esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In 1987, the IEEE (Institute of Electrical and Electronics Engineers) Standards [14] referred to the RM in the IRE Standards and established a complete resonance measurement method with complete assumptions regarding constitutive equations (d-form, e-form, g-form, and h-form) and simplified specimen dimensions for piezoelectric materials and five specimens with different geometric shapes, aspect ratios, polarization orientations, and electrode surface orientations. Some formula listed in Refs [13; 14] are more complicated, therefore the simplified form was proposed by subsequent researcher. In 1989, [PERSON] et al. [15] applied the thickness vibration theory in RM to the vibrations in a piezoelectric plate in which the length and width are assumed to be much greater than the thickness that they approach infinity. They used \(\alpha\)-quartz with two different cuts to measure the elastic, piezoelectric, anddielectric constants of \(\alpha\)-quartz. The material constants of Y-cut quartz were studied in the paper; hence, the method is not provided for obtained the complete transversely piezoelectric isotropic material constants. In 1998, [PERSON] et al. [16] conducted a detailed study on specimens with two types of shear mode shapes: thickness shear and length shear. They examined specimens with different aspect ratios and compared their measurements. The experiment results revealed that only the length-to-thickness ratio has a direct relationship and influences the values of the resonant and anti-resonant frequencies. A pure shear mode shape can be generated at l/20t, thereby reducing the errors in the resonant and anti-resonant frequency measurements. We used an impedance analyzer to measure the resonant and anti-resonant frequencies of piezoelectric materials and substituted them into the equations listed in RM to obtain 10 independent material constants. Although the material parameters of piezoceramics are practically independent from frequency in the order of several MegaHertz [17; 18; 19], in the frequencies of the order of kilohertz--several tens of kilohertz--the dependence on frequency is very strong. It performs on the main resonances in longitudinal, transverse, shear, or radial modes. We then substituted the measured material constants into finite element numerical calculations to verify the in-plane and out-of-plane resonant frequencies and mode shapes of the piezoelectric materials. The experimental measurements of the out-of-plane resonant frequencies and mode shapes were obtained using electronic speckle pattern interferometry (ESPI), whereas the in-plane vibrations were measured using ESPI and an impedance analyzer. A comparison of the experiment results and the numerical calculations revealed fairly good accuracy in the material constants obtained using RM.
# A 13.44-Bit Low-Power SAR ADC for Brain-Computer Interface Applications
###### Abstract  
This paper presents a successive approximation register analog-to-digital converter (SAR ADC) specifically optimized for brain-computer interface (BCI) applications. Designed and post-layout-simulated using 180 nm CMOS technology, the proposed SAR ADC achieves a 13.44-bit effective number of bits (ENOB) and 27.9 \(\upmu\)W of power consumption at a supply voltage of 1.8 V, enabled by a piecewise monotonic switching scheme and dynamic logic architecture. The ADC supports a high input range of \(\pm\)500 mV, making it suitable for neural signal acquisition. Through an optimized capacitive digital-to-analog converter (CDAC) array and a high-speed dynamic comparator, the ADC demonstrates a signal-to-noise-and-distortion ratio (SINAD) of 81.94 dB and a spurious-free dynamic range (SFDR) of 91.69 dB at a sampling rate of 320 kS/s. Experimental results validate the design's superior performance in terms of low-power operation, high resolution, and moderate sampling rate, positioning it as a competitive solution for high-density integration and precision neural signal processing in next-generation BCI systems.  
BCI; SAR ADC; low power Article
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 6 Results and Discussion
### Novel Contributions  
This research introduces three fundamental innovations that advance mobile acoustic sensing:  
1. Integrated audio-mobility framework: The first demonstrated framework enabling concurrent high-quality audio acquisition (>90% packet delivery) and responsive motor control (<150 ms latency) on resource-constrained embedded platforms. This integration solves the fundamental resource allocation problem that has prevented mobile acoustic sensing applications on embedded systems, validated through comprehensive testing across 40-85 dB environments.  
2. Adaptive real-time compression algorithm: A novel mathematical model for dynamic audio compression that continuously optimizes the trade-off between audio fidelity and bandwidth utilization based on real-time system conditions. The algorithm achieves 75% bandwidth reduction while maintaining >90% perceptual quality through dual-time-constant adaptive dynamics, representing a significant advancement over fixed-parameter compression schemes.  
