esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Human Health Risk  
Our overarching goal is to inform science-led policy change in Malawi to assist the attainment of SDG targets. For a reduction in drinking water fluoride standard from 6 mg/L to 1.5 mg/L to be achieved, a direct link (risk) to human health from geologically controlled fluoride must be identified and quantified. The calculation of HQ values was completed for each water point sampled. HQ values provide a specific risk factor for each water point, per age group (Table 3) and support other data,identifying augen gneiss as the highest geological fluoride risk lithology (Table 4). HQ values expose the risk from drinking groundwater abstracted directly from high risk lithologies and provide the justification required to advocate policy change. When the HQ values from our sampled water points were compared to the fluoride map contained within the Malawi Hydrogeology Atlas [27], the difference in apparent risk was startling. Malawi's continued use of its 6 mg/L standard implies that the area is safe from dental fluorosis and (ground) water points within are safe to drink (limited to six water points). The HQ values for the same area are contrary, with 74% of water points considered unsafe for children aged 6 years old or under to drink, and 44% of water points unsafe for children under 12 (Table 3).  
HQ values have additional value as a means of local risk reduction in the interim. HQ values could be utilised in practice to cycle water use between low and high-risk water points to dilute overall fluoride consumption. For example: anyone over the age of 12 should be safe from developing dental fluorosis by drinking water consistently from water point 30 (F\({}^{-}\) = 0.95 mg/L), but a 6-year-old is vulnerable. Water Point 31 (F\({}^{-}\) = 0.33 mg/L), however, is safe for anyone to drink (Table 3). By cycling water intake for children aged 6 years and under (also pregnant and breastfeeding women) between Water Points 30 and 31 (50/50), the overall risk for those vulnerable at Water Point 30 is reduced by half, potentially preventing the development of dental fluorosis in those children. If this method can be applied by users at as many of the vulnerable water points as is possible (recognising variable distances between water points), the incidences of dental fluorosis in the study area could be vastly reduced, preventing potentially thousands of people from developing the condition. This method may prove fruitful in the short term via simple planning and informed decision making at user level.
# Spatiotemporal Dynamics of Net Primary Productivity in China's Urban Lands during 1982-2015
## 3 Results
### Spatiotemporal Dynamics of NPP in China from 1982 to 2015  
As shown in Figure 3, the annual total NPP in China increased significantly (R\({}^{2}\) = 0.84, \(p\) < 0.001) at a rate of 17.15 Tg C/yr. Based on piecewise regression, the turning point of the NPP trend was 1997. From 1982 to 1997, the total annual NPP in China increased slightly (R\({}^{2}\) = 0.31, \(p\) < 0.05) at a rate of 9.59 Tg C/yr. After 1997, the rate of increase in annual total NPP decelerated, reaching 8.71 Tg C/yr, and the trend was significant (R\({}^{2}\) = 0.45, \(p\) < 0.01).  
To describe the spatial patterns of NPP variation, the trend from 1982 to 2015 was analyzed at a pixel level (Figure 4). The NPP presented an increasing trend in most areas of China, and the pixels whose NPP change rate was greater than 5 g C/m\({}^{2}\)/yr were  
Figure 3: Trend of annual total net primary productivity (NPP) in China from 1982 to 2015. The mark (***) indicates relative levels of significance with \(p\) < 0.001, the mark (**) indicates relative levels of significance with \(p\) < 0.01, and the mark (*) indicates relative levels of significance with \(p\) < 0.05.  
Figure 2: Urban lands in the 1980s and urban expansion from the 1980s to 2015 in (**a**) China, (**b**) the Beijing-Tianjin–Hebei urban agglomeration (BTHUA), (**c**) the Yangtze River Delta urban agglomeration (YRDUA), and (**d**) the Pearl River Delta urban agglomeration (PRDUA).  
mainly distributed in the central, eastern, and southern regions. Moreover, the pixels with a decreasing NPP trend were mainly distributed in northern and northwest China and in the Yangtze River Delta and Pearl River Delta regions along the coast. Among them, the NPP in the Yangtze River Delta and Pearl River Delta decreased significantly (\(p<0.001\)), and the decreasing NPP trend was greater than 10 g C/m\({}^{2}\)/yr.
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
###### Abstract  
Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13-30% moisture content were detected, the coefficient of determination \(R^{2}\) was 0.936, and the sum of squares for error (\(SSE\)) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process.  
