esa-sceva/satcom-chunk-collection huggingface dataset:
# Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures
## 4 Experiment and Results  
### Experimental Settings  
The goal of the experiment is to verify the ability of the proposed approach to model terrain information. We validate the effectiveness of introducing TNUD to the traversability cost prediction by comparing it with the framework introduced in [24], where the Gaussian process was directly applied to cost prediction without non-uniformity detection. For this purpose, we compute the root-mean-squared prediction error (RMSE), which is defined by  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(f(\mathbf{x}_{i})-\mu_{i})^{2}}, \tag{24}\]  
where \(N\) is the number of test samples, \(\mu_{i}\) denotes the predicted mean vibration of the input image texture \(\mathbf{x}_{i}\) and \(f(\mathbf{x}_{i})\) is the corresponding ground truth vibration. The experiment was conducted at Sanaru Lake in Hamamatsu, Japan, in a wide portfolio of terrains, where a full database, including terrain images and acceleration signals, was recorded and later randomly divided into training and test sets. The total number of samples is 2582, from which 90% of samples are allocated for training, and the remaining 10% of samples are used for testing. The resulting refined maps from multiscale analysis were weighted against a lightness contrast distance threshold \(contrastDistancethreshold=0.5\). The remaining experimental settings are given in [24]. All experiments were performed on an Intel Core i7-3770 3.4 GHz CPU with 8 GB of RAM.
###### Abstract
With our frequency-domain-processing method (hereafter, short-time Fourier transfer (STFT)-based method), the noise power in the power spectrum of the observed speech is first removed. The phase difference between the observed speech and clean speech is then calculated using the noise-suppressed power spectrum to obtain the phase spectrum of the enhanced speech. With our time-domain-processing method (hereafter, waveform-based method), the low-frequency waveform is first restored, then the high-frequency waveform is estimated using the restored low-frequency waveform. We conducted experiments to evaluate the proposed methods, and the results indicate that both methods can effectively handle most types of deterioration in the observed speech from LDVs, an improvement over conventional speech enhancement methods. This paper is organized as follows. Section 2 introduces the characteristics of observed speech from LDVs and the two proposed methods. Section 3 presents the evaluation experiments we conducted to evaluate the effectiveness of the proposed methods and the results. Section 4 concludes this paper and introduces future work.
## 3 Results and Discussion
### Optimization of Subsequent Voltammetric Detection
#### 3.2.2 Effect of Square-Wave Voltammetry Working Parameters  
From previously reported papers, it is known that applying a potential of \(-\)0.1 V for 60 s is sufficient to reduce VK1 to H2 VK1 [2, 33]. Hence, it was necessary to determine the optimal working parameters of SWV. At the constant potential step of 5 mV, an effect of other two working parameters for \(\alpha\)-TOH and VK1 peak current responses was investigated: potential amplitude from 5 to 50 mV and frequency from 5 to 100 Hz (Figure 2).  
Figure 2: Dependence of anodic peak current VK1 (red) and \(\alpha\)-TOH (blue line) on potential amplitude (**A**) and frequency (**B**). SWAdSV of 50-\(\mu\)mol L\({}^{-1}\)\(\alpha\)-TOH and 50-\(\mu\)mol L\({}^{-1}\) VK1 adsorbed from its 50% aqueous–acetonitrile mixture onto GCE at stirring rate of 400 rpm for 5 min. Voltammetric detection was carried out in 0.01-mol L\({}^{-1}\) HNO\({}_{3}\) containing 0.1-mol L\({}^{-1}\) KCl (pH 2.08) at \(E_{\text{dep}}=-0.1\) V, \(t_{\text{dep}}=60\) s, \(t_{\text{eq}}=5\) s and \(E_{\text{step}}=5\) mV. The effect of potential amplitude was examined at a constant frequency of 80 Hz and the effect of frequency was studied at a constant potential amplitude of 25 mV.
