esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem
## 5 Conclusions  
This study investigated an energy-aware dual-resource constrained flexible job shop scheduling problem (EDRCFjSP). The energy consumption model is first built to represent the energy consumption in the workshop. A mathematical model is subsequently established to optimize the total energy consumption. To deal with the problem, a modified migrating birds optimization algorithm (MMBO) is proposed according to the characteristics of the considered problem. Extensive experiments are conducted to demonstrate the effectiveness of the MMBO algorithm. As seen from the comparison results, the proposed improvement strategies can effectively enhance the search ability of the algorithm.  
Figure 4: Convergence curve of the compared algorithms in Instance RM14.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DF** & **Sum of Squares** & **Mean Square** & **F Value** & \(p\)**Value** \\ \hline Factor & 2 & 1555.056 & 777.528 & 115.41936 & 0 \\ Error & 57 & 383.98322 & 6.73655 & & \\ Total & 59 & 1939.03922 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA for ARPD of the compared algorithms in Table 4.  
Figure 3: Convergence curve of the compared algorithms in Instance RM09.  
In addition, the proposed MMBO algorithm outperforms the existing algorithms in comparison with three computational indicators, which shows the effectiveness of the algorithm in solving the problem under study. However, compared with the existing algorithms, MMBO has a longer computational time.  
In a future work, the EDRCFJSP will be further studied to make it more closely resemble realistic production. Some realistic constraints will be considered, such as controllable machining speeds, time of use (TOU) electricity price policy, job deterioration effects and transportation constraints. In addition, some uncertain events should be considered, such as machine breakdown and the arrival of new jobs. Furthermore, some efforts are necessary in the design of more efficient algorithms.  
Methodology, H.L. and H.Z.; writing--original draft, H.L. and T.J.; Writing--review & editing, H.Z.; Funding acquisition, T.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the Shandong Provincial Natural Science Foundation, grant number ZR2016 GP02 and the Project of Shandong Province Higher Educational Science and Technology Program, grant number J17 KA199. The authors declare no conflict of interest.
# Hardware-Based Architecture for DNN Wireless Communication Models
## 7 Conclusions  
MIMO OFDM is one of the most popular technologies in wireless communication today. However, the problem of high PAPR often occurs. In this paper, three DNN model types are proposed to solve the problem of non-linear distortions introduced by the PAs of the transmitters and replace the conventional digital signal processing (DSP) modules at the receivers in 2 \(\times\) 2 MIMO OFDM and 4 \(\times\) 4 MIMO OFDM systems. Proposed model type I uses the DNN model to de-map (de-modulate) the signals at the receiver. Model type II uses the DNN model to learn and filter out the channel noises at the receiver. Model type III uses the DNN model to de-map and detect the signals at the receiver. All three model types attempt to solve the non-linear problem. The robust performances of the proposed receivers are achieved. Both the software and hardware implementation results show that the BER performances of the DNN type I and type II models are higher than those of the conventional DSP model for the MIMO OFDM 4 \(\times\) 4 systems and the BER performances of the DNN type III model are higher than those of the conventional DSP model the MIMO OFDM 2 \(\times\) 2 systems. In addition, the hardware architectures of the three proposed models are successfully designed with the quantization technique to save the hardware resources, run-time, and power consumption. Therefore, our study would confirm that the hardware designs for wireless communication by applying DNN models are practical. In future works, we try to continue improving the performance as well as the simplicity of our systems. Moreover, we also develop the Multi-Users MIMO OFDM systems.  
Investigation, V.D.T.; Methodology, D.K.L. and T.H.T.; Project administration, D.K.L.; Supervision, D.K.L. and T.H.T.; Writing--original draft, V.D.T.; Writing--review and editing, D.K.L. and T.H.T. All authors have read and agreed to the published version of the manuscript.  
