esa-sceva/satcom-chunk-collection huggingface dataset:
# Cnoidal Wave-Induced Residual Liquefaction in Loosely Deposited Seabed under Coastal Shallow Water
## 5 Conclusions  
This study comprehensively studied the loose seabed of both the dynamic characteristics and the residual liquefaction process under a cnoidal wave loading. The integrated numerical model FSSI-CAS 2D is utilized as the computation platform, and the Pastor-Zienkiewicz Mark III soil constitutive model is adopted to describe the complicated nonlinear mechanical behaviors of the loose seabed soil. Based on our numerical analysis, the following recognitions are obtained:  
1. Due to the accumulation of pore pressure leading to the gradual loss of the stiffness of the soil skeleton, significant lateral expansion and vertical settlement can be observed on the loosely deposited seabed.
2. The accumulation of pore pressure in the loose seabed is not infinite but limited by the liquefaction resistance line Lr at any time and any buried depth. The effective stress in some zones in the upper seabed floor could be reduced completely to zero, becoming fully liquefied. At this moment, the wave-induced shear stress becomes zero. The fully liquefied seabed soil behaves like a type of heavy fluid with great viscosity, which cannot transmit any shear wave. The successful capture of this physical phenomenon shows that FSSI-CAS 2D is powerful in dealing with the problems of seabed dynamics. Overall, the wave-induced variation of pore pressure and effective stress in the loose seabed is highly complicated. We cannot understand them by a linear way of thinking.
3. The residual liquefaction first occurs at the surface of the loose seabed floor, and then gradually enlarges downward. The frontier of the liquefaction zone will eventually reach a stable depth; for example, the stable depth of the liquefaction zone is z = 16 m in the case involved in this study. Furthermore, the depth of the liquefaction zone is negatively related to the soil permeability and positively related to the saturation of seabed soil. Finally, we must keep in mind that the nonlinear cnoidal wave theory must be adopted for the waves with great height in shallow water. It will give out much more accurate and reliable results of the hydrodynamic loading on the surface of the seabed floor.
4. There are still some factors that are not considered in the present study. Specifically, the rotation of the principal stress on the dynamic characteristics of the seabed under wave loading is not considered. This problem will be studied in the future.  
[PERSON] contributed to the running of all numerical cases and the plotting of all figures. [PERSON] contributed to the data analysis and the plotting of some figures. [PERSON] contributed to the results analysis and manuscript writing, as well as the financial support. All authors have read and agreed to the published version of the manuscript.  
The authors of this study are grateful for the funding support from the Special Fund Project for Production Safety of Hubei Province, China ((2020) No. 12).  
Not applicable.  
The authors declare no conflict of interest.
# Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems
## 2 Materials and Methods
### Job Shop Scheduling Problem  
Job shop scheduling (JSSP) is another well-known NP-hard optimization problem. There is a set of jobs (\(J_{1}\),..., \(J_{n}\)) and a set of machines (\(m_{1}\),..., \(m_{m}\)). Each job \(j\) consists of a list of operations that have to be processed in a given order. Each operation within a job must be processed on a specific machine, only after all preceding operations of this job were completed.  
The makespan is the total length of a schedule, i.e., the total time needed to process all operations of all jobs. The objective is to find a schedule that minimizes the makespan.  
A solution is represented as sequence of jobs of the length \(n\times m\). There are \(m\) occurrences of each job in such sequence. When examining a sequence from the left to the right, the \(i\)-th occurrence of the job \(j\) refers to the \(i\)-th operation of this job.
