esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of Camelina Cake and Diets
#### 2.2.2 Fatty Acid Composition  
The fatty acid composition was determined as methyl esters (FAME) according to [PERSON] et al. (2007) [35] on the finely ground cake (400 mg) and diets (800 mg), using nonadecanoic methyl ester as the internal standard. FAMEs were injected into a gas chromatograph Agilent 7890 GC system (Palo Alto, CA) equipped with an on-column injector, and an FID (Flame Ionization Detector)  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Control (C)** & **Camelina 10\% (CAM10)** & **Camelina 20\% (CAM20)** \\ \hline  & \multicolumn{3}{c}{Ingredients, g/kg} \\ \hline Corn & 537.5 & 524.0 & 508.5 \\ Soybean meal & 320 & 247 & 174 \\ Calcium carbonate & 95 & 95 & 95 \\ Camelina cake & 0 & 100 & 200 \\ Soybean oil & 25 & 11.5 & 0 \\ Dicalcium phosphate & 12 & 12 & 12 \\ Vitamin premix * & 5 & 5 & 5 \\ Salt & 4 & 4 & 4 \\ Methionine & 1.5 & 1.5 & 1.5 \\ \hline  & \multicolumn{3}{c}{Metabolizable Energy, MJ/kg} \\ \hline  & 11.05 & 10.88 & 10.77 \\ \hline  & \multicolumn{3}{c}{Analytic Composition \%} \\ \hline Crude Protein & 18.08 & 18.32 & 18.54 \\ Crude Fat & 5.30 & 5.51 & 5.90 \\ Crude Fiber & 3.17 & 3.69 & 4.20 \\ Ash & 14.12 & 14.26 & 14.40 \\ L-Lysine & 0.90 & 0.96 & 1.02 \\ Methionine & 0.43 & 0.45 & 0.46 \\ Methionine + Cysteine & 0.75 & 0.66 & 0.56 \\ L-Threonine & 0.71 & 0.75 & 0.79 \\ Ca & 4.15 & 4.15 & 4.16 \\ P & 0.56 & 0.57 & 0.58 \\ Available P & 0.35 & 0.33 & 0.32 \\ Na & 0.17 & 0.16 & 0.16 \\ Cl & 0.22 & 0.22 & 0.22 \\ \hline \end{tabular}  
* Vitamins: A 10,000 IU; E 30.00 mg; D3 3000 IU; K 2.50 mg; B1 2.00 mg; B2 6.00 mg; B6 4.00 mg; B12 0.02 mg; H 0.05 mg; PP 30.00 mg; Folic Acid 1.00 mg; D-Part 8.00 mg; Coline 600.00 mg Fe 40.00 mg; Cu 12.00; Zn 75.00 mg; Mg 150.00 mg; Se 0.30 mg; I 1.20 mg.  
\end{table}
Table 2: Composition of experimental diets and calculated nutrient contents expressed as fed.  
detector. The separation was performed on a 100% dimethylpolysiloxane column (CP-Sil88 for FAME, 100 m \(\times\) 0.25 mm \(\times\) 25 \(\upmu\)m) adopting the conditions described in [PERSON] et al. (2019) [36]. FAMEs were identified by comparison of their retention times with known standards (37-component FAME mix, Supelco 47885-U) and expressed as g/kg of cake or diet.
## 1 Introduction  
Greater amberjack (_Seriola dumerili_) is a very successful fish candidate for aquaculture due to its attractive growth characteristics and high market prices [1, 2]. Farming of greater amberjack has gained global interest and is considered as one of the most important species for diversification of marine finfish production in the Mediterranean area [3]. Among various bottlenecks, disease outbreaks have become a significant limiting factor for the further expansion of greater amberjack farming, both in the Mediterranean region and elsewhere [4]. _Zeuxapta seriolae_, a gill monogen parasite, is considered one of the most devastating pathogens in caged greater amberjack [4]. The parasite causes heavy infestations mainly at high water temperatures, which have been associated with anaemia and severe losses in farmed greater amberjack [5, 6].  
