esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
Reaction time can be essential for assessing memory and reaction time [28] in cognitive tests [29], confirmation of the user's identity [8], and others, while organizational and methodological measures [23] for conducting experiments are important.  
The use of mobile devices such as smartphones and tablets in web surveys has increased markedly, especially smartphones [30] since they allow to participate in surveys wherever it is required. This is due to the widespread use of both the devices themselves and high-speed networks, and the habit of using mobile services, including in the field of medicine, for example, monitoring the health of the elderly [31], widespread introduction of online education during the pandemic [32]. Several studies [30; 33] show that the parameter characterizing the dispersion of the influence of users is their movement; therefore, additional smartphone capabilities, such as the accelerometer [34], are used to detect movement. However, in addition to the results of these studies, it should be noted that distractions (music in the background, conversation, etc.) are typical in general for web surveys, so when testing the reaction speed, the user can bend down for a fallen object, straighten his clothes, etc. The presence of multitasking and the diversion of resources to other processes carried out in devices [35; 36] can be passively registered by software.  
For studies in which the bias in the average estimates of the measured indicators may be insignificant, according to laboratory studies [37], a negligible effect on test results was found in the context of talking in the room and the presence of music. At the same time, it is difficult to estimate the value of the biased estimate. Both screen size and web page optimization [38] are a minor distraction. Thus, external distractions of the respondent are less likely to affect experimental results than delays caused by [39] application responses. Sometimes, laboratory tests change the response time significantly [40]; therefore, the choice of questions is also important for assessing the quality of the tests, as well as the choice of equipment [41] required to achieve a given level of bias in the response time estimates.
# Global Transcriptomic Response of _Staphylococcus aureus_ to Viulent Bacteriophage Infection
## 2 Materials and Methods  
### Strains and Growth Conditions  
Isolation and characterization of _Staphylococcus aureus_ strain SA515 and lytic phage vB_SauM-515A1 have been described previously [12; 25]. In brief, methicillin-susceptible _S. aureus_ SA515 was a host for bacteriophage and belonged to ST8 and spa-type 008, according to genotyping schemes [12]. The lytic bacteriophage vB_SauM-515A1 was isolated from a commercial bacteriophage cocktail and is a member of the _Herelleviridae_ family [12; 25]. The host strain was grown in Luria Bertani (LB) broth or on LB agar plates at 37 \({}^{\circ}\)C.  
The one-step growth curve of bacteriophage vB_SauM-515A1 on the host strain was performed as previously reported [25]. In brief, SA515 cells at an early exponential phase (OD600 = 0.12) were infected with vB_SauM-515A1 bacteriophage at a multiplicity of infection (MOI) of 0.01. After incubation at 37 \({}^{\circ}\)C for 7 min to allow adsorption, the mixture was centrifuged for 3 min at 10,000\(\times\)\(g\). The infected bacterial pellet was resuspendedin 10 mL of the LB broth. The aliquots (10 \(\mu\)L) were sampled periodically at 0, 5, 15, 20, 25, 30, 40, 50, 60, and 70 min, where the 0 min point corresponded to the initial infection of the culture with the bacteriophage from the start of infection. Samples were treated with 2% chloroform. The number of vB_SauM-515A1 particles was determined using the double-layer agar plating method.  
For the transcriptome analysis, vB_SauM-515A1 bacteriophage was added to SA515 bacterial culture (OD600 = 0.12) at an MOI of 10. Samples were taken at 5, 15, and 30 min after bacteriophage addition. The cells were harvested by centrifugation and immediately frozen at \(-\)70 \({}^{\circ}\)C. Moreover, the aliquots were taken at 0, 5, 15, 30, 40, 60, and 70 min post-infection to measure the growth curve of host strain by optical density detection and to determine the number of colony-forming units (CFUs). All the aforementioned experiments were carried out in triplicate.
