esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Evaluation of Time and Computation Complexity
According to Dr. [PERSON] from Qilu Hospital of Shandong University, the processing time of the proposed system appears to align well with practical requirements for timely diagnosis and decision-making in \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Size** & **Processing Time (ms/Image)** \\ \hline FCN-8s [54] & 1280 \(\times\) 720 & 54 \\ DeepLab [25] & 1280 \(\times\) 720 & **46** \\ U-Net [55] & 1340 \(\times\) 1030 & 48 \\ Manual features [56] & 1280 \(\times\) 720 & 62 \\ Proposed & 1280 \(\times\) 720 & 53 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of processing efficiency. Bold results refer to the best performance. Figure 12: Comparison of computation complexity on system inputs of 1, 10, 100, and 500 images from our proposed dataset. Values are generated on a virtual machine and Python 3.8.6, which will be only applied for comparison experiments. chest imaging tasks. Dr. [PERSON] from CNPC Central Hospital noted that the system's performance is promising and could significantly enhance workflow efficiency in the clinical environment. Their expert assessment adds depth to our evaluation of the system's processing time and reinforces its potential application in real-world scenarios.
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
## 3 Impact on _i_GNSS-R Altimetric Performance
### Altimetric Sensitivity  
The leading edge slope of the GNSS-R waveform indicates the altimetric sensitivity of the GNSS signals, which translates the uncertainty of the power measurement to that of the height estimation. By considering the speckle noise, the altimetric sensitivity is defined here as the normalized waveform slope at the delay of the specular point, i.e., \(s_{\mathrm{ALT}}=\left\langle Z_{S}(0)\right\rangle^{\prime}/\left(\dot{c}\cdot Z _{S}(0)\right)\), and it mainly depends on the auto-correlation properties of the transmitted signal. As the antenna footprint and the glistening zone are much larger than the pulse limited footprint in a spaceborne scenario, the weight function \(W(\tau)\) in Equation (2) could be approximated as a step function. The slope of the waveform is expressed as  
\[\left\langle Z_{S}(\tau)\right\rangle^{\prime} =W^{\prime}\left(\tau\right)\otimes\Lambda_{B}^{2}\left(\tau \right)=\Lambda_{B}^{2}\left(\tau\right) \tag{4}\] \[=\left|\int_{-\infty}^{\infty}S_{C}(f)e^{-j2\pi\tau f}\mathrm{d}f \right|^{2}\]  
in which \(S_{C}(f)\) is the power spectral density (PSD) of the GNSS signal at the input of the correlator by considering the effects of the front-end and baseband filters. The amplitude of the waveform at the delay of the specular point is expressed as  
\[\left\langle Z_{S}(0)\right\rangle =\int_{0}^{\infty}\Lambda_{B}^{2}\left(\tau\right)d\tau=\frac{1} {2}\int_{-\infty}^{\infty}\Lambda_{B}^{2}\left(\tau\right)d\tau \tag{5}\] \[=\frac{1}{2}\int_{-\infty}^{\infty}\left|S_{C}(f)\right|^{2} \mathrm{d}f\]  
the altimetric sensitivity can be rewritten in the frequency domain  
\[s_{\mathrm{ALT}}=\frac{2\left|\int_{-\infty}^{\infty}S_{C}(f)\mathrm{d}f \right|^{2}}{c\cdot\int_{-\infty}^{\infty}\left|S_{C}(f)\right|^{2}\mathrm{d}f} \tag{6}\]  
in which \(c\) is speed of light in vacuum.  
By using the same ideal low-pass filters for both direct and reflected signals, the altimetric sensitivities of the composite signals can be calculated with Equation (6) for two cases with different receiver bandwidths. As shown in Figure 7, including the IM component increases the altimetric sensitivity by a factor of \(\sim\)1.5 with the bandwidth larger than 10 MHz, as it concentrates more power further away from the carrier and increases the slope of the power waveform.  
Figure 6: SNR as a function of baseband bandwidth considering or not the IM component. Case 1: without IM, Case 2: with IM.
