esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussions
### PAPR Simulation Results
#### 6.3.1 PAPR Simulation Results on Transmitter Side  
This section shows the PAPR simulation results on the transmitter side to verify the performance of the proposed waveform and compare with other waveforms.  
Figure 8: The BER-SNR comparisons of the traditional OFDM, GFDM, WHT-GFDM, and WHT-LWT-GFDM implemented using Haar mother wavelets waveforms on the flat Rayleigh fading channel.  
The effects of the various mother wavelets on the PAPR performance of the WHT-LWT-GFDM waveform proposed for BPSK modulation are shown in Figure 9.  
In Figure 9, the influences of various mother wavelets as for example bior1.3, bior2.6, cdf1.1, cdf1.5, coif1, db7, lazy, and sym4 on the PAPR performance of the proposed waveform for BPSK modulation are presented. The best performance is obtained using the lazy mother wavelet and the worst performance is obtained using the db7 mother wavelets. The PAPR performance of the proposed waveform when one of the other mother wavelets considered is used falls between the performance obtained using the lazy or the db7 mother wavelets. Since the best PAPR performance of the proposed waveform is obtained using the lazy mother wavelets, we will use in the rest of this sub-section this mother wavelets for the implementation of the proposed waveform.  
The comparison of PAPR performance of the conventional OFDM, GFDM, WHT-GFDM, and proposed waveforms is illustrated in Figure 10 for the BPSK modulation.  
Figure 10: The PAPR performance comparison of the conventional OFDM, GFDM, WHT-GFDM, and WHT-LWT-GFDM waveforms.  
Figure 9: The effects of the different mother wavelets on PAPR performance of proposed WHT-LWT-GFDM waveform for BPSK modulation.  
When the PAPR performance given in Figure 10 are examined, it is noticed that the WHT-GFDM waveform surpasses conventional OFDM and GFDM waveforms. For \(1\times 10^{-3}\) CCDF value, it is seen that it gives approximately 1.3 dB PAPR enhancement versus the conventional OFDM waveform and approximately 2.3 dB PAPR enhancement versus the GFDM waveform. However, for \(1\times 10^{-3}\) CCDF value, it is observed that the proposed WHT-LWT-GFDM waveform provides approximately 0.4 dB PAPR gain versus WHT-GFDM waveform, provides roughly 1.5 dB PAPR enhancement versus conventional OFDM waveform and gives approximately 2.5 dB PAPR enhancement versus GFDM waveform. On the other hand, there appears to be a trade-off in the WHT-GFDM waveform. Although unsuccessful results were acquired in BER-SNR curve with the WHT-GFDM waveform, good results are obtained for PAPR.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Modeling the Parameters
#### 4.1.7 Other Parameters  
The parameters presented thus far are the focus of this work, but others are shown in Figure 1, which will be sketched next. Since we do not consider these parameters central, they will only be summarized. Again, one may refer to [26] for more details.  
* The _energy capacity_ refers to the initial energy of a node, measured in joule.
* The _energy consumption_ refers to the states of a Wi-Fi connection between a pair of nodes, namely RX (receive), TX (transmit), IDLE, and SLEEP. It is measured in ampere.
* Besides the types of different packets to be sent (sequence types), one can also consider different _packet sizes_ and _number of packets_.
* The _observation length_ is the length that we consider the communication in a network, in our case, the length of the network simulation.
* The _number of consumers and producers_ can largely affect the amount of communication in the network: The more producers there are, the smaller we expect the average paths to be between consumers and producers. Furthermore, one may have _overlapping_ consumers and producers, meaning one node fulfills both roles.
* The _Content Store size_ is the number of packets that can be stored on a node.
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 4 Revisit the Regulatory Mechanisms of tRNA and tsRNA
### tRNA: Focus on Both Main and Side Businesses
#### 4.1.1 Being Primers for Reverse Transcription Transposons  
Transposable elements (TEs) play a significant role in genome evolution, with reverse transcription transposons representing a crucial category. These elements can utilize RNA as an intermediary, reverse transcribe it into DNA, and subsequently integrate it into the host genome [136]. Reverse transcription transposons are primarily divided into two categories: Long terminal repeat-retrotransposons (LTRs; also known as Endogenous Retroviruses, ERVs) and non-LTR reverse transcription transposons (e.g., long interspersed nuclear elements (LINEs), short interspersed nuclear elements (SINEs)) [136]. Research indicates that the 3' end of mature tRNAs can serve as a primer for LTR reverse transcription, anchoring to the highly conserved primer binding sequence (PBS), thereby initiating the reverse transcription process [137]. This mechanism has been observed in a range of retroviruses, including human immunodeficiency virus (HIV), human T cell leukemia virus type 1, Rous sarcoma virus, Moloney murine leukemia virus, and Mouse mammary tumor virus [129]. Further exploration of this relationship could provide deeper insights into both the mechanisms of viral replication and the broader implications for host genome evolution.
