esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization Algorithm
### Specific Steps of the PSO-MC Algorithm  
The specific steps of the PSO-MC algorithm can be summarized as:  
Step 1: Input the operation parameters of all distributed generators, including power flow parameters and economic parameters related to distributed system operation, and set the confidence level of the objective function and the chance constraints.  
Step 2: Initialize the optimization parameters of the particle swarm algorithm, including the number of particles, the maximum number of iterations, the learning factor, the learning rate, the initial inertia weight, and the final inertia weight.  
Step 3: Based on the probability distribution of wind turbine power, solar power, and electrical demands, generate daily wind output, PV output, and load curves of the proposed system with the MC simulation.  
Step 4: Generate daily output curves of CHP, fuel cells, and battery systems randomly to form a particle.  
Step 5: Test the feasibility of the randomly generated particle under the condition of the daily wind output, PV output, and loads generated by the MC simulation in Step 3, according to Equations (15)--(25). If the particle is not feasible, regenerate another particle until the number of feasible particles is equal to the number of particles defined in Step 2.  
Step 6: Calculate the fitness value of each particle, and then compare the fitness value with the local extreme. If the fitness value of the particle is superior to the local extremum, then the current local extremum will be replaced by this particle. In addition, compare the fitness value of the local extrema with the global optimum, and if the local extremum value is superior to the global optimum, update the current global optimum with the local extremum.  
Step 7: After calculating the fitness value of each particle, it is necessary to update the current speed and positions of all particles based on Equations (15)-(25). It is worth noting that the speed and location of each particle may not be feasible; therefore, the speed and location of the unsuitable particle needs to be regenerated.  
Step 8: Check the number of the iteration. If it reaches the maximum number of iterations, calculate the power flow of the system and implementing cost of the system. Otherwise, return to Step 6.
## 3 Materials and Methods
### Detection of Dry Beach Area of Tailing Ponds
#### 3.2.5 Edge Detection  
The dry beach was segmented from the detected image based on the dry beach region mask. The segmented image needs to undergo preprocessing to remove color and noise effects to allow precise determination of the boundaries of the dry beach mask [46]. In this study, the Canny edge detection algorithm was used for image preprocessing.  
The Canny edge detection technique is a widely used method in computer vision. It is known for its precise edge detection in images and its strong noise resistance. The Canny edge detection algorithm mainly includes the following four steps:  
1. Noise reduction. There are noises in the image, which may affect the accuracy of edge detection, so the image needs to be denoised first. The common denoising method is to use Gaussian filtering, which convolves the image with the Gaussian filter to reduce noise in the image. Gaussian filter is a commonly used image processing filter
## 3 Results and Discussion
### Emissions from the Bio-Based Fertilisers
The EFs of tested fertilisers for N\({}_{2}\)O were in the range of 0.02-0.11 g per 100 g\({}^{-1}\) N applied which is lower than the IPCC Guidelines for National Greenhouse Gas Inventories [21]. The IPCC default value of direct N\({}_{2}\)O emissions is equivalent to 1% of total N applied on the soil. However, the IPCC guidelines state EFs from the data culminated over the year, and this study focuses on the short-term emission of 18 days which is one of the reasons for lower EFs. In a study on short-term GHGs [37], EFs of 0.1-0.49% for urea applied at 200 kg N ha\({}^{-1}\) were found, which were quite similar to the results in this study. A review study [38] indicated that the N\({}_{2}\)O emission factors from urea range between <0.1 to about 2% of applied N.  
From Table 5, AS has shown the least emissions across all three gases. Moreover, LFD has performed better than MFs in the case of N\({}_{2}\)O and CH\({}_{4}\). From these results, we can conclude that the GHG emissions of BBFs resulting from the secondary treatment of raw manure (AS) are less than that of untreated/primary treated animal manure (i.e., LFD and KC).
