esa-sceva/satcom-chunk-collection huggingface dataset:
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
###### Abstract  
Time-varying digital filters are widely used in dynamic signal processing applications, but their transient response can significantly impact performance, particularly in real-time systems. This study focuses on reducing transient time in time-varying filters through second-order section (SOS) optimization. By employing a numerical optimization approach, we selectively adjust the coefficients of a single SOS within a higher-order filter to minimize the transient period while maintaining overall stability. Using a sequential quadratic programming (SQP) algorithm, we determine a time-varying coefficient trajectory over a finite horizon, ensuring a rapid convergence to steady-state behavior. Experimental results demonstrate that this targeted coefficient adaptation reduces transient time by up to 80% compared to conventional static designs, with minimal computational overhead. Additionally, a comparative analysis with traditional linear time-invariant (LTI) filters highlights the advantage of this method in suppressing transient oscillations while preserving long-term filter characteristics. The proposed approach provides a practical and efficient strategy for enhancing filter responsiveness in applications requiring both stability and real-time adaptability. These findings suggest that selective time variation in SOS decomposition can be a valuable tool in digital filter design, improving efficiency without excessive memory or processing demands.  
title: 2025 15, 6512
## 3 Results and Discussion
### Analysis of Disaster Effects Caused by Different Types of Dynamic Loads
#### 3.4.2 Evolutionary Law of Stress Field Through Numerical Simulation
This study provides a theoretical basis and technical path for precise disaster prevention and control by revealing the source characteristics and propagation laws of different strong dynamic load events. First, based on the quantitative difference in waveform attenuation coefficient (roof-type 4.53 > coal pillar-type 1.57 > fault-type 1.41), the support system can be optimized in a targeted manner. For fault-type dynamic loads with slower attenuation, a larger safety distance needs to be reserved in the mining planning stage, and a flexible support structure with anti-shock wave propagation characteristics should be designed; conversely, for roof-type dynamic loads with faster attenuation, high-density energy-absorbing anchors can be laid out in the source area. Secondly, the spectrum feature analysis shows that the main frequency band is concentrated in the characteristic spectrum peak of 30-60 Hz, which provides the core parameters for the development of intelligent monitoring systems. By installing a distributed micro-seismic sensor array to  
Figure 18: Curves of difference in principal stresses before and after disturbance. capture the spectrum features in real-time, a dynamic load source identification model can be constructed to achieve a disaster warning closed loop of \"source location-type discrimination-energy assessment\". It is worth noting that the spatial aggregation law of dynamic load events found in the study (the first 500 m return air channel area) guides the implementation of graded prevention and control strategies in engineering practice: increase the density of hydraulic supports in the high-incidence area of roof-type, and use grouting reinforcement technology to block the energy transfer path in the fault influence zone. This refined prevention and control system based on the difference in dynamic load characteristics can increase the energy dissipation efficiency of the support system by more than 30%, effectively reducing the probability and degree of damage of dynamic disasters.
# Detection of On-Chip Generated Weak Microwave Radiation Using Superconducting Normal-Metal SET
## 3 Experimental Setup and Results  
The fabricated chips were mounted into a microwave-tight (although not vacuum-tight) single-wall-shielded sample holder and measured in a dilution refrigerator with a base temperature \(T\approx 15\,\mathrm{mK}\). Each signal line was equipped with a Thermocoax coaxial filter [33] having a \(1\,\mathrm{m}\) length and located at mK-stage of the fridge. The measurement results including the dependence of the detector signal on the frequency and power of microwave radiation as well as the contribution of the substrate-mediated phonons are presented in the following subsections.
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
## 3 Simulation Results and Analysis
### Read Stability Comparison  
One of the major drawbacks of the conventional 6T SRAM cell is its vulnerability to reading instability. During a read operation, the storage node holding logic '0' is directly connected to the pre-charged bit line at VDD, allowing a read current to flow. This current may cause a voltage rise at the '0'-storing node, potentially flipping the stored data. As the voltage increases, the noise margin of the cell decreases, reducing its tolerance to disturbances. Therefore, ensuring read stability is essential for reliable SRAM functionality. Read stability is typically evaluated using the read static noise margin (RSNM), which is measured by applying a DC voltage sweep to the storage nodes and generating the butterfly curve through DC analysis [22]. The RSNM is quantified as the side length of the largest square that can fit within the lobes of the butterfly curve. Figure 7 illustrates the  
Figure 5: Comparison of RAT between the proposed LDRH16T and previous works.  
