esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarization Differential Visible Light Communication: Theory and Experimental Evaluation +
## 3 Measurement Setup  
### Transmitter Side  
The transmitter side consists of two power LEDs (Bridgelux BXRE-50C3001-D-24, Bridgelux, Fremont, CA, US) driven at 300  mA. The LED currents are controlled using two separate drivers (Analog Devices DC2257A, Analog Devices, Norwood, MA, US). Both LEDs are equipped with identical 50 \(\times\) 50 mm linear plastic polarizers (Edmund Optics 50 \(\times\) 50 mm Linear Plastic Polarizer XP42-200, Edmund Optics, Barrington, NJ, US) with a specified tolerance on the polarization axis of \(\pm 2^{\circ}\). The polarization axes of these polarizers are aligned orthogonally to one another. The transmitter construction is shown in Figure 4, where the polarization axes of the linear polarizers are indicated.
## 3 Methods  
The proposed method builds on a generalized ML model to approximate the PL according to an advanced model, in an indoor environment (5G sources at 3.5 GHz). Then,this ML model is combined with a GA for finding optimal wireless network deployment in different environments and coverage scenarios. Finally, the deployments obtained with the GA are evaluated with the original advanced PL model to verify the achieved coverage percentage. Figure 1 shows the flow graph used for the solution.  
In our research, an ML algorithm is trained/validated with variables easy to be measured, such as the distance between the AP and the receiver locations, the number of walls in the direct line to the source, and material characteristics (e.g., penetration loss) of walls. To assess the accuracy of the ML algorithm, we compare the PL estimated in the simulated scenarios with the PL calculated by a state-of-the-art approach (WHIPP) [12]. The WHIPP approach is built on the Indoor Dominant Path Loss Model and was shown to give good PL estimation results in real scenarios [33]. Compared to typical log-distance or multi-wall models, it is more advanced, as it searches the physical path between transmitter and receiver, along which the signal encounters the lowest path loss. It has been shown that most of the signal energy is received via that path [34]. This PL model is used as PL ground truth and is used to obtain a PL dataset in the considered buildings. The goal of the ML model is to accurately estimate this PL value, based on a set of relevant inputs.
# Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
## 2 Background
### Federated Reinforcement Learning (FRL)  
RL is capable of solving a variety of complex problems with high performance. However, there are some challenges in its applications to practical scenarios, e.g., sampling is a decisive factor in the agent's experience, which entails that learning efficiency relies on sample efficiency [23]. In fact, sample efficiency refers to the manifestation of the actual decision-making challenge in reinforcement learning theory [82]. Although some efforts have focused on distributed RL [83; 84], protecting agents' privacy represents an issue to be faced. Moreover, the simulation-reality gap is another challenge once simulated environments can present some limitations compared to real environments [23].  
Moreover, federated reinforcement learning (FRL) aims to enable agents to jointly learn how to solve a given RL task performing uniformly well across multiple environments [18]. FRL focuses on privacy-preserving sequential decision-making, in which the sample, feature, and label are not used and the environment, state, and action are included [23]. In fact, FRL extends RL capabilities regarding distributed learning, new sampling techniques, generalization, and privacy. Conversely, it also entails new challenges to be faced (e.g., poisoning threats [85]). Figure 3 illustrates the federated reinforcement learning (FRL) training process.  
FRL is a recent topic that has been successfully adopted in several works. For example, there has been FRL application to 5G [88], autonomous driving [89], robotics [90], healthcare [91], and transportation [92]. These examples demonstrate how FRL can tackle several problems faced nowadays and foster the development of new approaches related to this combined framework. Moreover, the use of deep reinforcement learning (DRL) in a federated environment is referred to as federated deep reinforcement learning (FDRL). Finally, FRL can be applied to IoT solutions in several different ways and environments [91; 93].
## 3 Results and Discussion
### Possible Action Principle of the LC Material  
The process of negative plates during the cycle is briefly shown in Figure 7. The ionic reaction of the negative plates being discharged is as follows.  
\[\text{Pb}+\text{HSO}_{4}^{-}\rightarrow\text{PbSO}_{4}+\text{H}^{+}+2\text{e}^ {-} \tag{1}\]  
Figure 7: The schematic diagram for the effect of LC and HPC.  