NIR spectroscopy; rice grain moisture; 1450 nm; rapid-detection sensor +
Footnote †: journal: Applied Physics Letters
## 6 Localization Systems
### Indoor Localization Systems  
Indoor localization has recently shown increased interest because of the wide range of services it can provide by leveraging IoT technology and the ease of radio connectivity of mobile devices [179].
# Design Strategies for Mobile Click-and-Load Waiting Scenarios
## 4 Results
### Loading Symbol Strategies for Hierarchical Page Transitions
#### 4.2.2 Content Page Scenario  
(1) Icons  
In the content page scenario involving hierarchical page transitions (i.e., transitions between higher-level and lower-level pages), repeated measures of ANOVA were conducted to examine the effects of different loading icon strategies on user responses. The results indicated that there was no significant difference in time perception across the icon conditions, although the no icon condition resulted in the highest average perceived duration. No significant difference was found for the loading speed either, with the hedonic-style icon condition producing the highest average speed ratings. However, satisfaction showed a significant difference (F = 9.104, \(p\) < 0.001), with mean values ranked as follows: hedonic-style icon > regular icon > no icon. Similarly, emotional valence also exhibited a significant difference (F = 7.272, \(p\) < 0.01), with the same ranking order: hedonic-style icon > regular icon > no icon. No significant difference was found for the arousal level, although the hedonic-style icon condition had the highest average arousal score. LSD post hoc tests were conducted for the dimensions that showed significant within-subject effects. The detailed results are illustrated in Figure 13.  
Figure 12: Results of text strategy for the tab page scenario (Experiment 2). Vertical lines: standard deviations (SDs). *: \(p\) < 0.05.  
Figure 13: Results of icon strategy for the content page scenario (Experiment 2). Vertical lines: standard deviations (SDs). *: \(p\) < 0.001, *: \(p\) < 0.01, *: \(p\) < 0.05.  
1. Satisfaction: The hedonic-style icon condition resulted in significantly higher satisfaction ratings compared to both the regular icon and no icon conditions, suggesting that users prefer hedonic-style icons, likely due to their engaging and entertaining appearance. There was no significant difference in satisfaction between the regular icons and no icon conditions.
2. Emotional Valence: Emotional valence scores were significantly higher in the Hedonic-style icon condition than in the other two conditions, indicating that hedonic-style icons elicit more positive emotional experiences. No significant difference was found between regular icons and no icon conditions.
2. Text In the content page scenario, the effects of different loading text strategies were examined using repeated measures of ANOVA. The results showed that there was no significant difference in time perception across the text conditions, with personified text yielding the lowest average perceived duration. There was also no significant difference in loading speed, though personified text again had the highest average speed rating. No significant differences were found for satisfaction, emotional valence, or arousal level. However, both satisfaction and emotional valence reached the highest average values under the personified text condition. The no-text condition led to the highest average arousal rating. Detailed results are presented in Figure 14.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 3 Liquid-Assisted Antennas
### Other Tuning Techniques  
Other design variations proposed for antenna reconfiguration that are based on liquid dielectric materials but did not fit in the previous sections are discussed here.
## 2 Experimental Method  
The samples were prepared from a mixture of ZnS, Eu\({}_{2}\)O\({}_{3}\) and Li\({}_{2}\)O powders as precursors, with purities of 99.99, 99.99 and 99.5%, respectively. In all cases, the Eu\({}_{2}\)O\({}_{3}\) was maintained at 5 wt%, while the Li\({}_{2}\)O content was varied from 0 to 10 wt%. The notation of the samples is provided in Table 1.  
The powders were mixed and homogenized by ball milling (20 mm agate balls) for 5 h. The centrifugal ball mill was operated at 300 rpm. After the milling process, the powders were compacted under a compressive load of 2 T in the form of disk-shaped pellets with a diameter of about 7 mm and a thickness of 2 mm. The samples were placed on an alumina boat and inserted into the central (hottest) part of the quartz tube of a horizontal furnace (CHESA). The pellets were then submitted to a thermal treatment at 950 \({}^{\circ}\)C for 10 h, under an N\({}_{2}\) flux of 1.5 L/min. The experimental setup for vapor-solid growth has been described elsewhere [9].  