3. Dual-thread concurrent architecture: A mathematically optimized real-time scheduling framework that guarantees the concurrent execution of time-critical motor control and bandwidth-intensive audio processing. The architecture achieves 83% theoretical utilization while maintaining hard real-time constraints for both subsystems, enabling applications previously impossible on embedded platforms.  
Additional technical innovations:  
* Noise floor calibration protocol: Automated ambient noise assessment and adaptive parameter adjustment enabling optimal performance across varied acoustic environments without manual tuning.
* Power-optimized Bluetooth protocol: Efficient bandwidth allocation strategy enabling concurrent control and streaming within Bluetooth Classic constraints while minimizing power consumption for battery-operated platforms.
* Accessible open design: Complete framework implementation using commercially available components, enhancing reproducibility and accessibility for research and educational applications.  
These contributions establish new foundations for mobile acoustic sensing, enabling applications from environmental monitoring to autonomous exploration that were previously impossible on resource-constrained embedded platforms.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 2 Massive MIMO System Model
### MMSE Detection  
Applied at the recipient side of a massive MIMO communication, the MMSE iterative scheme offers near-optimal detection performance by degrading inaccuracies of the transmitted and received signals [24]. Estimation of the transmitted signal using the MMSE detection method is expressed in terms of [25]  
\[\hat{s}=(H^{H}H+\sigma^{2}I_{U})H^{H}y=(G+\sigma^{2}I_{U})H^{H}y=A^{-1}y_{MF} \tag{6}\]  
where \(G=H^{H}H\) denotes the Gram matrix, and \(y_{MF}=H^{H}y\) represents the matched filter. The MMSE detection method has \(O(U^{3})\) computational complexity, so it is difficult to apply in practice.
# Duty Factor Reflects Lower Limb Kinematics of Running
## 3 Results
### Pelvis and Foot Segment Angles
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Running Speed** & **DF Group** & \(\theta_{\rm TS}\) & \(\theta_{\rm IA}\) & \(\theta_{\rm MS}\) & \(\theta_{\rm TO}\) & \(\Delta\theta_{\rm a}\) & \(\Delta\theta_{\rm b}\) & \(\Delta\theta_{\rm c}\) \\ (**km/h**) & **DF Group** & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** \\ \hline \multirow{2}{*}{8} & DF\({}_{\rm high}\) & \(12.2\pm 8.4\) & \(0.5\pm 2.5\) & \(-37\pm 1.6\) & \(-32.4\pm 5.3\) & \(-11.7\pm 6.4\) & \(-15.9\pm 7.8\) & \(-28.6\pm 5.3\) \\  & DF\({}_{\rm low}\) & \(-24\pm 10.5\) & \(-3.3\pm 31\) * & \(-5.1\pm 2.2\) & \(-29.3\pm 5.0\) & \(-10.0\pm 8.2\) & \(-2.8\pm 9.4\) & \(-24.1\pm 4.0\) \\ \hline \multirow{2}{*}{10} & DF\({}_{\rm high}\) & \(16.3\pm 8.2\) & \(0.6\pm 2.4\) & \(-40\pm 1.6\) & \(-33.9\pm 5.3\) & \(-15.7\pm 6.3\) & \(-20.2\pm 7.7\) & \(-29.9\pm 5.2\) \\  & DF\({}_{\rm low}\) & \(-0.1\pm 11.8\) & \(-3.2\pm 2.9\) * & \(-5.4\pm 2.4\) & \(-30.2\pm 5.7\) & \(-3.1\pm 9.6\) & \(-5.3\pm 10.7\) & \(-24.8\pm 4.5\) \\ \hline \multirow{2}{*}{12} & DF\({}_{\rm high}\) & \(19.0\pm 7.9\) & \(0.4\pm 2.3\) & \(-4.2\pm 1.6\) & \(-35.0\pm 5.4\) & \(-18.6\pm 6.1\) & \(-23.1\pm 7.2\) & \(-30.9\pm 5.1\) \\  & DF\({}_{\rm low}\) & \(22.2\pm 12.9\) & \(-3.2\pm 3.0\) * & \(-5.6\pm 2.6\) & \(-31.6\pm 5.7\) & \(-5.4\pm 10.6\) & \(-7.9\pm 11.5\) & \(-25.9\pm 4.4\) \\ \hline \multirow{2}{*}{14} & DF\({}_{\rm high}\) & \(20.1\pm 8.6\) & \(-0.3\pm 2.2\) & \(-4.5\pm 1.8\) & \(-35.7\pm 5.7\) & \(-20.4\pm 6.9\) & \(-24.6\pm 7.8\) & \(-31.2\pm 5.3\) \\  & DF\({}_{\rm low}\) & \(4.0\pm 1.36.\) & \(-3.3\pm 31\) * & \(-6.0\pm 2.9\) & \(-32.4\pm 6.0\) & \(-7.4\pm 11.3\) & \(-10.1\pm 12.3\) & \(-26.3\pm 4.5\) \\ \hline \multirow{2}{*}{16} & DF\({}_{\rm high}\) & \(20.6\pm 8.8\) & \(-0.9\pm 2.2\) & \(-4.7\pm 1.9\) & \(-36.4\pm 5.6\) & \(-21.5\pm 7.2\) & \(-25.3\pm 7.9\) & \(-31.7\pm 5.1\) \\  & DF\({}_{\rm low}\) & \(4.6\pm 13.9\) & \(-3.6\pm 3.2\) * & \(-6.2\pm 3.2\) & \(-33.0\pm 6.3\) & \(-8.2\pm 11.5\) & \(-10.9\pm 12.2\) & \(-26.7\pm 4.7\) \\ \hline \multirow{2}{*}{18} & DF\({}_{\rm high}\) & \(20.6\pm 8.5\) & \(-1.4\pm 2.2\) & \(-4.8\pm 2.0\) & \(-36.5\pm 5.6\) & \(-22.0\pm 7.1\) & \(-25.5\pm 7.8\) & \(-31.7\pm 4.9\) \\  & DF\({}_{\rm low}\) & \(5.1\pm 14.2\) & \(-3.9\pm 33.3\) * & \(-6.5\pm 3.2\) & \(-33.9\pm 6.8\) & \(-9.0\pm 11.9\) & \(-11.5\pm 12.7\) & \(-27.
