esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Factor Identification by eGA-DT
#### 4.5.2 Results of eGA-DT Model  
The result presented in Figure 10 is the trend of the best fitness during 100 iterations. Considering that the model always selects the individual with better fitness, the reciprocal of the deviation is regarded as the value of fitness in the process of population evolution. It could be clearly observed that as the number of iterations increased, the fitness value of the population rose gradually until it reached the maximum value.  
Figure 10: The trend of model fitness in iterations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \(x_{1}\) & \(x_{2}\)**(s)** & \(x_{3}\)**(s)** & \(x_{4}\)**(s)** & \(x_{5}\)**(s)** & \(x_{6}\)**(s)** & \(x_{7}\)**(s)** & \(x_{8}\)**(s)** & \(x_{9}\)**(s)** & \(x_{10}\) & \(x_{11}\) & \(x_{12}\) \\ \hline
30 & 20 & 20 & 115 & 90 & 90 & 501 & 501 & 516 & 27 & 4 & 2 \\
34 & 20 & 30 & 80 & 80 & 80 & 418 & 418 & 397 & 27 & 4 & 2 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & & \\
35 & 10 & 19 & 170 & 140 & 135 & 127 & 287 & 388 & 27 & 4 & 2 \\ \hline \end{tabular}
\end{table}
Table 3: Data samples of feature vectors.  
The result in Table 4 clearly indicates that the overall fitting effect of bus headway by the proposed hybrid eGA-DT was significantly improved, with a nearly 20% reduction in MAE compared to single DT and more than a 50% reduction compared to ET.  
There are slight differences between the results of the four different types of routes. For regular routes and main routes, the best chromosome was [1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0]. For express routes, the best chromosome was [1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0]. For branch routes, the best chromosome was [1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0]. Considering all four different types of routes, the best chromosome could be concluded as [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0], representing that \(x_{1}\) (the number of the bus stop j), \(x_{2}\) (the dwell time of bus i \(-\) 1 at stop j), \(x_{4}\) (the time of bus i travel from stop j \(-\) 1 to stop j), \(x_{5}\) (the time of bus i travel from stop j \(-\) 1 to stop j), \(x_{6}\) (the historical average travel time between stop j\(-\)1 to stop j), and \(x_{7}\) (the headway of bus i at stop j \(-\) 1) are the most significant factors of bus bunching.
# A Review of Indoor Air Quality in Social Housing Across Low- and Middle-Income Countries
## 3 Results of the Literature Survey
### Direct Measurements (DMs)  
This section presents findings on pollutant levels, their sources, and the structural and behavioral factors that worsen them. In their global study of in-kitchen aerosol exposure, [PERSON] et al. [31] analyzed PM2.5 and PM10 levels in kitchens across 12 cities and found that smaller kitchens (room volume less than 15 m\({}^{3}\)), showed higher PM2.5 levels even when using cleaner fuels. For example, PM2.5 levels averaged 200 ug/m\({}^{3}\) in Dhaka due to common frying practices, while improved ventilation in Medellin resulted in significantly lower concentrations. Similarly, the study by [PERSON] et al. [32] found average PM2.5 levels of 70 ug/m\({}^{3}\), exceeding WHO guidelines (24 h average exposures should not exceed 15 ug/m\({}^{3}\) for more than 3-4 days per year), due to substandard building materials and inadequate ventilation. This highlights a distinct set of concerns compared to the global kitchen study [31], emphasizing structural issues over behavioral practices. Furthermore, [PERSON] et al. [33] identified dhoop burning as the highest emitter of polycyclic aromatic hydrocarbons (PAHs), with average concentrations reaching around 50 ug/m\({}^{3}\). This study differs from the wider particulate matter research aspect, because it shows how culturally specific behaviors affect IAQ. In another study, pollutants like PM2.5, CO2, and VOCs were monitored across 30 indoor locations in low- and middle-income residential homes in India over a six-month period [34]. The study monitored the real time impact of daily activities like cooking and cleaning on IAQ. The findings showed that pollutant levels rose significantly during cooking, especially in kitchens, due to insufficient ventilation, consistent with the above stated studies. Moreover, it was observed that these pollutants dispersed beyond the cooking areas into adjacent living spaces, which highlights the need for improved airflow management. The findings also support how the design and limited space within homes influence pollutant levels, emphasizing the importance of incorporating ventilation solutions to address IAQ challenges in densely populated, low-income settings.