The characterization of the samples was carried out using a variety of techniques. Morphology was studied by Scanning Electron Microscopy (Secondary Electron mode, SEM-SE), and images were recorded either in a Leica 440 SEM (Wetzlar, Germany) or FEI Inspect SEM (Hillsboro, OR, USA). Cathodoluminescence (CL) spectra were recorded using an Hitachi S2500 SEM at room temperature with beam energy between 15 and 20 keV using a HAMAMATSU PMA-11 (Hamamatsu, Japan) charge-coupled device camera. Composition was assessed by X-ray microanalysis (EDS) performed with a Bruker AXS Quantax system (Billerica, MA, USA) attached to a Leica 440 SEM. Electron Backscattered Diffraction (EBSD) analysis was carried out using an FEI Inspect SEM equipped with a Bruker Quantax e-Flash 1000 EBSD system. Optical spectroscopy measurements (micro-photoluminescence (\(\mu\)-PL) and Raman) were performed at room temperature using an Horiba Jobin Yvon LabRAM HR800 (Kyoto, Japan) confocal microscope with a He-Cd (\(\lambda_{\mathrm{ex}}\) = 325 nm) or He-Ne (\(\lambda_{\mathrm{ex}}\) = 632.8 nm) laser as an excitation source, respectively. For \(\mu\)-PL, a custom-designed module allowed us to separate excitation and collection points [34]. Finally, XRD measurements were performed using a Philips diffractometer using Cu K\(\alpha\) radiation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **ZnS** & **Eu\({}_{2}\)O\({}_{3}\)** & **Li\({}_{2}\)O** \\ \hline Eu5 & 95 & 5 & 0 \\ \hline Eu5\_Li1 & 94 & 5 & 1 \\ \hline Eu5\_Li5 & 90 & 5 & 5 \\ \hline Eu5\_Li10 & 85 & 5 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation of the samples.
## 3 State-of-the-Art Results on the Detection of Identity Deception
### Detection Features
#### 3.2.1 Social Context Models  
Social context models achieve detection of malicious actors by examining features related to an account's social presence. This includes features such as relationships to other accounts, similarities to other users' behaviors, and a variety of graph-based features [21; 22; 23; 2; 2]. These types of features are valuable when classifying various types of malicious behaviors such as social bots and Sybil networks [24; 25]. The approach allows for a high-level view of how users are interacting. There are relatively common patterns that benign users follow with regards to their social interactions on OSNs. They are typically the friends with an equal number of people who are friends with them (reciprocity of friendship), and the friends that they do have will likely be related to one another in a cluster. Users tend to further have common secondary interests (e.g., following the same categories of interest in a social network) [26]. On the other hand, a malicious account will typically not obey this sort of predictable behavior. For example, a spam bot will follow or send friend requests to a large number of unrelated users. It is through this juxtaposition of normal social behaviors against irregular social behaviors that allows models to determine malicious actors from a birds-eye view of the OSN on which the model is operating. Based on this focus on the greater social context of an account, graph-based  
Figure 1: Typical identity deception detection experimental workflow. Data are selected from public sources or are simulated. Features are selected based on social and/or user behavior data. A model is constructed based on a machine learning algorithm, and it is further evaluated using metrics such as the f-score.  
features are typically used in detection models. These include features such as the different numbers of connections that a user has, the degree to which they connect different clusters of users in the network (sometimes indicated by betweenness centrality), and other social network metrics that measure the level of a user's influence in the network [25; 26].
# Long-Term and Seasonal Changes in Sources of Urban Atmospheric Particulates in the Western Pacific
## 4 Conclusions  
In order to clarify long-term and seasonal change in contributions of vehicles and coal combustion to urban atmospheric suspended particulate matter (TSP) in the Western Pacific, combustion-derived particulates (P\({}_{\rm c}\)) in TSP collected in ten cities from 1997 to 2018 were analyzed using the NP method. The method separates P\({}_{\rm c}\) into particulates originating from high-temperature (P\({}_{\rm h}\)) and low-temperature (P\({}_{\rm l}\)) combustion sources. Using this method, ten cities in the Asia-Pacific region were separated into three classes.  
Class 1: commercial cities whose major contributor to P\({}_{\rm c}\) was from vehicles and which showed lower [P\({}_{\rm c}\)] values with larger [P\({}_{\rm h}\)]/[P\({}_{\rm c}\)] ratios. Sapporo, Kanazawa, Tokyo, Sagamihara (Japan), Shanghai (China), and Busan (Korea) were contained in this class.  
Class 2: cities whose main source of P\({}_{\rm c}\) was from coal heating, and which showed much higher winter [P\({}_{\rm c}\)] with larger [P\({}_{\rm l}\)]/[P\({}_{\rm c}\)] ratios. Beijing, Shenyang (China), and Vladivostok (Russia) were contained in this class.  
Class 3: steel manufacturing city which showed lower [P\({}_{\rm c}\)] with larger [P\({}_{\rm l}\)]/[P\({}_{\rm c}\)] ratio. The low [P\({}_{\rm c}\)] appeared to be due to the elimination of P\({}_{\rm l}\) from coke oven plants. Only Kitakyushu was contained in this class.  