## 2 Materials and Methods
### Methodology
#### 2.6.2 The Global and Local Models
In Algorithm 3, an evolutionary population \(N_{p}\) with \(NP\) individuals is generated at first, and the skill factors of them are computed by predicting fitness using \(n\) RBF models and a dynamic ensemble of surrogates. The parent population generates an offspring population through an assortative mating strategy, subsequently utilizing vertical cultural transmission to designate skill factors to the offspring population. A decision variable transformation strategy is employed to map each offspring to a new location. The scalar fitness of the current populations is computed using surrogates, and then, individuals with better scalar fitness are chosen to become the next parent population. Algorithm 4 details the procedure of generating the initial evolutionary population. If the number of accurately assessed samples in the database is less than \(NP/4\), all the samples in the database are added to the evolutionary population; if the number is greater than \(NP/4\), but less than \(NP/2\), \(NP/4\) samples are randomly selected from the database to be added to the evolutionary population; otherwise, the \(NP/2\) best samples are picked out from the database at first, and then, \(NP/4\) samples are selected from them to be added to the evolutionary population. Finally, the LHS is used to generate the remaining evolutionary individuals. This initial evolutionary population is generated in such a way that both historical data and new data are included, which can lead to better algorithmic performance. In addition, in order to avoid falling into the local optimality, this paper selects an individual with better fitness from the most promising and the average individual for exact evaluation and archiving in the database. The average individual is as follows:  
\[\alpha=\frac{NFE}{NFE_{max}} \tag{15}\]  
\[P=\frac{\sum_{i=1}^{N}P_{i}}{N}\times\alpha\times|Gauss(0,1)| \tag{16}\]  
where \(\alpha\) represents a parameter that rises with the number of exact evaluations, \(N\) denotes the number of samples, and \(Gauss(0,1)\) denotes a random number that conforms to the Gaussian distribution. Figure 2: The \(NRMSE\) values of the DSES and the DES on functions F1 and F3 with 20 dimensions in the search process. (**a**) The \(NRMSE\) values of the DSES and the DES on functions F1 with 20 dimensions in the search process; (**b**) The \(NRMSE\) values of the DSES and the DES on functions F3 with 20 dimensions in the search process.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 5 Case Study
### Implementation of the Architecture Models for the Case Study
#### 5.1.2 Functional View for the Case Study  
As shown in Figure 8, the large blocks show the functional elements of the warehouse management system including scanning, storing, managing, and communication. The smaller blocks inside the functional elements define the entities used for those functional elements; for example, the transportation is carried out using the four-wheeler and conveyor belts. The interfaces are shown using arrows. The bold arrows show the connection using Ethernet plus WiFi based on the element, whereas the dashed arrows show the wireless connection. The wireless communication is based on the publisher-broker-subscriber model, as is explained in further subsections.
