esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Influence of FOD Spectra on Selected Wavelengths for Estimating ChlF Parameters  
This study further investigated the influence of different FOD spectra on LASSO's selected wavelengths for ChlF parameter estimation (Figures 9 and 10). As shown in Figure 9, the selected bands span the VIS, red-edge, NIR, and SWIR regions, highlighting the complexity of estimating ChlF parameters from reflectance data. Consistent with previous research in temperate deciduous forests, the strong relationship between leaf traits and ChlF parameters reinforces this complexity [20]. From the perspective of the relationship between reflectance spectra and leaf traits, our results visually demonstrate how spectral information from different regions can serve as a surrogate for estimating ChlF parameters.  
On the other hand, this study revealed that the number of features selected by the LASSO algorithm within the red-edge region exhibited a gradual change in low-order (i.e., \(<\)1) FOD spectra, while it increased rapidly in higher-order spectra. Conversely, for the VIS, NIR, and SWIR regions, the number of features selected generally increased in the low-order range and decreased in the high-order range (Figure 10). It is noteworthy that for PSIImax, NPQ, \(\Phi\)P, and \(\Phi\)N, the maximum number of features observed at the 0.6-order spectra were in the VIS region compared to the other spectral regions. This phenomenon could be explained by the fact that low-order FOD spectra amplify absorption characteristics and signal details that are closely related to these ChlF parameters. A previous study has shown that the spectral reflectance slope in the blue and red regions is a reliable indicator of chlorophyll content [58]. Given the strong relationship between ChlF parameters and chlorophyll content, enhancing chlorophyll-sensitive regions with FOD spectra may improve the accuracy of ChlF parameter estimation.  
Figure 9: The distribution of wavelengths selected by the LASSO-based PLSR model across different FOD spectra for estimating ChlF parameters. Different spectral regions are represented by distinct colors.
# Industrial-Grade Edge Computing Device for Smart Furniture Products
## 5 Discussion  
A manufacturer interested in creating a new product, attracting new customers, and gaining a broader market reach has expressed its interest in researching new options and opportunities on how to upgrade their initial proof-of-concept-level smart furniture project.  
The initial project was based on proof-of-concept ESP32 hardware with a minimalistic software solution and reduced connectivity and operability functionalities.  
The proposed upgrades enable planning new variants of their furniture, utilizing industrial-grade devices freely available on the market for easier design, planning, manufacturing, maintenance, and marketing. The proposed device is standardized in size and requirements, future-proof in its capabilities, and enables the creation and maintenance of elaborate firmware, software, and platform (application) software, adding extra value to the original product. Market and industry trends show significant market maturity for stakeholders to continue with this proposal.  
Even though there are commercially available products similar to multimedia hubs and the like, the hereby proposed product is an industrial-grade product and rugged, assuring the proper functionality of the system in various scenarios. Decision-makers have an option to choose their path when it comes to selecting an operating system and a platform.  
Since the product is standard in size, there are no-to-minimal risks in maintaining the long-term mechanical and thermal dissipation design. The additional items required for this product to run are a minimalistic chassis, power supply, and external antennas (if desired). Additional expansions, such as local speakers, lights, and wireless charging cradles, can be installed. A comparison between the existing hardware and the new candidate is shown in the table below (Table 8).  
The true value of this product (hardware-wise) is in its standard format, ruggedness, expandability, stability, interconnectivity, and general ability to serve as a central point of a smart home, connecting all items present in the household. Software-wise, it uses platform-agnostic hardware that enables the optimum choice of an operating system and associated applications. Energy-footprint-wise, this product can replace several other/parallel ones and enable the customer to save on electricity consumption. The solution is vendor-agnostic and can serve as a future-proof concept, as the furniture manufacturer can easily select different vendors to supply the standardized industrial IT product by selecting the favorable specification among those offered in the given form factor.  
Security aspects can be additionally underlined with the proper choice of adequate communication modules that are ready to accept certified/secure communication protocols.
## 4 Results
### Comfort Conditions Analysis
#### 4.1.2 Operative Temperature and Relative Humidity Analysis  
The comfort range for operative temperature (19-23.5 \({}^{\circ}\)C) was divided into several intermediate bands in order to discretize the analysis of monitored data in the room center. The Figure 6 shows the percentage of time in which the calculated value (starting from monitored air temperature and radiant temperature) falls within these ranges, considering the whole monitoring period for the three systems.  
Figure 5: PMV and PPD calculation: radiant floor system.  
Figure 6: Percentage of time in comfort range for all systems—operative temperatures.  