Hydrogen peroxide baths (75-100 ppm for up to an hour) have been proved sufficient to confront _Z. seriolae_[7]; however, an effective dietary medicine would be an ideal measure to overcome the drawbacks associated with bath applications in large cages, including time-consuming and labor-intensive processes that are stressful and occasionally toxic to fish and dependent on weather conditions.  
Praziquantel (PZQ) is a synthetic drug that has been used in human [8], and veterinary medicine and is considered an ideal antiparasitic compound against fish platyhelmiths [9]. Its mathematic action leads to disruption of the parasitic integument and induction of spastic muscular paralysis [10]. To date, however, PZQ lacks full registration in aquaculture practices, though potential selection of such compounds is possible in aquaculture under the 'off-label' cascade (Council Directive 90/676/EEC, Directive 2001/82/EC and Commission Regulation 37/2010). For those cases, a standard withdrawal time of 500-degree days is enforced to secure consumer safety, considering that a zero maximum residue level has been established for livestock, where the use of PZQ is permitted [11].  
A comprehensive review on the use of PZQ against platyhelmith fish parasites has been published [9]. Praziquantel given at different dosing schedules, seemed a very effective anthelmittic of farmed fish species [12; 13; 14]. The pharmacokinetics of dietary PZQ in farmed greater amberjack have been recently determined with attractive absorption properties [15], however, its PZQ efficacy against fish monogeneans is missing in the Mediterranean region. Consequently, to acquire a complete picture on the anthelmittic value of the drug, the aim of this study was to evaluate the efficacy of dietary PZQ in _Z. seriolae_-infected greater amberjack.
# _EntangleNet:_ Theoretical Reestablishment of Entanglement in Quantum Networks +
## 2 The Idea of Restoring Entanglement
### Our Proposal
#### 2.2.1. Exemplification  
A representation of the protocol for the first and second particular cases is depicted in Figures 6 and 7, respectively. It should be noted that CS and IQS are not pictured explicitly; however, their actions contribute to the functionality of the scheme. In Figure 6, we consider \(k=1\), so there are three AQS between [PERSON] and [PERSON]. Nodes \(A,O_{A},B\) distribute generated entangled qubits directly to \(A^{\prime}\) and \(B^{\prime}\), which then perform a BSM and provide [PERSON] and [PERSON] with entanglement. In Figure 7, we consider \(k=2\); therefore, seven AQS are present on the path from [PERSON] to Bob. This time the distribution of particles propagates towards \(A^{\prime}\) and \(B^{\prime}\) by intermediate Bell measurements performed by the other AQS. Finally, \(A^{\prime}\) and \(B^{\prime}\) are the last ones to perform a BSM, which entangles the qubits kept by [PERSON] and [PERSON].  
Figure 5: A scenario that involves two inactive quantum switches (concentric circles) between two active ones (circular shapes) on a forward distribution segment. Active quantum switch \(S_{a_{i}}\) follows the protocol and sends qubit \(e_{i}^{\prime}\) to next node \(S_{a_{i+1}}\). The receiver is not part of the chosen subset to create entangled pairs or measure qubits, thus it forwards the particle to \(S_{a_{i+2}}\). The particle is again routed to node \(S_{a_{i+3}}\), which is active and can therefore perform a Bell measurement on its qubits.  
_Appl. Sci._**2018**, \(8\), 1935  
**Figure 6. An illustration of the protocol for \(k=1\). Nodes \(A,O_{A}\) and \(B\) locally prepare entangled pairs of qubits. Nodes \(A^{\prime}\) and \(B^{\prime}\) each receive two qubits (from \(A\) and \(O_{A}\) and from \(O_{A}\) and \(B\), respectively). A Bell measurement can then be performed on the qubits from different entangled pairs, regardless of the order, followed by classical communication of outcome (not pictured). For example, \(A^{\prime}\) is the first to perform a Bell measurement, followed by \(B^{\prime}\). After these steps, entanglement has propagated via swapping towards [PERSON] and [PERSON], who finally share one ebit as entangled pair \((e_{0},e_{4})\).  
