esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Analysis of Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Acronym** & **Metrics** & **Median** & **Standard** \\ \hline \multirow{3}{*}{Perception of the effectiveness} & PEOU & Perceived ease of use & 5.60 & 0.48 \\  & PUM & Perceived usefulness & 5.40 & 0.50 \\  & ITU & Intention to use & 5.70 & 0.40 \\ \multirow{3}{*}{Correctness range of scope Collaboration} & PUSO & Perceived usefulness & 5.40 & 0.64 \\  & PUSO & of obtained scope & 5.70 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of perception metrics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Artifact** & **Participation Degree (PD)** & **Factor Artifact History (HFar)** \\ \hline Initial meeting & SPL profile & 75 & 0.70 \\ Identify features & List features & 1520 & 0.84 \\ Identify products & List products & 7 & 0.75 \\ Identify functional & Functional & 15 & 0.69 \\ domains & domain list & 211 & 0.68 \\ Classify features in functional domains & 20 & Product map & 1340 & 0.87 \\ Tabulate products & & Product map & 29 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of quantitative indicators of the collaboration. method and the tasks, they consider that CoMeS-SPL facilitates the communication of ideas, contributions, and knowledge, evidencing the importance of the different roles. Furthermore, the company participants concluded that the role of the method named \"representative of potential customers\" must be a mandatory role and not an optional role as has been proposed. They consider this role to be imperative to get a correct scope. The team valued the software tool used to facilitate the exchange of contributions because it helped increase the number of proposals. This tool made it possible for the participants to know and consider the opinions of the other participants. The team indicated one improvement opportunity, that it is necessary to make an agreement on the wording and description granularity of the features before starting to propose and document them. In the study carried out, not agreeing in the level description of the features increased the time and effort that were required the after task identify features.
# Angioplasty Using 4-Hexylresorcinol-Incorporated Silk Vascular Patch in Rat Carotid Defect Model
## Results
### Artery Patency after Patch Angioplasty  
Gross artery patency was confirmed via the empty and refill test, and an acceptable degree of patency was observed in all groups during the operation (Figure 4). In the PTFE group, blood leakage was observed around the patch-covered area, and a sufficient amount of time was required to confirm hemostasis (Figure 4b). Less blood leakage and good adhesion to the covered arterial tissue and wall were observed in the 4-HR silk patch group (Figure 4c).  
Figure 1: Western blot analysis for 4-hexylresorcinol (4-HR). The application of 4-HR increased the expression level of vascular endothelial growth factor-A (VEGF-A) in (**a**) RAW264.7, and (**b**) HUVEC cells (C: untreated control).  
Figure 2: Blood coagulation test results. The absorbance of the 4-HR was significantly different from that of the control (\(p\) = 0.034).  
The artery patency of each group is presented in Figure 4. Successful artery patency was observed in eight rats in the 4-HR silk group (80%), as compared to only three rats (30%) in the PTFE group. The direct closure group showed success in six rats (60%). There was a statistically significant difference between the 4-HR silk group and PTFE group (\(p\) = 0.035). However, there was no significant difference between any other group (\(p\) > 0.05).  
The results of angiography are shown in Figure 5. Narrowing of the carotid artery was observed at the repaired site in the direct closure group (Figure 5a). In the PTFE group, loss of vascular patency was manifested as a signal void between the wire tags (Figure 5b). The arterial diameter of the animals in the 4-HR silk group did not change after patch angioplasty, and was stably maintained better than that on the contralateral side of the carotid artery (Figure 5c).  
Figure 4: Repair of the artificial carotid artery defect: (**a**) direct closure of the artery defect as control group; patch angioplasty with (**b**) PTFE patch, and (**c**) 4-HR silk patch.  
Figure 5: Three-week post-operation angiography: (**a**) direct closure as the control group; (**b**) PTFE patch angioplasty; (**c**) 4-HR silk patch angioplasty.  
Figure 3: SEM images of patch materials: (**a**) 4-HR-incorporated silk patch and; (**b**) polytetrafluoroethylene (PTFE) patch.
