esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Transcriptomic Analysis (RNA-Seq)  
The hippocampus was dissected from the brain tissue. The total RNA of three mice of each group was extracted using PureLink(tm) RNA Mini Kit (ThermoFisher Scientific, Waltham, MA, USA) according to the manufacturer's protocol. The RNA was incubated for 1 h at 37 \({}^{\circ}\)C with DNase I (ThermoFisher Scientific, Waltham, MA, USA) before heating to 90 \({}^{\circ}\)C for 30 min. Quantity/quality and RNA integrity were assessed by running the RNA sample on an Agilent Bioanalyzer(r) RNA 6000 Nano/Pico Chip. mRNA was extracted using NEBSext Poly(A) mRNA Magnetic Isolation beads following the manufacturer's protocol. cDNA libraries were constructed using the NEBSext Ultra II Directional RNA-Seq library. Sequencing was carried out using the Illumina NovaSeq 6000 SP v1.5 Lane (150 PE) libraries at the Earlham Institute Genomics core facility. The raw data were processed by the Trinity RNA-seq assembly package [17]. Briefly, the Trimmomatic tool was used in order to remove reads containing adapters. Then, the reads were quantified and mapped to the reference transcript of GRCm39 (Ensembl, Cambridge, UK) by Bowtie v0.12.1. software [18], RSEM v1.1.6 [19] had been used for transcription quantification.  
Differential expression analysis of expected read counts was performed by EdgeR (version 3.0.0, R version 2.1.5) [20]. Quantification of transcript expression levels was presented by FPKM (fragments per kilobase of exon per million fragments mapped). The 3 rd replicate of SFPWH (150 mg/kg) was excluded due to the lack of consistency.
## 2 Proposed Method
### Conventional Method  
A conventional iterative algorithm [14] optimizes a phase-only hologram to reconstruct the desired complex amplitude, in which a blank area is introduced around the original complex amplitude \(o(x,y)\). The size of the original complex amplitude is \(N\times N\) pixels. The expanded object plane \(u_{o}(x,y)\) with the blank area has \(mN\times mN\) pixels, where \(m\) denotes the magnification of the expansion. The magnification \(m\) indicates the ratio between the sizes of the original object plane to the expanded one. The size of the hologram plane \(u_{h}(x,y)\) is the same as that of \(u_{o}(x,y)\).  
The flow of the conventional algorithm is shown in Figure 1 and is performed in the following steps:  
1. We initially set random phase values in the hologram plane \(u_{h}(x,y)\);
2. We compute the diffraction calculation from \(u_{h}(x,y)\) to the object plane \(u_{o}(x,y)\) with the propagation distance \(+z\);
3. As an object plane constraint, the area where the original object in \(u_{o}(x,y)\) exists is replaced by \(o(x,y)\), while the calculated value in the blank area remains;
4. The updated \(u_{o}(x,y)\) is back-propagated to the hologram plane \(u_{h}(x,y)\) by the same diffraction calculation with the propagation distance \(-z\);
5. We introduce two constraints to the hologram plane. The first constraint is to calculate \(u_{h}(x,y)=u_{h}(x,y)/|u_{h}(x,y)|\) because our target is a phase-only hologram. The second constraint is the support of the hologram. We set zero values in the blank area of the hologram plane, because the size of the hologram is \(N\times N\);
6. We repeat Steps 2 to 5 until the number of iterations reaches a predefined number, or the image quality of the reconstructed complex amplitude reaches a predefined quality, or the image quality of the reconstructed complex amplitude decreases;
7. To obtain the final hologram, we crop only the central part of \(u_{h}(x,y)\) with \(N\times N\) pixels.  
The computational complexity of the algorithm is \(O(Lm^{2}N^{2}\log_{2}mN)\), where \(L\) is the number of the iterations. Memory usage requires \(O(m^{2}N^{2})\).