## 1 Introduction
Accuracy is important, though the ML models should also be interpretable. One previous work introduced a stress monitoring system utilizing heart rate (HR) variability, galvanic skin response (GSR), and skin temperature (ST), but without interpretation using machine learning techniques [14]. Another work have highlighted the benefits of physiological measures to detect anxiety states, but did not integrate multiple biosignals and machine learning models [15]. A review of various stress detection methodologies emphasized the effectiveness of multisignal biosignal analysis, but noted the absence of systematic integration [16]. Physiological sensors for stress detection were used in real-world driving tasks, but did not focus on model interpretability using explainable AI techniques, underscoring the potential of various biosignal features but without detailed integration [17]. The need for explainable ML in medical applications has led researchers to develop methods for interpreting complex models and explaining the rationales for model predictions. A number of methods, such as local interpretable model-agnostic explanations (LIME), explain predictions of a classifier or regressor by locally approximating the model as a simpler interpretable surrogate model [18, 19]. Another approach to interpreting ML models is SHapley Additive explanations (SHAP), which assigns each feature variable an importance value for each output prediction, with positive or negative values indicating the direction of influence [20, 21]. In this work, we investigate the importance of the physiological signals collected using a wearable wristband device for the detection of APS. We combine data from various physiological sources, including HR, GSR, ST, and three-axis accelerometer (ACC) data, for a comprehensive analysis of APS indicators using wearable devices. An XGBoost model is developed with weighted training for classification of APS and non-stress (NS) [22]. Employing XGBoost, an optimized gradient boosting algorithm, this work achieves an 99.93% accuracy for the classification of APS and NS states. In Section 2, we first describe the study protocol for the data collection experiments, provide a brief overview of the methods used for data preprocessing, and detail the weighted training method for handling imbalanced classes along with the XGBoost and SHAP algorithms used for detecting APS and assigning an importance to each feature variable when predicting a particular data sample. The results of the XGBoost prediction model and SHAP algorithm are detailed in Section 3, followed by a discussion of the key findings of the study in Section 4. The use of SHAP values ensures explainability of the global importance of physiological signals, which indicates that the order of importance for the variables (from most to least) is GSR, HR, ST, and motion sensor (accelerometer) readings. In addition, this approach elucidates the local signal importance for particular samples. Finally, Section 5 provides concluding remarks.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 3 Experimental Analysis
### Effectiveness of Fault Maintenance Decision Methods
It can be seen from the results in Figures 6 and 7 that the method in this paper can effectively improve the average accuracy of information system data transmission under different operating distances and reduce the communication data transmission delay. Figure 6: Data transmission average correct rate comparison result. Figure 7: Communication delay comparison results.
## 3 Results and Discussion
### Experimental Tensile Properties under Various Heat Treatments
#### 3.3.3 Comparison of Mechanical Properties under Various Heat Treatment Conditions
Moreover, there is a large stress field at the coherent interface between the matrix a-Mg and the a phase, which hinders the dislocation movement. During the aging process, a large number of precipitated-phase Mg\({}_{12}\)Nd particles precipitated from a matrix. The smaller \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **\(\sigma_{\rm b}\) (MPa)** & **\(\sigma_{\rm 0.2}\) (MPa)** & **\(\delta\) (\%)** & **Hardness (HV)** & **Strengthening** \\  & & & & & **Mechanisms** \\ \hline As-cast & 153.36 & 108.13 & 3.14 & 54.12 & M.S. + S.S.S. \\ As-cast T6-SSAT & 230.32 & 149.65 & 4.63 & 70.50 & M.S. + S.S.S. + P.S. \\ As-cast T6-TSAT & 263.16 & 178.75 & 4.07 & 72.05 & M.S. + S.S.S. + P.S. \\ As-compressed & 232.16 & 139.75 & 10.67 & 63.26 & M.S. + F.G.S. \\ As-compressed + T4 & 208.42 & 115.81 & 12.96 & 59.63 & M.S. + S.S.S. + F.G.S. \\ As-compressed + T5 & 255.88 & 146.28 & 10.75 & 75.82 & M.S. + F.G.S. + P.S. \\ As-compressed + T6\_SSAT & 268.35 & 157.65 & 11.52 & 78.92 & M.S. + S.S.S. + F.G.S.+ P.S. \\ As-compressed + T6\_TSAT & 294.46 & 188.72 & 10.93 & 82.56 & M.S. + S.S.S. + F.G.S.+ P.S. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of tensile properties, hardness at room temperature, and strengthening mechanisms under various conditions. size and higher dispersion of the a\" phase were able to be obtained by decreasing the aging time within a certain temperature range [20], influencing the mechanical properties. The precipitation plays a pinning role for the dislocation and prevents the grain boundary from sliding, thus increasing the strength [42; 43]. Therefore, after the two-stage aging treatment, the tensile strength is obviously improved, and there are slight changes in the elongation, which not only improves the mechanical properties but also reduces the heat treatment time. In all, by studying the effect of the TSAT process on the microstructure and properties of ZM6 alloy, the aging time can be shortened effectively, allowing better properties to be obtained and better quality control of ZM6 products to be achieved.
