esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparisons of LC\({}_{50}\) Values with Those of Other Species of Molluscs
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Molluscs** & **Species** & **Water Hardness** & **Live** & **Test** & **LC50** & **References** \\  & & **(mg/L)** & **Stage** & **Duration** & **(mg/L)** & **References** \\ \hline Bivalve & _Mussel Madiolus_ & NA & NA & 96-h EC50 & 2.88 & [PERSON] et al. [82] \\  & _phillipipinarum_ & & & & & \\ \hline Gastropods & _A. hyporum_ & 60.9 & NA & 96-h EC50 & 1.34 & [PERSON] et al. [100] \\  & _Viviparus bengalensis_ & 165 & NA & 96-h EC50 & 2.54 & [PERSON] and [PERSON] [70] \\  & _L. emarginata_ & 150 & NA & 96-h EC50 & 14 & [PERSON] et al. [101] \\  & _E. livescens_ & 150 & NA & 96-h EC50 & 71 & [PERSON] et al. [101] \\  & _Filopalhalina_ sp. & NA & Adult & 24-h EC50 & 319 & [PERSON] et al. [102] \\  & _Filopaludina_ sp. & NA & Adult & 48-h EC50 & 271 & [PERSON] et al. [102] \\  & _Filopaludina_ sp. & NA & Adult & 72-h EC50 & 235 & [PERSON] et al. [102] \\  & _Filopaludina_ sp. & NA & Adult & 96-h EC50 & 192 & [PERSON] et al. [102] \\  & _Melanoides tuberculula_ & 18.7 & Adult & 96-h EC50 & 6.82 & [PERSON] et al. [9] \\  & _Snail Certhedia_ & NA & NA & 96-h EC50 & 15.5 & [PERSON] et al. [82] \\  & _cingulata_ & & & & & \\ _Freshwater snail_ & NA & Adult & 96-h EC50 & 18 & [PERSON] et al. [7] \\  & _Theodoxins niloticus_ & & & & \\ _Archachatina papyracea_ & Land snails & Adults & & & \\ _Archachatina papyracea_ & Land snails & Adults & & & \\ _Pomacea insularum_ & & & & & \\ (small) & 65 & Juvernile & 48-h EC50 & 10.44 & This study \\  & & & & & \\ \end{tabular}
\end{table} 
Table 5: Comparison of LC50 values (mg/L) of Pb in _Pomaca insularum_ with other mollusts reported in the literature.
## 4 Methodology
### Representation Model Construction
With node attributes disseminated and aggregated by the network, the user representation vector \(\mathbf{U}\), \(\mathbf{U}\in\mathbf{R}^{N_{ll}\times H}\) and the location representation \(\mathbf{L}\), \(\mathbf{L}\in\mathbf{R}^{N_{ll}\times H}\) are obtained. Among them, \(H\) is the dimension of the representation vector, \(N_{U}\) is the number of users, and \(N_{L}\) is the number of locations. \[\mathbf{\Theta}^{(K)}= \begin{bmatrix}\mathbf{U}\\ \mathbf{L}\end{bmatrix} , \tag{12}\]  
where \(\mathbf{\Theta}\) is representation matrix of users \(U\) and locations \(L\), \(\mathbf{U}\) is the users' representations, and \(\mathbf{L}\) is the locations' representations. \(\mathbf{\Theta}^{(K)}\in\mathbf{R}^{(N_{ll}+N_{L})\times H}\), \(\mathbf{U}\in\mathbf{R}^{N_{ll}\times H}\), \(\mathbf{L}\in\mathbf{R}^{N_{ll}\times H}\). The recommendations are based on the output of our model, the user representation \(\mathbf{U}\) and the location representation \(\mathbf{L}\). We take the idea of collaborative filtering of matrix decomposition that the interested score of each user \(u\) to location \(\vec{l}\) where they have not visited before is represented by the inner product of \(\mathbf{U}\) and \(\mathbf{L}\). The formula is  
\[\hat{f}_{u\vec{l}}=\mathbf{U}_{u}^{T}\mathbf{L}_{\vec{l}}. \tag{13}\]  
Then according to the interesting score \(\hat{f}_{u\vec{l}}\), the top-k locations are selected as recommended locations for users.
