esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
## 1 Introduction
Unlike traditional methods, the network can generalize to new inputs after training on different data sets, eliminating the need to recalculate parameters for each system change. This approach builds neural network layers over multiple iterations using advanced learning techniques to achieve unprecedented results [20; 24; 25; 26]. Various deep unfolding-based algorithms for MIMO channel detection are reported, including trainable projected gradient detectors [27] and the conjugate gradient descent technique [28; 29], with other alternative approaches in [11; 12; 30]. Despite these developments, there is still a significant research gap in improving these approaches, especially when it comes to dealing with the complexity and variability of harsh situations. This emphasizes the need for further advances in this area and the usefulness of the proposed approach in improving MIMO detection technology. The deep unfolding approach also offers significant advantages in computational efficiency and hardware implementation [26]. This method is particularly beneficial for physical applications with hardware constraints and operational efficiency requirements. By predetermining the neural network's structure to mimic specific algorithm iterations, it reduces the need for extensive training data and computational resources, addressing major challenges faced by traditional DNNs. This study presents a significant advance in the field of MIMO signal detection by introducing a unique detection strategy that combines Tikhonov regularization and CG method with deep unfolding. Detection of transmitted symbols over a multipath fading channel is considered as an ill-conditioned problem [31] that may result in slower or even a non-existent convergence. Using the matrix \(L\) as a regularization term enhances the detection process in CG-based detection methods by resolving the input signal while effectively suppressing the degrading effects. Thus, the regularization allows for significant improvements in detection performance over conventional methods for different channel conditions and antenna layouts. The main contributions of this study are summarized below:To the best knowledge of the authors, this is the first study where Tikhonov regularization is integrated with the conjugate gradient method for MIMO detection in a deep learning-based approach. 2. Performance of the proposed method has been compared with both iterative and model-driven techniques for different channel models such as Rayleigh, Kronecker, Tapped Delay Line A (TDL-A), and TDL-E. The remaining sections of this study are organized as follows: Section 2 presents the relevant work and subjects. Section 3 provides a thorough explanation of the proposed approach. A comprehensive analysis of computational complexity is provided in Section 4. The simulation results are given in Sections 5 and 6, and conclusions are drawn and suggestions for further work are explored.
## 2 thermoRIDE-Tire Thermodynamic Model
### Physical Model
#### 2.2.4 Heat Exchange with External/Inside Air  
The whole mechanism of the heat transfer between a generic surface and a moving fluid at different temperatures is described by natural and forced convection equations. The convection heat transfer is expressed by [PERSON]'s law of cooling, as before:  
\[h_{conv}\big{(}T_{fluid}-T_{i}\big{)}\Delta X\cdot\Delta Y \tag{12}\]  
Therefore, the heat exchange with the outside air is modeled by the mechanism of forced convection, occurring when there is relative motion between the motorcycle and the air, and by natural convection, when such motion is absent.  
Natural convection is also employed to characterize the heat transfer of the inner liner with the inflating gas. The determination of the convection coefficient \(h\), both forced \(h_{forc}\) and natural \(h_{nat}\), is based on the classical approach of the dimensionless analysis.  
Supposing the tire invested by the air similarly to a cylinder invested transversely from an air flux, the forced convection coefficient is provided by the following formulation [8,9]:  
\[h_{forc}=\frac{K_{air}}{L}\cdot\left[0.0239\cdot\left(\frac{V_{x}\cdot L}{v_{ air}}\right)^{0.805}\right] \tag{13}\]  
in which:  
* \(K_{air}\) is air conductivity, evaluated at an average temperature between the effective air one and outer tire surface one, in \(\left[\frac{W}{m\cdot K}\right]\);
* \(V_{x}\) is considered to be equal to the forward speed, in \(\left[\frac{m}{s}\right]\);
* \(v_{air}\) is the kinematic viscosity of air, in \(\left[\frac{m^{2}}{s}\right]\);
* \(L\) is the characteristic length of the heat transfer surface, in [m];
* \(T_{m,air}\) is the arithmetic mean between the temperatures of the tire outer surface and the external air in relative motion, in [K].  
