esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 5 Performance Evaluation  
In this section, resilience against attacks, energy consumption, communication, and computation overheads are utilized to assess the proposed protocol. This choice is informed by the fact that these parameters are some of the most common metrics for evaluating authentication and key agreement schemes.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 3 Proposed Method  
Cloud computing supports several service types with resource management. Thus, SP schemes have to consider various service types and objectives (energy efficiency [41; 42; 43; 44; 45; 46], performance [47], and cost). In the proposed system, the input metric for multi-objective factors, such as performance and energy efficiency, is not limited.  
We propose SP schemes that include the following functions.  
* Determine services that need to be placed through resource monitoring for the detection of overload or underload.
* Determine the target machine based on the AHP system by calculating weights such as prediction values for resources.
## 4 Results and Discussion
### Consensus on Benefits
#### 4.2.4 Reduce Time of Data Finding  
Reducing time of data finding was a benefit reported with consensus by 11 papers. This data finding ability that AR offers reduces wasted time and enhances the flow of information [30; 44]. Finding building elements without looking at maps or complex 3D models is considered to be time saving [45]. In addition, [PERSON] et al. (2013) stated that AR had a positive impact on site monitoring, task management, and real-time information sharing [44]. AR improves the performance of existing on-site management processes and gives direct access to project information databases resulting in finding the location of construction resources on site without any time-consuming effort. Therefore, AR application offers the benefits of quick access to soft data and reduces the time for data searching regardless of the type of device in use. Having a consensus between a number of papers on this benefit highlights the potential of AR during communication use cases for inspections and sharing of information.
## 4 Results and Discussions
### Key Findings  
#### 4.2.1 Performance of the Three Setups  
Initially, the three setups were executed without employing the proposed machine learning-based assistive algorithms to validate whether significant differences in terms of average shopping time among them were present. This baseline assessment aimed to ensure that all three setups were unbiased before proceeding with validation runs. As depicted by the solid lines shown in Figure 8, over the course of 100 shoppers, the average time per item for every block of shoppers was consistent across all three setups, with variation within half a second. For clarity, the trend lines (dash lines) for of the three setups reveal a smooth pattern, interpreted as a consistent performance throughout the entire duration of 100 shopping scenarios, thereby underscoring the reliability of the baseline assessment.  
The same data were scaled using the scaling Equation (1), and its output is graphed to show if there are significant changes during the scaling process. As shown in Figure 9, the scaled data are almost identical to the data in Figure 8, verifying the legitimacy of the equation used. Furthermore, this result allowed us to continue the validation runs while ensuring the scaled data are valid.
# Application of the Random Decrement Technique in Damage Detection under Moving Load
## 5 Conclusions  
This paper proposed an output-only method in which the random decrement technique was employed to produce auto- and cross-RDSs under moving load. Then, AI was calculated to find the energy content of each individual RDS. These AIs were normalized, and a parameter \(\beta\) was defined as a normalization factor, thereby highlighting the maximum normalized AI along the structure, which could show the vicinity of damage location accurately. Since the proposed method could calculate the \(\beta\) for an intact structure using a linear pattern, there was no need to calculate the response of the non-damage structure.  
The main results of the research can be summarized as follows:  
1. It was proved that the damage location could be detected using acceleration data, recorded along a structure.
2. Investigating the effect of load-moving velocity, it was shown that the normalizing factor had an inverse linear relationship with velocity.
3. The accuracy of the proposed method was examined through two models, namely a simply supported beam and an arch cable bridge.
4. It was indicated that the proposed method could easily find the location of a single damage on a noisy signal.  
At the end of the research, it is noticed that the proposed method could locate the damage with no need to calculate the modal parameters; this could effectively reduce the time and cost of SHM through this method compared to other approaches to SHM. Hence, the proposed method provided an efficient, real-time, modal-free tool for damage detection of complex structures. This topic will be further studied by the authors in their future research under seismic loadings.  
At this time, the proposed method is only verified for structures subjected to the moving load. Other type of excitations, such as seismic loading, will be considered in our future works.  
