esa-sceva/satcom-chunk-collection huggingface dataset:
# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 3 Materials and Methods
### Ribosome Profiling Data Extraction  
The Ribosomal profiling technique (Ribo-seq) is currently the most effective tool to study the protein synthesis process in vivo. The advantage of this method, over other approaches, lies in its ability to monitor translation by precisely mapping the position and number of ribosomes on an mRNA transcript. Ribo-seq involves the extraction of mRNAmolecules associated with ribosomes undergoing active translation and a digestion phase, during which the RNase enzyme processes all the RNA molecules, with the exception of the \"protected\" parts, to which ribosomes are attached. This step is followed by rRNA depletion and preparation of the sequencing library as in an RNA-seq approach. However, since reads are obtained relating only to actively transcribed mRNA molecules, Ribo-seq better reflects the translation rate than mRNA abundance alone, although it requires particularly laborious preparation and is only applicable to species that have a reference genome available. The strategy employed in our work consists in the identification of high resolution Ribo-seq profiles through the systematic comparison of Ribo-seq datasets referring to experiments performed independently in different laboratories and in different time periods.  
In particular, the approach is composed by the following phases:  
* **Preprocessing--the ORF-specific ribosome profiling data from multiple datasets are collected and then processed by a bioinformatic pipeline;**
* **Signal digitalization--Ribo-seq profiles are digitalized by associating to each nucleotide a slow or fast label;**
* **Comparison of digital profiles--Digital profiles are used to quantify similarities and differences between Ribo-seq profiles of different datasets referring to the same ORF.
* **Identification of significantly reproducible Ribo-seq profiles--A set of highly reproducible profiles is obtained and, among them, reproducible sub-sequences are identified.**
# Integrative Assessment of Sediment Quality in the Sao Francisco River (Mina Gerais, Brazil)
## 3 Results
### Linking Physicochemical and Benthic Community Results  
The relation between benthic fauna abundance and physicochemical variables was studied using the BIOENV procedure (Table 3). This statistical analysis selects environmental variables 'best explaining' community patterns by maximizing the correlations among their respective similarity matrices. This approach is advantageous, since it identifies not only the best correlation but also the best combination of them.  
The results of the BIOENV analysis showed that 91.5% of the variance in the different stations was due to the nitrate, ammonium, ammonia, Al, and Zn contents; the nitrate, ammonia, ammoniacal nitrogen, Al, and Zn contents; or the ammonium, ammonia, ammoniacal nitrogen, Al, and Zn contents. This analysis always showed Al and Zn as the  
Figure 3: Bray–Curtis similarity dendrogram based on abundance, showing the classification of each station replicates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**n** & **Variables** & **r\({}_{\text{s}}\)** \\ \hline
5 & Nitrate + ammonium + ammonia + Al + Zn & 0.915 \\
5 & Nitrate + ammonia + ammoniacal nitrogen + Al + Zn & 0.915 \\
5 & Ammonium + ammonia + ammoniacal nitrogen + Al + Zn & 0.915 \\
1 & Nitrate & 0.879 \\
2 & Nitrate + SEM-AVSs & 0.879 \\
3 & Nitrate + ammonium + SEM-AVSs & 0.879 \\
2 & Nitrate + ammonium & 0.879 \\
2 & Nitrate + ammoniacal nitrogen & 0.867 \\
5 & Nitrate + ammonium + ammonia + ammoniacal nitrogen + SEM-AVSs & 0.867 \\ \hline \hline \end{tabular}
\end{table}
Table 3: BIOENV correlations (Spearman rank correlation coefficients, \(r_{\text{s}}\)) between benthic community structure and environmental variables (\(n\)).  
contaminant metals of concern in the analyzed macro-benthic community. This variance was lower (87.9%) when one (nitrate), two (nitrate and SEM-AVSs), or three variables were considered (nitrate, ammonium, and SEM-AVS; or nitrate, ammoniacal nitrogen, and SEM-AVS). These results associate the nitrogen species with those measured changes in the macro-benthic community structure.
