esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Proposed Method
### Simulation Model
#### 2.2.3 Normal Mode Interpolation  
During milling, the tool moves along the workpiece, moving through successive areas where different vibration modes dominate. Thanks to modal analysis, according to the EAOVP procedure, the modal properties of the workpiece are known. However, they are identified only for areas close to the accelerometers' locations. For the purposes of simulation using hybrid models, it is needed to provide modal parameters for each simulation point. This means that for points between the known positions of sparsely distributed sensors, normal modes must also be determined. To solve this problem, it is proposed to adapt the technique of linear interpolation of normal modes clearly described in [30]. For a position lying between locations where normal mode no. \(i\), \(i=1\), \(\ldots,mod\), has been identified (i.e., between the sensor positions for which a FRF has been calculated), the appropriate value of this normal mode component is calculated by the following formula (Figure 3):  
\[\Psi_{i}(\xi(t))=\Psi_{ij}+\frac{\xi\left(t\right)-\xi_{j}}{\xi_{j+1}-\xi_{j}} \big{(}\Psi_{i,j+1}-\Psi_{ij}\big{)}. \tag{14}\]  
This means that in (13), the matrix of transformation \(\mathbf{W}_{l}(t)\) becomes not only time- but also position-dependent and takes the following form:  
\[\mathbf{W}_{l}(t,\xi(t))=\begin{bmatrix}\mathbf{\Theta}_{l}(t)_{3\times 3}& \mathbf{0}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{\Theta}_{l}(t)_{3\times 3}\end{bmatrix} \mathbf{C}_{W}\mathbf{\hat{\Psi}}(\xi(t)), \tag{15}\]  
where  
\[\mathbf{\Theta}_{l}(t)=\begin{bmatrix}\cos\varphi_{l}&-\sin\varphi_{l}&0\\ \sin\varphi_{l}&\cos\varphi_{l}&0\\ 0&0&1\end{bmatrix}\]  
Here, there is a matrix of directional cosines between the axes \(y_{l1},y_{l2},y_{l3}\) of CE no. \(l\) and the axes of immovable coordinate system \(x_{1},x_{2},x_{3}\) (Figure 2).  
\[\mathbf{C}_{W}=col(0,\ 0,\ 1,\ 0,\ 0,\ 0),\]  
\[\mathbf{\hat{\Psi}}(\xi(t))=\begin{bmatrix}\Psi_{1j}+\frac{\xi(t)-\xi_{j}}{ \xi_{j+1}-\xi_{j}}\big{(}\mathbf{\Psi}_{1,j+1}-\mathbf{\Psi}_{1j}\big{)}\\ \vdots\\ \Psi_{ij}+\frac{\xi(t)-\xi_{j}}{\xi_{j+1}-\xi_{j}}\big{(}\Psi_{i,j+1}-\Psi_{ij} \big{)}\\ \vdots\\ \Psi_{mod,j}+\frac{\xi(t)-\xi_{j}}{\xi_{j+1}-\xi_{j}}\big{(}\Psi_{mod,j+1}- \Psi_{mod,j}\big{)}\end{bmatrix}^{T}.\]  
Using the same approach, extrapolation is also possible for points beyond the outermost sensor positions.  
In order to obtain an interpolated modal model along the workpiece in accordance with the planned tool path, the original proprietary software written in C was used, as well as the commercial FEGRAPH version 14 package (vMACH Engineering GmbH, Markt Indersdorf, Germany).
