esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Once a fish is vaccinated with the trial vaccine, the duration and intensity of protective immunity developed is examined to help provide a basis of whether the antigen is immunogenic and elicits the right 'type' of response, so that researchers can identify restrictions within the vaccine that need to be addressed [15]. The ideal vaccine combines the most efficient antigens and adjuvant systems to produce an effective response against a specific pathogen with minimal side effects [14]. Recent studies in fish immunology have discovered that using cytokines as adjuvant's instead of oil adjuvants has the advantage of stimulating the expression of co-stimulatory molecules and polarizing antigen-presenting cells [14]. Studies have also reported that using interferon responsive genes (IRGs) provides a form of innate immunity when encountering viral infections [16; 17]. Advanced information in the field of fish immunology and microbiology is also directing new approaches toward fish vaccinology. Modern vaccine technology includes developments in the realm of recombinant DNA, which targets specific components within the pathogen using various expression systems. mRNA vaccines are also currently being studied and have been found to provide greater levels of immunity. In terms of vaccine administration, oral vaccines are used as an alternative strategy to commercially vaccinate fish, with the added advantage that these cause no stress to fish and are easier for fish farmers to administer [10]. Live attenuated vaccines are more efficacious, as they mimic natural infection conditions and have the ability to stimulate both humoral and mucosal immunity to generate a strong antibody response [18; 19; 20]. Some live attenuated vaccines were developed in the 1990s and have been successfully implemented in aquaculture to increase production and reduce the use of antibiotics [21; 22]. Recombinant attenuated _Edwardsiella pisciida_ vaccines (RAEVs) have been explored as a vaccine delivery platform for heterologous antigens in aquaculture [23]. In carp, an _E. tarda_ live attenuated vaccine-induced greater levels of cellular immunity and conferred higher levels of protection compared to a formalin-killed vaccine against the wild-type strain. While overall progress in fish vaccination is promising, certain limitations persist within the field. One notable challenge lies in the efficiency of administration, particularly given the individualized nature of vaccine delivery. Additionally, the aqueous environment poses a potential hindrance to the optimal efficacy of the vaccines. Various review articles have extensively detailed the techniques employed in vaccine administration, current vaccination models, and the challenges confronting the fish vaccinology industry today [10; 13; 24]. Bearing these considerations in mind, we will delve into the trends in fish vaccination, explore different vaccine types and administration methods, assess their advantages and limitations, and discuss the prospects of effectively controlling infectious fish diseases through vaccination.
# Review Rating Prediction Based on User Context and Product Context
## 4 Experiments and Evaluations
### Experimental Settings and Research Questions  
We divided each dataset shown in Table 1 into two parts: training set and test set. We use 80% of each dataset as the training set and the rest is used as the test set. All of the hyper-parameters of our methods are selected based on the performance on the training set. For comparison, we summarize our proposed models and baseline methods as follows.  
* LR + global: RRP method based on linear regression trained and tested on all users and products.
* LR + global + UPF: We extract user features [36] and corresponding product features (denoted as UPF) from training data, and concatenate them with the features in baseline LR + global.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **\#Users** & **\#Reviews** & **\#Items** & **\#Reviews/User** & **\#Reviews/Product** \\ \hline Douban 1 & 1476 & 22,593 & 3041 & 15.31 & 7.43 \\ Douban 2 & 1079 & 13,858 & 2087 & 12.84 & 6.64 \\ Yelp 2014 & 4818 & 231,163 & 4194 & 47.97 & 55.12 \\ Yelp 2013 & 1631 & 78,966 & 1633 & 48.42 & 48.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical Information of Yelp 2014, Yelp 2013, and Two Douban Movie Review Datasets.  
* LR + individual + product: RRP method based on linear regression trained and tested on individual product.
* LR + individual + user: RRP method based on linear regression trained and tested on individual user.
* KNN: RRP method based on k-nearest neighbor.
* MF: RRP method based on matrix factorization.
* RRP + UC: RRP method based on review content and user context.
* RRP + PC: RRP method based on review content and product context.
* RRP + UC + PC: RRP method based on user context and product context.  