This research is funded by University of Information Technology (UIT), Vietnam National University, HoChiMinh City (VNU-HCM).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **The 4 \(\times\) 4 Model Type I** & **The 4 \(\times\) 4 Model Type II** & **The 2 \(\times\) 2 Model Type III** \\ \hline LUT & 50,972 (11.77\%) & 312,123 (72.05\%) & 192,633 (44.47\%) \\ \hline LUT RAM & 15 (0.01\%) & 112 (0.06\%) & 44 (0.03\%) \\ \hline FF & 35,027 (4.04\%) & 186,990 (21.58\%) & 130,400 (15.05\%) \\ \hline BRAM & 11.5 (0.78\%) & 384 (26.12\%) & 127.5 (8.67\%) \\ \hline DSP & 402 (11.17\%) & 1536 (42.67\%) & 3344 (92.89\%) \\ \hline IO & 61 (7.18\%) & 43 (5.06\%) & 99 (11.65\%) \\ \hline BUFG & 1 (3.13\%) & 1 (3.13\%) & 2 (6.26\%) \\ \hline Power (W) & 2.029 & 7.531 & 6.48 \\ \hline Frequency (MHz) & 188.679 & 119.047 & 135.135 \\ \hline Latency of inference phase, (cycles) & 2065 & 16,521 & 4106 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the hardware complexity implementation on FPGA.
# Numerical Insight into the Kelvin-Helmholtz Instability Appearance in Cavitating Flow
## 3 Numerical Procedure
### Turbulence Modeling  
Increased available computational power has made possible advances in computational fluid dynamics, among them Large-Eddy Simulation (LES) and Detached-Eddy Simulation (DES). These relatively novel approaches are in research used to accurately resolve all scales of turbulent fluctuations, while for engineering purposes there is usually no need for such complexity. In above mentioned Reynolds-averaging models, turbulent fluctuations are not resolved, and the turbulence model is used instead. The k-\(\upomega\) and k-\(\epsilon\) turbulence models are the most common models used to simulate mean flow characteristics for turbulent flow conditions. Both are two-equation models that provide a general description of turbulence utilizing two transport equations. The Menter's Shear Stress Transport (SST) k-\(\upomega\) turbulent model [15], combines both k-\(\upomega\) and k-\(\epsilon\) turbulence models such that it merges the robust, accurate, and less y+ sensitive performance of the k-\(\upomega\) model in the near-wall region with the solid freestream operation of the k-\(\epsilon\) model in the far-field. Nowadays the SST k-\(\upomega\) turbulent model is the preferred two-equation turbulent model and the preferred model for a wide range of applications.  
For the modeling of nonstationary cavitation flows, among them, periodic cavitation cloud shedding, two-equation turbulent models in the original form are not the preferred option. Two equation models prevent the reentrant jet from appearing at the rear of the cavitation cloud, all this being caused by too high prediction of turbulent viscosity. For a better non-stationary turbulence modeling performance, [PERSON] et al. [16] proposed a modification of the turbulent model. In the modification, the turbulent viscosity of the mixture is reduced in regions with high void fractions and the equation of turbulent viscosity becomes as follows, with \(k\) representing turbulence kinetic energy and \(\omega\) specific turbulence dissipation rate:  
\[\mu_{t}=f(\rho)\frac{k}{\omega} \tag{3}\]  
The density function is set by Equation (4), here indices \(l\), \(v\), \(m\) represent liquid, vapor, and mixture.  
\[f(\rho)=\rho_{v}+\frac{\left(\rho_{m}-\rho_{v}\right)^{n}}{\left(\rho_{l}- \rho_{v}\right)^{n-1}}\;n\gg 1 \tag{4}\]  
Various values were proposed for the exponent n. [PERSON] et al. [17] proposed value \(n=10\), while the described turbulent model extension was used and validated for various applications, among them in Venturi channel and a hydrofoil [2; 3; 18; 19; 20].
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art
### [PERSON] et al.'s AIM-dDE-Based Proposal  
[PERSON] et al. [55] proposed an Adaptive Invasion-based Model (AIM) to tackle range IR problems. Their AIM-dDE method made use of a distributed DE (dDE) algorithm which considers a migration model. The latter is inspired in the natural phenomenon known as biological invasion. Briefly, at each generation \(t\), the subpopulation \(P_{p}(t)\) of each node \(p\) performs a sequential DE until \(t\) equals \(t_{max}\) generations, and at every \(T\) generations, neighbouring subpopulations exchange individuals. Then, at a given invasion time, \(t=kT\), the pool of individuals in each subpopulation \(P_{p}(t)\) sends to its propagule (i.e., neighbours) according to \(M_{P_{p}(t)}\) and it is carried out by gathering the individuals of \(P_{p}(t)\) which are better than its average fitness. Next, a founding subpopulation formed by both native and exogenous individuals is built in each node by means of adding this huge number of invading individuals to the local subpopulation. In this manner, the authors provided a novel mechanism based on the founding subpopulation as a source of diversity exploitable by their algorithm to properly enhance its capability to search for new niches of solutions. Moreover, their AIM-dDE made use of an adaptive strategy for searching for the optimum control parameters. Specifically, they proposed two updating schemes: RandAvg and ChAvg. In the experimental section, they collected several range image datasets from a well-known source in the field, i.e., the SAMPL at the Ohio State University. They provided parallel versions of the proposed algorithms by making use of the Message Passing Interface (MPI) paradigm. Their proposed range IR method was compared against several algorithms in the SoTA. The reported statistical results suggested that the AIM-dDE-ChAvg variant turn out to be the best one in all the three considered tests.
