esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network-Assisted DPD of Wideband PA Nonlinearity for Sub-Nyquist Sampling Systems
###### Abstract
However, most of these methods have significant limitations. They are particularly vulnerable to noise and often fail to deliver satisfactory performance under the conditions of low signal-to-noise ratios (SNRs). Artificial neural networks (NNs) have been rapidly developed in recent years and can be used for PA and DPD modeling as they can exhibit strong nonlinear feature representation without pre-conditioning [12; 13]. Ref. [14] proposed a method for combining recurrent neural networks with PA modeling and DPD. Ref. [15] proposed the application of long short-term memory (LSTM) networks in the fields of amplifier modeling and digital predistortion. However, these models do not account for the downsampling environment. In [16], a digital predistorter, modeled by an augmented real-valued time delay neural network (ARVTDNN), was proposed to realize the correction of PA nonlinearity in broadband transmitters.The structure of this network is simple. However, its application scenarios are limited, and its performance decreases in some specific scenarios. In [17], a nonlinear correction method for power amplifiers is proposed, which combines a global optimization search algorithm with adaptive linearization to adaptively find the best parameters for nonlinear correction. In [18], a deep neural network (DNN) model was proposed for low-feedback estimation of DPD. However, this approach first requires the recovery of the model parameters of the PA before solving for the inverse model coefficients. This additional step increases the overall system complexity. Attentional mechanisms are inspired by the human visual system and are widely used in neural network models for their ability to improve model performance and model flexibility and generalization [19]. However,the method does not account for sub-Nyquist sampling scenarios. Moreover, the sparse characteristics of the undersampled data stream can negatively impact the training process of neural networks (NNs). This issue is demonstrated in the simulation results and needs to be carefully addressed. This paper proposes an NN-assisted wideband PA DPD method for sub-Nyquist sampling communication systems, where a dual-stage architecture is designed to handle the ambiguity caused by the down-sampled signals. In the first stage, a time-delayed memory polynomial reconstruction (TDMPR)-based method is implemented with the undersampled signals for the preliminary estimation of DPD parameters, which demands a limited number of pilot signals. Then, an attention-based NN is proposed for the refinement of DPD, where virtual training is performed with transmitted data signals, reducing the training overhead significantly. It is demonstrated through simulations that the proposed method can mitigate the wideband PA nonlinearity efficiently for sub-Nyquist sampling systems while providing outstanding performance gains compared to the existing downsampling DPD methods.
## 1 Introduction
### Technological Challenges  
In recent years, the interest in detecting and interpreting emotions in speech as well as synthesizing emotional speech has grown in parallel for a variety of applications. The research work done on emotions in speech is very extensive [8]. Speech Emotion Recognition (SER) consists of the identification of the emotional content of speech signals, the task of recognizing human emotions and affective states from speech. In the SER field, there are three important aspects being studied and discussed in the literature: the choice of suitable acoustic features [9], the design of an appropriate classifier [10] and the generation of an emotional speech database [11; 12; 13]. Some works propose multimodal approaches combining visual and speech data to improve and strengthen emotion recognition systems [14; 15]. It is also well attested that speech recognition systems function less efficiently when the speaker is in an emotional state [16]. On the other hand, some works synthesize emotions in speech by systematically manipulating some of the parameters of human speech [17]. We can say then, that there is an important body of work about the effects of emotions in Automatic Speech Recognition (ASR), classification of emotions from speech or emotional speech synthesis, but the literature is scarce on the effects of emotions in Speaker Identification (SI) [18] and even less abundant about the influence of stress specifically.  
One of the problems in this type of systems is the availability of databases recorded with emotional and neutral speech. Those existing are either recorded by actors simulating speech under those emotions, or by people to whom different emotions have been induced. This last option is truly complicated to implement--especially for negative emotions--and as a consequence, there are very few databases in which stressed speech is either simulated or recorded under real conditions, such as SUSAS [19], VOCE [20] or UT-Scope [21]. The main difficulty with these data relies on the labelling process.  