All the authors contributed to the research. [PERSON] conceived and designed the experiments. He, together with [PERSON]-[PERSON], performed the laboratory models and recorded the data. [PERSON]-[PERSON] and [PERSON]-[PERSON] guided the research and provided advice to ensure the accuracy of results. The paper was finally written by [PERSON] and confirmed by all the authors.  
Special thanks to Zhejiang University, which supported this work, and provided financial sources ([PERSON] foundation in China and Natural Science Foundation of China (NSFC, No. 51178416)).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Skeleton-Based Dynamic Hand Gesture Recognition Using an Enhanced Network with One-Shot Learning
## 4 Experiments
### Datasets
#### 4.1.2 MSRA Hand Gesture Dataset  
The public MSRA [19] hand gesture dataset, which contains skeleton-based sequence data of 17 right-hand gestures performed by 28 participants, is chosen to verify the robustness of the GREN network. The 17 right-hand gestures are manually chosen and are mostly from American Sign Language, to span the space of finger articulation as much as possible. Additionally, the length of each gesture varies from 490 to 500 frames. Each of these frames contains the coordinates of the 21 joints in the 2D depth image space and 3D world space, and those joints are shown in Figure 3.
# Photo-Transformation of Effluent Organic Matter by ZnO-Based Sunlight Irradiation
## 3 Results and Discussion
### Estimating the Impact of EOM on Receiving Waters  
The three PARAFAC components had a strong correlation with the EOM contributions to the receiving water (Table S3), which suggested that these components could be used as indicators for the assessment of the impact of wastewater. Discharge from the WWTP accounts for a large proportion of the river flow for most of the year, meaning that the impact of the wastewater on the river can be evaluated based on the river and wastewater flow data. In the present study, we estimated the impact of the wastewater (i.e., % wastewater contribution) at the downstream sampling point in the Han River using the PARAFAC components and the flow rates. Flow data were recorded for three months (from 1 May to 31 August 2019) at both upstream and downstream points obtained from the Han River Flood Control Office (Table S4). The daily discharge volume from the WWTP into the Han River was available from the Seoul Metropolitan Government (Table S5). Using the PARAFAC component-based method, the EOM levels were measured as the total mass of organic carbon (kg) discharged from the WWTP, which could be calculated using DOC and flow data from the WWTP. The total mass of organic carbon at the downstream site was assessed in terms of the %EFOM, which was calculated using Equation (1). Using the flow-based method, the percentage wastewater impact at the downstream site was calculated using Equation (2).  
\[\%EfOM=\ \frac{\text{mass of organic carbon from WWTP effluent to the downstream site (kg)}}{\text{mass of organic carbon present at the downstream site (kg)}}\times 100 \tag{1}\]  
\[\%\text{WW}_{\text{flow}}=\ \frac{\text{flow of WWTP effluent to the downstream site \left(m^{3}\right)}}{\text{river flow at the downstream site (m^{3})}}\times 100 \tag{2}\]  
Based on the flow data at the downstream site, %EFOM and %WW\({}_{\text{flow}}\) were determined to be \(3.8\pm 1.7\%\) and \(3.7\pm 1.6\%\), respectively. This indicates that combining the PARAFAC components with the flow data can be successfully used to assess the impact of wastewater on receiving waters.
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 2 Materials and Methods  
### Study Area  
As a case study in this article, a section of the Cigher River was selected, a left tributary of the Crisul Alb River, with a length of 18.3 km, located between 46\({}^{\circ}\)17\({}^{\prime}\)41.425\({}^{\prime\prime}\) N, 21\({}^{\circ}\)55\({}^{\prime\prime}\)9.597\({}^{\prime\prime}\) E (upstream) and 46\({}^{\circ}\)20\({}^{\prime}\)55.980\({}^{\prime\prime}\) N, 21\({}^{\circ}\)48\({}^{\prime}\)52.661\({}^{\prime\prime}\) E (downstream). The Crisul Alb River Basin (Figure 1), which includes the Cigher River sub-basin, belongs to the Crisuri hydrographic space, situated in the western part of Romania, largely overlapping with Arad County [54]. The basin extends approximately along the SE-NW direction.  
