esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 3 Preliminaries
### Traffic Signal Control  
Traffic Network: A traffic network can be represented as a directed graph, where nodes represent intersections and edges represent roads. Each road consists of multiple lanes, with lanes serving as the basic units for vehicle movement, determining how vehicles traverse the intersection--such as turning left, going straight, or turning right. The traffic network provides a structured framework for implementing traffic signal control strategies. A typical traffic network with a 4 \(\times\) 4 intersection pattern is seen in Figure 1.  
Traffic Movement: Traffic movement refers to the movement of vehicles through a specific road segment or intersection over a given period. Each type of traffic movement is influenced by the operation of the current signal phase. Understanding the different  
Figure 1: Illustration of a traffic network and an intersection. (**a**) A traffic network composed of a grid of 4 \(\times\) 4 intersections. (**b**) A typical intersection with four signal phases. Each phase allows certain traffic movements while restricting others.  
directional traffic movements is crucial for designing signal control strategies that can flexibly adapt to changing traffic demands.  
Phase: A signal phase, denoted by '\(\mathbf{p}\)', is a set of traffic movements that are allowed to proceed simultaneously. It defines which directions of traffic have the right of way at an intersection during a specific period. A phase topology with four phases--east-west straight, north-south straight, north-south left turn, and east-west left turn--is shown in Figure 1b, where traffic lights typically do not regulate right turns. The proper management of signal phases is key to ensuring a smooth and safe traffic flow at intersections.  
Phase Duration: Phase duration, represented by '\(\mathbf{d_{p}}\)' refers to the length of time a specific signal phase is maintained. It is a critical factor that influences the efficiency of traffic flow and vehicle waiting times at intersections. Optimizing phase durations can improve intersection throughput, reduce vehicle queues, and minimize delays.
## 1 Introduction
* Complex heterogeneous environments at mmWaves: The consideration of heterogeneous environments in terms of electromagnetic fields and, specifically, in the exposure assessment and dosimetric characterization, is pivotal due the advent of new wireless communication systems and their unstoppable widespread use. It is a reality that current and future communication systems have become and will be increasingly heterogeneous, providing services and applications relying on coexisting merged heterogeneous networks in order to comply with coverage/capacity relations. Technically, when considering a complex heterogeneous system in the proposed enhanced simulation tool, a full analysis of the spectrum use needs to be performed in order to provide an adequate exposure assessment and evaluation based on the fact that multiple systems are operating at the same time in different frequency ranges, increasing the overall spectral usage. Thus, this corresponding electromagnetic influence is characterized in a new synchronized and integrated module by implementing merging techniques, as more intensive spectrum uses with multiple systems have unique characteristics. In addition, these challenging environments need to consider high complex scenario spatial designs in terms of the morphology and topology, with special attention on the scatterers' frequency dispersive material properties. In this sense, the material properties' database library of the new enhanced version of the simulation tool has been upgraded to consider material properties up to mmWaves. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{Ref.} & \multicolumn{2}{c}{EMF Assessment} & \multicolumn{2}{c}{Carrier Frequency} & \multicolumn{2}{c}{**Beamforming**} & \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Simulation**} & \multirow{2}{*}{**Measurements**} & \multicolumn{2}{c}{**Channel Model**} & \multirow{2}{*}{**Description**} \\ \cline{2-2} \cline{4-14}  & DL & UL & \multicolumn{2}{c}{**Sub-6 GHz**} & & & & & & & & & & & & & & & \\ \hline
[6] & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ & UMi & ✓ & ✓ & ✗ & \begin{tabular}{c} XGP [UMi-Struct \\ Cartoon Model \\ Relense 16 [25]] \\ \end{tabular}  & \ \begin{tabular}{c} XGP [UMi-Struct \\ Cartoon Model \\ localization levels of User \\ Equipment (E). \\ \end{tabular}  & \begin{tabular}{c} XGP [UMi-Struct \\ traffic flows are tuned in \\ coordinate works with the uncertainty \\ Equipment (E). \\ \end{tabular}  \\ \hline
[7] & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ & UMa & ✓ & ✗ & RT & \begin{tabular}{c} Computational method to estimate \\ human MLP outputs in DL \\ base stations in outdoor macro environments. \\ \end{tabular}  \\ \hline
[8] & ✓ & ✗ & ✓ & ✗ & ✓ & ✗ & Industrial env. & ✓ & ✗ & \ \begin{tabular}{c} Hybrid approach: \\ RT/FDTD \\ \end{tabular}  & \begin{tabular}{c} Numptical approach for massive \\ MIMO hardware response assessment in industrial environments. \\ \end{tabular}
# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 3 Survey Results on Local Explanation in Industrial AI Applications
### Qualitative Analysis
#### 3.2.4 Finance  
In the finance sector, the majority of researchers have focused on using LIME and SHAP methods with an agnostic and post hoc approach for tabular and time-series datasets (as shown in Table 2). Some researchers, including [PERSON] et al. [69], [PERSON] et al. [70], [PERSON] et al. [72], and [PERSON] et al. [73], have applied the SHAP method on tabular datasets, while [PERSON] et al. [75] and [PERSON] et al. [74] have developed their SHAP methods on time-series datasets. Additionally, [PERSON] et al. [71] have utilized the LIME method for a tabular dataset.  