Furthermore, from these data, it is clear that operative temperature was within the comfort range almost entirely throughout the considered period; it was lower than 19.5 \({}^{\circ}\)C only for 1.0% of the acquisition period in the case of the radiant floor, and 11% when the fan-coils were switched on. The opposite situation occurred in the case of baseboards, since the operative temperature was lower than 19.5 \({}^{\circ}\)C for around 48% of the monitored values. This indicates that the baseboards needed more time to reach the set-point values and were more prone to a temperature decay during the day.  
In addition, it is evident that while with the fan-coils the operative temperature was positioned for 78% of the time between 20 \({}^{\circ}\)C and 21 \({}^{\circ}\)C, with the radiant floor, around 64% of the measures were characterized by values higher than 21 \({}^{\circ}\)C. This analysis allowed us to conclude that for this system, a lower set-point temperature could be used with consequent energy savings, assuring, at the same time, the comfort zone.  
With reference to relative humidity values, with fan-coils, it was found that it was less than 40% (generally between 37% and 39%) 88% of the time, and, globally, it was always lower than 50%. In the case of the radiant system, the relative humidity was lower than 40% for 58% of the monitoring period, and during the remaining period, it was still lower than 50% of the period. When the baseboards were active, the relative humidity varied between 40% and 50% only for 38% of the time, and it was lower than 40% for 58% of the monitored values.  
In all cases, it is clear that the emitters were not able, for their characteristics, to control the latent load.
## 4 Application Features
### Components
#### 4.2.3 Operations on Food and Meal  
The concept associated with partial food consumption in a meal is very important for nutritionists since it determines the number of nutrients consumed according to a prescribed food plan. Therefore, if a user only consumes 80% of a particular food, it is important to provide such feedback to the nutritionist since this will provide a realistic view of the food plan consumption and infer the real amount of nutrients consumed. This is presented in the first row of Figure 10, where the user registers the consumption of only 90% of the defined amount of carrots.  
The concept of food change in a meal is associated with the ability of the user to change food from the food plan (with an equivalent) related to the user's food preferences and the availability of specific food. This allows users to adjust the food plan, maintaining the base nutrients that compose a meal (second row of Figure 10, where a soup is changed with another soup). To this end, the user interface was created considering that food equivalent to the same nutritional level can be exchanged with another food.  
Finally, adding food to a meal is an option that has to be provided to users. Consuming food outside a prescribed food plan is not a desirable option since the plan is adjusted to nutrients for each user. However, adding this feature allows positive feedback to the nutritionist and implements a realistic view of ingested food (Figure 9). Moreover, it allows the nutritionist to adjust periods of food ingestion and types of meals by considering the user's food ingestion habits.
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
## 3 Results  
In this section, the transmittance of graded-index AR coatings with linear index distribution, exponential index distribution, and polynomial index distribution are first designed at working wavelengths from 200 nm to 1200 nm, and then the standing wave field distributions in these designed coatings are obtained by the numerical calculation of electromagnetic wave equation.  
The numerical calculation of Equation (1) is conducted by fourth order Runge-Kutta method which is an effective algorithm to solve differential equations. In our calculation, for convenience, the value of electric field amplitude of incident wave at surface \(\overset{\rightarrow}{E}_{0}^{+}\) is taken as 1 and the ratio \(\frac{\overset{\rightarrow}{E}^{2}}{\overset{\rightarrow}{E}_{0}^{2}}\) is taken as the intensity of standing wave field.  
To make the graded-index AR coatings working at wavelengths from 200 nm to 1200 nm, a numerical searching method is used to find the optimum matching thickness of coating as mentioned in Section 2. In our work, the range to optimize thickness \(d\) is taken from 0 nm to 3 nm. So, to keep the maximum searching thickness of entire coating \(N\times d\) close to maximum wavelength (1200 nm) of designed wavelength range 200 -1200 nm, we chose the number of layers \(N\) = 450 (450 \(\times\) 3 nm > 1200 nm). For searching method, the number of layers \(N\) does not need to be 450 and the specific number is not important. We also can chose N = 700. In this case, the range to optimize thickness \(d\) can be taken as 0-2 nm (700 \(\times\) 2 = 1400 nm > 1200 nm). The aim of the searching is to find the optimize thickness \(N\times d\)1 of graded-index AR coating, in which the coating has maximum transmittance. For ideal graded-index coating, the index distribution is continuous. Thus, \(d\) should be much smaller than the wavelength of incident laser and it is not a thickness for real fabrication of graded-index AR coating. For real fabrication of graded-index AR coating, one can re-divide the obtained optimized thickness. Figures 1d, 2d and 3d are examples for the re-divisions of obtained optimize thickness of linear index distribution, exponential index distribution,and polynomial index distribution, respectively. In all our re-divisions, the minimum thickness is large than 5 nm and the minimum index contrast among layers is 0.01.