_Appl. Sci._**2018**, \(8\), 1935  
Figure 7: Example for \(k=2\). Nodes \(A^{\prime}\) and \(B^{\prime}\) can no longer be reached directly by \(A,B\) or \(O_{A}\). Links of entangled pairs are established according to the instructions received by each node. Without needing to be synchronized, Bell measurements are performed at each station on the four segments of the path (1). Middle nodes \(A^{\prime}\) and \(B^{\prime}\) can execute the same task after receiving the qubits from their adjacent nodes (2), thus providing \(A\) and \(B\) with entangled pair of particles \(e_{0}\) and \(e_{8}\) (3).
## 4 Design and Methodology
### Methodology  
The proposed detection involves applying FL integrated with blockchain on a fog node. Each fog node represents a different bank with its local learning model. Federated learning ensures the accurate training of our models by sending the initial model parameters from the global learning model (cloud server) to the local learning model in each fog node (bank) individually. The FL algorithm is usually based on the following steps [53; 54; 55; 56]:  
Figure 4: Imbalanced dataset ECC.  
1. Initialization: A global learning model is initialized on a cloud server with random parameters generated to create and initialize it.
2. Model Distribution: Each local model (bank) downloads the global model from the cloud server. The parameter initialization is distributed to fog nodes (banks), which serve as local learning models.
3. Local Training: Each bank trains its local learning model using its local data, starting from the downloaded global model. During training, the local model updates its parameters based on its local dataset.
4. Model Aggregation: Upon completing local training, each local model sends its updated parameters back to the cloud server (global model). The global model aggregates these updates from all local models to prepare a new global model with the recent parameter updates.
5. Global Model (Updated Parameters): The updated model parameters are frequently communicated back to the cloud server after training at each bank.
6. Iteration: Steps 3 to 5 are iteratively repeated until the target with the minimum loss is achieved. Each round involves local training on individual banks, followed by model aggregation at the cloud server.
7. Convergence: After multiple rounds of training, the global model converges to a state where it has captured knowledge from all local models (fog nodes) while preserving privacy and data protection.
8. Deployment: Once training is complete, the final global model is deployed, while individual banks retain their local data without sharing them with the cloud server.  
We assumed three banks for experimental purposes, but the framework can accommodate any number of banks. Each bank will train its local learning model using the parameters received from the global model. Using FL helps keep the data on their local server more confidential and preserves privacy. Additionally, our system involves blockchain technology, which provides an immutable ledger to facilitate faster information reception, more accurate data processing, risk reduction, transaction recording, asset tracking in a business network, and more [16; 17].
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 2 Architecture of Digital Twin Monitoring System  
### Overall Architecture of Digital Twin Monitoring System  
According to the specific functional requirements for the remote monitoring of the EBZ roadheader and combined with the five-dimensional general model of the digital twin, the Digital Twin Monitoring System for Roadheader (DTMSR) is proposed, as shown in Figure 1.  
It consists of five basic parts: multi-valued attribute physical entity (MAPE), digital twin virtual entity (DTVE), data processing system (DPS), user interaction system (UIS), and network transmission system (NTS).
## 2 Materials and Methods
### Hybrid Adaptive PID Controller Design
#### 2.3.2 LSTM-Enhanced RLSE Implementation  
The Recursive Least Squares Estimation (RLSE) algorithm is enhanced by LSTM predictions to improve parameter estimation accuracy. The combined prediction model is defined as follows:  
\[\hat{\text{y}}(\text{t})=\phi^{\text{T}}(\text{t})\hat{\theta}(\text{t})+\text{ LSTM\_pred}(\text{t}) \tag{15}\]  
where  
\(\hat{\text{y}}(\text{t})\) is the predicted output  
\(\phi(\text{t})\) is the regression vector  
\(\hat{\theta}(\text{t})\) represents estimated parameters  
LSTM_pred(t) is the LSTM network prediction  
The LSTM enhancement provides several advantages:  
Improved prediction accuracy;  
Better handling of nonlinearities;  
Enhanced disturbance rejection;  
Faster adaptation to system changes.
