esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
## 2 Brief Review of Common Models
### COST Action 231 Model  
The European Cooperation in Science and Technology (COST) Action 231 [15] proposed a new propagation model, plus an extension to the previous Hata model. The COST-Hata PCS Extension simply extended the original Hata model to be applicable to frequencies up to 2 GHz. The COST-Walfisch-Ikegami (COST W.I.) model, however, provided a revised version of the Walfisch and Ikegami models, except that it does not require a 2/3D model of the environment. Eliminating this requirement is perhaps the main advantage of COST W.I., making it the most commonly used outdoor model. COST W.I. has been recommended by ITU and the European Telecommunication Standards Institute (ETSI) [16]. The model still requires the height of the buildings, width of the streets and some other environmental information, and therefore, it is not a fully deterministic model [17]. Although COST W.I. (or models similar to it) may seem to be an acceptable compromise between accuracy and computational complexity, it is rather a granulated set of formulas which has the following disadvantages:  
* Some of the model parameters lack clear physical or practical interpretation. For instance, the angle of arrival of the beam relative to the axis of the road (\(\phi\)) and road width (\(w_{s}\)) are vague at a crossroad, junction, riverside or a wide highway with partial line of sight (LoS), and undefined where there is no road. These conditions and many others that may occur are not considered in the COST W.I. formulas.
* The constant factors that control the loss due to intensity of multiscreen diffraction are discontinuous, resulting in abrupt changes in the result. For instance, scatter loss \(L_{trs}(\phi)\) has sharp transitions as it has been approximated with only three piecewise linear functions. Therefore, a small variation in \(\phi\) can affect the estimation drastically.
* There are several different permutations and possibilities to calculate the total path loss. The model first differentiates between LoS and NLoS (non-line of sight) conditions. Next, there are two ways of deriving the path loss depending on the \(d_{LoS}\). The NLoS has been further subdivided to scatter and diffraction losses. Next, there are three different conditional statements to calculate the scatter loss depending on the \(\phi\). Finally, there are another 11 further subdivisions (depending on BS and MS heights and environment type) to calculate the multiscreen diffraction loss. However, in reality, even differentiating between LoS and NLoS is difficult, resulting in the creation of near-LoS or partial-LoS conditions [18].
# Smart Grid for Industry Using Multi-Agent Reinforcement Learning
## 2 Fundamentals
### Multi-Agent Reinforcement Learning  
In reinforcement learning (RL), the focus lies on finding a control policy that is able to achieve a given goal. In this context, RL is formalized as a Markov decision process and builds upon iterative learning. During this process, a RL agent selects an action \(a_{t}\) based on the current policy and state \(s_{t}\), which is executed in a given environment. Afterwards, the environment transitions into a new state \(s_{t+1}\) and the agent receives a reward \(r_{t+1}\) (see Figure 4). By interacting with the environment, the policy is iteratively updated using a RL method to maximize the long-term reward. Various methods are used to compute the optimal policy, value-based and policy-based approaches being the most common ones. Regarding complex problems, neural networks are widely applied in order to predict the optimal policy or value function [30].  
For large problems, the RL approach can be extended to several agents, all of which are interacting in the same environment in a collaborative or competitive manner, which then is known as multi-agent reinforcement learning. In the case of collaborative agents, cooperative behavior can be reinforced by giving all agents a common reward. They thus learn to maximize this common reward together. One of the main challenges thereby is the so-called credit assignment problem [31]: In a multi-agent environment, it may be difficult for agents to assess their individual contribution to globally assigned rewards, thus making agents struggle to optimize their own policy.  
RL has proven able to solve very large and complex problems, e.g., Alpha Go [32]. Although a noteworthy computational effort is needed for training, the policy can then be executed within seconds while using little computation effort. However, RL--and especially MARL--require a detailed model of the environment. In addition, aspects like the reward assignment, the state space of the agents, and the hyper parameter of the learning algorithm have a strong impact on the solution quality [13].
