esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Device Based on an LED Matrix for Water Sterilization
## 2 Materials and Methods
### Optical Characterization and Degradation Tests  
To assess and compare the optical performance of various commercial LEDs, a characterization was conducted using UV-NIR spectrophotometric measurements. This was achieved using a fiberoptic spectrophotometer (AvaSpec2048-USB2, Avantes, Apeldoorn, Netherlands), which provided spectra within the wavelength range of 200 nm to 1100 nm. The LEDs were powered by a current-regulated power supply. Qualitative spectra, representing UV light emissions, were acquired before and after encapsulation for each commercial LED (Table 1).  
Additionally, to investigate potential degradation due to moisture exposure, tests were conducted underwater, in a volume of 5 L and at a depth of 20 cm, lasting for 700 h.
# QiOi: Performance Isolation for Hyperledger Fabric
## 8 Conclusions  
In cloud data centers, the Fabric services may run concurrently with co-located services. In such an environment, Fabric services may experience performance interference. We demonstrate that performance interference of Fabric indeed occurs and it is due to the scheduling delay caused by co-located services. To mitigate performance interference, we present QiOi, component-level performance isolation technique for Hyperledger Fabric. The technique is based on dynamically controlling the _CPU share_ of Fabric components. Our evaluation results show that QiOi mitigates performance degradation of Fabric by 22% and improves Fabric latency by 2.5 times without sacrificing the performance of co-located services.  
Conceptualization, J.K. and K.L. ([PERSON]); methodology, J.K. and K.L. ([PERSON]); software, J.K. and J.I.; validation, K.L. ([PERSON]) and G.Y.; investigation, J.K., K.L. ([PERSON]), G.Y., K.L. ([PERSON]) and J.I.; writing--original draft preparation, J.K. and K.L ([PERSON]); writing--review and editing, J.K., K.L. ([PERSON]), G.Y., K.L. ([PERSON]), J.I., and C.Y.; visualization, J.K.; supervision, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2015-0-00280 (SW Starlab) Next generation cloud infra-software toward the guarantee of performance and security SLA). This research was also supported by Next Generation Engineering Researcher Program of National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (No. NRF-2019H1 DSA2105513).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We appreciate the anonymous reviewers.  
The authors declare no conflict of interest.
## 2 Experimental Section
### Characterization and Electrochemical Measurements  
The morphology of the rGOP was investigated by field emission scanning electron microscopy (FE-SEM, S-4800, Hitachi Co. Ltd., Tokyo, Japan) at an accelerating voltage of 10 kV. The distribution of elements in crystals was characterized using an energy dispersive X-ray spectrometer (EDX) that was attached to SEM. Raman spectrum (SENTERRA, Karlsruhe, Germany) was used for showing the intrinsic structure between GOP and rGOP. The compositional analysis of the samples was checked by X-ray photoelectron spectroscopy (XPS, ESCALAB250 Xi, Thermo Scientific, Waltham, USA). Chronoamperometry, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were carried out using an electrochemical workstation (CHI-760E CH Instruments, Shanghai, China). Both CV and EIS measurements for rGOP were performed in PBS (pH 7.4). Chronoamperometry was used to investigate the sensing performance of electrodes. Different potentials of 0, \(-\)0.05, \(-\)0.1, \(-\)0.15, \(-\)0.2, \(-\)0.25 and \(-\)0.3 V were set to carry out the optimization. Before measurement, all solutions used in electrochemical tests were bubbled with high pure nitrogen for 15 min to thoroughly remove oxygen from the solution.
## 3 Methodology
### Databases and Keyword Searching  
To ensure a comprehensive literature search, this research review will adhere to the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines [63]. The literature review process was divided into several major phases, as illustrated in Figure 2. The Scopus and Web of Science (WoS) databases were utilized as primary sources, while additional exploration of references and citations were conducted to identify relevant studies. The search strategy encompasses scholarly journals, conference studies, and proceedings, with a restriction on publication years to no earlier than 2017.