## 3 Three Methods for Calculating Satellite Repeat Shift TTime
### Broadcast Ephemeris Method  
The broadcast ephemeris provides the relevant parameters of the satellite, which includes the semi-major axis of the orbit and the correction to the mean motion of the satellite. Hence, the effective mean motion is obtained by a small correction to the third Kepler law, which is expressed by:  
\[n=\sqrt{GM}/a^{3/2}+\Delta n, \tag{5}\]  
where \(\sqrt{GM}\) is the Earth's universal gravitational constant (\(1.996498\times 107\) in SI units), a denotes the semi-major axis of the orbit ellipse, and \(\Delta n\) denotes the correction to the mean motion, which is the difference between the mean motion of the satellite calculated by the precise ephemeris and the mean motion obtained by the given parameters (rad/s). Therefore, the accurate satellite operation period, \(T_{SOP}\), in inertial space can be expressed by:  
\[T_{SOP}=2\pi/n. \tag{6}\]  
Hence, the satellite repeat shift time, \(T_{SRFT}\), can be defined as:  
\[T_{SRFT}=DT_{MSD}-NT_{SOP}, \tag{7}\]  
where \(D\) is the approximate solar days of the satellite repeat period, which is calculated in Section 2. \(T_{MSD}\) is the duration of a mean solar day in seconds. \(N\) denotes the number of laps of the satellite operations around the orbit of itself in \(D\) solar days. \(T_{SOP}\) is the precise satellite repeat period in seconds, which is obtained by Equations (5) and (6).  
Although the orbit operation period of GNSS satellites are roughly the same, their satellite repeat period varies largely. As for the GPS system, in the reference frame on the rotating Earth, since the satellite repeat time of the satellite relative to a static position in the ground is two times more than its satellite operation period, the precise satellite repeat period, \(TG_{SRP}\), for GPS satellites is given by:  
\[TG_{SRP}=2 TG_{SOP}. \tag{8}\]  
However, unlike the GPS system, the satellite repeat period of other navigation systems has its own characteristics. For example, as for the Galileo system satellite, the repeat period of the satellite relative to the ground is 17 times its orbit operation period during 10 sidereal days. Then, the repeat period \(TGA_{SRP}\) for Galileo satellites is determined by:  
\[TGA_{SRP}=17 TGA_{SOP}. \tag{9}\]  
Similarly, the repeat period of BDS satellites can also be derived, and is summarized as follows:  
\[\left\{\begin{array}{c}TB_{SRP}=TB_{SOP}\\ TBM_{SRP}=13 TBM_{SOP}\end{array}\right., \tag{10}\]  
where \(TB_{SRP}\) and \(TBM_{SRP}\) denote the repeat period of BDS GEO/IGSO and MEO satellites, respectively.  
Then, the satellite repeat shift time of the same satellite can be determined by:  
\[\left\{\begin{array}{c}TG_{SRFT}=1\times 86400-TG_{SRP}\\ TG_{SRFT}=10\times 86400-TGA_{SRP}\\ TB_{SRFT}=1\times 86400-TB_{SRP}\end{array}\right., \tag{11}\]where \(TG_{SRTT}\) and \(TGA_{SRTT}\) mean the repeat shift time of the GPS and Galileo satellite, respectively. Additionally, \(TB_{SRTT}\) and \(TBM_{SRTT}\) denote the repeat shift time of the BDS GEO/IGSO and BDS MEO satellite, respectively.
## 4 Discussion
### Promising Acoustic Voice and Speech Features  
#### 4.1.1 Total Phrase Duration  
For a continuous speech task, the \"total phrase duration\" feature was given by the sum of the durations of all the speech phrases in that utterance. In other words, this measure represents the total duration of the recording minus any pause time. The effect size ([PERSON]'s \(d\)) for this measure was \(-0.5\) for both the Rainbow Passage and the second reading passage. This value indicates that total phrase duration tended to decrease from admission to discharge, with a moderate effect size [44]. The Rainbow Passage text did not vary from day to day, and the second reading passages were all approximately equal length and presented in random order. A decrease in the amount of time needed to read these texts, then, indicates that participants were able to read faster at discharge compared to admission.  
In our pilot study [28], total phrase duration in the Rainbow Passage decreased from admission to discharge for most participants. In that study, participants read the same text each day. Therefore, we initially hypothesized that the decrease in total phrase duration may have been a practice effect: participants were able to read the text more quickly because they were more familiar with it. We later added the second passage-reading task to control for any familiarity effect. Since there were 10 additional reading texts, participants read an unfamiliar one each day. Total phrase duration also decreased for this second reading task, with an equally large effect size (\(-0.5\)) as for the Rainbow Passage task. This finding indicates that participants' ability to read faster at discharge is not due only to familiarity with the reading text. In future work, it may also be useful to look at the speech rate in the spontaneous speech tasks, since that task would not depend on the speaker's reading ability.  