The chemical action would be available both on the surface and inside the negative plates due to the unique structure of HPC, thereby well improving the utilization of active materials. HPC played the role of steric hindrance by separating the grown PbSO4. Sponge lead first generated small PbSO4 particles on the carbon surface. The nanopores of carbon materials limit and separate the growth of PbSO4. It avoids the formation of irreversible PbSO4. Meanwhile, the formed porous-conductive network structure in NAM based on the porous structure of HPC material would provide more nucleation sites for the PbSO4 particles to defer the accumulation on the surface of Pb particles and inhibit the formation of irreversible PbSO4 particles. Small PbSO4 particles showed high solubility during charging, which helped to form lead-carbon dendrites. This ensured the stability of the electrodes structure [38].  
The ionic reaction in the negative plates when being charged is shown in Equation (2).  
\[\text{PbSO}_{4}+\text{H}^{+}+2\text{e}^{-}\rightarrow\text{Pb}+\text{HSO}_{4} ^{-} \tag{2}\]  
The electron transfer resistance on the interface between carbon and H2 SO4 is less than that between Pb and H2 SO4[40]. Enough electrons are necessary to promote the transformation from PbSO4 to Pb. The HPC could provide more transfer passages for electrons by the formation of the porous-conductive network structure. Therefore, the addition of HPC to the cells is beneficial. It is most important to make HPC evenly dispersed in NAM. When HPC was added to the negative plates, the hybrid energy storage system including the electric double-layer capacitor and Faraday capacitor was formed. The micropores and mesopores of HPC provided electric double-layer capacitor energy storage--an energy storage system with a fast response but low specific energy used for high current density charge and discharge. The Faraday capacitor generated by the Pb \(\Longleftrightarrow\) PbSO4 reaction of lead-acid cells provided an energy storage system with a slow charge and discharge speed but high specific energy. The addition of carbon materials with a high specific surface area offset the shortage of LABs. It could improve the high-rate performance of the cells [41].
# Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
## 4 Experiment
### Parameter Sensitivity  
In this part, we explore how the hyperparameters of the proposed DCSNA model affect performance. We conduct several experiments to analyze the results by varying the hyperparameters. There are three main parameters in the proposed DCSNA, the dimension \(d\) of node embeddings, the negative sample ratio \(r_{n}\) (i.e., the number of labeled anchor nodes used in the training set divided by the number of node pairs we obtain from the negative sampling for training predictive function), and sampling ratio \(r_{a}\) (i.e., the number of labeled anchor nodes used in the meta-learning constraint divided by the number of labeled anchor nodes used in the training set). To investigate the impact of these parameters on model performance, we set the default values of \(d=200\), \(r_{n}=0.5\), and \(r_{a}=0.3\). We then vary one parameter at a time while keeping the other parameters constant and assess its effect on network alignment tasks using the three evaluation metrics on four datasets.  
* Figure 7a shows that performance improves with increasing node size \(d\), but performance degrades when \(d\) is more than 200. The reason is that we represent each node as a distribution containing both mean and variance embedding, as the dimension \(d\) increases, the uncertainty (i.e., the information of variance embedding) will accumulate and result in the poor performance of matching potential anchor nodes.
* From Figure 7b, we can observe that our model could achieve competitive performance on different \(r_{n}\). It indicates that although the negative sampling mechanism is helpful for better training of the model, it plays a limited role. Improper determination of the number of negative samples may even reduce the accuracy of the model.
* From Figure 7c, we can observe our model can achieve competitive performance by using fewer labeled anchor nodes, which demonstrates the effectiveness of meta-learning constraint and the robustness of our model. Since the number of labeled anchor nodes is usually small in the real task, the robustness of the model can guarantee the model to be applied to the alignment task well.  
Figure 6: Result of ablation study.  
Figure 7: Results of parameters study.
# Supervoxels-Based Histon as a New Alzheimer's Disease Imaging Biomarker
## 3 Materials and Methods
### Dataset: OASIS  
The Open Access Series of Imaging Studies (OASIS) [47] is a project aimed at making MRI data sets of the brain freely available to the scientific community. OASIS is made available from the Washington University Alzheimer's Disease Research Center, Dr. [PERSON] at the Howard Hughes Medical Institute (HHMI) at Harvard University, the Neuroinformatics Research Group (NRG) at Washington University School of Medicine, and the Biomedical Informatics Research Network (BIRN).  