## 5 Experimental Results
### Axial Resolution Performance Depending on the Spectrometer  
We measured the point spread function (PSF) using a flat mirror at the same position of \(\sim\)210 \(\upmu\)m in OPD to calculate axial resolutions. The results seen in Figure 8 showed the same values of 7.8 \(\upmu\)m, which was in good agreement with the theoretical axial resolution of 6.2 \(\upmu\)m. The variation may be due to the imbalance of dispersion in the interferometer. Although the aberration effect of the imaging lens was different, the difference in the axial resolution due to the aberration effect could not be distinguished and similar spectra were obtained by the two spectrometers. If we used a light source with larger spectral bandwidths, the difference in the axial resolutions due to imaging lens aberration may become more apparent.  
Figure 8: Axial resolution calculated from the point spread function (PSF) measured by **(a)** the S-ADL and **(b)** the S-TFL according to the optical path length difference (OPD).  
Figure 7: Spectrum measured by **(a)** the S-ADL, and **(b)** the S-TFL.
# Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries
## 3 Methodology
### Game-Based Priority Coordinating
#### 3.2.1 Independent Solving of Process Control Schemes
According to cost functions (17)-(20), each agent \(v_{i}\) obtains its optimal independent control scheme \(\Delta\mathbf{u}_{p_{i}^{\text{PC}}}^{\text{PC}}(k)\) by solving constrained optimization problem (21). \[\min_{\Delta\mathbf{u}_{p_{i}^{\text{PC}}}^{\text{PC}}(k)}J_{i}^{\text {FC}}(k)=\left\|\mathbf{y}_{t}(k)\otimes\mathbf{I}_{y}-\mathbf{y}_{p_{i}^{\text{PC}}}^{ \text{PC}}(k)\right\|_{\mathbf{Q}^{\text{PC1}}}^{2}+\left\|\Delta\mathbf{u}_{p_{i}^{ \text{PC}}}^{\text{PC}}(k)\right\|_{\mathbf{Q}^{\text{PC2}}_{i}}^{2}+\left\|\mathbf{u}_{ p_{i}^{\text{PC}}}^{\text{PC}}(k)\right\|_{\mathbf{Q}^{\text{PC3}}_{i}}^{2} \tag{21}\] \[\text{s.t. }\left\{ \begin{array}{l}u_{\min,i}\otimes\mathbf{I}_{u} \leq\mathbf{u}_{p_{i}^{\text{PC}}}^{\text{PC}}(k)=u_{i}(k-1)\otimes\mathbf{I}_{u}+\mathbf{ M}_{u,i}\Delta\mathbf{u}_{p_{i}^{\text{PC}}}^{\text{PC}}(k)\leq u_{\max,i}\otimes\mathbf{I}_{u} \\ \Delta u_{\min,i}\otimes\mathbf{I}_{u}\leq\Delta\mathbf{u}_{p_{i}^{\text{PC}}}^{\text{PC }}(k)\leq\Delta u_{\max,i}\otimes\mathbf{I}_{u}\\ \mathbf{y}_{\min}\otimes\mathbf{I}_{y}\leq\mathbf{y}_{p_{i}^{\text{PC}}}^{\text{PC}}(k) \leq\mathbf{y}_{\max}\otimes\mathbf{I}_{y}\end{array} \right.\]  
where \(u_{\min,i}\) and \(u_{\max,i}\) are the lower and upper bounds of the control input of agent \(v_{i}\), \(\Delta u_{\min,i}\) and \(\Delta u_{\max,i}\) are the lower and upper bounds of the control input increments of agent \(v_{i}\), and \(\mathbf{y}_{\min}\) and \(\mathbf{y}_{\max}\) are the lower and upper bounds of the system outputs. The forms of \(\mathbf{y}_{\min}\) and \(\mathbf{y}_{\max}\) are provided in Appendix B. After the optimal independent control scheme is obtained, each agent informs all its neighbors about the first action of its scheme and determines its priority by playing the game introduced in Section 3.2.2. For agent \(v_{i}\), the first item \(\Delta u_{p_{i}^{\text{PC}}}^{\text{PC}}(k|k)\) in \(\Delta u_{p_{i}^{\text{PC}}}^{\text{PC}}(k)\) is broadcast to the adjacent agents.