## 4 Discussion
Applications, as outlined in Table 1, are possible right now and--as shown within the present study--can be approached with easily accessible tools possessing a reasonable degree of complexity. In the future, opportunities of sentiment analysis might become even broader. [PERSON], [PERSON], [PERSON] and [PERSON] [40] point out that sentiment analysis does not need to be bound to textual data and mention further applications of automatically extracting opinions from data such as \"facial expression, body movement, or a video blogger's choice of music or color filters\" (p. 19). This is yet another link to sports science where body language plays a large role, however, not analyzed algorithmically so far. Other visions of sentiment analysis refer to the use of questionnaires: The necessity to find participants to fill out questionnaires and ask for their opinion might be avoidable as a virtually endless number of people give away their opinion on the internet voluntarily. The necessity to ask for recreational behavior in questionnaires might be avoidable if the data can be extracted from social media or related online data without having the problem of social desirability in questionnaires [41]. However, technological, methodological and ethical questions will need to be overcome in order to establish social media analysis in general and sentiment analysis in particular as a tool in sports science. Technical feasibility for non-computer scientists is crucial as tools need to be manageable. We addressed this issue by testing easily accessible tools possessing a reasonable degree of complexity. The possibly most serious methodological issues concern representativeness, as users of social media cannot be assumed to be a representative sample of the population, e.g., in terms of demographic groups or age, as has been criticized in the domain of election forecasting [7]. While studies like this one, analyzing existing textual data for sentiments excluding any personal data are unproblematic in terms of ethical questions, such questions would become relevant if metadata like personal characteristics or geographical information would be analyzed. Carrying out experiments manipulating the behavior of users in social media deliberately, would impose even further issues with regard to informed consent.
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 4 Discussion
### Influence of Clay Content, Water Content, and Cementation on Shear Strength Parameters
#### 4.2.2 Internal Friction Angle with Variable Clay Content  
Figure 10 shows the relationship between water content and internal friction angle under both uncemented and cemented conditions. Overall, the internal friction angle decreases with increasing water content. However, the rate of decrease varies with clay content. Samples with high clay content exhibit a more pronounced reduction in internal friction angle compared to low-clay-content samples [50].  
Figure 10: Correlation between water content and internal friction angle of fault gouge with different clay contents under various cementation conditions: (**a**) 0% cementation degree; (**b**) 1–5% cementation degree (Data source: [PERSON] et al. [28], [PERSON] et al. [31], [PERSON] et al. [41]).  
Figure 9: Correlation between water content and cohesion of fault gouge with different clay contents under various cementation conditions: (**a**) 0% cementation degree; (**b**) 1–5% cementation degree (Data source: [PERSON] et al. [28], [PERSON] et al. [31], [PERSON] et al. [41]). Note: \(w_{\rm c}\) represents the clay content, and \(w_{\rm d}\) represents the degree of cementation.  
For example, in Figure 10a, the internal friction angle of a sample with 2.2% clay content decreases only slightly--from 29.72\({}^{\circ}\) to 28.5\({}^{\circ}\)--as water content increases from 5% to 22%, a reduction of just 4.1%. In contrast, a sample with 50% clay content shows a dramatic decrease--from 33.06\({}^{\circ}\) to 9.87\({}^{\circ}\)--as water content rises from 10% to 30%, corresponding to a 70.1% reduction. This pattern is also evident in Figure 10b under cemented conditions. Similar to cohesion, increasing the degree of cementation raises the absolute value of the internal friction angle but does not alter its trend with respect to water content or its sensitivity to clay content.  
This phenomenon can be explained by the fact that fine clay minerals, in contrast to coarse particles like quartz, absorb water more readily and become enveloped in water films. This reduces interparticle bonding and friction, especially under high-water-content conditions. Therefore, fault gouge with a higher clay content is more sensitive to increases in water content in terms of shear resistance reduction.
## 5 Latency Mitigation Strategies  
Most delay compensation or mitigation approaches can be broadly classified into control, perception, and network optimisation methods. These methods are reviewed further in the following subsections. However, it is worth noting that, in this paper, it is assumed that the network optimisation methods are already performed. Hence, they are covered very briefly. This paper primarily focuses on the control and perception approaches. A comprehensive list of latency mitigation strategies for control and perception is provided in Table 5 and Table 6, respectively. A practical summary of these strategies highlighting their advantages and disadvantages is later provided in Table 7.  