For this study, the \"Cross-sectional MRI Data in Young, Middle-Aged, Non-demented and Demented Older Adults\" (OASIS-1) have been selected. This collection is a cross-sectional dataset of 416 individuals of both genders, all right-handed, aged between 18 and 96. The set includes 100 patients (aged over 60) with a clinical diagnosis of [PERSON]'s disease ranging from very mild to moderate. A summary of the demographic characteristics of for the OASIS-1 is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Non-Dementa**} & \multicolumn{4}{c}{**Dementia**} \\ \hline
**Age Group** & **N** & **n** & **b** & **M/F** & **CDR** & **MMSE** & **n** & **Mean** & **M/F** & **CDR** & **MMSE** \\ \hline \(<\)20 & 19 & 19 & 18.53 & 10/9 & & & 0 & 0 & 0 & 0/0/0 & \\
20 s & 119 & 119 & 22.82 & 51/68 & & & 0 & 0 & 0 & 0/0/0 & \\
30 s & 16 & 16 & 33.38 & 11/5 & & & 0 & 0 & 0 & 0/0/0 & \\
40 s & 31 & 31 & 45.58 & 10/21 & 29.5 \(\pm\) 1.5 & 0 & 0 & 0 & 0/0/0 & \\
50 s & 33 & 33 & 54.36 & 11/22 & 29.4 \(\pm\) 0.8 & 0 & 0 & 0 & 0/0/0 & \\
60 s & 40 & 25 & 64.88 & 7/18 & 29.1 \(\pm\) 1.3 & 15 & 66.13 & 6/9 & 12/3/0 & 22.9 \(\pm\) 5.5 \\
70 s & 83 & 35 & 73.37 & 10/25 & 29.1 \(\pm\) 1 & 48 & 74.42 & 20/19 & 32/15/1 & 24.6 \(\pm\) 4 \\
80 s & 62 & 30 & 84.07 & 8/22 & 28.7 \(\pm\) 1.2 & 32 & 82.88 & 13/19 & 22/9/1 & 24.5 \(\pm\) 3.9 \\
90 s & 13 & 8 & 91.00 & 1/7 & 28.3 \(\pm\) 1.6 & 5 & 92.00 & 2/3 & 4/1/10 & 23.8 \(\pm\) 1.9 \\  & 416 & 316 & & 119/197 & 29.1 \(\pm\) 1.1 & 100 & 41/59 & 70/28/2 & 24.3 \(\pm\) 4.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of the population studied (OASIS-1 database). Clinical Dementia Rating scale (CDR) values are indicated as the number of patients diagnosed as very mild dementia/mild dementia/moderate dementia. Mini-Mental State Examination (MMSE) values are indicated as mean \(\pm\) standard deviation.  
Figure 1: Flowchart of the proposed method.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 1 Introduction
* EOR Framework: Developed to integrate SDF and SSC into a non-linear optimization framework that leverages insights from scenario features and object importance to improve localization accuracy and robustness. Figure 1: Schematic Representation of Camera-HD map Alignment. The remainder of this paper is organized as follows: Section 2 reviews highly related works, Section 3 describes the proposed enhanced object registration method, Section 4 presents the experimental evaluation conducted on the Argoverse 2 dataset, Section 5 discusses the results of experiments, and finally, Section 6 concludes the paper, summarizing the key findings and discussing avenues for future research.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 2 Principles
### Beam Steering Based on WGAs
#### 2.3.4 2D Steering Completely Based on Wavelengths  
Based on the waveguide delay lines and dispersion of WGAs, 2D beam steering can be achieved solely through wavelength tuning. 2D beam steering with beamwidth around 4\({}^{\circ}\)\(\times\) 4\({}^{\circ}\) in a range of 50\({}^{\circ}\)\(\times\) 15\({}^{\circ}\) was achieved by a wavelength shift of 100 nm [67]. As shown in Figure 3d, 2D beam steering can be achieved by the serpentine OPA structure used for delay-accumulating. The serpentine waveguide structure normally requires no active phase shifters [63]. For a fixed delay length, the wavelength-based 2D OPAs cannot be used to achieve flexible patterns.  
Besides, a wavelength-based 2D OPA combining wavelength division filters and a WGA array was proposed to realize 2D optical scanning. The filter module, which consists of a Mach-Zehnder interferometer (MZI) and microring array, divides the input light into different microrings and the corresponding emitters. The 64-channel OPA structure with an FOV of 6\({}^{\circ}\)\(\times\) 4\({}^{\circ}\) was realized by changing the input wavelength from 1.55 to 1.56 \(\upmu\)m. Voltage adjustment is required in the scanning process to change the refractive index of the upper MZI arm [68]. This 2D OPA with large WGA spacing enables only a limited FOV.