In a relevant finding, [PERSON] [45] found that, especially among older speakers, speaking and reading rates were correlated with overall level of health. Similarly, [PERSON] et al. [46] examined relationships between various speech production measures in women aged 67 and older. They found that reduced reading rate correlated with increases in phonatory instability, including jitter and F0 SD. They suggest that \"generalized loss of physiological control\" could alter both reading rate and phonatory stability. In their case, the suggested change in physiological control was primarily related to age, but HF-related changes in physiological status might similarly affect vocal control. In particular, [PERSON] et al. [46] found a \"significant correlation\" between reduced reading rate and increased vocal fold edema on a laryngoscopic exam. The causes of their speakers' vocal fold edema were not specified, but this finding strongly suggests that HF-related vocal fold edema may have direct physiological effects on voice and speech measures, including speaking and reading rate.
## 5 Discussion  
This work elucidated the efficacy of integrating diverse analytical approaches to harness the potential of a comprehensive dataset enriched with an array of recorded factors, aiming to bolster occupational safety within the forestry sector. It delineated a methodological triad comprising classical descriptive statistics, traditional machine learning techniques, and advanced probabilistic models, each contributing uniquely to the analysis and interpretation of the data.  
In the initial phase of the descriptive statistical examination, patterns emerged that aligned with findings from previous research [7; 12], highlighting two peaks in the frequency of work-related accidents within the daily work schedule: notably between 10:00 and 11:00 and between 15:00 and 16:00. Additionally, the analysis revealed that forestry workers predominantly encountered accidents involving interactions with branches, tree trunks, encounters with slippery soil surfaces, and navigating through areas laden with stones and rocks and gravel. A discernible seasonal trend in the incidence of accidents was also observed, with a higher occurrence rate during the winter months spanning November to March, albeit with a notable dip in December, attributed to the holiday season's reduction in work activities.  
Within the scope of this study, probabilistic modeling techniques were utilized to validate or refute expert opinions on the causality of accidents. The applied explanatory model, for instance, revealed that the likelihood of a young forestry worker (under 20 years of age) experiencing a severe occupational accident (entailing more than 3 days of sick leave) during the winter season was estimated to be 35.8%.  
Machine learning methodologies were applied to enhance the interpretability of the dataset. Specifically, a decision tree model was employed, integrating six variables from the dataset--including the time of the accident, month, age of the worker, day of the week, nature of the injury, and cause of the injury--to ascertain the likelihood of an accident leading to downtime. This model attained a commendable balanced accuracy score of 0.923, accompanied by a mutual information (MI) score of 0.422.  
This paper effectively illustrates that multiple methodologies can be deployed to distill insights from a dataset, offering valuable perspectives on practical queries. The choice of the approach is contingent upon the nature of the question at hand, with different techniques potentially offering the most significant insights.  
Figure 15: Naive Bayes model with two parent RVs (age and season) and one predicted RV (inactivity due to hospitalization days). Each row of each Conditional Probability Table (CPT) – also called Conditional Probability Distribution (CPD) – contains the probabilities (posterior) for each of the outcomes conditioned on one of the combinations of the values of the parent RVs. RVs that have no parents do not need conditioning.
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 3 Results and Discussion  
### Calculation Conditions  
#### 3.1.1 Multi-Phase Aircraft Infrared Signature  
A big commercial aircraft was used as the analysis target and its infrared signature during take-off and cruising phases was simulated. The skin material of the aircraft was white paint, the emissivity was 0.8 and the reflectivity was 0.2. When the thrust was reduced by 25% during the take-off phase, the initial skin temperature was 288 K and EGTwas 1000 K. During the cruising phase, the skin equilibrium temperature was 255 K, and the cruising altitude was 10 Km [37; 38; 39]. The results of the spectral radiant intensity in the two phases are shown in Figure 3.  
