esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Statistical Attenuation  
As we mentioned before, the Beer-Lambert law overestimates the link attenuation because it abandons all the scattered photons that have possibilities to return to the aperture, especially in the coastal and turbid harbor waters. Thus, we investigated the deviation of the BL path loss by exploiting the numerical simulation. After the simulation, the link attenuation is determined by \(PL(dB)=\lg\left(\sum\limits_{i=1}^{N_{r}}W_{i}/N_{T}W_{0}\right)\), where \(N_{r}\) represents the number of received photons. Figure 8 shows the comparison of the path loss obtained by BL computation (dashed line) and PTA simulation (solid line). We can verify that the BL results are much larger and there is a growing gap with the increase of water turbidity and distance. The more turbid water and longer communication range implies more interactions with particles and scattering times such that a bigger difference is observed.  
Figure 9 depicts the BL-based three-dimensional spatial distribution of optical power on a 3 \(\times\) 3 m\({}^{2}\) receiver plane after 10 m transmission under different water conditions. It may seem a little strange that the received power is so much lower in turbid harbor water than that in other water types, but the fact that its corresponding attenuation coefficient is 2.1 m\({}^{-1}\), whereas the value is 0.398 m\({}^{-1}\) in coastal ocean needs to be considered. Therefore, according to \(PL(dB/m)=10\lg(e^{-c})\approx-4.343c\), a big difference of path loss in a 10 m channel is reasonable and consistent with the data in Figure 10.  
Figure 10 illustrates the statistical results of path loss versus communication distance derived from the proposed PTA numerical simulation with different Rx apertures in coastal and turbid harbor waters. The total attenuation consists of the geometry loss and seawater optical loss. As the aperture rises from 1 cm to 20 cm, a decrease of the link attenuation can be witnessed. This is because the larger Rx area means a greater probability to capture scattered photons in such poor quality water medium.  
Figure 7: Spatial illumination distribution at the received square plane for LED-based diffusive link in (**a**) 10 m coastal water, (**b**) 5 m harbor water, (**c**) 6 m harbor water, and Laser-based collimated link in (**d**) 35 m coastal water, (**e**) 5 m harbor water, (**f**) 6 m harbor water.  
Figure 8: Path loss derived by the BL-analytical method and PTA simulation.  
Figure 10: Path loss for different ranges with different apertures (**a**) coastal water, (**b**) turbid harbor water.  
Figure 9: Spatial distribution of optical power in the received plane.
# Application of Canola Oil Biodiesel/Diesel Blends in a Common Rail Diesel Engine
## 3 Results and Discussion
### Emission Characteristics  
The effects of ULSD, BD20, and PCO on CO, NOx, and PM emissions according to brake mean effective pressures under an engine speed of 1500 rpm are shown in Figure 8. As shown in Figure 8a, the CO emission decreased considerably as BMEP increased. In particular, the CO emission is almost zero above 0.39 MPa, which is the same as for the combustion pressure where the fuel injection pressure and turbulence intensity in the combustion chamber increased as BMEP increased. This improves the fuel atomization and air/fuel mixture, which promotes combustion. In addition, the CO emission was lower for PCO than for ULSD because the canola oil biodiesel is an oxygenated fuel that can combat more fully.  
Figure 8b shows the effects of ULSD, BD20, and PCO on NOx emission according to brake mean effective pressures under an engine speed of 1500 rpm. As shown in Figure 8b, the NOx emission changed slightly as the brake mean effective pressures increased. As the value of BMEP increased from 0.26 MPa to 0.52 MPa, the NOx emission increased only slightly by 6% and 14% at 0.39 MPa and 0.52 MPa compared with 0.26 MPa. This can be explained in that the temperature increased as BMEP increased from 0.26 MPa to 0.52 MPa. In addition, changing from ULSD to PCO fuel, the NOx emission also increased because biodiesel is an oxygenated fuel with more intense combustion and increased temperature in the combustion chamber [41; 46]. So, the NOx emission can be reduced by decreasing the temperature of the combustion chamber. The EGR was an effective way to reduce NOx emission that was reported in our previous studies [16; 41].  
