esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Discussion
### Modeling
#### 3.3.1 Algorithms
[PERSON] et al., (2017) [96] employed the reduced optimal interpolation (ROI) method using data from 65 stations in the Oklahoma Mesonet Network and compared it with other methods such as Co-kriging and Inverse Distance Weighting (IDW). They found that ROI was significantly more accurate than IDW and outperformed Co-kriging in their results. In a comprehensive review of ML for SM assessment, [PERSON] et al., (2022) [97] concluded that machine learning algorithms like ANN and SVM disfavor the understanding of the relationship between dependent target variables and independent input variables due to their complex model structures; in contrast, algorithms such as CART and RF are favored for their transparency and ease of interpretation, facilitating a clearer comprehension of how input variables influence model outcomes. The performance of algorithms for SM measurement significantly depends on the quality and volume of available data. High-resolution spatial and temporal data, which are accurate and consistent, including multispectral and hyperspectral information along with in situ measurements, are crucial. While no specific data quantity threshold for SM modeling is established, extensive datasets with broad temporal and spatial observations are preferred. Long-term satellite data with high temporal frequencies and repeated UAV/in situ measurements across different seasons are recommended. Employing cross-validation techniques that integrate satellite, UAV, and in situ data is essential to reduce uncertainty and increase the reliability of SM models.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 3 Implementation
### Solar Module  
A 100 W solar panel (shown in Figure 3a) was used to build a solar power source, which was connected to a solar controller (shown in Figure 3b), and powered the entire system, including wireless access points and satellite terminals. The 100 W solar panel was chosen because it is sufficient for  
Figure 2: Implementation of the designed solar-powered satellite access point.  
providing the energy of the system, and is the most cost-effective in terms of output cost per watt while also powering the satellite access point.  
A 720 Wh lithium-ion battery (as shown in Figure 3c) was selected to store the power generated by the panel. The 720 Wh battery has the advantage of being widely used and has a good price/performance ratio (Wh/cost). Battery capacity allows the use of solar energy for at least three consecutive days, which can be beneficial for events such as the rainy season or failure of a solar panel system.  
The pulse width modulation (PWM) solar charge controller was selected because it costs less than the cost of a maximum power point tracking (MPPT) charge controller with the maximum acceptable power loss, which can transmit still more power from the solar panel to the access point.  
The solar system is expected to be exposed to outdoor weather. The solar system was built to withstand vertical dripping, and the manufacturer of the selected solar panel evaluated the solid particle protection level (level 5) and liquid ingress protection level (level 5) of the International Electrotechnical Commission's (IEC's) 60529 [15] standard. The solar panel was connected through a waterproof MC4 connector.  
The output voltage of the solar system was equal to the voltage of a 720 Wh lithium-ion battery, with a range of 10.0 V to 12.6 V. The satellite terminal and single-board computers required stable voltage from the solar system. Therefore, a direct current to direct current (DC-DC) voltage converter module was required. The PWM solar charge controller had a USB output that provided 5 V and 2 A power. However, the satellite terminal required 19 V to operate, so the XL6009E1 DC boost  
Figure 3: Components in solar module. (**a**) The solar panel that provides the electricity to the system. (**b**) The pulse width modulation (PWM) solar controller that converts the electricity to the system. (**c**) The 720 Wh battery. (**d**) The XL6009E1 DC boost converter.  
converter module (shown in Figure 3d) was connected to the solar system to provide power to the satellite terminal.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
###### Abstract  
In this paper, we provide a systematic comparison of distribution matching (DM) and sphere shaping (SpSh) algorithms for short blocklength probabilistic amplitude shaping. For asymptotically large blocklengths, constant composition distribution matching (CCDM) is known to generate the target capacity-achieving distribution. However, as the blocklength decreases, the resulting rate loss diminishes the efficiency of CCDM. We claim that for such short blocklengths over the additive white Gaussian noise (AWGN) channel, the objective of shaping should be reformulated as obtaining the most energy-efficient signal space for a given rate (rather than matching distributions). In light of this interpretation, multiset-partition DM (MPDM) and SpSh are reviewed as energy-efficient shaping techniques. Numerical results show that both have smaller rate losses than CCDM. SpSh--whose sole objective is to maximize the energy efficiency--is shown to have the minimum rate loss amongst all, which is particularly apparent for ultra short blocklengths. We provide simulation results of the end-to-end decoding performance showing that up to 1 dB improvement in power efficiency over uniform signaling can be obtained with MPDM and SpSh at blocklengths around 200. Finally, we present a discussion on the complexity of these algorithms from the perspectives of latency, storage and computations.  
