esa-sceva/satcom-chunk-collection huggingface dataset:
# Marine Gelatine from Rest Raw Materials
## 1 Introduction
### Opportunities for by-Catch Utilization  
By-products from marine processing industry represent a major environmental and economic challenge due to inadequate disposal options and/or costs associated with disposal at landfills. Processing leftovers such as bloodstream, trimmings, fins, frames, heads, shells, skin, viscera, and stickwater/effluent are currently used in Ireland for the production of fish meal, fish oil, fertilizer, and animal feeds [6]. Solid waste from surimi processing, which can amount for 50 to 70% of the utilized raw material, is also an important source of by-products [7]. Boarfish (_Capros aper_) and blue whiting (_Micromesitis poutassou_) are two pelagic species which represent specific challenges for the fish processing industry. They are currently viewed as lower value species, due to their small size which makes their processing demanding, although some advances have been made in the field of production of blue whiting skinless fillets [8]. Another option for processing of these species would be for production of surimi products, especially in the case of small specimens which are unsuitable for machine filleting operations.  
Boarfish (Figure 1) [9] is a small species (up to 23 cm in length) of mesopelagic shoaling fish, characterized by its orange colour, large eyes, and protrusible mouth. They can be found inhabiting shallow seas and shelf slopes from 400-600 m depth, and are widely distributed across the eastern Atlantic from Norway to Senegal, including the Mediterranean region [10]. Although it is considered a sub-tropical fish species, in recent decades boardfish has become very abundant throughout its range, which may be explained by rising ocean temperatures due to climate change [11]. Although the 2017 boardfish quota for Ireland is 36% lower than previous year's quota, the allowed 18,850 tonnes limit is still among the highest among European countries [12]. The main utilization of landed boardfish in Ireland includes export to Denmark for production of fishmeal [13], but other potential uses are also considered. The Irish Sea Fisheries Board (Bord Iascaigh Mara, BIM) currently recommends use of Boarfish for direct human consumption, with marketing options either in the form of commodity products including 20 kg blast frozen blocks of mince or as a headed and gutted product suitable for frying [14]. Other authors have recently discussed alternative means of boardfish biomass exploitation, which include hydrolysis of its proteins to obtain protein hydrolyses and extraction of valuable peptides and biomolecules [15; 16]. However, large-scale production of gelatine from boardfish by-products is not sufficiently researched as an option for valorisation of this biomass currently.  
The focus of this review is to explore the feasibility of producing gelatine sourced from marine processing by-products specifically from blue whiting and boardfish processing by-products including skins and bones.
## 5 Conclusions  
Traditional open-field and greenhouse agriculture are prone to contamination by fungal species, among which _A. flavus_ and _A. parasiticus_ reportedly generate AFBs that contaminate agricultural products during crop cultivation. Therefore, target agricultural crops should be protected from fungal attack, and chemical control measures against fungal contamination using fungicides have generally been employed in the field. Notwithstanding, non-aflatoxigenic _A. flavus_ and _A. parasiticus_ strains are considered effective controllers of fungal infection in crops when competitively grown together with aflatoxin strains in the field. Several researchers have reported that non-aflatoxigenic strains suppress aflatoxinic _A. flavus_ and _A. parasiticus_ survival and AFB contamination. In our study, a non-aflatoxigenic _A. flavus_ strain, ATCC 9643, exhibited its potential to mitigate AFB production and aflatoxinic _A. flavus_ strain (ATCC 22546) growth. Increasing the inoculum rate of ATCC 9643 relative to ATCC 22546 on solid agar significantly suppressed the growth of ATCC 22546 and reduced AFB (aflatoxin B) production by approximately 84%, 95%, and 97%, with inoculum ratios of 1:1, 1:3, and 1:5, respectively. Therefore, ATCC 9643 may be an excellent candidate for the biocontrol of AFB contamination in target crops as a biological agent against aflatoxin _A. favus_ and _A. parasitic_ strains. Further studies that validate the bioregulatory capacity of ATCC 9643 against aflatoxinic strains in the target crop cultivation field are urgently required.  