###### Abstract  
With the rapid development of agile Earth observation satellites (AEOSs), these satellites are able to conduct more high-quality observation missions. Nevertheless, while completing these missions takes up more data transmission and electrical energy resources, it also increases the coupling within each satellite subsystem. To address this problem, we propose a reasoning-based scheduling method for an AEOS under multiple subsystem constraints. First, we defined the AEOS mission scheduling model with multi-subsystem constraints. Second, we put forward a state variable prediction method that reflects the different coupling states of a satellite after analyzing the coupling relationships between various subsystems and identifying the primary limiting coupling states for each subsystem. Third, we established the reasoning rules corresponding to the planning strategies of different coupling states of the satellite by adding two planning strategies based on the planning strategies of existing planning methods. By comparing the proposed method to three heuristic scheduling methods and a meta-heuristic scheduling method, the results show that our method has better performance in terms of scheduling results and efficiency.  
a pacs: **Keywords:** agile satellite; Earth observation; mission scheduling; multi-subsystem; knowledge reasoning; state prediction
## 3 Energy-Saving Optimization Model
### Energy-Saving Optimization Algorithm Flow
#### 3.3.1 Time Dimension  
Assuming time-type appliances only have two operating statuses (on/off), the key to energy-saving optimization for general time-type appliances (such as lights, televisions, and so on) in the time dimension is to decide whether an appliance should be on or off at a certain moment.  
The time-type appliance optimization algorithm uses the real-time energy-saving costs as the evaluation basis, and the whole process is shown in Figure 2.  
The above process can be divided into the following steps:  
1. Recognize the operating status of the electrical appliance using the automatic recognition method of the appliance operation status with real-time collected operating power data;
2. Select the power demand mining method which applies to the appliance according to the characteristics of electrical appliances. Then, the current demand for power consumption is discriminated;
3. Evaluate the real-time time utility according to the electrical appliance operation status and the power demand;
4. Calculate the energy-saving costs if the electrical operation mode is changed (previously turned on, assumed to be off; or closed initially, and assumed to be on);
5. Determine the relationship between the energy-saving costs and the energy-saving threshold \(\xi\). If the energy-saving cost value is lower than \(\xi\), the optimization strategy is to change the electrical operation mode; if it is higher than \(\xi\), the optimization strategy is to keep the original operating mode unchanged.
# Investigation of Aerosol Climatology and Long-Range Transport of Aerosols over Pokhara, Nepal
## 2 Methodology
### Cimel Sun Photometer  
An automatic sun-and-sky scanning Cimel Sun Photometer is located on the roof of the Shangrila Village Resort in Pokhara's south-western suburbs, as a part of AERONET. Descriptions of this network and methods for retrieving aerosol optical data have been published before [12; 14; 20].  
The sun photometer measures direct sun radiances at 0.34 um, 0.38 um, 0.44 um, 0.50 um, 0.675 um, 0.87 um, 1.02 um and 1.64 um wavelengths during the daytime. The aerosol optical depth (\(\tau_{\text{AOD}}\)) is derived by correcting attenuation due to Rayleigh scattering, absorption by ozone, and gaseous  
Figure 1: The map with the aerosol ground monitoring station (Pokhara, Nepal) has an inset in the bottom right corner that shows the Aerosol Robotic Network (AERONET) sun photometer in Pokhara (photo by [PERSON]) and a compass rose on the left corner. This image is adopted from online resources, and horizontal and vertical scales in 200 miles are shown on the map.  
components in direct spectral measurements [12; 14; 15]. In addition, the sun photometer also measures diffuse sky radiance at four wavelengths 0.44 \(\mu\)m, 0.675 \(\mu\)m, 0.87 \(\mu\)m, and 1.02 \(\mu\)m [16; 14; 20]. These solar extinction measurements are used to retrieve aerosol columnar inversion products, such as volume size distribution and single scattering albedo. Three data quality levels, Level 1.0 (unscreened), Level 1.5 (cloud screened), and Level 2.0 (cloud-screened and quality-assured), are provided for analysis of aerosol data [16; 20], with Level 2.0 data made available after the instrument is returned to NASA during annual swap outs. All products of AERONET are automatically computed and made available within the AERONET website [15]. The estimated uncertainty in computed AOD is reported to range from \(\pm\)0.01 to \(\pm\)0.02, which is spectrally dependent, and is found higher on the UV region [12; 16; 23].