shaping gap; constellation shaping; probabilistic amplitude shaping; distribution matching; sphere shaping +
Footnote †: journal:  
Article
# Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018
## 4 Results
### Temporal Trends of NTL in Different Light Pollution Categories  
There is an increasing temporal trend in polluted PA worldwide over the past decades, especially in Europe and South America (Figure 5). The continuously polluted PAs in  
Figure 4: Spatial distribution of different light pollution categories (**a**) and their composited visualization using continuously polluted (R), non-polluted (G), and discontinuously polluted (B) (**b**).  
Europe account for 51% of the world, dominated by increasing NTL (Figure 5a). Among these PAs, 24% of them show a significant increased trend with \(p\)-values below 0.05. Most of the discontinuously polluted PAs with a significant increase are distributed in South America, West Africa, India, and South Asia (Figure 5b) (Table 1). For example, the proportion of PAs with a significant increase in South America is larger than 50%, suggesting a notable growth of NTL luminance. The difference between Europe and South America in different pollution categories indicates the rapid growth of city light in South America's developing areas due to urban sprawl. At the continental scale, the polluted PAs (i.e., continuously and discontinuously polluted) show a distinct spatial heterogeneity (Figure 5), especially in South America, West Africa, South Asia, and Oceania. The dominant trend of NTL change in South America is increasing in continuously and discontinuously polluted PAs, suggesting a considerable intervention of human activities related to the NTL luminance (e.g., urban sprawl). The discontinuously polluted PAs in Africa show an increasing trend (accounting for 50%), especially in West Africa.  
Figure 5: The temporal trends of NTL worldwide in continuously polluted (**a**) and discontinuously polluted (**b**) PAs. The visualization of kernel density maps is composited by channels of increase (R), decrease (G), and no change (B). Note: the search radius of the kernel density is 5 degrees, and the weight is the natural logarithm of the area.  
Global polluted PAs with a decreasing temporal trend are mainly distributed in the USA and Japan, and such a pattern is consistent in both the continuously and discontinuously polluted categories (Figure 5). The proportion of polluted PAs with a decreasing trend in Japan is about 85%, of which 35% of PAs are statistically significant. The proportion of discontinuously polluted PAs with a decreasing trend reaches 90% in Japan, and around 50% of these PAs pass the significance test. This phenomenon is mainly attributed to the well-planned conservation policies of light pollution in these two regions. For example, Japan is the first country to develop guidelines for mitigating the effect of light pollution [57]. The continuously polluted PAs with decreasing trends are mainly (~65%) distributed in the Great Lakes region of the USA. The discontinuously polluted PAs in the USA are also dominated (75%) by a decreasing trend. This phenomenon is related to the phase of urbanization and population loss; e.g., there is a noticeable outflow of the population in the Great Lakes region [58].
###### Abstract  
Rapid urbanization has threatened sustainable urban development in many cities across the globe, causing green space loss and vegetation cover degradation which reduce carbon sequestration. Optimal land management practices (LMPs) in an urban context are known as ways capable of promoting urban vegetation growth and contributing to carbon sequestration. Due to variations of physical, biological, and social structures in urban areas, policymakers often lack relevant information to decide and implement site-specific LMPs. Here we try to extract the areas in need of the optimal LMPs, identify location-dependent optimal LMPs, and assess how much more carbon can be captured by applying a combination of segmenting homogeneous urban environments and neighborhood-based analysis. As one of the most developed cities in China, the greater Guangzhou area (GGA) was selected as a case study. We found that the carbon uptake from the urban vegetation in GGA could be improved on average by 185 gC m\({}^{-2}\) yr\({}^{-1}\) in flux (or 1.3 TgC yr\({}^{-1}\) in total) with optimal LMPs, equivalent to a -30% increase considering the current level of 662 gC m\({}^{-2}\) yr\({}^{-1}\) in flux (4.4 TgC yr\({}^{-1}\) in total). The carbon uptake potential was found to differ considerably across locations and among different ecosystem types, highlighting spatially varied priorities for implementing optimal LMPs over the space. This study reveals the usefulness of the model in assessing carbon uptake potential from optimal LMPs and emphasizes that future urban planning may consider the importance of optimal LMPs in enhancing vegetation carbon uptake in urban planning.  
