esa-sceva/satcom-chunk-collection huggingface dataset:
# Super-Twisting Sliding Mode Control with SVR Disturbance Observer for PMSM Speed Regulation
## 2 Sliding Mode Control for PMSM  
### PMSM Modeling  
A dynamic model of a PMSM is nonlinear. The disturbances of the load torque change over time and depend on external conditions. The reference model of a PMSM can be expressed as follows [23]:  
\[\begin{array}{l}\frac{d{{\it{i}}_{d}}}{dt}=-\frac{{{\it{R}}_{i}}}{{{\it{i}}_{d }}}+\frac{{{\it{L}}_{d}}}{{{\it{L}}_{d}}}\omega{{\it{i}}_{q}}+\frac{1}{{{\it{ L}}_{d}}}{{\it{u}}_{d}},\\ \frac{d{{\it{u}}_{q}}}{dt}=-\frac{{{\it{R}}_{i}}}{{{\it{i}}_{q}}}-\frac{{{\it{ L}}_{d}}}{{\it{u}}_{d}}\omega{{\it{i}}_{d}}-\frac{\omega}{{{\it{v}}_{f}}}+\frac{1}{{{ \it{L}}_{q}}}{{\it{u}}_{q}},\\ \frac{d{\omega}}{dt}=\frac{{{\it{T}}_{e}}}{{{\it{f}}}}-\frac{{{\it{T}}_{f}}}{{{ \it{T}}}}-\frac{{{\it{B}}_{d}}}{{{\it{T}}}},\end{array} \tag{1}\]  
where \(u_{d}\) and \(u_{q}\) are the stator \(d\)- and \(q\)-axis voltages, \({{\it{i}}_{d}}\) and \({{\it{i}}_{q}}\) are the stator \(d\)- and \(q\)-axis currents, and \(L_{d}\) and \(L_{q}\) are the stator \(d\)- and \(q\)-axis inductances, respectively; \(R\) is the stator resistance, \(\omega\) is the rotator angular velocity, \(\psi_{d}\) and \(\psi_{q}\) are, respectively, the stator \(d\)- and \(q\)-axis flux linkage, \(\psi_{f}\) is the flux linkage, \(T_{e}\) is the magnetic torque, \(T_{L}\) is the load torque, \(B\) is the viscous friction coefficient, and \(J\) is the moment of inertia.  
The rotor flux is aligned by the decoupled \(d\)- and \(q\)-axis current components in the rotating reference. It controls the current in terms of both magnitude and phase. As the dynamics modeling of surface mounted PMSM with the permanent magnet of uniform thickness is used in this paper, \(L_{q}\) is equal to \(L_{d}\). Therefore, \(T_{e}\) is represented by  
\[T_{e}=\frac{3n_{p}\psi f_{iq}}{2}, \tag{2}\]  
where \(n_{p}\) is the number of pole pairs. The angular speed \(\omega\) is the PMSM output. From Equation (1), the motor dynamics can be rewritten as follows:  
\[\frac{d\omega}{dt}=\frac{3n_{p}\psi_{f}}{2J}i_{q}-\frac{B}{J}\omega-\frac{T_{L }}{J}+D(t). \tag{3}\]  
The \(q\)-axis stator current \(i_{q}\) can be replaced by the reference \(q\)-axis current \(i_{q}^{*}\) for a speed controller design. \(D(t)\) denotes the lumped disturbances which includes the system uncertainty, unmodeled dynamic, and environmental disturbance. Equation (3) can then be rewritten as:  
\[\frac{d\omega}{dt}=bi_{q}^{*}+a(t), \tag{4}\]  
where \(a(t)=-(B/J)\omega-T_{L}/J-b(i_{q}^{*}-i_{q})+D(t)\) and \(b=3n_{p}\psi_{f}/2J\). Here, \(a(t)\) can be considered as system disturbance, and \(b\) is a positive constant. The term \(a(t)\) includes friction, load disturbances, and tracking errors, which degrades the performance of the PMSM controller.