## 3 Results
### Pinch Point Temperature Difference  
In Figure 17, the effect of pinch point temperature difference (\(\Delta T_{pp}\)) on net power production for the proposed novel cycle and performance improvement in comparison with the reference cycle for each fluid are given. Power production increases as the pinch point temperature difference decreases because smaller pinch point temperature differences result in higher heat transfer to ORC evaporator and increased mass flow rate in the power cycle. By increasing \(\Delta T_{pp}\), decrease in net power has followed similar trends across all working fluids, suggesting that the difference between the lowest and highest net power-producing fluids remains stable throughout the research range. Increasing \(\Delta T_{pp}\) from 5 \({}^{\circ}\)C to 12 \({}^{\circ}\)C reduces net power by an average of 21.2 kW. Additionally, at higher pinch point temperature differences, the improvement of net power production over the reference cycle increases, reaching up to 11.9% for R601, while the lowest improvement is 9.3% at 5 \({}^{\circ}\)C for R290.
## 3 Results and Discussion
### Comparison with Other Optimization Models  
In the current section, various examinations were carried out to confirm that the synthesis of BFS, SVR, and GO produces the most reliable returns. This experiment engaged three optimizers to obtain the hyperparameters of SVR. One of these techniques was PSO, which explains the regular optimization performance for adjusting the SVR's hyperparameters. Another method was the Cuckoo Optimization Algorithm (COA), which was broadly employed for fine-tuning the parameters of ML algorithms [38]. The last optimizer was the Neural Network Algorithm (NNA), one of the most advanced optimization techniques [39]. The SVR model was optimized with PSO, COA, and NNA. Seven of the inputs that SVR-GO-BFS\({}_{\text{n}}\) used were used again when the new optimized models were made.  
The precision of the optimized models is presented in Figure 11. The performance results of the tuned SVR models by the optimized techniques are displayed in Table 6. For the granite quarry dataset, the precision of the SVR-GO-BFS\({}_{7}\) model was higher than that of the SVR-PSO-BFS\({}_{7}\), SVR-COA-BFS\({}_{7}\), and SVR-NNA-BFS\({}_{7}\) models. As a result, the capacity of the GO technique to search the SVR's hyperparameters was more effective than NNA, PSO, and COA. Simply put, the SVR-GO-BFS\({}_{7}\) method achieves high accuracy for AOp forecasting and has the best efficiency and consistency among all basic techniques. Overall, the SVR-GO-BFS\({}_{7}\) technique obtained great precision for AOp prediction and had the greatest performance and cohesion amongst all basic methods. Consequently, in this research, the SVR-GO-BFS\({}_{\text{n}}\) model is used for prediction, and further studies are suggested to utilize this method in other investigations based on the authors' concerns.  
Figure 10: Results of predictions by various models.  
Figure 9: Real and predicted AOp values.  
In comparison with the previous work, the model developed in this study showed better performance. For instance, [PERSON] et al. [27] applied an ANN-PSO to the same inputs, and they did not utilize any input selection technique and only used their model for AOp estimation. The best R\({}^{2}\) that they achieved was 0.8836. The SVR-GA-BFS7 model achieved a better R\({}^{2}\) while using a fewer number of inputs, which decreased the model complexity. The authors of this study believe that the current study and its process and results are able to add value to the available literature.
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
## 2 Structure and Theoretical Model of PEH-C
### Theoretical Modeling  
In order to establish a coupling control equation, this article uses the generalized Hamilton principle. \[\int_{t_{1}}^{t_{2}}[\delta(T_{K}-U)+\delta W_{nc}]dt=0 \tag{1}\]  
where \(T_{K}\) is the whole kinetic energy of the proposed system, U is the whole potential energy of the proposed system, and \(W_{nc}\) is the external work applied to the system. The whole kinetic energy of the proposed system can be expressed as:  
\[T_{K}=\frac{1}{2}\int\limits_{V_{b}}\rho_{b}\dot{u}^{2}(X,t)dV_{b}+\frac{1}{2} \int\limits_{V_{p}}\rho_{p}\dot{u}^{2}(X,t)dV_{p}+\frac{1}{2}m_{0}\dot{u}^{2}( L,t) \tag{2}\]  
where \(u(X,t)\) is the transverse displacement of the beam; \(V_{p}\) and \(V_{b}\) are the piezoelectric and substrate layer volume, respectively; \(\rho_{p}\) and \(\rho_{b}\) are the piezoelectric and substrate layer density, respectively; and \(m_{0}\) is the quality of mass. The whole potential energy of the proposed system can be expressed as:  
\[U={W^{*}}_{b}+{W^{*}}_{p} \tag{3}\]  
where \({W^{*}}_{b}\) is the elastic potential energy of the piezoelectric beam and \({W^{*}}_{p}\) is the electric potential energy of the piezoelectric layer. The elastic potential energy of the piezoelectric beam can be expressed as:  
\[{W^{*}}_{b}=\frac{1}{2}\int\limits_{V_{b}}T_{1}S_{1}d_{V_{b}} \tag{4}\]  
where \(T_{1}\) and \(S_{1}\) represent the axial stress and the axial strain, respectively. The electric potential energy can be expressed as:  
\[{W^{*}}_{p}=\frac{1}{2}\int\limits_{\Omega_{p}}(T_{1}S_{1}-E_{3}D_{3})dV_{p} \tag{5}\]  
Figure 1: Schematic diagram of PEH-C.  