## 4 Application and Discussion
### Risk Assessment of the Coupled Risk Factors
#### 4.4.3 Risk Calculation of Coupled Risk Factors  
The probability and consequence are classified into four levels and five levels, respectively, and the risk index matrix is established based on the historical risk values of all their state combinations; for instance, the historical risk is 19 when both levels are the highest [42]. Moreover, Equation (9) is fitted based on the method in Section 3.5 to determine the preference corrections \(a=2.2,\ b=3.3\). The amended risk evaluation model is shown in Equation (15).  
\[R_{i}=\sqrt[2.2*P(F_{i})^{\prime}]^{2}+\left(3.3*S_{i}^{\prime}\right)^{2}\,. \tag{15}\]  
The risk of each factor considering the coupling relationship can be calculated by introducing \(\mathrm{P}(F_{i})^{\prime}\) and \(S_{i}^{\prime}\) to Equation (15), with the result shown in Table 10.  
Some conclusions can be drawn from Figure 8:  
* The top 20% of factors belong to the key risk factors according to the Pareto rule, so the risk factors \(F_{11}\) and \(F_{12}\) have a higher risk level, which needs to be further analyzed to propose the preventive measurements.
* It can be concluded that the factors \(F_{21}\) and \(F_{13}\) belong to the medium risk region, and the risk level of the factor \(F_{43}\) is the lowest.
* According to the location distribution of risk factors in each category, the average risk level of human factors is the highest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline  & \(\mathbf{F_{11}}\) & \(\mathbf{F_{12}}\) & \(\mathbf{F_{13}}\) & \(\mathbf{F_{21}}\) & \(\mathbf{F_{22}}\) & \(\mathbf{F_{23}}\) & \(\mathbf{F_{24}}\) & \(\mathbf{F_{31}}\) & \(\mathbf{F_{32}}\) & \(\mathbf{F_{33}}\) & \(\mathbf{F_{34}}\) & \(\mathbf{F_{41}}\) & \(\mathbf{F_{42}}\) & \(\mathbf{F_{43}}\) \\ \hline \(R\) & 2.15 & 2.11 & 1.73 & 1.83 & 1.19 & 1.30 & 1.28 & 1.22 & 1.47 & 1.06 & 1.12 & 1.15 & 1.13 & 0.50 \\ \hline \end{tabular}
\end{table}
Table 10: The risk of the coupled risk factors.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \(\mathbf{P(F_{11})^{\prime}}\) & \(\mathbf{P(F_{12})^{\prime}}\) & \(\mathbf{P(F_{13})^{\prime}}\) & \(\mathbf{P(F_{21})^{\prime}}\) & \(\mathbf{P(F_{22})^{\prime}}\) & \(\mathbf{P(F_{23})^{\prime}}\) & \(\mathbf{P(F_{24})^{\prime}}\) & \(\mathbf{P(F_{31})^{\prime}}\) & \(\mathbf{P(F_{32})^{\prime}}\) & \(\mathbf{P(F_{33})^{\prime}}\) & \(\mathbf{P(F_{34})^{\prime}}\) & \(\Based on the above analysis, it can be concluded that both \(F_{11}\) and \(F_{12}\) occupy high importance throughout the mission and belong to the cause category. They have a great impact on other risk factors and are less susceptible. Moreover, \(F_{41}\) and \(F_{42}\), respectively, have the greatest impact on \(F_{11}\) and \(F_{12}\). Therefore, the essence of reducing the risk level of human factors is to reduce the risk level of management factors. It is necessary to further strengthen the safety training and safety supervision system in order to enhance professional skills and safety awareness, so as to ensure the safety of the mission process of the system.
# Reliable Visual Exploration System with Fault Tolerance Structure
## 4 Visual Localization and Mapping
### Submap-Based Back-End  
After obtaining the keyframes and constraints in the front-end, a pose-graph can be built, where keyframes are represented as vertexes in the graph. To build a submap-based back-end, instead of maintaining a single global graph, multiple submaps are maintained, and the visual tracking is always run within one of the submaps for overcoming tracking failure. The submap-based back-end can be seen in Figure 6.  