The values of \(h_{forc}\) evaluated with the above approach are close to those obtained by means of CFD simulations for a motorcycle tire, represented in Figure 4.  
Figure 4: Example of motorcycle Computational Fluid Dynamics (CFD) simulations, useful for both front and rear tires convection parameterization.  
The natural convection coefficient \(h_{nat}\), however, can be expressed as:  
\[h_{nat}=\frac{N\text{u}\cdot k_{air}}{L} \tag{14}\]  
in which, for this case:  
\[Nu=0.53\text{-}G^{0.25}\text{-}P\text{r}^{0.25} \tag{15}\]
# Meta-Analysis of the Performance of Previous Concrete with Cement and Aggregate Replacements
###### Abstract  
In recent years, pervious concrete (PC) has gained much attention as one of the strategies for low-impact development (LID) in pavements due to its structural, economic, and road-user benefits. This study sought to review and evaluate changes in the mechanical, hydraulic, and durability performance of PC produced with cement and aggregate replacements. A meta-analysis was conducted to elucidate the feasible range of the replacement percentage and the number of materials that could be used to replace cement and aggregates; single or binary replacements were considered. Results indicated that cement-replacing materials, industrial wastes (IWA), and recycled aggregates (RA) met the minimum requirement for the mechanical, hydraulic, and durability properties of PC. The use of a single cement replacement material provided PC with better performance than when cement was replaced with two or more materials or when cement alone was used. Industrial waste was found to be a better replacement to aggregates than RA. The combined replacement of cement and aggregates with IWA and other cement-replacing materials was the most effective method for improving the mechanical, hydraulic, and durability performance of PC. Replacements of up to 40% was considered viable for cement replacement, while up to 50% replacement was considered practical for aggregate and combined replacement. PC incorporating different cement-replacing materials exhibited equivalent or improved mechanical properties and maintained hydraulic performance compared to cement-based PC. Nonetheless, limited studies are available on the durability performance of PC made with cement and/or replacements. Thus, the durability of PC coupled with the applicability of replacement materials acquired from different locations need to be evaluated to address the viability of producing more durable PC with the use of replacements.  
pervious concrete; cement replacement; aggregate replacement; recycled aggregate; industrial waste materials; mechanical; hydraulic; durability +
Footnote †: journal:  
_Buildings_
## 3 Verification through a Simple Beam Under Sinusoidal Loads  
### Collecting Training and Test Data  
To verify the proposed method, a simple beam with a span of 3000 mm (see Figure 4) was utilized. Because simple beams are used as the main girder in bridges, it is very important to predict the displacements of simple beams. To collect the training and test data, a sine load of 1 kN with periods of 1, 0.5, and 0.1 s was applied to the center of the beam for 1 s. For example, in the case of the 0.5 s period, a total of two cycles was applied. To obtain the data, numerical analysis was carried out. Additionally, the beam was divided into 41 nodes (interval 75 mm) to obtain the strains and displacements at each time step. Therefore, one dataset consisted of 41 strains (input) and 41 displacements (output). Figure 5 shows the graph for sine loads. Also, Table 2 shows the number of datasets.  
\begin{table}
\begin{tabular}{l l} \hline Number of input nodes (strain values) & n\({}_{\text{s}}\) \\ Number of hidden layers & 1 \\ Number of hidden nodes & n\({}_{\text{s}}\) \\ Number of output nodes (displacement values) & Overall displacements \\ Training algorithm & Scaled conjugate gradient backpropagation \\ Transfer function & Logsig (hidden), Purelin (output) \\ Learning rate & 0.01 \\ Momentum & 0.9 \\ \hline \end{tabular}
\end{table}
Table 1: General information for the artificial neural network (ANN) model.  
Figure 4: Specification of the simple beam (Unit: mm) [17].  