## 3 Coherence Detection and Inland Water Body Monitoring
### Retrieval of Inland Water Body (Or Lake Water Body) Surface Height
The raw IF data of spaceborne GNSS-R has important application value in the retrieval of inland water body height. Carrier-phase measurement can provide a high accuracy and spatial sampling resolution, but the systematic effects of ionospheric correction and orbit determination residuals will also limit phase delay height measurement. In addition, due to the surface roughness driven by wind, it will be difficult to extract coherent phase measurement values from high-altitude satellites (phase information can still be obtained from the grazing angle reflection [100]). In future research, continuous exploration of GNSS-R height analysis under different specular reflection conditions is needed to continuously explore and develop the ability of future GNSS-R missions to monitor global inland water bodies [24].
## 2 Optimization Methods and Design Procedure
### Principle of Particle Swarm Optimization Algorithm
#### 2.2.1 Multiobjective Particle Swarm Optimization Algorithm with Mutation Operation  
Limited by the search method, standard particle swarm algorithms tend to fall into local optimal solutions by incomplete global search. To avoid this situation, a variational operation is added to expand the search range during the optimization process ([PERSON] et al., 2017). The specific idea of the mutation strategy adopted in this paper is to divide the whole population into three parts equally. The first part retains the original characteristics of the particles, i.e., lets them keep their original flight state, to ensure that the particles in the population retain at least \(1/3\) of the initial population information. The second part randomly selects individuals for uniform mutation according to a certain proportion. Uniform variation means that the proportion of the variation does not change with the increase of population optimization generations. The third part of the nonuniform variation is used to simulate the phenomenon that as the number of population generations increases, the particles will gradually gather around the optimal solution when the global search is completed. In this case, it is better to reduce the proportion of variation of particles in order to avoid finding the optimal global solution and then abandoning it. The multiobjective particle swarm optimization algorithm with mutation operation is called the M-MOPSO algorithm. The  
Figure 2: Optimization process of particles in search space.  
flowchart of the M-MOPSO algorithm is shown in Figure 3. For multiobjective optimization theory, the final optimization result is a set of solutions rather than a single optimal solution, called the Pareto front solution.
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 1 Introduction
Especially, some of the ideas have attempted to tackle the situation of spectrum sensing mismatch (SSM). However, the concept of SSM is simple, using direct statistical observation, and neither explicit mathematical models nor systematic deduction has been ever presented. Thus, the deterministic analysis of TDCSs under SSM is still an open problem. In this paper, we focus on the performance metric introduced by SSM on TDCS systems in terms of bit error ratio (BER). As per theoretical analysis, two different kinds of modulation schemes of TDCS are given, called cyclic code shift keying (CCSK) and quadrature phase shift keying (QPSK). CCSK is widely adopted in TDCS because of its low probability of interception [25], while QPSK is usually adopted as an embedded symbol to enhance the spectral efficiency [30]. A general definition is provided to measure the level of SSM. According to the theoretical analysis and simulation results, the influence of SSM can be modeled as the product of coefficient multiplied and the traditional signal to noise ratio (SNR). The expression of the mismatch factor, which only depends on the spectrum sensing results, can be easily modeled. The rest of this paper is organized as follows: In Section 2, we simply introduce two types of TDCS systems with different modulation methods. Then, the definition of SSM is given in Section 3, and the theoretical analysis of BER performance is given to provide the convinced evaluation for the influence of SSM. Finally, comprehensive simulations and analysis are presented in Section 4 and conclusions are given in Section 5.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 4 Experimental Result
### Basic Experiment
Finally, to validate the generalization capability of the TransECA-Net model, we conducted tests using ten normal traffic types from the USTC-TFC2016 dataset. The experimental results demonstrate that the TransECA-Net model maintains robust classification performance in cross-dataset scenarios. Although the recognition accuracy for the Weibo and Outlook types showed a slight decrease of 0.6-2% compared to the average accuracy achieved on the ISCX dataset, the model attained exceptional classification accuracy exceeding 99% for other traffic types, particularly in the BitTorrent, FTP, and Skype categories. These findings effectively verify the strong generalization capability of TransECA-Net, ensuring its reliable classification performance across diverse network environments.