butterfly curves of all compared cells. Among the designs, SIS10T, EDP12T, and SEA14T exhibit relatively low RSNM values due to their weak pull-up or pull-down strength at the storage nodes. In addition, WE-QUATRO, QUCCE12T, and SAW16T are susceptible to read upsets because two access transistors are positioned near each bit line, exposing not only the storage nodes but also the internal nodes to potential bit line noise. RHBD10T, which has the highest cell ratio (CR = 3), shows improved RSNM performance by keeping the voltage of the '0'-storing node relatively low during a read operation. As a result, it achieves higher RSNM than all comparison cells except for the proposed LDRH16T. As shown in Figure 7, the LDRH16T cell exhibits the highest RSNM among all evaluated designs. This is mainly attributed to its read-decoupled architecture, in which only WL is activated during a read, while WWL remains OFF [1; 20]. This ensures that only the internal nodes are connected to the bit lines through a single pair of access transistors, while the storage nodes Q and QB remain completely isolated. Therefore, even if the voltage of the internal node increases to a level that could flip its state, it does not affect the stored data at Q and QB. This read-decoupled technique is a structural-level approach that is inherently independent of process technology, enabling its applicability across various technology nodes. Furthermore, the proposed cell is designed with a relatively high cell ratio (CR = 2.5), which lowers the voltage at the '0'-storing internal node S0, further enhancing read stability. Consequently, the LDRH16T achieves the highest RSNM among the compared designs, ensuring robust read operation under radiation-prone conditions.
## 2 Materials and Methods
### Data Analysis  
Data were first analyzed with commercially-available software (AcqKnowledge, BIOPAC Systems, Goleta, CA, USA) to monitor any abnormality in EMG traces. Then, data were exported to Matlab (version R2012b; The Math-Works, Natick, MA, USA) for quantitative analysis using a number of custom scripts. The root mean square voltage (RMS) was used as an index of the global EMG amplitude [15]. For each muscle, the RMS EMG was calculated during the active period of the muscle (EMG burst) of each pedal thrust (64-ms window). Then, the RMS-EMG was averaged every six crank revolutions (corresponding to about 5 s for 70 rpm), as previously described [15].
## 6 Simulation Results
In contrast, the proposed DPTC method reduces the torque ripple to 17.9%, 29.4%, and 38.6%, respectively. This shows that compared with the MPTC method, the proposed method reduces the torque ripple by 53.14%, 29.67%, and 27.44%, respectively. Since the PWM-DITC method also calculates the duty cycle, it has a similar torque ripple as the proposed DPTC method. Moreover, Table 2 clearly shows that the copper loss index \(I_{RMS}\) of the PWM-DITC method at the speeds of 200, 400, and 800 rpm are 4.6230, 4.8113, and 5.2329, respectively. On the other hand, the proposed DPTC method is optimized for phase torque allocation, so the index \(I_{RMS}\) are 4.2109, 4.4360, and 4.9058, respectively. The result shows that the proposed DPTC method reduces the copper loss index in the PWM-DITC method by 8.96%, 7.80%, and 6.25%, respectively. Although the PWM-DITC can reduce the copper loss by trial and error of the turn-on angle, the proposed method avoids this work. It is worth noting that the optimization degree of the proposed DPTC method for copper loss decreases with the increase of the speed. This is because as the speed increases, the operable optimization time decreases. At this point, the optimization operation may generate undesired negative torque and reduce the optimization effect, which can be seen from the index \(I_{RMS}\) of the MPTC method at 800 rpm. In the proposed DPTC method, this situation is avoided by setting a speed limit to ensure the optimization effect of the copper loss. Thus, the proposed method has a significant advantage when the speed is lower or the copper loss is dominant. To sum up, the above comparison results illustrate the advantages of the proposed DPTC method in torque control. Figure 8: Simulation results of (**a**) the DITC method, (**b**) the FCS MPTC method in [31], (**c**) the PWM-DITC method in [36], and (**d**) the proposed DPTC method at 800 rpm with a load of 5 Nm.