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 2 Literature Review
### Link- and System-Based Approaches
EXplora-Capture (EXplora-C) in [26] equalizes the ToA of UL packets, keeping the balance regarding SF allocation in a single and multi-GW environment, and takes the capture effect into account. The performance of the EXplora-C approach has been compared to EXplora-AT and EXploRa-SF, where EXplora-C shows improved results in terms of the data extraction rate. In particular, EXplora-C shows enhanced results of up to 38% on average over the legacy ADR mechanism. To improve the power efficiency of ADR, a proposed ADR is presented in [27]. Firstly, it takes the average of the SNRs of \(M\) UL packets (i.e., \(M\)=\(20\)). Secondly, it takes the standard deviation of the SNRs of \(M\) UL packets. The performance of the proposed ADR shows better performance than ADR in terms of power efficiency. However, the proposed work in [27] completely ignores the slowconvergence time of the ADR, which is affected by variable channel conditions [28; 29; 30]. Due to variable channel conditions, the ADR is not suitable when the EDs are mobile; therefore, the authors propose an enhanced ADR (E-ADR) in [31]. The E-ADR is primarily based on the trilateration technique, which aims to estimate the next position of a mobile ED with a predefined trajectory. In different use cases, the E-ADR reduces energy consumption and ToA due to quick adaption. However, E-ADR is solely based on a preset path of the EDs.
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 2 Program Algorithm
### Improvement of Program Performance
In this paper, when  
Figure 4: (**a**) Proposed program scheme using predictive verifying concept. Reprinted/adapted with permission from Ref. [36], 2020, IEEE. (**b**) The number of verifies is decreased by 47% using the predictive program scheme. Reprinted/adapted with permission from Ref. [36], 2020, IEEE. Figure 3: High-speed program (HSP) of V-NAND. Reprinted/adapted with permission from Ref. [35], 2016, IEEE. the number of slow bits at the final verify level is smaller than that of the predetermined number of reference cells, the program operation for the state ends by not applying the next program pulse, which can reduce the number of programming loops and program disturbance. Along with the predictive verify concept above, there is a possibility that left widening may occur in the highest Vt distribution due to the slow bits. Nevertheless, this approach can improve the performance and the reliability of the NAND chip if the number of slow bits can be properly controlled, which is sufficiently corrected with error correction codes (ECC) [38]. A string-based start-bias control (SSBC) has been proposed as an alternative way to reduce program and verify numbers [39]. In 3D NAND, considering the cell variation between chips, a programming start bias is found in the wafer-level test stage, and the same ISPP operation is conducted for all strings using this value. However, in the proposed method, the optimal programming bias is recalculated from the programming operation of the first string once again, and the number of programming loops can be further reduced by applying the optimal programming start bias from the second to the last string based on this value. An additional circuit that corrects the programming start bias for each string is added, sacrificing the chip area, but as the number of strings gradually increases, there will be an advantage of securing a larger improvement. Instead of reducing the number of programs and verify operations, a method to reduce the time required for data transfer in the page buffer has been proposed [40]. As described above, 3D NAND enhances the program performance by simultaneously programming 3 bits/cell in one ISPP operation. Therefore, after programming 3 bits in the WLn cell, 3 bits of data to be written to the WLn + 1 are transferred to the page buffer, and the WLn + 1 program operation is performed using this value. However, the proposed technique has shown us how to reduce the overall programming sequence by transferring the first and second bits of WLn + 1 in advance, while the third bit of the WLn cell performs a program operation.