where \(E_{3}\) and \(D_{3}\) represent the electrical field and the electrical displacement, respectively. The electrical displacement and the axial stress can be expressed as:  
\[T_{1}=c_{11}^{E}S_{1}-e_{31}E_{3} \tag{6}\]  
\[D_{3}=\varepsilon_{33}^{S}E_{3}+e_{31}S_{1} \tag{7}\]  
where \(\varepsilon_{33}^{S}\) and \(e_{31}\) represent the permittivity component at constant strain and the piezoelectric constant, respectively, and \(c_{11}^{E}\) is the piezoelectric material elasticity coefficient. The external work applied to the system can be expressed as follows:  
\[\delta W_{nc}=-\int\limits_{V_{p}}f_{i}\delta udV_{p}-Q_{k}\delta v-\int \limits_{V_{b}}cu\delta udV_{b} \tag{8}\]  
where \(f_{i}\) and \(Q_{k}\) represent the external incentives and effective current, respectively. In this paper, based on the Rayleigh-Ritz principle, it is assumed that a single-mode approximation of the beam deformation is sufficient and the vibrational displacement of the beam can be expressed as follows:  
\[u(X,t)=\sum\limits_{i=1}^{n}\psi_{i}(X)r_{i}(t) \tag{9}\]  
where \(\psi_{i}(X)\) is the \(r_{i}(t)\) mode shape of the beam and \(r_{i}(t)\) is the time-dependent generalized coordinate. Under low-frequency excitations, the vibration of the beam is mainly concentrated in the first-order mode, so it is sufficient to consider one mode to obtain the reduced-order model.
# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
## 6 Interactive Process-Model Discovery through Merge and Abstraction  
As discussed in Section 3, in our previous approach in [3], we provided a framework supporting the interactive and \"step-by-step\" discovery of the process model, based on the _merge_ semantic operations. In this section, we extend it, providing experts, as well as the possibility of adopting the _abstraction_ semantic operations described so far. Notably, merge and abstraction operations are orthogonal and complementary operations, working along two different dimensions: the \"level of abstraction\" (of the terms used to describe the process: abstraction operations) and the \"level of repetition\" (of equal patterns in the process model: merge operations). They can, therefore, easily be integrated within a unique model discovery procedure, supporting experts with a powerful and flexible framework to achieve the desired balance in the process model: in particular, we refer to not only the balance between overfitting and underfitting [2], but also to the balance regarding the \"level of abstraction\" of the terms used in the model description. Specifically, in [3] we proposed a framework supporting process model versioning, based on a tree-of-models data structure, the _versioning tree_. Each node in the versioning tree corresponds to a process model. Arcs in the versioning tree correspond to merge operations. As motivated in [3], the root node of the versioning tree is the log tree. Based on their domain knowledge, experts can analyze the current model (possibly taking advantage also of the retrieval facilities proposed in [3]; see the \"_analyze_\" option in Algorithm 5 below) and decide whether it achieves the desired balance or whether it is better to move forward to further improvements (through the application of merge operations) or even backtrack to previously built models. Given the orthogonality discussed above, such a general process can be extended by supporting, besides merge operations, also abstraction operations. The extended algorithm, called _GenerateModel_, is shown below (see Algorithm 5). For the sake of brevity, the options \"_analyze_\", \"_merge_\", and \"_back_\" are not detailed (they are expanded and widely explained in [3]).