# Numerical Simulation of the Force Acting on the Riser by Two Internal Solitary Waves
## 2 Materials and Methods
### Mathematical Models
#### 2.3.2 Internal Isolated Wave Theory  
The common inner isolated wave theory solutions are KdV, mKdV, eKdV, and MCC theories, and [PERSON] et al. have investigated the applicability of different theories based on the inner isolated wave dispersion and nonlinear parameters. Based on the established dimensions of the simulated computational domain, the eKdV theory is chosen as the theoretical equation to describe the internally isolated waves in this paper [50].  
The full name of the eKdV equation is the extended Korteweg-de Vries equation. It includes nonlinear and dispersive cubic terms in the wave amplitude derived from the water-wave equations and the Lagrangian for the water-wave equations. Since there is not necessarily a balanced relationship between nonlinearity and dispersion, the KdV equation does not apply to isolated waves within a large amplitude, where a cubic nonlinear term can be introduced, i.e., a nonlinear term of significant order is added after KdV to obtain the eKdV equation:  
\[\frac{\partial H}{\partial t}+(c+\alpha_{0}H+\alpha_{1}H^{2})\frac{\partial H }{\partial x}+\beta\frac{\partial^{3}H}{\partial x^{3}}=0 \tag{8}\]  
Of which:  
\[\alpha_{1}=\frac{3c}{h_{1}{}^{2}h_{2}{}^{2}}\Bigg{[}\frac{7}{8}\bigg{(}\frac{ \rho_{1}h_{2}{}^{2}-\rho_{2}{h_{1}}^{2}}{\rho_{1}h_{2}+\rho_{2}h_{1}}\bigg{)} ^{2}-\frac{\rho_{1}{h_{2}}^{3}-\rho_{2}{h_{1}}^{3}}{\rho_{1}h_{2}+\rho_{2}h_{1 }}\Bigg{]} \tag{9}\]  
Equations (2)-(10) has the following inner isolated wave solution, called the eKdV theoretical solution:  
\[H(x,t)=\frac{H_{0}}{B+(1-B)\cosh^{2}\Bigl{[}\frac{(x-c_{eKdV}t)}{l_{eKdV}} \Bigr{]}} \tag{10}\]Of which:  
\[c_{eKdV}=c+\frac{H_{0}}{3}\bigg{(}\alpha_{0}+\frac{1}{2}\alpha_{1}H_{0}\bigg{)} \tag{11}\]  
\[l_{eKdV}{}^{2}=\frac{24\beta}{H_{0}(2\alpha_{0}+\alpha_{1}H_{0})} \tag{12}\]  
\[B=\frac{-H_{0}\beta}{2\alpha_{0}+\alpha_{1}H_{0}} \tag{13}\]  
\(c_{eKdV}\) is the phase velocity of the internal isolated wave calculated with theory. The theory corresponds to the extreme value of the internal isolated wave amplitude as:  
\[H_{\text{max}}^{eKdV}=\frac{4h_{1}h_{2}(h_{1}-h_{2})}{h_{1}^{2}+h_{2}^{2}+6h_{1 }h_{2}} \tag{14}\]
# A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
## 2 Literature Review
### Machine Learning Enabled Intrusion Detection for IoT Appliances
#### 2.1.3 IoT-Enabled Networks
The authors have suggested the use of FL to protect the IIoT framework from DDoS attacks. They trained the model locally so that edge nodes can include modules that perform analysis and detection for DDoS. These modules include traffic policies that all the network traffic must adhere to before a connection can be established. As per their scheme, upon sensing any malicious cyber activity, the IDS moves the application to the sandbox, and related information is updated to the cloud to make other edge nodes aware of this type of intrusion. Data security is a big concern in smart healthcare industry applications. [PERSON] et al. [PERSON] et al. (2018) addressed the problems related to machine learning-based modeling attacks by proposing a physically-unclonable function (PUF)-based authentication mechanism. This proposed scheme has two phases: registration and authentication, to ensure secure connections and protection against man-in-middle attacks, replay, etc. However, weak PUF (WPUF) and one-time PUF (OPUF) can put the entire network at risk. Although their proposed scheme has shown decent protection against a few attacks, it does not protect against DDoS, Brute force, and bot attacks. These inferences and findings motivated us to develop a robust IDS for enhanced protection against potential attacks. Deep learning-based intrusion detection was proposed by [PERSON] and [PERSON] [PERSON] and [PERSON] (2017) to protect IoT networks. This model was evaluated using the NSLKDD dataset. Through this model, the author examined an IDS with the customary shallow model methods. This proposed IDS is executed with focused and appropriated validations. Test results showed that the distributed attacks detection schemes performed better in terms of accuracy. Additionally, the deep model is much better than the shallow model concerning the precision, recall, reliability, and F1 measures. Additionally, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed an IDS based on anomalies that make use of a gradient boosting machine (GBM) as a recognized automated machine. The GBM model ideal parameters are used in the grid-based search and the IDS uses three datasets, namely, UNSW-NB15, NSL-KDD, and GPRS, to approve using hold-out and cross-fold strategies. The proposed model was compared with previous approaches like fuzzy classifiers, GAR forest, and tree-based ensembles. It was found that the suggested model is better concerning accuracy, specificity, sensitivity, and area under the curve (AUC). A detailed summary of the highlighted paper is given in Table 2.