# Designing an Adaptive Underwater Visible Light Communication System +
## 1 Introduction
Specifically, we explore how this automatic adjustment affects the received signal and whether it enables the selection of the optimal direction for signal reception. The received signal strength relative to the receiver angular position is recorded as a power graph versus angular measurements. The findings indicate that the receiver records the maximum power when it is aligned with the direction of the signal. The paper suggests that incorporating an automatic receiver adjustment mechanism based on power measurements at different angles could significantly enhance the performance of underwater communication systems. This approach could be a key advancement in improving the efficiency of underwater communication systems, particularly in dynamic or moving scenarios where maintaining proper alignment is difficult. The transceiver design proposed in this paper can be easily integrated into wireless sensor networks (WSNs) to support IoUT application through edge computing, as the algorithms can be executed locally.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 2 Multi-Frame PCA-Based Coding Method
### Principle of PCA
Ideally the original matrix \(X\) can be restored by reduced-dimensional matrix \(Y\) and rotation matrix \(P\) with  
\[X\approx X_{restore}=Y\times P^{T} \tag{2}\]  
in which \(X_{restore}\) is the matrix restored in decoder and \(P^{T}\) is the transposition rank of matrix \(P\). Then, \(X_{restore}\) is reconstructed to spectral coefficients. \begin{table}
\begin{tabular}{l} \hline \hline
**Algorithm: PCA (Principle Component Analysis)** \\ \hline (i) Obtain matrix by columns: \(X_{\lfloor}\begin{array}{cc}m&n\end{array}\end{array}\) \\ (ii) Zero-mean columns in \(X_{\lfloor}\begin{array}{cc}m&n\end{array}\end{array}\) \\ (iii) Calculate \(C=\frac{1}{m}X^{T}X\) \\ (iv) Calculate eigenvalues \(\lambda_{1},\lambda_{2},\lambda_{3}\ldots\lambda_{n}\) and eigenvectors \(a_{1},a_{2},a_{3}\ldots a_{n}\) of \(C\) \\ (v) Use eigenvector to construct \(P_{\lfloor}\begin{array}{cc}n&n\end{array}\end{array}\) according to the eigenvalue \\ (vi) Select the first \(k\) columns of \(P_{\lfloor}\begin{array}{cc}n&n\end{array}\end{array}\) \\ (vii) Complete dimensionality reduction by \(Y_{\lfloor}\begin{array}{cc}m&k\end{array}\end{array}=X_{\lfloor}\begin{array} []{cc}m&n\end{array}\end{array}\times P_{\lfloor}\begin{array}{cc}n&k\end{array}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PCA ALGORITHM.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
###### Abstract  
Thanks to the rapid development of vehicle-to-everything (V2X) and sensor technology, states of vehicles can be accurately measured and stored jointly in the cloud. These states can be viewed as a set of infinite attributes, such as the density around the motor vehicle, signal strength and so on. As such, the vehicle can be viewed as a moving object. The vehicle state can be measured, and its entropy is large. In vehicle networking, unicast communications between vehicles must be encrypted. The previous approach was to negotiate a session key through the Diffie-Hellman algorithm and then use the session key to encrypt the communications. This method not only needs to know in advance the peer IP of the communication, but it also needs a long time to establish a session key. Therefore, it is not suitable for vehicle networking. For the fast-changing vehicle networking, the session key establishment process should be shortened or even canceled. In this paper, we propose a method of Negotiation-free encryption for securing vehicular unicasting communications to improve the efficiency of vehicle ad hoc network transmissions.  
vehicular ad-hoc network; unicast communication; encrypted transmission; property set; vehicle safety +
Footnote †: journal: Applied  
Article
## 4 Proposed Resource Allocation Scheme
### Optimal Problem Formulation
#### Constraints  
The constraints of the aforementioned objective function can be listed as follows:  
* **BBU Constraint:** The total number of BBUs in a pool must be constrained as \[|B|\leq B_{max},\] (8) where \(|B|\) denotes the total number of BBUs in a pool and \(B_{max}\) represents the threshold value of the maximum number of BBUs that can be pooled.
* **Capacity Constraint:** The capacity constraint represents that the sum of the processing capacities of the BBUs in a pool must be constrained by the total capacity of a BBU pool. This can be represented as \[\sum_{b\in B}\eta_{b}\leq\eta,\] (9) where \(\eta_{b}\) is the size of each BBU and \(\eta\) is the total capacity of a BBU pool.
* **Request Assignment Constraint:** It ensures that at a time, each request b \(r\in R\) of one RRH \(h\in H\) is always assigned to one BBU \(b\in B\) of a BBU pool, \[\sum_{r\in R}\xi_{r,b}\leq 1,\quad\forall r\in R,\ \forall b\in B,\] (10) where \(\xi_{r,b}\) is a Boolean variable, equal to 1 if a request \(r\in R\) is assigned to a BBU \(b\in B\) of a pool; otherwise, it is 0.  