Moreover, stress is not considered a proper emotion, although it is intimately related to anxiety and nervousness. It can be defined as a state of mental or emotional tension resulting from adverse or demanding circumstances. Stress may be induced by external factors (workload, noise, vibration, sleep loss, etc.) and by internal factors (emotion, fatigue, etc.). Among the physiological consequences of stress are respiratory changes, increased heart rate, skin perspiration and increased muscle tension of the vocal cords and vocal tract. All of these factors may, directly or indirectly, adversely affect the quality of speech [22] and help us discriminate between stressed or neutral speech using machine learning algorithms.
## 6 Conclusions  
This study presents an enhanced ORB-SLAM3 method to tackle the difficulties visual SLAM systems face in open-pit mining settings. Dense mapping is accomplished by integrating point-line feature extraction and improved reprojection error computation, utilizing RGB-D camera depth data. Experimental validation utilizing the TUM dataset and authentic mining environments demonstrate that, in comparison to the ORB-SLAM3, the enhanced algorithm decreased the absolute trajectory error (APE) by an average of 44.33% and the root mean square error (RMSE) by 44.15%. Regarding relative trajectory error (RPE), it reduced the average by 14.34% and the RMSE by 15.64%. The algorithm in dense reconstruction effectively creates environmental representations with intricate geometric details by implementing dense point cloud mapping threads while markedly enhancing the storage efficiency via octree structures. In simulated assessments within authentic open-pit mining settings, the algorithm can precisely replicate intricate terrain characteristics, such as gravel rock walls and undulating surfaces, thereby offering dependable environmental perception assistance for the autonomous navigation of mining robots.  
Despite the enhanced algorithm exhibiting superior performance in various dimensions, certain limitations remain. Drift issues persist during the dense point cloud map's construction for large-scale scenes. Additionally, because of experimental constraints,  
Figure 22: Simulated open-pit mine scene: (**a**) scene mapping; (**b**) dense mapping; (**c**) OctoMap.  
validation using real mining scenarios was not conducted. Factors such as dust, lighting variations, and irregular terrain in actual mining environments, which are difficult to replicate in simulations, may significantly impact performance. Future work will address these issues, improve its robustness in dynamic settings, optimize cumulative errors, enhance real-time dense mapping, and validate the method in real mining scenarios.  
Methodology, W.X. and B.L.; software, Y.X.; validation, Y.X.; investigation, Y.X.; resources, Y.X. and W.Z.; data curation, B.X. and H.Z.; writing--review and editing, Y.X.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangxi Major Science and Technology Special Project (AA23062077) and Central Guidance for Local Science and Technology Development Fund Special Project (2023 PRJ0101).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Numerical Simulation and Analysis of Heart-Aorta Fluid-Structure Interaction Based on S-ALE Method
## 2 Materials and Methods
### Simulation Boundary and Output Settings  
The normal physiological boundaries and constraints of the heart-aorta FSI model are shown in Figure 4a. Reasonably constrained boundary conditions were set to the FSI model based on anatomical structural features. A full constraint was applied at the base of the heart (orange area in the dashed line) to simulate a fixed connection between the heart and the diaphragm. Radial constraints were set in part of the descending aorta (red dots) to achieve attachment between the intercostal arteries and the thoracic spine. Figure 4b shows the output locations of aortic hemodynamic parameters, analyzed for blood velocity, flow rate, and WSS at the aortic section.  
Figure 4: Simulation boundaries and output settings for the heart–aorta FSI model. (**a**) Normal physiological boundary and constraint; (**b**) output locations of the aortic section.  
