esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion, Conclusions, and Limitations  
### Discussion  
This study focuses on the mixed traffic scenario of gasoline vehicles and electric vehicles, aiming to propose an intersection signal timing optimization strategy based on  
Figure 12: Average vehicle waiting time at different penetration rates.  
Figure 13: Average vehicle CO\({}_{2}\) emissions under different penetration rates.  
deep reinforcement learning (Rainbow DQN) to improve intersection vehicle throughput and reduce CO2 emissions.  
Theoretically, this study utilizes the state-of-the-art \(Q\)-learning model, Rainbow DQN, providing a novel theoretical framework for optimizing traffic signal timing. Considering the increasing proportion of electric vehicles in real-world scenarios, this study fills the gap in existing literature by addressing the mixed traffic scenario involving both electric and gasoline vehicles. It incorporates the emission differences between gasoline and electric vehicles, selects appropriate carbon emission models, and designs a multi-objective reward function considering both traffic efficiency and CO2 emissions. Additionally, the introduction of the ACmix module, which combines convolutional and self-attention mechanisms, enhances the model's computational efficiency and expressive power. This also provides insights into mitigating the high computational and decision-making costs of real-time optimization while improving the potential application of reinforcement learning algorithms in complex traffic signal control problems.  
In addition, by applying reinforcement learning algorithms to real-time traffic signal optimization, this study significantly improved traffic throughput and reduced CO2 emissions through the dynamic adjustment of intersection signals. This provides an efficient, low-carbon, and intelligent optimization solution for intersection signal timing, offering valuable practical insights for urban traffic management. Through SUMO simulations, the optimization effects of the model were analyzed under different electric vehicle proportions and traffic volumes. In scenarios with a high proportion of electric vehicles, the optimization effect of the model weakened, and the performance of the DQN and D3 QN models was lower than that of the conventional traffic signal control. This finding provides useful guidance for urban traffic managers in formulating strategies and further promotes the development of low-carbon cities, reducing traffic emissions. However, the model still needs to be validated in real-world traffic scenarios, and further consideration of the computational power required by the model is necessary to assess its applicability under real traffic conditions, providing stronger data support for broader future applications.
## 2 Materials and Methods
### Data Collection and Measurements
#### 2.3.3 Grain Yield and Water Use Efficiency  
In order to study the relationship between irrigation quality and final wheat yield, Collect only at maturity Measure wheat yield, wheat plants from each plot were randomly sampled for the determination of grain yield. Yield measurement sampling areas were set at 15 m, 30 m and 45 m away from the reference border of each plot, and samples were taken at equal intervals along the radial direction of both sides of the micro spraying tapes (4, 6, and 8 samples were taken for 23 m, 33 m and 53 m border width treatments, respectively). After threshing and air drying, the total mass and 1000-grain weight of the samples were determined. Sampled plants in each plot were hand harvested and air dried for 2 wk until constant mass, and then the grain was separated, cleaned, and weighed. Grain yield was calculated on a dry-matter basis (13%) using an electronic balance. Water use efficiency was calculated as the grain yield (in kg ha\({}^{-1}\)) produced per unit of evapotranspiration. The latter was the sum of soil water at sowing minus soil water at harvest plus the growing season precipitation and irrigation.
## 4 Experimental Simulation Analysis
### Influence of Semi\(-\)Spread Spectrum Factor on Performance  
Using an improved Logistic map with an initial value of 0.40, we selected 20, 50, 100, and 200 semi\(-\)spread spectrum factors for simulation experiments. From the simulation results in Figure 10, it can be concluded that selecting different semi\(-\)spread spectrum factors will have different effects on the system. Through simulation, it can be seen that the smaller the semi spread spectrum factor, the better the bit error performance of the system. With the increase of the semi-spread spectrum factor, the bit error performance of the system also increases.  
The correlation characteristics of the sequence will affect the bit error performance of the system during demodulation. With the increase of the spread spectrum factor, the sequence correlation characteristics become better and better. At the same time, with the increase of the semi-spread spectrum factor, the noise introduced is also increasing. There is a balance point between these two influencing factors, and this balance point is the optimal semi\(-\)spread spectrum factor value.  
Figure 10: BER curves of the system with different semi\(-\)spread spectrum factors.  