Several researchers have focused on applying explainable AI models to tabular datasets in finance. For example, [PERSON] et al. [69] proposed a model that uses Shapley values and similarity clustering to explain why customers either buy or abandon non-life insurance coverage. In contrast, [PERSON] et al. [70] utilized SHAP to identify important variables in stock market crises and provide local explanations of the probability of a crisis at each date. [PERSON] et al. [72] developed a Shapley-based model to predict the expected return of small and medium-sized enterprises based on their credit risk and expected profitability. Similarly, [PERSON] et al. [73] combined SHAP with a LightGBM (light gradient-boosting machine) model to interpret explanatory variables affecting credit scoring, which outperformed the bank's logistic regression model. Additionally, [PERSON] et al. [71] proposed a model that uses long short-term memory (LSTM) and efficient machine learning techniques to accurately predict stock prices based on user sentiments derived from news headlines. They suggest further research directions to include automated prediction of financial news headlines and adding emotion-based GIFs to enhance the model's appeal. This model can be used as a decision maker for algorithmic trading. Future research may explore applying these methods to other situations in the finance sector, such as underwriting and claims management.  
[PERSON] et al. [75] have suggested using SHAP with a popular deep reinforcement learning architecture, DQN, to explain an agent's actions in financial stock trading, in timeseries data. Additionally, they advised expanding the method to continuous action space and using DRL models, such as deep deterministic policy gradient (DDPG) and advantage actor-critic (A2C), to improve the explanation by adding more technical indicators as features. In another study, [PERSON] et al. [74] created an explainable AI model for fintech risk management, particularly for assessing the risks associated with peer-to-peer lending platforms' credit borrowing. They used Shapley values to interpret AI predictions based on underlying explanatory variables. Future research could be conducted on enhancing prediction understanding by clustering the Shapley values.