Conceptualization, K.-S.J., H.-M.K. and S.-E.L.; methodology, K.-S.J., H.-M.K. and J.L.; software, K.-S.J., H.-M.K. and J.L.; validation, D.G. and S.-E.L.; formal analysis, K.-S.J., H.-M.K. and J.L.; investigation, K.-S.J., [PERSON]-M.K., [PERSON] and [PERSON]-[PERSON]; resources, [PERSON] and S.-E.L.; data curation, [PERSON]-S.J., [PERSON]-M.K., [PERSON], [PERSON] and S.-E.L.; writing--original draft preparation, [PERSON]-S.J., [PERSON] and S.-E.L.; writing--review and editing, [PERSON]-S.J., [PERSON] and S.-E.L.; visualization, [PERSON]-S.J., [PERSON], D.G. and S.-E.L.; supervision, S.-E.L.; project administration, S.-E.L.; funding acquisition, S.-E.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Biological Materials Specialized Graduate Program through the Korea Environmental Industry and Technology Institute (KEITI) funded by the Ministry of Environment (MOE). This research was also supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) through a grant funded by the Ministry of Education (2021R1A6C101A416), and quantitative PCR was carried out the at KNU NGS Center (Daegu, South Korea). This study was partially supported by a grant (15162 MFDS044) from the Ministry of Food and Drug Safety.  
Not applicable.  
Data Availability Statement: Data are contained within the article.
## 3 System Model and Assumptions
### Proposed Slice Allocation System
#### 3.2.2 Proposed Adaptive Slice Allocation System (ASA)
\begin{table}
\begin{tabular}{c c} \hline \hline
**5G Slice** & \(ASP_{i}\) \\ \hline URLLC & \(ASP_{1}=\frac{R_{v}^{1}}{P_{\text{URLLC}}\times R}\) \\ \hline eMBB & \(ASP_{2}=\frac{R_{v}^{2}}{P_{\text{AMBS}}\times R}\) \\ \hline mMTC & \(ASP_{3}=\frac{R}{P_{\text{MITLC}}\times R}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The ASP of each slice’s request \(i\) in the ASA system. However, when the total current RBs occupied by the requests of slice i drops below the predefined virtual resource partition \(R_{v}^{i}\), the ASP value of the slice's request i is decreases, and it receives a higher priority. For example, suppose the \(R_{v}^{i}\) value of the URLLC slice is 50 RBs, whereas the current \(R_{c}^{i}\) value is 40 RBs. The \(R_{v}^{i}\) value of the eMBB slice 35 RBs, and the current \(R_{c}^{i}\) value is 5 RBs. The \(R_{v}^{i}\) value of the mMTC slice is 15 RBs, whereas the current \(R_{c}^{i}\) value is 10 RBs. Using Equation (16), the ASP value for the URLLC slice is 0.8, 0.4 for the eMBB slice, and 0.6 for the mMTC slice. As a result, when we receive requests from all slices, the eMBB slice request will have the highest priority and obtain the service first since it has the lowest ASP value. When three different slice requests have the same ASP value, the request with the lowest priority index (i.e., URLLC) will be served first. For example, suppose the \(R_{v}^{i}\) value of the URLLC slice is 50 RBs, whereas the current \(R_{c}^{i}\) value is 25 RBs. The \(R_{v}^{i}\) value of the eMBB slice 35 RBs, and the current \(R_{c}^{i}\) value is 18 RBs. The \(R_{v}^{i}\) value of the mMTC slice is 15 RBs, whereas the current \(R_{c}^{i}\) value is 8 RBs. Using Equation (16), the ASP value for the URLLC slice is 0.5, 0.5 for the eMBB slice, and 0.5 for the mMTC slice. As a result, when the ASA system receives requests from all slices, the URLLC slice request will have the highest priority and obtain the service first since it has the lowest priority index. Therefore, the unutilized RBs of one slice request can be utilized by another slice request, as needed. Furthermore, the ASP value achieves slice isolation by preventing slice requests from affecting each other. Table 6 summarizes the CAC procedures of the ASA system, Figure 5 illustrates the pseudocode for the arrival event of the request \(i\) algorithm, and Figure 6 illustrates the pseudocode for the departure event of the request \(i\) algorithm.