## 1 Introduction
[6] fabricated an AZ91/CNT composite by a powder metallurgy technique and described an improvement in the strength and elongation. A Mg (AZ91) alloy reinforced with a graphene nanopowder composite showed an enhancement in mechanical properties [5]. [PERSON] et al. [15] and [PERSON] et al. [16] studied a AZ31 alloy reinforced with eggshell particulates and Ti particles and discussed various mechanical properties such as the Young's modulus, impact strength, elongation, wear behavior, etc. According to [PERSON] et al. [17], Mg/Si\({}_{3}\)N\({}_{4}\) nanocomposites containing 1.0 vol% Si\({}_{3}\)N\({}_{4}\) nanoparticles have 1.4 times increase in tensile strength and 2 times increase in ductility while maintaining an elastic modulus of the composite that is within the range of the local cortical bone elastic modulus. Moreover, the authors immersed the nanocomposites in simulated physiological fluids to assess in vitro degradation and bioactivity and found 2.2 times reduction in degradation rate in comparison to pure Mg, and thus provided the basis for their use as biodegradable implant materials for osteosynthesis. [PERSON] et al. [1] studied the various methods used to fabricate AZ91 alloy-based composites and finally reported that stir casting and liquid settling techniques were the best \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Alloying** & **Aluminum-** & **Aluminum-** & **Rare Earth** & **Zinc-** & **Zinc-** & **Zirconium-** & **Zinc-** & **Zirconium-** & **Zinc-** & **Manganese** & **Zirconium** \\
**Element** & **Manganese** & **Zinc-** & **Manganese** & **Zirconium** & **Manganese** & **Metal** & **Zirconium** & **Mental** \\ \hline ASTM & AM & AZ & AS & EZ & ZC & ZE & ZK & M & K \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main alloying elements with ASTM designation of magnesium alloys. methods for the preparation of AZ91 composites because of commercial use. [PERSON] et al. [2] discussed the mechanical properties of an AZ91D Mg alloy including density (1.81 g/cm\({}^{3}\)), ultimate tensile strength (240-250 MPa), and Young's modulus (45 GPa). In the past decade, numerous studies have been conducted on MMMCs, with a focus primarily on powder metallurgy routes, infiltration techniques, and other methods for fabricating these composites [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. However, the stir casting method was found to be less investigated; furthermore, there are gaps in the existing literature regarding AZ91/Si\({}_{3}\)N\({}_{4}\) composites fabricated through a stir casting technique that need to be addressed. Therefore, in the present study, AZ91/x-Si\({}_{3}\)N\({}_{4}\) composites at different wt.% are fabricated using a stir casting technique. This study includes an experimental analysis of the physical properties, mechanical properties, and wear characteristics, as well as a microstructural analysis of the composites.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 8 Biomedical Applications of PLA-Based Nanofibrous Structures
### Tissue Engineering
#### 8.3.1. PLA Electrospun Structures for Musculoskeletal Tissue Engineering  
Musculoskeletal Disorders are injuries that affect the human body's movement or musculoskeletal system (i.e., bones, tendons, muscles, ligaments, nerves, etc.). Ignoring defects in bone, muscle, or tendon can lead to reduction and even loss of their function and subsequently lower quality of life for those affected. Due to the considerable role of PLA in the orthopedic field, the continued use of PLA in many musculoskeletal tissue engineering strategies is not surprising [125]. Worldwide, over two million bone grafting procedures are annually performed but in the cases of large defects and complex healing optimal bone regeneration and repair is still challenging [126]. Innovative approaches are based on the application of new materials or growth factors in order to mimic the physiology of the tissue. Scaffolds for bone tissue engineering should provide mechanical  
Figure 15: (**a**) In situ combination of encapsulated particles and fibers via electrospinning and electrospray techniques. (**b**) SEM (**left**) and confocal microscopy (**middle** and **right**) images of particle-fiber composites produced with different solvent compositions: EA:BA = 1:9, Reproduced with permission from [110] (License number: 5232980582910). stability and an environment conducive to bone regeneration [127; 128]. On the other hand, a lot of tissues in our body have been demonstrated piezoelectric effects [129]. Bone is a tissue with piezoelectric constants similar to those of quartz, the main component of the bone organic ECM. For this reason, the application of piezoelectric materials, such as polyvinylidene fluoride (PVDF) and PLA in bone tissue engineering has been invoked to support tissue function [126]. Scaffolds with (3D) nanofibrous structures have received a lot of attention in the field of bone regeneration since nanofiber mimic the bone ECM [130]. Electrospinning is one of the most important techniques for the fabrication of 3D scaffolds with nanoscale features and interconnected pores. Electrospun PLA nanofibrous scaffolds have a greater surface area than raw PLA material, which consequently results in higher protein adsorption but also faster degradation. The degradation kinetics and mechanical properties of PLA-based scaffolds can be adjusted by changing the racemic mixture of lactic acids composing the polymer chains [131]. Table 3 shows different studies in which electrospun PLA fibers have been used for bone tissue engineering.
# Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
## 1 Introduction
### Contributions and Paper Outline  
The contributions of this study are as follows: First, we propose the heterogeneous traffic flow mixed toll station lane allocation problem (TSLAP for short) and develop a mixed-integer nonlinear bilevel programming problem (MINLBP for short) model to solve it. Second, using the Liulin toll station (Lianhuo Expressway) in Zhengzhou, China as a case study, we validated the model's effectiveness through extensive numerical experiments and analyzed the impact of vehicle composition and arrival rates on system performance, providing valuable management insights. Third, we derived the relaxed analytical solution of the MINLBP model, offering a theoretical explanation for the observed behavior of all ETC-HVs selecting ETC lanes, and providing toll station managers with a quick and efficient lane allocation strategy.  
The remainder of this study is organized as follows: Section 2 presents the problem statement, notations, and assumptions. Section 3 details the TSLAP bi-level model and the equivalent single-level model. Section 4 validates the effectiveness of the proposed model through numerical experiments, analyzes the impact of vehicle composition and arrival rate on system performance, and explains the numerical results using the relaxed analytical solution. Section 5 concludes the study and discusses future research directions. Appendix A presents the necessary conditions for the existence of feasible solutions, while Appendix B provides the derivation of the relaxed analytical solution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Country** & **Sample** & **Methods** & **Key Findings** \\ \hline
[19] & United States & driving simulator & driving simulator & Dynamic message signs and arrow pavement markings reduce lane changes. \\ \hline
[20] & China & driving simulator & driving simulator & Toll station guidance signs can improve drivers’ lane change behavior. \\ \hline
[13] & Serbia & E/70 & \(M/M/1\), RNNs accurately predicted vehicle arrival rates, enhancing model robustness. \\ \hline
[8] & China & Shanghai ring expressway & \(M/M/1\), active set & The proposed method reduces queue time by up to 57.4\% compared to proportional allocation. \\ \hline
[14] & China & Nanjing toll station & \(M/M/1\), enumeration & The benefit decreases initially, then rises with increasing ETC vehicle ratio. \\ \hline
[23] & Egypt & simulation & \(M/M/N\), simulation & MTC is inefficient and can cause significant delays to highway traffic. \\ \hline
[24] & Ethiopia & Addis-Adama expressway & \(M/M/N\), simulation & Toll service performance depends on the number of servers, service time, and interarrival time. \\ \hline
[25] & United States & New Jersey expressway & \(M/G/1\), fluid mechanics & The vehicle security distance impacts toll booth traffic capacity. \\ \hline
[22] & China & Dongshe toll station & \(M/G/N\), PSO-LSTM & The lane scheduling model saves 147,825 RMB annually at Dongshe toll station \\ \hline \hline \end{tabular}
\end{table}
Table 1: Existing related work.