Figure 3: Flowchart for optimizing strain-measurement points using the GA in conjunction with the ANN.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 5 Experiment
### Experimental Analysis
#### 5.4.2 Ranking Accuracy  
To evaluate the performance of the LBIA algorithm more comprehensively, we used the Kendall metrics to assess the effectiveness of each algorithm. To make the results convincing, the infection probability \(\beta\) in the SIR model started at 0.02 and increased by 0.01 each time. Different networks were propagated with different infection probabilities and the ranking results of each algorithm were compared. Figure 6 and the table demonstrate the Kendall values of each algorithm with different infection probabilities, which show that the LBIA algorithm achieved good results on the Dolphins, USAir97, Bio-celegans, Eurroad, RT-assad CA-GrQc, and CA-Erdos992 networks, and significantly outperformed the rest of the algorithms on the Soc-hamster and CiteSeer networks. Table 3 presents the average Kendall values of nine algorithms under different propagation probabilities.  
Figure 6: Kendall values of different algorithms on different networks and the legend: The different colored lines represent the kendall values obtained by the different algorithms on the corresponding networks as the probability of propagation changes. The higher the kendall value, the more consistent the node ordering.
# A Hierarchical Coordinative Control Strategy for Solid State Transformer Based DC Microgrids
## 2 Overview of the SST-Based DC Microgrid System
### Problems Existing in the Independent Control Strategy
Since the CLLC can be equivalent to one submodule, so the transfer function of \(i_{2}\) to \(i_{L}\) will be the same as \(i_{2i}\) to \(i_{Li}\), as illustrated in Equation (7):  
\[\frac{i_{2}}{i_{L}}=\frac{G_{uo}G_{ps}}{G_{uo}G_{ps}{-}sNC_{L}} \tag{7}\]  
Similarly, the transfer function of the MMC output DC current \(i_{dc}\) to the CLLC input current \(i_{1}\) can be expressed as:  
\[\frac{i_{dc}}{i_{1}}=\frac{3E_{d}G_{udc}G_{idd}}{3E_{d}G_{udc}G_{idd}{-}2U_{dc }C_{He}} \tag{8}\]  
where \(G_{idd}\) is the transfer function of the grid connected current in the d-axis, and its expression is:  
\[G_{idd}=\frac{i_{d}}{i_{dref}}=\frac{G_{id}}{G_{id}{+}L_{0}s} \tag{9}\]  
According to the control parameters listed in Table 1 and the system hardware simulation parameters listed in Table 2 in Section 4, the current response Bode diagrams of HVDC and LVDC links can be drawn, as shown in Figure 7. In Table 1, the proportion and integral parameters of PI controllers are distinguished by the subscripts P and I. For example, \(K_{uo}=K_{Puo}+K_{luo}\). It can be seen from Figure 7 that the closed-loop bandwidth of \(i_{2}\) to \(i_{L}\) is 2930.81 rad/s, which is much larger than the 547.19 rad/s of \(i_{dc}\) to \(i_{1}\). It indicates that the power adjustment speed of the CLLC is much faster than that of the MMC. Therefore, when the output power of the microgrid fluctuates dramatically, such as  
Figure 6: Topology and control strategy of the photovoltaic (PV) and energy storage system (ESS): (**a**) the interface converter for the PV; (**b**) the maximum power point tracking (MPPT) control diagram for the PV; (**c**) The interface converter for the ESS; (**d**) The control strategy for the ESS. switching from absorbing electricity to generating electricity to the distribution network, the power response speed of the CLLC is much faster than that of the MMC. According to the dynamic Equation (1), the capacitance voltage fluctuation of the HVDC link depends on the current difference between its two sides, that is, the capacitance voltage itself is determined by the difference of the current integral value:  
\[U_{dc}=\frac{1}{C_{He}}\int\ i_{dc}dt-\frac{1}{C_{He}}\int\ i_{1}dt \tag{10}\]
