esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 1 Introduction
### Problem Description  
The idea is to secure and improve driver and passenger safety on roads. Urban vehicle safety is an idea to predict the road and accident conditions before any accident. The scenario is based on data dissemination which is periodic or not enabling the state of the road and surrounding vehicles. The continuity of the dynamic connections, limited coverage, fast vehicular movement, connection handover, and service interruptions lead to VANET content sharing degradation and high delay in content sharing. So, we focused on delay-sensitive services in VANET. It is better for service delivery to pre-cache the content when vehicles approach zones. The short time connecting vehicles to zones shows a delay in the content response process. The novelty of benefiting from early content prediction is enhancing road safety in urban areas.  
The problem lies when content pre-aching is manual in recent research, which causes a delay in the network performance. In Geocast, a data dissemination message is sent only to the vehicle inside a specific geographical area called a zone of relevance (ZoR). The proposed mechanism disseminates safety messages in V2V and V2I scenarios. It is specially designed and suitable for the urban environment. The main goal of Geocast communication is to perform a safe information dissemination process within the zone of relevance (ZoR) and without the zone of forwarding (ZoF). This work defines the new secure pre-caching strategy to handle urban vehicle road and incident conditions through zone-based techniques. With high accuracy, the machine learning algorithm is used for early content prediction of road and traffic incidents.  
In this paper, we have considered the below-mentioned questions during the task scheduling.  
1. How can a machine learning algorithm predict the pre-caching strategy to control traffic congestion?
2. How can congestion be controlled in VANET?
3. How to handle the pre-caching through the manager node from every zone?
## 5 Enhancement of Brillouin Stokes Signal (II): Influence of Core Diameter and Fiber Length
### Brillouin Linewidth Narrowing Effect  
Although the Brillouin linewidth of a PFGI-POF has been reported to be 105 MHz at \(P_{\text{in}}\) of 20 dBm in [27], detailed investigations were difficult because the Stokes power was extremely low. Here, by making use of the enhanced Stokes power, we investigated the Brillouin linewidth dependence on \(P_{\text{in}}\).  
Figure 10(a) shows the measured BGS of the 200-m PFGI-POF with 62.5-\(\upmu\)m core at \(P_{\text{in}}\) of 14.5, 17.5, and 23.5 dBm, where the Stokes power is normalized so that the maximum power is 0 dB. Figure 10(b) shows the Brillouin linewidth dependence on \(P_{\text{in}}\). From these figures, we can see that, with the increasing \(P_{\text{in}}\), the 3-dB linewidth of the BGS decreases, but that its slope gradually becomes small. This behavior agrees well with the experiment and theory of the linewidth narrowing effect in silica-based SMFs [39].
# Double-Peaked Mid-Infrared Generation Based on Intracavity Difference Frequency Generation
## 1 Introduction
[34] designed a single-cavity dual-comb OPO pumped by a dual-comb solid-state laser, generating an idler beam with a dual-peaked spacing of 29 nm and FWHMs of 134 and 130 nm, respectively. The third technique is nonlinear frequency conversion with pulsed pumping sources. The pulsed sources that are improved by the technique effectively change the optical properties of atomic systems [35; 36]. At the same time, pulsed pump sources for OPO systems are also excellent at generating broadband mid-infrared lasers [37; 38; 39]. In this paper, the CW pump source is an ASE fiber source, and the TFBG is chosen as the spectral modulation device, which generates a double-peaked pump source. TFBGs are essential in gaining suppression and wavelength selection as excellent passive fiber devices. Previous applications have mainly suppressed nonlinear effects that impact laser performance [40]. There is little research that suppresses the gain extremum point. In this research, the modulation property of TFBG on the spectrum is utilized to modulate the pumping spectrum, which generates a double-peaked pumping source. In terms of nonlinear frequency conversion, the intracavity DFG technique is utilized for frequency conversion of the double-peaked pump source [41; 42]. It is reported that a double-peaked pumping source is achieved by TFBG modulation, and intracavity DFG generates a tunable double-peaked mid-infrared laser. By modulating the TFBG bandwidth and transmission spectrum depth, it is essential to generate mid-IR lasers with a different spacing of peaks. The double-peaked mid-infrared generation is located at 3688-3786 nm. The spacing of the two peaks is 23-37 nm, and the FWHM of each peak is 12-30 nm. The double-peaked mid-infrared generation is 0.18 W, and the pump-idler conversion efficiency is 8.5%. The double-peaked mid-infrared generation with adjustable central wavelength, controllable double-peaked spacing, and controllable FWHM has excellent potential for DIAL application. The research also demonstrates the feasibility of modulating the mid-infrared laser spectrum by modulating the pump spectrum. It has essential applications in the investigation of nonlinear frequency conversion.