Figure 8c shows the effects of ULSD, BD20, and PCO fuels on PM emission according to brake mean effective pressures under an engine speed of 1500 rpm. As shown in the Figure 8c, the PM  
Figure 7: Effect of test fuels on COVIMEP according to BMEP.  
emission decreased considerably as BMEP increased, especially at 0.39 MPa and 0.52 MPa of BMEP, respectively, where it decreased 59% and 65% of their values at 0.13 MPa of BMEP. In addition, the PM emission also decreased when changing from ULSD to PCO fuel. This phenomenon is similar to that for CO emission: increasing BMEP can change the combustion environment to promote combustion and burn the fuel more fully.
## 4 Results and Discussion
### Uniqueness Test  
The zero-watermarking algorithm does not directly embed watermark information into the original data but relies on the feature information in the original maps to construct zero-watermarks, as such, the generated zero-watermarks must be unique to ensure the uniqueness of copyright verification. To test the uniqueness of zero-watermarks under different algorithms and in different datasets, the zero-watermarks generated by watermarking schemes in ZC and SZ are extracted and used to verify each other. As shown in Table 6, the extracted watermark information is compared and the obtained _NC_ values are between 0.37 and 0.41 with no valid watermark information identified, meaning that zero-watermarks generated using different datasets and algorithms are identical.
# A Dynamic Parameter Tuning Strategy for Decomposition-Based Multi-Objective Evolutionary Algorithms
## 1 Introduction
The PBI decomposition approach provides high search efficiency and optimization quality for interactability problems and nonlinear and nonconvex optimization problems [22]. The PBI decomposition approach has higher feasibility and validity for initial population individuals and better reflects the characteristics of the problem than the other two decompositions in MOEA/D [23]. In addition, the PBI decomposition approach allows for the selection of appropriate penalty parameters according to the needs of the specific problem [24]. This can result in a more effective adaptation of the generated population to the objective function and a quicker discovery of the globally optimal solution during the optimization process. The size of the penalty parameter in the PBI algorithm significantly affects its convergence and diversity. In 2019, when solving hybrid energy system optimization problems [25], [PERSON], [PERSON], and [PERSON] first set the penalty parameter to 0.5, when the diversity of the algorithm was low, and then changed the value of the penalty parameter to 3.0 when the diversity of the algorithm was significantly increased. In 2015, in response to the multi-target backpack problem [26], [PERSON], [PERSON], and [PERSON] found that the convergence rate for a penalty parameter value of 0.1 is significantly higher than that for a penalty parameter value of 5.0. The algorithm's diversity increases with larger penalty parameter values, while smaller penalty parameter values increase its convergence [27; 28; 29]. Therefore, the value of the penalty parameter should be different depending on the problem; in contrast, in the traditional MOEA/D-PBI decomposition approach, the penalty parameter is usually designed as a fixed value. Fixed penalty parameters do not capture the characteristics of the problem well when dealing with problems of high complexity. Also, when there is a large conflict between the objective function value and the constraints, the two need to be balanced by adjusting the penalty parameter, and a fixed penalty parameter cannot flexibly cope with this trade-off problem. To tackle the aforementioned situation, this paper suggests a novel penalty approach, namely the boundary-crossing strategy with dynamically adjusted penalization parameters (MOEA/D-DPA). MOEA/D-DPA adaptively adjusts the penalty parameter values according to the state of the uniform distribution of the solutions around the weight vectors for the iteration period currently in progress. This paper is structured as follows: Section 2 explains the MOEA/D-PBI algorithm and the significance of the penalty factor in it. Section 3 proposes an improved penalization strategy for the PBI algorithm. Section 4 presents an experimental study of the proposed punishment strategy. Section 5 summarizes the research presented in this paper.
## 2 Theoretical Background
### FIR Filter  
The FIR filter is one of the most commonly used digital filters in digital signal processing systems. In general, the output signal \(y[n]\) of a \(K\)-tap FIR filter can be calculated in the time domain as (1), where \(x[n]\) is the input discrete-time signal, \(c_{i}\) is the filter coefficient, and \(K\) is the tap of the FIR filter.  
\[y[n]=\sum_{i=1}^{K}x[n-i]\cdot c_{i} \tag{1}\]  
In the practical hardware implementation, the filter coefficient \(c_{i}\) is usually normalized as (2) to keep the dynamic range of output signals the same with input signals and avoid the calculation overflow error. The filter coefficients are all normalized in the following.  
\[c_{i}^{\mathrm{norm}}=c_{i}/\sum_{j=1}^{K}|c_{j}| \tag{2}\]  
Figure 1: Conventional hardware architecture of FIR filter [2].  