## 1 Introduction
Offline digital signal processing (DSP) is required to be implemented in OINs for reconstructing the constellation shaping signal since it would be degraded in the long distance by the influence of impairments related to dispersion and nonlinear phase noise. Compared with the conventional DSP algorithm, the DSP assisted by machine learning (ML) algorithm performs better. The K-means clusters algorithm is the preferred method for classifying constellation points influenced by nonlinear effects of optic-fibers among the various ML algorithms available. Researchers introduced a weighted K-means cluster algorithm for PS-64 QAM in [21], which significantly addressed the issue of incorrect centroid judgement precipitated by fewer constellation points on the constellation's corners. With the likelihood of each constellation point being distributed equally in the GS scheme, the aforementioned issues are prevented, and the common K-means clustering algorithm could be used straight, eliminating computational complexity to a certain degree. To sum up, constellation shaping has the potential to improve OIN's capacity and narrow the gap with the Shannon limit. Simultaneously, the K-means algorithm could recover the deteriorated signal and minimize the bit error rate (BER). There have not been full investments in merging machine learning algorithms with it to address nonlinear deterioration. Therefore, this paper proposes a complete state-of-the-art scheme that applies the K-means clustering algorithm to geometric shaping based on iterative polar modulation, which not only brings better performance to the transmission system but also increases the intelligence of the system. A coherent optical communication simulation system was established to verify the superiority of the proposed scheme. The simulation results show that the IPM modulation format excels QAM in terms of mutual information (MI) under various SNR and transmission lengths. When the modulation order is 16,64, 256, respectively, the BER performance of the proposed scheme is compared with QAM adopting blind phase searching (BPS) in the same order. The results reveal that the proposed scheme has a lower BER, suggesting that our proposal can enhance capacity without sacrificing BER performance. The paper is organized as follows. We begin in Section 2 by discussing the principles of IPM and K-means and generate the IPM constellations of different orders. In Section 3, we describe the configurations of the MATLAB simulation system and then evaluate the simulation results. We discussed the potential application scope in Section 4. Finally, we provide some concluding remarks in Section 5.
## 1 Introduction  
Silicon-organic hybrid (SOH) photonics has gained a lot of attention in recent years due to its potential for high-speed modulators [1; 2]. Electro-optical (EO) polymers incorporated into silicon-on-insulator (SOI) slot-waveguides with gap sizes below 180 nm have lead to unrivaled results in high-speed performance. There are two EO effects present in EO polymers; namely the linear [3] and the quadratic EO effect [4]. The former one has been widely investigated in SOH modulators. Recently, SOH Mach-Zehnder modulators with exceptionally low energy consumption of \(W_{\text{BH}}=8.5\,\text{fJ}/\text{Bit}\) have been demonstrated using the linear EO effect having record-high in-device EO figures of merit of up to \(n^{3}r_{33}=2300\,\text{pm}/\text{V}\)[5]. Furthermore, data rates of up to 100 Gbit/s in on-off keying (OOK) operation and voltage-length products as low as \(U_{n}L=1.0\,\text{Vmm}\) have been reported [6].  
The performance of those devices is achieved by the introduction of SOI slot-waveguides with polymer-clad. Within a narrow, low-index gap region, slot-waveguides offer a high optical field confinement [7], which can be exploited to efficiently modulate the guided mode. The large optical and electrical field overlap in SOI slot-waveguides allows for ultra-efficient EO modulation. The EO response is provided by the polymer-clad. A widely used polymer system consists of the linear conjugated dye Disperse Red 1 (DR1) doped into a poly[methyl methacrylate] (PMMA) matrix [8]. It was used to demonstrate the feasibility of employing the linear [9] as well as the quadaratic EO effect in SOI slot-waveguides demonstrating its high potential for SOH photonics [4]. The polymer system PMMA+DR1 is commercially available (Sigma Aldrich, St. Louis, MO, USA) and can be used as a test material since it is cost effective and the sample preparation is relatively simple. However, the optical properties of the polymer is changed by the EO dye DR1. Therefore, it is desirable to study the influence of this change on the modal-field distribution in slot-waveguides. For high scalability and reduction of costs this approach requires compatibility to complementary metal-oxide semiconductor (CMOS)-technologies.  