# Three-Dimensional-Printed Polymeric Cores for Methane Hydrate Enhanced Growth
## 2 Materials and Methods
### Gas Hydrate Formation Test  
A 27.5 mL in-volume visual cell with two leucosapphire windows (Figure 3) coupled with the Drop Shape Analyzer DSA 100 ([PERSON], Hamburg, Germany) was employed. Methane hydrate formation was studied at 1 \({}^{\circ}\)C and about 9.1 MPa (subcooling was 11 \({}^{\circ}\)C). The morphology of the hydrate being formed was registered with the DSA 100 photo and video-recording system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Polymer** & **Extrusion** & **Layer** & **Extrusion** & **Table** \\  & **Width (\(\mu\)m)** & **Thickness (\(\mu\)m)** & **Temperature** & **Temperature** \\  & & & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) \\ \hline PLA & 200 & 50 & 205 & 60 \\ PLA* & 400 & 200 & 205 & 60 \\ ABS & 200 & 50 & 250 & 100 \\ PolyFlex & 200 & 50 & 225 & 60 \\ UltraX & 200 & 50 & 300 & 100 \\ ePC & 200 & 50 & 260 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Printing options.  
Figure 3: High-pressure visual cell; 1—coolant circulating channels connected to a thermostat, 2—shielding windows, 3—sapphire windows, 4—cell’s working volume, 5—air space with a moisture absorber.  
Figure 2: Three-dimensional-printed plastic cores: (**A**)—PLA, (**B**)—PLA* (400 \(\mu\)m extrusion width), (**C**)—ABS, (**D**)—PolyFlex, (**E**)—UltraX, (**F**)—ePC.  
When the growth of the hydrate stopped for 1-2 h (no visual and pressure changes), the system was usually kept under the specified conditions for about 12 h; then, the pressure was fixed, and the hydrate was decomposed. A 700G30 reference pressure transducer (Fluke, Everett, WA, USA) with a measuring range of 0-34.5 MPa and a maximal error of 0.017 MPa was used to check the initial and final pressures in the cell (without recording). A temperature sensor (Pt100) was embedded in the cell body to control temperature constancy. Methane consumption was calculated based on \(P\), \(T\) data using the Peng\(-\)Robinson equation. X-ray computed tomography data on polymer inserts volume were used when calculating the free cell space. Pressure and volume deviations may result in some errors in the counted amount of gas passed into the hydrate.
## 3 Results and Discussion
### Volatile Compound Profile in Lentil Wholemal Samples
Most of the SMF samples contained VCs belonging to aldehydes and alkanes for the SMF\({}_{\rm LR}\)24 and SMF\({}_{\rm L}\)24 sample groups, while alcohols and ketones were the largest newly formed VC classes in the SMF\({}_{\rm L}\)48 sample group. Most of VCs detected in lentils are known to be associated with specific flavour and aroma characteristics. The 1-hexanol is described having light apple, sweet, fruity, ethereal, and herbal flavour notes with herbal and oily aroma characteristics [65; 66]. Some of the most abundant aldehydes--hexanal and pentadecanal--have similar flavour and aroma traits and are described as fatty, grassy, fresh, and green [65; 66; 67]. Organic acids, such as hexanoic acid, are described as having sour, fatty, and cheesy flavours and aromas [65]. \(\beta\)-Damascenone contributes to woody, fruity, stewed apple, and sweet aroma and flavour notes [65]. Eugenol is known to contribute to flavour and aroma with clove, spicy, and honeylike notes [65]. Figure 3: Volatile compound (VC) profile (% of the total volatile compounds content) in lentils. C—control samples (non-fermented); L—lentils obtained from pure stands; LR—lentils obtained from relay incorporated with winter rye; SM—submerged conditions (lentils/water ratio 1:5, \(w/w\)); SS—submerged conditions (lentils/water ratio 1:1, \(w/w\)); SMF—fermented under submerged conditions; SSF—fermented under solid state conditions; 24, 48—duration of fermentation (in hours). The aliphatic aldehydes and ketones, such as hexanal or oct-3-en-2-one, are associated with fatty acid oxidation [68]. The detected aromatic aldehydes and alcohols, benzaldehyde phenylacetaldehyde and phenylethyll alcohol, showed a statistically significant correlation with the aromatic amino acid phenylalanine (see supplementary file: Table S11). Aromatic aldehydes and alcohols are associated with the catabolism of aromatic amino acids; in this case, phenylalanine [69]. The respective amino acid is converted to \(\alpha\)-keto acid by the respective aminotransferase, which can be converted to aldehyde by decarboxylless [53; 69]. Furthermore, the respective aldehyde can be oxidised to form carboxylic acids or reduced to form alcohols [53; 68]. The ester formation has four main biosynthesis pathways, as follows: alcohol and acid conversion to ester via esterase; hemiacetal, which is formed via spontaneous reaction between aldehyde and alcohol, with dehydrogenation via its dehydrogenases; ketone conversion to ester via Baeyer-Villiger monooxygenases; and alcohol and acyl-CoA conversion to ester via alcohol acyltransferases [70]. The production of hydrocarbons, such as tetradecane, tridecane, or 1-tetradecene, is associated with fatty acid catabolism [71]. Finally, all the factors and their interactions were statistically significant for most of the VCs in lentils (see supplementary file: Table S12).