# Exploring the Use of Landmarks to Aid Pedestrian Wayfinding
## 3 Method
### Design  
This study used a 'between-subjects' design in which participants were randomly assigned to one of the three groups: the LM, GM, or PM Group. In Phase 1, participants in the LM and GM Groups followed the pre-set route guided by their respective booklets. The LM Group's route consisted of eight segments with an on-route landmark assigned to each turn (a decision point) and almost every segment (a confirmation point). Sixteen on-route landmarks were shown in the LM booklet. The GM Group's booklet provided Google Maps screenshots of the route, showing each of the eight route segments. Figure 9 depicts the locations of the 16 on-route landmarks shown to the LM Group in their booklet and the route used by the LM and GM Groups. Participants in the PM Group were given the A4 map and instructed to take the quickest route to the destination. All participants were timed and instructed to walk at a comfortable pace. Route-following accuracy was to be prioritized over travel speed. Timing was paused when stopped at traffic lights. The researcher provided instructions directing LM and GM Group participants back to the last decision point/intersection to help them re-orientate and attempt the route again if they made an error that took them off the route for 20 s. Hesitations of 20 s or more by the LM or GM Group participants were noted. Participants in the PM Group were asked to give a quick overview of their planned route before starting their walk, enabling the researcher to know if/when a participant had deviated from their planned route. All walks were completed between 10:30 a.m. and 4:00 p.m. during winter. Timing was captured on an Apple iPhone stopwatch, and the researcher relied on his substantial local knowledge of the test area to guide participants back to their route if they strayed from it.  
Figure 8: Overview of the materials used.  
Figure 9: The route followed by the LM and the GM Groups in Phase 1, with the 16 on-route landmarks used in the LM Group’s booklet indicated. Also shown are the off-route landmarks used in Phase 3. Map data © Mapbox, © OpenStreetMap.  
In Phase 2, all participants drew the route they had traveled as accurately as possible on the unlabeled A3 paper map that depicted the start point and the destination. In Phase 3, participants attempted to locate 16 numbered landmarks (eight on-route; eight off-route--on another identical unlabeled A3 paper map). The photographs of landmarks were arranged in random order with the numbers applied in sequential order. Eight of the on-route landmarks were used in the LM Group's booklet in Phase 1, and the eight off-route landmarks were not. The same order was used for all participants. The off-route landmarks were located between 100 and 500 m from the route. Participants located the landmarks by writing the number of each photograph showing the relevant landmark on the map. A landmark located within 100 m of its actual position was regarded as correct. Figure 10 summarizes the study design.
## 3 Methodology
### Performance Metrics  
The study employed four established evaluation metrics for regression machine learning tasks to assess the performance of the models.  
Mean Absolute Error (MAE): This metric is frequently used in regression tasks to gauge model accuracy. It computes the average of absolute differences between predicted and actual values, providing insight into the average deviation of the model's predictions from the real data (Equation (20)).  
\[MAE=\frac{1}{n}{\sum_{i=0}^{n-1}}(|y_{i}-\hat{y_{i}}|) \tag{20}\]  
where \(y_{i}\) is true value, \(\hat{y_{i}}\) is predicted value, and n is the total number of observations.  