In this work, we consider CMOS-compatible slot-waveguides based on SOI substrates covered with a polymer-clad consisting of the guest-host polymer system PMMA+DR1. For the first time, our investigation focuses on the influence of the nonlinear optical dye concentration on the modal properties of slot-waveguides and compares it with the influence of geometric parameters. The precise knowledge of the effective refractive index is crucial for the design of waveguide couplers and for highly efficient phase-matched second harmonic generation [10; 11]. Beside that, the field confinement factor is from great importance for the optimization of EO modulators. Therefore, we provide precise values for the effective refractive index and a guideline to optimize the field confinement factor.
## 4 CAS Development
### CAS Design
#### 4.2.1 Ontology Conceptualization
* The W3C OWL-Time ([[http://www.w3.org/2006/time](http://www.w3.org/2006/time)]([http://www.w3.org/2006/time](http://www.w3.org/2006/time))), an ontology of temporal entities, describes the temporal properties of resources. Another well-known ontology reused in this conceptualization is the Ontology of Units of Measure and Related Concepts (OM) [21]. In some cases, the reused ontology elements matched the intended use in the developed ontology; therefore, they were adopted without modification. Some examples of this case are the classes **sosa:Platform, sosa:FeatureOfInterest** or **om:Unit**. However, in other cases, some additional properties or constraints had to be added to the reused elements. Then, a new subclass of reused concepts was created to attach the new semantics to the subclasses created. For example, the class **casc:Observation** is a specialization of the class **sosa:Observation** since **sosa:Observation** represents any computation activity in general. **casc:Observation** uses the object property prov:wasInformedBy to identify the inputs of the computation. The class **casc:Deduction** is a specialization of **casc:Observation**. A deduction is also a computation or a measurement that has a state for a result. Figure 5: Overview of the CASO. Finally, the conceptualization was modularized into two ontologies: CASO and IRRIG. The IRRIG ontology imports and extends CASO. The CASO ontology is illustrated in Figure 5. This IRRIG ontology is depicted in Figure 6. The entities defined in the CASO and IRRIG ontologies are preceded by the prefixes \"casco\" (yellow boxes) and \"irrig\" (blue boxes), respectively. The goal of CASO is to model any context processing. IRRIG is a specialization of CASO used to describe all the context processing involved in the IRRINOV\({}^{\circledotimes}\) method. For this reason, it specializes some elements of CASO, as shown in Figure 6. For example, IRRIG specializes the class **ssn:Property** in different subclasses related to moisture, stress, and growth. Several subclasses of **casco:Observation** specify different types of observations. Each subclass is dedicated to the observations of one specific instance of **ssn:Property**. During the conceptualization phase, several versions of the ontology model are produced and stored in a private repository of INRAE. Figures 5 and 6 are parts of the lastest version (v201912).
###### Abstract
More recently, the authors of the present work have used the statistical moments in the frequency domain to characterize the seismicity of Vesuvius [16]. Following this latter work, the detection procedure we provide in the present paper is based on the coherence and statistical moments of seismic signals and turns out to be more empirical than many modern approaches that are quite articulated and based almost entirely on neural networks. Our main objective is the detection of deep LF events occurring beneath Mt. Vesuvius at depths greater than the predominant VT seismicity [12]. The aim of improving the analysis of recorded seismic signals is to identify and classify in a semi-automatic manner the occurrence of LF events, possible signals due to landslides and other phenomena such as thunderstorms, in order to distinguish them from seismic signals associated with events arising outside the volcano, such as regional earthquakes and transient signals of artificial origin. To this end, the proposed approach focuses on the analysis of the spectral content of seismic signals using two techniques jointly, one based on the spectral parameters analysis through the computation of statistical moments, and another based on the coherence analysis. The effective detection of low-amplitude seismic signals such as LF events and volcanic tremor is the first step toward the detailed analysis of their source and driving mechanisms, which is of fundamental importance for assessing the eruptive potential of volcanoes [17]. In the work of [PERSON] et al. [16], spectral parameters were used for earthquake classification. In the present paper, the innovative aspect lies in the application of statistical moments for detection purposes and the additional use of coherence as a multichannel technique. The newly designed procedure was tested on continuous seismic signals recorded at Mt. Vesuvius from 2019 to 2021. After a brief outline of used data and methodologies, the main results are illustrated and discussed in the final part of the paper.