# Human Cognitive and Motor Abilities in the Aging Workforce: An Information-Based Model
## 3 A Review of Experimental Tests of Motor-Cognitive Abilities from Information Theory Perspective
### Tests on Cognitive Abilities
#### 3.1.2 Reaction Time (from CANTAB)  
The CANTAB (Cambridge Neuropsychological Test Automated Battery) is a set of tests defined for multiple purposes [52]; the test version adopted in this paper is the reaction time test and can be found in [53]. The test measures the reaction time (RT) of subjects; RT is defined as the time between a stimulus and a response. During the test, the subject holds down a button at the bottom of a screen. The screen shows some circles (one for the simple mode, and five for the five-choice mode) and in one of them a yellow dot will appear. Once the yellow dot appears, the subject has to release the button as quickly as possible. Every time the subject releases the button (when she/he sees the yellow dot), the amount of information processed is 1 bit (\(\log_{2}(2)\)). The IPR of the subject can be evaluated as follows:  
\[\mathrm{IPR}_{\mathrm{CANTAB}}=\frac{\log_{2}2}{\mathrm{RT}}\left[\frac{ \mathrm{bit}}{\mathrm{s}}\right]. \tag{3}\]RT is the observed time elapsed between the stimulus (the appearance of the yellow dot on the screen) and the response (the release of the button). This test is not associated with memory, as it does not require the subject to memorize any figure, symbol, digit, or letter.
## 3 Results and Discussion
### Experimental Measurements
#### 3.1.1 Plasma Potential
Thus, the whole plasma neighborhood of the ion beam undergoes a sudden change in its electric potential when the emissive probe enters the beam. This confirms the hypothesis that the additional sink of electrons on the probe surface inside the beam is compensated for by the enhancement of the SEE, which requires a higher potential difference between the plasma and the surface of the vacuum vessel. The amplitude of the potential jump should depend on the strength of the disturbance. Any object placed in plasma acquires a floating potential defined by the condition of zero  
Figure 8: Radial profile of plasma potential determined by the emissive probe at different axial positions (**left**). Dependence of measured potential of the fixed tungsten filament during emissive probe movement through the beam (**right**). electric current. This condition implies that any floating object _consumes_ a constant flux of electrons and ions that recombine on its surface. The amplitude of the disturbance should be proportional to the area of the object. The four probes used in the present study cause a potential jump of approximately \(400\,\mathrm{V}\). Minimization of the disturbance can be achieved by using a single probe with a small surface area of the holder exposed to the beam. Preliminary tests with a single emissive probe confirm that the value of the potential jump is indeed reduced. The amplitude of the jump still depends on the axial position of the downstream probe insertion and will be investigated in a future study. Partially Compensated Beam  
The comparison of the potential profiles for compensation currents of 0 and \(1\,\mathrm{mA}\) is shown in Figure 9. A noticeable decrease in the potential jump by \(200\,\mathrm{V}\) can be observed when \(1\,\mathrm{mA}\) compensation is employed. This indicates that the beam disturbance is significantly reduced by the presence of electrons that are thermionically emitted by the cathode. Similar behavior is observed for the fixed electrode case, for which the measured values reduce to the range of \(-10\) to \(10\,\mathrm{V}\), though in this case, the measured values do not represent the actual plasma potential because a fixed electrode is used as a thermionic emitter of electrons.
## 2 Characteristic Analysis of the Iridium System
### The Signal Level
#### 2.3.2 The Variations in Doppler  
The high relative velocity between Iridium satellites and ground receivers generates significant Doppler frequency variations in the carrier of LEO signal. Figure 4 presents the simulated Doppler frequency and Doppler rate dynamics across three distinct Iridium orbital planes. The maximum Doppler frequency and Doppler rate values exhibit positive correlation with the satellite's maximum elevation angle. During zenith passes (90\({}^{\circ}\) elevation), the absolute Doppler frequency approaches 40 kHz, while the maximum Doppler rate reaches 400 Hz/s. These extreme dynamics necessitate real-time parameter adaptation in positioning algorithms based on instantaneous Doppler characteristics.  