Predictive methods have been shown to be well suited for delay compensation on both the downlink and uplink of teleoperation [70; 113]. In the realm of predictive methods, passivity-based and predictive display-based methods have been most commonly used. They have been shown to reduce inconsistency. However, the performance degrades with increasing and variable time delays. A broad classification of latency mitigation methods is demonstrated in Figure 7.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 6 Conclusions
The configuration optimization and constellation design of LEO satellites remain to be explored. Besides, there are some limitations in current observation simulation. For example, the simulation and processing of atmospheric residuals do not take into account the effects of high-order errors, and the ionospheric delay simulated on GIM calculations may cause distortions. Moreover, the actual built LEO navigation augmentation system in future may also has many unknown errors like inter-frequency and inter-system biases, causing problems of whether the existing error-source models are suitable for LEO-satellite scheme and of how to improve the current multi-constellation fusion algorithm, which should be carefully considered in real data processing. With the continuous development of LEO satellites, more attentions should be paid to the new problems and challenges when achieving LEO-augmented positioning in practical use. Though more available observations contribute to better positioning performance, they also bring a higher requirement for hardware equipment to possess more computing power and speed in data processing. Meanwhile, the algorithm of the high-dimensional ambiguity resolution will also face challenges and need to be further optimized. Considering the appearance of larger and diversified constellation, the ground receiver must be capable of supporting so many signal channels, and the corresponding ability to receive and storage data also needs to be improved. In addition, the problem of multi-signal interference may happen due to the harmonic distortion or insufficient band-pass filtering. Once these negative side effects haven been solved or eliminated, higher precision and better performance from LEO-satellite augmentation positioning can be expected. **Author Contributions:** All authors contributed to the writing of this manuscript. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments. [PERSON], [PERSON], [PERSON], and [PERSON] processed the data, drew the pictures and investigated the results. [PERSON] and [PERSON] validated the data. **Funding:** This study is financially supported by the National Natural Science Foundation of China (Grant No. 41774030), the Hubei Province Natural Science Foundation of China (Grant No. 2018 CFA081) and the National Youth Thousand Talents Program. **Acknowledgments:** We are very grateful to the reviewers for their helpful remarks for improving the manuscript. Meanwhile, we would also like to express our gratitude to NGS, IGS, MGEX, GFZ, DLR, and CODE for providing multi-GNSS data and products, as well as the help from [PERSON] and [PERSON]. In addition, the numerical calculations in this study have been done on the supercomputing system in the Supercomputing Center of Wuhan University. **Conflicts of Interest:** The authors declare no conflict of interest.
# An Analysis of MPTCP Congestion Control
## 3 Path Selection
### Path Diversity
#### 3.1.2 Window Limitations  
When using a transmission layer protocol such as TCP, a sender typically uses the minimum of _cwnd_ and the receiver buffer (_rwnd_) to transfer data. A problem called window blocking can occur when a large portion of the receive buffer is occupied by out-of-order packets. In such cases, the receive window is filled with out-of-sequence packets and hence the sender shrinks its send window. The problem affects available paths with higher performance (lower delay, higher bandwidth) since no data can be transferred until head-of-line blocking data are positively received and acknowledged [1].  
Another issue caused by a full receive buffer is called send buffer blocking. When a sender transmits data, the segments stay in its buffer until the data are acknowledged. This causes the sender to hold on to the out-of-order packets until the blocking packets are received [1].  
Figure 1: Simple Scheduler.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### Features Extraction
#### 2.4.2 The Median Absolute Deviation (\(MAD\))  
The median absolute deviation (\(MAD\)) was discovered by [PERSON] (1974). The median is a measure of central tendency that has the advantage of being very insensitive to the occurrence of outliers. Therefore, fluctuations in the RR values relative to the median value could be used to explain the difference in AF patients [33]. The median absolute deviation (\(MAD\)) is a robust variable of numerical series. The \(MAD\) formulation is shown in (2), where the RR interval is \(RR_{i}\), and the RR intervals within the segment are denoted as \(RR_{seg}\). [PERSON] (2016) suggested that the \(MAD\) of AF is a significant variable, showing a higher value for NSR than for AF [34].  
\[MAD=median\left(\left|RR_{i}-median\left(RR_{seg}\right)\right|\right) \tag{2}\]