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Applications
#### 4.3.5 Optical FSSs Based on Nano-Particle Arrays (NPAs)  
While FSSs are well-known for spatial filtering applications in the microwave domain, but recently, FSS technology has attracted great interest for even higher frequencies. Far infrared stacked FSSs filters  
Figure 21: (**a**) Photograph of the un-stretched and stretched meta-skin, wrapped around dielectric nylon rod, flexibility demonstration with a glass flask [182]. (**b**) Topology of the third-order stopband optical FSS. (**a**) Adapted from [182] and reproduced from under Creative Commons Attribution 4.0 International License.  
with good response are offered in [184] and with single-layer topology showing strong stop-bands in [185]. Near-infrared FSS made by metallic sub-micron patches of circular elements is presented in [186]. It should be noticed that metallic periodic inclusions, such as patches or apertures on the dielectric substrate, are key elements of FSSs. FSSs in the visible range is proposed based on plasmonic metasurfaces, where the EM wave interacts with metal plasmons and create plasmonic resonance.  
An optical filter is proposed [187], which is based on subwavelength gratings in an array of protrusions extending in the substrate. Most recently, nano particle arrays are used as spatial angular filters and also as metasurfaces [188; 189] in the optical frequency regime, the possibility of cascading two or more NPAs is also explored. NPAs exhibit a sharp frequency response with peak always centering the same resonant frequency, independent of the EM incidence angle. A synthesis procedure is demonstrated to design a stopband optical FSS [190] with 2D periodic array element of nano-particles having subwavelength dimensions of unit cell and cascaded NPA layers as coupled resonators, as shown in Figure 21b. The closed-form solution is used to derive equivalent circuit for an NPA and excited by a plane wave in the boundary of the dipole approximation. This comprises a function derived from individual and collective plasmonic modes.
## 3 Method  
### Pipelined Digital Geological Mapping System  
From field collection to geological mapping, data need to be exchanged between different hardware and programs. An important criterion for evaluating a digital geological mapping system is that data should not need any transformation in the data flowing between different modules. In the DGSS, according to the industry requirement, digital geological mapping is divided into modules of base map preparation; indoor design of the geological survey route; field data collection; data cleansing and modification; PRB (Point-Route-Boundary) database; primitive map, and geological map compilation, and geological map database. Moreover, the DGSS also supports the logging of the cross-section for making geological cross-section maps and stratigraphic histograms.  
Unified data formats (Figure 2) are designed for data flow in different modules and hardware equipment. Vector data are divided into three types: point, polyline, and polygon. The geometrical information of geological features is stored in spatial feature files (e.g., *tm, *lm, *pm), while properties of geological features are stored in SQLite database files (e.g., * ta, *la, *pa). All the files of point, polyline, and polygon are organized by a project index (*gpj). This way, the data can be processed by different modules without any data transformation and provides a base data format for the pipelined digital geological mapping system. In addition, the DGSS can also input raster and image data as the base map.