## 3 Results and Discussion
### Spectral and PC Analysis
Reflectance spectra of samples containing sea sand, red soil, and brown soil mixed with 8% and 10% _w/w_ petroleum, are shown in Figure 3. Changes in spectral features for soils and sea sand samples, with and without the pollutant, are visible. The solid black trace spectra shown in Figure 3a-c represent the reference standard with 10% petroleum in KBr. The spectral signals shown in light blue are from soil samples without petroleum. When these samples are mixed with oil (red solid trace), high intensity bands at 1450 and 1380 cm\({}^{-1}\) appeared clearly in the spectra (Figure 3b,c). However, as shown in Figure 3a, these signals were feeble (red dotted trace) apparently due to the same reason that mid to high intensity bands are not observed in the spectrum of uncontaminated sea sand. Bands at 1450 and 1380 cm\({}^{-1}\) are associated with the asymmetric and symmetric deformations of CH\({}_{3}\), respectively [61]. Figure 2: Scores plot of PC 2 vs. PC 1 (principal components) for sea sand (sand), montmorillonite (mont), bentonite (bent), brown soil, and red soil. The 95% confidence level is also shown.
## 4 Results and Discussion
### Force Acting on the PM
Here defects are simulated by through holes. As shown in Figure 7a, in the \(z\)-direction, more intensive signals can be obtained for larger defects. Note that for a large defect, the two peaks we observed in Figure 5a overlap with each other. We calculate the areas under the corresponding curves by integrating the voltage above the baseline (baseline voltage = 0.285 V in Figure 7a) and the results are shown in Figure 8. The graph shows a strong linear correlation between the integral and the volume information of defects within experimental error. This correlation is confirmed by theoretical analysis in the range where the size of the defect is comparable or smaller than the PM probe (1 mm\({}^{3}\)). Therefore, the z-component of our force can be used to quantitatively determine the size of the defect thin test sample with small defect. Figure 7: Measured force acting on the PM for through-hole defects of various diameters. (**a**) z-component and (**b**) x-component. In the \(x\)-direction, we observed stronger signals for larger defects, which is similar to the observations in the \(z\)-direction but with a different feature. We may utilize this feature specific to the horizontal component to evaluate the lift-off effect, which will be discussed next.
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 4 Performance Analysis  
In this section, we first give the DLSTM network training results. Then, we evaluate the decoding delay, complexity, and error-correction performance of the proposed algorithm and other existing algorithms. Simulations were performed with AWGN channel and BPSK modulation on an Intel(R) Xeon(R) Gold 6132 CPU @ 2.60 GHz server.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation
### Goals, Metrics, and Methodology
#### 5.1.2 Routing Algorithms for Performance Evaluation  
For performance evaluation, we use the following three geographic greedy routing (GRP) algorithms:  
* Geographic greedy routing without a trust model (Pure GRP);
* Pure GRP with the beta trust model (Beta GRP);
* Pure GRP with our model (Hybrid GRP).  
The Pure GRP (geographic routing protocol) [19] provided by the OPNET Modeler is a geographic greedy routing system that works very similarly to the GPSR that we introduced in Section 2.1. We implemented two trust-based geographic greedy routings (beta GRP and hybrid GRP) based on the GRP and two trust models (the beta trust model and our hybrid trust model) in the OPNET Modeler.  
The main difference between the three routings is the neighbor selection algorithm (NSA), by which a sending node S finds a next hop H to forward its packet to H. Algorithm 1 shows the neighbor selection algorithm of the Pure GRP. In the Pure GRP, as with the GPSR, a sending node S selects a next hop H that is the closest one to the destination from its neighbor set **NS**.  
```
1:\(\text{d}_{\text{MIN}}\): = dist (S, D);
2:for each node n\({}_{i}\) in **NS do**
3: calculate dist (n\({}_{i}\), D);
4:if (dist (n\({}_{i}\), D) \(<\) d\({}_{\text{MIN}}\)) then
5: H: = n\({}_{i}\);
6:\(\text{d}_{\text{MIN}}\): = dist (n\({}_{i}\), D);
```  
**Algorithm 1** (NSA in the GRP)  
On the other hand, in the trust-based GRPs such as the Beta GRP or the Hybrid GRP, as shown in Algorithm 2, a sending node S finds a next hop H that is the closest one to the destination among its neighbors whose trust value is equal to or higher than the trust threshold TH. The only difference between the two trust-based GRPs is the difference in the trust models that they use.  