The optimal spread factor of the MC\(-\)ODBR\(-\)DCSK system is simulated. The experiment, results of which are shown in Figure 11, is to use an improved Logistic map at the SNR of 10 dB and 11 dB to carry out the BER performance curve with semi\(-\)spread spectrum factor transformation. With the increase of the spreading factor, the BER performance of different chaotic maps changes gently at first and then becomes worse. Different chaotic maps are applied to the MC\(-\)ODBR\(-\)DCSK system with different optimal spreading factors.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 5 SST--An Efficient Heuristic Algorithm for the GPP Problem
### Heuristics and Methods
#### 5.5.1 Scoring--The Information Gain  
Given \(\mathcal{V}\) as a baseline, planning a trajectory \(T\) at altitude \(h\) requires finding a subset \(\mathcal{W}^{h}\subset\mathcal{V}\) that includes the most informative waypoints for the drone to traverse. The scores attached to each candidate waypoint can be any heuristic representing its information gain. As an example, according to the probability map and flight altitude, the information gain of each point can be measured by the following two properties:  
\[\begin{split}\forall v\in\mathcal{V}:\ max(v)&= \max_{\{m,n\}\in J(v)}p_{m,n}\\ sum(v)&=\sum_{\{m,n\}\in J(v)}p_{m,n}\end{split} \tag{8}\]  
By sorting the waypoints in \(\mathcal{V}\) according to these properties in decreasing order (first by \(max(\cdot)\), then by \(sum(\cdot)\)), the algorithm is able to choose as many waypoints as possible for \(\mathcal{W}^{h}\) while concentrating on those with the highest probabilities. In this manner, we will favor waypoints from which the highest probabilities can be observed, even if they are isolated, as required by the optimization problem (Equation (4)).
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 10 Limitations and Future Directions  
This study has reviewed, analyzed, and discussed in detail LiFi from various aspects in studies that have not been reviewed comprehensively. However, despite the strengths and contributions of this review, it still has several limitations that can be elaborated based on the following points. First, the papers downloaded and cited in this work were gathered from four main databases. Thus, future studies can include these four databases and may add more literature that they might find suitable in studying LiFi and communication. Also, other areas of focus within LiFi, including LiFi modulation with its types and usages across the literature, can be considered in future works in this domain. In addition, other related studies outside the eligibility criteria of this review (e.g., hardware equipment experiments in LiFi) can be investigated and reviewed in the future.
## 3 Proposed Methodology
If the revised number f(\(\varkappa\)) is greater than our present best guess, then f(\(\varkappa\)) - f(\(\varkappa^{*}\)) is optimistic. If we analyze f at the new location \(\varkappa\), I(\(\varkappa\)) yields the change, which is how significantly we will enhance our existing best answer. \[ \begin{split} I(\varkappa)&=\max(f(\varkappa)-f( \varkappa^{*}),0)\\ PI&=CDF(\frac{\mu-\mu^{*}}{\
u})\end{split}  \tag{8}\]  
where CDF is the cumulative probability function, \(\mu\) is the average of surrogate function, \(\mu^{*}\) shows the optimal average of the surrogate function bounds so far, and \(\
u\) is the standard deviation of the surrogate function. Exploitation (evaluating at locations where the surrogate mean is low) and exploration (evaluating at sites where the surrogate variance is less) are trade-offs in acquisition functions. The employed acquisition function O(F) is optimized over the surrogate model to determine the next hyperparameters to assess. (In this case,\(f\) =\(\arg\min O(F)\) and F\({}^{\prime}\) is the new hyperparameter value.) We add probabilistic modules in one of the most commonly utilized acquisition functions in the literature is the Probabilistic Expected Improvement Criterion. Expected Improvement (EI) is given as:  
\[PEI=E_{P}(0,\mu_{P}(f_{opt}-O(f))) \tag{9}\]  
where  
\[ \begin{split} Z=\frac{\mu_{P}(f_{opt})-O(f^{{}^{\prime}})-\xi}{ \sigma(\mathbf{f})}\text{ if }\sigma(\mathbf{f})&>0\\ 0\text{ if }\sigma(\mathbf{f})&=0\end{split}  \tag{10}\]  
In the end, we leveraged the assumption that the PDF of a normally distributed is symmetrical, therefore \(\theta(\eta_{0})=\theta(-\eta_{0}))\). Therefore, this equation may appear frightening, but it is not. So, whenever would EI(x) take on a bigger number and when \(\mu\geq f(\varkappa^{*})\). That is, the Gaussian Process's average value is large at \(\varkappa\). When there is a bunch more ambiguity, the expected improvement increases, so when \(\sigma>1\). Through the meantime, the equation above applies for \(\sigma(\varkappa)\geq 0\); alternatively, if \(\sigma(\varkappa)=0\) (as at the recorded data points), EI(\(\varkappa\)) = 0. While we go, there is one more thing. We can smooth the AHT algorithm's exploitation vs. exploration by inserting a (hyper)parameter(F) into the equation for EI(x). Therefore, the complete equations are (11) and (12). \[ \begin{split}\text{EI}(\varkappa)\equiv\mathbb{E}[I(\varkappa) ]&=\int_{-\infty}^{\eta^{*}}I(\varkappa)\varphi(\eta)\,\text{d} \eta\\ \varphi(\eta)&=\frac{1}{\sqrt{2\pi}}\exp\left(-\eta^ {2}/2\right)\end{split}