This study has elucidated the contribution of sources to urban atmospheric TSP in the Western Pacific which was previously unknown. Overall, [P\({}_{\rm c}\)] was decreasing with seasonal difference in this region, mainly due to the decreasing contribution of vehicles to P\({}_{\rm c}\), but not due to a decrease in coal combustion.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12042149/s1](https://www.mdpi.com/article/10.3390/app12042149/s1)]([https://www.mdpi.com/article/10.3390/app12042149/s1](https://www.mdpi.com/article/10.3390/app12042149/s1)), Text S1: Determination of PAHs and NPAHs [27; 28]; Figure S1: Sampling cities in Western Pacific Ocean; Table S1: Characteristics of the sampled cities; Table S2: Atmospheric concentrations of TSP, 1-NP and Pyr in 10 cities from 1997 to 2018; Table S3: x and y values for 10 cities from 1997 to 2018.  
All authors have contributed substantially to this paper. Conceptualization, methodology and writing, K.H.; data calculation, N.T. and A.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a Grant in Aid for Scientific Research (No. 17H06283) from the Japan Society for the Promotion of Science, the Environment Research and Technology Development Fund (5-1951) of the Environmental Restoration and Conservation Agency of Japan, and the research fund from the Japan Automobile Research Institute.  
Not applicable.
## 3 Discussion
### Discussions on AM Applicability and Analysis Methods
Research shows that other popular areas for AM-based applications are military, automotive, and medical emergency situations (COVID-19 condition).
### Remote Sensing Estimation of Gross Primary Productivity and Light Use Efficiency
#### 2.4.2 Remotely Sensed Gross Primary Productivity  
Initial estimates of GPPCASA were used to calculate a baseline error against GPPin situ; this is the foremost accuracy assessment in Figure 1. The output map of biomass production from PySEBAL expresses GPP in kg/ha/day as calculated in Equation (2) [11; 12; 23].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site Name** & **Site Code** & **Years Active** & **Crop Species** & **Observations** \\ \hline Cheorwon Rice paddy & KR-CRK [31] & 2015–2018 & Rice & 15 \\ Philippines Rice & PH-BiF [32] & 2012–2014 & Rice & 19 \\ Institute flooded & & & & \\ Humnoke Farm Rice & US-HRA [33] & 2017 & Rice & 4 \\ Field A & & & & \\ Humnoke Farm Rice & US-HRC [34] & 2017 & Rice & 4 \\ Field C & & & & \\ Mead-irrigated & US-Ne1 [35] & 2001–2013 & Maize & 47 \\ continuous maize site & & & & \\ Mead-irrigated & US-Ne2 [36] & 2001–2013 & Maize (odd & 33 \\ maize-soybean rotation & & & years), soybean & \\ site & & & (even years) & \\ Mead-rainfed & US-Ne3 [37] & 2001–2013 & Maize (odd & 46 \\ maize-soybean rotation & & & years), soybean & \\ site & & & (even years) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flux tower sites used to derive GPPin situ and LUEin situPySEBAL was run with the recommended \(\text{LUE}_{\text{max}}\) values of 2.5 gC/MJ/day for C3 crops (rice, soy) and 4.5 gC/MJ/day for C4 crops (maize). This output map was multiplied by 0.1 to convert from kg/ha to gC/m\({}^{2}\).
