlobalPointer [44] to complete it. \(S(sh,\;oh|\;p)\) is to identify the specific relationship \(p\) of \((sh,\;oh)\) pair; here, we also use GlobalPointer to complete it, but to avoid the \(sh>oh\) to remove the default lower triangle mask of GlobalPointer; finally, \(S(st,\;ot|\;p)\) is the same as \(S(sh,\;oh|\;p)\).
## 2 Results and Discussion
### Gene Expression Analysis  
Semi-quantitative RT-qPCR was performed to characterize the expression of moringa genes based on relative amplification. Densitometry was conducted to quantify and interpret semi-quantitative gels, and the relative intensity differences between different salt concentrations compared with the control were evaluated. The PCR products were prepared using specific primers for the genes _Cu/Zn-SOD_, _APX_, _GGP_, and _TPS_, resulting in a single band (Figure 7). Their upregulation with elevated temperature demonstrates that the cellular defensive strategy involves combating the detrimental impact of the temperature inside the cytosol. At 30 \({}^{\circ}\)C, the higher expression of _Cu/Zn-SOD_ (Figure 7a) and _APX_ (Figure 7b) indicates resistance to the detrimental effects of temperature.  
Figure 7: _Cont._  
In contrast, the expression of these genes was reduced at 35 \({}^{\circ}\)C. The expression levels peaked at 30 \({}^{\circ}\)C and declined at 35 \({}^{\circ}\)C. _GAPDH_ was constitutively and stably expressed under the different temperature regimes (Figure 7e). The higher expression of these genes at 30 \({}^{\circ}\)C indicates that PKM-1 might be exploited as a temperature-resistant variety owing to its superior genetic characteristics. Thehalose-6-phosphate (_TPS_) acts as a specific signal of sucrose status and a regulator to modulate carbon metabolism within the plant. Quantitative real-time PCR analysis showed that the expression patterns of _TPS_s displayed group specificities in _M. oleifera_. _MoTPS_ genes were closely related to reproductive development and Group II _MoTPS_ genes were closely related to high-temperature resistance in leaves, stem, stem tip, and roots [83]. [PERSON] et al. [84] studied the expression of _APX_, _GGP_, and _TPS_ genes under different abiotic stresses, such as drought, salinity, heat, and cold, and their results indicated that each gene was differentially upregulated, which might show their varying roles in response to different abiotic stress. [PERSON] et al. [72] reported that the concomitant upregulation of _Cu/Zn-SOD_, _APX_, and _GPX_ genes in sorghum genotypes  
Figure 7: Semi-quantitative analysis of candidate genes in moringa. (**a**) Cu/Zn superoxide dismutase 1 (_Cu/Zn-SOD_); (**b**) ascorbate peroxidase (_APX_); (**c**) trehalose-6-phosphate synthase (_TPS_); (**d**) GDP-L-galactose phosphorylase (_GGP_); and (**e**) glyceraldehyde-3-phosphate dehydrogenase (_GAPDH_) for normalization based on constitutive expression under different temperature regimes. M: molecular marker. Statistical difference was calculated by standard deviation.  
is suggestive of tolerance under stress conditions [85]. [PERSON] et al. [86] aimed to establish reliable reference genes for moringa via RT-qPCR analysis of gene expression and observed that, out of eighteen candidate reference genes, only ribosomal protein L1 (_RPL1_) and acyl carrier protein 2 (_ACP2_) could be considered suitable reference genes in all tested samples. Identifying these potential genes may provide a molecular basis for identifying temperature-resistant moringa cultivars.
## 3 Improved PSO-RBFNN Model
### Improved PSO Algorithm
#### 3.1.1 Time-Varying Learning Factors  
In Equation (4), two learning factors, \(c_{1}\) and \(c_{2}\), influence the exploration and exploitation capabilities of particles in the solution space, respectively. Exploration means to explore all over the search space to find promising solutions, whereas exploitation means to exploit the identified promising solutions to tune the search for global optimum [32]. The ultimate goal is to find a trade-off between exploitation and exploration in the population-based PSO algorithm [33]. However, it is challenging to find a good balance due to the stochastic behavior of PSO when the learning factors are fixed. Moreover, exploration and exploitation are always in conflict with each other, i.e., enhancing one always sacrifices the other one. Unfortunately, the addition of \(\mathbf{gbest}\) to the velocity vector has weakened the exploration phase, since it establishes a permanent element of velocity updating in PSO. In view of this, we adopt an adaptive scheme for determining \(c_{1}\) and \(c_{2}\) as follows:  
\[\begin{array}{l}c_{1}=c(1-(\frac{k}{\text{{MaxIter}}})^{\alpha})\\ c_{2}=c(\frac{k}{\text{{MaxIter}}})^{\alpha}\end{array} \tag{8}\]  
where _MaxIter_ is the maximum number of iterations; \(c\) and \(\alpha\) are constants.  