It can be observed that \(K\) multipliers and \(K-1\) adders are required for a \(K\)-tap FIR filter in the conventional hardware implementation schemes, illustrated as Figure 1. It would be a huge complexity for practical DSP systems with high-order FIR filters.
## 3 SD-GPSR Routing Method
### Data Layer Forwarding  
Finding an optimal path during the routing process is crucial for improving network performance. We combine flow table matching with greedy computation to achieve data forwarding in the routing process. When a node forwards data, it first attempts to match the flow table for data forwarding. If the flow table matching fails, the node employs a greedy forwarding strategy. If greedy forwarding also fails, the node needs to report to the controller, requesting the controller to calculate the route path and install a new flow table. The node then re-matches the flow table for data forwarding. Specifically, as shown in Figure 2, the entire routing process involves the following three stages:  
(1) In the first stage, upon receiving a data packet, the node prioritizes flow table matching by default (considering that flow tables have better performance in terms offorwarding rate, security, QoS, and load balancing). If the flow table matching is successful, the node executes the instructions in the action set to complete data forwarding, and the data packet processing process ends.  
(2) In the second stage, if flow table matching fails, the node queries the Table-miss entry of the flow table. It then initiates the GPSR-DA algorithm according to the instructions. The node relies on neighbor information and destination node position information extracted from the data packet header to calculate the next-hop forwarding node using the greedy forwarding strategy. If a better destination node is found than itself, the node forwards the data packet, and the data packet processing process ends.  
(3) In the third stage, when the node encounters a routing hole while forwarding the data packet using the greedy forwarding strategy, it reports the data packet to the controller through a Packet-in message. The controller utilizes the globally maintained position information and neighbor relationships to calculate a route path using a centralized algorithm. It then sends Flow-mod messages to the reporting node to install the flow table and resends the data packet to the reporting node using a Packet-out message. Upon receiving the Flow-mod and Packet-out messages, the node first parses the Flow-mod message to obtain the route path for node forwarding and updates the local flow table information. It then parses the Packet-out message to retrieve the data packet and adds the route path to the data packet header. Finally, the node re-matches the flow table to complete data forwarding. We will elaborate on the GPSR-DA algorithm in Section 4.
# Using Markov Random Field and Analytic Hierarchy Process to Account for Interdependent Criteria
## 2 Literature Review
### Mrf  
MRFs are a class of probabilistic graphical models that capture statistical relationships between random variables through an undirected graph [40]. MRFs have gained attention in various fields due to their ability to model complex dependencies and provide an elegant mathematical framework for modeling and reasoning under uncertainty. MRFs have been used in computer vision, natural language processing, and social network analysis, among other domains [41; 42; 43]. Several extensions and variations of MRFs have been proposed, such as CRFs by [44], Gaussian MRFs by [45], and factor graphs by [46]. Despite the success of MRFs in modeling complex dependencies, challenges remain in scalability, handling large-scale and high-dimensional problems, and developing more robust and flexible MRF models [44; 47].  
The mathematical model of a discrete MRF is defined by an undirected graph \(G=(V,\,E)\), where \(V\) is a set of nodes representing random variables, and \(E\) is a set of edges denoting the pairwise interactions between the variables. A discrete MRF is associated with a set of potential functions \(\varphi_{j}(w_{C_{j}})\) that capture the strength of the relationship between the variables [40]. The joint probability distribution of the random variables in an MRF, defined by the Gibbs distribution, is given by:  
\[P(X=x)=\frac{1}{\zeta}exp(-\sum_{j=1}^{J}\varphi_{j}(w_{C_{j}})), \tag{6}\]where \(C_{j}\) denotes the set of maximal cliques, \(X\) represents the set of random variables, \(x\) is a specific assignment of values to these variables, \(w_{C_{j}}\) denotes the values of the variables in clique \(C_{j}\), and \(Z\) is the normalization constant known as the partition function.