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 4 Preliminary Design of an IoT-Aided Building Fire Evacuation Control System
### Potential Services and Applications
#### 4.3.2 Fire Risk Assessment  
The major function of the fire risk assessment module is to help identify the hazardous areas of the fire event so that occupants can bypass the area and evacuate smoothly. By collecting the ambient status from various environmental sensors, monitoring of the indoor fire environment is enabled. Afterwards, by comparing these monitoring data with the criteria for occupants to safely evacuate the building, such as at 2.0 m height of the enclosure space, air temperature below 100 \({}^{\circ}\)C, concentrations of carbon monoxide below 2800 ppm, etc. [70], the areas at risk can be identified. Finally, by linking these identified hazardous areas to the BIM platform, hazardous areas can be directly viewed on BIM models.  
In addition to identifying the hazardous areas in a fire, the collected data can also be used to mine more necessary information regarding fire characteristics, such as fire origins, fire sizes, stages of fire development, etc. This sort of problem is called a fire inverse problem, which involves using the observed sensor data to infer the causal factors that produced it. Many studies have explored the inference of fire origins, fire sizes, and stages of fire development from temperature observations [71; 72; 73; 74]. With the machine learning approach to find out the relationship between the fire characteristics and a large volume of simulated fire data, dynamic inference of the various fire characteristics from a series of on-site sensor data collection can be enabled. Understanding the characteristics of the ongoing fire provides the necessary information basis for building managers and ICs to predict the risks in the evacuation process, so it is very important for evacuation decisions.
## 2 Materials and Methods  
### Study Area  
#### 2.1.1 Chirp Pulse Waveform  
In the radar system, the wider the bandwidth, the smaller is the object that can be detected. However, implementation of the wider bandwidth is a burden to the system, and the development costs increase due to expensive hardware. To solve this problem, the chirp pulse has been widely used in SAR systems. The chirp pulse for linear frequency modulation (LFM) has a very large time bandwidth product (TBP) characteristic. The other advantage of using chirp is its correlation property [17], which can acquire received signals, omitting noise and other unwanted reflections.  
The radar emits the signal and acquires two-dimensional data: azimuth and range direction. The resolution of the radar can be expressed as:  
\[\Delta R_{xz}\approx\frac{L}{2}\text{ and } \tag{1}\]  
\[\Delta R_{rg}=\frac{ct_{p}}{2}, \tag{2}\]  
where \(\Delta R_{xz}\) is the azimuth resolution, \(L\) is the antenna length, \(c\) is the speed of the light, \(\Delta R_{rg}\) is the range resolution, and \(\tau_{p}\) is the pulse width without modulation. Instead of using the short pulse in Equation (2), SAR increases the bandwidth using chirp for better resolution. The range resolution using chirp modulation is given by:  
\[\Delta R_{rg}=\frac{c}{2B}=\frac{c}{2(f_{1}-f_{0})}, \tag{3}\]  
where \(B\) is the bandwidth, \(f_{1}\) is the last frequency, and \(f_{0}\) is the initial frequency. The wide bandwidth is easily implemented by the frequency variation. The chirp pulse is a rectangular shape and contains amplitude and phase terms:  
\[\text{Chirp signal}=rect(\frac{t}{T})[cos(\pi Kt^{2})+jsin(\pi Kt^{2})], \tag{4}\]  
where \(rect(t/T)\) is amplitude modulation, \(cos(\pi Kt^{2})+jsin(\pi Kt^{2})\) is the phase modulation, \(t\) is the time, \(T\) is the pulse width, and \(K\) is the chirp rate, which is the increment of the frequency over time. Baseband chirp signal is implemented using Equation (4).  