Root Mean Square Error (RMSE) is a key metric for assessing the accuracy of regression models, reflecting the discrepancy between predicted values and actual observations. RMSE is calculated as the square root of the average of the squared errors, thereby emphasizing larger deviations (Equation (21)).  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=0}^{n-1}}(y_{i}-\hat{y_{i}})^{2}} \tag{21}\]  
where \(y_{i}\) is true value, \(\hat{y_{i}}\) is predicted value, and n is the total number of observations.  
R-squared (R\({}^{2}\)) is a metric that indicates how effectively a model accounts for the variability of the dependent variable, with values ranging from 0 to 1. An R\({}^{2}\) value close to 1 suggests a good fit between the predicted and actual values, while lower values may indicate poor model quality (Equation (22)).  
\[R^{2}=1-\frac{SS_{res}}{SS_{tot}} \tag{22}\]  
\(SS_{res}\) represents the sum of squared residuals, which measures the differences between actual and predicted values, while \(SS_{tot}\) denotes the total sum of squares, reflecting the differences between actual values and their mean.  
Average Localization Error (AE) is a metric that quantifies the average discrepancy between predicted values and actual values within a dataset. This metric offers valuable insight into the accuracy of a predictive model or algorithm. The AE is presented below:  
\[AE={\sum_{i=1}^{n}}\bigg{(}\bigg{(}X_{real}-X_{pred}\bigg{)}^{2}+\bigg{(}Y_{ real}-Y_{pred}\bigg{)}^{2}\bigg{)}^{0.5} \tag{23}\]  
where \(X_{real}\) and \(Y_{real}\) are actual coordinates, \(X_{pred}\) and \(Y_{pred}\) are predicted coordinates, and n is the total number of data points.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
###### Abstract  
Groundwater is a critical yet understudied resource in Peru, where surface water has traditionally dominated national assessments. This study provides the first country-scale analysis of groundwater storage (GWS) variability in Peru from 2003 to 2023 using satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions. We used the GRACE Data Assimilation-Data Mass Modeling (GRACE-DA-DM GLV3.0) dataset at 0.25\({}^{\circ}\) resolution to estimate annual GWS trends and evaluated the influence of El Nino-Southern Oscillation (ENSO) events and anthropogenic extraction, supported by in situ well data from six major aquifers. Results show a sustained GWS decline of 30-40% in coastal and Andean regions, especially in Lima, Ica, Arequipa, and Tacna, while the Amazon basin remained stable. Strong correlation (r = 0.95) between GRACE data and well records validate the findings. Annual precipitation analysis from 2003 to 2023, disaggregated by climatic zone, revealed nearly stable trends. Coastal El Nino events (2017 and 2023) triggered episodic recharge in the northern and central coastal regions, yet these were insufficient to reverse the sustained groundwater depletion. This research provides significant contributions to understanding the spatiotemporal dynamics of groundwater in Peru through the use of satellite gravimetry data with unprecedented spatial resolution. The findings reveal a sustained decline in GWS across key regions and underscore the urgent need to implement integrated water management strategies--such as artificial recharge, optimized irrigation, and satellite-based early warning systems--aimed at preserving the sustainability of the country's groundwater resources.  
GRACE; GRACE-FO; droughs; national analysis; trends; water resource management 2025
# Effect of Soil Washing with Ferric Chloride on Cadmium Removal and Soil Structure
## 2 Materials and Methods
### Batch Experiment  
Batch-scale soil desorption experiments were performed to confirm the extraction of Cd from the Cd-contaminated paddy soil by washing agents. Briefly, 4.0 g of soil samples and 40 mL of 0.01 mol\(\cdot\)L\({}^{-1}\) NaCl were placed in 50 mL centrifuge tubes with screw caps and spiked with FeCl\({}_{3}\) and HCl, keeping a soil-to-suspension ratio of 1:10 (g/mL) in each sample. The suspensions were shaken at a constant shaking speed of 150 rpm at room temperature (25 \({}^{\circ}\)C), under the influence of initial solution pH values (2.0, 3.0, and 4.0) on Cd extraction from the soils adjusted by different amounts of FeCl\({}_{3}\); the extraction kinetics (at 0.17, 0.33, 0.50, 0.67, 0.83, 1.0, 3.0, 6.0, 12, 24, 72, 144, and 288 h) were evaluated under the above conditions. The experiment with an initial solution pH value of 2.0, adjusted by HCl, and the control experiment without pH adjustment were conducted under the same conditions. Each sample was collected using a 2.5 mL syringe and then filtered through a 0.22 \(\upmu\)m Millipore filter for Cd determination; we also analyzed dissolved organic carbon (DOC) and Fe (II). After the reaction, the residual soils were separated by static settling and supernatant removal for analyzing the speciation of Cd, soil mineral components, and total organic carbon (TOC). Each treatment was replicated three times.