## 1 Introduction  
In the context of the development of several innovative neural network models used in machine translation, the Recurrent Neural Network (RNN) has become a classic model used for neural machine translation (NMT) [1]. However, RNN still faces some obstacles in the training process. First, because the sequential input of RNN is adopted, early information is easily lost, especially when there is a large input. Second, the basic RNN uses back propagation, and back-propagated gradients are usually vanished or exploded because of the use of a finite-precision number in computing. With the development of Long Short-Term Memory (LSTM) [2, 3], the gradient-vanishing problem has been reassessed. In a different manner to the standard feed forward network, LSTM has its own feedback connections: input gate, forget gate, and output gate. However, unlike the Convolutional Neural Network (CNN), both RNN and LSTM are time-sequence models that still suffer from the parallel computing problem--i.e., the GPU cannot be used to speed up while training.  
A state-of-the-art model called the Transformer [4] proposed by Google applies the Multi-Head Self-Attention Mechanism and leverages the parallel computing in Neural Machine Translation. The Transformer has been the best NMT model to be proposed to date. In recent years, several researchers have used this self-attention mechanism to improve their models. Using GPU computing, the self-attention mechanism has improved the NMT model to a certain extent, and more researchers have chosen to stack more GPUs or model parameters, such as BERT. Although this parameter improves performance, its higher training cost also implies that these pre-trained models can only be owned by specific researchers. Therefore, in this paper, we focus on adjusting the sub-layers of the Transformer model, whilst maintaining its accuracy and efficiency.  
To prove that our model is better than the state-of-the-art model Transformer, in this paper we use two different language pairs: the English-German and English-French news commentary translation corpus of the World Machine Translation (2014) [5]. The proposed X-Transformer, which is an NMT model, includes the following features:  
* The X-Transformer modifies the structure of the Transformer by reducing the number of model parameters in both the encoder and decoder.
* The basic building block of the encoder in the X-Transformer is refined. We believe that applying the self-attention mechanism in the feed forward layer of the encoder is helpful for the model to understand natural languages, as will be shown in the experimental results.
* Two different language pairs are applied to train the Transformer and X-Transformer at the same time. The experiment results reveal that the proposed X-Transformer has an outstanding performance.
* The visual heat map is used to aid the comprehension ability of the trained models. The results again demonstrate the superiority of the X-Transformer. The refined encoder structure of the X-Transformer elevates the comprehension ability to a token-to-token level.