## 3 Results
### Evaluating Different SIF-GPP Relationships
ENF: evergreen needleleaf forests, EBF: evergreen broadleaf forests, DBF: deciduous broadleaf forests, MF: mixed forests, OSH: open shrulands, WSA: woody savannas, SAV: savannas, GRA: grasslands, CRO: croplands, and WET: wetland. Figure 1: Scatterplots of solar-induced chlorophyll fluorescence (SIF) and tower gross primary production (GPP): (**a**) site level; (**b**) grid cell level. The site-level relationship was derived using OCO-2 SIF in the nadir mode and tower GPP from 64 EC sites, and the grid cell level relationship was derived based on the predicted 0.05\({}^{\circ}\) SIF and tower GPP from 91 flux sites. Figure 2: Validation of estimated GPP from eight SIF-GPP relationships. The boxplot shows the RMSE between estimated GPP and tower GPP for 91 EC flux sites. The SIF-GPP relationships 1–4 are based on OCO-2 SIF soundings and tower GPP: 1—the site-level, universal, non-zero intercept relationship; 2—the site-level, biome-specific, non-zero intercept relationship; 3—the site-level, universal, zero intercept relationship; 4—the site-level, biome-specific, zero intercept relationship, respectively. Relationships 5–8 to are similar to relationships 1–4, but based on gridded SIF and tower GPP.
## 2 Materials and Methods  
Our goal is to restore low-quality finger vein images \(x\) to high-quality finger vein images \(y\). Specifically, we propose the DRL-FVRestore that can self-select the optimal combination of restoration tasks and links according to the quality status of the finger vein image itself, enabling an adaptive restoration of low-quality images of finger veins. When applying DRL to finger vein image restoration task selection, it is initially necessary to consider how the Markov decision process (MDP) can be built in this area to achieve reliable and effective results. The overall flow chart of the proposed method for DRL-FVRestore is shown in Figure 2. During the processing of DRL-FVRestore, the set \(S\{s_{1},\ldots,s_{i},s_{i+1},\ldots,s_{n}\}\) of low-quality finger vein images is obtained from the environment. Each finger vein image is considered as a state, and the different image restoration tasks are considered as actions to be executed, called action spaces, denoted as \(\{a_{1},\ldots,a_{n}\}\). If the current input finger vein image to the trained model is \(s_{i}\), the agent finds the best image restoration method \(a_{j}\) from the action space to process the low-quality finger vein image based on the weights trained from the training data. It can be found that the design of the DRL-FVRestore method is divided into three sections: (a) building of the reinforcement learning interactive environment; (b) method realization of the image deblurring restoration, image damage inpainting restoration, and image enhancement and denoising restoration; and (c) training of an agent for adaptive selection of image restoration tasks based on DRL. The focus of the environment is the design of the dataset for training the agent, which will be described in detail in Section 3. In Section 2.1, we introduce the structure of the DeblurGAN-v2 network based on the Inception-Resnet-v2 backbone and its loss function design applied to the finger vein image deblurring restoration task. In Section 2.2, we then introduce the structure of a vein feature-guided finger vein image damage inpainting network and the design of its loss function applied to the image damage inpainting restoration task. In Section 2.3, we describe representative algorithms used for the restoration task applied to finger vein image enhancement and denoising. Finally, in Section 2.4, we will discuss in detail the network structure of the DRL-FVRestore method proposed in this paper, the design ideas, and the design of the reward function for the method.
## 5 Concluding Remarks  
The aerodynamic features of a train and flat closed-box bridge system with a train model mounted on the upstream track was investigated via wind tunnel tests. Compared to the train-only and bridge-only cases, the aero interactions of the train and bridge system were examined. Compared to the same train and bridge system but with the train model mounted on the downstream track, the effects of the occupied track (with the train model mounted on) variation of the train model on the aerodynamic properties of the train and bridge system are revealed. Core findings are summarized as follows.  
(1) The aero interactions of the train and flat closed-box bridge system can be roughly categorized as the underbody flow restraining effect (UFR), bridge deck shielding effect (BDS), quasi-Reynolds number effect (Q-Rey), flow transition promoting effect (FTP), and flow separation intensifying effect (FSI). Among these, the FTP was insensitive to the occupied track variation of the train model, whereas the UFR, BDS, FSI, and Q-Rey were dependent on the occupied track variation, especially for Q-Rey, which disappeared when the train model was mounted on the upstream track. The Q-Rey disappearance was mainly attributed to the speed-up leading-edge shear layer of the bridge-deck not reattaching on the rounded shoulder of the train model mounted on the upstream track.  
