esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems
## 5 Experimental Results
### Comparison of CSA Algorithms Incorporated with Variants of the NEH  
To validate the effectiveness of each initialization approach, the CSA was combined with several variants of the NEH-based algorithm and their respective performance levels were compared. A new  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **Parameter** & **Value** \\ \hline
**CSA** & & \\  & Number of rows & 20 \\  & Number of iterations \(t_{max}\) & 1000 \\  & Number of independent runs \(R\) & 20 \\  & Flight length \(fL\) & 10 \\  & Awareness probability \(AP\) & 0.25 \\ \hline SA & & \\  & Initial temperature \(T\) & 100 \\  & cooling coefficient \(\beta\) & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings of the proposed algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Problem** & **Jobs** & **Machines** \\ \hline ta01 & 20 & 5 \\ ta11 & 20 & 10 \\ ta21 & 20 & 20 \\ ta31 & 50 & 5 \\ ta41 & 50 & 10 \\ ta51 & 50 & 20 \\ ta61 & 100 & 5 \\ ta71 & 100 & 10 \\ ta81 & 100 & 20 \\ ta91 & 200 & 10 \\ ta101 & 200 & 20 \\ ta111 & 500 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Taillard [50] benchmarks for the permutation flow shop scheduling problem (PFSP).  
priority rule and a new tie-breaking rule based on the NEH [16] algorithm were used; elements included NEHD [17], NEHKK1 [18], NEHKK2 [19], CL [20], and NEHLIJP1 [22]. The BRPD and ARPD values for the simulation of the CSA coupled with six initialization NEH algorithms are summarized in Table 5 and the values in bold font represent the algorithms that achieve superior performance.  
The best BRPD and ARPD values of the tested NEH-based algorithms were 1.88 and 2.18, respectively. From the aforementioned simulation results, it can be concluded that the CSA combined with the NEHLIJP1 heuristic is more effective than other NEH-based heuristic algorithms. In addition, the original NEH algorithm had better ARPD values than the NEHD and NEHKK1 variants. Therefore, we incorporated NEHLIJP1 into the CSA algorithm as the initialization strategy. To present the previous experimental results more clearly, the data from Table 5 are depicted visually in Figure 3. The \(x\)-axis shows the different sizes of the benchmarks, and the \(y\)-axis shows the ARPD and BRPD values for each test function.
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 5 Experiments
### Results
Again, the time between the detection of the conflict and the communication of the optimal solution to the USM was less than 1 s. Figure 18 shows the initial flight plans for the UAS and their reserved Operational Volumes, and the situation right after the jamming attack. Despite not having conflicts initially, the creation of a new geofence provoked an eventual conflict, which was resolved with an alternative flight plan for UAS\({}_{2}\) (see Figure 19). Figure 16: A perspective view of the conflict resolution in the use case 1. A new flight plan for UAS\({}_{1}\) (with a final _go down_ maneuver) was selected to keep the safety distance with UAS\({}_{2}\). The other alternative maneuvers generated by the TD module (_go left_ and _go right_) are also shown. Figure 17: Timeline of the use case 2, where a jamming attack and a geofence conflict are resolved. Single arrows indicate isolated interactions between modules, whereas double arrows indicate periodic communication. Finally, it is important to recall that the experiments were carried out with a setup where the UTM system ran at a remote distance of the UAS stations. Despite that, the communication delays and response times by the UTM system were adequate for a real-time resolution of the unexpected conflicts. In particular, we measured a reception of the UAS telemetry data at the USM of an average rate of 1 Hz with a maximum delay of 40 ms.
## 4 Computational Methods
### Criticality Analysis  
The traditional depletion analysis in the lattice (2D) or Monte Carlo (2D-3D) codes cannot reflect real operational parameters of the fuel assembly in the reactor core since they simulate the fuel assembly in radially reflective boundary conditions. In addition, data like water density and fuel temperature are based on an initial estimate since neither thermodynamic nor thermal-hydraulic models are incorporated into the lattice codes. To overcome these limitations, the authors performed depletion analysis of the BWR fuel assembly using axial coolant density distributions and axial burnup profiles obtained with the nodal analysis of the reactor core. The BWR spent fuel composition resulting from these calculations were used in further criticality analysis of the GBC-68 storage cask.  