## 4 Analysis of Simulation Findings
### Introduction of Small Load Experimental Simulation Analysis  
During the process of adjusting the motor speed to 1000 rpm, a load torque of 5 Nm was increased over the duration of 0.1 s. We performed a comparative analysis of the motor speed waveforms under small load disturbances for the three controllers, SMO, ADRC, and Fuzzy-ADRC, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Numerical Value** \\ \hline Rated voltage U/V & 220 \\ Rated power P/W & 200 \\ Rated speed n/(r/min) & 3000 \\ Rated torque Te/(N-M) & 10 \\ Rated current I/A & 1.5 \\ Rotor inertia J/(kg\(\cdot\)m\({}^{2}\)) & \(0.17\times 10^{-4}\) \\ Permanent magnet flux \(\varphi_{\mathrm{f}}/\omega_{\mathrm{b}}\) & 0.3477 \\ Stator resistance (between lines) Rs/\(\Omega\) & 11.6 \\ Stator inductance (between lines) Ls/H & 0.022 \\ Switching frequency fs/kHz & \(1\times 10^{4}\) \\ Polar logarithm P & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main parameters of PMSM.  
Figure 6: The corresponding motor speed curves of the three controllers when the set speed is 1000 rpm.  
Based on Figure 7, it is clear that different controllers experience a temporary decrease in speed when the load torque is increased. Nevertheless, with internal adjustments, the controllers eventually achieve a stable speed that aligns with the desired set value. The SMO was measured to maintain the speed at 980 rpm, the ADRC at 990 rpm, and the Fuzzy-ADRC at 998 rpm. This suggests that the SMO has a quick dynamic response yet a high landing speed. The speed of landing of ADRC is only less than that of the SMO. The Fuzzy-ADRC has the least speed landings and is almost unaffected by load disturbances. To summarize, the Fuzzy-ADRC has the capacity to efficiently compensate for and rectify minor disturbances. It accurately observes external small perturbations and provides suitable compensation, surpassing the compensation abilities of ADRC.
## 2 Construction of Intelligent Automatic Blasting Vibration Monitoring System
### Development of Intelligent Data Analysis and Prediction Software  
The intelligent data analysis and prediction software were written in C++ based on the application of TC-6850, including wireless data receiving interface (provided by CST), data FFT analysis MATLAB interface, data HHT analysis MATLAB interface, rough set fuzzy neural network model MATLAB interface, and parameter writing interface. When the blasting vibration data are received with the TC-6850, the data will be uploaded to the designated database of the cloud server through 4G or WIFI signals, and the software will be called. On the one hand, the software automatically performs time-frequency processing on the received data and obtains the spectrum data, HHT spectrum data and instantaneous energy data, which are stored in the database. On the other hand, the software automatically calls the corresponding rough set fuzzy neural network model for data training according to the location of the data site and stores the training data in the server. When users open the software, the service data will be automatically uploaded to the client. When making data prediction, the tester shall input the parameters into the software in advance. After the software receives the input data, the corresponding rough set fuzzy neural network model will be called according to the users' setting to predict the specified blasting vibration characteristic value automatically. The predicted data can be displayed on the first page and alerted according to the set threshold. With the software and hardware, the intelligent automatic blasting vibration monitoring system is built (see Figure 4).
# Cell Stress Reduction by a Novel Perfusion-Culture System Using Commercial Culture Dish
## Materials and Methods  
### Study Design  
We constructed a perfusion culture system that was compatible with commercial culture dishes based on a tube pump (Icomes Lab, Iwate, Japan) (Figure 2A). Tube pumps are well suited for perfusion cultures because they are not limited by reservoir size or the amount of time for which they can operate; moreover, they have a low minimum flow rate and low dead volume, and do not require any preprocessing of the culture medium. An adapter named as culture dish adapter (CD-Adapter) was mounted on the culture dish (cat. no. 3000-35; AGC Techno Glass Co., Ltd., Shizuoka, Japan) [16]. The CD-Adapter has a function of sealing the culture dish to ensure a constant medium volume in the dish. Culture medium in a reservoir can be constantly injected into the closed dish using the pump. The tubes connecting the reservoir, the pump, the CD-Adapter, and a waste were polytetrafluoroethylene (PTFE) tubes with an inner diameter of 0.5 mm (TUF-100; Chukoh Chemical Industries, Ltd., Tokyo, Japan). Between the pump and the CD-Adapter, a filter with a pore size of 0.2 \(\upmu\)m (SLLGH04 NL; Merck Millipore, MA, USA) was connected for bubble removal. A 50 mL tube (cat. no. 52070; Corning, New York, NY, USA) with a silicone rubber stopper (cat. no. 1-7650-02; AS ONE Corp., Osaka, Japan) was used as a reservoir. The flow rate was controlled by a software on a laptop PC, which was connected to the tube pump.  