## 4 Theoretical Analysis of Different Detection Schemes
### BER Analysis of LCML Detection  
The average BER over Nakagami-\(m\) fading is given by:  
\[\bar{P}_{b}=\int_{0}^{\infty}Q\Big{(}\sqrt{2\kappa\gamma}\Big{)}f_{\gamma}( \gamma)\,d\gamma \tag{41}\]  
Using the identity:  
\[Q(x)=\frac{1}{\pi}\int_{0}^{\pi/2}\exp\biggl{(}-\frac{x^{2}}{2\sin^{2}\theta} \biggr{)}d\theta \tag{42}\]  
We substitute into the BER integral:  
\[\bar{P}_{b}=\frac{1}{\pi}\int_{0}^{\pi/2}\biggl{[}\int_{0}^{\infty}\exp\biggl{(} -\frac{\kappa\gamma}{\sin^{2}\theta}\biggr{)}f_{\gamma}(\gamma)d\gamma\biggr{]}d\theta \tag{43}\]  
Substituting the Nakagami-\(m\) PDF:  
\[\bar{P}_{b}=\frac{m^{m}}{\pi\Gamma(m)\tilde{\gamma}^{m}}\int_{0}^{\pi/2} \biggl{[}\int_{0}^{\infty}\gamma^{m-1}\exp\biggl{(}-\gamma\biggl{(}\frac{ \kappa}{\sin^{2}\theta}+\frac{m}{\tilde{\gamma}}\biggr{)}\biggr{)}d\gamma \biggr{]}d\theta \tag{44}\]  
The inner integral is a gamma integral:  
\[\int_{0}^{\infty}x^{\
u-1}e^{-\alpha x}dx=\frac{\Gamma(\
u)}{a^{\
u}} \tag{45}\]Letting \(A(\theta)=\frac{\kappa}{\sin^{2}\theta}+\frac{m}{\gamma}\), we get:  
\[\bar{P}_{b}=\frac{m^{m}}{\pi\gamma^{m}}\int_{0}^{\pi/2}\biggl{(}\frac{1}{A( \theta)}\biggr{)}^{m}d\theta \tag{46}\]  
Thus, the average BER under Nakagami-\(m\) fading is:  
\[\bar{P}_{b}\approx\frac{m^{m}}{\pi\gamma^{m}}\int_{0}^{\pi/2}\biggl{(}\frac{1}{ \frac{\kappa}{\sin^{2}\theta}+\frac{m}{\gamma}}\biggr{)}^{m}d\theta \tag{47}\]
## 4 Experiments
### Experimental Details
#### 4.2.2 Evaluation Indicators  
Mean average precision (mAP) is used to measure the positioning accuracy of an index in a dataset that contains multiple categories of situations. It calculates the average precision (AP) value for each category and then computes the average of these AP values. AP is calculated based on the area under the precision-recall (PR) curve. The PR curve plots precision on the x-axis and recall on the y-axis. The area under this curve is a crucial indicator for evaluating the performance of a detector. A higher AP value indicates better detector performance. The core indicator for evaluating the performance of a detector is the AP value, with higher values indicating better performances. Precision measures the ratio of correctly detected airport field targets to all targets detected by the sensor. Recall measures the ratio of correctly detected airport field targets to all actual targets on the ground. The calculation method for both _Precision_ and _Recall_ is shown in Formula (8).  
\[\left\{\begin{aligned} & Precision=\frac{TP}{TP+FP}\\ & Recall=\frac{TP}{TP+FN}\end{aligned}\right. \tag{8}\]  
The mean average accuracy of the object keypoint similarity (OKS) calculation is chosen as the criterion for judging the accuracy of human keypoint detection. OKS determines the network performance by calculating the similarity between the predicted skeleton points and the actual locations on the ground and setting different scaling ranges. It is  
\begin{table}
\begin{tabular}{c c} \hline
**IIAR-30 Dataset** & **Detailed Information** \\ \hline Target detection & Eight volunteers of different ages and genders acquired 1735 images from various angles at various times, totaling 3889 human targets. The average height of the volunteers was about 1.65 m-1.8 m, and their weights were about 60 kg-100 kg. \\ \hline Human keypoints & Each human body is labelled with 13 skeletal points: 0 nose, 1 left shoulder, 2 right shoulder, 3 left elbow, 4 right elbow, 5 left wrist, 6 right wrist, 7 left \\ dataset & hip, 8 right hip, 9 left knee, 10 right knee, 11 left ankle, 12 right ankle. \\ \hline Behavioral & 506 videos were used as the behavioral recognition dataset, and six types of actions were included in the dataset, i.e., standing, walking, running, crouching, touching the paddle, and climbing. The duration of each video was in the range of 2–8 s. Volunteers usually walked at a speed of about 3 to 4 miles per hour (4.8 to 6.4 km per hour). Their running speed usually ranged from 6 to 8 miles per hour (9.7 to 12.9 km per hour). \\ \hline \end{tabular}
\end{table}
Table 1: Detailed information on the IIAR-30 dataset.  
calculated as shown in Formula (9). \(s\) and \(d_{k}\) represent the category and Euclidean distance of the skeleton points, respectively, and \(\delta\) is the scale factor of the target.  
\[\left\{\begin{aligned} KS&=\exp\left(-\frac{d_{k}^{2}}{2 s^{2}\sigma_{k}^{2}}\right)\\ OKS&=\frac{\sum_{k}\exp\left(-\frac{d_{k}^{2}}{2 s^{2}\sigma_{k}^{2}}\right)\delta(v_{k}>0)}{\sum_{k}\delta(v_{k}>0)}\end{aligned}\right. \tag{9}\]