# On Vulnerability of Selected IoT Systems to Radio Jamming--A Proposal of Deployment Practices
## 5 Presentation of the Selected IoT Systems: The Weightless and the Sigfox
### Weightless  
The system can be viewed as one of the major players in the Low Power Wide Area Network (LPWAN) space, beside the better known LoRa [22; 23]. Similar to its LoRa counterpart, Weightless, recently renamed as plain \"Weightless\", also takes advantage of the spread spectrum (SS) transmission for improving system immunity. However, as opposed to LoRa (that uses multi-state chirp spreading, see also [24]), the spectrum is direct sequence spread (DSS) [25], along with other techniques, such as interleaving and data whitening. This spreading only takes place in the OQPSK (Orthogonal Quadrature Phase Shift Keying) modulation mode (MOD), whereas in the other mode, GMSK (Gaussian Minimum Shift Keying), transmission without spectrum spreading is used (i.e., \(SF=1\)). It is an open communication standard dedicated to operating at long distances under harsh attenuation and interference conditions, invented and supported by Weightless SIG. It is designed for bidirectional, fully synchronized, low-power wide area public or private networks with end devices satisfying the Internet of Things (IoT) requirements such as the limited throughput and relaxed latency, fitted for operation in sub-gigahertz ISM bands. Due to legal constraints in Europe, investigations presented in the paper were performed for the 'V band' (863-870 MHz) ISM Band, especially its 'M' and 'P' sub-bands defined in [26], with the following limits on Equivalent Radiated Power (ERP) and duty cycle (DC):  
* 'M' sub-band: 868-868.60 MHz, ERP = 25 mW, DC \(\leq\) 1%;
* 'P' sub-band: 869.40-869.65 MHz, ERP = 500 mW, DC \(\leq\) 10%.  
Figure 2: A diagram of the Internet of Things systems classification.  
In order to ensure a greater capacity, 12.5 kHz channels can be combined into bundles of eight channels, with the aggregate bandwidth (BW) of 100 kHz (a wideband mode, WB). Although operation in a single 12.5 kHz sub-channel (i.e., a narrowband mode, NB) implies lower data rates but increases the number of logical channels, leading to improved uplink cell capacity, a feature particularly valuable in congested networks. These two channel bandwidths, along with OQPSK and GMSK modulations, two values of coding rate (i.e., 0.5, 1) and three spreading factors (i.e., eight, four, one), define eight operational modes differing in data rates, as stated in Table 1.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 6 Conclusions  
In this paper, a time domain spherical harmonic analysis method for spatial sound field recording over 3D space has been developed with the goal to minimize processing delay. This favours the specific application of spatial ANC. With the proposed FIR filter design, the time domain spherical harmonic coefficients can be obtained from the sound pressure measurements of an open spherical microphone array. The filters are designed based on the inverse of the Legendre function. Additionally, the filters are modified with considerations of stability and practical implementation. We have provided simulation results proving the validity of the proposed method.  
We note that by obtaining the proposed time domain spherical harmonic coefficients, the desired sound field can be efficiently captured and reconstructed over space. The proposed time domain spherical harmonic coefficients can be related to the conventional frequency domain coefficients, where both have the same location independent property. The proposed method has the prominent advantage of lower delay since it is developed in the time domain without the introduction of a Fourier transformation or inverse Fourier transformation. Furthermore, the proposed time domain filtering method can support sample based signal processing instead of frame based, which indicates that the frame size can be one sample if necessary. As a result, we consider the proposed time domain spherical harmonic analysis method to be highly suitable for a spatial ANC system where accurate spatial recording with low delay is desired.  
Figure 11: Delay analysis between the desired signal and the reconstructed signal at a point inside the region of interest.  
The most important future work is practically introducing the proposed spatial recording method to a spatial ANC system. Currently the proposed method utilizes open spherical microphone arrays, where the difficulties of constructing this array limit the potential applications. Hence, applying the proposed method to alternative optimised open microphone arrays is another direction for future work.  
