esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, the rapid growth of vehicles has imposed a significant burden on urban road resources. To alleviate urban traffic congestion in intelligent transportation systems (ITS), real-time and accurate traffic flow prediction has emerged as an effective approach. However, selecting relevant parameters from traffic flow information and adjusting hyperparameters in intelligent algorithms to achieve high prediction accuracy is a time-consuming process, posing practical challenges in dynamically changing traffic conditions. To address these challenges, this paper introduces a novel prediction architecture called Multiple Variables Heuristic Selection Long Short-Term Memory (MVHS-LSTM). The key innovation lies in its ability to select informative parameters, eliminating unnecessary factors to reduce computational costs while achieving a balance between prediction performance and computing efficiency. The MVHS-LSTM model employs the Ordinary Least Squares (OLS) method to intelligently reduce factors and optimize cost efficiency. Additionally, it dynamically selects hyperparameters through a heuristic iteration process involving epoch, learning rate, and window length, ensuring adaptability and improved accuracy. Extensive simulations were conducted using real traffic flow data from Shanghai to evaluate the enhanced performance of MVHS-LSTM. The prediction results were compared with those of the ARIMA, SVM, and PSO-LSTM models, demonstrating the innovative capabilities and advantages of the proposed model.  
traffic prediction; heuristic selection; LSTM; deep learning +
Footnote †: journal: Medical and Medical and Medical Engineering, Shanghai Normal University,  
1
## 4 Discussion  
### Complex Relationship Between DE and GD  
Our research findings indicate that both the DE and GD levels in China are steadily improving, aligning with the results in [49; 59]. The DE, as a new economic form, serves as a crucial driving force for enhancing efficiency and optimizing economic structures. Meanwhile, GD paves the way to sustainable development, inseparable from enhancing the industrial structure and improving allocation efficiency. Therefore, green sustainable development is intrinsically linked to the advancement of the DE.  
A substantial body of research indicates that the DE significantly enhances GD [60; 61; 62]. This phenomenon can be attributed to the DE's ability to facilitate green production through industrial transformation [63] and improve ecological quality and living standards through effective digital governance [64], ultimately achieving green and sustainable development. Nevertheless, our findings indicate that the initial effect of the DE on GD is negative. However, this impact gradually diminishes over time. Regional consistency is demonstrated by this trend, with the notable exception that, in the later stages, a positive impact is observed in the western region. A plausible explanation for this phenomenon is that the early development of the DE contributes to increased production scales, which in turn leads to higher carbon emissions that negatively impact GD. As technological advancements and the implementation of green technologies expand, the positive and negative effects start to counterbalance each other. Furthermore, disparities in resources and policies across various regions also play a significant role in shaping this outcome.  
Additionally, the influence of GD on the DE is shown to follow a consistent trend of transitioning from negative to positive across the eastern, central, and western regions. In this process, a significant role may be played by green technological innovation, as it is  
Figure 10: Changes in the GD level in provincial areas from 2021 to 2023.  
regarded as a critical mediating mechanism in the facilitation of high-quality GD by the DE. Initially, considerable capital investments are necessitated by technological innovations, and a substantial time frame is required before practical application, which may result in adverse impacts on the DE during the early stages. As technologies mature and are effectively implemented, continued growth in market demand for these innovations is observed. Moreover, the dynamic is further shaped by policies that actively promote high-quality GD. Collectively, renewed vitality is injected into the advancement of the digital economy by these factors.  
Given the complex relationship between the DE and GD, our analysis using a machine learning prediction model revealed a consistent trend of lagged effects between the two. This finding further underscores their close interconnection and reaffirms the results of our study.
## 3 Results
### Degradation of Soil Pesticides Under Different Tillage Methods
#### 3.5.3 The Degradation of Pesticides at the Mid-Incubation Period  
The mid-incubation phase, approximately 40 days post-application, serves as a critical time point for evaluating relative differences in degradation rates among various pesticides under uniform tillage conditions. This study reveals that mesotrione consistently exhibited the highest degradation efficiency across all tillage treatments at this stage.  
Under CK conditions, mesotrione's degradation efficiency was significantly greater than that of atrazine, acetochlor, and alachlor. Nicosulfuron showed comparable degradation to mesotrione at 40 days under CK.  
