esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Resource and Power Allocation for Underlay Multicast Device-to-Device Transmission
## 3 System Model and Problem Formulation
\[P_{m}^{D2D_{m}}\gamma_{m}^{D2D}\geq\Gamma_{\min}^{D2D},\forall m \in\mathcal{M},n\in\mathcal{N} \tag{12a}\] \[P_{n}^{CU}\gamma_{n}^{CU}\geq\Gamma_{\min}^{CU}\] (12b) \[\sum\limits_{n=1}^{N}x_{m,n}\leq 1,\forall m\in\mathcal{M}\] (12c) \[\sum\limits_{m=1}^{M}x_{m,n}\leq 1,\forall n\in\mathcal{N}\] (12d) \[\frac{\Gamma_{\min}^{D2D}}{\min\frac{\Gamma_{\max}^{D2D}}{\sum \limits_{n=1}^{N}x_{m,n}P_{n}^{CU}\zeta_{n,k}^{CD}+\sigma_{n}^{2}}}\leq P_{n}^ {CU}\leq P_{max}^{CU}\,\forall m\in\mathcal{M},n\in\mathcal{N} \tag{12e}\]  
where \(\eta_{all}\) represents the overall energy-efficiency of D2D clusters. Constraints (12a) and (12b) define the minimum SINR requirement. Constraints (12c) and (12d) ensure that each D2D cluster reuses the RBs of CU at most once. Constraints (12e) and (12f) ensure that the transmit power both D2D users and CUs fall into certain range. According to Equation (12), in order to improve the total energy-efficiency, the possible solution is either to decrease the transmit power or to increase the aggregate transmission rate. This is interpreted as we have to find the optimal resource reuse relationship \(x_{m,n}\) between D2D clusters and CUs, as well as to determine the optimal transmit power of both CUs and D2D clusters which also guarantees the minimum SINR requirement. As a matter of fact, when D2D multicast transmission undelaying with a cellular network, the resource allocation method and power allocation strategy are actually interacted with each other [14]. Once the resource reuse relationship between CUs and D2D clusters varies, the transmit power of each UE will also be influenced owing to changing interference condition. On the other hand, if the transmit power of different UE alters, the interference condition between co-channel CUs and D2D clusters will also change, which conversely affects the resource assignment results. In addition, the existence of integer assignment variable \(x_{m,n}\) makes the optimization problem more complicated. Consequently, the above optimization problem in (12) is a non-convex optimization problem which is proved to be a NP-Hard problem and there are no efficient solutions [15]. Moreover, when the problem size increases, the computational complexity also increases exponentially. A possible solution for above problem might be using the bipartite matching based optimal resource allocation scheme, as suggested by [16]. However, such scheme is actually based on exhaustive searching method, which results in high computational complexity. In order to deal with this challenge, we propose a heuristic resource allocation and power control algorithm to balance the system performance and complexity.
# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
###### Abstract  
This paper proposes a new hybrid hierarchical roadside unit (RSU) deployment algorithm that combines traditional RSU deployment strategies with temporary RSUs (t-RSUs) selected from many parked vehicles. The driving vehicles in the corresponding RSU coverage section are divided into clusters and the demand is thus summarized. We first solve the problem of how to choose the appropriate cars serving as t-RSUs and then optimize the RSU deployment with the dynamic existing t-RSUs. We simulate the deployment application in a downtown area of Shanghai, China, using the simulation of urban mobility (SUMO), version 1.11.0, an open-source traffic simulation package, and its traffic-control interface (TraCI) with Python. The simulation results show that, within a given economic cost, the proposed hybrid hierarchical RSU deployment algorithm outperforms the maximum vehicle coverage deployment algorithm (MVCD) and the random deployment algorithm (RandDeploy) in terms of overall vehicle coverage and point-to-point vehicle connectivity ratio. Our scheme provides new solutions and ideas for the deployment of RSUs in future urban traffic environments.  