## 1 Introduction
The key contributions of this study are as follows: (1) the novel DNN-based signal detection model can tackle the particular difficulties facing MIMO systems, such as high computing demands and inter-antenna interference; (2) the proposed DNN-based detector's efficacy is carefully assessed and compared with traditional techniques (MLD, MMSE, and ZF), covering performance measures including computing time, SNR, and SER; (3) a better understanding of the capabilities and limits of the model is developed before the study is expanded to complex MIMO situations by adding SISO system values as a baseline, which is used to test the DNN's performance in less complicated circumstances; (4) 3D visualizations provide more profound understandings of the relationship between training functions, network architecture, and SER performance, which helps optimize DNN configurations for particular signal detection applications in MIMO systems; (5) the door to further investigations into the flexibility and constraints of DNN designs in MIMO scenarios is opened by highlighting the advantages and disadvantages of using DNN-based detectors for sophisticated wireless communication systems. The remaining sections of this paper are structured as follows: Section 2 presents the theoretical foundation for SISO and MIMO detection. Section 3 provides a comprehensive review of the relevant literature. Section 4 details the system description and methodology employed in this research. Section 5 presents the results and discussions, followed by the conclusion in Section 6.
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
###### Abstract  
An iterative QR-based soft feedback segment interference cancellation (QRSFSIC) detection and decoder algorithm for a Reed-Muller (RM) space-time turbo system is proposed in this paper. It forms the sufficient statistic for the minimum-mean-square error (MMSE) estimate according to QR decomposition-based soft feedback successive interference cancellation, stemmed from the a priori log-likelihood ratio (LLR) of encoded bits. Then, the signal originating from the symbols of the reliable segment, the symbol reliability metric, in terms of an a posteriori LLR of encoded bits which is larger than a certain threshold, is iteratively cancelled with the QRSFSIC in order to further obtain the residual signal for evaluating the symbols in the unreliable segment. This is done until the unreliable segment is empty, resulting in the extrinsic information for a RM turbo-coded bit with the greatest likelihood. Bridged by de-multiplexing and multiplexing, an iterative QRSFSIC detection is concatenated with an iterative trellis-based maximum a posteriori probability RM turbo decoder as if a principal Turbo detection and decoder is embedded with an iterative subordinate QRSFSIC detection and a RM turbo decoder, exchanging each other's detection and decoding soft-decision information iteratively. These three stages let the proposed algorithm approach the upper bound of the diversity. The simulation results also show that the proposed scheme outperforms the other suboptimum detectors considered in this paper.  
R 2017 19, 426; doi:10.3390/e19080426
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 5 SMT-Based Scheduling Optimization for Fault-Aware Job Assignments
### Optimization Problem Overview  
The scheduling problem is formulated as a multi-objective optimization task, where the primary goal is to minimize the total predicted fault probability across all machines and jobs. This must be achieved while satisfying job deadlines, machine availability, job precedence constraints, and other system-specific constraints. The SMT solver plays a pivotal role in handling this optimization by considering both the predicted fault probabilities and the various operational constraints inherent to the production environment.
## 3 Test Results and Analysis  
### Experimental Phenomena  
For the axial compression specimens, the damage process was similar for all five specimens. The damage pattern is illustrated by the example of I-PSRPC-3, where the specimen is in the elastic phase at the beginning of the loading period and no cracks are evident. When loaded to 30% of the ultimate load (P\({}_{\mathrm{max}}\)), a small crack appears at the top end of the column. When loaded to 75% P\({}_{\mathrm{max}}\), small cracks appear at both ends of the column in different directions. As the load continues to 90% P\({}_{\mathrm{max}}\), the crack extends from the end to the middle of the column as the load increases, and the RPC at the lower end of the column spalls off extensively. When the load reaches P\({}_{\mathrm{max}}\), several vertical main cracks can be seen on the surface of the column, the external RPCs are spalling off, and the load-carrying capacity of the specimen is reduced, with damage occurring at the bottom end of the specimen.  
