esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In summary, existing research has successfully applied deep reinforcement learning (DRL) algorithms for multi-objective traffic signal control optimization, but there are still some shortcomings in the following areas: (1) Methodological limitations: Deep reinforcement learning algorithms such as DQN, DDQN, and D3 QN have been widely used in traffic signal control optimization. [PERSON] et al. [39] proposed the Rainbow DQN algorithm, which integrates six advanced modules and has shown significant advantages in areas like lane-changing and path planning [40; 41]. However, current studies have not yet applied Rainbow DQN to intersection signal timing optimization. (2) Carbon emission optimization in DRL: Existing literature has demonstrated the potential of DRL in carbon emission optimization, with most studies focusing on fuel-powered vehicles. However, with the development of new energy electric vehicles (EVs), the proportion of EVs is expected to rise in the future, and the mixed operation of fuel-powered and electric vehicles will become a prevailing trend. [PERSON] et al. [42] pointed out that there are significant differences in carbon emissions between fuel-powered and electric vehicles. Therefore, in optimizing signal timing, it is essential to consider the carbon emission differences between fuel and electric vehicles and select appropriate carbon emission models for accurate calculation. To date, there is a lack of signal optimization strategies based on DRL that focus on carbon reduction in scenarios involving both fuel-powered and electric vehicles. To fill the gaps mentioned above, this study explores the use of the Rainbow DQN algorithm for multi-objective optimization of urban intersection signals, fully leveraging the capabilities of DRL algorithms in optimizing signal timing, improving intersection traffic efficiency, and reducing energy consumption and pollutant emissions. The main contributions of this paper are as follows:  
(1) Based on the Rainbow DQN algorithm framework, the ACmix module [43] is introduced to combine the advantages of convolution and self-attention mechanisms. By utilizing V2X (Vehicle-to-Everything) technology to obtain dynamic vehicle state information, the traffic signal control actions are defined as parameterized actions. A reward function is constructed with the aim of reducing carbon emissions and improving traffic efficiency while considering the differences between fuel-powered vehicles and electric vehicles. Different methods are employed to accurately calculate the instantaneous carbon emissions of vehicles, and the signal timing is dynamically adjusted based on real-time traffic flow at the intersection. (2) The model's effectiveness is validated through a comparative analysis with DQN, D3 QN, Webster signal timing, and actuated signal timing methods, using real-world traffic data from an actual intersection. Finally, the model's applicability is tested under various scenarios, including different proportions of electric vehicles, varying electric vehicle carbon emission factors, different traffic volumes, and the consideration of autonomous vehicle penetration rates.
# Angiography Simulation and Planning Using a Multi-Fluid Approach
## 4 Results and Discussions
### Experiment 1
#### 4.1.1 Stenosis Vessels  
In Figure 6a, we imitated the development condition of the stenosis cardiovascular by using a stenosis cylinder. The diameter and length of the stenosis cylinder were 0.4 cm and 3 cm, respectively. Lumen diameter of the target coronary artery for angiography simulation was about 0.34 cm around  
Figure 5: Construction of an artificial velocity for boundary particles.  
injection position. At the initial state, the pressure on both sides of the stenosis was significantly larger than that of at the stenosis, and blood flows were moving faster at the stenosis, satisfying the Bernoulli's principle. When increasing the contrast media injection speed, the velocity of the blood flow around the vascular approach increased too, and blood particles were pushed forward, increasing the pressure at the front of the stenosis. Contrast media diffused fast after entering the vessel and mixed with the blood to become a blood-contrast media mixture. During this process, the pressure propagated quickly and the condition of the pressure and the velocity of the contrast media were restored to the previous values, and the contrast media at the center of the vessel propagated faster than that at the boundary. While the contrast media diffusing, it had been pushed forward by the influx of the blood flow and diffuses forward and backward further. The pressure condition and the velocity were stable and similar to the contrast media injected before.  
Figure 6: Contrast media diffusion simulation in two typical vessels. (**a**) Stenosis vessels; (**b**) Y-type vessels.  
The results of the simulation, thus, was a close representation of the real-life case. We then tested the simulation algorithm under different structural conditions to see if the realistic results could be reproduced.