## 7 Routing in CRAHNs
However, in this case, the priority for the end-to-end traffic flow is given to routes that offer minimum delay and maximum stability, while also considering the latency introduced by the spectrum handoff. The algorithm is based on four modules: route discovery, route decision, transmit power control (ORTPC) and route maintenance. The route discovery module discovers all possible paths between the source and the sink node by a neighborhood exchange of spectrum opportunities via RREQ packets. The module arranges its discovery according to various latency values offered by each potential route. The route decision module choses the latency value from a sorted list to best fit the requirement of the traffic at hand. ORTPC then adjusts the transmit power according to the traffic requirement. The route maintenance module is responsible for ensuring that all entries in the routing table are updated. Although the transmit power varies in this scheme, the continuous exchange of spectrum opportunities among neighboring nodes and the continuous updating of routing entries can be very taxing on the energy-constrained nodes in a WSN. Other schemes consider the full knowledge of the network topology with respect to what they offer before making the routing decision. For example, [46] uses the elaboration of algebraic connectivity metrics to capture network connectivity, network diameter and average node distance. Then, weights are assigned to paths according to PU interruption. The gymkhana routing scheme then makes the final route decision after collecting all of the possible route information. However, having full knowledge of the network topology in a WSN is virtually impossible because of the associated cost and the density of node deployment. In summary, routing in CRNs can be categorized into routing with spectrum decision, routing with joint spectrum decision and PU awareness and routing with joint spectrum decision and re-configurability.
## 1 Introduction  
Production of global fisheries has reached catch volumes of up to 179 million tons of fish, with 205 million tons forecast to be reached by 2030. Worldwide, only 50-60% of marine catches are used for human consumption [1], increasing waste and byproducts of fishing. In this industry, as much as 70-75% of catches can be considered waste or byproducts [2, 3]. Waste may include entrals, skin, scales, shells, gills, dark meat, heads, bones, spines and effluent. These products are usually thrown into the environment without any treatment or added value [4, 5].  
In recent years, due to limited biological resources, the need has arisen to use and make the most of fishing resources so that a large part of the byproducts generated are destined for use in fishmeal and surimi-like products [6]. However, with the elaboration of these products, an intermediate effluent commonly called stickwater (SW) is generated [7].  
SW has a high concentration of insoluble matter on its surface (5-10% solids), mainly comprising proteins, amino acids, and collagen. This high concentration of biomolecules causes the deterioration of ecosystems in which it is discharged [8, 9]. That is why alternatives have been sought for its use. Among the technologies that stand out is ultrasonic pulses as pretreatment to produce protein hydrolysates. Ultrasound affects hydrogen bonds and hydrophobic interactions by alternating molecular conformation through cavitation, heating, vigorous agitation, and shear stress [10]. Ultrasound pulses create high and low-pressure regions that induce a series of compressions and decompressions, causing molecular displacement by interrupting dipole-dipole forces, hydrogen bonds, and other weaker interactions (high-intensity ultrasound in the range of 10-1000 W/cm\({}^{2}\) with frequencies of 20-100 kHz) [9; 11].  
Additionally, this technique aid in extraction of functional compounds with improved performance, short production time, low cost, and less solvent used [12]. Recent studies have focused on identifying the type of proteins present in SW. However, most have only focused on collagen/gelatin, one of the most abundant proteins in this byproduct [13], leaving aside other important protein fractions that can also possess functional and antioxidant properties. Therefore, the aim of this study aims is to produce protein hydrolyses from the soluble fraction of SW by using ultrasonic pulses, characterizing its functional properties (solubility, foaming, and emulsifying capacity) and antioxidant properties (ABTS, DPPH, and FRAP).
## 4 Results
### Model Evaluation at the Xiangtang Station  
For the calibration period (2017), model performances were reasonably good, with predictions in the GPP and ET matching the dynamic characteristics and the peaks of measured GPP and ET (Figure 5). The RMSE in GPP generally peaks in the stage of the resume growth before jointing; however, the RMSE is generally less than 50% of the mean GPP (Figure 6). In addition, the mean bias in the GPP for the early rice is close to 0 on average and relatively small compared to the mean GPP during the growing period of early rice. Compared to GPP, the RMSE in ET predictions from the PML-V2 model is even smaller, about 10% of the mean ET. The overall good performances in the simulations of  
Figure 4: Key parameters in the PML-V2 model in different stages of early rice (red) and late rice (blue). (**a**) Quantum efficiency \(\beta\); (**b**) Carboxylation efficiency \(\eta_{z}\); (**c**) notional maximum catalytic capacity of Rubisco \(V_{m25}\); (**d**) stomatal conductance coefficient \(m\), and (**e**) Soil evaporation coefficient \(\alpha_{s}\).  
both GPP and ET indicate that the PML-V2 model is capable of predicting crop yield and its water use for rice paddies if the proper parameters are used.  