Figure 5 represents the boundary load curves for the normal physiological state. The model simulated the systole and diastole of the heart by applying displacement loads to the LV (Figure 5a), thereby realizing the pumping function of the heart. The load was directed toward the interior of the LV and was oriented perpendicular to the element. The opening and closing of the aortic valve were achieved by controlling the coupling between the aortic valve and the blood. The point I on the curve indicated that the LV started to contract. At the point II, the LV began to eject. At this point, the FSI was turned off to simulate the open state of the aortic valve. The LV stopped ejection at the point III, and the FSI was turned on to simulate the closed state of the aortic valve. Flow resistance in blood vessel was simulated by applying a time-dependent pressure load at the aortic outlet [35]. As shown in Figure 5b, the pressure curves are converted into time-dependent relative volume curves and assigned to the EOS to realize the pressure change at the outlet. When the parameters in the Gruneisen EOS equation (Equation (9)) are set to specific values \(S_{1}=1\), \(S_{2}=S_{3}=0\), \(\gamma_{0}=2\), \(E=0\), it degenerates into a linear equation:  
\[p=\rho_{0}C^{2}\zeta \tag{11}\]  
By combining Equations (10) and (11), the following can be obtained:  
\[V=\frac{\rho_{0}C^{2}}{p+\rho_{0}C^{2}} \tag{12}\]  
Parameter \(C\) can be calculated using the Newton-Laplace equation:  
\[C=\sqrt{\frac{K}{\rho_{0}}} \tag{13}\]  
where \(K\) is the blood bulk modulus and \(\rho_{0}\) is the blood density. The speed of sound propagation in blood was calculated to be 1543 m/s.
# Research on Snow Load Characteristics on a Complex Long-Span Root Based on Snow-Wind Tunnel Tests
## 3 Experimental Research on Snowdrifts on Complex Long-Span Roof
### Experimental Setup
#### 3.1.2 Setup Parameters  
The prototype stadium was constructed in a suburban area of Tongren City, Guizhou province, China. The reference wind pressure is 0.35 kN/m\({}^{2}\) (100 year return period); whereas, the reference snow pressure is 0.35 kN/m\({}^{2}\) (100 year return period) [2]. The prototype wind velocity at a standard height (10 m) during a snowstorm is assumed as 0.45 of the reference wind velocity, calculated based on the reference wind pressure [21]. The air density there is about 1.22 kg/m\({}^{3}\), and hence the reference wind velocity is close to 24 m/s. The snow density is about 150 kg/m\({}^{3}\), and the maximum snow depth during a snowstorm could reach 0.23 m [2]. The threshold friction velocity and snowfall velocity are set to 0.15 m/s and 0.2 m/s, respectively. Regarding the snowfall duration, as no outdoor measuring record was available; the experimental duration was provided based on an eight-year-long outdoor measurement in China by the authors. This work was carried out from 2010 to 2017 and is ongoing. The measurements included the snowfall duration, interval time between two snowfalls, accumulated snow density, and snow depth. Based on the measurement data, a single snowfall usually lasted for nearly 6 to 7 h. The probability of snowfall lasting for less than 12 h was approximately 72% (Figure 7). Therefore, the prototype duration was set as 12 h.  
The artificial snow particles were selected as the experimental particles. Prior to the experiment, the snow was sieved into uniform sphere particles. Based on the similarity criterion introduced in Section 2.2, the experiment reference velocity was 1.2 m/s at 0.06 m, and the experimental snowstorm duration was set to 0.6 h. A total of 700 L of artificial snow was poured into the sieve-like feeder during the experimental duration, and the snowfall flux was approximately 0.007 kg/m\({}^{2}\)s. As this stadium exhibited biaxial symmetry and the wind direction frequency was provided with a 22.5\({}^{\circ}\) interval in the annual wind rose diagram, five cases were designed and conducted for different inflow directions at 22.5\({}^{\circ}\) intervals, namely 0\({}^{\circ}\), 22.5\({}^{\circ}\), 45\({}^{\circ}\), 67.5\({}^{\circ}\), and 90\({}^{\circ}\), from the x-axis to y-axis (Figure 8).  
Figure 8: Study cases conducted for different inflow directions at 22.5\({}^{\circ}\) intervals (0\({}^{\circ}\), 22.5\({}^{\circ}\), 45\({}^{\circ}\), 67.5\({}^{\circ}\), and 90\({}^{\circ}\)).  