# A Model of Information Visualization Interpretation
## 2 Materials and Methods
### Side Length Probability Density Function  
In this section, the focus is on the determination of the probability density function of the height of bar \(D_{12}\) (Equation (4)). As previously mentioned, the next step is to normalize the variables using the transform given in Equation (24). This highlights the need for a simplifying assumption. The elements of the diagonal of the covariance matrix are seen inthe denominator. In Case A, these values are equal. So, as has been done in the previous investigations of the [PERSON] model, Case A is assumed. Namely,  
\[\psi_{A}=\hat{\sigma}_{1}=\hat{\sigma}_{2}=\sqrt{2\sigma^{2}(1-\rho)}.\]  
Hence,  
\[X_{1}=\frac{\eta}{\psi_{A}}\qquad X_{2}=\frac{\zeta+h}{\psi_{A}}.\]  
This allows one to write  
\[D_{12} =\sqrt{\psi_{A}^{2}\left(\left(\frac{\eta}{\psi_{A}}\right)^{2}+ \left(\frac{\zeta+h}{\psi_{A}}\right)^{2}\right)}\] \[=\sqrt{\psi_{A}^{2}\left(X_{1}^{2}+X_{2}^{2}\right)}\]  
or  
\[\frac{D_{12}}{\psi_{A}}=\sqrt{X_{1}^{2}+X_{2}^{2}}.\]  
It follows that  
\[\text{E}(X_{1})=0 \text{E}(X_{2})=h/\psi_{A}\]  
which is the aforementioned situation in which the means of the variables are different, prompting the need for a simple covariance structure.  
The variable \(y=D_{12}/\psi_{a}\) has the generalized Rayleigh distribution, which is also called the noncentral chi distribution (not to be confused with the chi-squared distribution) or Rice distribution [27; 28]. The noncentrality parameter is  
\[y_{0}^{2}=\sum_{j=1}^{2}\text{E}(X_{j})^{2}=h^{2}/\psi_{A}^{2}.\]  
According to [PERSON] [29], the probability density function is  
\[g(y;y_{0}) =y\exp[(y^{2}+y_{0}^{2})/2]I_{0}(yy_{0})H(y)\] \[g(y;h,\psi_{A}) =y\exp[(y^{2}+h^{2}/\psi_{A}^{2})/2]I_{0}(y(h/\psi_{A}))H(y).\]  
In this expression, \(I_{0}\) is a modified Bessel function of the first kind of order zero, and \(H(y)\) is the Heavyside function.
## 1 Introduction  
Surgico-orthodontic therapy has become the standard procedure for correcting skeletal discrepancies between the maxilla and mandible [1]. Recently, several studies also reported successful results for patients with severe skeletal Class II malocclusion through the supra-orthodontic procedure [2; 3; 4]. To achieve optimal outcomes, post-surgical stability is crucial, which depends on both the patient's factors and the operative technique [5]. Additionally, establishing a sufficient occlusion before surgery was found to play an important role in maintaining stability after surgery [6; 7; 8].  
In most malocclusions accompanied with a severe skeletal deformity, the dentition may often be compensated to secure interdigitation [9]. Orthodontists employ various techniques to achieve presurgical decompensation in all three planes.  
The treatment of adults with Skeletal Class II relationships is frequently complicated by the underlying transverse discrepancies. [PERSON] and [PERSON] [10] reported that untreated Class II malocclusions are narrower in terms of overall maxillary dental arch width than Class I subjects. [PERSON] suggested treating Class II division 1 patients with rapid palatal expansion due to their narrow upper alveolar intermolar width [11].  
There have been many attempts to correct the transverse issues. Expanding the constricted upper dental arch often involves transpalatal arch (TPA), rapid palatal expansion (RPE) or miniscrew-assisted RPE (MARPE), surgically assisted RPE (SARPE) and so forth [12, 13].  
With advancements in orthodontic methods, nonsurgical maxillary skeletal expansion procedures have emerged as effective means to address transverse deficiencies prior to surgery [12, 14]. These advancements have alleviated the burden on patients who had to undergo another step of surgery, and also for orthodontists who had to deal with post-surgical relapses of expansion. Despite the increasing popularity of these techniques and the recognized importance of achieving sufficient occlusion before surgery, there is a lack of research exploring the relationship between transverse deficiency and post-surgical stability in skeletal Class II malocclusions.  
Cone-beam-computed tomography (CBCT) technology allows for the acquisition of three-dimensional images with less distortion [15]. The utilization of this technique has facilitated the accurate localization of the center of resistance (CR), a critical landmark for defining tooth position and its displacement [16, 17].  