## 3 Holdback Bar Device Connection Relationship and Contact Mechanics Model
### Connection Relationship of Holdback Bar Device  
During catapult-assisted takeoff, the launch bar of the nose landing gear is connected to the catapult shuttle on the deck. To ensure the initial traction load during catapult-assisted takeoff, the nose landing gear of the aircraft is connected to the holdback bar device on the deck by the holdback bar, and the front joint of the holdback bar is connected to the hinge point of the nose landing gear torque arm, as shown in Figure 3.  
Figure 3: Schematic diagram of the connection between the holdback bar and the nose landing gear and the deck.  
To simulate the actual holdback bar release process, a dynamics simulation model is needed based on the constraint relationships between the mechanisms of the holdback bar device. Constraints can be used to describe the kinematic relationship between two components, and each independent component in the model can be linked by defining constraints. The kinematic relationship between the components is abstracted through the definition of constraints, and the appropriate constraint unit is selected for description of such relationship. In order to establish the kinematic pairs of the holdback bar device, it is necessary to analyze the connection relationship between the parts of the holdback bar device, which contains the kinematic pairs and the contact relationship. A sketch of the holdback bar connection relationship and kinematic pairs is shown in Figure 4.  
According to the connection relationship between each mechanism of the holdback bar, the type of kinematic pairs that meets the motion needs is selected, and the kinematic pairs are defined in the dynamics simulation analysis software using the geometric coordinate system.  
Cylindrical Joint is defined by choosing one axis of the two contact coordinate systems respectively. The cylindrical joint is used to connect the front joint with the lock mechanism, the release mechanism with the load transfer bar, and the load transfer bar with the load adjustment device.  
The revolute joint is defined by choosing one axis of the two contact coordinate systems and the plane perpendicular to this axis, which means that the two coordinate systems share the same axis and one plane is always in contact. The revolute joint is used for rear joint and the deck.  
The bracket joint is defined by selecting two coordinate systems in the corresponding position, respectively. The fixed joint is used to achieve a fixed connection between some components, the load transfer bar, and the rear joint.  
The contact joint is defined by selecting two planes of contact coordinate system respectively. Contact Joint is used to contact two components, including locking mechanism and load transfer bar, locking mechanism and release mechanism, release mechanism and load adjustment device, etc.
###### Abstract  
Equalization-enhanced phase noise (EEPN) can severely degrade the performance of long-haul optical fiber transmission systems. In this paper, the impact of EEPN in Nyquist-spaced dual-polarization quadrature phase shift keying (DP-QPSK), dual-polarization 16-ary quadrature amplitude modulation (DP-16 QAM), and DP-64 QAM optical transmission systems is investigated considering the use of electrical dispersion compensation (EDC) and multi-channel digital backpropagation (MC-DBP). Our results demonstrate that full-field DBP (FF-DBP) is more susceptible to EEPN compared to single-channel and partial-bandwidth DBP. EEPN-induced distortions become more significant with the increase of the local oscillator (LO) laser linewidth, and this results in degradations in bit-error-rates (BERs), achievable information rates (AIRs), and AIR-distance products in optical communication systems. Transmission systems using higher-order modulation formats can enhance information rates and spectral efficiencies, but will be more seriously degraded by EEPN. It is found that degradations on AIRs, for the investigated FF-DBP schemes, in the DP-QPSK, the DP-16 QAM, and the DP-64 QAM systems are 0.07 Tbit/s, 0.11 Tbit/s, and 0.57 Tbit/s, respectively, due to the EEPN with an LO laser linewidth of 1 MHz. It is also seen that the selection of a higher-quality LO laser can significantly reduce the bandwidth requirement and the computational complexity in the MC-DBP.  