There are two types of digital chirp generation methods: memory-map-based waveform generator and algorithm-based waveform generator.
# Auditory Device Voice Activity Detection Based on Statistical Likelihood-Ratio Order Statistics
## 2 Auditory Device VAD Implementation  
As described in Section 1, the auditory filter bank should have uniformly spaced narrow frequency bands and at least 60 dB stopband attenuation, preferably higher [20; 21]. Furthermore, the low computational cost and low time latency of less than 10 ms are demanded for the filter bank. These restrictions are satisfied with a uniform polyphase DFT filter bank based on the fast Fouriertransform (FFT). In this paper, a 32-channel filter bank is employed with an 8 ms time delay under a 16 kHz sampling rate condition [20; 21; 27] on which the LRT VAD is implemented, as depicted in Figure 1.  
We set the number of channels \(Q=32\), the frame shift length via the STFT approach R = 16, and FFT size \(K=128\) for the oversampled perfect reconstruction [22; 27; 28]. The input time-discrete signal \(x(n)\) is buffered to form the input frame signal \(\mathbf{x}\), of which length is equal to the FFT size \(K\). In other words, the \(\ell\)th frame signal \(\mathbf{x}(\ell)\) is formed as \(\mathbf{x}(\ell)=[x(\ell\mathcal{R}),\,x(\ell\mathcal{R}+1),x(\ell\mathcal{R }+2),\ldots,x(\ell\mathcal{R}+K-1)]^{T}\), where \(T\) is the transpose operator. Then, the prototype low-pass filter (LPF) \(\mathbf{w}=[w(0),\,w(1),w(2),\ldots,w(K-1)]^{T}\) is applied to \(\mathbf{x}(\ell)\) as a window, and the windowed frame signal \(\hat{\mathbf{x}}_{\ell}=[\hat{x}(\ell\mathcal{R}),\,\hat{x}(\ell\mathcal{R}+ 1),\hat{x}(\ell\mathcal{R}+2),\,\ldots,\,\hat{x}(\ell\mathcal{R}+K-1)]^{T}\) is transformed into the complex-valued spectral component \(X_{k}(\ell)\) at the \(k\)th frequency bin \((k=0,1,\ldots,\,K-1)\) by an FFT. Here, 128 window sequences in [27] are used for the prototype LPF \(\mathbf{w}\) of the DFT filter bank.  
Whether the target speech has been activated (\(H_{1}\)) or not (\(H_{0}\)) is determined by applying a VAD algorithm to the \(X_{k}(\ell)\) at the \(k\)th frequency bin \((k=0,1,\ldots,K/2)\). Simultaneously, the 16 down-sampled speech signals at the \(q\)th frequency band, \(\hat{s}_{q}\big{(}\eta_{116}\big{)}\), can be obtained based on the real value from the complex component \(X_{2q}(\ell)\), which can be used to estimate the envelope power of each band.
## 6 Discussion
### Capacity Evaluation  
This section introduces the discussion on the evaluation method of traffic capacity of bus stations under a CAVs environment. The scenarios considered in the simulation of this paper include more classic simulation scenarios. From Figures 7-9, we can find that there is a significant correlation trend between FD, FLR, and traffic capacity, which shows that there is a clear relationship between these two indicators and traffic capacity. In the analysis process, it is found that the improvement in the penetration rate of autonomous driving has a good performance in alleviating traffic congestion, which is consistent with the research conclusions of references [9; 10; 11; 12]. In the second section, we found that the indicators have a great impact on the traffic capacity through reading of the literature. Therefore, in the fourth section, we propose a model of the relationship between FD, FLR, and traffic capacity for the simulation scenario in this paper. Now, let us discuss the research and analyze the relevant literature on this issue. It is found that the traffic capacity evaluation of bus stops under a CAVs environment is currently very rare, but the analysis of the traffic capacity of the bus stop under traditional traffic environment is still one of the hot issues in the bus field.  