# Quorum Sensing and Quorum Quenching in _Agrobacteriumum_: A \"Go/No Go System\"?
## 6 Quorum Sensing and Quorum Quenching as Two Parts of an Integrated Regulation Mechanism
Second, it is clear that BlcC and AiiB activities are modulated in response to environmental factors, such as carbon and nitrogen starvation, growth arrest, and concentration of important metabolites, such as the plant hormone salicylic acid, the stress metabolites GABA and proline, or the amino acids alanine, valine, and the agrocinopines opines [37; 38; 39; 45; 46; 47; 53; 69; 70]. In an effort to integrate part of these data, several regulatory models have been proposed, suggesting that the QS/QQ systems in the bacterial cell permits a fine tuning of the conjugation of the Ti plasmid throughout the _Agrobacteriumum_ infection cycle [69; 70]. _Agrobacteriumum_ is one the bacterial models in which regulatory balance between QS and QQ was the most deeply investigated [70]. Other authors have also proposed that the QS/QQ systems of _Agrobacteriumum_ are a way to reversibly convert bacteria, phenotypically, from plasmid recipient to donor [71]. While all these models are elegant and sound, our hypothesis (Figure 2) goes one step further and proposes that QS (TraR/TraI) and QQ (TraM/AiiB/BlcC) define an integrated regulatory \"go/no go system\" that finely controls the biologically costly transfer of the Ti plasmid in response to multiple environmental cues, and contributes to limit QS-hijacking. This last feature can be viewed as a self-protection of the regulatory system. This go/no go decision mechanism permits the bacteria to sense whether it is in a gall or not, in a living or decaying tumor, in stressed plant tissues, etc., and whether Ti plasmid conjugation has a chance to lead in fine to a successful event. In this scheme, the role of the lactonases is pivotal. As a support to this assertion, and in addition to the data presented above, an _blcC_ mutant is less competitive than its wild-type parent in tomato and _Arabidopsis_ tumors [37; 47]. If indeed \"_nothing in biology makes sense except in the light of evolution_\" [72], the previous observation confirms that the presence of BlcC lactonase (and likely that of AiiB) has been selected and maintained in the course of Ti plasmid and agrobacterial evolution, because it is indeed beneficial to the bacteria. Experimental evolution experiments, as well as additional competition experiments performed in stressed plants or decaying tumors, could indeed help to verify the central role of lactonases in the go/no go control of Ti plasmid transfer in agrobacteria. This work has been supported by an annual support from CNRS (2017-2018-I2 BC). Both [PERSON] and [PERSON] contributed to the writing of the chapter and preparation of the figures. The authors declare no conflict of interest.
The design of the unit element allows for the incorporation of PIN diodes between adjacent elements for switching to a lower C-band spectrum at 6.6 GHz. The biasing network is on the bottom layer of the substrate to avoid effects on the shielding performance. A PIN diode configuration for the switching operation is also proposed. In simulations, the PIN diode model is incorporated to observe the switchable operation. Two prototypes are fabricated, and the switchable operation is demonstrated by etching copper strips on one fabricated prototype between adjacent unit cells (in lieu of PIN diodes) as a proof of the design prototypes. Comparisons among the results confirm that the design offers high angular stability and excellent performance in both bands. We would like to take this opportunity to thank all authors of the respective works for their high-quality contributions. We also hope that readers will find new and useful information on antennas and related technology for 5G, IoT, and sensing applications. **Funding:** This research was supported in part by the European H2020 Marie Sklodowska-Curie Individual Fellowship under grant # 840854 (VisionRF). **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments and Analysis
### The Influence of STD on the Parallel PPP Receiver
The \"fixSTD-fix\" strategy suffered from large noise due to the fact that the unmodeled errors and observations noise in the STD modeling were not eliminated. The influence of observation noise was eliminated for the \"fixSTD-fix (Self)\" strategy, thus the obtained positioning coordinate sequence was rather smooth. Moreover, the positioning convergence time could be shortened, and the accuracy of PPP ambiguity-float solutions and the fixing rate of ambiguity resolution were further improved. The BDS PPP suffered longer convergence time compared to GPS due to its less-accurate orbit and clock solutions. Therefore, the performance of different troposphere corrections on BDS PPP were further evaluated. As the phase biased products for BDS ambiguity-fixed solutions were not available, only ambiguity-float solutions using different tropospheric correction strategies are shown in Figure 9. It was observed that the positioning accuracy with STD constraints were significantly improved compared to the ZTD constraints. The positioning errors in the E and U directions obtained by the \"dropZTD\" strategy around 19:00 were significant, while the \"dropSTD\" strategy could decrease the positioning errors. The results indicated that the unmodeled residuals in the STD improved the positioning accuracy, and that the elimination of multipath error and observation noise in STD modeling could further smooth the results. However, the contribution of the different strategies to the  
Figure 7: The improvement of different troposphere modeling strategies to PPP ambiguity-fixed solutions. (**a**) North. (**b**) South. (**c**) Up. convergence was not obvious, which demonstrated that the orbit and clock accuracy, especially the poor GEO orbits, were the primary reasons limiting the convergence of BDS PPP. Figure 8: The PPP ambiguity-float solutions and fixed solutions of CUTC obtained by different slant tropospheric delay (STD) models. (**a**) North. (**b**) South. (**c**) Up. Figure 9: The PPP ambiguity-float solutions of CUTC using different constraint strategies. (**a**) North. (**b**) South. (**c**) Up.