## 5 Performance Analysis and Simulation
### Analysis of Communication Overhead
#### 5.2.2 Communication Overhead of the Routing Phase  
Suppose that the SPS, EPUSBRF and RPBMP schemes use the same network topology, and the PNs are all represented as \(\text{p}_{\text{c}}\), and the path of the data packets transmitted from \(\text{p}_{\text{c}}\) to the SN does not pass through the visible area of the source node.  
The communication overhead of the EPUSBRF scheme in the routing phase is  
\[\text{Hop}_{\text{EPUSBRFc}}=\text{Hop}_{\text{pc,sink}}. \tag{29}\]  
The communication overhead of the RPBMP scheme in the routing phase is  
\[\text{Hop}_{\text{RPBMPc}}=3*\text{Hop}_{\text{pc,sink}}. \tag{30}\]  
The communication overhead of the NRSPS scheme in the routing phase is  
\[\text{Hop}_{\text{NRSPSc}}=\text{Hop}_{\text{pc,sink}}. \tag{31}\]  
The communication overhead of the RSPS scheme in the routing phase is  
\[\text{Hop}_{\text{RSPSc}}=\text{M}*\text{Hop}_{\text{pc,sink}}. \tag{32}\]  
The communication overhead of the NRDPS scheme in the routing phase is  
\[\text{Hop}_{\text{NRDPSc}}=(\text{Hop}_{\text{pc,pr}}+\text{Hop}_{\text{pr,sink }}). \tag{33}\]  
The communication overhead of the RDPS scheme in the routing phase is  
\[\text{Hop}_{\text{PRDPSc}}=\text{M}*(\text{Hop}_{\text{pc,pr}}+\text{Hop}_{ \text{pr,sink}}). \tag{34}\]  
In summary, the following relationships are obtained:  
\[\text{Hop}_{\text{RPBMPc}}>\text{Hop}_{\text{RDDPSc}}=\text{ Hop}_{\text{NRDPSc}}>\text{Hop}_{\text{PEPIUSBRFc}}=\text{Hop}_{\text{RSPSc}}=\text{ Hop}_{\text{NRSPSc}},\] \[\text{Hop}_{\text{RPBMPc}}>\text{Hop}_{\text{PDPDPSc}}>\text{ Hop}_{\text{PSPSc}}>\text{Hop}_{\text{NRDPSc}}>\text{Hop}_{\text{EPUSBRFc}}=\text{ Hop}_{\text{NRSPSc}},\] \[\text{M}=2\]\[\text{Hop}_{\text{RDPSC}}>\text{Hop}_{\text{RPSPSC}}\geq\text{Hop}_{\text{RPBMPc}} >\text{Hop}_{\text{NRDPS}}>\text{Hop}_{\text{EPUSBRFc}}=\text{Hop}_{\text{NRSPSc}}. \qquad\text{M}\geq 3\]  
Therefore, as the number of PNs actually used increases, the communication overheads of the RSPS scheme and the RDPS scheme increase accordingly.
## 2 Atmosphere Channel
### Log-Normal Model  
Log-normal model describes the optical signal intensity PDF with weak turbulence as:  
\[f(I)=\frac{1}{\sqrt{2\pi\sigma_{I}^{2}}}\frac{1}{I}\exp\left\{-\frac{(\ln\frac {I}{b_{0}}-E[l])^{2}}{2\sigma_{I}^{2}}\right\}\;I\geq 0 \tag{5}\]  
For satellite-to-ground downlink channel, the value of the scintillation index expressed in (4) is typically 0.07-0.23 [19] and it can get smaller at the presence of aperture averaging. For weak turbulence, the Rytov variance \(\sigma_{I}\) in (5) is simply the scintillation index [20]. After normalization, the average irradiance \(I_{0}\) is set to be 1. The Log average irradiance value \(E(l)\) is obtained as:\[E(l)=-\frac{\sigma_{l}^{2}}{2} \tag{6}\]  
with the Rytov variance \(\sigma_{l}\) increasing, more sample values move towards zero and more low SNR bits appear in the communication link.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 3 Experimental
### Signal-To-Noise Ratio  
The signal-to-noise ratio \(r_{\text{SNR}}\) in XPCS experiments is given by [62, 63]  
\[r_{\text{SNR}}\propto\beta I_{0}\sqrt{N_{\text{pix}}\cdot T\cdot\Delta t_{ \text{min}}}, \tag{21}\]  
where \(I_{0}\) denotes the incident coherent flux, \(N_{\text{pix}}\) is the number of detector pixels, \(T\) the total acquisition time and \(\Delta t_{\text{min}}\) the shortest accessible time, e.g., defined by the repetition rate of the detector. As a result, an increase of a factor \(x\) of intensity will result in a reduction the accessible time scale \(\Delta t_{\text{min}}\) of \(x^{2}\) at the same \(r_{\text{SNR}}\). This demonstrates that XPCS benefits strongly from an increased brilliance at new X-ray sources and high-speed detectors.  