In the course of the study, it is found that bus flow rate, bus dwell time, and bus stop type are the main factors affecting the change in traffic capacity, which is consistent with the literature [11; 22; 35]. For the specific evaluation method, we can obtain more objectivedata and indicators by counting the values of FD and FLR through the proposed indicators, which can be used to analyze the traffic situation within the bus stop. Through the use of evaluation indicators, different traffic strategies can be quantitatively analyzed and compared to select the best solution or make decisions under CAVs environment.  
In the existing research, the quantitative representation of traffic capacity is mostly calculated by the reduction coefficient. The advantage of using the reduction coefficient to evaluate the traffic capacity is that it can more accurately consider various factors in the real environment and make reasonable adjustments. It can provide more realistic and reliable capacity assessment results and help to develop more effective traffic planning and management measures. However, relatively speaking, the more comprehensive the factors considered, the better the data requirements. These algorithms of reduction coefficients are mostly applicable to traditional traffic flow, and their applicability under a CAVs environment still needs to be determined.  
In addition, the index evaluation method proposed in this paper also has the advantage of easy realization. Although the accuracy is less than some existing evaluation methods, it can be easily realized in some application scenarios with low accuracy requirements, which can help the beneficiaries to quickly understand the capacity of bus stops.
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
## 5 Factors Affecting Fermentation Process of Pharmaceutical Terpenoids
### Dissolved Oxygen  
Under aerobic conditions, yeast converts glucose to carbon dioxide and water to produce energy. However, at high glucose concentrations, it also produces ethanol from glucose, which is known as the \"Crabtree effect\". In yeast cultivation, the biomass increases very fast in the exponential phase, accompanying with high amount of oxygen consumption, so dissolved oxygen (DO) decreases quickly. Simultaneously, ethanol is produced because of the Crabtree effect. When glucose concentration decreases to a relatively low level, growth of yeast cells slows down due to inadequate carbon source in the medium; therefore, the DO increases promptly. If the glucose is added to culture medium, DO will again slowly decrease with the consumption of glucose. Thus, this approach of controlling DO plays an important role on cell growth and product formation, especially in fed-batch fermentations. The optimum glucose consumption rate and the yeast growth rate can be tightly controlled by the DO [106]. In a study for ergosterol production by fed-batch fermentation of _S. cerevisiae_, DO was a significant factor in its production and highest yield was obtained when DO was kept at 12%, explaining that the accumulation of this sterol was oxygen dependent [106]. Although oxygen is a vital component for aerobic organisms, it can also be a toxic agent that can damage cells by the action of reactive oxygen species (ROS). ROS can be generated through many different stress conditions, including high oxygen content in the culture medium [107]. Because of this, selection of the optimum DO level during terpenoid fermentation has significant effect on health and growth of cells as well as product formation. In many studies of terpenoid production, DO levels were kept above 30%, or 40%, by controlling agitation and aeration (cascade mode) in fed-batch processes (Table 3). During stationary phase of cultivation, DO levels reached up to 1.5%, which maintain micro aerobic fermentation in order to drive metabolic flux towards the production of fermentative products, like terpenoids [108].
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 5 Experiment and Analysis
### Example 2 Experiment  
To further validate the model's generalizability, out-of-sample experiments were conducted using the May 2006 (final spring month) and June 2006 (initial summer month) load data from an Australian region.  
In order to further verify the strong generalization ability of the model, the data of the last month of spring (May) and the first month of summer (June) in an area of Australia in 2006 were selected for experimental verification. According to the decomposition results of  
Figure 14: Evaluation indicators of Tetuan City in four months.  
Example 1, this example uses VMD for the experiment. Since the prediction effect of MLP is worse than that of LSTM and the GRU, this example uses the GRU, BiLSTM, and other models as comparison algorithms. In addition, this experiment also introduces the SOA to compare with the ISSA, aiming to further highlight the advantages of the ISSA.  