Figure 1: The localization of the study area (processed based on [54; 55]).  
In the Cigher River basin, according to the Digital Elevation Model [56], the elevation varies between 95 and 792 m. Low-altitude areas are predominant in the northern half, while the southern half is dominated by hills. This elevation variation indicates a diverse topography, with steep slopes in the south, influencing the distribution and \"behavior\" of the rivers. In high-altitude areas, water flows at a higher speed and can accumulate in low-lying areas where the slope and, consequently, the flow velocity decreases. These areas become susceptible to flooding, particularly during periods of intense rainfall coupled with rapid snowmelt. The high-altitude areas are prone to slope processes (erosion, landslides), which affect slope stability, soil structure, and sediment regimes and, consequently, may impact the hydrological regime and the \"behavior\" of rivers.  
From a climatic perspective, in the high-altitude areas of the Cigher basin, the annual average temperatures range between 4 and 6 \({}^{\circ}\)C, and precipitation exceeds 1000 mm. In the hilly areas, temperatures range from 8 to 10 \({}^{\circ}\)C with precipitation between 600 and 800 mm, while in the lowland areas, temperatures are 10-11 \({}^{\circ}\)C with precipitation between 500 and 600 mm [54; 57].  
Generally, there is a tendency for the multi-annual average precipitation to increase with altitude; however, the annual variation of precipitation is particularly significant. In most cases, abundant spring precipitation combined with the sudden melting of snow causes flash floods and overflows, resulting in severe environmental impacts.
## 1 Introduction
In particular, the acoustic impedance (Z) of aluminum is 17.33 MRayls, so the impedance mismatch at the boundary of aluminum and skin (Z = 1.53 to 1.68 Mrayls) causes acoustic shadow artifacts that appear as anechoic signals in the image, causing the aluminum to act as an absorber [28]. However, in US images, tissues at the attachment site are not observed because of these acoustic artifacts, and the skin surface at the attachment site is also covered with an opacity absorber, which hinders the confirmation of the attachment site with the naked eye [28, 29]. Additionally, the risk of injury when using thin aluminum requires caution. However, polyurethane, which is a non-metallic material, has a Z of 1.80 MRayls, resulting in a lower Z difference with the skin than that of aluminum [28]. Furthermore, the degree of acoustic shadow artifacts is low; therefore, it is hypoechoic in the image [22]. Thus, in diagnostic US images, both metallic and non-metallic absorbers can aid in controlling the signal intensity from anechoic to hypoechoic because of the Z difference between the body tissues and the absorber. With the advent of various non-metallic materials, materials that are suitable as absorbents are becoming increasingly diverse. In particular, the transparent nature of polyurethane allows visual confirmation of its attachment site on the skin [22, 28]. In addition, it can be manufactured in various shapes and as thin biomedical phantoms [30, 31], exhibits elasticity and waterproofing properties, and serves as a dressing tool to prevent infection in wounds through a sterile dressing tool [32; 33]. Polyurethane is characterized by a high degree of safety because it is consumable in existing clinical practice and can also act as an absorber in US diagnosis. However, despite the use of various types of absorbers, most absorbers use metals and involve the risk of allergies and injuries. Recently, non-metallic absorbers have been used to compensate for the shortcomings of these metals. Because low-echoic images can be confirmed at the location of the absorber, new ways to use them as absorbers must be devised. In addition, because metal absorbers appear anechoic, no research has been conducted to evaluate the signal intensity in images. However, polyurethane is non-metallic and appears hypoechoic; therefore, the changes in signal intensities in images can be compared. In this study, we examined the efficiency of distinguishing between the fascia and surrounding tissues and evaluated a new function to confirm the location of the absorber by adding shading to the image. A polyurethane absorber (PU) was attached to the RA, a large, long, and flat muscle of the abdomen comprising various tissues, and US images were obtained to compare and quantitatively analyze the changes in the signal intensities of the fascia and muscle depending on whether the absorber was used.