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 3 Results
### Merging Images from Sentinel 2 Images with High Resolution Images. NVDI Estimation  
Figure 3 shows the first example of the application of this work. On the left (Figure 3a,c) are two NDVI images calculated from Sentinel 2 images (processing level 2A) obtained on 25 July and 18 September On the right (Figure 3b,d) shows the result of merging these images with the corresponding images from a drone (July image and Homogenized September image). In Figure 3a,c, due to the lower spatial resolution of the Sentinel 2 images, NDVI values can only be observed corresponding to large areas of the plot and, as expected, without being able to distinguish between soil and vegetation.  
After the fusion procedure, the NDVI values are reassigned in the high-resolution images, and therefore, in the (b) and (d) images, both soil and vegetation are clearly distinguished.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 4 Numerical Simulations
### Seismic Forward Model  
For the numerical simulation of the forward wave propagation problem, we consider an earthen water reservoir dam with the dimensions as shown in Figure 8. The depth of the water on the upstream slope of the dam is 3.0 m. We consider a line crack (5.6 cm wide and 10 m long) starting at 1.5 m below the centre of the free surface of the dam. The crack extends towards the far-field at an angle of 45 degree to the vertical. The crack is filled with extremely soft saturated soil. The water table inside the dam is represented by the draw-down curve, as shown in the figure. A realistic range of wave propagation is considered by adopting the elastic medium parameters as suggested in [40]. The medium parameters for the model are shown in Table 3 and the effect of the air-water and the air-solid interface is considered a free surface. A synthetic time signal (Ricker wavelet) of unit amplitude and centre frequency of 0.8 kHz acting vertically is considered. The seismic source fired at the top of the upstream slope of the dam. Twenty-five seismic receivers are positioned along the dam's upstream and downstream slope with a spacing of 0.53 m (\(dx=0.5\) m, \(dz=0.17\) m). A source function of unit amplitude is considered; thus, the seismograms recorded at the receivers are the unitless values normalized by the excitation function. The dam is initially (at \(t=0\)) considered at rest.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Hardware** & **CPU/GPU** & **Memory** & **OS** \\ \hline CPU1 & 11 th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40 GHz & 8 GB & Ubuntu 20.04 \\ \hline CPU2 & AMD Ryzen Threadripper 3970X( 2.9 GHz) & 64 GB & Ubuntu 20.04 \\ \hline GPU1 & Nvidia GeForce GTX 1650 & 4 GB & Ubuntu 20.04 \\ \hline GPU2 (installed with CPU2) & Nvidia GeForce GTX 2080 Ti & 8 GB & Ubuntu 20.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CPU and GPU hardware used for computation in this study.  
Figure 8: A physical model of dam including a crack and soil-water interface to perform forward seismic wave simulation. The material parameters are as given in Table 3 and 55 seismic receivers are located along the dam surface with spacing of 0.53 m.  
Figure 11: Vertical component of particle velocity \((v_{z})\), normalized to the velocity wavelet at the excitation point, in the forward seismic model.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 4 Uplink Transmission
### Maximum Ratio Combining  
The Maximum Ratio (MR) combining technique is the most straightforward method for combining received signals. It is defined as  
\[\mathbf{v}_{kl}^{\text{MR}}=\mathbf{D}_{kl}\hat{\mathbf{h}}_{kl}, \tag{80}\]  
where \(\hat{\mathbf{h}}_{kl}=c_{kl}\mathbf{H}_{l}\mathbf{e}_{k_{l}}\) represents the MMSE estimate, which is computationally efficient. This technique enhances the desired signal power in the numerator of Equation (78). The uplink Spectral Efficiency (SE) achievable with MR combining has been explicitly derived in [39].  