## 4 Experimental Simulation
### Algorithm Complexity Analysis  
To assess the complexity of an algorithm, one needs to consider the number of multiplication and addition operations performed within the algorithm. The computational complexity of the method proposed in this paper is primarily composed of three parts: the Runge-Kutta algorithm for the stochastic resonance system, the quantum particle swarm optimization algorithm, and FFT. When the system proposed in this paper processes an OFDM signal with one symbol period, the number of sampling points is \(n\), and the maximum number of iterations for the quantum particle swarm algorithm is \(I\). The number of real multiplication and addition operations required to implement the enhancement and demodulation process of an OFDM signal with one symbol period using the two systems is shown in Table 1. From Table 1, it can be observed that compared to the OFDM signal enhancement and demodulation method based on the FUBSR system, the proposed method has a higher computational complexity but achieves better enhancement and demodulation performance as well as improved noise resistance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Number of Multiplications** & **Number of Additions** \\ \hline FUBSR & \((133I+40)n+2n(1+I)\log_{2}n\) & \(n(22+18I)+7I+3(I+1)\log_{2}n\) \\ SUUBSR & \((201I+67)n+2n(1+I)\log_{2}n\) & \(n(27+45I)+2I+(I+1)3n\log(2n)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of computational complexity between the two systems.  
Figure 15: The BER of OFDM signal demodulation under different subcarrier modulation schemes for two systems.
###### Abstract
The peak power adaptation, in comparison to the average power adaptation, protects and guarantees instantaneous interference prevention at PU, and in many cases, the PU QoS would be limited by the instantaneous signal to interference plus noise ratio (SINR) at the receiver [8; 15; 16]. Therefore, in this work, we will consider the peak power adaptation mechanism. It is also worth noting that the insights from peak power adaptation will still be valid even if average power adaptation is considered. The power adaptation schemes require the knowledge of channel state information (CSI) at SU-Tx to function properly. Recent literature studies have pointed out that this CSI information can be obtained by acknowledgment/non-acknowledgement (ACK/NACK) packet transmissions or by detecting the transition of modulation and coding schemes (MCS) [17; 18; 19; 20]. Since our main aim is to show the performance of interference spreading, we assume that the CSI information is available at the SU-Tx. In summary, the main contributions of this letter are as follows:  
Figure 1: Interference spreading in an OFDM-based cognitive radio network showing subcarrier collisions and no subcarrier collisions. * Theoretical expressions for SINR of collision and SNR of no-collision cases for different modulation orders of M-QAM are compared and validated;
* Bit error rate (BER) expressions for the collision and no-collision cases are compared for different modulation orders of M-QAM;
* Detailed analysis of interference spreading on BER. To our knowledge, this is the first work to study the BER performance of interference spreading in CRN. The rest of the letter is organized as follows. In Section 2, a system model with important considerations will be laid out. Section 3 details the important mathematical preliminaries, and Section 4 gives a thorough BER performance analysis. Finally, the conclusions are given in Section 5.
# Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
## 3 Example Application
### Slope Stability Analysis Based on the DFN-DEC Model  
The _Fs_ of the slope were calculated using the strength reduction method (SRM). The Mohr-Coulomb failure criterion is adopted in SRM to compute the factor of safety, _Fs_, which is carried out by artificially diminishing rock strength characteristics such as the cohesion and tangent of the friction angle until the rock slope fails or collapses. In SRM, the _Fs_ corresponds to the strength reduction factor (SRF). _Fs_ is the ratio of the rock mass's shear strength to the shear stress on the surface that is sliding or failing along two sets' fractures. Figure 4 shows the x-displacement contours for an identical slope with two distinct fracture arrangements. The two slopes in Figure 4a,b have different _Fs_ values due to their different structures, which indicates that the rock mass structure dramatically influences the slope stability. In addition, these two _Fs_ values (1.45 and 1.73) are greater than 1 (the critical value for slope instability), which means that these two slopes will not be destabilized.  
As shown in Figure 4a, the six non-through fractures near the slope are more concentrated and gradually break up into a through fracture. Because the slope is very prone to instability damage along the through fracture, the slope with such a structure has lower stability (_Fs_ = 1.45). As shown in Figure 4b, the discrete fractures near the slope are more scattered and unconnected, causing the rock slope to have higher stability than that in Figure 4a. Further, the failure mode of such slopes is usually stair-stepping along the horizontal and inclined fractures.  
Figure 4: Horizontal displacement contours for an identical slope with two distinct fracture arrangements ((**a**)—a realization of DFN with _Fs_ = 1.45; (**b**)—another realization of DFN with _Fs_ = 1.73).  