Figure 3 investigates the effects of changing \(c\) and \(\alpha\) on the learning factors, respectively. It is observed from Figure 2(a) that the learning factors with a larger \(\alpha\) experience a longer exploration phase (i.e., \(c_{1}>c_{2}\)) and a shorter exploitation phase (i.e., \(c_{2}>c_{1}\)), and vice versa. That is, the choosing of the power \(\alpha\) means the trade-off between exploitation and exploration abilities. Additionally, it is observed from Figure 2(b) that as the iteration increases, the curve with a larger \(c\) has a steeper slope than that with a smaller \(c\). That is, the learning factors with a larger value of \(c\) provide a better ability to learn information among individuals, and vice versa. Further, we adaptively decrease \(c_{1}\) and increase  
so that the masses tend to accelerate towards the best solution as the PSO reaches the exploitation phase. Since there is no clear border between the exploration and exploitation phases, the adaptive approach is the best option for allowing a gradual transition between these two phases. Additionally, this adaptive approach emphasizes exploration in the early stage of iterations and then exploitation in the later stage.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants  
### Silver-Based NPs  
A comprehensive overview focused on phytotoxicity assessment of AgNPs and their interaction with plants, including whole plant and organs, as well as interactions on the cellular and molecular levels, was presented by [PERSON] et al. [178]. Coating of AgNPs can affect their toxicity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NPs** & **Medical Plant** & **Dose (Length** & **SM** & **Multiple of Control** & **Refs.** \\  & & **of Treatment)** & **SM** & **SM content** & \\ \hline ZnO & _Hyuscymus reticulatus_ L. & 100 mg/L (48 h) & hyoscyamine & 4.61 & \\  & 100 mg/L (72 h) & scopolamine & 3.20 & [67] \\ \hline Fe\({}_{2}\)O\({}_{3}\) & _Hyuscymus reticulatus_ L. & 900 mg/L (24 h) & hyoscyamine & \(\approx\)5.0 & \\  & 450 mg/L (48 h) & scopolamine & \(\approx\)5.0 & \\ \hline Fe\({}_{2}\)O\({}_{3}\) & _Draccephalum kotschyi_ Boiss & \(75\) mg/L (24 h) & rosmarinic acid & 9.7 & \\  & & xanthomicrol & 11.87 & \\  & & crismaritin & 3.85 & \\  & & isokaempreferide & 2.27 & \\ \hline SiO\({}_{2}\) & _Draccephalum kotschyi_ & 100 mg/L & rosmarinic acid & 8.26 & \\  & & xanthomicrol & 13.00 & \\  & & crismaritin & 13.42 & \\  & & isokaempreferide & 10.00 & \\ \hline Ag-SiO\({}_{2}\) & _Artemisia annuua_ L. & 900 mg/L (3 days) & artemisinin & 3.90 & [164] \\ \hline Ag & _Datura metel_ L. & 20 ppm & & \\  & & (12 h) & atropine & 1.147 & \\  & & (24 h) & & 2.420 & \\ \hline CuO & _Brassica raqu_ L. spp. _pckinensis_ & 50, 100, 250 mg/L for & glucoplasmic & \(\approx\)1.38 & \\  & & 24, 48, 72 h & 4-methoxyglucobrassicin & \(\approx\)1.51 & \\  & & neoglucobrassicin & \(\approx\)1.54 & \\  & & 4-hydroxyglucobrassicin & \(\approx\)1.82 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Enhanced production of secondary metabolites (SM) of medicinal plants elicited by various NPs using hairy root cultures compared to corresponding bulk elicitors.
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
###### Abstract  
We conduct a thorough comparison of two basic notch filters employed to mitigate the pattern effect that manifests when semiconductor optical amplifiers (SOAs) serve linear amplification purposes. The filters are implemented using as the building architecture the optical delay interferometer (ODI) and the microring resonator (MRR). We formulate and follow a rational procedure, which involves identifying and applying the appropriate conditions for the filters' spectral response slope related to the SOA pattern effect suppression mechanism. We thus extract the values of the free spectral range and detuning of each filter, which allow one to equivocally realize the pursued comparison. We define suitable performance metrics and obtain simulation results for each filter. The quantitative comparison reveals that most employed metrics are better with the MRR than with the ODI. Although the difference in performance is small, it is sufficient to justify considering also using the MRR for the intended purpose. Finally, we concisely discuss practical implementation issues of these notch filters and further make a qualitative comparison between them in terms of their inherent advantages and disadvantages. This discussion reveals that each scheme has distinct features that render it appropriate for supporting SOA direct signal amplification applications with a suppressed pattern effect.  
semiconductor optical amplifier (SOA); pattern effect; optical delay interferometer (ODI); microring resonator (MRR) Article