roadside unit deployment; parked car; mixed traffic flow; vehicular ad hoc networks Article
## 1 Introduction and Literature Review
Section 2 describes the methodology used; in Section 3, details of the case study are given, the criticality indices are evaluated, and the possible solutions are defined. In Section 4 the results are analyzed, and in Section 5 the discussions are presented, outlining the main limitations. Section 6 concludes the work with future research directions. This paper aims to explore the use of artificial intelligence for 3D human pose estimation using individual snapshots or video sequences. A new body postural assessment system is developed that can automatically calculate joint angles based on keypoint detection using an Artificial Intelligence (AI) model for pose estimation fed with images of operator activity. The output is obtained, which consists of a 3D representation of the human skeleton with 17 keypoints; then, the joint angles are calculated using the keypoints and the image processed via image-based motion capture technology. Based on the joint angle and RULA matrix, the main inferential rules are created to build a fuzzy IE for the evaluation of each body region. The system calculates a global \"criticality index\", which is able to provide a measure of ergonomic stress for each task performed by the operator and define the impact of each upper-body quadrant on the final result. Then, the collaborative robot is implemented to reduce ergonomic stress and increase production capacity. The methodology developed is based on a temporal convolutional model that takes 2D keypoint sequences as the input and 3D human pose estimation as the output and allows the parallel processing of multiple frames, unlike recurrent networks. Therefore, the measurement of the joint angle is more accurate, and as wearable measurement sensors are not used, ergonomic risk assessment can take place without hindering the operator's activities.
# Precise Channel Estimation Approach for a mmWave MIMO System
## 6 Computational Complexity  
In this section, four benchmark algorithms, namely, TSSR [46], VAMP [47], OMP [16] and ADMM [18], are compared with the proposed Ex-ADMM algorithm in terms of complexity.  
For the general case, TSSR is much faster than any one-stage method. The computational complexity of TSSR is dependent upon the calculation of its maximum diagonal elements and the smaller number of off-diagonal elements in its sparse matrix. The complexity order of the TSSR algorithm is \(\mathcal{O}(\text{nlog}(\text{n}))\), [46], where n is the full column rank of the targeted sparse matrix.  
In VAMP, the complexity is dominated by the matrix vector multiplication. The computational complexity of VAMP is the order of \(\mathcal{O}(\text{N}_{\text{R}}\text{N}_{\text{T}}\text{Llog}(\text{N}_{ \text{R}}\text{N}_{\text{T}}\text{L}))\)[47], where \(\text{N}_{\text{R}}\) and \(\text{N}_{\text{T}}\) is the number of receivers and transmitters, respectively, and L is number of channel paths or the sparsity level of the channel.  
In OMP, the computational complexities depend upon the sparsity of the dictionary matrix and the number of grids. Mathematically, this can be expressed as \(\mathcal{O}\big{(}\text{LlnG}^{2}\big{)}\). Here, L is the number of paths or the sparsity level of the channel and G is the grid of the dictionary matrix in which the AoAs and AoDs are distributed uniformly [16]. The computational complexity of ADMM reflects the number of iterations, \(\text{I}_{\text{max}}\), and the number of transmitting and receiving antennas, i.e., \(\text{N}_{\text{T}}\) and \(\text{N}_{\text{R}}\), respectively. In ADMM, appropriately, matrix factorization can be performed offline, and only the matrix vector product has to be calculated online. This reduces the computational complexities of the ADMM in a significant way. The complexity of ADMM can be expressed as \(\mathcal{O}\big{(}\text{N}_{\text{R}}^{2}\text{N}_{\text{T}}\big{)}\)[18], where \(\text{N}_{\text{R}}\) and \(\text{N}_{\text{T}}\) are the numbers of receivers and transmitters, respectively. The proposed Ex-ADMM algorithm is originally derived from ADMM. Therefore, the complexity of the proposed Ex-ADMM algorithm remains the same as that of ADMM, i.e., \(\mathcal{O}\big{(}\text{N}_{\text{R}}^{2}\text{N}_{\text{T}}\big{)}\).