The purpose of this study is to examine the relationship between the presurgical maxillomandibular transverse index using CBCT and post-surgical stability one year after mandibular advancement. We hypothesized that post-surgical stability would differ depending on the transverse differences before surgery.
# A Cumulant-Based Method for Acquiring GNSS Signals
## 4 Simulation and Experimental Results
### Real GNSS Signal Acquisition Results  
In the next part of this section, we evaluate the performance of our method using real signal data collected via a GNSS receiver. The device used to capture the GNSS satellite signals is a SiGe GN3S Sampler with version 2 firmware. The specific parameters of the data captured from this GNSS module are as follows:  
* Sampling frequency: 8.1838 MHz
* Intermediate frequency: 38.400 KHz
* Data format: 2-bit I/Q samples (1-bit I & 1-bit Q) in binary format (sI0, sQ0, sI1, sQ1, sI2, sQ2\(\ldots\))  
Figure 11 shows the sky plot of GPS satellites during the experiment. The experiment was conducted at approximately 12:40 p.m. local time. The elevations of each satellite observed during the experiment are listed in Table 1.  
Table 2 presents the satellite signal parameters (such as code phase and Doppler shift) acquired using the traditional method and the corresponding parameters obtained using the proposed method. Note that this is only for a matter of comparison, showing that both methods acquired the same code offset and Doppler shift; also note that PRN21 is not acquired using the traditional method. Figure 12a illustrates the acquisition results for PRN 12 using the traditional method, and Figure 12b shows the acquisition results for PRN 12 using the proposed method.  
Figure 11: Sky plot showing the positions of visible GPS satellites during the experiment. Each numbered point represents a satellite, with its position on the plot indicating its azimuth and elevation relative to the receiver’s location. The blue structures in the background represent the local environment and are not relevant to the satellite data. The center of the plot represents the zenith, while the edge represents the horizon.
## 3 Results
### Identification and Characterization of Candidate Terpenoid Biosynthesis Genes
RACE technology was used to amplify the 5' RACE and 3' RACE of the GbHMGS1 and GbHMGS2 enzymes (Table 1). Then, the Sanger sequencing results of the 5' RACE and 3' RACE terminal fragments were compared and spliced to obtain the full-length cDNA sequence of _GbHMGS1_ (2198 bp) and _GbHMGS2_ (1681 bp) (Supplementary data S1). The _GbHMGS1_ and _GbHMGS2_ genes contain 1422 bp and 1413 bp ORFs, respectively, and TAA is the termination codon in both genes. The GbHMGS1 and GbHMGS2 proteins had predicted molecular weights of 52.66 and 52.01 kDa, theoretical isoelectric points of 5.36 and 6.24, aliphatic indexes of 78.16 and 75.8, and total mean hydrophilicities of \(-\)0.234 and \(-\)0.209, respectively. They were all unstable proteins (instability index >40). The secondary structure analysis of GbHMGS1 and GbHMGS2 proteins showed that alpha helices were 210 bp and 172 bp long, accounting for 44.3% and 36.29% of the protein. Moreover, DNAMAN V6.0 was used to compare the two putative GbHMGS protein sequences with those of other species. The results show that all the HMGS amino acid sequences contained a conserved structural domain \"NxD/NE/VEGI/VDx(2)NACF/YxG\" (Figure 2a, marked with a red box). The five red circles represent the active amino acid sites (Figure 2a). Phylogenetic analysis results show that GbHMGS1, GbHMGS2, _Pinus sylvestris_, and _Taxus x media_ clustered into gymnosperms and that other angiosperms clustered into one branch. The relationship between the HMGSs of the analyzed plants could better reflect the relationship between gymnosperms and angiosperms (Figure 2b).
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 4 Findings and Discussion
### Obtained Results Discussion
#### 4.4.3 Cybersecurity  
In [88; 89], the authors describe a firewall implementation that effectively blocks malicious traffic and can manage up to 1,024 flows with minimal added delay. Another study on firewalls, presented in [66], reports that security rules are updated within 10 ms with a confidence interval of 95%. Meanwhile, the DoS detection scheme described in [63] is evaluated based on the time that programmable switches take to extract features from packets, which is 110 \(\upmu\)s. This is significantly faster than CPU-based solutions. In contrast, the DDoS mitigator outlined in [90] incurs a latency below 150 \(\upmu\)s while managing 1 Gbps of traffic. In [91], a defense mechanism against cellular botnets restricts traffic to 40 requests per second. Additionally, the secret permutation implemented in [87] encodes and decodes IoT packet payloads at a line rate of 6.4 Tbps, which the authors claim to be the fastest rate reported in the literature.