# Design and Testing of Two Haptic Devices Based on Reconfigurable 2R Joints
## 3 Methodology of the Experiment
### Statistical Tools  
In order to process the large amount of data that were obtained during the experiment, statistical analysis tools were used. These tools can indicate if the difference between compared sample means is statistically significant. In order to do this, the probability of means being the same is calculated. If this probability is smaller than a certain number \(\alpha\), the difference between the sample means is considered to be significant. In most cases a value of \(\alpha\) equal 0.05 [44] is acceptable.  
In order to select proper statistical tools, the analyzed data distribution type should be identified. If the data have a normal distribution, parametric methods should be used; in the other case, or when the samples have a mixed data type--non-parametric methods should be used. The normality check can be implemented in two ways--graphical and numerical. The graphical method consists of a comparison of the sample distribution graph with the normal distribution graph. It is simple and fast; however, in some cases it cannot provide a definite result. The numerical methods are more reliable and provide definite results. There are many variations of numerical normality checks. In this study, the Shapiro-Wilk test was used as it provides higher statistical power [45; 46] than the most  
Figure 3: The trajectories (in orange) used in task three (**a**) and used in task four (**b**).  
commonly used Kolmogorov-Smirnov test (KS-test) [47]. The Shapiro-Wilk test can be defined as  
\[W=\frac{\left(\sum_{i=1}^{n}a_{i}x_{(i)}\right)^{2}}{\sum_{i=1}^{n}(x_{i}-\overline {x})^{2}} \tag{8}\]  
where \(x_{(i)}\) is the \(i\)-th smallest value of the sample (not to be confused with \(x_{i}\), the \(i\)-th value of the sample); \(a_{i}\) is a coefficient, taken from the special tables related to this test; and \(\overline{x}\) is the sample mean.  
In this test, the null hypothesis, or the first assumption, was that the studied sample had a normal distribution. If the value of \(W\) that was obtained from the calculation exceeds the critical value appropriate for the test, the null hypothesis has to be rejected and the data distribution is not normal. The critical value is taken from special tables, depending on the sample size and the value of \(\alpha\).  
In this study, the tasks required the analysis of two samples at a time, which was accomplished using paired and unpaired \(t\)-tests for data with a normal distribution and Wilcoxon's signed rank test and tge Mann-Whitney test for the mixed and non-normally distributed data [48]. In the comparisons which involved samples with interconnected data (such as tasks 1 and 2, the control mode comparisons), the paired \(t\)-test and [PERSON]'s signed rank test were used. In cases where data did not have any connection (joint performance comparison), the unpaired \(t\)-test and the Mann-Whitney test were used.
## 2 Materials and Methods
### Research Methodology
#### 2.3.2 Accuracy Verification  
In this paper, the impermeable surfaces are extracted in the form of an image. The extraction accuracy is verified by calculating the coefficient of determination (\(R^{2}\)), root mean square error (\(RMSE\)), and systematic error (\(SE\)) [48] for 200 sample points of images per region. The \(R^{2}\) and \(SE\) can be used to measure the systematic error and the goodness of fit of the simulated impervious surface coverage values with respect to the true coverage values, respectively. The \(RMSE\) is highly sensitive to very large or small errors in a set of measurements, and can effectively reflect the precision of the measurements [49]. The expressions for calculating \(R^{2}\), \(RMSE\) and \(SE\) are as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\left(x_{1}-x_{i}\right)^{2}}{\sum_{i=1}^{N}\left( x_{1}-x_{2}\right)^{2}}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(x_{i}-x_{1}\right)^{2}}{N}}\]  
\[SE=\frac{\sum_{i=1}^{N}(x-x_{i})}{N}\]  
where \(x\) is the impervious surface estimate, \(x_{1}\) is the true impervious surface value, \(x_{2}\) is the mean of the true impervious surface value, and \(N\) is the sample size. The smaller the values of \(RMSE\) and \(SE\), the smaller the impervious surface estimation error. Values of \(SE>0\) and \(SE<0\) mean that the impervious surface is overestimated and underestimated, respectively.  