# Shear Strength Improvement of Clay Soil Stabilized by Coffee Husk Ash
## 2 Materials and Methods  
### Clay Soil  
The clay soil used for this investigation was collected from Paya Kameng, Aceh Besar District, Aceh Province-Indonesia as in Figure 1. The sample collecting pit was located at coordinates 05\({}^{\circ}\)32\({}^{\prime}\)37.0320\({}^{\prime\prime}\) N, 95\({}^{\circ}\)30\({}^{\prime}\)02.6280\({}^{\prime\prime}\) E. It is located approximately 30 km east of Banda Aceh, the capital city of Aceh Province. The chemical composition of clay soil was measured by utilizing gravimetric, titrimetric, and atomic absorption spectroscopy (AAS) methods, as shown in Table 1. Silica is a major component in the chemical composition of the soil.  
The clay soil was initially stored outside the laboratory to dry naturally and then placed inside an oven for 24 h to completely dry. To prevent temperature influences on the soil characteristics [41], the-oven drying temperature was maintained at 100-110 \({}^{\circ}\)C. Following that, the dried clay was filtered through a 4.75 mm sieve to obtain a homogeneous sample. Figure 2 illustrates the grain size distribution of the clay. The ASTM D422-63 standard [42] was used to determine the grain size distribution of the clay. The grain size distribution, which included sieve analysis and hydrometer measurements, revealed that the sample has a consistent size distribution. Soil is composed of particles of clay (56.9%), silt (32.4%), and sand (10.7%). Table 2 presents the physical parameter of untreated clay. The liquid limit (LL), plastic limit (PL), and plasticity index (PI) of natural clay are 70.90, 27.77, and 43.13%, respectively. According to the Unified Soil Classification System (USCS), the clay soil is classified as a high plastic clay (CH) and A-7-6 according to AASHTO, which may be considered an expansive clay soil.  
Figure 1: Soil sample location map of Aceh Province—Indonesia.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **MnO** & **P\({}_{2}\)O\({}_{5}\)** & **LOI** \\ \hline
42.99 & 5.63 & 2.67 & 3.01 & 1.69 & 0.05 & 0.10 & 0.03 & 0.28 & 1.50 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition (%) of the dry untreated clay.
# Compliant Human-Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator
## 3 Experiments and Results  
### Experimental Setup  
The tests are performed using a HIWIN RA605 6-DOF robot arm with a Robotiq 2F-85 gripper attached to the end effector. An ATI Gamma F/T sensor with analog output sampled at 1 kHz was used to measure the external force applied to the end effector. See Figure 4.  
In this section, the two experiments conducted to validate the proposed approach are detailed. They include a linear motion test and a 3D path test. For these tests, the robot moves with constant orientation. Furthermore, the robot tool actual position is obtained from the robot joint encoders. The sliding mode control parameters used are summarized in Table 1. Regarding the admittance law parameters, the limits for damping \(c\) were chosen as \(c_{min}=300\) N\(\cdot\)s\(\cdot\)m\({}^{-1}\) and \(c_{max}=1200\) N\(\cdot\)m\(\cdot\)s\({}^{-1}\). From these values, the maximum speed is 266 mm\(\cdot\)s\({}^{-1}\), which allows us to comply with the relative speed limits defined in Annex A of ISO/TS 15066:2016 [35] for applications where contact between body areas and the robot system is possible. In our case, these body areas include the lower arm and hand/finger, where the speeds are restricted to 1300 and 650 mm\(\cdot\)s\({}^{-1}\) for transient and quasi-static contact, respectively, considering an estimated robot mass of 20 kg. In addition,  
Figure 4: Experimental setup used for the tests showing the robot equipped with the force/torque (F/T) sensor and a gripper on the end-effector.  
the user has an emergency button at hand throughout the experiments conducted. The time constant of \(G(s)\) is the ratio \(\frac{m}{c}=0.0167\) s. Finally, \(f_{th}=1.2\) N and \(f_{v}=40\) N.  