* **Virtual BBU Allocation Constraint:** The BBU allocation constraint defines that at a given time, one BBU will be allocated for one request, which is represented as \[\sum_{b\in B}\Psi_{r,b}\leq 1,\quad\forall r\in R,\;\forall b\in B,\] (11) where \(\Psi_{r,b}\) is a Boolean variable, equal to 1 if a BBU \(b\in B\) of a BBU pool is allocated for a requested request \(r\in R\); otherwise, it is 0.
* **Profit Constraint:** The profit constraint can be represented as \[\rho_{r,b}\geq\rho_{min},\] (12) where \(\rho_{r,b}\) is the profit that is gained by the cloud service provider for executing a request, \(r\in R\) on a BBU \(b\in B\) of a BBU pool and \(\rho_{min}\) is the minimum profit that must be gained by the service provider for executing that request.
* **QoS Constraint:** The QoS constraint can be represented as \[\tau_{r,b}\leq q_{r},\] (13) where \(\tau_{r,b}\) is the total time to get any response after executing a request \(r\in R\) on a BBU \(b\in B\) of a BBU pool, and \(q_{r}\) is the required QoS of a request or the maximum allowable time to obtain a result.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 2 Finite Element Analysis
### Debonding Mechanism Using Lap Shear Strength
#### 2.3.2 Incorporating the Debonding Mechanism into a 12.7 Mm (0.5 inch) Discontinuous IRP System  
This section presents the integration of the shear strength of Diglycidyl ether of bisphenol A (DGEBA) epoxy resin, which has been determined to be 20 MPa (2.9 ksi) [25], into the numerical modeling framework of an IRP system subjected to deflection loading. The shear strength value assigned to the DGEBA epoxy adhesive matches with that employed in the experimental tests conducted at the University of Colorado Boulder (CUB). This alignment ensures that the numerical simulations accurately reflect the behaviour of the actual IRP system in the experimental setup at the CUB shown in Figure 1.  
The stress results of the IRP system are depicted in Figure 15, while the maximum equivalent stress measured at the midspan of the invert of the IRP alone is depicted in Figure 16. The stress value is measured to be 53.87 MPa (7.81 ksi), which is considerably lower than the maximum allowable stress of 79.42 MPa (13.70 ksi) for the IRP. This observation indicates that the IRP does not experience failure when subjected to the ultimate load of 71.2 kN (16 kip), with a safety factor (SOF) of 1.47. The safety factor represents the ratio of the maximum allowable stress to the actual stress experienced by the IRP, demonstrating that the structure can withstand the applied load with a significant margin of safety. Additionally, Figure 17 provides a visual representation of the progressive debonding process that occurs within the adhesive layer as the applied load increases. It can be observed that the adhesive layer starts to weaken at 28.5 kN (6.4 kip) and experiences substantial debonding at the bottom layer when subjected to the ultimate load of 71.2 kN (16 kip).  
Figure 16: Equivalent stress distribution in the IRP of a 12.7 mm (0.5 inch) discontinuous IRP system employing the debonding mechanism at a maximum load of 69.1 kN (15.5 kip) (magnified 13 times).  
Figure 15: Equivalent stress results of a 12.7 mm (0.5 inch) discontinuous IRP system employing the debonding mechanism at a maximum load of 69.1 kN (15.5 kip) (magnified 29 times).  
To further evaluate the effectiveness of incorporating an adhesive layer, Figure 18 and Table 6 present a comprehensive comparison between load versus displacement data obtained from the FEA approach. This incorporates the debonding mechanism in the adhesive layer and the corresponding experimental results from different locations. The outcomes clearly indicate that the inclusion of an adhesive layer significantly improves the agreement between the predicted and experimental results. Specifically, the adhesive layer exhibits a certain level of debonding after reaching approximately 28.5 kN (6.4 kip).  