## 2 Materials and Methods
### Antioxidant Activity
#### 2.8.3 ABTS Radical Cation Scavenging Effect  
The ABTS radical cation (ABTS*) scavenging activity of the _Pinus_ spp. bark extracts was measured according to a described procedure [23; 32] and adapted to the 96-well plate scale. Firstly, stock solutions (1 mg mL\({}^{-1}\)) of three bark polar extracts from each _Pinus_ species were prepared in methanol/water (50:50, \(v/v\)). Moreover, stock solutions (1 mg mL\({}^{-1}\)) of ascorbic acid and trolox (antioxidant standards) were prepared in methanol/water (50:50, \(v/v\)) and methanol, respectively. The ABTS radical cation was prepared by reacting 7 mM ABTS with 2.45 mM potassium persulfate. The reactional mixture was kept in the dark at room temperature for 16 h and then diluted with methanol, to give an absorbance value of 0.700 at 750 nm. In each microwell, 250 \(\mu\)L of ABTS\({}^{\bullet+}\) solution was mixed with 50 \(\mu\)L of sample, obtaining the 0.5-21.2, 0.5-15.9, and 0.5-19.4 \(\mu\)g mL\({}^{-1}\) concentration ranges for the extracts, ascorbic acid, and trolox, respectively. The control was constituted by 250 \(\mu\)L of ABTS\({}^{\bullet+}\) solution and 50 \(\mu\)L of methanol. After 30 min, protected from the light, the absorbance was recorded at 750 nm by a Thermo Scientific MultiskanTM FC microplate reader.  
The IC\({}_{50}\) was calculated by plotting the percentage of scavenged ABTS radical cation as a function of the sample concentration logarithm. Each concentration was tested in triplicate.
# Modified Joint Source-Channel Trellises for Transmitting JPEG Images over Wireless Networks
## 4 Simulated Results
The robustness of our proposed JSCC model to variations in the average channel SNR in a slow-fading Rayleigh channel is evident in Figure 10. We observed that the quality of the reconstructed images remained acceptable in our experiments and these simulations, although the performance of our JSCC model declined when executed over a static AWGN channel. Figure 7: Performance of the JSCC scheme on a JPEG image over an AWGN channel. \[MSE=\frac{1}{N}\text{$\sum_{i=1}^{512}\sum_{j=1}^{512}(I_{0}(i,j)-I_{r}(i,j))^{ 2}$} \tag{9}\]0 Performance of the JSCC scheme on a JPEG Lena image over an AWGN channel at various compression ratios. \[MSE=\frac{1}{N}\text{$\sum_{i=1}^{512}\sum_{j=1}^{512}(I_{0}(i,j)-I_{r}(i,j))^{ 2}$} \tag{9}\]1 Performance of the JSCC scheme on a JPEG image over a slow Rayleigh fading channel.
# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
## 3 Experiments and Analysis
### Datasets  
To validate model effectiveness, we collect both environment-only data and environment-target data from two areas.  
Dataset A: the data were collected in a certain sea area, for approximately 6 min, with a sampling rate of 40 kHz. The noise data collected at this location is relatively clean, with only a few nearby vessels, and the data primarily consists of ocean environmental noise. Based on the target underwater acoustic signal simulation algorithm and the sea trial data, a training set of 5000 samples was constructed, each lasting 40 s. The frequency range is from 700 to 1200 Hz, ensuring that no factors (such as nearby vessel noise) except ocean noise are included. A schematic diagram of a training sample is shown in Figure 3.  
Figure 2: The structure of convolutional denoising model.  
The test dataset A1 is constructed with 500 samples based on the target underwater acoustic signal simulation algorithm and the aforementioned ocean noise data, consistent with the training data, selecting a frequency range from 700 to 1200 Hz. Based on the signal-to-noise ratio (SNR) indicators in the time-frequency spectrogram, six SNR intervals, \(-10\) to \(-5\) dB, \(-5\) to \(0\) dB, \(0\) to \(5\) dB, \(5\) to \(10\) dB, \(10\) to \(15\) dB, and \(15\) to \(20\) dB, are adopted in the simulation. For each SNR interval, 100 samples are generated.  