(2) The aerodynamic features of the train and flat closed-box bridge deck models are very sensitive to aero interactions and occupied track variations. The aero interaction resulted in obvious reductions in the aerodynamic forces of the train at the wind angle of attack \(\alpha\) of \([-4^{\circ},\,12^{\circ}]\) and in the static stall angle of the bridge, and led to sensible increases in the absolute values of the aerodynamic forces of the bridge in \(\alpha\) of \([-4^{\circ},\,12^{\circ}]\). The occupied track variation caused the disappearance of the drag crisis and other saltatory aerodynamic behaviors of the train-bridge system in \(\alpha=[0^{\circ},\,12^{\circ}]\).  
Figure 10: Strouhal numbers of the models: (**a**) the train model; (**b**) the bridge-deck model; and (**c**) the whole system.  
**Author Contributions:** Conceptualization, X.H.; methodology, H.L.; experiments, H.W. and H.L.; software, H.W.; validation, H.L. and X.H.; writing, H.W. and H.L.; supervision, X.H.; funding acquisition, X.H. and H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Natural Science Foundation of Hunan province under Grant No. 2021 JJ40744, and by National Natural Science Foundations of China under Grant No. 51925808 and U1934209.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within this article.  
**Acknowledgments:** The authors thank the reviewers for their great help on the article during its review progress.  
**Conflicts of Interest:** The author(s) declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article.
## 4 Problem Formulation and Methodology
### Proposed Methodology
#### 4.2.3 Cross Validation Criteria  
Prior to analyzing the output produced by SLDA, it was pertinent to determine whether the seed words effectively revealed the personality insights of social media users. As mentioned earlier, even though the personality traits are not mutually exclusive in real-world cases [21], the reliability of SLDA in generating the topics still can be determined using the traits correlation between the Big 5 (Conscientiousness and Agreeableness) and PEN (Psychoticism) [14; 21; 51]. Meanwhile, personality theorists seem to have accepted that Extraversion and Neuroticism are two fundamental personality dimensions of human beings and are strongly convergent [52]. Therefore, to bridge the gap between the two personality systems, the information about personality scores provided by _myPersonality_ provider was used to cross-examine the topics generated by SLDA aggregated to the PEN traits based on the following criteria:  
1. Any training instances labeled as Psychoticism by SLDA must be correlated to Conscientiousness or Agreeableness scores provided in _myPersonality_. The two traits seem to be correlated with antagonism characteristics and Psychoticism [14]. Texts that were labeled as Psychoticism also may be correlated to Neuroticism due to the negative coverage;
2. Any training instances labeled as Extraversion or Neuroticism by SLDA must be directly correlated to the Extraversion or Neuroticism scores, respectively, as provided in _myPersonality_;
3. Any training instances labeled as Psychoticism or Neuroticism by SLDA may have the element of Extraversion due to boundary ambiguities among the traits [21] and positivity biases in the human language [53].  
The above criteria (i.e., (1-3)) were applied to develop the multiclass-based distribution. On other hand, the development of one vs. all distribution followed the rules in (1) and (3) as well as slight changes for rule (2) where the instances will be representing both Extraversion and Neuroticism traits as well as neutral instances. This implied that _myPersonality_ and _Sentiment140_ transformed into a distribution termed one vs. all [54], where \"one\" referred to Psychoticism instances and \"all\" constituted Extraversion, Neuroticism, and neutral instances and was labeled as \"not_Psychoticism\" (also referred to as \"non-Psychoticism\"). There were two reasons to employ the one vs. all analysis. First, our intention is to evaluate the performance of SLDA in the context of Psychoticism and non-Psychoticism where this experiment can be a stepping-stone to highlight the potential of psychological representation in forensic areas. Second, except for the sentiment coverage, the pilot study had shown the non-existence of unique structural relationships between the instances of Extraversion and Neuroticism despite the two traits being rooted in opposite sentiment polarities. The following sections will disclose the findings of our experiments.