Figure 7: Probability density curve of single snowfall duration, derived from an eight-year field measurement result.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 3 The Effect of Index Distribution
### QIM Steganography  
The core of QIM steganography is the codebook division algorithm. It divides the codebook into several parts, and each part is mutually exclusive. A codebook is \(L_{1}\) which is divided into \(N\) subcodebooks \(L_{11},L_{12},\ldots,L_{1N}\). The subcodebook satisfies the conditions in Equation (2).  
\[L_{1}=L_{1i}\cup L_{1j},L_{1i}\cap L_{1j}=\phi,i,j\in[1,N],i\
eq j \tag{2}\]  
CNV-QIM algorithm was first used in QIM steganography in literature [18], which divided \(L_{1}\) into \(L_{11}\) and \(L_{12}\). CNV-QIM is a hidden method with good concealment. When the embedded information bits is \"0\" and \"1\", the subcodebooks for finding the optimal codewords are \(L_{11}\) and \(L_{12}\) respectively. At the receiving end, by judging the codebook \(L_{11}\) or \(L_{12}\), you can know the embedded information \"0\" or \"1\". The binary codebook flow chart is shown in Figure 1[19].  
For the G.723.1 speech coder, \(L_{1,i}\), \(L_{1,j}\) denote two different codewords of the codebook \(L_{1}\). \(i\), \(j\) are positive integers, \(i\
eq j\). In CNV-QIM algorithm, \(L_{1,i}\) and \(L_{1,j}\) are divided into two different subcodebooks in PSVQ (Predictive Split Vector Quantization). They are marked as 0 and marked as 1, and the codeword and its neighbour vertices must be located in two subcodebooks respectively, which reduces distortion due to steganography.
## 2 Modeling
### Non-Ideal Equivalent Circuit Model
For this purpose, a recursive linearization process can be carried out to obtain the best approximation of the value of each parameter, depending on the operating point for each sampling period. Therefore, the developed model is valid and general, and to update it, it is sufficient to modify the linearized parameter to define an LPV model for each sampling period [28]. This allows for the design of model-based adaptive controllers [29]. Finally, as usual, the maximum switching frequency of the switching devices will be much higher than that corresponding to the operating frequency of the converter. Likewise, assuming, as already mentioned in this section, that components are of professional quality, their leakage currents can be neglected. Thus, the non-idealities considered in the modeling of the non-ideal push-pull converter are listed below:  
For the transformer, series resistances and leakage inductances, and the capacitances of the primary and secondary windings (\(R_{Lp}\), \(R_{L_{S}}\), \(L_{P_{12}}\), \(L_{S_{12}}\), \(C_{P_{12}}\) and \(C_{S_{12}}\), respectively), as well as transformer core losses associated with magnetization current, eddy currents and parasitic fluxes (\(L_{M}\) and \(R_{Nu}\)), were considered. In this case, the parameters of the parallel branch of the transformer model were concentrated in the upper primary winding. For the power-switching devices, the series drain-sink saturation resistances (\(R_{DS}\)) and parallel output capacitances (\(C_{OSS_{12}}\)) of the transistors, as well as the conducting resistance (\(R_{D}\)) and the corresponding threshold voltage (\(V_{\gamma}\)) of the rectifier diodes, were considered. Finally, for the converter output LC filter, the series resistance of both the inductance (\(R_{L_{F}}\)) and the filter capacitor (\(R_{C_{F}}\)) were considered. Please see \"Nomenclature\" for the meaning of each parameter. Based on this, Figure 2 shows the complete non-ideal equivalent model reduced to the secondary of the push-pull converter in Figure 1.
# Neighbor Discovery for IPv6 over BLE Mesh Networks
## 3 6 LoBLEmesh Neighbor Discovery
### IPv6 Address Auto-Configuration and Update Mechanism
The mesh gateway shares the context table with all the nodes in the domain. Therefore, the nodes can only perform IP header compression and decompression when they share the same context table, which is the key technology for transmitting IPv6 over BLE. Figure 4: IPv6 address auto-configuration and update mechanism. For the security of BLE mesh networks, the node may use its device private address instead of the static address. The private address is suggested to change every 15 min. When the device address changes, the address update mechanism is triggered by the node. The node sends the NS and RS messages to the mesh gateway to register the new IPv6 address. Due to the same connection between the node and the relay node, unicast communications are performed during the address update. The procedure is shown in Figure 4.