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
## Abbreviations  
\begin{tabular}{l l} RUL & remaining useful life \\ IG & inverse Gaussian \\ EM & expectation-maximization \\ PDF & probability density function \\ CDF & cumulative distribution function \\ Ra & surface roughness \\ Y(\(t\)) & degradation process with a simple IG process model \\ \(\mu\) & degeneration rate of Y(\(t\)) \\ \(\lambda\) & fluctuation coefficient of Y(\(t\)) \\ \(\Lambda(t)\) & monotone increasing function of Y(\(t\)) \\ \(\omega\) & failure threshold \\ T & failure time \\ Tr & residual life \\ \(F_{\rm T}(t)\) & CDF of T \\ \(f_{\rm T}(t)\) & PDF of T \\ P(\(\cdot\)) & probability of an event \\ E(\(\cdot\)) & expectation operator \\ N(a, b) & uniform distribution with boundary [a, b] \\ \(\Phi(\cdot)\) & CDF of standard normal distribution \\ \(N\big{(}\alpha_{\mu},\sigma_{\mu}^{-2}\big{)}\) & distribution of Parameter \(1/\mu\) \\ \(\Lambda^{\prime}(t)\) & derivative function of \(\Lambda(t)\) \\ \(t_{\rm k}\) & \(k\)th measurement time \\ R\({}_{\rm k}\) & RUL corresponding to the equipment at the current measurement time \(t_{\rm k}\) \\ \end{tabular}
# Design and Application of a Distribution Network Phasor Data Concentrator
## 4 The Test of the Key Performance Indicators of DPDC  
In this study, equipment including \(\upmu\)MPMU, time source, power signal source, etc., and some software (e.g., Main station Emulator and PMU Emulator) are deployed to build the distribution network WAMS system for DPDC test. The laboratory test environment for DPDC is shown in Figure 6.  
The \(\upmu\)MPMUs used in the test are produced by XUJI Electrics Company. The model is \(\upmu\)M-PMU-851, which can collect three-phase voltages, three-phase currents and zero-sequence current at the same  
Figure 5: DPDC panel. (**a**) Front panel; (**b**) rear panel.  
time and make uMPMUs achieve \"three remote\" function as the distribution network terminals. The time source is synchronous with GPS and provides time service to uMPMUs and DPDC, manufactured by Xu Ji Group, model DDS100-S, time accuracy \(\leq 1\upmu\)S. A relay protection tester works as a power signals source. It is manufactured by Guangdong only Electrical Automation Co., Ltd. and its model is ONLLY-AQ430. PC1 has two network ports, Intel(R) Xeon (R) E5-2603 v4 processor, 8 G RAM with 1.7 GHz working frequency. PC2 is a laptop with an Intel (R) Core (TM) i7 6700 HQ processor, 4 G RAM with 3.5 GHz working frequency. PMU Emulator and Main station Emulator software are developed for DPDC testing. PMU Emulator is based on Visual C ++ development, supports the standard GB/T 26865.2-2011 protocol, and adopts a multi-thread design. The software can simulate 30 PMUs at the same time, each PMU has three output streams, the data length can be modified; support 1-200 frames/s report rate. The data time of each PMU is initially generated by the computer time. The timestamp can be consistent or a fixed delay can be set to simulate the time difference between different PMUs arriving at the PDC through different channels. Main station Emulator is also developed based on Visual C ++. It can connect multiple PMU and PDC devices and adopt a multi-thread design. The simulated master station displays the received data based on a table. The packet capture software Wireshark is also installed on the DPDC. Its function is to capture network packets and display the most detailed network packet data as possible, with time accurate to microseconds. In the following key indicator tests, PDC Emulator and Main station Emulator are mainly used to test DPDC.
## 2 Materials and Methods  
The current mainstream spaceborne receiver products rely on GPS/COMPASS dual-frequency observation data to establish pseudo-ambiguities of carrier phase observations, the parameters to be estimated, and their corresponding random models, which are used to compensate for errors in broadcast ephemerides. These error parameters can absorb the propagation path delay of broadcast ephemerides due to their inherent slow-varying characteristics and the constraints imposed by the dynamic model. However, if the dynamic model loses its constraining effect, the error parameters will no longer be able to absorb the propagation path delay of the broadcast ephemerides. The accuracy of the dynamic model is primarily determined by the gravity field model [27; 28].