# Anthropometric Profiles in Table Tennis Players: Analysis of Sex, Age, and Ranking
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Between-Subjects’ Main Effects**} & \multicolumn{6}{c}{**Between-Subjects’ Interaction Effects**} \\ \cline{2-13}
**Variable** & \multicolumn{2}{c}{**Sex**} & \multicolumn{2}{c}{**Age**} & \multicolumn{2}{c}{**Ranking**} & \multicolumn{2}{c}{**Sex’Age**} & \multicolumn{2}{c}{**Sex’Ranking**} & \multicolumn{2}{c}{**Age*Ranking**} \\ \cline{2-13}  & \multicolumn{1}{c}{**P**} & **ES** & **P** & **ES** & **P** & **ES** & **P** & **ES** & **P** & **ES** & **P** & **ES** \\ \hline \multicolumn{13}{c}{Stature (cm)} & 0.036 * & 0.01 & \(<\)0.001 * & 0.51 & 0.026 * & 0.01 & \(<\)0.001 * & 0.11 & 0.012 * & 0.01 & 0.109 * & 0.02 \\ Body mass (kg) & 0.113 & 0.01 & \(<\)0.001 * & 0.38 & 0.146 & \(<\)0.001 * & 0.07 & 0.015 * & 0.01 & 0.019 * & 0.02 \\ BMI (kg/m\({}^{2}\)) & 0.697 & \(<\)0.01 & \(<\)0.001 * & 0.09 & 0.477 & \(<\)0.01 & 0.194 & 0.01 & 0.124 & \(<\)0.01 & 0.072 & 0.02 \\ Arm relax (cm) & 0.088 & 0.01 & \(<\)0.001 * & 0.18 & 0.056 & 0.01 & \(<\)0.001 * & 0.04 & 0.044 * & 0.01 & 0.695 & \(<\)0.01 \\ Arm reflex (cm) & 0.092 & 0.01 & \(<\)0.001 * & 0.22 & 0.040 * & 0.01 & \(<\)0.001 * & 0.06 & 0.031 * & 0.01 & 0.614 & 0.01 \\ CSA Arm (cm) & \(<\)0.001 * & 0.06 & \(<\)0.001 * & 0.28 & 0.011 * & 0.01 & \(<\)0.001 * & 0.26 & 0.109 & 0.01 & 0.578 & 0.01 \\ CSA Leg (cm) & \(<\)0.001 * & 0.05 & \(<\)0.001 * & 0.26 & 0.131 & \(<\)0.01 & \(<\)0.001 * & 0.07 & 0.776 & \(<\)0.01 & 0.252 & 0.01 \\ CSA High (cm) & 0.004 * & 0.02 & \(<\)0.001 * & 0.33 & 0.106 & 0.01 & \(<\)0.001 * & 0.08 & 0.577 & \(<\)0.01 & 0.326 & 0.01 \\ Bone (\%) & 0.009 * & 0.01 & \(<\)0.001 * & 0.23 & 0.496 & \(<\)0.01 & 0.044 * & 0.02 & 0.472 & \(<\)0.01 & 0.031 * & 0.02 \\ Fat mass (\%) & \(<\)0.001 * & 0.27 & 0.437 & 0.01 & 0.761 & \(<\)0.01 & 0.022 * & 0.02 & 0.268 & \(<\)0.01 & 0.005 * & 0.03 \\ Lean mass (\%) & \(<\)0.001 * & 0.14 & \(<\)0.001 * & 0.11 & 0.872 & \(<\)0.01 & 0.672 & 0.00 & 0.152 & \(<\)0.01 & 0.015 * & 0.03 \\ Endomorph & \(<\)0.001 * & 0.06 & 0.703 & \(<\)0.01 & 0.703 & \(<\)0.01 & 0.026 * & 0.02 & 0.131 & \(<\)0.01 & 0.038 * & 0.02 \\ Mesomorph & 0.028 * & 0.01 & 0.395 & 0.01 & 0.828 & \(<\)0.01 & 0.531 & 0.01 & 0.186 & \(<\)0.01 & 0.065 & 0.02 \\ Ectomorph & 0.421 & \(<\)0.01 & 0.083 & 0.02 & 0.736 & \(<\)0.01 & 0.213 & 0.01 & 0.594 & \(<\)0.01 & 0.142 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effects of sex, age, ranking, and their interactions on morphological characteristics of table tennis players for sex and age (n = 495).
## 4 Thin-Film Microextraction--TFME  
It arises from the theory of microextraction that the total amount of analyte, extracted into a solid phase in a solution in which equilibrium has been established, is directly proportional to the volume of the sorption phase according to the Equation:  