Under SS conditions, mesotrione's degradation efficiency surpassed that of all other herbicides, including incosulfuron.  
Under ST conditions, mesotrione showed significantly higher degradation efficiency compared to the strongly adsorbed and slowly degrading chloroacetamide herbicides acetochlor and alachlor. However, no statistically significant differences were found between mesotrione and either atrazine or incosulfuron at day 40. Alachlor exhibited a relatively high degradation rate during the early stage under ST, with 39% degradation observed by day 7. Nicosulfuron showed a steady degradation pattern throughout the incubation period under the same treatment.  
In summary, mesotrione consistently showed the highest degradation efficiency among the five tested herbicides across all tillage treatments at the mid-incubation stage (approximately 40 days after application). Under CK and SS conditions, mesotrione degraded more rapidly than acetochlor and alachlor. Its advantage was especially notable under SS, where it also outperformed nicosulfuron. Under ST, mesotrione remained more efficiently degraded than the chloroacetamide herbicides. However, no significant differences were found between mesotrione and either atrazine or nicosulfuron. These findings highlight mesotrione's relatively rapid dissipation and its distinct response to tillage practices compared to other commonly used herbicides.
## 5 Attributes and Characteristics of FL
### Data Privacy
#### 5.1.1 Introduction to Data Privacy in FL  
Safeguarding data privacy is crucial in today's digital life. Federated learning is at the forefront of this effort, dedicated to protecting user data through advanced techniques like secure multi-party computation (SMC) and differential privacy [39]. As the digital environment evolves with challenges like quantum computing and deepfakes, FL must enhance its privacy measures. Communication within FL, especially across numerous devices, is vital. [PERSON] et al., in [45], highlight the importance of efficient data transmission and resource management. Compression techniques can help to reduce the data transmission volumes, making FL feasible even in bandwidth-limited scenarios. Building on [23], FL is promising for decentralized data frameworks and extends to ensemble models, complex collaborative filtering, and more intricate architectures. The decentralized nature of FL means that models are trained directly on edge devices or nodes, minimizing the risk of data exposure and breaches by sending only model updates or essential information to the central model.
## 3 Application to Classical Computing Device: Resonant Tunneling Diode
### Numerical Results
We therefore define the power spectral density due to the unmodified system, i.e., the system where the protocol is not applied, as \(\mathbb{P}_{unmodified}=\mathcal{F}\{\langle I_{T}(t_{2})I_{T}(t_{1})\rangle\}\) while \(\mathbb{P}_{modified}=\mathcal{F}\{\langle I_{T}(t_{2})\rangle\langle I_{T}(t_ {1})\rangle\}\) for the modified system where the protocol is implemented. Here \(\mathcal{F}\) represents the Fourier transform operator. Finally we can define the relative error (RE) as:  
\[\text{RE}=\frac{\mathbb{P}_{modified}-\mathbb{P}_{unmodified}}{\mathbb{P}_{ modified}}, \tag{12}\]  
which is plotted in Figure 3. The RE in Equation (12) contains quantum noise at all frequencies. Since all electrical devices are, in fact, a low pass filter, it is interesting for engineering purposes to quantify the error as a function of the frequency. A value of the RE equal to zero indicates that the quantum uncertainty (of the system and _ancilla_) has been eliminated which has a direct correspondence with the increase in the transport electrons in the device that can be further attributed to a large transmission or a large average current. Besides this it is also easy to see that to arrive at a zero quantum uncertainty we do not need an infinite number of electrons. Figure 2: A 3D schematic of the RTD where the green material represents GaAs while the red represents AlGaAs. The alternating AlGaAs-GaAs-AlGaAs structure results in a potential well with discrete resonant energies. In our simulation the total number of electrons injected is given by \(N=t/\tau_{in}\) where \(t\) is the total simulation time (the time after which the mean current reaches a constant value) and \(\tau_{in}\) is the injection time of electrons. Now we know that \(\langle I_{T}\rangle=\frac{qTf_{e}}{\tau_{in}}\) where \(T\) is the transmission coefficient and \(f_{e}\) is the Fermi function which we assume to be unity. So it is straightforward to see that \(N=\frac{t\langle I_{T}\rangle}{qTf_{e}}\). We used the total time of simulation as 50 ps, \(T=0.7\), \(f_{e}=1\) and \(\langle I_{T}\rangle=5\) uA. These values correspond to a point in graph where the uncertainty starts to disappear which gives us the value of \(N\approx 2232\). Which is a large value but not infinite.