# SIV: Raise the Correlation of Second-Order Correlation Power Analysis to 1.00
## 3 Sparse Intermediate Value in AES
### The Performance Improvement of SOCPA Using SIV  
In Section 3.1, we demonstrated the existence of SIV using the operational characteristic. In the case of AES, the cardinality of all possible outcomes of SIV is only 2. Moreover, the Hamming weights of outcomes are different. Note that constant multiplication with random variables only affects the sign of the correlation coefficient:  
\[\text{Corr}\left(aX,bY\right)=\text{sign}\left(ab\right)\cdot\text{Corr}\left(X,Y\right),\]  
where \(a\) and \(b\) are the arbitrary constants, and sign is the sign function. Thus, the theoretical correlation coefficient of Equation (1) is the same as the 1-bit correlation presented in Table 2, although the length of the SIV is 8. For example, let \(u,v\) be the \(S_{2}\) intermediate value, then the theoretical correlation of the SIV is the same as that of some 1-bit intermediate value.  
\[\text{Corr}\left(\text{HW}\left(u\oplus v\right),\;pre\left( \text{HW}\left(u\right),\text{HW}\left(v\right)\right)\right)\] \[=\text{Corr}\left(\text{HW}\left(4u_{7}\oplus 4v_{7}\right),\;pre \left(\text{HW}\left(4u_{7}\right),\text{HW}\left(4v_{7}\right)\right)\right)\] \[=\text{Corr}\left(4\times\left(u_{7}\oplus v_{7}\right),\;pre \left(4u_{7},4v_{7}\right)\right)\] \[=\text{Corr}\left(u_{7}\oplus v_{7},\;pre\left(u_{7},v_{7}\right) \right).\]  
Therefore, when the preprocessing functions are AD and PC, the SIV can increase the correlation coefficient of SOCPA from 0.24 to 1.00 and from 0.35 to 1.00, respectively. That is, the SIV theoretically allows to reduce the minimum trace to disclose the secret key, \(\frac{1}{0.24^{2}}\approx 17.3611\) times and \(\frac{1}{0.35^{2}}\approx 8.1633\) times, respectively [9].
## 3 Experiments and Results
### Dynamic Target Speed Measurement  
While measuring the distance to the moving target, the proposed method also calculated its velocity. Figure 11 shows the velocity measurements. The average value of the target velocity was 500.44 mm/s, with a deviation of 0.44 mm/s from the set value. The precision (1\(\sigma\)) of the velocity measurement was 0.58 mm/s.
## 2 Theoretical Background
### Fuzzy Set Theory  
Fuzzy set theory is a powerful technique for dealing with concepts and rules that concern uncertainty, imprecision, and nonlinearity [38]. A significant feature of fuzzy set theory is its ability to represent vague data [39]. UX involves multiple aspects, some of which are characterized by vague and linguistic variables [40]. There are various types of fuzzy numbers used to represent vague data, including triangular, trapezoidal, G-Bell, and sigmoid functions. In this study, triangular fuzzy numbers were adopted to quantify fuzzy linguistic variables concerning the risk of UX because they are intuitive, straightforward to calculate, and easy to comprehend [41]. A triangular fuzzy number can be defined using a triplet (\(a_{1}\), \(a_{2}\), \(a_{3}\)), and its membership function can be formulated as follows [42]:  
\[\mu(x)=\left\{\begin{array}{ll}0,&x<a_{1}\\ \frac{x-a_{1}}{a_{2}-a_{1}},&a_{1}\leq x\leq a_{2}\\ \frac{x-a_{1}}{a_{2}-a_{3}},&a_{2}\leq x\leq a_{3}\\ 0,&x>a_{3}\end{array}\right.. \tag{2}\]  
To obtain the crisp output for fuzzy calculation, defuzzification is required. Defuzzification can be performed by using various methods, such as center of gravity, mean of maxima, and center of area. Center of gravity is the most popular and precise method due to its clear geometric significance [43]. Therefore, the center of gravity was adopted for defuzzification in this study, and the formula for computing the defuzzification value is as follows:  
\[DF=\frac{\int x\cdot\mu(x)\mathrm{d}x}{\int\mu(x)\mathrm{d}x}, \tag{3}\]  
where \(DF\) is the defuzzification value and \(\mu(x)\) is the membership function.