Figure 3: The signal-to-noise ratio (SNR) of each real Iridium epoch during the 2 min observation window.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 2 Ship Wake Bubbles Laser Detection Characteristics and Analysis
### Laser Detection Characteristics and Analysis of Ship Wake  
Due to the strong attenuation of the seawater medium to the laser and the low reflection coefficient of the bubble, the laser receiving device can only receive the weak signal returned by the wake bubble target, and the weak target signal is also superimposed on the background of the strong close-range water scattering signal, making it more difficult to distinguish. This has always been the reason that restricts the slow engineering process of wake laser detection. The wake laser detection needs to suppress the backscattering of seawater, thereby improving the detection signal-to-background ratio (SBR) of the bubble echo signal. Usually, distance gating can be used to suppress the backscattering of seawater, but the distance between the wake bubble and the laser detection device is mostly within 20 m, and the laser detection time is in the order of hundreds of nanoseconds. Limited by the underwater platform, the wake laser detection device needs to meet the requirements of compact installation. At present, the acousto-optic and electro-optic distance gating devices cannot meet the requirements in technical performance and size. At present, relevant scholars have mostly used laser backward detection mode and laser imaging detection mode to detect ships' wakes [16]. Among them, the laser backscatter detection system has a simple structure design, low cost, and reliable structure. It can suppress the backscatter of seawater and improve the detection SBR of the echo signal of the ship's wake. However, the receiving field of view is narrow, and only the echo signal in the close-range part can be received. If the bubble is not in its receiving field of view, the wake bubble target cannot be detected, which cannot meet the needs of the large field of view reception. The laser imaging detection system has the advantages of a large detection field, wide detection range, and high precision. It can also suppress the backscattering of seawaterand improve the detection SBR of the ship's wake echo signal. However, the back-end image processing and target detection are difficult. The existing target detection algorithm cannot effectively detect the ship's wake bubble target, and its signal processing rate is slow compared to the backscatter detection, which cannot adapt to the high-speed underwater weapon carrier. According to [17; 18; 19], it can be concluded that the echo signal or image has the following characteristics:  
* weak echo signal (target feature);
* random discrete distribution of echo signals (targets);
* large environmental interference;
* large amount of target echo signal data.  
The front-end detection system needs to have the characteristics of high sensitivity, large dynamic range, high resolution, and a large field of view, so as to meet the engineering requirements of laser wake detection.
# Grain Orientation and Hardness in the Graded Interlayer of Plasma Sprayed W on CuCrZr
## 3 Results and Discussion
The case of W is different because the droplets impacting on the Cu phase give rise to the incipient melting of Cu due to its lower melting point [32]. Owing to the occurrence of this phenomenon, the orientation of the new W grains forming after solidification is scarcely affected by that of grains already present in the surface of deposition. Therefore, as was made evident by comparing the specific texture index values, the [211] and [100] texture components of W are weaker than those of Cu. Recent results published by [PERSON] et al. [33] showed that the nano-indentation behavior of pure W is strongly dependent on grain orientation because this affects nucleation and the movement of dislocations under the indenting tip. Through high-resolution electron backscatter diffraction (HR-EBSD) around nano-indents, these investigators were able to reveal pile-up for Burgers vectors inclined to the surface at an angle greater than \(\sim\)20\({}^{\circ}\), whereas dislocation transport channels without pile-up were observed for Burgers vectors near-parallel to the sample surface. The same effect was also observed in other materials, such as WC-Co [34], TiAl [35], Cu [36; 37; 38], Ni, and Fe [38]. In fact, the orientation of the indented grain can determine the onset of either hardening or softening mechanisms. On these grounds, the increasing hardness observed in W zones of the interlayer moving from substrate towards the coating can be related to the progressive evolution of the texture to the strengthening of the [100] component. Grains with this orientation present the localization of plasticity and pile-up around the indent [33] with a consequent increase in the measured hardness. Analogous considerations can be made for Cu, where the mechanical performances of [111] oriented grains are superior to those of any other grain [36]. Near the substrate and in the middle of the interlayer, the strongest texture components of Cu are [100] and [110]: the prevalent orientations of the grains are those corresponding to the lowest mechanical strength. The hardness increase observed near the coating corresponds to the texture evolution towards random grain distribution. This change involves a larger fraction of grains having a (111) orientation with higher resistance to indentation. We hope that the main result of this work, namely the presence of a hardness gradient along the interlayer and its dependence on grain orientation, will stimulate further investigations in the fusion community for predicting the behavior of W armors during service in a tokamak, with particular attention to the interaction of H and He with lattice defects of W, that is not yet completely understood, and contradictory results have been published [38; 39; 40].
# Construction Progress and Aviation Flight Test of BDSBAS
## 4 BDSBAS Aviation Flight Test
### Flight Test-II  
On 18 September 2020, the take-off and landing operation test and the great-circle flight operation test were constructed in a circular airspace with a radius of 10 km above the airport in order to simulate the flight status of civil aviation aircraft during the approach phase. The flight lasted 2 h and 15 min, and the trajectory is shown in Figure 14.  
Similarly, the PL can be calculated by the SF augmentation message and the RTK output is used as the reference to obtain the PE. The histogram of HPE and VPE are shown in Figures 15 and 16, respectively. The PE and the PL sequences are shown in Figure 17, and the horizontal and vertical Stanford triangle charts are shown in Figure 18.  
Figure 14: Flight trajectory of test-II. The green line is the trajectory, while the flight distance and azimuth are marked by the red line and the yellow text.  
Figure 15: The HPE histogram of flight test-II. The green line in the figure is the horizontal accuracy (95%), which is 1.27 m.  
Table 3 shows the statistics of the performance indicators. It can be seen from Figures 17 and 18, and Table 3 that for the flight test-II conducted on 18 September 2020, the BDSABS SF augmentation positioning accuracy meets the requirements of the civil aviation APV-I accuracy index, and the PL can completely cover the PE. No integrity risk events occurred during the test, and the availability and continuity of the service reached 100%.  