The comparison of the prediction results of a regional model in Australia is shown in Figure 15. As can be seen from the figure, compared with the method of single fusion VMD, the method that added an optimization algorithm to optimize model hyperparameters has a relatively better prediction effect and higher prediction accuracy. Since the load data of two months are used in the experiment in this example, the data volume is larger than that of Example 1, and the model proposed in this paper can extract data features more fully and capture the nonlinear relationship of the load sequence, which has a better fitting effect on the trend of the load data and a higher prediction accuracy.  
The prediction error indicators of each model are shown in Table 8. It can be seen from the table that models using the ISSA and GRU optimization algorithms have better prediction effects than models such as the GRU and LSTM, among which VMD+ISA-LSTM has a particularly prominent prediction effect. Compared with other models, such as the GRU and LSTM, the \(E_{RMSE}\) decreases by 55.53%, 73.93%, 70.23%, 43.85%, 8.63%, 10.73%, and 41.14%, respectively. The prediction of different regional data proves that the proposed model has a high prediction accuracy and good generalization performance.  
At the same time, by comparing the ISSA's optimization algorithm with the SOA's optimization algorithm, it can be seen that the ISSA has a better prediction effect, among which, the \(E_{RMSE}\) of VMD+ISA-LSTM and VMD+SOA-GRU are decreased by 10.73% and 41.14%, respectively. The \(E_{RMSE}\) of VMD+ISA-GRU decreased by 2.3% and 35.58% compared with VMD+SOA-LSTM and VMD+SOA-GRU.  
Through the comparison between the ISSA and SOA in practical cases, the models optimized by the ISSA exhibit lower RMSE values and better prediction performances, further demonstrating the robustness and generalization ability of the ISSA.  
Figure 15: Comparison of model prediction results of region in Australia.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 3 Theoretical Background and Considerations
### Statistical Properties and Analysis  
To evaluate the statistical properties of the sequence, we can analyze the autocorrelation function, which measures the similarity between the sequence at different time shifts. For a sequence \(\{f_{o}\}\), the autocorrelation function \(R_{f}(\tau)\) is defined as:  
\[R_{f}(\tau)=\mathbb{E}[f_{o}f_{o+\tau}]-\mathbb{E}[f_{o}]\mathbb{E}[f_{o+\tau}] \tag{6}\]  
Figure 1: Flowchart of the frequency hopping sequence generation and synchronization process.  
For a true random sequence, \(R_{f}(\tau)\approx 0\) for \(\tau\
eq 0\), indicating that there is no predictable pattern over time.  
The power spectral density (PSD) of the frequency-hopped signal provides insight into how the signal's power is distributed across the frequency spectrum. The PSD of a signal \(S(t)\) is defined as:  
\[S_{SS}(f)=\lim_{T\to\infty}\frac{1}{T}\mathbb{E}\bigg{[}\bigg{|}\int_{-T/2}^{T /2}S(t)e^{-\beta\pi ft}dt\bigg{|}^{2}\bigg{]} \tag{7}\]  
For a system using true random FHSS, the PSD is expected to be flat across the hopping band, indicating uniform distribution of signal power.  
This detailed mathematical foundation underscores the robustness and security advantages of using true random sequences for frequency hopping, as opposed to deterministic or pseudo-random sequences. The next sections will delve into the security analysis and practical considerations for implementing such a system.
###### Abstract  
Modal decays and modal power distribution in acoustic environments are key factors in deciding the perceptual quality and performance accuracy of audio applications. This paper presents the application of the eigenbeam spatial correlation method in estimating the time-frequency-dependent directional reflection powers and modal decay times. The experimental results evaluate the application of the proposed technique for two rooms with distinct environments using their room impulse response (RIR) measurements recorded by a spherical microphone array. The paper discusses the classical concepts behind room mode distribution and the reasons behind their complex behavior in real environments. The time-frequency spectrum of room reflections, the dominant reflection locations, and the directional decay rates emulate a realistic response with respect to the theoretical expectations. The experimental observations prove that our model is a promising tool in characterizing early and late reflections, which will be beneficial in controlling the perceptual factors of room acoustics.  
reflection power; room response; directional decay rates; room modes; eigenbeam processing; spatial correlation +
Footnote †: journal: Applied Sciences