In this research, MR combining [39] is utilized with \(\mathbf{v}_{\text{MR},kl}=\mathbf{D}_{kl}\mathbf{h}_{kl}\). As a result, the expectations in Equation (78) are expressed as follows [4]:  
\[\mathbb{E}\{g_{ki}\}_{l}=\begin{cases}\sqrt{\eta_{k}\eta_{l}\tau_{p}}\mathbf{ D}_{kl}\mathbf{R}_{il}\mathbf{R}_{il}\mathbf{R}_{il}^{-1}\mathbf{R}_{kl},&\text{if }i\in P_{k}\\ 0,&\text{otherwise},\end{cases} \tag{81}\]where \([\mathbb{E}\{g_{ki}g_{ki}^{H}\}]_{lr}=[\mathbb{E}\{g_{ki}\}]_{l}[\mathbb{E}\{g_{ki} \}]_{r}^{H}\) for \(r\
eq l\), and  
\[\mathbf{v}_{\text{MR},kl}=\mathbf{h}_{kl}^{\text{DMD}}. \tag{82}\]  
Moreover,  
\[\mathbb{E}\{g_{ki}g_{ki}^{H}\} =\eta_{k}\tau_{p}\text{tr}\Big{(}\mathbf{D}_{kl}\mathbf{R}_{il} \mathbf{R}_{kl}\Psi_{t_{kl}^{-1}}^{-1}\mathbf{R}_{kl}\Big{)} \tag{83}\] \[\quad+\begin{cases}\eta_{k}\eta_{l}\tau_{p}^{2}\Big{|}\text{tr} \Big{(}\mathbf{D}_{kl}\mathbf{R}_{il}\Psi_{t_{lk}}^{-1}\mathbf{R}_{kl}\Big{)} \Big{|}^{2},&\text{if }i\in\mathcal{P}_{k}\\ 0,&\text{otherwise}.\end{cases}\]  
Additionally,  
\[[F_{k}]_{ll}=\sigma_{ul}^{2}\eta_{k}\tau_{p}\text{tr}\Big{(}\mathbf{D}_{kl} \mathbf{R}_{kl}\Psi_{t_{kl}^{-1}}^{-1}\mathbf{R}_{kl}\Big{)}, \tag{84}\]  
and  
\[\mathbf{\Psi}_{ll}^{j}=\Bigg{(}\sum_{l,i\in\mathcal{P}}p_{li}\tau_{p}\mathbb{ R}_{li}^{j}+\sigma_{UL}^{2}\bar{\psi}_{M}^{j}\Bigg{)}^{-1}. \tag{85}\]  
When Dynamic Mode Decomposition (DMD) is applied, Equation (85) is adjusted to  
\[\mathbf{\Psi}_{ll}^{j}=\Bigg{(}\sum_{l,i\in\mathcal{P}}p_{li}\tau_{p}\Big{(} \mathbb{R}_{li}^{j}\Big{)}^{\text{DMD}}+\sigma_{UL}^{2}\bar{\psi}_{M}^{j} \Bigg{)}^{-1}. \tag{86}\]
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 3 Modifications of Information Criteria for the Case of Hankel Noise
### Approach to Comparison  
One of the criterion quality characteristics used in practice for model order estimation is the proportion of correct order (rank) estimates or the bias of the average rank estimate. However, overestimation or underestimation of the rank can have different effects on the result, because the decomposition components are arranged by decreasing their contribution, and therefore, overestimation of the rank leads to a smaller increase in the signal estimation error compared to underestimation of the rank. Therefore, we will consider the RMSE of the signal estimation as the main characteristic. Note, also that the trend identification methods [26; 37] are robust to rank overestimation when the number of identified components is chosen according to the estimated rank.  
In the problem statement considered in this paper, the correct order of models is generally not defined. Therefore, we will compare the estimated ranks (model orders) with the optimal rank \(r\), which gives the minimum error of the signal estimation obtained by (2). Accordingly, we will compare the RMSE of the signal estimation at the estimated rank with the minimum error at the optimal rank. Since the best approximation depends on the noise level, we will consider the quality of the criteria depending on the noise level.  
Thus, in most cases, we will compare the signal estimation error with the average minimum error and the average rank with the average optimal rank. In addition, we will consider the proportion of rank estimate matches with individual optimal ranks that yield the minimum errors for each series separately.
# Occurrence of Zeralenone and Enniatin B in Swiss Wheat Grains and Wheat Flours  
[PERSON]  
1 Research Group Food Chemistry, ILGI Institute of Food and Beverage Innovation, School of Life Sciences and Facility Management, ZHAW Zurich University of Applied Sciences, 8820 Wadenswil, Switzerland 1,1  
food contaminated by mycotoxins is of concern for both human and animal health, causing both acute intoxications and long-term effects [6].  
Among the microorganisms producing mycotoxins, _Fusarium_ species often infect wheat grains. Most _Fusarium_ species can produce one or more mycotoxins. Among the most studied are zearalenone (ZEA), deoxynivalenol (DON), and the emerging mycotoxins of the Enniatin family. While the quantity of ZEA and DON in cereals is controlled and regulated by the European Food Safety Authority (EFSA), enniatins have not yet been considered in the legislation. Despite the statement of the EFSA that an acute exposure to enniatins does not indicate a concern for human health [7], scientific evidence has proven the cytotoxic effects of enniatin B (ENB) and has shown concern about chronic exposure [7; 8; 9].  