urban vegetation; carbon sequestration; flux; land management; environment heterogeneity Article
## 3 Results and Discussion
### Choice of Place to Live
#### 3.2.2 Adapted Audiometric Tests  
Adapted audiometric tests were performed as a complement to the objective dimension analysis and were used to determine the hearing threshold of \"exposed\" and \"unexposed\" volunteers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Noise** & **Yes** & **No** & **Did Not Answer** \\ \hline Crackle/Sizzle & 8.0 & 1.0 & 1.0 \\ Humming & 24.0 & 3.0 & 0.0 \\ Sparks & 4.0 & 0.0 & 0.0 \\ Did not answer & 7.0 & 2.0 & 14.0 \\ Not applicable & 0.0 & 36.0 & 0.0 \\ Total & 43.0 & 42.0 & 15.0 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Percentage of interviewees who claimed there was another noise, besides the road traffic, which caused discomfort, and how they characterized this noise in Serzedelo.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Distance from Source (m)**} & \multicolumn{3}{c}{**Serzedelo**} & \multicolumn{3}{c}{**Abacão (S. Tome)**} \\ \cline{2-7}  & **Yes** & **No** & **Total** & **Yes** & **No** & **Did not Answer** & **Total** \\ \hline
50–99 & 0 & 0 & 0 & 7 & 3 & 0 & 10 \\
100–149 & 3 & 1 & 4 & 8 & 6 & 0 & 14 \\
150–199 & 3 & 0 & 3 & 5 & 6 & 0 & 11 \\
200–249 & 5 & 3 & 8 & 3 & 4 & 0 & 7 \\ \(\geq\)250 & 53 & 32 & 85 & 26 & 31 & 1 & 58 \\ Total & 64 & 36 & 100 & 49 & 50 & 1 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Percentage of interviewees who have a health problem regarding distance from the source (highway) in Serzedelo and in Abacão (S. Tome).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Analyzed Areas** & **Yes (\%)** & **No (\%)** & **Did not Answer (\%)** & **Total (\%)** \\ \hline Serzedelo & 73.0 & 24.0 & 3.0 & 100.0 \\ Abacão (São Tome) & 64.0 & 34.0 & 2.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Interviewees living near a highway and/or national road in Serzedelo and Abacão (São Tome).  
Thus, the tests were performed with eight \"exposed\" individuals (Serzedelo--Figure 8) and with six \"unexposed\" individuals (Abacao (Sao Tome)--Figure 9).  
The differences recorded for the hearing threshold for pure sounds and recorded sound for both groups were significant. For the recorded sound, the difference between the average of the hearing threshold was 27.2 dB [54]. The data shows that the \"exposed\" group revealed being used to low frequency noise, and corroborates the results shown in Figure 6; Figure 7, which clearly illustrates the low sensitivity of the \"exposed\" group below 39 Hz when compared to the \"unexposed\" group [54].
## 3 Related Work  
Substantial work on the eccentricity distribution dates back to 1975, when the term \"eccentric sequence\" was introduced to denote the sequence that counts the frequency of each eccentricity value [10]. The facility location problem is an example of the usefulness of eccentricity as a measure of centrality. When considering the placement of emergency facilities such as a hospital or fire station, assuming the map is modelled as a graph, the node with the lowest eccentricity value might be a good location for such a facility. In other situations, for example for the placement of a shopping center, a related measure called closeness centrality, defined as a node's _average_ distance to every other node (\(c(v)=\frac{1}{n}\sum_{w\in V}d(v,w)\)), is more suitable. Generally speaking, the eccentricity is a relevant measure when some strict criterion (the firetruck has to be able to reach _every_ location within 10 minutes) has to be met [11]. An application of eccentricity as a measure on a larger scale is the network routing graph, where the eccentricity of a node says something about the worst-case response time between one machine and _all_ other machines.  
The most well-known eccentricity-based measure is the diameter, which has been extensively investigated in [5; 6; 7]. Several measures related to the eccentricity and diameter have also been considered. [PERSON] _et al_. [12] study the effective radius, which they define as the 90 th-percentile of all the shortest distances from a node. In a similar way, the effective diameter can be defined, which is shown to be decreasing over time for many large real-world graphs [13]. Each of these measures is computed by using an approximation algorithm [14] to determine the neighborhood of a node, a technique on which we will elaborate in Section 5.3.  
To the best of our knowledge, there are no efficient techniques that have been specifically designed to determine the exact eccentricity distribution of a graph. Obviously, the naive approach, for example as suggested in [15], is too time-consuming. Efficient approaches for solving the APSP problem (which then makes determining the eccentricity distribution a trivial task) have been developed, for example using matrix multiplication [16]. Unfortunately, such approaches are still too complex in terms of time and memory requirements.