Depending on the setup and sample, an optimal \(r_{\text{SNR}}\) can be found. For SAXS geometries the optimal \(r_{\text{SNR}}\) is obtained when the speckle size matches the detector pixel size, although the contrast will be smaller than the maximum value [63]. Furthermore, considering dose limitations for radiation-sensitive samples, a linear increase of \(r_{\text{SNR}}\) with \(I_{0}\) was reported [64]. For XSVS, the signal-to-noise ratio is given by [59]  
\[r_{\text{SNR}}\propto\beta I_{0}\sqrt{\frac{N_{\text{pix}}N_{\text{patt}}}{2(1 +\beta)}}, \tag{22}\]  
with the number of patterns \(N_{\text{patt}}\). Depending on the sample, number of images and delay times, this values can be larger than the one for XPCS [65].
## 4 Conclusions  
A novel fault diagnosis method is proposed, based on a GAN-SDAE system. This proposed system combines the advantages of GAN, which is to learn an original sample distribution, and SDAE, which is to accurately diagnose faults. We drew conclusions from the experimental results for two cases of bearings and gears:  
1. The optimal network structures of the bearing and gear fault diagnostic systems are determined by the reconstruction errors. We established two GAN-SDAE systems for bearings and gears, respectively. It is feasible to apply a GAN-SDAE to fault diagnosis.
2. As a result of the adversarial learning process, the generator and the discriminator of the GAN approach are trained in turn. We improved the GAN approach using a cloud model analysis, which improved the data augmentation. The data augmentation in turn improved the classification ability of the GAN-SDAE system.
3. The system uncertainty of the GAN-SDAE was analyzed by repetitive experiments. The synthetic standard uncertainty \(u_{\text{C}}\) remained at a 0.2 float for bearing fault diagnosis and below 0.6 for the gear fault diagnosis. These results reflect the stability of the GAN-SDAE system.
4. The GAN-SDAE system was compared with other fault diagnosis models (SVM, BPN, DAE, and SDAE). The proposed system is obviously advanced.
5. The GAN-SDAE was showed an accuracy higher than 90% for both bearing and gear faults. This result reflects the adaptability of the proposed method.  
Conceptualization, Q.F. and H.W.; methodology, Q.F.; software, Q.F.; validation, Q.F.; formal analysis, Q.F.; investigation, Q.F.; resources, H.W.; data curation, H.W.; writing: original draft preparation, Q.F.; writing: review and editing, Q.F.; visualization, Q.F.; supervision, Q.F.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number U1833110.  
The authors declare no conflict of interest.  
Figure 22: The synthetic standard uncertainty.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 2 Related Works
### Human Pose Estimation Methods Based on Fusion of RGB Images and Other Modalities  
To address occlusion challenges in pose estimation, hybrid systems integrating RGB cameras with additional sensors, such as Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), and Infrared Radiation (IR), have been introduced [45; 46; 47]. Pose estimation methods utilizing RADAR [48; 49; 50] and LIDAR [51; 52] offer advantages in occlusion handling and privacy preservation. However, these approaches face challenges due to high system deployment costs and sparse data availability. Consequently, research involving RADAR and LIDAR has progressed more slowly than studies on wearable motion capture systems and multi-camera motion capture systems [53]. The MIT CSAIL research team focused on occlusion caused by objects obstructing individuals in 2D pose estimation. To alleviate this problem, they introduced RF-Pose [21], a method that utilizes wireless signals penetrating walls and reflecting off the human body with RGB images. RF-Pose aggregates vertically and horizontally recorded RF heatmaps captured simultaneously and utilizes a neural network to infer occluded body poses. This approach demonstrates robustness to occlusions caused by walls and achieves improved pose estimation performance in dark environments. However, the study identified limitations in addressing occlusions caused by overlapping human bodies. Specifically, challenges arise when correctly associating the same joints of the front and back individuals in multi-person occlusion scenarios. Additionally, in self-occlusion cases, pose estimation accuracy depends on the model's ability to infer occluded joints, highlighting an area requiring further research and improvement.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 1 Introduction
Simulation results confirm that the scheme would have better throughput while keeping fair access between LTE and Wi-Fi stations in either heavily or lightly loaded Wi-Fi environments. By adequately assigning weights to the utility function, when loads of Wi-Fi stations are high, our scheme favors Wi-Fi stations to preserve the access right of Wi-Fi stations. On the other hand, when loads of Wi-Fi stations are light, the remaining resources are allocated to LTE stations for maximal bandwidth efficiency. The rest of the paper is organized as follows. Section 2 presents related works. In Section 3, we define the system model and the formulated optimization problem for the time-division multiplexing between LTE and Wi-Fi networks. The proposed method is described in detail in Section 4. Section 5 presents the simulation settings and shows the numerical results accompanied by analysis and discussion. Finally, we draw some conclusions in Section 6.