Figure 16: The VPE histogram of flight test-II. The green line in the figure is the vertical accuracy (95%), which is 4.30 m.  
Figure 17: The PE, PL, and AL sequences of flight test-II. In both the horizontal direction (see the top panel) and the vertical direction (see the bottom panel), the PL can completely cover the PE and neither exceeds the PL, which means that no false alarms or missed alarms occurred during the test.
## 3 Results  
### Analysis of the Overall Flavor Profiles by E-Nose  
The PCA results of the E-nose response values are presented in Figure 2a, displaying the 95% confidence region. The cumulative variance contribution rates of the first principalcomponent (91.1%) and the second principal component (8.3%) sum to 99.4%, indicating that they capture most of the information in the original data. The distance between the confidence ellipses indicates that the data points of the two types of silage can be distinguished, suggesting that the electronic nose is capable of distinguishing between normal (ZCQZ) and abnormal (YCQZ) silage by reflecting the overall flavor profile of the samples. To further assess the contribution degree of each sensor in the electronic nose, the PLS-DA discriminant results are illustrated in Figure 2b. A VIP value exceeding 1.0 is typically regarded as indicative of significant importance in the PLS-DA discriminant process. The electronic nose sensors with VIP values above 1.0 include W5S, highlighting their crucial roles in differentiating the odors of normal and abnormal silage (\(p<0.05\)). The results of the significance analysis are shown in Table 2. The five sensors--W5S, W1C, W1W, W3C, and W2W--show significant differences in distinguishing between the two types of silage samples (\(p<0.01\)).  
Figure 2: Statistical analysis of volatile profiles of two samples based on E-nose. (**a**) The scores plot of PCA based on E-nose. (**b**) Important VOCs (VIP greater than 1.0) identified by PLS-DA based on E-nose. (**c**) The violin plot based on E-nose.
## 2 Materials and Methods
### Data Collection and Processing  
Kinematic data were obtained by 12 Vicon MX-13 cameras (Vicon, Oxford Ltd., Yarnton, UK) via 51 reflective markers of 14 mm diameter at a measuring frequency of 250 Hz. A Cleveland Clinic Marker set (Motion Analysis Corp, Santa Rosa, CA, USA) with clusters on the lower limbs [16] was used (Figure 1). Data were managed via Nexus 1.8 software (Vicon, Oxford Metrics Ltd., Oxfordshire, UK) and filtered according to Woltring [17]. A full-body 3D-model, segments, joints, and calculations of all variables were conducted via Visual 3D software (C-Motion Inc., Rocksville, MD, USA). The sagittal change of angle between two adjacent segments represented the rotation in the corresponding joint (shoulder: upper arm-torso; knee: shank-thigh) [13]. Two separate AMTI force plates (120 \(\times\) 60 cm; AMTI, Watertown, MA, USA) were placed parallel to each other. Each force plate collected ground reaction forces of one leg at 2000 Hz (FP1 for the dominant leg, FP2 for the non-dominant leg). A fourth-order low-pass Butterworth filter (50 Hz) was applied, and kinetic data were normalized to body weight.
# Multi-Level Feature Dynamic Fusion Neural Radiance Fields for Audio-Driven Talking Head Generation
###### Abstract  
Audio-driven cross-modal talking head generation has experienced significant advancement in the last several years, and it aims to generate a talking head video that corresponds to a given audio sequence. Out of these approaches, the NeRF-based method can generate videos featuring a specific person with more natural motion compared to the one-shot methods. However, previous approaches failed to distinguish the importance of different regions, resulting in the loss of information-rich region features. To alleviate the problem and improve video quality, we propose MLPF-NeRF, an end-to-end method for talking head generation, which can achieve better vector representation through multi-level feature dynamic fusion. Specifically, we designed two modules in MLPF-NeRF to enhance the cross-modal mapping ability between audio and different facial regions. We initially developed a multi-level tri-plane hash representation that uses three sets of tri-plane hash networks with varying resolutions of limitation to capture the dynamic information of the face more accurately. Then, we introduce the idea of multi-head attention and design an efficient audio-visual fusion module that explicitly fuses audio features with image features from different planes, thereby improving the mapping between audio features and spatial information. Meanwhile, the design helps to minimize interference from facial areas unrelated to audio, thereby improving the overall quality of the representation. The quantitative and qualitative results indicate that our proposed method can effectively generate talk heads with natural actions and realistic details. Compared with previous methods, it performs better in terms of image quality, lip sync, and other aspects.  
Keywords:talking head generation; neural radiance fields; audio-visual feature fusion; cross-modal content generation +
Footnote †: journal: applied sciences