For the eccentric specimens, the damage pattern of PSRPC columns under eccentric loading is similar to that of ordinary reinforced concrete columns under eccentric loading. The test results show that the main influencing factor on the damage pattern is the eccentricity distance, which is illustrated by II-PSRPC-2 and II-PSRPC-5. The eccentricity of II-PSRPC-2 is 0.4, and the specimen deforms elastically without significant changes in the early stages of loading. When the load reaches 48% P\({}_{\mathrm{max}}\), small cracks appear in the middle of the column on the tensile side, and longitudinal diagonal cracks appear at the end of the column. As the load continues to increase, the cracks continue to extend, accompanied by the tearing sound of the RPC, and the lateral deflection increases significantly. When the load reaches 80% P\({}_{\mathrm{max}}\), it is clear that the column buckles, with the RPCs at the top and bottom ends spalling off and the RPCs in the middle bulging out. When the load approaches P\({}_{\mathrm{max}}\), the central RPC is crushed, the main crack penetrates the cross-section, the specimen shows a \"broken\" state, and the load-carrying capacity of the specimen begins to decrease. When loaded to 20% P\({}_{\mathrm{max}}\), a horizontal crack appears in the middle tensile zone of the specimen. When loaded to 36% P\({}_{\mathrm{max}}\), a longitudinal crack appears at the upper end of the specimen. When loaded to 70% P\({}_{\mathrm{max}}\), RPC spalling in the upper compressive zone is evident, accompanied by a \"tearing\" sound of the RPC, and the horizontal cracks increase and extend to both sides of the cross-section. When loaded to the ultimate load, the RPC in the compressive zone breaks up, the RPC in the tensile zone cracks, and the load capacity of the specimen begins to decrease.
# An Internet of Things Model for Improving Process Management on University Campus
## 2 Materials and Methods
### Architecture of a University Campus
#### 2.1.1 Perception Layer  
This layer contains the devices that monitor or act on the environment. There are several types of devices, but those considered with regard to the IoT have the characteristic of responding to a type of communication (direct or indirect) that links it to the Internet [28]. Such a system has the ability to integrate sensor type devices that allow them to measure parameters such as temperature, position, amount of light, etc. They incorporate actuators that transform energy into action in order to automate a process [29]. These IoT-oriented devices are recognized as intelligent sensors in that, in addition to obtaining the value of the physical parameters which they measure, they can perform other functions. Most of these devices connect through wireless communication which enables autonomous interactivity between devices even if they are in motion [30].  
Other devices used in this layer are smart tags which allow the university to trace the entire administrative process. For example, in order to allow the university to properly manage the laboratory equipment that students use, it is necessary to answer questions such as where and when has laboratory equipment been provided? Who has lent it? Which equipment is the most frequently used? To answer such questions, it is necessary for the university to capture the relevant data and track the time it is used, using its trace and added features. There are several types of smart tags available, the best known of which are barcode labels and QR codes [8].
## 3 Experimental Results and Discussion
On the other hand, the measured phase delay is more accurate, as it will be discussed shortly later. For the Ka band network, the measured S11 ranges between \(-13\) and \(-17\) dB at the center frequency (see Figure 11a), while the simulated values are comprised between \(-14\) dB and \(-35\) dB (see Figure 5a). The measured transmission is between \(-7\) and \(-9.5\) dB at the center frequency (see Figure 11b), while the simulations show an S21 between \(-1.7\) and \(-2.4\) dB (see Figure 5b). On the other hand, as for the previous network, the measured phase delay is more accurate. Figure 10: Measured characteristics of the reflection (**a**), transmission (**b**), and phase delay (**c**) for the \(19.5\) GHz center frequency RF-MEMS phase shifter (K band). For what concerns the characteristics of reflection and transmission of both the phase shifters designs, the influence of the switches non-idealities previously reported leads to a significant worsening of the performance of the network, which is particularly severe for the losses. Nonetheless, the fact that the worsening of experimental S-parameters is rather independent on the particular network configuration and switch design corroborates the explanation that it is due to the additional contact resistance of the switches. Eventually, as discussed before, the technological non-ideality was already solved in the next wafers still to be finalized. In conclusion, despite the large divergence between experimental and nominal characteristics, the measured phase delay configuration exhibits a satisfactory match with the simulations, as highlighted by the phase difference plots reported in Figure 12, for a very narrow range around the K and Ka band center frequencies (Figure 12a,b, respectively). Figure 11: Measured characteristics of the reflection (**a**), transmission (**b**), and phase delay (**c**) for the 29.3 GHz center frequency RF-MEMS phase shifter (Ka band).