By combining the review content, user context and product context information, we propose a RRP method based on user context and product context. To analyze the performance of our method and the factors that affect the performance of our method, on the four datasets, we did three experiments to answer the following three questions:  
1. Whether our method performance is better than the benchmark method.
2. The impact of the number of reviews per user and reviews for each product on our methods
3. The impact of user context and product context on our methods.
## 2 Materials and Methods
### Spectral and Image Feature Extraction  
#### 2.3.1 Image Segmentation and Spectral Feature Extraction  
Image preprocessing, segmentation, and feature extraction are illustrated in Figure 2, which shows a series of steps to acquire data and develop the mathematical model. Initially, every image was calibrated with the dark current and white reference image with Equation (1). For extracting classification features for model building and testing, segmentation of the true seed images from the hyperspectral image background is a key step. In this study, adaptive threshold segmentation was selected because of its simplicity and high efficiency [18]. Adaptive segmentation and feature extraction were performed for each hypercube as follows. (1) The relative reflectance image at 782.6 nm was selected to identify the regions of interest (ROIs) because of its superior contrast outline in this image. (2) Pre-processing operations, such as image filtering and enhancement, were applied to remove noise  
Figure 2: Image segmentation procedure and feature extraction. (**a**) representative spectral hypercube with 233 band images of maize seeds within the wavelength region of 400–1000 nm; (**b**) raw hyperspectral image at 782.6 nm used for segmentation of the maize seeds; (**c**) image after filtering and enhancement; (**d**) image after threshold segmentation; (**e**) the obtained mask is applied to segment the true regions of maize seeds from the 233 wavebands in the hypercube; and (**f**) extraction of the average reflectance spectra from region of interest (ROI) images.  
on the obtained gray image. (3) Threshold segmentation was then performed to produce a binary mask. (4) The obtained binary mask was applied to segment the true regions of maize seeds from the 233 wavelengths in the hypercube. (5) The mean spectral features were calculated by averaging the intensity values of all pixels in the ROIs of the 233 wavelengths obtained from each maize seed.
# Video over DSL with LDGM Codes for Interactive Applications
## 5 Related Work
The authors of [39] considered packet interleaving as an alternative response to bursty losses. As in Pro-MPEG COP #3 (refer to Section 2.3) the intention was to spread packet error bursts so that they no longer affect consecutive packets of the source packet stream. Unlike Pro-MPEG COP #3, channel coding was not incorporated into the interleaving scheme. Instead, packet interleaving was introduced at the IP-packet level irrespective of content. However, in [45], the authors demonstrate that if interleaving is also introduced into rate-controlled streams then the possibility exists of an adverse interaction with the congestion control algorithm. For an interleaving block of 48 UDP/IP packets, the impact on end-to-end latency was estimated to be an acceptable 65 ms. Smaller interleaving block sizes could be employed, depending on error burst conditions. The scheme was most appropriate to the small burst-length conditions present in satellite links. In [46], packet interleaving took place before video compression. Though error resilience was improved, the loss of temporal redundancy, owing to interleaving before the codec, resulted in a degradation of coding efficiency. Interleaving of base-layer and enhancement-layer packets in scalable coding does not suffer from the same weakness and in [47], along with other techniques, resulted in greater resistance to error bursts. The work in [24] dynamically introduced Pro-MPEG COP #3 into an RTP video packet stream. Depending on bitrate constraints and the channel conditions, selected video frames were protected. In particular, I-frames were protected because of their impact on later frames. Apart from interleaving, network coding has also been deployed [48] to counter error bursts occurring in wireless multi-hop networks. Alternatively, if it is possible to adaptively route packets based on measured link conditions, as it is with the Cognitive Packet Network [49], then multimedia streams can gain in quality and become more resilient to error bursts. Again, in content-aware networks, transmitting redundant packets can counter packet losses [50].
## 2 Site and Data  
### GNSS Site Description  
The study selected the P043 site from the Plate Boundary Observatory (PBO) H\({}_{2}\)O network to acquire GNSS observations [38]. Basic information and the surroundings of this site are shown in Table 1 and Figure 1, respectively. In Figure 1, it can be seen that the terrain in this area fluctuates slightly, with no large shelter and sparse vegetation, which is conducive to SM retrieval. Since the SNR quality of the L2C signal is better than those of the L1 C/A and L2P signals [55], the L2C SNR data were used in this study within the elevation range of the satellite from 5\({}^{\circ}\) to 20\({}^{\circ}\), and the experimental period was from day of year (DOY) 96 to DOY256 in 2015. This ensured that the SNR had a clear and stable multipath periodic oscillation characteristic in the intercepted satellite elevation range. Based on the first Fresnel reflection principle [26; 38; 41; 56], the monitoring area corresponding to the 5\({}^{\circ}\) and 20\({}^{\circ}\) satellite elevation angles were drawn, as shown in Figure 1.