Considering the fragility of visual tracking, when no reliable constraint can be built for a new keyframe, a new submap is initialized for keeping updating the graph all the time. After submaps' initialization, the submaps are supposed to be merged by place recognition, and the scales in different submaps are alig  
Figure 5: Illustration of parallel design of new keyframe (middle image); besides a constraint from the last keyframe (blue arrow), multiple loop-closure constraints to existing reference frames (red arrows) are built in parallel.
## 2 Related Works
In recent years, it is noteworthy that the STAP technique has been increasingly used in MIMO radar systems [43; 44; 45; 46; 47]. Compared to conventional SIMO STAP, MIMO STAP can achieve better minimum detectable velocity (MDV) performance. This means that such a radar system is more capable of detecting slow-moving targets in the presence of strong clutter. When analyzing research on MIMO, attention should be paid to [48], where a novel secrecy energy-efficient hybrid beamforming design for satellite-terrestrial integrated networks operating in the mmWave band was presented. In addition, article [49] examined satellite multicast communication and antenna-integrated networks with rate division multiple access, where satellite and unmanned aerial vehicle components are controlled by the network management center and operate in the same frequency band. In paper [50], the joint optimization design of beamforming and power allocation in the downlink non-orthogonal multiple access based for satellite-terrestrial integrated networks operating in the mmWave band was investigated. On the other hand, in article [51], a novel beamforming design for cognitive satellite-terrestrial networks operating in the mmWave band was presented. As it turns out, in real-world conditions, determining the clutter covariance matrix and its inverse in this way is difficult. Furthermore, statistical algorithms fail when the data contained in the training bins do not reflect the statistical properties of the clutter of the bin under test, especially in an environment of heterogeneous clutter. These are natural drawbacks of our method, as well. Therefore, taking into account the extensive literature analysis presented in the introduction of our article, we noted that attention has now been focused worldwide on the study of non-statistical methods for estimating clutter covariance matrices in heterogeneous environments. Our goal was to break this convention and try to conduct research on new statistical methods for estimating clutter covariance matrices in STAP. It turns out that this possibility exists. Based on our knowledge of numerical methods, we saw the possibility of reducing the computational complexity and software implementation of the long-existing SMI method. This paper proves that it is possible to estimate the clutter covariance matrix using the MIMO radar geometry model and the statistical clutter matrix covariance method. The estimated clutter covariance matrix was determined using the LU method with partial pivoting. Hence, it can be said that our research fills a gap in current research on STAP. The rest of this paper is organized as follows. Section 3 establishes the signal model and formulates the waveform design problem. Section 4 develops a new algorithm for STAP in MIMO radar in the presence of clutter. Section 5 provides numerical examples to demonstrate the performance of the proposed algorithm. Finally, Section 6 presents our conclusions.
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 2 Materials and Methods
### Data Preprocessing  
[PERSON] et al. (2019) identified artifacts in the 400-500 nm spectral range of the instrument. Therefore, spectral data with bands between 400 and 499.5 nm were removed. Numerous studies have demonstrated that smoothing and first-order derivative transformations of spectra can effectively reduce noise, enhance the correlation between spectral data  
Figure 1: Sampling points of European Union.  
and soil properties, and improve model predictive accuracy [37; 38]. First, we applied a Savitzky-Golay (SG) smoothing filter with a window size of 101 points and third-order polynomials, followed by a first-order derivative (D1) transformation of the spectral data. Subsequently, the data obtained from the D1 transformation underwent Standard Normal Variate (SNV) transformation [39].  
To further optimize computational efficiency and reduce redundant information, the spectral data were down-sampled to 1 nm intervals, reducing the total number of spectral bands to 2000 (Figure 2). This approach balances the need to simplify the dataset while preserving essential spectral features critical for accurate soil property predictions. Although the resampling is not linear in terms of energy, the choice of 1 nm intervals ensures that key information is retained for a reliable model performance. This strategy reflects a trade-off between reducing data complexity and maintaining the integrity of predictive features necessary for robust model outputs.  