Figure 3: The slope model with random fractures.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 4 CLOR Protocol
### Process of CLOR
#### 4.2.4 Adaptive Transmission Process
The receiver sets an initial timer and sends an \(ACK\) packet back to the sender. The sender may send the packets, and the number of the packets can be smaller than the number required by the receiver, since some packets have been received by other nodes before current transmission. In addition, the receiver cannot accurately estimate the transmission redundancy of the sender. Thus, once a new packet is received, the receiver refreshes the timer according to the coding information in this packet. The time interval of this timer is calculated as follows. \[\tau_{sACK}=\left\{ \begin{array}{l}R_{M}\cdot N_{rq}\cdot\tau_{ED}+\tau_{ est},n_{r}=0\\ ((N_{out}-1)-k_{r})\cdot\tau_{ED},n_{r}>0\end{array} \right. \tag{25}\]  
where \(n_{r}\) represents the number of the packets that have been received from the sender; and \(R_{M}\) is the maximum transmission redundancy initialized by the receiver. \(N_{rq}\) is the number of the data packets requested by the receiver; \(\tau_{est}\) is the estimated time for the receiver receiving a data packet, which is related to the time interval of current negotiation; \(k_{r}\) is the index of the latest coded packet that has been received. On this basis, the time interval \(\tau_{sACK}\) is estimated by the number of residual packets. When the timer is expired, the receiver sends an \(ACK\) packet that carries the link quality and the decoding results. Meanwhile, the receiver updates the link quality by current transmission and refreshes the neighbor table. When the sender has received an \(ACK\) packet, it removes the packets according to the decoding mask in the \(ACK\) packet and then updates the forward link quality. Any node in the candidate set that has overheard the coded packet attempts to recover the original data packets by the currently accumulated information. The recovered packets are stored in the data queue for future transmission or for protection, since more than one node stores the same data packets.
## 2 Background on the Identification Problem
### Previous Results on DI Capacity  
The DI problem for DMCs subject to an average constraint, is studied in [32] and a full characterization of capacity is established. Therein, the codebook size similar to that of the TR problem [17], is shown to grow _exponentially_ in the codeword length, i.e., \(\sim\)2\({}^{nR}\)[32]. [PERSON] and [PERSON] studied the DI problem for the compound channels in [57]. Furthermore, recent observation for DI over continuous input alphabet channels including Gaussian channels with fast and slow fading [32], memoryless discrete-time Poisson channel (DTPC) [48], DTPC with inter-symbol interference (ISI) [52], and Binomial channel [59], revealed a new observation regarding the codebook size, namely, it scales _super-exponentially_ in the codeword length, i.e., \(\sim\)2\({}^{(n\log n)R}\), which is different than the standard exponential [32] and double exponential [49] behavior for DI and RI problems, respectively.
## 3 Obstacle Avoidance Scenario
### Obstacle Avoidance Techniques
#### 3.4.6 Neural Network  
Neural network algorithms are inspired by the human brain. The neural network takes in data, train themselves to recognize the patterns in the data, and then predict the outputs for a new set of similar data. A computational model repeats training or functions with a biological neural network system until the best result comes out. The dynamic neural network is capable of automatically adjusting its structure, following the complication of the vehicle's environment, understanding the mapping connection amongst the vehicle's state and its obstacle avoidance decision in real-time, and efficiently decreasing the vehicle's computational load [316].  
[PERSON] et al. [317] designed a Hopfield-type neural network with nonlinear analog neurons for path planning and obstacle avoidance. Using a reinforcement learning neural  
Figure 13: (a) 2D histogram grid map; (b) converted 1D polar histogram [41].  
network, an obstacle avoidance approach was proposed by [PERSON] et al. [318]. [PERSON] et al. [319] designed a controller to find the obstacle-free shortest trajectory in 3D space for a UAV, which used a vision-based Grossberg Neural Network. Later, using a modified Grossberg neural network [PERSON], [PERSON], and [PERSON] [50] proposed an algorithm to avoid dynamic obstacles in 3D space. [PERSON] and [PERSON] [320] proposed a neural network control system to guide mobile robots to avoid arbitrary obstacles in a maze. [PERSON] and [PERSON] [321] used a fuzzy neural network to avoid obstacles with a wheeled mobile robot. They used fuzzy sets as members of the neural network layer. [PERSON] et al. [322] proposed a vision-based trail following a UAV, which will avoid obstacles in the route using Convolutional Neural Network (CNN). [PERSON] et al. [323] also used CNN to learn schemes to avoid obstacles in an unknown environment for a quadrotor UAV. Using neural network algorithms for obstacle avoidance needs lots of training data, but it is suitable for real-time obstacle avoidance performance. Example training data, to train for a complex environment, are shown in Figure 14, but the result is better than a few other obstacle avoidance methods, shown in Figure 15.  
Figure 14: Neural network training data. (**a**–**h**) Generated paths with single and multiple obstacles with different placements during training phase [324].  