## 2 Materials and Methods
### Singular Spectrum Analysis (SSA)
The basic requirement to perform SSA forecasting is that the series satisfies a linear recurrent formula (LRF), where the series \(Y_{N}=\left\{y_{t};t=1,2,\ldots N\right\}\) satisfies an LRF of order \(L-1\) if \(y_{t}=a_{1}y_{t-1}+a_{2}y_{t-1}+\cdots+a_{L-1}y_{t-L+1},t=L+1+\cdots N\). This means that the series ruled by an LRF follows a natural recurrent continuation since each term of the series is a linear combination of several preceding terms. As in the case of Arima models, the SSA is based on the weighting of previous observations; these weights are obtained based on eigenvectors. As noted by [PERSON] et al. [60], the LRF does not imply that the series must be linear, but it might be a nonlinear structure ruled by the LRF. In the case of this study, we considered one of the two versions of the univariate SSA algorithm, also known as the recurrent SSA (RSSA). We also used the construction of the bootstrapped confidence  
A summary of the statistics for the first component shows the range of the altitude of plantations, which covers the entire sample range. For PTPM statistics, the maximum monthly values are near 2000 mm (about 6.56 ft), which exceeds the maximum upper limits considered in the index insurance design (Table 4). In particular, the first 25 locations for the first component contribute significantly to the cumulative proportion of the component variance (Figure 7). The departments that belong to the first component are Santander, Antioquia, Caldas, and Valle del Cauca (Figure 6c,d). The largest contribution of variance (15.3%) can be found in Samana in Caldas (Table 5 and Figure 7). The next location, with an 8% contribution of variance, is in Briceno, Antioquia, and then it is followed by San Francisco, Antioquia (7.1%), San Carlos, Antioquia (5%), and El Guacamayo, Santander (4%). The locations in Valle del Cauca have less than 1% of the contributions. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Min** & **1 st Qu.** & **Median** & **Mean** & **3 rd Qu.** & **Max** \\ \hline Altitude (m) & 996 & 1280 & 1440 & 1447 & 1550 & 1850 \\ PTPM (mm) & 0 & 69.95 & 209.89 & 258.79 & 393.30 & 1995.52 \\ \hline \end{tabular}
\end{table} 
Table 4: Summary statistics PCA for PTPM 1 st component. Figure 7: First PCA component contributions of individuals. Here, the weather stations and the percentage of variance contributions are identified. Source: author’s processing. In summary, statistics (Table 6) for the dry and wet seasons show possible adverse conditions to the optimal development of coffee because the second half of the stages for optimal coffee development show lower extreme rain levels [55].
# A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain
## 3 Scheme Design
### Cem-PoA  
Compared with proof of work, proof of authority does not involve mining mechanisms [24]. It is an energy-saving consensus algorithm with higher throughput and fast transaction processing. We designed CEM-PoA, introduced a committee endorsement mechanism, and added supervisory nodes to ensure the correctness of the generated blocks. Figure 3 shows the CEM-PoA architecture:  
Figure 2: Seven phases in the proposed scheme.  
As a consortium blockchain, the Committer used to generate blocks will be selected from the service nodes. All service providers are supposed to disclose their true identities, and the blockchain dynamically maintains a set of rankings based on identity credibility. One of the service nodes with the highest reputation ranking will be randomly selected as a Committer for several rounds. The essence behind the reputation mechanism is the certainty of the identity of the service node. The work of the Committer involves collecting transactions in the network, verifying the transaction, and packaging the transaction into a block. The block is added to its local blockchain after passing the endorsement.  
Additionally, the committee endorsement mechanism is introduced. After the verification of a new block, several nodes will be randomly selected from the regulatory nodes to form a committee to sign the submitted block as an endorsement. It is required that the Committer must collect enough regulatory node endorsements before adding relevant information to the new block to be generated. Moreover, if the Committer wants to destroy the blockchain in the current round of consensus, he must unite a certain number of regulatory nodes to do evil together:  
* The Committer selected from the service node must be complicit with the regulatory node.
* Among these conspiracy nodes, enough nodes must be randomly selected for endorsement.
* The above two conditions make it challenging for the Committer to utilize his rights in order to generate different blocks.
# Design and Verification of Large-Scaled Flapping Wings for High Altitude Environment
## 1 Introduction
The wind tunnel experiments were used to optimize the appropriate planform, structural layout, and film material. Backpropagation (BP) neural network models were built to perform the prediction of the trim state. CFD was used to provide recommendations for adjusting flight conditions in high-altitude environments. Flight tests at different altitudes were conducted to verify the performance of the flapping wing. This study broadens the existing knowledge of large-scale flapping-wing designs at high altitudes.