# Bias Effects on g- and s-Factors in Westcott Convention
## 3 Results and Discussions
### g-Factors for \({}^{241}\)Am and \({}^{151}\)Eu for Sample Temperatures of 300 and 1000 K  
The g-factor for \({}^{241}\)Am is calculated by inserting \(\sigma(E,\,T_{s})\) into Equation (6) as a function of neutron temperature \(T_{n}\), for two sample temperatures, i.e., \(T_{s}\) = 300 K (black) and 1000 K (red), and plotted in Figure 4a. Two curves are almost degenerated. The g-factor is almost equal to the unity in the neutron energy region below 400 K and gradually increases as the neutron temperature rises, since the first neutron resonance at 0.31 eV starts to contribute to this increase. Figure 4b is an expanded version of Figure 4a, showing the neutron energy region between 250 and 450 K. In this neutron temperature range, the deviation of the g-factor from unity is from 1% to 9%. If neutron temperature is determined with an uncertainty of 25 K, the g-factor can be estimated with an uncertainty of about 1% using the data shown in this figure.  
Figure 4c shows the ratio of g-factor at \(T_{s}\) = 1000 K to that at 300 K for \({}^{241}\)Am. Figure 4d is the expanded version of Figure 4c. The maximum deviation from the unity is about 0.5% at a neutron temperature near 600 K. It is only 0.3% for a limited region from 250 K to 450 K, as shown in Figure 4d. Therefore, for most applications not requiring accuracy below 1%, the sample temperature effect on the g-factor can be neglected. It should be remembered that this effect needs to be considered for special applications such as neutron standard cross section studies aiming at accuracy in the order of 0.1%.  
Figure 5 shows the g-factor calculated for \({}^{151}\)Eu. The change of the g-factor as a function of neutron temperature for \({}^{151}\)Eu is more moderate compared to the case of \({}^{241}\)Am, as shown in Figure 5a. This tendency can be explained by the fact that the first prominent resonance energy of \({}^{151}\)Eu is about 1.5 times higher than that of \({}^{241}\)Am. By comparing the g-factors of \({}^{241}\)Am and \({}^{151}\)Eu in a limited region from 250 K to 450 K, it is noticed that the difference of the slopes is significant, and the ratio of these g-factors is sensitive to neutron temperature. Although the effectiveness using a Zr-Au-Lu alloy is known to determine neutron temperature [12], the combinational use of these isotopes would provide additional and independent information on neutron temperature. The sample temperature effect for \({}^{151}\)Eu shown in Figure 5c,d is in the same order of that for \({}^{241}\)Am, as discussed above.  
Figure 4: (a) Westcott g-factor for \({}^{241}\)Am as a function of neutron temperature \(T_{n}\), for two sample temperatures, \(T_{\rm s}\) = 300 K (black) and 1000 K (red); (b) Expanded figure of (a); (c) Ratio of Westcott g-factor for \(T_{\rm s}\) = 1000 K to that for 300 K; (d) Expanded figure of (c).  
Figure 5: _Cont._
## 3 Results and Discussion
### Cluster Analysis and Raw Material Classification
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Factor**} & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** \\  & **(_n = 1_)** & **(_n = 4_)** & **(_n = 8_)** & **(_n = 2_)** \\ \hline Z-score (DPPH radical IC\({}_{50}\)) & 1.886 & 0.924 & \(-\)0.500 & \(-\)0.793 \\ Z-score (ABTS radical IC\({}_{50}\)) & 3.343 & 0.301 & \(-\)0.442 & \(-\)0.503 \\ Z-score (OH IC\({}_{50}\)) & 1.135 & 1.210 & \(-\)0.682 & \(-\)0.259 \\ Z-score (reducing power) & \(-\)1.396 & 0.970 & \(-\)0.583 & 1.091 \\ \multirow{2}{*}{Assigned treatment (1)} & \multirow{2}{*}{H} & \multirow{2}{*}{K, S, V, and L} & A-H, T-H, T-K, A-S, & A-K and T-L \\  & & & T-S, A-V, T-V, and A-L & A-K and T-L \\ \hline \hline \end{tabular}  \({}^{(1)}\) H, heart; K, kidney; S, spleen; V, liver; L, lung; A-, Alcalase-hydrolyzed; T-, trypsin-hydrolyzed. Cluster 2: moderate radical scavenging activities but high reducing power (porcine kidney, spleen, liver, and lung);  
Cluster 3: balanced radical scavenging activities but low reducing power (Alcalase- and trypsin-hydrolyzed porcine heart, trypsin-hydrolyzed kidney, Alcalase- and trypsin-hydrolyzed spleen, Alcalase- and trypsin-hydrolyzed liver, and Alcalase-hydrolyzed lung);  