Conceptualization, H.S., T.D.A. and P.N.S.; Funding acquisition, T.D.A. and P.N.S.; Investigation, H.S.; Methodology, H.S., T.D.A. and P.N.S.; Project administration, T.D.A.; Supervision, T.D.A. and P.N.S.; Validation, H.S.; Writing--original draft, H.S.; Writing--review and editing, T.D.A. and P.N.S. All authors have read and agreed to the published version of the manuscript.  
This work is funded by Australian Research Council (ARC) grant DP180102375.  
Not applicable.  
No applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANC & Active noise control \\
3D & Three-dimensional \\ IFFT & Inverse fast fourier transform \\ FIR & Finite impulse response \\ SNR & Signal to noise ratio \\ \end{tabular}
## 7 Datasets
### Healthcare  
In the healthcare domain, time series anomaly detection datasets are primarily utilized for monitoring and analyzing an individual's physiological parameters. This allows for the timely detection of health issues, abnormalities in diseases, or malfunctions in medical equipment. For instance, the MIT-BIH Arrhythmia Database (ECG) [95] contains a vast collection of electrocardiogram (ECG) records that are used for the detection and analysis of cardiac arrhythmias. The SVDB [98] offers a range of standard and variant electrocardiogram recordings, which are employed in research on cardiac health and the detection of arrhythmias. These datasets are instrumental in advancing our understanding of heart conditions and in developing more effective diagnostic and monitoring tools for healthcare professionals.  
Electrocardiogram (ECG) signals are commonly used to assess heart health. The heart, being a complex organ, can exhibit many different types of arrhythmias. Therefore, applying anomaly detection methods to analyze ECG signals can be highly beneficial. ANNet [120] utilizes LSTM networks and MLP to process ECG signals, and improves the accuracy of anomaly detection by combining feature vectors. The study also includes preprocessing steps for ECG signals, such as denoising with Discrete Wavelet Transform (DWT) and using a 60 Hz notch filter to remove power line interference (PLI). Similarly, [PERSON] et al. [121] proposed an unsupervised transformer-based anomaly detection method. The model architecture consists of two parts: a transformer model for feature extraction and a classifier for anomaly scoring, which is used to evaluate and detect anomalies in ECG signals.
## 3 Load Forecasting Based on GRU and TCN Hybrid Network
### Load Forecasting Based on VMD-GRU-TCN  
After VMD decomposition, different IMF components have different frequency characteristics. In this paper, different neural networks are selected for the data training and load forecasting based on the different frequency characteristics of the load modal components. As mentioned above, the GRU network can better capture the time-series characteristics and variation rules of stationary signals, and it can obtain higher prediction accuracy when the model parameters do not change much. It is suitable for the forecasting of the low-frequency load components. The convolutional layer of TCN has both atrous convolution and causal convolution structures. Atrous convolution allows the volume to obtain different receptive fields by controlling the sampling rate; it covers a suitable range of sequence values. In addition, through the parallel performance of hole convolution, the time-series tracking of high-frequency fluctuation signals can be improved significantly. The forecasting results of the high and low-frequency load components are rebuilt for the result of the load forecast; the process of the proposed method is shown in Figure 5. The specific forecast steps are as follows:  
Figure 4: Comparison of CNN and TCN convolution.  
1. Data preprocessing. The original load data contain abnormal data and missing data. First, the data are cleaned by DBSCAN clustering, the abnormal data are eliminated and the missing data are supplemented.
2. Decomposition of time-series load data. The load time series data are decomposed by VMD to obtain the different \(f(t)\), \(u_{k}(t)\), and \(\lambda(t)\), corresponding to the characteristic data with different frequencies.
3. Load forecasting. We construct a load forecasting model based on the GRU network and TCN network, select the load data on similar days to determine the training date and carry out model training to obtain the load forecasting model; then, the load data of different components in the future day are forecasted.
4. Load reconstruction. The forecast data of different components are superimposed and reconstructed to obtain the forecast daily load data.