## 7 Simulation Assessment
### Effects of Delay Caused by PMUs  
PMUs were installed at the substation and SDG to monitor the grid and SDG currents. The PDC for collecting the data sent by PMUs was placed at the substation. The total delay of using PMUs was a combination of PMU processing, PDC processing, and serial, routing, and propagation  
Figure 7: IEEE-34 node radial test feeder: (**a**) one-line diagram; (**b**) time–current curve of a recloser–fuse.  
delays. The PMU processing and PDC processing delays were set at 26 ms and 80 ms, respectively [36]. The serial and routing delays were determined in Section 5. The propagation delay was obtained by the propagation speed and total distance. The total distance was the sum of the distance from the PMUs to the PDC and the distance from the PDC to the IBDGs. The shorter the total distance was, the lower the propagation delay was. The propagation delays from the PMUs located at the substation to IBDG1 (32 km) and IBDG2 (57 km) were 0.16 ms and 0.285 ms, respectively. On the other hand, the propagation delays from the PMUs installed at the SDG to IBDG1 (67 km) and IBDG2 (92 km) were 0.335 ms and 0.46 ms, respectively.  
The single line to ground (SLG) fault was estimated at 80% for all kinds of fault occurrences [24]. From 0.14 to 0.44 s, phase \(b\) was short-circuited to the ground with 0.01 \(\Omega\) of fault resistance (\(R_{f}\)) in the section between nodes 834 and 842. The fault was detected by IBDGs, and the FIM strategy of separated phase-current controls was enabled at 0.155 s. The grid and SDG currents computed by PMU data are shown in Figure 8a. After the fault was detected, the grid and SDG currents computed by PMU data were noticeably different from the real-time grid and SDG currents as a result of the delay. This resulted in an error in the current reference of the FIM strategy calculated by using the PMU data. The total fault current amplitude was not reduced to the grid current amplitude, as shown in Figure 8b. Several cycles later, the grid and SDG currents computed by the PMU data became close to the real-time grid and SDG currents. The total fault current amplitude was decreased to the grid current amplitude. Nevertheless, it was too late because the fuse melted before the recloser operated, as shown in Figure 8c. The recloser-fuse coordination was thus compromised.  
Figure 8: Effect of delay caused by using phasor measurement units (PMUs) on separated phase–current controls: (**a**) real-time currents and currents calculated by PMU data; (**b**) total fault current and grid current; (**c**) operating time of recloser–fuse and Synchronous distributed generators (SDG) breaker.
## 1 Introduction
The least squares recursive algorithm was used to identify the system parameters and obtain the health state range of the variable parameters of the system under the same operating conditions. Then, under variable operating conditions, the identification algorithm was used to obtain variable parameters and compare them with the parameter range of health state to achieve hydraulic system health state evaluation. The main contributions and innovations of this paper can be summarized as follows:  
* The idea of evaluating the health state of a hydraulic system based on parameter estimation is introduced and implemented. The simulation results verify the feasibility of this idea. * A hydraulic system parameter identification model is given, and the parameter indicators for evaluating the health status of the hydraulic system are delineated. * The combination of a signal processing-based method and a model-based method is performed, and the problem of evaluating the health status under variable operating conditions is solved based on the data analysis of the same operating condition. The remaining parts of this paper are arranged as follows. Section 2 establishes a nonlinear mathematical model of the electro-hydraulic servo system for a valve-controlled cylinder system. Section 3 gives the system parameters based on the least squares recursive algorithm through the simulation experiment. Section 4 gives a specific process of evaluating the health status of the hydraulic system under variable operating conditions through the simulation experiment. Section 5 is a brief summary of this paper, together with the future research work.
# Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
## 6 Demonstration
### Quantum Compressive Sensing
In (**a**,**b**) we see (as anticipated) that the Decomposition protocol lowers the accuracy of reconstruction when measurements are introduced, since these classes of sensing matrix do not satisfy the condition that \(A\) is non-zero on only \(m\) columns. However, (**b**) and especially (**c**) demonstrate that sparse matrices can approximate this condition, increasing accuracy of the Decomposition protocol for larger \(m\). In (**d**), the performance of QITE and Rodeo fall off due to the restrictions of the sensing matrix, but the Decomposition performance is impressive. Finally, Figure 14d considers non-binary matrices in which \(m\) columns are sampled uniformly between 0 and 1, and the remaining \(n-m\) columns are left at zero. These sensing matrices are inherently flawed, in that they are guaranteed to only ever measure a restricted portion of the signal \(y\). Any distinguishing characteristics found in the unmeasured portion of the signal cannot be used to project the Born machine \(|\Psi\rangle\). Indeed, we see in this case that the QITE protocol, up to this point the gold standard, falls off significantly, and the rodeo algorithm fails all but completely. That said, this class of sensing matrices is the most general on which we can expect the Decomposition protocol to function as intended, and indeed we observe that it performs extremely well. We conclude that Quantum Compressive Sensing, implemented with a projection protocol suited to the particular sensing matrix in question, is an effective means of introducing incomplete information associated with a signal of interest.
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 4 ESPEA for Solving the PVSP-MSS
### Framework of the ESPEA  
In this subsection, the proposed algorithm is outlined in Algorithm 1. In steps S1-S2, we encode a pickup vehicle arrangement scheme using two one-dimensional sequences and further use the cooperative initialization strategy to provide an initial solution for the optimal schedule. In step S3, a null elite archive is created to store elite individuals. During the population evolution process, steps S4-S6 utilize insertion decoding to optimize the pickup vehicle sequence. Then, the objective value and the fitness value for each individual are calculated. Based on the fitness value, steps S7-S8 use the population environment to select elite individuals for crossover and mutation. In step S9, a local search technique based on the critical path is used to find a better scheduling solution.  
```
0: Initial number of iterations \(q=0\); maximum number of iterations \(q_{max}\); near neighbor threshold \(k\); elite archive size \(\eta\); population size \(NP\), crossover probability \(CP\), mutation probability \(MP\);
0: Pareto front solution for the elite archive \(A_{q_{max}}\);  S1: Particle coding for pickup vehicle arrangement scheme, go to S2;  S2: Generate population \(P_{0}\) using the cooperative initialization strategy, go to S3;  S3: Create a null elite archive \(A_{0}\), go to S4; while\(q<q_{max}\)do  S4: Insertion decoding for \(P_{q}\) and \(A_{q}\), go to S5;  S5: Calculate the object value according to (2) and (3) for \(P_{q}\) and \(A_{q}\), go to S6;  S6: Calculate the fitness \(F\) according to (15) for \(P_{q}\) and \(A_{q}\), go to S7;  S7: Select environment for \(P_{q}\) and \(A_{q}\), and save the elite individual to \(A_{q+1}\), go to S8;  S8: Crossover and mutation for \(A_{q+1}\), and save the results to \(P_{q+1}\), go to S9;  S9: Local search based on a critical path for \(P_{q+1}\) to generate new solution, and replace the parent solution as much as possible, go to S10;  S10: Update \(q=q+1\); endwhile
```  
**Algorithm 1** Framework of the ESPEA
## 2 Theoretical Background  
### Wireless Sensor Network Routing Protocols  
A wireless sensor network consists of a finite set of devices named sensors that can sense or regulate physical characteristics such as sound, light, temperature, humidity, and others in a geographical area. Wireless sensor nodes communicate with the base station or sink and other nodes through wireless channels as shown in Figure 1. Sensor nodes have limited energy, memory, and CPU capacity. A sensor node has a power unit, a processing unit, one or more sensing units, a transceiver, an antenna, and optional components like a position finding system, a power generator, and an actuator. The volume of sensor nodes varies from cubic nanometres to cubic decimetres. [7]. The node location of sensor nodes in a network may be known or unknown, actual, or logical communication between network nodes and all other devices determines the topology. A WSN can have different topologies depending on the network and node tasks. WSN relies heavily on the speed and reliability of data delivery. Routing protocols are in charge of establishing how data moves through the network. Routing methods for WSNs should take energy usage, coverage area, and other considerations into account. Based on the network topology, WSN's routing protocols may be classified as either flat, location-aware, or hierarchical [8].