# Analysis of 3D Printed Dielectric Resonator Antenna Arrays for Millimeter-Wave 5G Applications
## 4 Discussion and Conclusions
However, post-processing techniques, such as surface polishing, could reduce surface roughness and correct dimensional inaccuracies, potentially improving the final results. It is expected that future advances in FDM, as well as other 3D printing techniques, will allow for the fabrication of millimeter-wave DRA arrays at higher resolutions, which could also be integrated with 3D-printed circuits, including metallic conductive layers. Such technology for FDM printing of conductive and metallic layers already exists; however, it was not considered at this stage, as it would lead to significant deviations in performance at millimeter-wave frequencies. In the future, improvements in material science and multi-material 3D printing could allow for more seamless integration of dielectric, conductive, and flexible components within a single printing process. This would enable the development of fully 3D-printed, compact, and highly efficient antenna arrays, potentially reducing production costs and opening new applications in sectors such as 6G and beyond telecommunications, satellite systems, and IoT. Moreover, advances in printing at the nanoscale may further enhance the performance and miniaturization of millimeter-wave devices. Conceptualization, Z.C. ; Investigation, S.L. ; Supervision, B.S.I. and S.G. All authors have read and agreed to the published version of the manuscript. This research was funded by EPSRC, grant number: EP/S005625/1, and Royal Society--International Exchanges 2019 Cost Share (NSFC), grant number: IEC\(\backslash\)NSFC\(\backslash\)191780. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to no publicly accessible repository. But the authors would like to share the data included in the paper. The authors would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] (KEYENCE UK LTD) for their support. The authors declare no conflict of interest.
# Programming Real-Time Sound in Python
## 5 Discussion  
Table 3 reports statistics for the voltage-controlled filter implemented using the Cython + native C approach under different OS load conditions. In all cases, a chunk size of 64 samples was set. The real-time limit for this chunk is equal to 64 \(\times\) 22.6 \(\upmu\)s \(\approx\) 1.45 ms. According to the values reported in Table 2, the process takes 64 \(\times\) 0.14 = 8.96 \(\upmu\)s processor time for each chunk. However, in the best case, a chunk was computed in about 37 \(\upmu\)s in our test environment. The extension of this  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Processing Time** & **Memory Occupation** \\ \hline Interpreted Python & 199.3 \(\upmu\)s/sample & \textless{}1 KB \\ \hline Cython annotations & 6.5 \(\upmu\)s/sample & 16 KB \\ \hline Cython + native C & 0.14 \(\upmu\)s/sample & 12 KB \\ \hline Numba & 1.36 \(\upmu\)s/sample & 12 KB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Voltage-controlled filter: mean processing time and memory occupation of different implementations; chunk_size = 512; processing time averaged over 1000 chunks. (Real-time limit: 22.6 \(\upmu\)s/sample).  
extra time depends on the priority assigned to the real-time process by the OS. Using the highest load that we were able to reproduce, the consequent extra time (31.6 ms in Table 3) exceeded the real-time limit for this chunk size by no less than 20 times.  
Furthermore, Table 3 in the last row reports the percentage of violations of the real-time limit, measured as the time between two subsequent callbacks (idle time). Because no buffer underflow and consequent sound artifacts occur in consequence of such violations, certainly the system alternated audio chunks preemption with prioritization of non real-time processes. In fact, portaudio creates a further buffering level hence smoothing out delays due to algorithmic and/or OS bottlenecks, at the cost of introducing additional latency--about 50 ms in our tests. Figure 2 shows that, in practice, few longer delays interleaved with several short idle times. Such delays happen when the Python process waits for the next chunk of samples to become available from the audio driver.  
The total latency of the system depends on multiple factors: the audio interface hardware, the audio drivers, the buffering levels introduced by the system, and the chunk size. In our test environment, latency grew up to about 500 ms when the voltage-controlled filter was computed using the internal sound card instead of the Babyface. More generally, Python does not acquire special privileges from the OS or address possible priority inversion problems, and even an audio thread must contend with non-real-time Python threads for the interpreter lock. Furthermore, real-time Python programmers should always check the virtual memory occupation of their software, as page faults can have a huge impact on real-time performance. Unless these issues are rigorously managed, uninterrupted audio, in principle, cannot be guaranteed by a Python application.