# Efficient QC-LDPC Encoder for 5G New Radio
###### Abstract  
This paper presents a novel efficient encoding method and a high-throughput low-complexity encoder architecture for quasi-cyclic low-density parity-check (QC-LDPC) codes for the 5 th-generation (5G) New Radio (NR) standard. By storing the quantized value of the permutation information for each submatrix instead of the whole parity check matrix, the required memory storage size is considerably reduced. In addition, sharing techniques are employed to reduce the hardware complexity. The encoding complexity of the proposed method was analyzed, and indicated a substantial reduction in the required area as well as memory storage when compared with existing state-of-the-art encoding approaches. The proposed method requires only 61% gate area, and 11% ROM storage when compared with a similar LDPC encoder using the Richardson-Urbanke method. Synthesis results on TSMC 65-nm complementary metal-oxide semiconductor (CMOS) technology with different submatrix sizes were carried out, which confirmed that the design methodology is flexible and can be adapted for multiple submatrix sizes. For all the considered submatrix sizes, the throughput ranged from 22.1-202.4 Gbps, which sufficiently meets the throughput requirement for the 5G NR standard.  
quasi-cyclic LDPC code; channel codes; 5G New Radio; encoding +
Footnote †: journal: Electronics Electronics
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### Method of Determining K Value Based on the Mean Value of Instantaneous Frequency  
When using VMD to decompose the original signal, the value of the decomposition modulus K will greatly affect the decomposition effect. If the K value is too small, the decomposed subsequence will lose information or cause modal heterogeneity; on the contrary, if the K value is too large, it will lead to excessive decomposition, increase the amount of calculation of the subsequent neural network, and then affect the prediction effect [35]. Therefore, it is very important to select an appropriate K value and fully extract the characteristics of the original data. This article introduces the instantaneous frequency mean value method of the component to select the K value. The main idea is that the IMFs obtained based on VMD decomposition are with obviously different frequencies. By dividing the original sequence into sub-sequences with different modulus, the mean value of the component instantaneous frequency under various K values is calculated. If the average change corresponding to two adjacent K values is significantly reduced, it is considered that the over-decomposition has caused the modal confusion phenomenon at this time, and this critical K value is the optimal decomposition modulus. In order to verify the effectiveness of this method, an analog signal is established, which is shown in Formula (7).  
\[x(t)=\cos(4\pi t)+\frac{1}{8}\cos(48\pi t)+\frac{1}{32}\cos(576\pi t) \tag{7}\]After the signal is decomposed by VMD, three IMFs should be obtained, as shown in Figure 1:  
Using the instantaneous frequency mean value method, the decomposition is divided into deformed sub-sequences with different modulus, and the instantaneous frequency mean values of the components under different K values are calculated. The specific calculation results are shown in Figure 2. It can be seen that when K \(\geq\) 4, each component has a certain degree of modal confusion. Therefore, for analog signals, K = 3 is the best decomposition modulus, which is consistent with the preset K value. It is proved that the method is effective in selecting the modulus of VMD.
# Power Factor Correction Application Based on Independent Double-Boost Interleaved Converter (IDBIC)
## 3 Simulation Results  
The simulation was performed based on a MATLAB Simulink model of the described PFC solution within this paper. The whole structure is depicted in Figure 4.  
The \"PFC\" sub-system represented in Figure 5 contains the control law of the power stage [30]. The reference rectified voltage was obtained from the input voltage waveform by reversing the negative half-cycle. Based on two proportional-integrator (PI) regulators, the control law was implemented, and the output \"Duty_Cycle\" drive signal was computed as result. The \"Voltage_PI\" regulator maintains the output DC voltage constant, and the \"Current_PI\" regulator limits and shapes the input current.  