Cluster 4: strong DPPH and ABTS radical scavenging activities and high reducing power (Alcalase-hydrolyzed kidney and trypsin-hydrolyzed lung). As demonstrated, the distinct antioxidant properties of porcine organs and their hydrolyzates are important considerations when selecting initial raw materials for future  
Figure 7: Dendrogram on antioxidant property-related variables (DPPH, ABTS, and OH radical IC\({}_{50}\) values and reducing power) of Alcalase- and trypsin-mediated protein hydrolyzates from pork by-products. H, heart; K, kidney; S, spleen; V, liver; L, lung; A-, Alcalase-mediated hydrolyzate; T-, trypsin-mediated hydrolyzate. industrial applications. Specifically, since all hydrolyzates exhibited improved radical scavenging activities compared to the intact raw materials, it may be possible to produce hydrolyzates with antioxidant properties without separating specific porcine organs from bulk raw material supplies. However, when producing premium hydrolyzates with superior antioxidant properties, it may be preferable to separately process porcine kidneys and lungs with Alcalase and trypsin, respectively, as supported by Cluster 4 in Table 2. Thus, this study suggests that the selection of porcine organs as raw materials and the application of specific enzymes should be tailored to ensure that the desired antioxidant properties are achieved.
## 3 A Two-Phase Iterative Solution Methodology  
Figure 1 illustrates the above-mentioned approach: the first phase solves the FJSP + T, a FJSP augmented with only intermachine transportation times alongside unlimited transporters [25] for good machine-operation assignments that are next used to generate and solve its corresponding JSPT instance. The incorporation of transportation constricts the solution space and guides the solution process to generate assignments that result in FJSP + T instances that closely approximate its respective derived JSPT instances.  
Conventional decomposition methods add infeasibility cuts based on the second subproblem. In this work, machine-operation assignments generated by the FJSP + T are eliminated by adding cover cuts. It is _not_ necessary to solve the FJSP + T to optimality, as it is _only_ used for generating good assignments.  
The JSPT problem is always feasible, as it is constructed from the FJSP + T solution. Thus, to expedite solving, instead of solving it for each iteration, an _objective cut_ constraint bounded by the current best makespan decremented by one is added. This renders JSPTs that do not generate better solutions _infeasible_, and thus they are left unsolved. Each JSPT instance generates a corresponding solution instance of the original FJSPT. After a predefined number of iterations, the best solution across all JSPT instances is returned as the overall solution.  
Algorithm 1 shows the pseudo-code of the two-phase solution methodology.  
Figure 1: High level schematic of two-phase iterative methodology.  
```
Input: solution set, \(sol\leftarrow\varnothing\), Input: iteration counter, \(iter\gets 1,1\leq iter\leq iter_{max}\) Input: Objective value of best solution, \(obj_{best}\),\(\leftarrow\infty,sol_{best}\in sol\) Output: Best solution, \(sol_{best},sol_{best}\in sol\) Output: objective value of best solution, \(obj_{best}\) Output: Current solution, \(sol_{iter},sol_{iter}\in sol\), \(1\leq iter\leq iter_{max}\) Output: objective value of current solution, \(obj_{iter}^{j},1\leq iter\leq iter_{max}\) repeat  Solve the FJSP + T and obtain \(U_{ik}\) for each \(i\in I_{j},k\in M_{i}:U_{ik}=1\) Add (cover) cut \(\sum_{(i,k)}U_{ik}\leq N-1\) to FJSP + T Construct basic JSPT network Enhance and cluster basic JSPT network Formulate current JSPT problem If \(iter\geq 2\): Add objective cut, \(C_{max}{}^{\prime}\leq obj_{best}-1\) to current JSPT problem Solve the JSPT problem If problem is infeasible: \(sol_{iter}\leftarrow\varnothing\) else if\(obj_{iter}<obj_{best}\): \(sol_{best}\gets sol_{iter}\) \(sol_{best}\gets sol\cup sol_{iter}\) \(iter\gets iter+1\) until\(iter=iter_{max}\) return\(sol_{best},obj_{best}\)
```  
**Algorithm 1** General scheme of two-phase iterative approach