# Exploring the Probiotic Potential of Dairy Industrial-Relevant Lactobacillus
## 2 Materials and Methods
### In Vitro Survival in Simulated Oro-Gastrointestinal Transit  
The oro-gastrointestinal transit (OGT) tolerance assay was performed in a previously designed gastrointestinal system, in which three compartments and five digestion phases (i.e., t1-t5) are simulated [28; 29]. LAB were grown until mid-exponential phase (OD\({}_{600\text{nm}}\) = 1) and resuspended into sterile saline solution (0.86% NaCl) (stage t0). In the oral phase, the bacterial suspension was supplemented with a gastric electrolytic solution (6.2 g L\({}^{-1}\) NaCl; 2.2 g L\({}^{-1}\) KCl; 0.22 g L\({}^{-1}\) CaCl\({}_{2}\); 1.2 g L\({}^{-1}\) NaHCO\({}_{3}\)) pH 6.0 and incubated for 5 min at 37 \({}^{\circ}\)C with lysozyme 150 mg L\({}^{-1}\). Then, the gastric environment was simulated by the addition of pepsin (3 g L\({}^{-1}\)) and the pH was progressively reduced to 3.0 (t2) and 2.0 (t3) with HCl 1M. For the small intestinal phase, pH was neutralised to 6.5, and bile salts (3 g L\({}^{-1}\)) and pancreatin (1 g L\({}^{-1}\)) were added (t4). Finally, the large intestine was mimicked by dilution (1:1 _v/v_) with intestinal electrolytic solution (5 g L\({}^{-1}\) NaCl; 0.6 g L\({}^{-1}\) KCl; 0.25 g L\({}^{-1}\) CaCl\({}_{2}\)) (t5). The viability was determined by spreading serial dilutions from bacterial samples at the different stages on MRS agar and the survival rate was calculated comparing the number of CFU/mL from unstressed and OGT-stressed samples. Survival rate was expressed as LOG (CFUt(1-5)/CFUt0).
###### Abstract  
The aim of this study is to establish a new method for predicting the effective elastic and thermal behavior of heterogeneous materials through mathematical curve fitting. The research initiates with the collection of 3D microstructures comprising 200 spherical inclusions embedded within a linear elastic matrix. This database is acquired by varying the volume fraction of the inclusions (10%, 15%, 20%, or 25%) and the contrast between the two phases. The contrast is quantified in terms of the ratio of the Young's modulus and thermal conductivity modulus, \(\frac{E_{i}}{E_{n}}\) and \(\frac{\lambda_{i}}{\lambda_{n}}\), with values ranging from 10 to 200. These microstructures are then used to estimate the elastic and thermal properties by calculating the effective bulk, shear, and thermal conductivity moduli via the finite element method (FEM). The compiled database is a crucial asset for the development of mathematical equations that precisely fit the curves of various moduli based on changes to input parameters such as the volume fraction and the contrast. The process includes analyzing the data, identifying patterns, and establishing mathematical relationships that effectively reflect the moduli's observed behavior. By integrating these individual equations and taking into account their interdependencies, the resultant comprehensive 3D model provides an extensive representation of the material's behavior and takes into consideration the impacts of varying the volume fraction and contrast on the different moduli. This approach enables a better understanding of the material's response under diverse conditions. Results exhibit the accuracy and reliability of the chosen mapping functions and parameters. Thereby, these proposed functions, with respect to boundary conditions and analytical limits, confirm the relevance of the proposed model to capture such information with a suitable level of precision.  
numerical homogenization; curve fitting; heterogeneous materials; mathematical model Article
# HyFusER: Hybrid Multimodal Transformer for Emotion Recognition Using Dual Cross Modal Attention
## 5 Experimental Evaluation  
### Dataset  
The data used in this study include the Korean multimodal emotion datasets KEMDy19 [23] and KEMDy20 [24], provided by the Electronics and Telecommunications Research Institute (ETRI). The KEMDy19 dataset was collected from 40 Korean voice actors, where pairs of participants participated in one session per pair to collect data. Similarly, KEMDy20 was collected from 80 adults aged 19-39 fluent in Korean speech, with pairs participating in one session each to collect data. The collected datasets include various modalities such as speech utterances, contextual meaning of the utterances, and physiological signals (e.g., skin conductance, pulse intervals, wrist skin temperature). However, this study only used text and speech data.  