Figure 6 represents the sub-system of the \"PWM_generator\" which, depending on the signals received by the \"PFC\" subsystem, realizes the control logic of the four transistors.  
Figure 4: The generic output voltage control loop and PWM generator. \(\mathrm{V_{in}}\)—AC input voltage; \(\mathrm{V_{rec}}\)—rectified voltage; \(\mathrm{V_{out}}\)—DC output voltage; \(\mathrm{V^{*}}_{out}\)—reference DC output voltage; \(\mathrm{I_{rec}}\)—rectified current; \(\mathrm{I^{P}}_{rec}\)—reference rectified current; \(\mathrm{I_{in}}\)—input current; PWM T1, PWM T2, PWM T3 and PWM T4 — Gate-Source voltage for \(\mathrm{T_{1}}\)–\(\mathrm{T_{4}}\) transistors.  
Figure 5: MATLAB Simulink inside “PFC” sub-system. K—Current calibration factor.  
Following Figure 7, the input current (I\({}_{\text{in}}\)) and voltage (V\({}_{\text{in}}\)) can be analyzed in conjunction with the output voltage (V\({}_{\text{out}}\)). Additionally, the inductor L\({}_{1}\) and L\({}_{2}\) currents (i\({}_{\text{L}1}\) and i\({}_{\text{L}2}\)) synchronized at low frequency are represented, followed by the rectified input voltage (V\({}_{\text{rec}}\)), all these signals being introduced in Figure 4.  
Figure 8 shows the L\({}_{1}\) and L\({}_{2}\) coils' current evolution at both low- and high-frequency representations. One can see that during a low-frequency cycle, the boost converters are  
Figure 6: MATLAB Simulink inside “PWM_generator” sub-system.  
Figure 7: Low—frequency waveforms representation of the input voltage (Vin), input current (lin), output voltage (V\({}_{\text{out}}\)), inductor L\({}_{1}\) and L\({}_{2}\) currents (i\({}_{\text{L}1}\), i\({}_{\text{L}2}\)) and the input rectified voltage (Vrec).  
working from DCM (Figure 8a) and BCM (boundary conduction mode, Figure 8b) to CCM. In CCM, two current waveforms can be noticed at duty cycles bigger then 0.5 (Figure 8c) or smaller then 0.5 (Figure 8d).  
Figure 8: L\({}_{1}\) and L\({}_{2}\) inductor currents’ (I\({}_{\text{L1}}\), I\({}_{\text{L2}}\)) representation at low and high frequency representations; (**a**) DCM; (**b**) BCM; (**c**) CCM for D \(>\) 0.5; (**d**) CCM for D \(<\) 0.5.
## 4 Results and Discussion
### Situational Awareness  
In our experiment, SART results showed that participants using the Oculus Quest 2 and the monitor-based group performed better than a traditional paper-based group. They were also aware of their surroundings because of Oculus Quest 2 ability to switch on the camera when double tapping on the gear. This feature makes it unique and assists it in better performance. The 3D design model of the simulated VE on the monitor, and Oculus Quest 2 seemed to help the participants understand their task; concerning SA, both VEgroups performed better than the traditional group. Attentional D and attentional supply S were less for the latter two environments than in the first one but understanding (U) was vice versa. Participants who used the Oculus Quest 2 focused solely on the immersive VE, making it easy to comprehend their surroundings by utilizing the cognitive resources of attentional supply (arousal, concentration, attentional division, and mental capacity). It is a fact that the design reviewer must perform various cognitive tasks simultaneously, such as analyzing, comprehending, remembering, and making assumptions of alternatives, if any. They must be fully aware of their surroundings to perform better. In general, using these virtual headsets or HMD would potentially impact the industry with better performance and reduce the risk and errors coming forth in the execution of the project.