Figure 15: Collision avoidance performance using training data, comparing with Particle Swarm (PSO), the dynamic A * algorithm, and Artificial Potential Field (APF) [324].
# Structural Damage Identification Using a Modified Directional Bat Algorithm
## 2 Theoretical Background
The flexibility matrix can be obtained by the following formula:  
\[[F]=\sum_{i=1}^{s}\frac{1}{\omega_{i}^{2}}\{\phi_{i}\}\{\phi_{i}\}^{T} \tag{6}\]  
The objective function is defined as follows:  
\[Function=\sum_{i=1}^{s}\left[\Delta_{1}(1-MAC_{i})+\Delta_{2}MDLAC_{i}+\Delta_ {3}(1-MACF_{i})\right] \tag{7}\]  
where \(s\) is the order obtained. In order to distinguish the sensitivity of natural frequency, mode shape and flexibility matrix in structural damage identification, the objective function is weighted [43; 21]. The weight coefficient of the objective function is determined to be \(\Delta_{1}=0.82,\Delta_{2}=0.13,\Delta_{3}=0.05\) and \(\Delta_{1}+\Delta_{2}+\Delta_{3}=1\). Considering the influence of uncertain factors on modal parameters, random noise is added. The expression is as follows:  
\[x_{noise}=x(1+\eta R_{n}) \tag{8}\]where \(x_{noise}\) and \(x\) are the modal parameters with and without noise, respectively; \(\eta\) is the noise level; \(R_{n}\) is a random matrix, and each number in the matrix is a random number of [0, 1].
# Systematic Literature Review of Challenges in Blockchain Scalability
###### Abstract  
Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network.  
blockchain; distributed ledger technology scalability; consensus model; scalability solution; throughput +
Footnote †: journal: Applied Sciences
###### Abstract  
A high-efficiency DC-DC converter employing a modified architecture called the hybrid switched inductor-capacitor series (MHSLCS) is proposed in this paper. The primary goal is to achieve a notably ultra-high voltage gain for renewable energy systems (RESs). Furthermore, the use of only one input capacitor in the MHSLCS eliminates pulsations in the input current at both low and high duty ratios. The proposed converter integrates the MHSLCS with a modified switched capacitor (MSC) that interleaves with the main MOSFET, effectively doubling the voltage transfer gain. Additionally, a modified hybrid switched inductor-capacitor parallel (MHSLCP) is incorporated in parallel with an interleaved auxiliary MOSFET. Both MOSFETs, combined with the MSC, contribute to achieving an ultra-high voltage gain. In addition, the inductors of the MHSLCP operate in a discontinuous conduction mode (DCM), which results in significant stress reductions in the power diodes and switches at high output voltages. The advantages of the proposed converter are multifaceted, demonstrating a high efficiency while minimizing the voltage in power device diodes and MOSFETs. The use of low inductance and capacitance values at high switching frequencies further enhances the performance. Wide-bandgap (WBG) power devices are employed to achieve the desired high voltage gain and efficiency. The proposed converter was designed with a PCB and underwent experimental testing to validate laboratory results. The proposed converter boosted the input voltage from 30 V to a variable output voltage between 325 V and 500 V, with a power output of 325 watts and an efficiency of 95.5%.  
MHSLCS; MHSLCP; switched capacitor; switched converter; high-efficiency DC-DC converter
## 4 Discussion
### Future Directions and Practical Implications  
To address automation trust calibration, this study recommends designing transparent ATS interfaces that provide clear, real-time feedback, enabling dispatchers to maintain an appropriate level of trust. Integrating targeted training programs to enhance dispatchers' system knowledge can further optimize trust calibration. For instance, our research team is currently developing a next-generation ATS human-machine interface (see Figure 3) that features transparent information channels, prototype completion, and ongoing interaction design and human factors validation. This interface aims to enhance dispatchers' understanding of system operations and improve trust calibration. Additionally, we are designing an adaptive training system (see Figure 4) that incorporates adaptive learning modules and usability testing, ensuring dispatchers can effectively build and maintain system proficiency. Future research should explore the interaction between system transparency, feedback mechanisms, and trust calibration, aiming to identify the optimal level of transparency that balances trust with cognitive workload. Advancements in artificial intelligence and deep learning also offer promising avenues for automating trust assessment and calibration. While this study focused on Chinese subway dispatchers, extending the research to other regions and operational contexts will help establish a more comprehensive understanding of automation trust in safety-critical systems.  
Figure 3: Next-generation ATS human–machine interface under development, featuring transparent information channels, prototype completion, and ongoing interaction design and human factor validation.