Figure 17: Progressive debonding in the adhesive layer at various load levels for a 12.7 mm (0.5 inch) discontinuous IRP system (true scale).
## 3 Results
### Chronic Effects of Complex Training  
Table 5 presents the characteristics of studies that analyzed the long-lasting performance of CT. Table 6 shows the results of studies analyzing the long-lasting effects of CT on performance in basketball players. The table presents the values obtained in the tests pre- and post-chronic application of CT, along with the reported effect size and \(p\)-value (directly taken from the studies). The effects on jumping ability were analyzed by assessing different metrics (i.e., jump height, relative peak power, and contraction time) in the CM) [7; 20; 21; 22; 33; 35; 36; 37; 38], squat jump (S) [20; 21; 22; 33; 35; 37; 41], drop jump (DJ) [21; 22; 37; 41], Abalakov jump ABK [22], and standing long jump and Sargent jump test [7]. Sprinting performance was measured using 10-30 m linear sprint tests [7; 20; 21; 29; 35; 37; 38], while change of direction (COD) performance was assessed using the \(t\)-test [20; 41], Illinois test [35; 40], and Shuttle Test [33]. Additionally, maximal strength (i.e., 1 RM) was evaluated through exercises such as the Half Squat [7; 20; 21; 29; 38], Back Squat [37], Bench Press [20], and Hip Thrust [20]. Furthermore, some studies utilized field tests to assess strength, such as the medicine ball throw [35] and sit-up back test [34]. The intervention periods lasted for 6 [20; 26; 36; 38], 8 [33; 35; 37; 40], and 10 [7; 22; 29; 34] weeks, with sessions conducted 2 [7; 20; 22; 26; 29; 33; 36; 38] or 3 [34; 35] times per week. The studies utilized loading parameters of 1 [37], 2-4 [20; 22; 26; 33; 36; 38], or 5-12 [7; 21; 29] sets, of 3-6 [20; 36; 37], 4-12 [7; 26; 33; 38], or 6-15 repetitions [22], and 40-80% [26; 38], 80% [20], or 85% [33; 37] of 1 RM, 5-12 RM [21; 22; 29] optimal load [20], and relative resistance of 0.8-1.0 m/s [36]. Some studies did not clearly specify the moderators' (i.e., volume, intensity, series, and reps) characteristics [35; 40]. Additionally, one study analyzed the application of CT in core training, performing 3 sets of 9-10 reps or 30 s for 10 core exercises, with rest intervals of 2-30 s within groups and 1-5 min between sets [34]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Groups** & **n** & **Body Mass (kg)** & **Age (yrs)** & **Experimental Design** & **Exercise Intervention** & **Group Specific Pre-Post Change** \\ \hline \multirow{9}{*}{CPX} & \multirow{9}{*}{13} & \multirow{9}{*}{85.9 \(\pm\) 10.9} & \multirow{9}{*}{24 \(\pm\) 6} & \multirow{9}{*}{} & CYU training 2-4 sets of 5-12 \\  & & & & & rep at 85\% 1 RM with 90-120 rest & CM Height (cm) \(\uparrow\) 2.3 \(\pm\) 5.5\% \\  & & & & & (estimate increase/