## 3 Results and Discussion
### Characterization of Optimized ETB-NE  
#### 3.4.1 Particle Size Analysis and Zeta Potential Determination  
The droplet size of ETB-NE was found to be 179.6 \(\pm\) 4.21 nm with a PDI value of 0.373 \(\pm\) 0.02. It has been demonstrated that eucalyptus oil nanoemulsion with polysorbate as a surfactant can yield droplet size of 2.27-2771.00 nm, and with a PDI value range of 0.256-1.000, depending on the eucalyptus oil concentration and the ratio of eucalyptus oil with polysorbate [34]. Further, the PDI value of the prepared ETB-NE was low enough for considering it as a monodisperse system. Thus, the observations in the present study were in concurrence with previous studies [35]. Further, the values of both droplet size and PDI  
Figure 2: Results of analysis of data of PDI; (**a**) contour plot, (**b**) response surface plot, (**c**) individual factor effects, and (**d**) predicted versus actual plot.  
were very close to their predicted values during numerical optimization. The zeta potential value was \(-10.9\pm 1.01\) mV for the ETB-NE and was in good agreement with a reported value of \(-9\) mV for eucalyptus oil nanoemulsion [35].
# Shear Behavior of Superposed Perfobond Connectors Considering Lateral Constraints
## 4 Equation for Calculating Shear Capacity
### Determining the Shear Capacity Equation  
Based on the analysis in Section 4.2, the shear capacity calculation equation of the perfobond connector can be expressed as  
\[Q_{\rm u}=k_{0}nA_{0}f_{\rm cu}^{0.57}+k_{\rm T}(T_{\rm c}+T_{\rm s}+T_{\rm st}) \tag{18}\]  
where \(n\) is the number of holes in the perfobond rib (i.e., \(n\) = 1 for single-hole perfobond connectors).  
According to the test results in this study, the coefficients \(k_{0}\) = 4.9 and \(k_{\rm T}\) = 0.96 were obtained via the least-squares method. The final calculation equation for the shear capacity of the superposed perfobond connectors is given as follows  
\[Q_{\rm u}=4.9 nA_{0}f_{\rm cu}^{0.57}+0.96(T_{\rm c}+T_{\rm s}+T_{\rm st}) \tag{19}\]  
To further verify the accuracy of the shear capacity equation proposed in this paper, the shear capacity of all specimens listed in Table 5 was calculated using Equation (19) and compared with the calculations of Equations (2)-(5). The overall standard deviation coefficient between the calculation results of Equation (19) and the corresponding experimental results was 0.14, and the mean value was 0.99. As shown in Figure 19, the proposed equation outperformed the existing calculation methods, especially for the shear capacity of multi-hole perfobond connectors. Meanwhile, for Specimens III [22] and PS-22 [23], the deviation remained relatively large. One reason for this is the inconsistent specimen design method. Another is the discreteness of the perfobond connectors lacking a perforating rebar. Therefore, more push-out tests designed with uniform methods are required. To conclude, the shear capacity calculation equation proposed in this paper for multi-hole perfobond connectors under lateral constraints exhibits a smaller error and is more universal than those previously obtained.
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 3 Results
### Rainfall Anomalies between 2000 and 2018  
The spatial distribution of the performance of IMERG's monthly resolution products in detecting anomalies observed by the gauges is shown in Figure 11. In general, IMERG showed a limited performance for detecting anomalies across the state, considering \(+/-1.96\) monthly standard deviation. The best performance was observed in the south-central region of Parana, with POD above 0.75, CSI above 0.50, and FAR below 0.50. The worst performance occurred in the northeast region, the region with the lowest annual rainfall, and in the coastal and southwest regions, which correspond to the regions with the highest annual rainfall in the state (Figure 1), agreeing with the worst performance of the daily and monthly product metrics (Figure 8).  
Figure 9: Spatial distribution of the qualitative metrics of IMERG performance on a daily and monthly scale.  