## 4 Conclusions  
Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@TiO\({}_{2}\) (FS-NT (0 mL)) and N-doped Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@N-TiO\({}_{2}\) (FS-NT (2-5 mL)) photocatalysts with magnetic core-shell structures were prepared using a multi-step synthesis method. The prepared materials were characterized by DRS, XRD, FESEM, XPS, TEM, and SAED. The results of the characterizations showed that the prepared samples had an anatase structure, and N was successfully doped. FS-NT (0 mL) and FS-NT (2-5 mL) with different amounts of nitrogen doping were used for the study of photocatalytic degradation of methyl orange (MO) in a pure MO solution and a MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution, respectively. Under high-pressure mercury lamp irradiation, the average photocatalytic degradation rate of MO in the pure MO solution using three different batches each of FS-NT (0 mL) and FS-NT (3 mL) for the first time reached 85.25% \(\pm\) 2.23% and 95.53% \(\pm\) 0.53%, respectively. The average photocatalytic degradation rate of MO in the MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution using three different batches each of FS-NT (0 mL) and FS-NT (3 mL) for the first time under the same irradiation conditions reached 90.46% \(\pm\) 3.33% and 97.79% \(\pm\) 2.09%, respectively. The results showed that Na\({}_{2}\)SO\({}_{4}\) can promote photocatalytic degradation of MO by FS-NT (0 mL) and FS-NT (3 mL). The reason for this may be that SO\({}_{4}\)\({}^{-}\) was produced by the interaction with h\({}^{+}\), and SO\({}_{4}\)\({}^{-}\) can degrade MO. The experiment of recycling photocatalysts showed that there was still a good degradation effect after five cycles. The results of the recycle experiment indicated that the FS-NT (3 mL) photocatalyst exhibited better degradation performance in the MO solution and MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution, respectively. Finally, the first-order kinetic model and the photocatalytic degradation mechanism showed that the synergistic effects of N doping and SO\({}_{4}\)\({}^{-}\) can enhance the photocatalytic degradation effect of MO in the MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution. The FS-NT photocatalytic can be magnetically recycled and reused without pollution to the environment; thus, it has broad development prospects in wastewater treatment.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Methodology
### Model Significance Test Method
#### 3.3.2 \(t\)-Test Method  
F-test could only indicate that the coefficients of the model \(b_{i}\) are not all 0, but it is not possible to exclude the case that several of them are 0. Under the circumstance, \(t\)-test can test the significance of the coefficients of each variable in the model, so it is necessary to assume that:  
\[H_{0}^{(i)}:b_{i}=0,i=0,1,\ldots,m. \tag{15}\]  
When \(H^{(i)}{}_{0}\) holds, the statistic is:  
\[t_{i}=\frac{b_{i}/\sqrt{b_{ii}}}{\sqrt{SSE/(n-m-1)}}\sim t(n-m-1) \tag{16}\]  
where the symbolic meaning is the same as that of the above equation, \(b_{ii}\) is the (\(i,\,i\)) element in \(XX^{T}\), and \(x_{j1},\,x_{j2},\,\ldots,\,x_{j18}\) (\(j=1,\,2,\ldots,\,n\).) are the observed values of \(x_{1},\,x_{2},\,\ldots,\,x_{18}\). At the confidence level \(\alpha\), if \(\mid t\mid<t_{\alpha/2}(n-m-1)\), then is accepted \(H^{(i)}{}_{0}\) and otherwise rejected, under which it indicates that the independent variable has a significant relationship with the dependent variable.
## 5 Results and Discussion
### Investigating Error Sources
#### 5.4.1 Excluding Vegetation  
The largest error was apparent in Test 3 and, upon investigating this position, the reason was found to be nearby vegetation. Vegetation was an issue because trees were not incorporated in the campus model and therefore \"leaf-off\" conditions were assumed. However, as this test was carried out in early summer, a leaf-off scenario was not a true representation of reality. For this reason, areas adjacent to large trees resulted in exaggerated errors. Point 9 was also recorded in the vicinity of trees and it also displayed a prediction error. It is important to note that Point 10 was recorded near trees, however, it did not display an error. This was because the obstructing vegetation at Point 10 was to the south east (azimuth 156\({}^{\circ}\)), whereas it has been demonstrated that the GNSS constellation at that time was concentrated above the north-western horizon and, therefore, the trees could not obstruct the the GNSS signal. Trees could potentially be included in a future version of the model as the 2D OSi mapping contains layers with information on vegetation. The tree height could be estimated, surveyed or measured from shadows on aerial photography if trees were an important part of the obstacles in the surrounding area. However, the goal of our novel methodology was to produce a quick, efficient procedure that eliminates the need for surveying and therefore trees were not included in the obstruction model.
## 4 Experiments
### The Impact of the ON/OFF Cycle  
To investigate the effect that \(T_{cycle}\) has on the QoS of the packets, we repeat the experiments with \(T_{cycle}=50s\) and the same values of \(R_{on}\). The parameters of this second set of seven experiments are shown in Table 2.  