## 5 Experiment Results  
### Comparison of Camera Calibration  
The coordinates of the screen are obtained by the phase-shifting algorithm in three methods. For EPCM, the distance \(d_{p}\) between the external stop and the front surface is obtained as \(d_{p}=23.96\) mm (see Appendix A). The experimental setup of the PMD is shown in Figure 6, where the LCD screen on the optical experiment platform displays a sixteen-step sinusoidal fringe pattern. The resolution of the CCD camera is \(1296\times 966\) pixels, and one pixel is \(3.75\) um. The light emitted from the LCD screen is reflected by a high-quality flat element and captured by a camera with an external stop, which can be removed. The coordinates of the external stop can be accurately positioned with CMM and the camera focus on the element regardless of the screen being out of focus.  
Three methods, including EPCM, PM, and MPMD, are selected for comparison. In EPCM, the camera coordinates are obtained with Equation (4) according to the geometric system in Figure 1a. In PM, the 16 poses of the checkerboard are captured (the last one is restored at an identical position to the SUT); then, the internal and external parameters are obtained by the calibration toolbox [29]. Finally, the projection center of the camera is calculated with Equation (7). Likewise, in MPMD, [PERSON]'s algorithm [15] is used to obtain the relationship between the camera and the screen. Additionally, we then calculate the camera coordinates with Equation (7), which is equivalent to the PM in principle. According to the calibration processes mentioned above, the camera coordinates are obtained in EPCM, MPMD, and PM, respectively. Additionally, they are transformed into the same coordinate system, as shown in Table 2. It can be seen from Table 2 that the camera coordinates obtained by using the above three methods are different in the \(x\), \(y\), and \(z\) directions. According to Table 1, if the camera coordinate in the x direction is inaccurate, the error will mainly be induced in the 4 th and 6 th terms of the Zernike polynomial; inaccuracy in the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Zernike Terms/Degree of Freedom** & **Z4** & **Z5** & **Z6** & **Z7** & **Z8** & **Z9** & **Z10** & **Z11** & **RSS** \\ \hline \multirow{8}{*}{The error of 1 mm is artificially introduced, \(z_{m}\)} & \(x_{c}\) & \(20.92\) & \(-0.01\) & \(9.75\) & \(0.00\) & \(2.27\) & \(0.00\) & \(-0.03\) & \(-0.01\) & \(23.19\) \\  & \(y_{c}\) & \(-0.11\) & \(9.91\) & \(0.05\) & \(2.32\) & \(0.00\) & \(y direction will be largely embodied in the 5 th term; and error in the z direction will be expressed in the 4 th term and will have a great influence on the reconstruction accuracy. Therefore, if the three sets of coordinates in Table 1 are separately used to measure the SUT, the inaccuracy of the camera coordinates will be mostly reflected in the defocus and astigmatism error.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**KPI** & **Linguistic** & **a** & **b** & **c** & **d** \\ \hline \multirow{4}{*}{Supplier proximity} & Very low & 0 & 0 & 15 & 20 \\  & Low & 15 & 20 & 25 & 30 \\  & High & 25 & 30 & 40 & 50 \\  & Very High & 40 & 50 & 100 & 100 \\ \hline \multirow{4}{*}{Number of supply chain nodes} & Very low & 0 & 0 & 3 & 4 \\  & Low & 3 & 4 & 5 & 6 \\  & High & 5 & 6 & 8 & 10 \\  & Very High & 8 & 10 & 15 & 15 \\ \hline Supplier flexibility/Supplier involvement in & Very low & 0\% & 0\% & 50\% & 55\% \\   product development/Production mix & Low & 50\% & 55\% & 70\% & 75\% \\   flexibility/Delivery frequency & High & 70\% & 75\% & 88\% & 95\% \\  (actual/planned)/Delivery & & & & & \\   punctuality/Timeliness/Support service & & & & & \\ evaluation/Returns flexibility (handling from multiple sources) & & Very High & 88\% & 95\% & 100\% & 100\% \\ \hline \multirow{4}{*}{Supplier