## 3 Results
### AIGC Input-Output Classification
#### 3.3.3 Text to Audio/Speech  
Text-to-speech (TTS) and text-to-audio techniques both involve converting text into human-like speech and generating music with various vocal styles, respectively. TTS is commonly used in applications such as voice assistants, voice navigation systems, and audiobooks. One notable approach in this field is AdaSpeech [113], which is an efficient custom voice synthesis technique. It utilizes two acoustic encoders: one for extracting the sequence of phonemes and another for extracting utterance-level information. By incorporating conditional layer normalization in the mel-spectrogram decoder, AdaSpeech [113] enhances the quality of the synthesized voice. Several diffusion-based models, such as Diff-TTS [63], Grad-TTS [152], and ProDiff [53], have been developed to achieve more human-like speech synthesis using diffusion techniques. Additionally, a hybrid model called DiffGAN-TTS [128] combines both diffusion and GAN architecture to further enhance the quality of speech synthesis. Another interesting technique is GANSynth [153], which focuses on generating high-quality musical clips from text. Jukebox [145], on the other hand, is a VQ-VAE based model that can generate music from lyrics, thereby accomplishing the lyrics-to-singing (LTS) task. In the domain of audio classification, Microsoft's CLAP [47] is a notable pioneer. It utilizes audio and text encoders to classify audio samples, such as identifying sounds like audience claps, bird sounds, and other environmental audio cues. This technique can be considered an example of audio-to-text conversion. Additionally, there are techniques like MusCaps [48], which aid in captioning music by genre, and SecGAN [82], a speech enhancement technique that uses conditional GAN to achieve speech-to-speech conversion. These techniques showcase the versatility and broad range of applications within the realm of audio and speech processing.
# Safe Three-Dimensional Assembly Line Design for Robots Based on Combined Multiobjective Approach
## 2 Modeling of Assembly Line Layouts Based on the Safety Indicator  
Many researchers have proposed layout design models [33], and a large number of researchers have proposed equal-area and unequal-area layout models, and a linear layout of sewing assembly line equipment of equal area [17], which lacks consideration of the layout of robot equipment in the assembly line. There are also some researchers who focus on the layout optimization of the robot cell [25], but the model only uses the oblique grid for the sequence pair method for the special scene of the robot cell, and is not suitable for the assembly line layout. We propose that the safety assembly model (SLM) is different from the model that has been proposed from the modeling object to the optimization goal. Moreover, a single optimization goal cannot meet the complex assembly line layout, and the two important indicators of the smallest logistics cost and the smallest floor space are considered to be multiple objectives for optimization, and the assembly line safety layout optimization model is established. The modeling object is an assembly line with industrial robots. The entire assembly line consists of three main components: workstations, traditional industrial robots, and industrial robots, which can replace or assist in assembling heavyweight parts. However, robots have large working range, many degrees of freedom, and the dangerous situation of arbitrary movement direction during work, which brings challenges in establishing safe assembly lines. We build an industrial robot simulation model while using the Monte Carlo method in order to simulate the actual physical process in a workspace. A quantitative safety indicator is added to the model for assembly line scenarios with robots in order to judge the collaboration assembly status and the position of robot arms. In safety layout design, hardware devices for safety protection, such as warning lights, electronic sirens, and safety sensors, are not considered. Instead, the location data of components in assembly lines are optimized in the early planning process, and the desired output is an optimal safe layout that ensures the safety of robots in operation.