The error in extracting the impervious surface area is obtained as follows:  
\[relative\ error=\frac{x_{i}-x}{x}\]  
where \(x\) and \(x_{1}\) are the estimated and true impervious surface areas, respectively.
# Zinc-Decorated and Nitrogen-Functionalized Hierarchical Porous Carbons for Carbon Dioxide Capture
###### Abstract  
This study developed a highly facile method to synthesize Zn-decorated and nitrogen-doped hierarchical porous carbons for carbon dioxide (CO\({}_{2}\)) adsorption. Zeolitic imidazolate framework-8 (ZIF-8) was used as the raw material, which was subjected to a thermal treatment to obtain ZIF-8-derived carbons (ZDCs) in order to develop nanocarbons with a stable framework structure, a high CO\({}_{2}\) adsorption capacity, and high selectivity under normal pressure. The crystallinity evolution of the samples changed from the typical ZIF-8 structure to having features of graphite carbons upon heating. The average particle sizes of the products were between 34 and 105 nm, and the specific surface areas ranged from 618 to 1862 m\({}^{2}\)/g. The nitrogen and zinc contents gradually decreased with increasing carbonization temperatures, but the changes in the distributions of the functional groups were different. The interactions between CO\({}_{2}\) and the ZDCs were significantly enhanced, resulting in a higher isosteric heat of adsorption. The ZIF-8 carbonized at 1123 K exhibited the highest CO\({}_{2}\) uptake, i.e., 3.57 mmol/g at 298 K and 101.3 kPa, while higher CO\({}_{2}\) uptakes at 15 kPa occurred on the ZIF-8 carbonized at 923 and 1023 K due to their high isosteric heat of adsorption of CO\({}_{2}\). The higher adsorption selectivity of Z8-650 for CO\({}_{2}\) over N\({}_{2}\) may be due to its higher \(V_{\text{-0.7 nm}}/\text{V}_{\text{mi}}\) ratio and nitrogen and zinc contents. Consequently, the micropore area ratio and surface functional groups primarily determined the CO\({}_{2}\) adsorption capacity at 15 kPa. In addition, an appropriate metal Zn to Zn\({}^{2+}\) ratio may have a positive effect on CO\({}_{2}\) adsorption. On the other hand, the ultramicropore volume ratio, micropore area, and SSA played more significant roles at 101.3 kPa of pressure.  
z pacs: 15, 5743 +
Footnote †: journal: Applied Sciences
## 2 Field Monitoring Program and Data Collection
### Leakage Current Measurement  
The leakage current of the testing insulator string in the test station was measured and recorded from June 2016 to December 2018. According to the measurement results, the leakage current of the testing insulator string was around 0.1 mA\(\sim\)0.3 mA in the first 6 months (from June 2016 to December 2016) because the surface of the insulator was clean enough. However, the leakage current gradually increased in November and showed a significant positive correlation with humidity. Figure 7 illustrates the relationship between the leakage current and humidity.  
Throughout the monitoring period, the long-term trend in leakage current reveals a significant and sharp variation from October to March of the following year. This phenomenon is attributed to occasional monsoons that introduce heavy salt contamination and industrial pollution. Moreover, the test station experienced low rainfall during this period, leading to the gradual accumulation of contamination and pollution on the insulator's surface, resulting in an increase in leakage current. After March, the trend in leakage current stabilizes, indicating a substantial reduction when rain occurs due to the prevention of rain washing on the insulator's surface. The primary reason for this stabilization is the low amount of salt contamination.  
Figure 5: The ratio of water-soluble constituents.  
Figure 6: The ratio of non-soluble constituents.  
surface [23]. Both wind and humidity are significant factors contributing to the increase in leakage current. A strong correlation between humidity and leakage current is evident in Figure 7. Therefore, in this study, a set of weather stations was placed around the test station to measure and record local weather data, including temperature, relative humidity, pressure, wind speed, ultraviolet radiation, and other parameters, around the test insulator every minute.