response time (lead time)} & Very High & 0 & 0 & 2 & 3 \\  & High & 2 & 3 & 4 & 5 \\  & Low & 4 & 5 & 8 & 10 \\  & Very low & 8 & 10 & 20 & 20 \\ \hline \multirow{4}{*}{Total production time} & Very High & 0 & 0 & 3 & 4 \\  & High & 3 & 4 & 5 & 7 \\  & Low & 5 & 7 & 10 & 15 \\  & Very low & 10 & 15 & 30 & 30 \\ \hline \multirow{4}{*}{Overtime hours} & Very High & 0 & 0 & 2 & 3 \\  & High & 2 & 3 & 4 & 5 \\  & Low & 4 & 5 & 6 & 7 \\  & Very low & 7 & 8 & 10 & 10 \\ \hline \multirow{4}{*}{Defective products percentage} & Very low & 0.00\% & 0.00\% & 2.00\% & 3.00\% \\  & Low & 2.00\% & 3.00\% & 4.00\% & 5.00\% \\  & High & 4.00\% & 5.00\% & 6.00\% & 7.00\% \\  & Very High & 6.00\% & 7.00\% & 10.00\% & 10.00\% \\ \hline \multirow{4}{*}{Number of bottlenecks/Inventory turnover index/Issue resolution time (support requests)/Return request response time} & Very low & 0 & 2 & 3 \\  & Low & 2 & 3 & 4 & 5 \\  & High & 4 & 5 & 6 & 7 \\  & Very High & 6 & 7 & 10 & 10 \\ \hline \multirow{4}{*}{Delivery error percentage} & Very low & 0 & 0 & 3 & 4 \\  & Low & 3 & 4 & 7 & 9 \\  & High & 7 & 9 & 11 & 15 \\  & Very High & 11 & 15 & 30 & 30 \\ \hline \multirow{4}{*}{Delivery flexibility} & Very High & 0\% & 0\% & 50\% & 55\% \\  & High & 50\% & 55\% & 70\% & 75\% \\  & Low & 70\% & 75\% & 88\% & 95\% \\  & Very low & 88\% & 95\% & 100\% & 100\% \\ \hline \multirow{4}{*}{Inventory-to-sales transformation speed} & Very low & 0 & 0 & 15 & 20 \\  & Low & 15 & 20 & 25 & 30 \\  & High & 25 & 30 & 35 & 40 \\ \cline{1-1}  & Very High & 35 & 40 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fuzzy range of agile KPIs. \begin{table}
\begin{tabular}{c c c} \hline
**KPI** & **Linguistic Judgment** & **Degree of Membership** \\ \hline Quantity of non-compliant supplies & Very High & 0 \\  & High & 1 \\ Inventory level (raw materials) & High & 1 \\ Inventory cost & Very High & 1 \\ Productivity & Low & 1 \\ Production capacity utilization & Low & 1 \\ Overall equipment effectiveness & Low & 1 \\ \hline \end{tabular}
\end{table} 
Table 10: Linguistic judgment and membership degree of lean KPIs.
## 2 Calculation Model of Particle Impact Strength Corrosion
### Parameter Analysis of the Impacting Strength Erosion Model  
The independent variables in Equation (6) are particle volume \(V_{p}\), impact velocity \(v_{p}\), impacting time \(n\), and impact angle \(\alpha_{n}\). There is a clear positive correlation between impacting time \(n\) and the dependent variable bond radius multiplier \(\lambda\). However, further analysis is needed to characterize the relationship between the other parameters and the bond radius multiplier \(\lambda\). This study investigated the effects of particle volume \(V_{p}\), impact velocity \(v_{p}\), and impact angle \(\alpha_{n}\) on bond radius multiplier \(\lambda\). Figure 2a-c illustrates the influence of impact angle \(\alpha_{n}\), impact velocity \(v_{p}\), and particle volume \(V_{p}\) on bond radius multiplier \(\lambda\), respectively (assuming \(n_{c}\) = 60 and K = 0.5 mm/s).  
According to Figure 2, the strength corrosion rate of the target material increases with the number of impact times \(n\). When particles impact the target material vertically  
Figure 2: Bond radius multiplier changing chart. (**a**) Bond radius multiplier changing with different angles; (**b**) Bond radius multiplier changing with different particle velocities; (**c**) Bond radius multiplier changing with different particle volumes.  
(\(\sin\alpha=1\)), the bond radius of the affected area decreases the fastest with the impact time \(n\). When \(n\) is fixed, the impact angle that results in the slowest decrease in bond radius satisfies \(\alpha=\arcsin\left(\frac{K}{v_{p}}\right)\). The strength corrosion rate of the target material is directly proportional to both impact velocity \(v_{p}\) and particle volume \(V_{p}\).