To verify the model's performance on real measured data, test dataset A2 is constructed, including six samples and each lasting 60 s. The frequency range is from 1200 to 1700 Hz. The center frequencies of the line spectrum are annotated. This range contains a significant number of line spectrum from other target signal (civilian vessels), which are used for testing the model's effectiveness on real measured data. The data are shown in Figure 4, where the red triangles represent the manually labeled line spectrum frequencies.  
Dataset B: collected from a maritime area, where the place is different from the place of dataset A, with a duration of approximately 40 min and a sampling rate of 64 kHz. The noise data collected at this location is relatively complex, with several vessels nearby and the sound of waves crashing against the shore. Test dataset B1 consists of 500 simulated samples generated by target underwater acoustic simulation algorithm and measured data within the frequency range from 700 to 1200 Hz, divided into six signal-to-noise ratio (SNR) intervals. Similar to test dataset A2, test dataset B2 contains 60 measured data samples, each lasting 60 s, resulting in a total of 60 samples. The frequency range for this set is 1200 to 1700 Hz.  
Figure 4: Real measurement data.  
Figure 3: Training samples.
## 2 Results
### Conversion of VO to VO\({}_{2}\) Defect Versus Isovalent Dopant Covalent Radius  
Figure 4 shows the conversion ratio (a\({}_{\text{VO2}}\)/a\({}_{\text{VO}}\)) of the VO to the VO\({}_{2}\) defect as a function of the isovalent dopant covalent radius (information about the samples from Table 2). As seen in Figure 4, the larger the isovalent dopant radius, the smaller the conversion ratio. In other words, the formation of VO\({}_{2}\) defects is more suppressed as the covalent radius of the isovalent dopant increases. This behavior could be explained [143] as follows. The two main reactions that govern the annealing of the VO defect are: VO +O\({}_{\text{i}}\) \(\rightarrow\) VO\({}_{2}\) and VO + Si\({}_{\text{I}}\) \(\rightarrow\) O\({}_{\text{i}}\). Sources of the self-interstitials are large defect clusters [144] formed in the irradiated material. As a result of the local strains induced in the Si lattice due to the substitutional incorporation of the larger radius isovalent dopants, the latter reaction is affected. It has been argued [143] that the binding energy of the self-interstitials decreases as a result of the presence of the larger isovalent dopants. This makes the liberation of the self-interstitial from the defect clusters easier, facilitating their enhanced emission as the temperature increases in the course of annealing. It means that a larger number of VO pairs is dissociated due to the latter reaction, or an equivalently less VO participating in the former reaction. In other words, the formation of VO\({}_{2}\) defects is suppressed. Thus, the larger isovalent radius, the smaller the VO\({}_{2}\) formation. This is represented in Figure 4. Next, we shall study the production and evolution of the VO defect as a function of the Ge dopant concentration in Si.  
Figure 4: The conversion ratio a\({}_{\text{VO2}}\)/a\({}_{\text{VO}}\) of the VO to VO\({}_{2}\) defect as a function of the isovalent dopant covalent radius Reproduced with permission from [143]. American Institute of Physics 2012.
## 3 Results and Discussion
### Linear and Dynamic Coupling
In 2021 and 2020-2021, there was an identical increase in influence at both locations from January to April. Beyond April 2020 and May 2021, the influence declined towards December. It can be inferred from the correlation and transfer entropy analysis that the VTEC has little or no influence on the DFS in the months of May through July during the combined period of 2020-2021. Figure 11: Seasonal variation in correlation values between VTEC and DFS at **(top)** Lagos and **(bottom)** Abuja. Figure 10: Monthly variation in correlation values between VTEC and DFS at **(a)** Lagos and **(b)** Abuja. The linear relationship between VTEC and DFS measurements suggests that an increase in VTEC will cause a decrease in DFS measurements. When there is an increase in the total electron content within the ionosphere, there are more electrons for the signals to interact with. This interaction causes the degradation of signals. However, the correlation analysis does not suggest direct causality between the VTEC and DFS. The transfer entropy provided a conclusive argument that changes in VTEC cause significant changes in DFS. By exploring the probability distribution space of both variables, it was concluded that changes in VTEC cause changes in DFS.