# New Method for Simultaneous Arsenic and Selenium Speciation Analysis in Seafood and Onion Samples
## 3 Materials and Methods
### Samples and Sample Preparation  
The samples were collected from several convenience shops in urban areas in Poznan (Poland). Red shrimps, clams, octopus, squids, Argentine red shrimps, white blanched shrimps Vannamei, white shrimps Vannamei, bio white onion, pink onion, sweet white onion, and bio red onion were chosen for analysis. Tuna Fish Tissue BCR-627, Institute for Reference Materials and Measurements (IRMM), Geel, Belgium, with certified contents of arsenobetaine and dimethylarsinic acid, was used. The certified values were arsenobetaine, 52 \(\pm\) 3 \(\upmu\)mol\(\cdot\)kg\({}^{-1}\), and dimethylarsinic acid, 2.0 \(\pm\) 0.3 \(\upmu\)mol\(\cdot\)kg\({}^{-1}\)[56]. All food samples were first frozen and lyophilized for 48 h in a freeze dryer GT2E type 11 with a vacuum pump Leybold Trivac D5E. The mass 1.00 \(\pm\) 0.01 g of the dried samples, which included red shrimps, clams, octopus, squids, Argentine red shrimps, white blanched shrimps, Vannamei white shrimps, bio white onion, pink onion, sweet white onion, and bio red onion, were weighed precisely in plastic tubes. The extraction process was carried out with 10 mL of distilled ultrapure water obtained from a Milli-Q Direct 8 purification unit (Millipore, Burlington, MA, USA, Merck) at 40 \({}^{\circ}\)C. Times of 20, 40, and 60 min were chosen to check the effectiveness of the water extraction process. The concentration of each determined species was constant, and because of that, the time of 20 min was chosen for further analysis. The water extracts of samples were cooled to room temperature and centrifuged (5.0 min, 14,500 rpm, MiniSpin plus, Eppendorf, Warszawa, Poland). Supernatants (water-soluble extracts) were carefully decanted, filtered through a 0.45 \(\upmu\)m pore sized polyvinylidene difluoride syringe filter (Whatman, GE Healthcare Life Sciences, Piscataway, NJ, USA), and introduced directly to the HPLC-ICP-MS system. The samples (3 replicates) of CRM were prepared and analyzed with the same procedure as all the food samples. Moreover, portions of the foods and CRM extracts were analyzed by ICP-MS to determine the water-soluble concentration of analyzed metals. The parameters of the ICP-MS spectrometer for analysis are presented in Table 4.
# Polymeric and Crystalline Materials for Effective and Sustainable CO\({}_{2}\) Capture
## 2 Background
### General Principals of Carbon Dioxide (CO\({}_{2}\)) Capture and Sequestration
Its main goal is the establishment of an electrochemical, energy-efficient, and near-to-CO\({}_{2}\)-neutral process to produce bulk chemical ethylene from CO\({}_{2}\), water, and renewable energy [25]. To tackle the capture of CO\({}_{2}\), several approaches have been proposed. Firstly, technologies such as zero emission are poised to significantly simplify the separation process, which is often the most expensive step in CO\({}_{2}\) recovery. Also, membrane separation processes offer several advantages over traditional techniques such as a high selectivity for CO\({}_{2}\) over oxygen or nitrogen while utilizing polymeric and/or inorganic materials [26; 27; 28]. Porous membranes, when combined with supporting amine solutions, have demonstrated effectiveness in CO\({}_{2}\) separation [13; 29; 30]. Specifically designed materials, capable of separating molecules based on molecular weight or size, show promise for CO\({}_{2}\) adsorption.
## 3 Methodology
### Recurrent Neural Network
#### 3.3.3 GRU  
GRU was developed by [43] to simplify LSTM. Figure 5c shows the basic structure of GRU. A typical GRU unit contains two types of gates: A reset gate (\(r_{t}\)) and an update gate (\(z_{t}\)). The reset gate (\(r_{t}\)) controls how much information from the previous state is written into the current candidate hidden layer vector \(\widetilde{h}_{t}\). The smaller the reset gate (\(r_{t}\)), the less information from the previous state is written. The update gate (\(z_{t}\)) is used to control the degree to which the state information \(h_{t-1}\) at the previous time step \(t-1\) will be brought into the current time step \(t\). The larger the value of the update gate (\(z_{t}\)), the more the state information at the previous time step is brought in. The reset gate (\(r_{t}\)) and update gate (\(z_{t}\)) can be defined by the following formula:  
\[r_{t}=\sigma(W_{rx}x_{t}+W_{rh}h_{t-1}+b_{r}) \tag{13}\]  
\[z_{t}=\sigma(W_{zx}x_{t}+W_{zh}h_{t-1}+b_{z}) \tag{14}\]  
The candidate hidden layer vector \(\widetilde{h}_{t}\) is defined as follows:  
\[\widetilde{h}_{t}=\tan\ln(W_{hx}x_{t}+W_{hh}(r_{t}\odot h_{t-1})+b_{h}) \tag{15}\]  
The output of the GRU unit can be formulated as follows:  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\widetilde{h}_{t} \tag{16}\]  
where \(W_{rx}\), \(W_{rh}\), \(Wzx\), and \(W_{zh}\) are the weight matrices; \(b_{r}\) and \(b_{z}\) are the bias terms.