To fully evaluate the PML-V2 model, we used the \"calibration-validation\" strategy and re-ran the model in 2018 using the parameters that were calibrated in 2017. The seasonal changes in GPP predictions generally coincided with the change patterns in the GPP measurements throughout the year in 2018; however, GPP was underestimated in the early rice stage (Figures 5a and 6). Compared to the \"optimized\" simulations, where parameters were optimized in every growing stage in 2017 and 2018 (Figure 6), RMSE in GPP increased \(\sim\)0.2-1.0 gC\(m^{-2}\)d\({}^{-1}\) when the model was run at the early rice period,  
Figure 5: Daily predictions in (**a**) GPP and (**b**) ET components from the PML-V2 model when the parameters in 2017 were used for both 2017 and 2018 at the Xiangtang station.  
Figure 6: Prediction accuracy from the PML-V2 model in different growing stages of early rice (red) and late rice (blue). The solid lines represent model simulations when the optimized parameters were used in each growing stage, and the dotted lines represent model simulations in 2018 when the parameters of 2017 were used.  
and the mean bias reached over \(-\)2 gCm\({}^{-2}\)d\({}^{-1}\) at the first growth stage of the early rice. In comparison, model simulations were much better for the late rice (Figures 5a and 6). Compared to the GPP estimation, ET estimation in 2018 showed more robust results. The predictions in ET and its three components were generally consistent with the optimized simulations. RMSE in ET predictions increased only \(\sim\)0.1-0.2 mm/day, and the mean bias was confined reasonably well.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Capture  
This study investigated the use of remotely-sensed multispectral imagery from both UAV and satellite platforms for detection of weed areas in irrigation channels.
# Fractional-Order Sliding Mode with Active Disturbance Rejection Control for UAVs
## 4 Experiments and Results
### Analysis of Experimental Results Without Interference  
The simulation effect of the controller controlling the attitude angle, as well as the position information without interference, is shown in Figure 3(a)-f.  
Figure 4: _Cont._  
Figure 4a-c show the attitude angles in the resultant graphs, where the proposed controller suppresses the high-frequency switching oscillations of the output of the SMC well, which makes the output smoother and more stable and has a higher control accuracy in the attitude angle \(\psi\). And, the control accuracy is higher in the attitude angle \(\psi\). Figure 4d-f of the total position information show that all three algorithms can track the desired value in a finite time and the tracking effect is better. A more visual comparison can be made by referring to the steady-state error table (Table 4); the controller proposed in this paper has a higher control accuracy, and the response speed in the altitude channel results in a certain degree of improvement compared with the other two kinds of controllers.
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 4 Simulation Environment and Results  
In this section, we describe our simulation setup and present the evaluation results.  
Figure 4: Node willingness selection based on the number of received HELLO messages.
# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
### Data Fusion for Data Analysis
#### 6.4.1 Data Types
* _Fusion and analysis for unstructured data:_ Unstructured data refer to data with an irregular or incomplete data structure, data without a predefined data model, and data that are not easily represented by a two-dimensional logical table of the database. These data mainly include video, audio, picture, image, document, text, etc. Technically, it is more difficult for unstructured information to be standardized and understood than for structured and semi-structured information. Therefore, the application fields of fusion and analysis for the unstructured data are also more abundant and diversified, such as medical imaging systems, educational video on demand, video monitoring, file servers, media resource management, intelligent retrieval, knowledge mining, content protection, and the value-added development and utilization of information. Unstructured data fusion mainly relies on semi-supervised machine learning, reinforced learning, representation learning, deep learning, and tagless learning due to their complex characteristics and performance requirements on data-mining technology.