A series of Python scripts were developed to transfer data from the nodal calculations into the KENO Monte Carlo solver used for the cask modelling. The flowchart of the modelling sequence is presented in Figure 4. After each simulation of the operational cycle in DYN3D, the coolant density and burnup profiles of the discharged FAs are extracted from the code's output file and saved to the text file by the Dyn3 dPars script. When the required number of cycles N has been simulated, the saved data is used by the PolOPinp script to create input files for the depletion and decay calculations in Polaris/ORIGEN sequence. At this point, Polaris is used again to produce an isotopic composition for the BWR spent fuel for further criticality analysis with KENO. The PolOPinp script also performed an automatic composition extraction from the ORIGEN binary concentration file using the OPUS post-processor. To reduce the required amount of calculations, the PolOPinp also averages burnup and coolant density obtained for each of 25 axial layers in DYN3D down to 6 layers for the KENO cask model-top and bottom natural uranium blankets and the 4 layers within the FA as shown in Figure 5. Next, Polaris/ORIGEN performs depletion/decay calculations for each layer in the traditional lattice approach, with layer's corresponding to coolant density and burnup value, as well as with a fixed set of cooling times. The fuel temperature is equal to 900 K as the estimated average for BWR fuel simulations during normal operation [27], and 560 K, for the coolant [31]. Then, the NumDensTrans script converts number densities obtained with Polaris/ORIGEN for each layer from the binary concentration file to KENO Va input.  
This study considered the set of 28 leading nuclides for criticality analysis--major and minor actinides and major fission products (AFP set) as presented here:  
* Major actinides set [27]--U234, U235, U238, Pu238, Pu239, Pu240, Pu241, Pu242, Am241.
* Minor actinides and major fission products set [27]--U236, Am243, Np237, Mo95, Tc99, Ru101, Rh103, Ag109, Cs133, Sm147, Sm149, Sm150, Sm151, Sm152, Nd143, Nd145, Eu151, Eu153, Gd155.  
The AFP set is formed from nuclides playing the most important role for burnup credit analysis of LWR spent fuel [6].
# Numerical Study of Mode Conversion between Fundamental Lamb and Rayleigh Waves
## 3 Results  
It is convenient to study the mode conversion phenomenon in terms of transmission and reflection coefficients, which will be a measure of the mode conversion efficiency. The time averaged power flow for a propagating mode is given by [1]:  
\[P=\frac{1}{t}\int_{0}^{t}\int_{0}^{D_{i}}\Big{[}\sigma_{xx}(\dot{U}_{x})+\sigma_{ xy}(\dot{U}_{y})\Big{]}dydt \tag{2}\]  
where \(\sigma_{xx}\) and \(\sigma_{xy}\) are the axial and shear stress, respectively, and \(\dot{U}_{x}\) and \(\dot{U}_{y}\) are the particle velocities. The integration is performed over the section \(D_{i}\), for which the Lamb modes correspond to the thickness of the plate, i.e., \(-h/2\) to \(h/2\). For Rayleigh waves, the integration was performed from the surface of the quarter space to a depth corresponding to \(1.5\times\lambda_{R}\)[20]. The transmission coefficient, otherwise termed as conversion efficiency, and reflection coefficients were calculated using [21]:  
\[T_{c}=P_{t}/P_{i},\ \ \ R_{c}=P_{r}/P_{i} \tag{3}\]  
where (\(P_{i}\)) is the incident power, (\(P_{i}\)) is the transmitted power and (\(P_{r}\)) is the reflected power. Using the transmission and reflection coefficients, one can use the power flux balance [17,21], \(T_{c}+R_{c}+P_{s}=1\), to obtain the scattering power coefficient (\(P_{s}\)). Similarly, the power flow for the bulk modes, the longitudinal (\(P\)) and shear (\(SV\)) waves, were obtained using the 1 st and 2 nd terms of Equation (2) [1].