```
1:\(\text{d}_{\text{MIN}}\): = dist (S, D);
2:for each node n\({}_{i}\) in **NS do**
3: calculate dist (n\({}_{i}\), D) \(<\) d\({}_{\text{MIN}}\)) then
4: H: = n\({}_{i}\);
5: H: = n\({}_{i}\);
6:\(\text{d}_{\text{MIN}}\): = dist (n\({}_{i}\), D);
```  
**Algorithm 2** (NSA in the trust-based GRP)
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## System and Threat Models
### Threat Models  
Assume that sensor networks have a short safe bootstrapping phase right after network deployment, during which the adversary cannot compromise nodes or launch attacks. Subsequent to that, the adversary can compromise multiple nodes. However, the authors assume that the BS will not be compromised because it is usually well-secured. When a node is compromised, all the information it holds will also be compromised, and the adversary can take full control of it to launch different kinds of attacks. The following attacks are the focus of this paper:  
* False report injection attack [5]. The adversary might control the compromised nodes to inject forged event reports, which might cause false alarms as well as the exhaustion of the constrained resources of the network.
* Report disruption attack [6]. The compromised sensor node might contribute invalid endorsement to the event report. As a result, the report of a real event will be dropped by some forwarding node or the BS. Additionally, the compromised sensor node might refuse to participate in report generation, thus the CH cannot collect enough legitimate endorsements for the event report. Furthermore, if a CH node is compromised, it might refuse to send out the signed event report or send out an event report with an illegitimate signature, resulting in the real event not being reported to the BS.
* Selective forwarding attack [6]. The compromised forwarding node might selectively drop the legitimate reports, which will severely damage data availability and disrupt the event report service.  
The study objective is to design an effective scheme to mitigate the impact of these attacks. The authors assume that most \(t-1\) sensor nodes in an event area can be compromised in the study models, otherwise the adversary can successfully generate fake reports to fool the BS with non-existing events. Additionally, the authors assume that at least \(t\) sensor nodes in an event area are not compromised, otherwise the CH cannot generate legitimate reports for the real events. Therefore, at least \(2t-1\) sensor nodes in an event area should be deployed--\(Nc\geq 2t-1\).
# A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification
## 2 Related Work
### Feature Fusion  
The information fusion method is generally divided into the fusion of the pixel level, feature level, and decision level [13]. Feature-level fusion reduces the dimensionality of the input data in the feature space, and then combines multiple dimensionality-reduced feature vectors. Compared with the other two methods, feature-level fusion has better discrimination because it retains important information of the original feature vector. Feature-level fusion often fuses different features extracted from different modalities into a more abstract, more compact feature representation, which can improve classification performance. Among them, canonical correlation analysis (CCA)-based feature fusion [13] and discriminant correlation analysis (DCA) [14] are the most prominent technologies.  
CCA-based feature fusion mainly seeks the projection directions of two sets of feature vectors to maximize correlation after projection. Considering the differences between and within classes, [PERSON] et al. [15] proposed a discriminant CCA (DCCA) method for face recognition and achieved competitive performance. With the development of deep learning technology, it is necessary to explore information fusion technologies that are suitable for deep learning. [PERSON] et al. [16] proposed the ResC3D deep learning network to extract deep features. The model adopted typical correlation analysis methods for feature fusion and obtained competitive performance in gesture recognition. [PERSON] et al. [14] considered the class information in the classification and proposed a discriminative correlation analysis method for multimodal feature fusion which can effectively remove correlations between classes and suppress the correlation within classes.  
Feature fusion can optimize the combination of different feature vectors, thereby retaining effective discriminate information for multiple sets of features and eliminating redundant information between feature vectors. In this paper, the feature vectors extracted by different deep learning networks are considered to be different modalities. Feature fusion method is employed to fuse the features to obtain deep feature vectors with robust discriminative capacity and lower dimensions.