The flight route used for analysis in this paper was obtained from the measured Flight Operations Quality Assurance (FOQA) data. The FOQA flight data come from the real parameters of various instruments and equipment of the aircraft. When an aircraft is in flight, the FDIMU data collector on the aircraft collects thousands of high-quality original parameters on the aircraft bus per second according to the ARINC 429 protocol. The data are acquired in real time on-board the aircraft and downloaded by the airline once the aircraft reaches the destination gate [40; 41]. The altitude and speed changes of the flight route are shown in Figure 4. The total flight time was about 1 h and 22 min, and its cruising time was about 2400 s. The detection optimization method in this paper focuses on the cruising phase, as shown in the red box in Figure 4.  
The distribution of radiant intensity of a large commercial aircraft with different spectrum bands is shown in Figure 5. It can be seen that during the daytime, the r the target radiant intensity is mostly hundreds of W/Sr, and at nighttime without sunlight, the target radiant intensity is extremely reduced, even below 10 W/Sr.  
Figure 3: Big commercial aircraft spectral radiant intensity; (**a**) reduced-thrust takeoff (daytime), (**b**) reduced-thrust takeoff (nighttime), (**c**) cruise (daytime), (**d**) cruise (nighttime).
## 2 Routing Protocol for LLNs
### Downward Traffic
#### 2.3.2 Storing Mode  
In storing mode, each node keeps routing table entries for its entire sub-DODAG. A message destined to a node within the DODAG travels upward along the default route until it reaches a common ancestor. From there, the message is routed downward by examining the routing table of consecutive hops. To build those routing tables, each node unicasts DAOs to its preferred parent using a link-local source and destination address. A node must attach to those DAOs a Target option for each of its self-owned addresses via which it wishes to be reachable together with all Target options it received from its children. A single Transit Information option mainly serves a maintenance purpose.
## 2 Categories of Robotic Teleoperation
### Ego-Centric and Eco-Centric Teleoperation  
Depending on the operator's perspective in observing the manipulation process, robot teleoperation can be divided into ego-centric and eco-centric teleoperation. In egocentric robotic teleoperation, the user observes the remote manipulation process from the same perspective as the robot and perceives himself as one with the robot [14]. The operator controls the remote robot with human-level dynamics through immersive and multimodal control-feedback schemes. Ego-centric robotic teleoperation transfers human-level manipulation and proficiency in telemanipulation applications and equips robotic systems with human-level motor skills. The user does not need to consider the mapping relationship between the robot's actions and the user's input commands [15; 16].  
Eco-centric teleoperation usually involves inspecting the robot and its interactive environment from a third-person perspective while manipulating the robotic system with a broader view of observation (Figure 1) [17]. With the help of mixed reality technology, users can still experience coexistence with the robot in the same virtual space but without the immersion and intuition of being one with the robot.
## 2 Theoretical Analysis of the Model
### Atmospheric Attenuation  
The laser beam propagation through the atmosphere causes attenuation of the optical power due to scattering, absorption, and turbulence. Thus, many accurate theoretical and empirical models for the estimation of the corresponding coefficients, i.e., \(a_{sc}\), \(a_{ab}\), \(a_{tur}\), were proposed and verified [18; 32]. Furthermore, in order to estimate thetotal received optical power, \(P_{r}\), due to the total atmospheric attenuation, \(a_{tot}\), the Beer-Lambert's law equation was used [3; 4; 24; 32]:  
\[P_{r}=P_{rGL}\exp(-a_{tot}l) \tag{3}\]  
where \(a_{tot}=a_{sc}+a_{ab}+a_{tur}\). Then, using Equations (2) and (3), the total attenuation factor, \(a_{tot}\), could be estimated through the link's parameters and characteristics, as:  
\[a_{tot}=-\frac{1}{l}ln\left(\frac{P_{r}}{a_{GL}P_{t}}\right) \tag{4}\]