The CD-Adapter was fabricated by molding of polydimethylsiloxane (PDMS, CAT-106F, Shin-Etsu Chemical), which was cured at room temperature (25 \({}^{\circ}\)C). The mold master was made of epoxy resin (STYCAST 126J PTA, Henkel, Dusseldorf, Germany), as shown in Figure 2C, the fabrication method of which was based on a study by [PERSON] et al. [16]. The center of the CD-Adapter attached to the culture medium is a 1 mm thick membrane, as shown in Figure 2B, to improve the gas permeability of PDMS to maintain the cell condition.  
The medium volume in the dish can be changed by adjusting its height with a spacer, as shown in Figure 2B; in our case, a 2 mm spacer was used to design a 2 mm high container.  
On the other hand, although the dimensions of commercial dishes differ slightly depending on the manufacturer, we could apply the CD-Adapter without leakage, to all commercial dishes using a holding jg (Figure 2D). The jig made of acrylic is attached to the center membrane of the CD-Adapter (Figure 2B).
# Dynamic Response Analysis of Airport Pavement under Impact Loading
## 4 Sensitivity Analysis of Vibration Frequency
### Effect of Sinking Speed  
Aircraft landing is divided into the following stages: descent, leveling, level flight deceleration, floating touchdown, and skid. As shown in Figure 7, the flight trajectory is straight down to the ground (blue solid line), floating down to the ground (purple dotted line), and level flight floating to the ground (red dashed line). When an aircraft lands, the buffer system in the landing gear absorbs most of the kinetic energy of the impact, reducing the impact on the aircraft and improving occupant comfort. This process, which typically lasts a short time, is known as the aircraft landing impact.  
The sinking speed of the aircraft is the velocity of the descent during landing, and the design of the existing landing gear structure is based on the sinking speed of the aircraft and the maximum landing weight. According to the investigation, considering the maximum landing weight, the sinking speed of the aircraft should not exceed 3.05 m/s. Compared with the aircraft mass, the sinking speed plays a dominant role in the design of the landing gear strength. Therefore, the proper selection of the aircraft sinking speed will have a significant impact on the design of the landing gear. In this paper, under the  
Figure 6: Airport pavement dynamic response cloud charts at various landing pitch angles. (**a**) Conditions: tire pressure 2.0 mpa, landing pitch angle 3\({}^{\circ}\), sinking speed 3 m/s. (**b**) Conditions: tire pressure 2.0 mpa, landing pitch angle 4\({}^{\circ}\), sinking speed 3 m/s. (**c**) Conditions: tire pressure 2.0 mpa, landing pitch angle 5\({}^{\circ}\), sinking speed 3 m/s. (**d**) Conditions: tire pressure 2.0 mpa, landing pitch angle 6\({}^{\circ}\), sinking speed 3 m/s.  
assumption of tire pressure (2.0 MPa) and landing pitch angle (3\({}^{\circ}\)), the effect of different aircraft sinking speeds (3 m/s, 2 m/s, 1 m/s) on the impact resistance of the airport pavement was simulated, and the parameters of effective stress, effective strain, and the vertical displacement of the asphalt pavement under impact loading were calculated. The cloud charts of the simulation result are shown in Figure 8.
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 6 Sensitivity and Results Analysis
### Influence of the Production Cost  
The variation of the production cost \(c_{pro}\) indicates that it has a strong effect on the production policy, as can be seen in Figure 12. For this cost we analyze two different cost scenarios with values \(c_{pro}=0.5\) and \(5\). If the production cost is reduced to \(c_{pro}=0.5\), the production thresholds (\(Z_{1}\), \(Z_{2}\) and \(Z_{3}\)) increase, because it is less expensive to operate the machine. By increasing the production cost to \(c_{pro}=5\), the production thresholds (\(Z_{1}\), \(Z_{2}\) and \(Z_{3}\)) reduce considerably. The reason behind this decrement is that at increasing the production cost, the system reacts by maintaining just the necessary amount of inventory to palliate shortages caused by failures and defectives units.  