## 5 Simulation Results
### Rates for Different Layers and Antenna Polarizations  
We studied the impact of layers and polarization on the per-user rate. Furthermore, we examine whether it is better for an array configuration to deploy dual cross polarized antenna elements, or to double the number of single polarized antenna elements at the BS and UTs. This comparison can be seen between \(N_{t}=[36\times 2]\), \(N_{r}=1\) dual polarized and \(N_{t}=[72\times 2]\), \(N_{r}=[2\times 1]\) single polarized elements configurations in Figure 12. For a given couple of \(N_{ty}\) and \(N_{zz}\), the antenna array with dual polarized elements improves the rate of the UTs, as the number of antenna elements are doubled. Nevertheless, in the case of equal number of elements, the configuration with single polarized elements exhibits a lower outage percentage, as the increase in the array dimension allows a better interference rejection capability.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 5 Comparison Methods
### Saodv [9]  
This protocol utilizes a digital signature to provide non-repudiation of the non-mutable data in the route information (e.g., sequence numbers) and protect its integrity; and hash chains are used to authenticate the hop count information (HC). The protocol is summarized below.  
Let us consider a case in which \(\mathsf{S}\) attempts to establish a route to \(\mathsf{D}\). In this protocol, \(\mathsf{S}\) broadcasts an RREQ packet. At this point, \(\mathsf{S}\) determines the maximum number of hops to \(\mathsf{D}\) (MHC), and if the RREQ does not reach \(\mathsf{D}\) within the MHC, the RREQ is discarded. The following describes the calculation procedure implemented by the hash chains.  
Node \(\mathsf{S}\) generates a random number (seed) and stores its value in a field called Hash. The seed is input to the hash function \(h\) MHC times, the output is obtained, and the result is expressed as \(\text{TopHash}=h^{\text{MHC}}(\text{seed})\). This calculation result is stored in the TopHash field as defined in the RREQ. Subsequently, \(\mathsf{S}\) generates the RREQ by signing with its own private key. Before the generated RREQ is broadcast to the neighboring nodes, node \(\mathsf{S}\) inputs the seed, which is generated in advance, to \(h\). At this time, \(\text{Hash}=h(\text{seed})\) is stored in the hash field.  
The node that receives the RREQ verifies the attached signature using \(\mathsf{S}\)'s public key and verifies whether the number of hops is correct by applying the hash function. As a verification procedure using the hash function, the hash value contained in the RREQ is input to the hash function exactly the number of times equal to the difference between MHC and HC, which is contained in the RREQ as TopHash. This ensures that the same value has been compared and verified. This procedure can be written as  
\[\text{TopHash}==h^{(\text{MHC}-\text{HC})}(\text{Hash}), \tag{11}\]  
where \(a==b\) indicates that a node is attempting to verify whether \(a=b\).  
After these verification processes and the establishment of a reverse route to \(\mathsf{S}\) as AODV, the RREQ is forwarded to the destination-directed neighbor node. The node that receives this RREQ verifies the signature and calculates relevant hash chains in the same manner by repeating these procedures until the RREQ reaches D.  
When D receives the RREQ and generates the RREP as an RREQ, the signature can protect the integrity and provide non-repudiation of the non-mutable data in the route information. Furthermore, hash chains with a one-way hash function can authenticate the hop count. Each node updates its routing table when the signature and verification of the RREP by the hash chain are completed. After establishing a route to the destination node, it can then forward the RREP to 5. When the RREP reaches 5 and signature and hash chain verification are complete, a route from the source node 5 to the destination node 5 is conclusively established.
## 2 Materials and Methods
### Data Comparison Methodology
#### 2.4.2 Data Processing  
Because of the scale discrepancy between GPPs and rain gauge data (Figure 2), two approaches were used to assess the performance of GPPs: (i) point-to-grid, (ii) point-gridded approach. Point-to-grid compares the precipitation recorded at each rain gauge with the precipitation from the GPP grid (0.04\({}^{\circ}\), 0.05\({}^{\circ}\), and 0.1\({}^{\circ}\), respectively) that encompasses the rain gauge (Figure 2a). Because the location of rain gauges most often does not coincide with GPP grid centroids (Figure 2a), a second strategy was implemented: the point-gridded approach. In practice, a cell is delineated around each rain gauge (cell size of 0.04, 0.05, or 0.1\({}^{\circ}\) depending on the GPP; Table 1). Then, the rainfall value in those new cells was estimated as the area-weighted mean (max. 4) of the GPP grid cells overlapping with the new cell (Figure 2b). A third approach could have been to define an 'area of influence' (Thiessen polygon) around each rain gauge and calculate area-weighted averages for each grid to be compared with the gridded data ('grid-to-grid' approach). However, such an approach leads to very different situations from one grid cell to another, some grids being in the area of influence of a single rain gauge while other grids are in the area of influence of up to five rain gauges. In addition, because the spatial resolution varies across GPPs, such an approach may introduce bias in the comparison.