# Equivalent Dynamic Analysis of a Cable-Driven Snake Arm Maintainer
## 4 Model-Based Control  
Traditional \(PID\) control is widely used in industry due to its simplicity and reliability. However, when the system dynamic model is complex and variable, simple \(PID\) control cannot provide sufficient accuracy. Model-based control can effectively improve the robustness of the system. Therefore, the validity of the equivalent dynamics can be verified by introducing a simple dynamics model-based \(PID\) controller [37,38].  
As shown in Figure 8, in the working process of the snake robot, the first step is to perform online or offline trajectory planning to obtain the inverse kinematics solution \((\delta_{i},\gamma_{i})\) of each joint group. Then, the kinematics inverse solution is divided into two channels: one channel is transformed into the motor-driving signal (\(\mathbf{\theta}_{i}\)). Another channel imports the Lagrangian dynamic model to solve the driving torque of each joint and obtain the feedforward torque (\(\mathbf{T}_{id}\)) of the motor through equivalent transformation and decoupling calculation. The model-based feedforward torque (\(\mathbf{T}_{id}\)) of the motor plus the motor drive torque (\(\mathbf{T}_{ik}\)) after \(PID\) tuning jointly control the output torque (\(\mathbf{T}_{i}\)) of the motor, as well as the movement of the robot. The kinematic data (\(\mathbf{\theta}_{ip}\)) of the robot movement process are collected and fed back to the closed-loop controller.  
The torque (\(\mathbf{T}\)) of each joint is expressed as follows:  
\[\mathbf{T}=\mathbf{T}_{d}+\mathbf{T}_{k} \tag{31}\]  
\[\mathbf{T}_{k}=\mathbf{k}_{p}\mathbf{e}(\mathbf{t})+\mathbf{k}_{i}\sum\mathbf{e}(\mathbf{t})+\mathbf{k}_{d} \dot{\mathbf{e}}(\mathbf{t}) \tag{32}\]  
where \(\mathbf{e}(\mathbf{t})=\mathbf{\theta}(\mathbf{t})-\mathbf{\theta}_{\mathbf{p}}(\mathbf{t})\) is the motor-angle tracking deviation; \(\mathbf{k}_{p}\) is the proportional coefficient, which responds to the current deviation of the system (\(\mathbf{e}(\mathbf{t})\)); \(\mathbf{k}_{i}\) is the integral coefficient, which responds to the accumulated deviation of the system (\(\sum\mathbf{e}(\mathbf{t})\)); and \(\mathbf{k}_{d}\) is the differential coefficient, which reflects the rate of change of the system deviation signal  
Figure 8: Model-based equivalent dynamic control principle.  
(\(\dot{e}(t)\)). By combining the equivalent dynamics model (\(T_{d}\)) of SAM and the compensation output (\(T_{k}\)), the joint control torque (\(T\)) can be obtained.
## 3 Results
### Application of the New CL System
#### 3.2.2 Recovery Experiments and Analysis of Urine and Feed Samples  
Approximately 100 mL urine or feed prepared sample solution was divided into four portions: one was a blank solution, whereas the others were added with standard solution to determine recoveries. All test solutions were then diluted appropriately with pure water to within the linear range of determination. Recoveries were 95% to 105% for SAL and 95% to 108% for TEB in urine and feed samples, respectively. The results of the blank samples agreed with those obtained by the HPLC-UV method [32]. Chromatographic separation was performed on a CN column (10 \(\upmu\)m, 250 mm \(\times\) 4.6 mm i.d.). The mobile phase used consisted of acetonitrile and KH\({}_{2}\)PO\({}_{4}\) (0.075 M) in a ratio of 85:15 at a flow rate of 1.0 mL/min. UV detector wavelength was set at 215 nm according to UV scan spectra. The results by HPLC measurement were no detection. All results are listed in Table 2.