optical fiber communication; electrical dispersion compensation; multi-channel digital backpropagation; equalization-enhanced phase noise; achievable information rates +
Footnote †: journal: Sensors
# Power Quality Impact and Its Assessment: A Review and a Survey of Lithuanian Industrial Companies
## 5 Discussion
### Voltage Sag Tables
For example, let us examine IEC 61000-3-2:2018 [122], which limits harmonic current emissions of electrical and electronic equipment that has an input current up to and including 16 A per phase and is intended to be connected to public LV distribution systems. In this standard, the following groups are defined:  
Figure 13: Two dependent phase-to-ground voltage sags (occurred on 24 November 2023) in the 0.4 kV feeder of the metalworking plant (located in Marijample municipality). The time interval between their start thresholds is 4.4 s.  
Class A includes the equipment that is not specified as belonging to Class B, C or D, for example, balanced three-phase equipment, household appliances, vacuum cleaners, high pressure cleaners, non-portable tools, independent phase control dimmers, audio equipment, professional luminaries for stage lighting and studios;
2. Class B includes portable tools and non-professional arc welding equipment;
3. Class C includes lighting equipment;
4. Class D includes the following types of devices if their specified power is less than or equal to 600 W: personal computers and their monitors, television receivers, and refrigerators and freezers having one or more variable-speed drives to control compressor motor(s). Obviously, grouping of industrial equipment with higher than 16 A input current per phase is a more complicated question. Moreover, it can be carried out not only by type but also by duration of use, simultaneity of use, power consumption, frequency of use, etc. This is particularly relevant for surveys and PQ impact assessment research.
# Estimating Underwater Light Regime under Spatially Heterogeneous Sea Ice in the Arctic
## 3 Results  
### Comparing In Situ Downward Irradiance (\(E_{d}\)) and Upward Radiance (\(L_{u}\)) Measurements  
An example showing in situ downward irradiance (\(E_{d}\)) profiles and upward radiance (\(L_{u}\)) profiles at 16 visible wavelengths measured under-ice is presented in Figure 3. For the \(E_{d}\) profiles, subsurface light maxima at a depth of around 10 m are clearly visible between 400 and 560 nm. These peaks are not visible in the yellow/red region (580-700 nm). For the \(L_{u}\) profiles, no subsurface light maxima were found at any wavelength. To have a closer look at the shape of both \(E_{d}\) and \(L_{u}\) profiles, data below the 10 m depth were normalized to the value at 10 m (Figure 4). Below 10 m and between 400 and 580 nm, both \(E_{d}\) and \(L_{u}\) profiles presented the same shape (i.e., yield the same rate of attenuation with increasing depth). At longer wavelengths (\(\geq\)600 nm), differences between the shapes of \(E_{d}\) and \(L_{u}\) profiles increased. Irradiance and radiance diffuse attenuation coefficients (\(K_{d}\) and \(K_{Lu}\)) calculated for the layers of a 5 m thickness are compared in Figure 5 for all 83 profiles. In the blue/green/yellow regions (400-580 nm), the determination coefficients between \(K_{Lu}\) and \(K_{d}\) varied between 0.98 at the surface (10-15 m) and 0.64 at depth (75-80 m). For most of the surface layers, regression lines lined up with the 1:1 lines. Slight deviations from the 1:1 lines started to appear below 60 m where \(K_{d}\) was on average higher than \(K_{Lu}\). The relationships including orange and red wavelengths are presented in Figure 3 included in the Appendix C. A linear regression analysis between all in situ normalized \(E_{d}\) and \(L_{u}\) profiles showed that determination coefficients (\(R^{2}\)) range between 0.75 and 1 (Figure 4). A sharp decrease and a high variability of calculated \(R^{2}\) occurred beyond 575 nm. This suggests a gradual decoupling between \(E_{d}\) and \(L_{u}\) profiles at longer wavelengths, likely due to the effect of inelastic scattering (mostly Raman scattering).  