## 1 Introduction
According to existing seismic fragility analysis, usually, a single value or two seismic intensity measure (_IM_) values of ground motions are determined to represent the characteristics of ground motions and evaluate the damage stages of structures. However, due to the complexity of ground motions and structural dynamic response regularity, a single or two seismic _IMs_ cannot fully describe the characteristics of ground motions. Thus, it is highly necessary to ascertain the optimal _IMs_ that can comprehensively reflect the characteristics of ground motions as fully as possible. Thus, on this basis, proposing a new method for selecting ground motions is of great significance for quickly and accurately analyzing the damage status of engineering structures. Confronted with the above aspects, this study introduces the PCA method and the K-means clustering algorithm to develop a novel seismic fragility analysis approach and applies this approach to performing damage probability analyses of high-pier bridges undergoing pulse-like ground motions. First, a set of pulse-like ground motions and a suite of initial candidate seismic _IMs_ are chosen, respectively. Then, the weakly correlated and multicollinearity _IMs_ among the initial candidate _IMs_ are eliminated, to determine the optimal combination of _IMs_ that satisfy the requirements for the PCA method. After using the PCA method to reduce the dimensionality of the optimal combination of _IMs_ to obtain uncorrelated principal components, the K-means clustering algorithm is applied in order to cluster the selected pulse-like ground motions, based on the principal components. Finally, according to the IDA method, the 3D finite element model of the example of a high-pier bridge is established by OpenSees, while nonlinear dynamic time-history analyses are carried out to derive the dynamic responses while undergoing each class of pulse-like ground motions. The seismic fragility curves are also depicted and compared in terms of the high-pier bridge undergoing each class of pulse-like ground motions.
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.4 Dynamic Resource Allocation  
In [28], dynamic resource allocation (DRA) is presented as a new MAC algorithm that aims to achieve spatial reuse while avoiding interference among multiple WBASNs. Each WBASN creates a list of interfering sensors from other WBASNs within its transmission range and broadcasts this list to its neighbors. Each coordinator will assign channels to the interfering sensors orthogonally.  
The interference scenario was investigated in a system of many WBASNs with numerous sensors in each WBASN. Each WBASN's coordinator knows the received power of its sensor nodes. This algorithm starts with the orthogonal transmission of sensor nodes of each WBASN in the first round. The received power of each sensor is compared to the SINR threshold; an interference list is then created that consists of the sensors in the interference range between WBASNs. Each coordinator then creates its interference set that consists of its interfered sensors and the interfering sensors belonging to its neighbors. All coordinators broadcast their interference sets and assign new channels to the nodes with a high interference level.  
An example of channel assignment is shown in Figure 3a with three coexisting WBASNs labeled WBASN\({}_{1}\), WBASN\({}_{2}\), and WBASN\({}_{3}\). Each WBASN will create its interference list as follow, I1 = {(2, a), (3, F), (3, D)}, I2 = {(1, 5), (3, C), (3, D), (3, E)}, and I3 = {(1, 1), (2, e)} for WBASN\({}_{1}\), WBASN\({}_{2}\), and WBASN\({}_{3}\), respectively. The interference set is also created at each coordinator as follows: S1 = {(1, 1), (1, 5), (2, a), (3, F), (3, D)}, S2 = {(1, 5), (2, a), (2, e), (3, C), (3, D), (3, E)}, and S3 = {(1, 1), (2, e), (3, C), (3, D), (3, E)}. The channel assignment is shown in Figure 3b; nodes in the interference set are transmitted orthogonally.
# Learning Class-Specific Features with Class Regularization for Videos
## 1 Introduction  
Video-based action recognition has seen tremendous progress since the introduction of Convolutional Neural Networks (CNNs) [1; 2]. The hierarchical application of 3D convolutional operations has been shown to effectively capture descriptive spatiotemporal features.  
CNNs include multiple layers that are stacked in a single, hierarchical architecture. Features are calculated by successive convolutions. Kernels in early layers focus on simple textures and patterns, while deeper layers focus on more complex parts of objects or scenes. As these features become more dependent on the different weighting of neural connections in previous layers, only a portion of them becomes descriptive for a specific class [3; 4]. Yet, all kernels are learned in a class-agnostic way. Consequently, much of the discriminative nature of CNNs is achieved only in the very last fully-connected layers. This hinders easy interpretation of the part of the network that is informative for a specific class.  
We aim at forcing the network to propagate class-specific activations throughout the network. We propose a method named _Class Regularization_ that relates class information to extracted features of network blocks. This information is added back to the network by amplifying and suppressing activation values with respect to predicted classes. _Class Regularization_ has a beneficial effect on the nonlinearities of the network by modulating the effects of the activations. Owing to this, the architecture can effectively distinguish between the most class-informative kernels in each part of the network given a selected class. This procedure reduces the dependency on many uncorrelated featuresin the last fully-connected layers that are responsible for the final class predictions, essentially penalizing overfitting.  
Our contributions are the following:  
* We propose _Class Regularization_, a regularization method applied in spatiotemporal CNNs. The method does not change the overall structure of the architecture, but can be used as an additional step after each operation or block.