## 4 Results and Discussion
### Time-Averaged Unsteady Simulation Results  
#### 4.1.1 Numerical Method Validation  
The performance of SVD was measured in the centrifugal compressor test facility of the Shenyang Blower Works Group Corporation. The structure of the test rig is shown in Figure 4. More details about the measuring instruments, data acquisition, and experimental accuracy apparatus are given in [20].  
Figure 3: Time evolution of pressure coefficient with three different time steps for SVD configuration.  
To verify the current numerical methods, time-averaged unsteady simulation results are compared with the experimental measurement data. Figure 5 compares the predicted results with the measured performance results for SVD. The abscissa is the flow coefficient normalized by the design mass flow rate, while the ordinate is the total pressure ratio \(\varepsilon\) and total-total polytropic efficiency \(\eta_{\text{pol}}\). Parameter \(\varepsilon_{05}\) is the ratio of the total pressure mass-averaged on the cross-section at the compressor stage exit to that at the stage entrance; i.e., \(\varepsilon_{05}=P_{\text{t5}}/P_{\text{t0}}\). The \(\eta_{\text{pol}}\) is given in Equation (1):  
\[\eta\text{pol}= \frac{\kappa-1}{\kappa}\frac{\ln\frac{P_{\text{t5}}}{P_{\text{t0} }}}{\ln\frac{T_{\text{t5}}}{T_{\text{t0}}}} \tag{1}\]  
where \(\kappa\) is the isentropic exponent, which is equal to 1.4, and \(T_{\text{t0}}\) and \(T_{\text{t5}}\) are the total temperatures mass-averaged on the cross-sections at the compressor stage inlet and outlet, respectively.  
It is apparent from Figure 5 that the CFD-predicted total pressure ratio and total-total polytropic efficiency are in accord with the experimental measurement data. The difference between numerical results and experimental data may be due to disc friction and leakage  
Figure 4: Schematic sketch of the test rig.  
Figure 5: Comparison of CFD-predicted results with experimental data for the stage with SVD: (**a**) total pressure ratio; (**b**) total to total polytropic efficiency.  
flow caused by gaps that are neglected in CFD. Apart from these, the numerical scheme used in CFD also contributes some errors such as the discretization algorithms adopted, the turbulence model employed, and the data transformation method of rotor-stator (impeller-diffuser) interfaces. The error between numerical results and experimental data is less than 3% under all mass flow conditions. This demonstrates that the numerical method applied is reliable and can be used for further investigation.
## 5 Simulation, Experiment, and Comparison
### The Simulation
#### 5.1.2 The Fuzzy Logic Design  
The dataset collected above is processed to create the rule base of the fuzzy impedance controller as presented in Section 4.2. The number of regions of inputs and output is \(i=9\), \(j=9\), and \(k=49\). The maximum magnitudes of the inputs and output are \(A_{m}=10\), \(B_{m}=0.025\), and \(C_{m}=200\). The total number of rules created is 45.
# Design of Low-Power ECG Sampling and Compression Circuit
## 3 Proposed Scheme
### CS Module
#### 3.2.2 Storage  
The storage units include a register and latch, as depicted in Figure 5. The register typically comprises two levels of master and slave latches, with the input signal meeting the setup time and hold time, thus eliminating glitches and ensuring data accuracy. The primary latch can be used to store stable data. For this work, a fast clock pulse signal is set as the trigger condition of the pulsed latch by optimizing the circuit timing. By reducing the conducting time of the latch, the probability of generating glitches reduces, thus improving the accuracy of the stored data. Furthermore, the hardware consumption of the latch is about half the register, so using latches affords lower power consumption.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{b_{3}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{b_{1}}\) & \(\mathbf{b_{0}}\) & \(\mathbf{c}\) & \(\mathbf{b_{3}}^{*}\mathbf{b_{2}}^{*}\mathbf{b_{1}}^{*}\mathbf{b_{0}}^{*}\) & **Error** & **Probability** \\ \hline
1 & 1 & 1 & 1 & & & & +1 & 1/16 \\
1 & 1 & 1 & 0 & 1 & & & +2 & 1/16 \\
1 & 1 & 0 & 0 & 1 & & & +3 & 1/16 \\
1 & 1 & 0 & 0 & & & & +4 & 1/16 \\ \hline
1 & 0 & 1 & 1 & & & & \(-3\) & 1/16 \\
1 & 0 & 1 & 0 & & & & \(-2\) & 1/16 \\
0 & 1 & 1 & 1 & & & & +3 & 1/16 \\
0 & 1 & 1 & 0 & 0 & & & +2 & 1/16 \\
0 & 0 & 1 & 1 & & & & \(-3\) & 1/16 \\
0 & 0 & 1 & 0 & & & & \(-2\) & 1/16 \\
0 & 0 & 0 & 1 & & & & \(-1\) & 1/16 \\
0 & 0 & 0 & 0 & & & & 0 & 1/16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The approximate addend and corresponding error.  