## 3 Simulations and Data Analysis
### Simulation of Flow Fields and Beam Propagation  
Under the flow field simulation conditions described in the previous section, the distribution of the air density near the optical turret is shown in Figure 2.  
The density data obtained from the aforementioned flow field simulation are extracted, and the corresponding refractive index distribution is derived using Equation (1), with the wavelength of the optical beam being \(\lambda=1550\) nm. For the emission directions proposed in Equation (3), this study selected four typical emission angles, namely angles 1 to 4, as illustrated in Figure 3. Subsequently, the light field distribution is derived from Equations (4)-(6). Based on the phase distortion caused by flow field interference, corresponding phase screens are generated to filter the Gaussian beam, yielding the SNR expressed in Equation (8).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Altitude/km** & **Temperature/K** & **Static Pressure/Pa** \\ \hline
0 & 288.15 & 101,325 \\
5 & 255.67 & 54,048 \\
10 & 223.25 & 26,499 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between the temperature and the gauge pressure at different altitudes.  
Figure 1: Airborne turntable model. The turntable geometric model (**a**) and the turntable grid structure (**b**).  
Utilizing the Monte Carlo phase screen method, a sequence of subsequent phase screens is generated based on Equation (9) to simulate the impact of atmospheric turbulence on beam propagation over long distances. Figure 4 displays the normalized intensity distribution of the light spot emitted in direction (2) under the combined influence of aero-optics and atmospheric turbulence at an altitude of 5 km and a velocity of 0.4 Ma.  
Figure 2: Simulation results of the air density under different motion conditions. (**a**) \(h=0\) km, \(v=0.4\) Ma; (**b**) \(h=5\) km, \(v=0.4\) Ma; (**c**) \(h=10\) km, \(v=0.4\) Ma; (**d**) \(h=0\)km, \(v=0.8\) Ma; (**e**) \(h=5\) km, \(v=0.8\) Ma; (**f**) \(h=10\) km, \(v=0.8\) Ma.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Proof That Our Method Uses the Minimum Number of Drones
#### 3.6.4. Wastage of Coverage in _ThreeNeighbours_  
Considering three circles with centers \(C_{1}\), \(C_{2}\), and \(C_{3}\) and radii \(r_{1}\), \(r_{2}\), and \(r_{3}\), respectively, we want to find the center of the next circle \(C\) with radius \(r\). Let \(A\) be the intersecting point of circles with centers \(C_{1}\) and \(C_{2}\) and let \(B\) be that of circles with centers \(C_{2}\) and \(C_{3}\), as shown in Figure 16b; furthermore, let \(r_{1}=r_{2}=r_{3}=r\).  
\[\angle DC_{2}O=\angle EC_{2}O=90^{\circ}\Rightarrow\overline{DC_{2}E}\text{ is a straight line.}\]  
it is evident that  
\[\overline{DC_{2}}\parallel\overline{C_{2}E}\parallel\overline{C_{3}B} \parallel\overline{AC_{1}}\text{ and }\overline{C_{3}BAC_{1}}\text{ is a straight line.}\]  
Figure 16. Wastage of coverage in (**a**) _TwoNeighbours_ and (**b**) _ThreeNeighbours._\[\angle C_{3}DH = \angle C_{1}EI=\theta\] \[\angle C_{3}DC_{2} = \angle C_{2}EC_{1}=135^{\circ}-\theta\] \[2\angle C_{3}DC_{2}+2\angle DC_{3}B = 360^{\circ}\] \[\angle DC_{3}B = \frac{360^{\circ}-2(135^{\circ}-\theta)}{2}=45^{\circ}+\theta\] \[\angle C_{2}BA = \angle DC_{3}B\] \[\text{(Corresponding angles of }\overline{DC_{3}}\delta\overline{C_{2}B})\] \[\angle ABC = \angle C_{2}BA\text{ (Property of rhombus C}_{2}\text{ BCA)}\] \[\angle BC_{3}F = \angle ABC\] \[\text{(Corresponding angles of }\overline{BC}\delta\overline{C_{3}F})\] \[= 45^{\circ}+\theta\] \[2(Area(segmentBF)) = 2r^{2}\big{(}\frac{(45^{\circ}+\theta)\pi}{360^{\circ}}-\frac{ sin(45^{\circ}+\theta)}{2}\big{)}\]  
Similarly,  
\[2(Area(segmentGA)) = 2r^{2}\big{(}\frac{(45^{\circ}+\theta)\pi}{360^{\circ}}-\frac{ sin(45^{\circ}+\theta)}{2}\big{)}\] \[2(Area(segmentBF)) = 2(Area(segmentGA))\]  
hence, the wastage of the area of the circle centered at \(C\) is equally distributed among those centered at \(C_{1}\) and \(C_{3}\).