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper
### Analysis of the Influence of Particle Damper Materials and Structural Parameters  
#### 3.4.1 Particle Filling Rate  
The particle damper was filled with 2 mm steel particles; the other conditions remained the same. The number of particles and the corresponding mass ratios at different filling rates are shown in Table 5.  
From Figure 6, it can be seen that the total energy dissipation of the particle system shows a trend of increasing and then decreasing with an increase in the particle filling rate. The total energy dissipated by the particle system was highest at the 60% filling rate. There was an optimal particle filling rate for particle dampers in such conditions. When the particle filling rate was low, the number of particles in the container was low, the particle-particle and particle-container wall contact was limited, and less energy was dissipated through collisions and friction. As the particle filling rate increased, the momentum exchange between particles and between particles and the container wall increased, and the total energy dissipated by the particle system increased. When the filling rate of the particles was higher than the optimal filling rate, the space for movement of the particles inside the container wall was limited, and the energy dissipated through collisions and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Contact** & \begin{tabular}{c} **Coefficient** \\ **of Recovery** \\ \end{tabular} & \
\begin{tabular}{c} **Coefficient of** \\ **Static Friction** \\ \end{tabular} &
\begin{tabular}{c} **Coefficient of** \\ **Rolling Friction** \\ \end{tabular} \\ \hline Steel–Steel & 0.45 & 0.15 & 0.15 \\ Steel–Aluminum alloy & 0.45 & 0.17 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Contact parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial** & \begin{tabular}{c} **Density (\(p\))** \\ **(kg\(\cdot\)m\({}^{-3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Elasticity Modulus** \\ **(E) (GPa)** \\ \end{tabular} &
\begin{tabular}{c} **Poisson Ratio (\(\mu\))** \\ \end{tabular} \\ \hline Steel & 7850 & 200 & 0.3 \\ Aluminum alloy & 2800 & 68.9 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Filling Rate (\%)** & **Number of Particles** & **Mass Ratio (\%)** \\ \hline
10 & 203 & 6.68 \\
20 & 406 & 13.35 \\
30 & 609 & 20.03 \\
40 & 812 & 26.73 \\
50 & 1015 & 33.38 \\
60 & 1218 & 40.05 \\
70 & 1421 & 46.73 \\
80 & 1624 & 53.40 \\
90 & 1827 & 60.08 \\
100 & 2030 & 66.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of particles and mass ratios corresponding to different filling rate.  
friction decreased gradually. At a 100% filling rate, the particles had very little movement, but energy could still be dissipated through friction between the particles. Therefore, the total energy dissipated at this filling rate was slightly better than that at a 10% filling rate.
# Multi-View Network Representation Learning Algorithm Research
## 3 Our Method
### Complexity Analysis  
In the proposed MVNR, the training procedure can be divided into the following parts: \(M\) construction, \(M\) factorization using IMC and representations concatenation. In matrix \(M\), the number of rows equals the number of columns, meanwhile, the number of rows and columns equals the number of vertices in the network, which is defined as \(\left|\left.V\right|\right.\). Thus, the time complexity of constructing matrix \(M\) is \(O(|V|^{3})\).  