# 5G-MEC Testbeds for V2X Applications
## 5 Summary of Available Tools
### Networking  
In the 5G-MEC system, networking is crucial for connecting the UE to the MEHs and the MEC applications. Additionally, networking within the 5G CN to the MEC system needs to be established. The MEC orchestrator or MANO can have control over the 5G-MEC system via networking.  
**The Data Plane Development Kit** (DPDK) [79] was developed by the Linux foundation. The DPDK includes a collection of libraries for performing efficient packet tracing. The DPDK performs microservices that support the Container Network Interface (CNI) and is managed using a standard SDN controller.  
**Mininet**[80] is an open source network emulator used to create virtual networks that run on a kernel, switch, and application code. Mininet provides support for developing applications based on OpenFlow. Mininet hosts run standard Linux software and can provide efficient routing along with SDN.  
**Open vSwitch** (OVS) [81] is an open source tool for a multi-layer virtual switch that can redirect networking traffic within virtual machines. Furthermore, OVS operates as a soft switch running in the hypervisor. The Akraino connected vehicle implements OVS with OpenNESS to control the network traffic between containerized applications.
# Assessment of Student Music Performances Using Deep Neural Networks
## 3 Material and Methods
### Experiments  
The experiments are designed to gauge the performance of the DNN models at predicting the ratings given by human judges across the different assessment criteria. Considering the overall difference in musical proficiency across the two categories of students (Symphonic Band versus Middle School), which might influence the way the judges assess them, two separate experiments are designed. The first experiment investigates the performance data of Symphonic Band students from all three of the years and the three instruments combined. The data are randomly shuffled and split into training, validation, and testing sets (using a 80%, 10%, and 10% split). The three DNN models are separately trained for each of the four assessment criteria. The same steps are followed for the second experiment but with data taken from the Middle School recordings.  
The performance of the proposed models is compared against an SVR model trained using traditional low-level as well as hand-crafted features as proposed by [PERSON] et al. [18]. It is worth noting that, since the results for their model were obtained using only Middle School Alto Saxophone performances (a subset of the data considered in our experiments), the results reported here differ.  
To summarize, the following models are compared and contrasted in the two experiments:  
1. SVR-BD: SVR model using a feature set that combines both low-level and hand-crafted features. The low-level features are comprised of standard spectral and temporal features such as MFCCs, spectral flux, spectral centroid, spectral roll-off, zero-crossing rate, etc. The hand-crafted features, on the other hand, model note-level features such as note steadiness and amplitude deviations, as well as performance level features such as average accuracy, percentage of correct notes, and timing accuracy. The dimensionality of the combined feature set is 92 (68 for low-level, 24 for hand-crafted).
2. PC-FCN: Fully convolutional model with the PC representation as the input.
3. M-CRNN: Convolutional recurrent model with the MEL representation as the input.
4. PCM-CRNN: Hybrid model that leverages both the PC and MEL input representations.
###### Abstract  
This study proposes an asynchronous brain-computer interface (BCI) framework based on steady-state motion visual evoked potentials (SSMVEPs), designed to enhance the accuracy and robustness of control state recognition. The method integrates filter bank common spatial patterns (FBCSPs) and filter bank canonical correlation analysis (FBCCA) to extract complementary spatial and frequency domain features from EEG signals. These multimodal features are then fused and input into a dual-classifier structure consisting of a support vector machine (SVM) and extreme gradient boosting (XGBoost). A weighted fusion strategy is applied to combine the probabilistic outputs of both classifiers, allowing the system to leverage their respective strengths. Experimental results demonstrate that the fused FB(CSP + CCA)-(SVM + XGBoost) model achieves superior performance in distinguishing intentional control (IC) and non-control (NC) states compared to models using a single feature type or classifier. Furthermore, the visualization of feature distributions using UMAP shows improved inter-class separability when combining FBCSP and FBCCA features. These findings confirm the effectiveness of both feature-level and classifier-level fusion in asynchronous BCI systems. The proposed approach offers a promising and practical solution for developing more reliable and user-adaptive BCI applications, particularly in real-world environments requiring flexible control without external cues.  
asynchronous BCI; SSMVEP; feature fusion; ensemble classification Article
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 2 System Overview  
In this section, we present the physical layer overview. It describes the main elements that constitute the proposed system.
# Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots
###### Abstract  
This paper proposes a coupled-error-based formation control algorithm for the rapid formation completion of multi-robot systems. We consider a multi-robot system with omni-directional robots with swerve-driving mechanisms and a communication system with minimized constraints. This paper introduces a coupled error that links the distance errors with the leading robot and the following robot through a coupling ratio. We propose a controller using the coupled error to achieve the control objectives of this paper. Unlike existing results that only use the information of the preceding robot, this algorithm couples the information of both the preceding robot and one's follower. Using the proposed error-coupling-based formation control algorithm, multi-robot systems can quickly establish formations for collaboration, allowing tasks to commence swiftly and reducing deformations in formations due to speed variations. With stability analysis and simulation results for the practical application of the proposed algorithm, the approach has been verified to improve the speed of both the completion of the formation and overall system trajectory tracking, balancing trade-offs between them.  
multi-robot system; formation control; omni-directional robot; swerve-driving mechanism; error coupling; coupled-error; rapid formation completion
## 2 Methods
### Intervention Effectiveness Evaluation Indicators  
(1) Physical health  
Physical health indicators encompass various aspects, including body composition, cardiovascular health, and metabolic health. Body composition metrics (such as body fat percentage and Body Mass Index (BMI)) are used to assess participants' weight changes and their response to the intervention [23]. Cardiovascular health is assessed by measuring blood pressure, heart rate, and maximal oxygen uptake (VO\({}_{2}\)max) to understand the impact of the intervention on the cardiovascular system [24]. In terms of metabolic health, the focus is primarily on lipid levels (low-density lipoprotein LDL and high-density lipoprotein HDL) to assess the impact of exercise interventions on metabolic indicators [25]. Through a comprehensive assessment of these indicators, one can gain a thorough understanding of the specific effects of exercise interventions on physical health.  
(2) Cardiorespiratory endurance  
Cardiopulmonary endurance will be measured using two methods: maximal oxygen uptake (VO\({}_{2}\)max) and the 6 min walk test. Participants will undergo a standardized Bruce protocol treadmill test to assess VO\({}_{2}\)max and HRmax [26]. VO\({}_{2}\)max is the gold standard for assessing cardiovascular endurance. By measuring the maximum amount of oxygen consumed by participants under maximal exercise conditions, it accurately reflects their cardiovascular function and overall aerobic endurance [27]. Typically, normal values for young adult males range from approximately 35 to 45 mL/kg/min, while for females, they range from about 30 to 40 mL/kg/min. Meanwhile, the 6 min walk test will be used to assess the distance participants can walk in six minutes, thereby indirectly measuring their cardiovascular endurance and exercise stamina [28]. By combining these two testing methods, a comprehensive evaluation of participants' cardiovascular function can be achieved and changes before and after the exercise intervention can be compared to ensure an accurate assessment of the intervention's effects.  
Quality of life and psychological state  
At the end of the 16-week exercise intervention experiment, we evaluated exercise enjoyment and self-efficacy. The Perceived Enjoyment of Physical Activity Scale (PACES) was used to assess the perceived enjoyment of the three different interventions. Participants were asked to complete the scale to determine which intervention they found more enjoyable. PACES is a psychological measurement tool used to assess individuals' enjoyment during physical activities. This scale is designed to quantify participants' level of enjoyment of exercise, thereby revealing the impact of physical activity on their emotions and motivation [29]. In addition, the General Health Questionnaire (GHQ-12) is used to assess participants' mental health status and emotional distress [30].