Figure 3: Examples of in situ downward irradiance (\(E_{d}(z)\)) and upward radiance (\(L_{u}(z)\)) profiles measured under-ice on 20 June 2016. Note the presence of subsurface maxima in the downward irradiance profiles and the absence of subsurface maxima in the upward radiance profiles.  
Figure 4: Comparison of downward irradiance (\(E_{d}(z)\)) and upward radiance (\(L_{u}(z)\)) for one example light profile measured under-ice. Profiles were normalized to the measured radiometric value at 10 m depth (under the subsurface light maximum) in order to emphasize the similar shape between \(E_{d}(z)\) and \(L_{u}(z)\).
# Soriasis: From Pathogenesis to Pharmacological and Nano-Technological-Based Therapeutics
## 5 Nanotechnological Approaches for Psoriasis
### Lipid-Based Nanoparticles
#### 5.2.1 Liposomes  
Liposomes are closed vesicles, produced by one or more lipid layers overlapped from the core to the surface, having the same number of aqueous compartments between layers [137,139]. Liposomes are one of the most studied lipid-based nanoparticles in the treatment of dermatological disorders due to their unique features of being able to act like organic solvents to solubilise non-soluble drugs, getting in groups acting as local depots for a control releasing of the drug, enhancing penetration and diffusion of molecules with low solubility into the lipid covering of the stratum coroneus [139]. Additionally, liposome physicochemical characteristics are easily controlled by their composition. Features, such as charge, size, permeability, or durability, are malleable by researchers, giving different approaches for the same treatments.  
[PERSON] et al. studied the use of a loaded liposome of fusidic acid for a proficient treatment of plaque psoriasis [140]. These liposomes where prepared, obtaining a range in different physicochemical characteristics depending on the concentration parameters, such as size, ranging from 572.7 to 740.1 nm, or entrapment efficiency, ranging from 52.1% to 72.6%. This study showed that the formulations were stable and had an enhancing effect on the efficacy of the anti-psoriatic drug compared with a conventional preparation in in vivo studies in mouse tail models. In this sense, liposomes are effective for their use in a drug delivery system in skin disorders, such psoriasis.
## 1 Introduction  
High-Intensity Focused Ultrasound (HIFU) is a minimally invasive therapeutic method in which ultrasound beams are concentrated at a focal region, producing heating and selective ablation within the focal volume without compromising surrounding tissues [1]. HIFU has been proposed for the safe ablation of both malignant and benign tissues and as an agent for drug delivery, and Magnetic Resonance Imaging (MRI) has been proposed for guidance and monitoring of the HIFU therapy [2]. The combination of HIFU and MRI is known as Magnetic Resonance-guided HIFU (MRgHIFU). The HIFU treatment is performed with an ultrasonic transducer, acoustically coupled to the target tissue using water and gel-pads, as shown in Figure 1.  
In MRgHIFU therapy planning, identifying the regions allows for accurate targeting of tissue, positioning of the transducer and its geometric focus (focal point). If a proper automated identification of the transducer and tissue region can be achieved, with image segmentation, targeting would be facilitated, as well as therapy for planning using numerical methods for the calculation of acoustic energy distribution and thermal response in biological tissue.  
In the present research work, segmentation methods were studied to find internal parameters that provide an efficient identification of the regions of interest in MR images used for HIFU treatment planning. The involved segmentation methods were threshold, level set and watershed with markers, and they were applied to 112 transverse and 112 sagittal MR images, obtained from a study for the treatment of abscesses with MRgHIFU in a murine model [3]. In the images, the regions of interest were the air, tissue, gel-pad, transducer and water regions. The evaluation of the segmentation quality was performed using F-measure [4] supported by ground truth images obtained from manual delineation of the regions of interest. A parametric study consisting of tests with different values for the internal parameters of the implemented image segmentation methods was carried out. The F-measure results from the parametric study were analyzed independently by region of interest in order to determine which method is the most appropriate to identify each region.