Fortunately, the clock frequency of the data compression is relatively low, and the data are prohibited from being stored until each compression round stabilizes. Therefore, this paper uses latches to store the compressed data and accordingly generates a fast clock pulse signal as the trigger condition. On the premise of ensuring the accuracy of stored data, the number of latches in this work is reduced by half from 1600 (16 \(\times\) 50 \(\times\) 2) to 800.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 4 Detailed Description of the Proposed Algorithm
### Task Allocation
#### 4.3.1 Reinforcement Learning and Q-Learning Model
In our model, \(s^{(l)}\) denotes the targets that do not meet the tracking quality requirements in the surveillance area of the sensors at the \(l\)-th time step. (3) Reward  
The reward obtained by the agent executing the \(j\)-th action at the \(l\)-th time step is expressed as \(R_{j}^{(l)}\). The specific numerical setting of the reward function is given by Equation (14). The reward provides the evaluation of the actions executed by agents in a certain environment state. The reward function in our proposed model is designed for the trade-off between energy efficiency and tracking quality of the sensor in a multi-target-tracking scenario.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 4 Proposed Scheme
### Energy Propagation Model  
The acoustic channel attenuation in UWSNs over \(d\) is defined by the following formula [38]:  
\[10 logA(d,f)=k\cdot 10 logd+d\cdot 10 log\alpha(f).\]  
Two terms used in the above equation i.e., the spreading loss and absorption loss. \(k\) may have values i.e., 1, 1.5 and 2 for represents the spreading coefficient which defines the geometry of the propagation. If \(k=1\), the geometry of propagation is cylindrical spreading in shallow water, however \(k=2\) pertains to the geometry of the propagation spherically spreading in deep water, while, for \(k=1.5\), the geometry of the propagation is practical spreading where \(\alpha(f)\) represents the absorption coefficient. The underwater noise can be found by the following expression:  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f),\]  
where \(N_{t}(f)\) represents turbulence noise, \(N_{s}(f)\) represents shipping noise, \(N_{w}(f)\) represents waves noise and \(N_{th}(f)\) represents thermal noise. Noise in acoustic channels comprises site-specific noise and ambient noise. While site-specific noise only exists in certain places, backgrounds of quiet deep seas always have ambient noise present. For instance, snapping shrimps in warmer waters create acoustic noise and so does ice cracking in polar regions. Turbulences, rain, breaking waves and  
Figure 4: Speed of sound vs. salinity.  
-distant shipping give rise to ambient noise as shown in the above equation. Although this noise is not white, it's usually approximated as Gaussian. Contrary to ambient noise, significant non-Gaussian components are present in site-specific noise. Our simulation only considers narrow band ambient noise and that is taken to be Gaussian. Ambient noises' acoustic signal is distorted due to ambient noises that have completely different effects depending upon the location and frequency. Practically, the noises discussed above are the major contributors at frequencies from 10 to 100 KHz.  
Noise spectrum levels typically decrease from about 140 dB re 1 \(\upmu\)Pa\({}^{2}\)/Hz at 1 Hz to about 30 dB re 1 \(\upmu\)Pa\({}^{2}\)/Hz at 100 KHz. We have used a central acoustic signal frequency of 12 KHz with an approximate noise level of 50 dB re 1 \(\upmu\)Pa\({}^{2}\)/Hz to fair noise model close to practical values.