# A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks
###### Abstract  
With the development of modern society, there are not only many voice calls being made over wireless communication systems, but there is also a great deal of demand for data services. There are increasing demands from the general public for more information data, especially for high-speed services with elevated Gbps levels. As is well known, higher sending power is needed once data rates increase. In order to solve this problem, virtual cellular networks (VCNs) can be employed in order to reduce these peak power shifts. If a VCN works well, mobile ports will receive their own wireless signals via individual cells, and then, the signals will access core networks with the help of a central terminal. Power control can improve the power capacity in multi-hop networks. However, the use of power control will also have a negative impact on network connectivity, delay, and capacity. In order to address the problem, this paper compares specific control methods and capacities in multi-hop networks. Distributed chicken game algorithm power control (DCGAPC) methods are presented in order to reach acceptable minimum levels of network delay and maximum network capacity and connectivity. Finally, a computer simulation is implemented, and the results are shown.  
multi-hop network; chicken game; power control; receiving power; transmitting power +
Footnote †: journal:  
_Article_  
A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks
## 3 Analysis of Output Characteristics Variation  
The SS topology with the proposed design method has a self-limitation characteristic. As indicated by (17) and (18), unlike conventional compensation topologies in which the output voltage and current are constant, the output equation consists of complex structures, and the values of the output current and voltage depend on the load. As the load resistance value fluctuates, the ratio between the reactance and resistance components included in the denominator of the output equation varies. Accordingly, the output characteristic equation is divided into three modes.
## 3 Results and Discussion
### Essential Oil and Hydrosol Extract Chemical Profile Implementing GS-MS Analysis
#### 3.1.1 _O. vulgare_ Essential Oil and Hydrosol Extract Chemical Profile  
The essential oil of _O. vulgare_ contained eight major compounds, comprising 92.2% of the total EO. The major compound was carvacrol (63.0%), followed by its precursors \(\mathbf{p}\)-cymene (11.8%) and \(\mathbf{\gamma}\)-terpinene (8.4%). Other compounds were detected at relatively low levels: \(\mathbf{\beta}\)-myrcene (2.0%), \(\mathbf{\alpha}\)-pinene and \(E\)-caryophyllene (1.9%), thymol (1.8%), and \(\mathbf{\alpha}\)-terpinene (1.6%). Similar findings have been reported by other studies, demonstrating that the essential oil of _O. vulgare_, cultivated in Greece, consists of mainly carvacrol, \(\mathbf{p}\)-cymene, \(\mathbf{\gamma}\)-terpinene, while \(\mathbf{\alpha}\)-terpinene, thymol, \(E\)-caryophyllene, \(\mathbf{\alpha}\)-pinene were reported as minor constituents [19; 20; 42; 43]. [PERSON] et al. [23], reported that this oregano subspecies, which is characterized by carvacrol chemotype (72.4-88.2%), contains its isomer thymol in low percentages or traces, which is typical for Greek oregano (_O. vulgare_ spp. _hirtum_) [44; 45]. In contrast, other studies that reported the chemical profile of oregano derived from other Mediterranean areas showed differences in chemotype of _O. vulgare_ (Table 1). The main compound of Italian _O. vulgare_ EO was _trans_-sabinene hydrate [24], and was thymol and \(\mathbf{\gamma}\)-terpinene for Sicilian [46]. These data demonstrate that the chemotype of carvacrol _Origanum vulgare_ spp. _hirtum_ might be detected mainly in Greece. As shown in Table 2, the _O. vulgare_ HS was consisted almost entirely of carvacrol at (97.3%). Of note, carvacrol was detected in _O. vulgare_ HS, apparently in a much higher percentage compared to EO. This profound difference could be attributed to the fact that some non-polar molecules, especially terpene hydrocarbons (\(\mathbf{\alpha}\)-pinene, \(\mathbf{\alpha}\)-myrcene, \(\mathbf{\alpha}\)- and \(\mathbf{\beta}\)-terpinene), are detected only in EOs [47]. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Chemotype**} & \multicolumn{1}{c}{**Main Compounds**} & \multicolumn{1}{c}{**Country Origin**} & \multicolumn{1}{c}{**References**} \\ \hline \multicolumn{1}{c}{_Origanum vulgare_ ssp.
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 4 Problem Analysis and Strategy Design  
The proposed optimization problem is a mixed-integer programming problem, and the complexity of obtaining the optimal solution is NP-hard. To solve this optimization, we analyze the optimization problem and design an available backhaul capacity ratio to estimate the available capacity of satellites in the handover decisions. Then, we propose the maximum backhaul capacity handover strategy based on the available backhaul capacity ratio.