## 6 Evaluation  
In this section, we evaluate the effectiveness and performance of the proposed algorithm through a Python 3.10.16-based simulation of an IoV system with blockchain data. The experiments were conducted on a machine equipped with an Intel i5 processor and 16 GB of RAM, with system initialization based on the data described later in this paper. Each test randomly generates a varying number of RSUs and vehicle devices, and every experiment is executed 20 times to assess the algorithm's performance. For evaluation metrics, we focus on the storage hit rate of the collaborative storage scheme, the overall access cost for vehicles in the system, and the runtime of the algorithm. Since the proposed strategy can be described as a constraint-based caching scheme, the storage hit rate, similar to the cache hit rate, is defined as the percentage of block access requests completed within the vehicle's dwell time. A higher storage hit rate indicates that more vehicles can access block data without relying on the backhaul link to the remote cloud server.
## 2 Methods
### Procedures  
#### 2.3.1 Equipment Set-Up  
A schematic of the testing set-up for the Flightscope Mevo+ is provided in Figure 1, which is in accordance with the manufacturers' guidelines. The Flightscope Mevo+ is a 3D Doppler tracking radar (EDH, Inc., Orlando, FL, USA) and the manufacturer advises a minimum distance of 2.13 m [19] between the golfer and the launch monitor device. Taking into consideration the size of the laboratory space, the launch monitor was set up at a distance of 2.7 m from the golfer.  
Although somewhat anecdotal, and after speaking with participants directly, it was acknowledged that swinging a driver differs somewhat from swinging a 6-iron, and it is standard practice to progress in intensity during golf swing warm-ups. To ensure accurate data reflecting the golfer's maximal capacity, all participants started with 6-iron shots before swinging their driver afterwards. Prior to data collection, the participants were given time to conduct their own desired golf-specific warm-up routine, relative to how they practice and compete [11]. Once golfers felt warmed up, they were allowed to perform warm-up shots with each club until they felt ready for testing. The golfers were instructed to perform each shot with the aim of achieving maximal distance. The participants were provided with 60 s of rest between swings and 3 min of rest between clubs. All shots that produced data on the Mevo+ were analysed, regardless of impact location or shot outcome, and the average of all 10 shots (for each club) was used for the subsequent data analyses. The specific shot metrics that were analysed are presented in Table 1, with anaccompanying definition for each--all the metrics were recently validated against the gold standard Trackman launch monitor [18]. The golf ball was placed in the same tee spot for each shot to enable consistency with the distance between the netting (3.66 m) and the Mevo+ (2.7 m). The players used a rubber tee for shots with the 6-iron, but were given the option of three different rubber tee heights for the driver, with all recorded trials requiring the same tee height. When using a driver, trying multiple tee heights during practice attempts was allowed before deciding on one for the data collection. The golfers were instructed to aim for a \"target\" which lay in the centre of the 8-foot \(\times\) 8-foot netting, which was also calibrated for the Mevo+ prior to the data collection. To gain the most accurate measurements, Titleist 160 Pro V1x Radar Capture Technology golf balls were used, which have been specifically built for indoor use with radar-based launch monitors, so that reflective markers are no longer required. The manufacturers of the FlightScope Mevo+ reported that this launch monitor, along with others, was used to validate the golf balls in previous indoor testing sessions [20].  
Figure 1: Set up of indoor shot monitoring with the FlightScope (FS) Mevo+ launch monitor, which was aligned with the golf ball.