Illustrated in Figure 5 are all the coordinate frames and their corresponding relations with each other in the environment used for the experiments. In the linear motion test, we define a horizontal line in front of the robot to demonstrate the behavior as external forces are applied to the end effector from different directions. The line is defined from points \(A=[0.34,\ 0.45,\ 0.15]^{T}\) to \(B=[0.0,\ 0.6,\ 0.15]^{T}\) in robot base frame, see Figure 5 and Figure 6a, and parametrized with respect to \(s\) as in (19), where \(\overrightarrow{AB}=B-A\) is the \(3\times 1\) direction vector from \(A\) to \(B\) and \(s\in[0,1]\).  
For the second test, the 3D path we chose to demonstrate the path-tracking capability of our system is a helix, defined in (20)-(22) and parametrized with respect to \(s\in[0,1]\), where \(s=0\) marks the initial point of the path and \(s=1\) marks the end point of the path. \(R=0.1\) m is the helix radius centered at \(o=[0.24,\ 0.45,\ 0.15]^{T}\), \(n=2\) is the number of rounds for the helix, \(b_{o}=0.05\) m is the increase in \(z\) per round.  
\[x(s)=o_{x}+R\cos(2\pi ns) \tag{20}\]  
\[y(s)=o_{y}+R\sin(2\pi ns) \tag{21}\]  
\[z(s)=o_{z}+nb_{o}s \tag{22}\]
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 2 Literature Review
Five channels are used, and each is assigned an RD code; each channel carries 5 Gbps of information data that are transmitted on two polarization states. Weather conditions (clear sky and different fog conditions) are considered. An overall transmission capacity of 50 Gbps is reported. In [25], the mode division multiplexing (MDM) technique is used in an FSO transmission communication system. One channel is transmitted on two different modes. Weather conditions (clear sky and different fog conditions) are considered. A successful transmission of 10 Gbps is reported. In [26], the WDM technique is used in an FSO transmission system based on using binary shift keying (BSK) modulation. Four channels are used: each carries 1.56 Gbps of information data. Clear sky, different rain conditions, and different fog conditions are considered and a 6.24 Gbps overall transmission capacity is achieved.
## 3 Results
### Synthesis of Results  
Data extraction from the 20 articles evaluated allowed us to list a total of 350 FAMM and 136 i-FAMM flaps performed. Overall reported necrosis rate for FAMM flaps was 9.7%, 1.4% (5 cases) as total and 8.3% (29 cases) as partial necrosis. Overall reported necrosis rate for iFAMM flaps was 2.2%, 1.5% (2 cases) as total and 0.7% (1 case) as partial necrosis. Results are synthesized in Table 1; Table 2.  
Figure 1: Flow diagram of literature search and study selection.  
Figure 2: ROBINS-I Traffic Light Plot bias assessment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **FAMM** & **i-FAMM** & **FAMM Viability** & **i-FAMM Viability** \\ \hline [PERSON] et al. (1992) & 18 & & 1 flap loss & \\ [PERSON] et al. (1999) & & 12 & 1 marginal necrosis & None \\ [PERSON] et al. (2005) & 22 & & 2 partial necrosis & \\ [PERSON] et al. (2005) & 2 & 15 & 1 total necrosis & \\ [PERSON] et al. (2007) & 16 & & 2 flap loss & \\ [PERSON] et al. (2008) & 61 & & 15 partial necrosis & \\ [PERSON] et al. (2009) & 27 & 9 & 1 total necrosis & \\ [PERSON] et al. (2012) & 20 & 30 & 1 total necrosis & \\ [PERSON] et al. (2013) & 11 & & 2 marginal necrosis & \\ [PERSON] et al. (2015) & 12 & & 1 partial necrosis & \\ [PERSON] et al. (2015) & 36 & 14 & None & None \\ [PERSON] et al. (2016) & 17 with personal & & 1 distal necrosis & \\ [PERSON] et al. (2016) & 16 & & 1 marginal necrosis & \\ [PERSON] et al. (2017) & 3 & 3 & 2 partial necrosis & 1 flap loss \\ [PERSON] et al. (2017) & 17 & & & None \\ [PERSON] et al. (2018) & 55 (29 traditional vs. & & 1 distal necrosis in & \\  & 26 modified) & & traditional group & \\ [PERSON] et al. (2019) & 13 & & None & \\ [PERSON] et al. (2020) & 16 & & None & None \\ [PERSON] et al. (2020) & 21 & & None & \\ [PERSON] et al. (2020) & & 20 & & None \\ Total & 350 & 136 & 5 total necrosis & 2 total necrosis \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flap reconstruction details and additional reported complications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Anatomical Region to Reconstruct** & \
\begin{tabular}{c} **Defect Average Size** \\ **(mm)** \\ \end{tabular} & **Reported Complications** \\ \hline [PERSON] et al. (1992) &