To improve the model's training convergence rate and generalization, soil property values were normalized using MinMax scaling, adjusting the target values to a range of 0 to 1. This label normalization ensures consistency in target values, enhancing model performance. During performance evaluation, the estimated soil property values were inverse-transformed to their original scale for accurate comparison.
## 2 Overall Design for the Near-Bit MWD  
The MWD is installed near the drill bit for the real-time measurement of downhole pressure, temperature, and other parameters. The main structure includes the measuring sub-shell, sensor, circuit, battery, and connecting structure between the upper and lower drill pipes. Among them, the sensors are mounted on the measuring sub-shell and the circuit unit, which are used to measure the real-time downhole data; the circuit and battery are mounted on the circuit unit, which are used for signal acquisition, signal processing, and power supply. The vibration-damping device support frame is mounted between the vibration-damping device and the circuit unit, which is used to provide space for the sensor to be mounted. The vibration-damping devices at both ends are mounted tightly on the inside of the measuring sub-shell by the reducer union. The reducer union connects the MWD to the upper and lower drill pipes. The pressure detection hole is opened on the wall of the shell, which is used to measure the pressure of the annulus. According to the actual project background, the preliminary design of the MWD is 127 mm in diameter and 750 mm in length. The internal installation structure of the MWD is shown in Figure 1.  
In pneumatic DTH hammer drilling operation processes, a drill string will produce very complex coupled vibrations, of which longitudinal vibrations are the most intense. Although the focus of this study is on longitudinal vibrations, it is worth noting that transverse and torsional vibrations in the drilling string system can similarly affect the performance of a system. Although these forms of vibrations were not explored in depth in this study, their presence cannot be ignored. Future research efforts will be directed towards the development of more comprehensive vibration-damping solutions to cover all types of vibration. This will further improve the reliability and measurement accuracy of the system.  
To protect the circuit unit, this study adopts spring-damper elements at both ends of the circuit unit. These elements are flexibly connected to the upper and lower reducer  
Figure 1: Diagram of the preliminary design scheme of the MWD.  
unions. The elastic deformation of the spring element and the energy dissipation of the damping element can isolate the vibration. However, the structure and parameters of the stiffness damper directly affect the vibration-damping performance. Therefore, it is necessary to optimize the parameters of the damper.
## 2 Methods
### Data Processing and Statistical Analysis  
With the proposed power of 80% and an \(\alpha\) level of 0.05 for radiographical evaluations, a sample size of at least 25 was needed to derive significant differences between planned and post-operative alignment data, specifically the orientation of the tibial osteotomy and the mechanical axis of the lower limb. This computation assumes that the general mean difference between the two conditions was 2.0\({}^{\circ}\) in radiological evaluations with relevant standard deviation equal to 3.0\({}^{\circ}\).  
All data are presented in terms of mean \(\pm\) standard deviation. In detail, pre- and post-operative 3D kinematics and kinetics of the hip, knee and ankle joints from gait analysis were calculated in degrees (\({}^{\circ}\)) and in % patient's body weight (BW) times height (H), and versus % of gait cycle; these were compared with reference data from the literature [33] for the level-walking task only. Pre- and post-operative ML and antero-posterior (AP) coordinates (in mm and in % of AP and ML tibial plateau dimensions) of the intersections of the GRF vectors with the tibial plateau were calculated versus % of gait cycle.  
Statistical parametric mapping (SPM) [36], which has recently been applied for biomechanical analyses [37], was used for statistical comparisons between curves. The Kolmogorov-Smirnov test was applied preliminarily to check normality in data distribution; consequently, Student's \(t\) test or the Mann-Whitney-Wilcoxon test was utilized for data comparison between the pre- and post-operative data. Furthermore, the Pearson's product moment correlation coefficient (R) was calculated to determine the relationships between pre- and post-operative clinical and radiological scoring. In all comparisons, a \(p\)-value of less than 0.05 was taken to reveal statistically significant differences; otherwise, non-significance (NS) was indicated. All calculations were performed blinded to the pre/post-operative timeframe (Matlab(r), R2022a The MathWorks, Inc., Natick, MA, USA).  