The results for packet loss are presented in Section 4.1 (Figure 5), where the packet loss is relatively stable at approximately 6%, smaller than when \(T_{cycle}=25s\). Figure 10 shows the measured power consumption _versus_ the measured end-to-end delays for \(T_{cycle}=25s\) and \(T_{cycle}=50s\). In Figure 10 we can observe the system behaviour and the relative tradeoff between power consumption, delay and packet loss. Comparing between the two experiments, it can be seen that for larger \(T_{cycle}\) the delay increases. Thus, small \(T_{cycle}\) values result in smaller delays but larger packet losses.  
Figure 9: Observed relationship between average power consumption and average delay for different values of \(R_{on}\).
# Abrasion-Resistant and Temperature Control of
## 2 Anti-Scouring and Abrasion-Resistant Performance Test on Concrete
### Mix Design and Concrete Performance Test  
As indicated by the test on dosage selection for fly ash, with increasing dosage of fly ash, the compressive strength, splitting tensile strength and anti-scouring and abrasion-resistant strength of concrete all decreased. With the growth of test age, due to the late-stage effect of fly ash, the concrete with a higher dosage of fly ash witnessed a faster strength increase; at the age of 90 day, the strengths of concrete at different dosages of fly ash were relatively close to each other. Given that the anti-scouring and abrasion-resistant concrete for Dagangshan Hydropower Station had a design age of 90 day, to fully utilize fly ash's advantage of faster strength increase in the late stage, it was recommended to use a dosage of 20% for fly ash. As indicated by the test on dosage selection for silica fume, with increasing dosage of silica fume, the compressive strength and anti-scouring and abrasion-resistant strength of concrete increased as well; when the dosage of silica fume increased by 2%, the compressive strength of concrete increased by approximately 5%. Meanwhile, with increasing dosage of silica fume, the dry shrinkage rates of concrete at different ages would increase as well; when the dosage of silica fume increased from 5% to 9%, the increase in the dry shrinkage rate of concrete was within 8%. To guarantee the compressive strength and anti-scouring and abrasion-resistant strength of concrete and reduce the probability of concrete cracking, it was recommended to use a dosage of 5% for silica fume. The performance test result on the concrete at different dosages of PVA fiber shows that when the dosage of PVA fiber increased from 0.8 kg/m\({}^{3}\) to 1.0 kg/m\({}^{3}\), the compressive strength, splitting tensile strength, and anti-scouring and abrasion-resistant strength of concrete gradually increased, but the increase in amplitudes was not substantial [26; 27; 28; 29; 30; 31]. The test adopted the dosage of 0.9 kg/m\({}^{3}\) recommended by the manufacturer for PVA fiber. After several times of field trial mixingand optimal adjustment, the construction data listed in Table 7 were finally proposed for guiding field concrete casting.  
Based on the basic mix proportions of fly ash and silica fume for concrete construction provided in Table 7, after field sampling, an indoor test was conducted on the concrete specimen's compressive strength, splitting tensile strength, elastic modulus, anti-scouring and abrasion-resistant strength, dry shrinkage deformation, adiabatic temperature rise and other performance parameters. The test results are listed in Table 8.
## 1 Introduction
The results of the model are then applied to two different scenarios in which the orbital parameters of the reference elliptical orbit are consistent with those of the heliocentric orbit of the Earth and Mercury, as reported in Section 3. Furthermore, the limiting case of a circular reference orbit is also studied in order to obtain a set of numerical results in this important special case of a closed Keplerian orbit that can be used to rapidly approximate the Earth-based mission scenario. Finally, this last circular case is used to define a simplified, non-optimal control law, which allows for completing the phasing maneuver with an analytical form of the panels' switching strategy. The effectiveness of the analytical control law is investigated in Section 4, using a linearized formulation of the heliocentric dynamics of the spacecraft [42], which enables the flight time to be computed in an open loop as a function of the design parameters of the diffractive sailcraft as its characteristic acceleration. Consequently, the innovative contribution of this paper to the literature on Sun-facing diffractive sailcraft is essentially twofold. First, it investigates the performance of such sailcraft in the relevant mission scenario of orbital phasing along a generic elliptical orbit within a heliocentric framework. Second, it introduces a simplified control law that enables a rapid estimation of mission performance--specifically, the flight time--in cases where a linearized spacecraft's heliocentric dynamics around a circular orbit are adopted. The proposed analysis is confined to a Sun-facing attitude; however, an extension to the more general case, in which the sailcraft's attitude varies during interplanetary flight, is straightforward. The results related to these two aspects of mission planning are further discussed in Section 5, which presents the final remarks and concludes the paper.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 5 Experimental Simulation Scenarios Topology  
Two types of simulation were performed. The first part of the simulation considers LV lines, where the throughput is monitored for the verification of the expected trend due to the line distance (Sections 6.1-6.3). The second part considers MV lines with an experimental simulation setup using a G.hn implementation, where the input parameters are comparable with the IEEE P1901 compliant BPL modems (Section 7).