\begin{tabular}{c} Soft/hard palate, floor of mouth, \\ upper lip, lower lip, nasal mucosa \\ \end{tabular} & Not reported & None \\ [PERSON] et al. (1999) & Palate, alveolus, nasal septum, orbit & Not reported & None \\ [PERSON] et al. (2005) & Palate & \(12\times 12-40\times 30\) & None \\ [PERSON] et al. (2005) & Alveolus, lip, floor of mouth, palate & \(80\times 30\) (MAX) & Cheek edema and tightness \\ [PERSON] et al. (2007) & Hard palate & \(32\times 16\) (MAX) & Palate fistula \\ \hline \hline \end{tabular}
\end{table}
Table 1: Synthesis of results.  
Figure 3: ROBINS-I Weighted Summary Plot bias assessment.
## 2 Methods
### Principle of the Proposed Method  
As demonstrated in Section 2, a comprehensive investigation was carried out to analyze the spatial distribution characteristics of the sphericity error. Given the non-uniqueness of the ideal solution \(\mathbf{p}_{0}^{*}\) and the potential existence of local optima, the high-precision sphericity error evaluation directly from the global solution space presents a considerable challenge. However, through the adept partitioning of the global solution space into multiple local solutions, it becomes possible to approach the evaluation of the sphericity error as a unimodal function optimization problem within each local space. By employing computational geometry techniques, it is possible to attain high-precision solutions within the confines of these local solution spaces.  
Subsequently, the integration of the computational geometry method employed with the DE algorithm is performed. We establish the positions of local solution spaces by utilizing the individuals that are present in the population of differential evolution. During each iteration, the sizes of the local solution spaces are adaptively adjusted based on the distances between the individuals in the population and the global optimum. Additionally, incorporating Levy flights and simulated annealing techniques allows for the perturbation of the positions of individuals within the population, thereby augmenting the diversity of local solution spaces. After attaining high-precision solutions within the designated local spaces, the DE algorithm individuals are relocated to their respective optimal positions within these local solution spaces. With each successive iteration of the DE algorithm, the population progressively approaches convergence towards the global optimum. As a result, an optimal solution for minimizing the sphericity error is eventually obtained. The overall algorithmic process is depicted in Figure 3. We denote the computational geometric method integrated with the DE algorithm as \"Adaptive Local Enhancement\". The detailed steps of this approach are outlined in Section 3.2.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 3 IoT Architectures for EW Systems
### Reference Architecture
#### 3.1.1 Perception Layer  
The perception layer has the task to sense and collect data from the environment, usually through sensors. Wireless Sensor Networks are widely used in disaster monitoring scenarios: they consist of nodes equipped with sensing units and communication units that can harvest data from the environment and then forward it towards a gateway node that interfaces and communicates with higher layers. WSNs offer benefits such as scalability, dynamic reconfiguration, reliability, small size, low cost, and low energy consumption [24]. Some aspects of a WSN development are particularly important in disaster monitoring or disaster EW scenarios, such as battery life, coverage, and fault tolerance, and they will be explained in detail in Section 3.2.  
The choice of the right sensing unit can be essential in providing a timely and accurate response, and different parameters can contribute differently to a particular environmental hazard. Positioning sensors in certain zones or terrains can be particularly difficult, and while some applications monitor localized events (such as landslides), others might require deployments over large areas (such as river basins in flood EW, or the large geographical regions that can be affected by earthquakes), and this will enhance the cost of the solution and require ad hoc strategies to efficiently cover the entire area to be monitored, for example differentiating between nodes with long and short range coverage capabilities [25] or, for example, with a smart and optimized distribution of the sensors depending on the disaster probability of occurrence [26].