Upon analyzing the KEMDy20 dataset by emotion category, it was observed that there were 11,120 samples for the neutral emotion, while disgust and fear emotions had only 61 and 43 samples, respectively. This imbalance in emotion data poses challenges for accurate analysis. To address this issue, the KEMDy19 dataset was additionally used to augment the dataset. After merging the data, duplicate text entries and sentences with fewer than two syllables were removed. Furthermore, to resolve the imbalance among emotion categories, the number of neutral emotion samples was reduced to match the count of the emotion category with the highest number of samples.  
The total number of samples for each emotion category is summarized in Table 4. To further evaluate the reliability of the proposed model, the Korean emotion recognition dataset provided by AI-HUB [25] was used, following the same preprocessing procedures.
## 3 Results
### Overall Performance Metrics  
The model performance varied notably in terms of accuracy, IRT score, and response time (Table 1). GPT-o1 reached the highest mean accuracy (88.7 \(\pm\) 0.0%), while LLaMA 3 70B had the lowest (60.5 \(\pm\) 3.4%). Regarding efficiency, Deepsee-R1 had the slowest response (60.0 \(\pm\) 29.3 s), whereas LLaMA 3.2 450B had the fastest (2.1 \(\pm\) 0.5 s).  
Figure 2 provides a side-by-side comparison of the model accuracies and Rasch-based abilities, reinforcing the observed performance disparities.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Mean Accuracy (\%)** & **Mean Time (s)** & **Mean IRT Score** \\ \hline Gemini 1.5 Pro & 75.00 (1.01) & 28.81 (20.82) & 1.32 (0.07) \\ \hline GPT-4o & 83.90 (0.00) & 3.70 (7.67) & 1.96 (0.00) \\ \hline GPT-o1 & 88.70 (0.00) & 13.62 (8.48) & 2.42 (0.00) \\ \hline Qwen 2.5 & 71.00 (0.00) & 3.39 (2.15) & 1.08 (0.00) \\ \hline Deepsee-R1 & 86.30 (1.10) & 60.04 (29.32) & 2.17 (0.11) \\ \hline Llama 3.2 450B & 74.20 (2.30) & 2.11 (0.53) & 1.27 (0.14) \\ \hline Llama 3 70B & 60.5 (3.03) & 5.15 (2.81) & 0.52 (0.17) \\ \hline Anthropic Haiku & 73.4 (1.01) & 3.15 (2.23) & 1.22 (0.07) \\ \hline Anthropic Sonnet & 81.5 (1.01) & 4.01 (6.28) & 1.76 (0.09) \\ \hline \end{tabular}
\end{table}
Table 1: Performance metrics for nine large language models on the Chilean National Anesthesiology Certification Exam.  
Figure 1: Distribution of total raw scores between human examinees and large language Models.