## 5 Conclusions  
The effect of plant barriers on the irrigation quality of micro-sprinkling hose irrigation is noticeable, especially in the middle to late growth period of winter wheat. The uniformity coefficient of irrigation water distribution generally decreased with the distance perpendicular to the micro-spray tapes increasing. Compared with irrigation water distribution, the uniformity coefficient of soil moisture distribution was 25.8% higher after water redistribution due to the leaves and stems of wheat plants' interception, showing a positive effect of water redistribution on the irrigation quality of the micro-spraying system. Grain yield, total ET\({}_{\text{c}}\) and WUE of winter wheat were within 7528-8387 kg ha\({}^{-1}\), 327-367 mm and 2.11-2.56 kg m\({}^{-3}\), respectively. With the increase in border widths, grain yield generally increased. Though grain yields of 75 mm irrigation quota treatments were relatively stable, as indicated by its low variations in grain yields among treatments, 45 irrigation quota combined with 5.3 m border width obtained the maximum grain yield and WUE due to better irrigation uniformity and lower ET\({}_{\text{c}}\). In conclusion, the combination of a 5.3 m border and 45 mm quota is most effective in maximizing yield output while minimizing labor input for micro-spraying irrigation for winter wheat in the North China Plain.  
Conceptualization, S.W. and X.Q.; methodology, X.Q. and P.J.; software, P.J. and Y.W.; validation, S.W.; formal analysis, P.J.; investigation, P.J.; resources, X.Q. and M.W.; data curation, P.J. and H.Y.; writing--original draft preparation, P.J. and S.W.; writing--review and editing, X.Q.; visualization, H.Z. and H.L.; supervision, S.W.; project administration, X.Q.; funding acquisition, X.Q. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was funded by the Henan Water Conservancy Science and Technology Project (GG202121; GG202238), the Scientific and Technological Project of Henan Province (212102110069), and the Engineering Scientific Research Service Project of Yinjiang-Jihuai Engineering (Henan Section) (HNYJJHJSFWKY-2021002).  
Not applicable.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 4 CubeSat Design
### Attitude Dynamics  
Since the CubeSat is an earth observing satellite, it must always maintain a nadir-pointing orientation. The attitude dynamics is simulated based on the reaction wheels/magnetorquers available in the selected ADCS system from the COTS inventory according to Equation (8).  
\[J_{s}\overset{\rightharpoonup}{\omega}_{s}=-\overset{\rightharpoonup}{ \omega}_{s}\times\left(J_{s}\overset{\rightharpoonup}{\omega}_{s}+J_{RW} \overset{\rightharpoonup}{\omega}_{RW}\right)+\tau_{RW}+\tau_{m}+\tau_{d} \tag{8}\]  
where \(J_{s}\) is the moment of inertia matrix of the satellite, \(\overset{\rightharpoonup}{\omega}_{s}\) is the angular velocity vector of the satellite, \(J_{RW}\) is the moment of inertia matrix of the reaction wheel system, \(\overset{\rightharpoonup}{\omega}_{RW}\) is the angular velocity vector of the reaction wheel system, \(\tau_{RW}\) is the torque applied by the reaction wheels system, \(\tau_{m}\) is the torque applied by the magnetorquers, and \(\tau_{d}\) is the external disturbance torques, which is a sum of the gravity gradient, aerodynamic, and solar radiation pressure disturbances. The simulation of the attitude dynamics based on the selected ADCS system provides the pointing accuracy of the satellite, which is used for the optimization problem.