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Structural Optical Activity in Cuticle from Cetonia Aurata
#### 4.3.1 Method I  
Figure 6 shows transmission Mueller matrices from elytra from two specimens of the scarab beetle _Cetonia aurata_. Beetles are individuals and exhibit differences in their optical features, and data from one specimen with small depolarization (sample CA1) and one with large depolarization (sample CA2) are shown. The depolarization of the two samples are shown in Figures S1-S3 in Supplementary Materials. A common feature is that right-handed circularly polarized light is transmitted in a band around 550 nm as seen in the elements \(m_{41}\) and \(m_{14}\) indicating circular dichroism. Corresponding circular birefringence features are seen in elements \(m_{32}\) and \(m_{23}\).  
Contributions from CD and CB are also observed in elements \(m_{22}\) and \(m_{33}\) in accordance with Equation (4). The non-zero but small values of \(m_{31}=m_{13}\) and \(m_{42}=m_{24}\) show, however, that the elytra has some linear polarization effects. In fact, the elytra are biaxial as revealed by analysis of Mueller matrices measured in reflection mode [26]. If the Mueller matrices in Figure 6 are compared with Equation (4), we can make the qualitative conclusion that the cuticle from these beetles exhibits chirality with a resonance and transmits right-handed circularly polarized light in the spectral band 500-600 nm. Due to the complex structure, a quantitative analysis using method I is not recommendable.  
Figure 5: The birefringence \(C_{\text{B}}\) (solid curve) for an \(\alpha\)-quartz sample determined by differential decomposition of the MMSE data in Figure 4. The dashed curve shows unfolded \(C_{\text{B}}\) as obtained from the differential decomposition.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Global Spectrum Planning
#### 6.1.5 Spectrum Planning  
The spectrum planning module uses historical data, status data, constraints, and identified patterns or trends in spectrum usage to plan spectrum usage for a future period across the entire system. This planning is accomplished using various methods, including graph theory, genetic algorithms, and machine learning.
## 1 Introduction
Plasma treatment is one of a several methods to improve bonding between the substrate and any polymeric coating. Plasma not only changes the surface morphology of the substrate but also binds active sites to the surface, rendering the surface active for subsequent reactions [30]. Plasma treatment is a useful method to introduce functional radicals onto the surfaces of material without changing material's bulk properties [31]. Plasma technology has the advantages of convenience and environmental friendliness and more importantly, it has the capacity to activate the surface of substrates [32; 33]. During plasma processing, many electrons, ions and metastable ions in the ion flow can break the molecular chain on the surface in a short time, increase the number of active groups and unsaturated bonds, and meanwhile play a certain etching effect [34]. Plasma treatment occurred only on the surface without affecting the elemental composition and macroscopic mechanical properties of the substrate [35]. Therefore, low-temperature plasma treatment could be employed to treat substrates before coating with conductive pastes. The aim of this work was to develop and investigate the fabrics coated with formulation containing PEDOT:PSS, focusing on their electromagnetic properties and resistance performance. The developed fabrics are intended for electromagnetic radiation (EMR) shielding in microwave range likewise for absorbing microwaves in radar operating range, as to act as radar absorbing materials (RAM). Consequently, the measurements of reflection and transmission of developed textile fabrics were performed in a frequency range of 2-18 GHz, which covers the defined frequencies relevant to the application. To improve the resistance performance of developed samples, a conventional textile surface modification technique, i.e., atmospheric plasma treatment, was used and the plasma effects were studied.