## 5 Illustrative Examples
\[\{\mathbf{E}^{\rm s},\mathbf{H}^{\rm s}\}(\mathbf{r},t)=\frac{\big{\{}\mathbf{E}^{\rm s \times 0},\mathbf{H}^{\rm s\times 0}\big{\}}(\mathbf{\xi},t-|\mathbf{r}|/c_{0})}{4\pi|\mathbf{r}|} \big{[}1+O(|\mathbf{r}|^{-1})\big{]}, \tag{33}\]  
as \(|\mathbf{r}|\to\infty\), with \(\mathbf{\xi}=\mathbf{r}/|\mathbf{r}|=\cos(\phi^{\rm s})\sin(\theta^{\rm s})\mathbf{i}_{x}+\sin (\phi^{\rm s})\sin(\theta^{\rm s})\mathbf{i}_{y}+\cos(\theta^{\rm s})\mathbf{i}_{z}\) being the unit vector in the direction of observation, can be approximately evaluated from  
\[E_{x}^{\rm s\times 0}(\mathbf{\xi},t) \simeq(2\Delta_{y}/c_{0})\xi_{z}\partial_{t}k_{y}(t), \tag{34}\] \[E_{y}^{\rm s\times 0}(\mathbf{\xi},t) \simeq-(2\Delta_{x}/c_{0})\xi_{z}\partial_{t}k_{x}(t),\] (35) \[E_{z}^{\rm s\times 0}(\mathbf{\xi},t) \simeq(2\Delta_{x}/c_{0})\xi_{y}\partial_{t}k_{x}(t)-(2\Delta_{ y}/c_{0})\xi_{x}\partial_{t}k_{y}(t), \tag{36}\]  
where \(k_{x}(t)\) and \(k_{y}(t)\) directly result from the TD solution (24) via Equations (20) and (21). The corresponding magnetic-type far-field amplitudes simply follow from \(\mathbf{H}^{\rm s\times 0}(\mathbf{\xi},t)=Y_{0}\mathbf{\xi}\times\mathbf{E}^{\rm s\times 0}(\mathbf{\xi},t)\). It is further worth noting that Equations (34)-(36) are, as a matter of fact, exact in the directions normal to the plane of aperture, \(\theta^{\rm s}=0\) and \(\theta^{\rm s}=\pi\), and that the scattered wave fields in \(z<0\) are equal to the total fields transmitted through the aperture (cf. Equation (1)). For the sake of validation, the \(z\)-component of the far-field electric-type amplitude at \(\phi^{\rm s}=\pi/4\) and \(\theta^{\rm s}=5\pi/6\) was evaluated via Equation (36) using the CdH-MoM solution (24) and with the help of a \"far-field probe\" in CST Microwave Studio(r). Figure 5 shows the resulting pulse shapes. Again, the results correlate well with \(\text{ERR}\simeq 6.2\%\).
# Sound Environments in Large Public Buildings for Crowd Transit: A Systematic Review
## 5 Conclusions  
This paper reported on the sound environments in large public spaces for crowd transit. For this purpose, a systematic review in accordance with the PRISMA guidelines was performed. After the screening process, the dataset resulted in twenty-six items that were sorted into three groups, depending on the building types, that is: museums/exhibition spaces, shopping malls, and transposition hubs/stations, of the investigation. Having substantially different methodological approaches, the studies were qualitatively analysed. The review presented obvious significances in sound environments between such spaces and other ones. The main conclusions are:  
* The most commonly used objective measures were SPL, and more specifically often considered as \(L_{\text{Aeq}}\), and \(T\). The intervals across studies are currently inconsistent, and the selection is recommended to take the space scale factor into account.
* The used subjective measures can be classified into four categories, including annoyance, affective quality, room-acoustic quality, and acoustic spatiality. Four basic perceptual assessments for special consideration on dynamic content are accordingly suggested as \"annoying-not annoying\", \"crowed-uncrowed\", \"long-short (reverberation)\", and \"far away-nearby\". The other measures can be project-specific.
* The adopted methodologies involve measurement, questionnaire/interview, listening test and software simulation. It is necessary for the former two to consider temporal and spatial features of such spaces, and the latter two will lead to a better understanding of users' exposure in such spaces, e.g., acoustic sequence and user amount.
* The outputs of acoustic and/or indoor soundscapes investigations inform that improvement of background noise level, e.g., 90 dB in museum/exhibition spaces, and sound reverberation, e.g., over 4.0 to 5.0 s in shopping malls and transportation hubs/station are of fundamental importance. Sufficient acoustic comfort for building types can be achieved with integrated design of indoor soundscape.  
The review qualitatively showed an increasing interest on sound to enhance users' health and well-being in such large public spaces. Apart from the further work on association between objective and subjective measures, the supplement of dynamic content will hopefully renew the users' experience and improve the indoor environmental quality.  
Conceptualization, T.Y., F.A. and J.K.; methodology, T.Y., F.A. and J.K.; formal analysis, T.Y. and F.A.; investigation, T.Y.; resources, J.K.; data curation, T.Y.; writing--original draft preparation, T.Y. and F.A.; writing--review and editing, T.Y., F.A. and J.K.; supervision, F.A. and J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research programme (grant agreement No. 740696).