Figure 2: Workflow of the recently reported methodology for characterizing GRF data on tibial morphology [25]: functional data are collected via gait analysis during level walking, using a standard marker set protocol enriched with additional markers around the knee; medical images are acquired with CT scan, and related DICOM files are used to reconstruct 3D models for the bones and the additional markers; a single-value decomposition approach combining the reconstructed and tracked additional markers is then used to register the positions of the GRF vectors on the tibial bone model.
# Impact of Coil Misalignment in Data Transmission over the Inductive Link of an EV Wireless Charger
## 5 Conclusions  
The inductive link offered by a wireless charger has been used in some recent papers as the communication channel for the transmission of data. This work has studied how the communication capacity of the wireless channel is altered by coil misalignments, which are typical in EV wireless chargers. We have formulated a theoretical model to analyse this impact for three types of coil misalignments and the four uni-resonant compensation topologies. The model has been contrasted with the simulation results of an EV wireless charger prototype. The theoretical model and the simulation results demonstrate that the capacity offered by the inductive link greatly depends on the coil displacements for the SS and PP topologies. In contrast, the asymmetrical topologies were shown to be less sensitive to these circumstances. Modulation information techniques should take into account this behavior in order to transmit data efficiently.  
This work has been partially funded by Jose Castillejo Mobility Grant CAS 17-00318 of the Spanish Ministry of Education, Culture and Sports.  
The research problem addressed by the paper was identified by the three authors. [PERSON] proposed the methodology and conducted the simulation study. The simulation results were reviewed and analysed by the three authors. [PERSON] and [PERSON] wrote the paper. [PERSON] coordinate the complete work.  
The authors declare no conflict of interest.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### SAR Imaging Technology
#### 2.3.3 Application Status and Prospects  
The principal benefit of SAR is its all-day and all-weather imaging capability, enabling rapid mapping [156]. Therefore, the main application areas are related to emergency and security-related services, where the timeliness and availability of data are critical; for example, SAR data are operationally used in sea-ice mapping, which cannot be easily extracted using other remote sensing techniques, especially in cloudy winters. In addition, considerable scientific research has been conducted on agricultural monitoring, forest mapping and topographic mapping. Continuous environmental monitoring is possible using spaceborne SAR datasets. Moreover, some previously reported studies utilised SAR images for road extraction [72; 157; 158].
# Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network
## 3 Short-Term Emotion Recognition with Single-Pulse PPG Signal
### Single-Pulse Analysis of PPG Signal for Emotion Recognition  
The PPG signal measures the periodic blood flow in blood vessel and incorporates considerable information regarding emotions. We employed the raw signal data to ensure fast emotion recognition, as discussed above and shown in Figure 3. We assumed that a single PPG pulse contained sufficient information regarding emotions, hence this raw data could be used to extract features.  
Figure 3: Raw photoplethysmogram (PPG) signal.  
Figure 2: Emotion recognition procedure.  
We observed PPG pulse shapes with different emotions. We selected four subjects between 20 and 30 to measure PPG signals, and chose each two music videos with high valence, low valence, high arousal and low arousal by surveying 10 people, following a similar method to that for the DEAP dataset. The music videos evoked emotions corresponding to valence and arousal, and we prepared a silent room to ensure the participants would not be disturbed by their environment. PPG signals were recorded from participant's fingers without them moving, to establish their neutral state, and then, while watching the selected music videos. Totally, we acquired the three classes data of PPG signals for valence and arousal (high, neutral, and low).  
We split the PPG signals into a single pulse to check the tendency of the PPG signal with different valence, as shown in Figure 4 for one subject. The PPG single pulse data showed significant trends relative to valence and arousal for all subjects, e.g., pulses gathered together depending on the emotion. Similar pulses appear for similar emotions. Thus, we verified that emotions could be distinguished using a single PPG pulse, which could then be used as input for a deep learning framework such as CNN. The DEAP dataset included PPG data for a large group of people, which would provide good confidence in the proposed method outcomes.