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithms** & **Original** & **1\% R** & **3\% R** & **5\% R** & **10\% R** & **1\% A** & **3\% A** & **5\% A** & **10\% A** \\ \hline \(\varepsilon\)-greedy & 0.1690 & 0.1720 & 0.1756 & 0.1846 & 0.1935 & 0.1774 & 0.1896 & 0.2052 & 0.2405 \\ Boltzmann & 0.1318 & 0.1332 & 0.1460 & 0.1503 & 0.1568 & 0.1346 & 0.1469 & 0.1587 & 0.1805 \\ CNAME & 0.2648 & 0.2657 & 0.2712 & 0.2725 & 0.2829 & 0.2698 & 0.2841 & 0.3006 & 0.3315 \\ UCB & 0.2103 & 0.2088 & 0.2113 & 0.2289 & 0.2312 & 0.2091 & 0.2344 & 0.2409 & 0.2753 \\ UCB1 & 0.1510 & 0.1581 & 0.1584 & 0.1681 & 0.1780 & 0.1572 & 0.1695 & 0.1768 & 0.220 \\ FP-UCB & 0.1237 & 0.1180 & 0.1314 & 0.1400 & 0.1576 & 0.1190 & 0.1340 & 0.1392 & 0.1614 \\ BBANK & 0.2654 & 0.2684 & 0.2711 & 0.2755 & 0.2850 & 0.2745 & 0.2867 & 0.3011 & 0.3317 \\ MOSS & 0.1703 & 0.1736 & 0.1780 & 0.1855 & 0.1987 & 0.1786 & 0.1938 & 0.2082 & 0.2440 \\ R-MOSS & 0.2631 & 0.2640 & 0.2678 & 0.2719 & 0.2831 & 0.2704 & 0.2834 & 0.2983 & 0.2766 \\
**DG-** & **0.2681** & **0.2693** & **0.2723** & **0.2762** & **0.2853** & **0.2748** & **0.2868** & **0.3013** & **0.3322** \\
**MOSS** & **0.2681** & **0.2693** & **0.2723** & **0.2762** & **0.2853** & **0.2748** & **0.2868** & **0.3013** & **0.3322** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Random attacks (R) and average attacks (A) on the advertising dataset. under Grant No. 232102521005, and the Key Technologies Research and Development Program of Henan Province under Grant No. 222102210080. Data Availability Statement: Data of this paper are available and can be accessed via [[https://tianchi.aliyun.com/dataset/dataDetail](https://tianchi.aliyun.com/dataset/dataDetail)]([https://tianchi.aliyun.com/dataset/dataDetail](https://tianchi.aliyun.com/dataset/dataDetail)) (accessed on 15 March 2024), [[https://grouplens.org/datasets/movielens](https://grouplens.org/datasets/movielens)]([https://grouplens.org/datasets/movielens](https://grouplens.org/datasets/movielens)) (accessed on 15 March 2024), and [[https://segmentfault.com/a/1190000018871668](https://segmentfault.com/a/1190000018871668)]([https://segmentfault.com/a/1190000018871668](https://segmentfault.com/a/1190000018871668)) (accessed on 15 March 2024). Thanks to [PERSON] from Guangxi Youjiang Water Resources Development Co., Ltd., for assistance with the software used in this work. The authors declare that they have no conflicts of interest.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Discussion  
While our study demonstrates the effectiveness of the proposed fog generation model in improving dehazing performance, we acknowledge that the evaluation did not include comparisons with recent state-of-the-art dehazing architectures such as FFA-Net, Dehaze-Former, or DehazeGS. This was a deliberate decision to isolate the effect of fog generation by maintaining a consistent dehazing network (GridNet) across all experiments. While this helped ensure a controlled comparison among fog synthesis methods, we recognize that incorporating more advanced dehazing networks in future work will provide a broader and more generalizable validation of our synthetic fog generation approach.  
Additionally, we acknowledge that a component-wise ablation analysis was not conducted. In particular, we did not individually evaluate the contributions of components such as the perceptual loss, dark channel consistency, and the GridNet-based architectural design. We believe that such analysis would offer important insights into the role of each element in performance improvement. We plan to conduct systematic ablation studies in future work to more rigorously validate our model's design choices and improve its efficiency.
## 4 Experimental Methodology
### Feature Extraction Framework  
Within the inverse reinforcement learning framework, this study employs apprenticeship learning to model expert data features. With discount factor \(\gamma=0.99\), feature mapping functions calculate feature expectations \(\mu(\pi_{E})\) for expert policy and \(\mu(\pi_{g})\) for apprentice policy. Iterative approximation via convex optimization continues until the policy divergence metric \(\left\|\mu(\pi_{E})-\mu(\pi_{g})\right\|_{2}\) falls below threshold \(\epsilon=1\times 10^{-4}\), yielding optimal weight vector \(\omega^{*}\).