## 4 Experimental Results
### SACT Statistic Review
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & **UB** & **Better** & **Worse** & **Mean** & **\%RE** & **\(\sigma\)** & **t Sec** & **Median** & **Mode** \\  & **Units of Time** & **Units of Time** & **Units of Time** & **Units of Time** & **\%RE** & **\(\sigma\)** & **t Sec** & **Median** & **Mode** \\ \hline TA21 & 1642 & 1646 & 1772 & 1683 & 0.24 & 18.1 & 1938 & 1681 & 1665 \\ TA22 & 1600 & 1600 & 1680 & 1637 & 0 & 13.1 & 3476 & 1636 & 1644 \\ TA23 & 1557 & 1560 & 1628 & 1600 & 0.19 & 15.5 & 1681 & 1598 & 1598 \\ TA24 & 1646 & 1651 & 1693 & 1681 & 0.3 & 14.4 & 397 & 1683 & 1670 \\ TA25 & 1595 & 1597 & 1669 & 1633 & 0.13 & 17.9 & 2208 & 1634 & 1649 \\ TA26 & 1643 & 1651 & 1716 & 1684 & 0.49 & 15.6 & 4547 & 1681 & 1680 \\ TA27 & 1680 & 1682 & 1712 & 1701 & 0.12 & 6.5 & 1736 & 1700.5 & 1698 \\ TA28 & 1603 & 1617 & 1639 & 1625 & 0.87 & 6.6 & 1374 & 1623 & 1622 \\ TA29 & 1625 & 1627 & 1642 & 1631 & 0.12 & 4.9 & 4876 & 1628.5 & 1627 \\ TA30 & 1584 & 1584 & 1618 & 1607 & 0 & 6.0 & 6429 & 1608.5 & 1606 \\ DMU06 & 3244 & 3254 & 3381 & 3321 & 0.31 & 30.4 & 267 & 3319 & 3307 \\ DMU07 & 3046 & 3065 & 3223 & 3127 & 0.62 & 37.5 & 3192 & 3122.5 & 3118 \\ DMU08 & 3188 & 3192 & 3385 & 3255 & 0.13 & 41.8 & 1696 & 3253 & 3022 \\ DMU09 & 3092 & 3121 & 3231 & 3174 & 0.94 & 29.5 & 1912 & 3173.5 & 3228 \\ DMU10 & 2984 & 3001 & 3084 & 3042 & 0.57 & 23.1 & 246 & 3041 & 3032 \\ DMU46 & 4035 & 4133 & 4189 & 4171 & 2.43 & 13.9 & 3424 & 4172 & 4176 \\ DMU47 & 3942 & 4024 & 4094 & 4070 & 2.08 & 13.9 & 1244 & 4073.5 & 4074 \\ DMU48 & 3763 & 3856 & 3988 & 3907 & 2.47 & 20.9 & 858 & 3906 & 3908 \\ DMU49 & 3710 & 3822 & 3871 & 3851 & 3.02 & 21.3 & 8301 & 3906 & 3902 \\ DMU50 & 3729 & 3829 & 3907 & 3882 & 2.68 & 16.2 & 5281 & 3881.5 & 3881 \\ YN1 & 884 & 885 & 905 & 896 & 0.11 & 4.5 & 1558 & 895.5 & 896 \\ YN2 & 904 & 906 & 930 & 917 & 0.22 & 6.4 & 3353 & 916 & 913 \\ YN3 & 892 & 892 & 915 & 903 & 0 & 5.9 & 2459 & 903.5 & 904 \\ YN4 & 968 & 968 & 990 & 978 & 0 & 5.8 & 3525 & 977.5 & 974 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SACT results, large benchmarks JSSP (\(20\times 20\)). Figure 16: Relative error, average of five tests, JSSP \(15\times 15\). Figure 17 presents the average of 30 tests of the relative error obtained for the YN problems. It can be observed that the maximum RE occurs in YN2 and was not greater than 1.7; this problem had more difficulty approaching the upper bound of agreement in Table 6. The lowest average RE was in YN4, not greater than 1.2 with respect to the upper bound in Table 6.  
Figure 18 presents the average of 30 tests of the relative error obtained for the DMU (06-10) problems. It can be observed that the maximum RE occurs in DMU07 and was not greater than 2.7. The DMU10 reached lowest average RE < 2. DMU08 was easier to approach the upper bound of agreement in Table 6. For the other DMU problems, the maximum average RE is in the range 2.3 < RE < 2.7. Figure 17: Relative error, average of thirty tests, JSSP 20 \(\times\) 20. Figure 18: Relative error for thirty executions of problems DMU (06–10) of JSSP 20 \(\times\) 20.