* We demonstrate that the relationships between classes and features can be visualized by propagating class-based feature information through normalized neural weights for each of the model's building blocks.
* We report performance gains for benchmark action recognition datasets _Kinetics, UCF-101_, and _HMDB-51_ by including _Class Regularization_ in convolution blocks.  
We discuss advances in vision-based action recognition in Section 2. A detailed overview of _Class Regularization_ appears in Section 3. Experiments are presented in Section 4. We conclude in Section 5.
## 4 Modeling and Control of MR Dampers for Structural Buildings
### Semi-Active Controllers for MR Dampers
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Author and Year** & **Controller** & **Finding** \\ \hline \multirow{2}{*}{1} & [PERSON] and [PERSON] (2020) [190] & Clipped optimal with LQG control & Control of the seismic inelastic torque response of multi-story buildings. \\ \hline \multirow{2}{*}{2} & [PERSON] et. al. (2018) [191] & H2/LQG control & Modification of H2/LQG control to optimize the control system’s performance \\ \hline \multirow{2}{*}{3} & [PERSON] et. al. (2019) [192] & Neural network control & Efficacy of neural network control on a three-story small-scale structure using the Tohoku 2011 and Boumerdes 2003 earthquake data. \\ \hline \multirow{2}{*}{4} & [PERSON] and [PERSON] (2019) [193] & Adaptive Neuro-Fuzzy inference system & Neuro-fuzzy optimization adapted to genetic algorithms. \\ \hline \multirow{2}{*}{5} & [PERSON] and [PERSON] (2018) [194] & Sliding mode control Fuzzy system & Developed a sliding mode control method based on a fuzzy system. Fuzzy logic control mitigates the chattering phenomenon. \\ \hline \multirow{2}{*}{6} & [PERSON] and [PERSON] (2017) [195] & Adaptive Neuro-Fuzzy inference system & Verified the efficacy of neuro-fuzzy controllers in reducing the responses of building structures equipped with MR dampers. \\ \hline \multirow{2}{*}{7} & [PERSON] and [PERSON] (2021) [196] & Adaptive Neuro-Fuzzy inference system and Simple adaptive control & Reduction of the seismic response of three-dimensional combined buildings under two-way seismic excitation with adaptive neuro-fuzzy inference system control and simple adaptive control. \\ \hline \multirow{2}{*}{8} & [PERSON] (2020) [197] & Fuzzy logic controller & Use of wavelet networks and fuzzy logic controllers to copy the inverse dynamics of MR dampers and nonlinear isolators. \\ \hline \multirow{2}{*}{9} & [PERSON] and [PERSON] & Fuzzy logic controller & Fuzzy logic controls are better than traditional controls and algorithmic controls and are critical when optimizing the response of a structure to seismic loads. \\ \hline \multirow{2}{*}{10} & [PERSON] et. al. (2017) [186] & Fuzzy logic controller & Improving a fuzzy control system with a smart multi-objective fuzzy-genetic controller produced controls that were more effective than others. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recently published articles on controllers for MR dampers for structural buildings.
## 2 Speaker Identification Related Work
### Feature Extraction  
SI systems try to use acoustic features that differ between individuals to discriminate among them. Some of the features that exhibit good performance when used in neutral or emotionless conditions in speech-based systems are the Mel-Frequency Cepstral Coefficients (MFCC) [24]. Other prosodic features are widely used as well, such as intonation, stressed syllables and rhythm; and phonetic features, such as detection of the phones and their statistics. Finally, Linear Prediction is used in audio and speech processing for representing the spectral envelope of speech signals in compressed form. It is also a powerful speech analysis technique to provide estimates of speech parameters like pitch, duration and energy [25].  
In speech emotion recognition, the current state of the art focuses on the use of formant frequencies, their bandwidths, pitch, log-energy and the so-called Normalized First-Order Autocorrelation Coefficients [26], among other features. Although for speaker identification under stress conditions there is hardly any previous work, MFCCs [27] together with prosodic features as the pitch, energy and word duration [22] have been used with successful results [28].  
Beyond the previously mentioned hand-crafted features, automatic features extracted from raw data by DNN (Deep Neural Networks) is a successful trend achieving very innovative results [29; 30]. Nevertheless, in our case, the use of complex DNN approaches is not currently possible due to their high computational load, delay and the availability of sufficiently big training datasets, which are three very important limitations within the Bindi system.