\[\mathrm{n}=\frac{\mathrm{C_{o}V_{p}V_{s}K}}{\mathrm{V_{p}+V_{s}K}} \tag{1}\]  
in which \(\mathrm{C_{o}}\) is the concentration of the analyte in the original sample, \(\mathrm{V_{p}}\)--the volume of the sample, \(\mathrm{V_{s}}\)--the volume of the sorption phase (extract), and K--the equilibrium constant. Thus, with an increase in the thickness of the solid sorption phase, the sorption efficiency increases; at the same time, the sensitivity of the analytical method in which microextraction is used also increases.  
Simultaneously, in accordance with the Equation (2) [16]:  
\[\mathrm{t_{95\%}}=\mathrm{B}\cdot\frac{\delta\mathrm{Kd}}{\mathrm{D_{f}}} \tag{2}\]  
Figure 5: Types of SPME extraction: (**a**) direct extraction, (**b**) superficial extraction, (**c**) protective membrane extraction.  
where the time in which a state of equilibrium is established in the sorption system is prolonged with the increase in the thickness of the extraction phase. In Equation (2), B--denotes the coefficient related to the geometry of the sorption surface, \(\delta\)--the thickness of the boundary layer, d--the thickness of the sorption surface, K--the equilibrium constant (the partition coefficient of the analyte between the sorption phase and the sample), and D--the diffusion coefficient of the analyte in the sample.  
The rate of solid-phase microextraction is determined by the Equation (3) [18]:  
\[\frac{\mathrm{dn}}{\mathrm{dt}}=\left(\frac{\mathrm{D}_{\mathrm{f}}\mathrm{A}} {\delta}\right)\mathrm{C}_{\mathrm{o}} \tag{3}\]  
is directly proportional to the surface area of the extraction phase. In Equation (3), D--denotes the diffusion coefficient of the analyte in the sample, A--the sorption area, \(\delta\)--the thickness of the boundary layer, and C\({}_{\mathrm{o}}\)--the initial concentration of the analyte in the sample. As a result, the geometry of the extraction phase, in the form of a thin film, for which the surface-to-volume ratio is large, causes an increase in the following:  
1. The rate at which the system reaches equilibrium;
2. The sorption capacity of the solid phase.  
Hence, the use of a microextraction device with a developed sorption surface increases the speed of sample execution, and it is also a suitable solution for trace analysis [9].