# Green Production of Glycerol Ketals with a Clay-Based Heterogeneous Acid Catalyst
## 4 Conclusions  
Ketals from glycerol are considered potential oxygenates to be used in fuel formulation. Considering the TOF, 1 h HCl activated smectite AC is a good ketalization catalyst for the reaction of glycerol and several acyclic and cyclic ketones, even if it undergoes a first-order partial deactivation. During such activation, the original smectite RC porosity increases while its content in Fe, Mg, K and Al decreases, with a concomitant increase of the SiO\({}_{2}\) component. Kinetic studies performed for the ketalization of glycerol with acetone (system 1), MEK (system 2), and cyclohexanone (system 3) led to a reversible kinetic model with elemental ketalization and hydrolysis reactions and partial first order deactivation. The models were adequate for a wide range of catalyst concentration, temperature and ketone to glycerol molar ratios.  
Conceptualization, A.B., N.M., E.S., N.B. and M.L.; Data curation, M.L.; Formal analysis, N.B. and M.L.; Funding acquisition, E.S. and N.B.; Investigation, S.A., J.G. and A.B.; Methodology, S.A., J.G., E.S., N.B. and M.L.; Resources, M.L.; Supervision, A.B., N.M., E.S., N.B. and M.L.; Writing--original draft, S.A. and M.L.; Writing--review & editing, A.B., N.M., N.B. and M.L.  
Tunis-El Manar University is gratefully acknowledged for financial support. Also, a very special gratitude goes out to the Spanish Ministry of Science and Innovation and the Department of Chemical Engineering and Materials, Faculty of Chemistry, Complutense University of Madrid. We are really indebted to [PERSON], from the Department of Chemical Engineering and Materials of the Complutense University of Madrid, for her scientific support through fruitful discussions and for providing the FTIR equipment here employed for the kinetic experiments. The scientific support of Prof. [PERSON] and Prof. [PERSON] is also gratefully acknowledged.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**System 1**} & \multicolumn{2}{c}{**System 2**} & \multicolumn{2}{c}{**System 3**} \\ \hline T (\({}^{\circ}\)C) & 25 & 50 & 25 & 50 & 25 & 50 \\ T (K) & 298.16 & 323.16 & 298.16 & 323.16 & 298.16 & 323.16 \\ k\({}_{1}\) & \(1.03\times 10^{-6}\) & \(7.93\times 10^{-6}\) & \(1.77\times 10^{-7}\) & \(1.60\times 10^{-6}\) & \(3.50\times 10^{-7}\) & \(9.35\times 10^{-6}\) \\ k\({}_{2}\) & \(2.62\times 10^{-6}\) & \(2.38\times 10^{-5}\) & \(3.36\times 10^{-7}\) & \(5.05\times 10^{-6}\) & \(2.41\times 10^{-7}\) & \(1.34\times 10^{-5}\) \\ k\({}_{4}\) & \(2.69\times 10^{-2}\) & \(2.44\times 10^{-1}\) & \(2.14\times 10^{-4}\) & \(1.90\times 10^{-3}\) & \(2.54\times 10^{-4}\) & \(2.22\times 10^{-3}\) \\ \(\beta\) & 0.425 & 0.425 & 0.134 & 0.134 & 0.117 & 0.117 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Estimated kinetic parameters for the model fitted to all data from acetone (system 1), MEK or butanone (system 2) and cyclohexanone (system 3) ketalization reactions with glycerol at the higher and lower temperature values within the range under study.