# Investigation of Laser Cutting Width of LiCoO\({}_{2}\) Coated Aluminum for Lithium-Ion Batteries
## 2 Experimental Set-Up and Cathode Materials  
Single-mode Quasi-CW Ytterbium Fiber Laser (IPG YLM-200/2000-QCW) is used with CW mode in this experiment. Its M\({}^{2}\) value is 1.05. The wavelength is 1070 nm and the maximum average output power is 250 W. The cathode sample with a thickness of 50 \(\upmu\)m is used in the experiment, as shown in Figure 1. The current collector material is aluminum and its thickness is 20 \(\upmu\)m. Major cathode manufacturing parameters are tabulated in Table 1. The active electrode material consists of 90% of LiCoO\({}_{2}\), 5% SuperC65 (Conducting agent), and 5% of polyvinylidene fluoride (binder). The thickness of the active electrode material is coated on the current collector with the thickness of 30 \(\upmu\)m. It is noted that the total thickness of this cathode is relatively thin for high power applications. Since this study proposes a systematic analysis, this method will be applied to the laser cutting of thicker cathode and the transferability of this method will be tested for the future research. Figure 1 shows the experimental settings. The cathode specimen is fixed at the both ends, with fixtures placed at the top and bottom. Thus, the gap between the workpiece and the plate prevents the effect of thermal conduction between the workpiece and plate below. Vertical distance from the F-theta lens to the surface is set to be 100 mm. The laser spot size at focus is 23 \(\upmu\)m. The width of the cathode sample is 30 mm, and the cutting distance is 20 mm so that even though the laser cut sample is completely separated, the rest of the sample remains attached. Laser cutting is performed by changing the laser power and speed. Laser power is varied from 50 W to 250 W by an increment of 50 W. Laser scanning speed is changed from 500 mm/s to 5000 mm/s by an increment of 500 mm/s. Scanning Electron Microscope (SEM) is utilized to observe the cut surface of the laser-cutting sample.
# A Review of Modular Multilevel Converters for Stationary Applications
## 3 Mathematical Modeling and Control of Three-Phase MMC
### Output Voltage and Current Control
These harmonics cause voltage distortion, additional power losses in converters, and the malfunction of sensitive electronic devices. Therefore, it is necessary to apply a control method to eliminate multiple harmonics for the distorted grid. The harmonic components are normally at (6n \(\pm\) 1)\(\omega\)1, n = 1,2,3,..., in the three-phase system where the even-order and triple-order harmonics are naturally eliminated. The sequence of (6n - 1)\(\omega\)1 harmonics are consistent with the sequence of a fundament component (positive/negative), whereas (6n + 1)\(\omega\)1 harmonics are opposite to the fundamental sequence (negative/positive) [88]. Figure 8: Output voltage and current control under balanced grid condition. It is argued that the PI controller is not suitable for harmonics elimination in the distorted grid due to its limited performance when dealing with sinusoidal reference signals and periodic disturbances [89]. The preferred control schemes [90] are based on selective harmonic compensation by using proportional resonant (PR) controllers or nonlinear and predictive approaches and repetitive control. Among them, the PR controller is widely used to eradicate the harmonics in the specific order. Figure 10 illustrates two control approaches based on PR controllers in the \(\alpha\beta\) and \(dq\)-frame [91] and \(\alpha\beta\) frame under distorted grid condition [92]. Figure 9: Output voltage and current control under unbalanced grid condition.
## 2 Hybrid Path Planning Based on Improved ABC and BAS Algorithms
### Path Planning Based on IABCBAS Algorithm  
The path planning process based on the IABCBAS algorithm is illustrated in Figure 2, with the steps as follows:  
1. Set the relevant parameters for the ABC algorithm and generate the bee population with Tent chaotic perturbation using Equation (5).
2. \"Leading\" and \"following\" are set as the main behaviors, interspersed with scouting, to form a new distribution of the honeybee population; obtain the corresponding nectar concentration, and select the colony distribution position based on nectar concentration.
3. Mutate the bees after position update using Equations (6) and (7). If the nectar concentration after mutation is higher than that before mutation, update the position to the mutated one; otherwise, no operation is performed.
4. If the maximum number of iterations of the bee colony algorithm is not reached, repeat the above cycle; otherwise, increase the initial pheromone value of the path points corresponding to the current bee colony to form an optimized initial pheromone distribution and improve the quality of global search points.
5. Set the relevant parameters of the BAS algorithm and perform local numerical optimization using the optimized initial pheromone.
6. Randomly generate a new solution using Equation (12) and calculate the fitness value of it. If the new solution outperforms the previous one, the direction of the optimization search is adjusted accordingly.
7. The optimal solution is retained according to Equation (13), with local and global pheromones updated simultaneously.
8. Check if the maximum number of iterations of the BAS algorithm has been reached. If so, the results of the optimal search are saved, analyzed, and processed.