## 2 Methods
### The SIRT-MWTDM Iterative Algorithm  
The SIRT algorithm adopts the point-by-point update mode, and the contribution value of rays from all projection angles to the current point should be calculated for updating each point. The update of each image point is obtained by averaging the contribution value of all rays passing through the point. It has more advantages in suppressing statistical noise than ART and SART. The iterative equation of the SIRT algorithm is shown in Equation (10).  
\[f_{j}^{(n+1)}=f_{j}^{(n)}+\lambda_{\text{SIRT}}^{(n)}\cdot\sum_{i=1}^{N}{[a_ {ij}(p_{i}-\sum_{m=1}^{M}{a_{im}f_{m}})/\sum_{m=1}^{M}{a_{im}}]/\sum_{i=1}^{N}{ a_{ij}}} \tag{10}\]where \(n\) is the iteration number of the SIRT algorithm and \(f_{j}\) is the pixel value of the reconstructed image. Each ray beam is regarded as an infinitely narrow straight line, and the length of the ray passing through the reconstructed pixel is defined as \(a_{ij}\). Namely, \(a_{ij}\) is an element value of the system matrix. \(1\leq i\leq N\), \(1\leq j\leq M\), and \(N\) and \(M\) are the width and height of the sinogram. \(\lambda_{\text{SIRT}}\) is the relaxation factor ( \(0<\lambda_{\text{SIRT}}<2\)).  
Before the SIRT-MWTDM algorithm is used for iterative reconstruction, we use the FBP algorithm to obtain a reconstruction image \(f^{(0)}\) from sparse angle projections. \(f^{(0)}\) is used as the initial image of the SIRT-MWTDM algorithm. Then, the SIRT-MWTDM algorithm starts the process of iterative reconstruction according to the parameters that have been set. Each complete iteration process of the SIRT-MWTDM algorithm includes one SIRT iteration, one MRP, and one WTDM. When the stop condition is met, we can obtain the reconstructed image. In the initial stage of the SIRT-MWTDM algorithm, the estimated solution is far from the optimal solution, and a large \(\lambda_{\text{SIRT}}\) value can speed up the optimization process. As the iteration number increases, the estimated solution becomes closer and closer to the optimal solution. As the number of iterations increases, the smaller \(\lambda_{\text{SIRT}}\), the higher quality of the convergence. Therefore, we introduce the parameter \(\lambda_{red}\) of attenuation rate to our algorithm. To more clearly illustrate the SIRT-MWTDM algorithm, the organizational scheme of the SIRT-MWTDM algorithm is shown in Figure 1 and Algorithm 1.  
Figure 1: Organizational scheme of the SIRT-MWTDM algorithm. In the organizational scheme, \(\lambda_{\text{SIRT}}\) is the relaxation factor. \(\lambda_{red}\) is a decay ratio. \(\varphi\) is the weight value between gradient continuity and gradient sparsity constraints. \(\gamma\) is the noise suppression intensity parameter. \(N_{\text{iter}}\) is the maximum number of iterations. \(k\) is a parameter to adjust the threshold \(\omega\). \(n\) is the number of iterations.
# Ocean Currents Reconstruction from a Combination of
## 1 Introduction
Based on one year of modelled data (2017), we simulate synthetic altimeter-derived currents, and we combine them with the surface modelled Chl data. The validity of the reconstructed currents is evaluated using the CMEMS modelled surface currents as a validation benchmark. The Mediterranean Sea represents a quite challenging study area, mostly due to the scales of its circulation features, reaching only a few kilometers in some cases [36], and to the presence of distinct (and changing) trophic regimes [37; 38]. Moreover, the Mediterranean Area is of interest for several activities directly affected by ocean currents: it hosts 30% of the global sea-born trade, as well as more than 200 marine protected areas. Though challenging, this context clearly justifies a dedicated study to improve the monitoring of the Mediterranean surface circulation patterns. The paper is structured as follows: in Section 2, we present the materials and methods of our study. Then, we illustrate the main results of our analyses in Section 3. A discussion of the results is presented in Section 4, also providing the main conclusions and perspectives.