The production cost also influences the repair/major maintenance switching policy as observed on Figure 13. We use three different cost values \(c_{pro}=0.5,\ 1.25\) and \(2.5\) for the analysis. The results of Figure 13 clearly indicate that major maintenance is less recommended when the production cost is set to a reduced value of \(c_{pro}=0.25\). At increasing the product cost to \(c_{pro}=1.25\), more major maintenance is conducted. Further, the zone for major maintenance expands even more at increasing the production cost to \(c_{pro}=2.5\). To clarify matters, more major maintenance is recommended at increasing the production cost, because the operation of the machine becomes more selective, promoting more maintenance actions that mitigate completely the effects of deterioration \(c_{pro}\).  
Figure 12: Sensitivity of the production cost on the production policy. (a) \(c_{pro}=0.5\); (b) \(c_{pro}=5\).  
Figure 13: Sensitivity of the production cost on the repair/major maintenance policy.
# An Accuracy-Efficiency-Power Consumption Hybrid Optimization Method for CNC Milling Process
## 4 Results and Discussions
### Power Consumption Analysis with Cutting Load  
Since the experiment under cutting condition was repeated five times for each parameter, the average result will be used in the analysis. The result of experiment 1 is shown in Figure 13. The power consumption trend of X-axis motor for different chip loads is seen. Under the same spindle speed, a higher chip load (0.18 mm/t) did not consume higher power than the other two smaller chip loads. For eight different spindle speeds, there are four power consumptions of the 0.18 mm/t chip load smaller than the power consumption of the 0.15 mm/t chip load. Under the fixed spindle speed, the feedrate determines the chip load. Similarly, to maintain the same chip load, the feedrate must be proportional to the spindle speed. The chip load not only determines the spindle cutting load but also influences power consumption. The results do not exhibit significant affection for spindle power consumption due to the small difference of chip load.  
In experiment 2, a larger interval of spindle speed was used to obtain a larger feedrate difference and observe the power consumption characteristic of the feedrate servo motor. The power consumption results of experiment 2 are shown in Figure 14. It is seen, even when the spindle speed was increased to 1000 to 2250 rpm with the maximum feedrate of 270 mm/min, the power consumption of chip load of 0.18 mm/t is smaller than chip load of 0.12 mm/t and 0.15 mm/t. This is perhaps due to the width of cut 20 mm which has reached 80% of cutter diameter that is classified as heavy cutting, thus, generating large cutting resistance. Hence, the motor power consumption is increased. In addition, when the spindle speed was 2250 rpm and the feedrate 1620 mm/min, a spark was appearing during the cutting process, so it is not appropriate to increase the feedrate.  
Figure 13: X-axis motor power consumption vs. Spindle speed for experiment 1.
## 4 Discussion
These systems require reference stations with accurately defined positions, allowing deviations between measured positions and actual positions to be determined. These corrections can then be applied to improve the accuracy of measured positions of other GNSS user receivers [31]. Locating satellites can be a time-consuming process, significantly contributing to overall power consumption. Consequently, there remains a substantial gap in the availability of reliable, fully automated, and cost-effective RTK-GNSS technologies. This gap is especially prominent when considering the rigorous energy efficiency demands of low-power IoT devices intended for distribution along TSFs. When designing an IoT GNSS node for TSF monitoring, optimizing all operational parameters becomes imperative. This optimization must consider the unique challenges posed by potential multipath interference or satellite blockage within mine infrastructures. The primary parameters to be considered for an IoT GNSS solution include the following:  
* A power consumption goal, to ensure a lifespan of multiple years without the need for battery replacement. * Available bandwidth of communication channels, such as LoRa coverage. * Line of sight to the sky and any limitations imposed by the specific configuration of the TSF. Taking these parameters into account is crucial for the development of an efficient and effective IoT GNSS solution specifically tailored to meet the demands of TSF monitoring. Given this context and the potential added value of obtaining absolute position readings over time, as well as the advantages of enhancing techniques for measuring TSF displacement data, continuous efforts are being made to develop commercial technologies that can fulfill these requirements [32].