For the Equation (11), we introduce the optimization approach proposed by [PERSON] et al. [33]. For each \(k\)-step network, we use the known weight matrix \(W\) to factorize matrix \(M\) of network feature based on IMC algorithm. The output of the Equation (11) is the matrices \(X\) and \(Y\), where \(M\approx XYW\). Therefore, the time complexity of each iteration of minimizing \(X\) and \(Y\) is \(O(\text{nnz}(M)d+|V|dq+|V|d^{2})\) in Equation (10), where \(\text{nnz}\) (\(M\)) denotes the number of non-zero elements in \(M\), \(d\) denotes the vector length of the network representations. The time complexity of the representations concatenation is \(O(|V|kd)\).  
```
Input: Adjacency matrix \(A\) on network. Maximum transition step \(K\). Harmonic factor \(\lambda\). Dimension of representation vector \(d\). Output: Matrix of the network representation \(R\). Content:
1. Calculate \(A=1/d_{ij}\).
2. Calculate matrix forest index \(S=[S_{ij}]\): \(I=\text{sparse}(\text{GetIdentityMatrix}\ (\text{RowSize}(A)))\). \(D=I\). % The positions of 1 in Identity Matrix are set to the degree of vertex as follows: degree of vertex as follows: \(D(\text{ConvertTologicMatrix}\ (D))=\text{RowSum}(A)\). \(L=D-A\). \(S=\text{inv}(I+L)\). % inv is to computes the inverse. for\(k=1\) to \(K\) (1) Get \(k\)-step transition matrix \(A^{(k)}=[a^{(k)}_{ij}]\): \(1.1\) Compute \(A^{1},A^{2},A^{3},\ldots,A^{K}\). \(1.2\) Get the matrix \(A^{(k)}\): \(A^{(k)}=[a^{(k)}_{ij}]\) \(\text{where }a^{(k)}_{ij}=\begin{cases}0,&a^{k}_{ij}=0\\ 1,&a^{k}_{ij}\geq 1\end{cases}\). (2) Get the network structural feature matrix \(M^{(k)}\): \(M^{(k)}=(A^{(k)}+(A^{(k)})^{2})/2\). (3) Get the weight matrix \(W^{(k)}\): \(W^{(k)}=[w^{(k)}_{ij}]\), \(w^{(k)}_{ij}=s_{ij}\times a^{(k)}_{ij}\). (4) Construct the representation vector \(R^{(k)}=(X^{(k)})^{T}\): \(\underset{W,H}{\min}\|M^{(k)}-((X^{(k)})^{T}Y^{(k)}W^{(k)})\|_{F}^{2}+\frac{ \lambda}{2}(\|X^{(k)}\|_{F}^{2}+\|Y^{(k)}\|_{F}^{2})\). endfor
3. Concatenate all the \(k\)-step representations: \(R=R^{(1)}\oplus R^{(2)}\oplus\cdots\oplus R^{(k)}\).
```  
**Algorithm 1**MVNR Algorithm.
# A Robust Controller for Upper Limb Rehabilitation Exoskeleton
## 3 Controller for Trajectory Tracking  
Model-based controllers are not easy to implement and cannot provide precise control in the human-exoskeleton interaction, because exoskeleton-human interaction factors and sensor noise do not allow the exact dynamic model to be taken into account. Therefore, several systems are controlled independently in the active joints [22; 23].  
Effective rehabilitation exercises require precise, stable and reliable control techniques. The traditional PID controller is commonly used due to its good features, such as its robustness, simplicity and wide applicability [24].
# RF Energy Harvesting
## 3 Commercially Available RF Energy Harvesters
### GuRu Wireless--RF lensing  
The GuRu company was launched in 2017 by Caltech engineers. It provides solutions that allow for wireless charging of devices. In 2021, GuRu and Motorola announced cooperation to use GuRu modules to wirelessly charge Motorola devices [18]. An antenna matrix is used and operates at microwave frequencies of up to 24 GHz. The use of matrices makes waves that are directed exactly to the receiver on the end device; the waves can be transformed into energy to charge the device.  
The frequencies at which the GuRu solution operates are in line with high-band 5G mobile networks. RF lensing solutions use beamforming, which is also used in the fifth generation of mobile networks.