The contamination of wheat with zearalenone and enniatin B has been widely studied worldwide [5; 10; 11; 12; 13]. In Switzerland, a broad study conducted between 2007 and 2014 established an average contamination of wheat with zearalenone of 39 \(\mu\)g/kg; 7% of the wheat samples in this study exceeded the EFSA limit of 100 \(\mu\)g/kg [11]. However, no other recent publications on zearalenone contamination of wheat in Switzerland were available at the time of writing. Concerning the contamination of wheat grown in Switzerland by the emerging mycotoxin enniatin B, no data have been published to date. In France, a study conducted between 2012 and 2014 reported that enniatin B is the prevalent enniatin in small grain cereals, accounting for 68% of the total enniatin content [10].  
In the context of a broader research project aiming at proposing strategies to decontaminate wheat grains with active agents such as functional microorganisms and enzymes in order to reintroduce them into the food value-chain [14], wheat grains cultivated in Switzerland in 2020 and 2021, as well as wheat flours purchased in local stores in Switzerland were analyzed. The goal was to assess the contamination of wheat grains and flours by the regulated ZEA and to assess the contamination of the before-mentioned products with the not yet regulated mycotoxin ENB.
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 6 Conclusions  
This paper conducted a thorough performance assessment of the mean shift algorithm for individual tree delineation from airborne lidar data. Three main factors considered are kernel shape, kernel size adaptiveness, and kernel weighting. They were assessed in three different datasets, one simulated data, one UK forest data, and one benchmark data from Finland.  
The results suggested that the Pollock model used as the mean shift kernel can improve the segmentation, though there are a few parameters that would have to be fine-tuned. On the other hand, the Cylinder kernel, commonly used in other studies, can generate good results while maintaining simplicity. The continuous adaptive strategy works for certain data, but might not be reliable due to the complexity of crown structures, whilst being time consuming. The weighting in height is recommended to be tested for different datasets, whereas horizontal weighting should be undertaken with caution. Finally, the validation results can be affected by the ground truth quality, as real crown positions are difficult to determine from neither ground survey nor other data sources.  
The effectiveness of mean shift is proved by comparing with two raster-based methods, marker-controlled watershed and region growing, which performed well on single-layered data, and were extremely fast. Further improvements to the segmentation workflow by introducing additional steps, such as integrating point-based and raster-based methods, will be investigated in future work.  
[PERSON] conceptualized the research, proposed the methodology, prepared the software, and analyzed the results. [PERSON] and [PERSON] collected the data. [PERSON] and [PERSON] processed the data. [PERSON] contributed to the original draft preparation. All authors contributed to the review and editing of the paper.  
This research was supported by a Douglas Bomford Trust grant. The ground data collection was supported by the Natural Environment Research Council (NERC) studentship award [reference number: 1368552] and the airborne data was provided through NERC Airborne Research Facility (ARF) grant [GB 14-04].  
The authors acknowledge [PERSON], [PERSON] and [PERSON] (Newcastle University) for their help during the fieldwork in Aberfoyle.  
The authors declare no conflict of interest.
# 3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery--A Literature Review
## 2 Materials and Methods  
The research of this systematic review is based on the Population Intervention Control Outcome Study design (PICOS) format (Table 1). PubMed and Scopus are the databases adopted for our research. The considered keywords are: orthognathic surgery, facial dysmorphism, surgical planning, 3D, 3D face analysis, soft tissue, BSSO, bilateral sagittal split osteotomy, Le Fort I, prediction methods.  
The research has been set on different combinations of keywords; at first, we focused generically on the orthognathic surgery, then the type of interventions has been specified: BSSO, IVRO, SSRO and LFI. Finally, the research has been limited to soft tissue simulation and prediction.  
All the articles found were assessed by three authors and classified in: prospective study (PS), retrospective study (RS), case series study (CS).  
Single cases reports have been excluded from our analysis because they have been considered not clinically significant.  
Articles published before 2000 have not been considered.  
The following data have been recorded for each eligible study: first author and year of publication, journal, study design, sample size, gender, mean age, diagnosis, imaging technology, typologies of surgery, software used for the prediction, soft and hard tissue landmarks considered, time interval from surgery to post-surgical imaging, results and conclusions of the authors.