In the analysis of the boxplot of the metrics (POD, CSI, and FAR) used to assess the performance of IMERG in detecting anomalies, a relative deviation from the mean and extreme values of CSI and FAR were observed (Figure 11). Therefore, eight stations were selected randomly, in different regions of the state, to detect anomalies by the gauges and by IMERG, between the years 2000 and 2018, shown in Figure 12.  
Positive anomalies were detected by IMERG monthly products and gauges stations in all regions. In general, the gauges stations detected the anomalies with greater magnitude compared to those estimated by remote sensing. On the other hand, only IMERG detected negative anomalies, for example in the gauge stations 2349036, 2449040, and 2452062.  
Figure 11: Spatial distribution of performance metrics for anomaly detection.  
Figure 10: Seasonal distribution of the performance metrics for IMERG at a monthly scale.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Packet Delivery Ratio  
The packet delivery ratio is a metric that measures the successful data packets transmitted. In a normal scenario, with a rate of 60 packets, if the unit of time is one hour, it translates to one packet per minute. However, in an emergency scenario, this rate is likely to increase, such as 3 packets per minute. If the packet-sending rate is high, the Packet Delivery Ratio (PDR) tends to increase. In emergency scenarios, this rate is likely to be even higher, and with larger attribute sizes, the PDR is also expected to be higher. In case of the threshold sending full readings, a threshold also only raises the packet size when full readings are shared. The average packet size and its delivery will be in between the normal and emergency scenario. The high PDR in EHDA is because of effective data compression and encryption that allows the efficient and reliable transmission of sensitive healthcare data. The use of fuzzy logic for parent selection, considering factors like residual energy, load on the node, and relative node connectivity, allows a more effective mechanism for data transmission. This approach improves energy efficiency by selecting a suitable parent node that also increases PDR. The proposed scheme EE-FDAS has attained better PDR because of the utilization of Boolean digits for normal data that is small in size. It ultimately increases PDR. The DICA has a bit-lower PDR because of inappropriate parent selection for data transmission without knowing the type of data packet. Figure 6a illustrates that the FDAS has attained a delivery ratio of 50 for normal and 130 for emergency cases. Similarly, DICA has achieved 33 for normal and 100 for emergency cases. The DICA_EXT, EE-FDAS, and EHDA achieved ratios of 42, 60, and 55 for normal cases while for emergencies they attained ratios of 126, 180, and 163, respectively. The resulting values for the threshold based scenario.  
Figure 6: In (**a**), the graph displays Packet Delivery Ratio, representing successfully delivered packets per unit time, while (**b**) illustrates the Packet Loss Ratio.
## 4 Blockchain Group Authentication Scheme  
### Group Authentication Principles  
In this scheme, a hybrid authentication method combining group authentication and single authentication is used for security authentication of spatio-temporal big data devices. In group authentication, the uploading node calculates the ID of all devices in the group with the MHT algorithm to obtain the Root Hash of the group and stores it on the blockchain. When the group device needs authentication, the group device transmits the ID to the verification node by using PUF technology. The verification node calculates the Root Hash of the group through the MHT algorithm. Whether the devices in the group are legitimate is determined by comparing the Root Hash with the Root Hash stored in the blockchain. If there are illegal devices in the group, the zero-knowledge proof is used for single authentication to accurately find the illegal nodes in the group. The experimental analysis suggests that the combination of group authentication and single authentication improves the overall authentication efficiency.  
In actual data collection, the data-receiving end needs to authenticate the identity of each spatio-temporal big data authentication device to prevent illegal devices from transmitting wrong data to the data-receiving end. Therefore, the identity authentication of the device is imperative, which is directly associated with the accuracy of the collected data.  
1. Each device's ID is sufficiently random. As displayed in Figure 7, there are three main components in this solution: group, blockchain, and client. Their functions are: 2. Group: This is the unit of spatio-temporal big data collection, and the number of devices in each group can be specified by the user. Each group consists of several collection devices and an upload node with strong computing power. The role of the upload node is to receive the ID Hash of the collection device, build the MHT to obtain the Root Hash, and upload the Root Hash to the blockchain. 3. Blockchain: As a decentralized security storage module, its main function in this system is to store the Root Hash of the group sent by the upload node in the group. 4. Client: As a user of the system, it issues verification notifications and receives verification results.  
This solution is divided into two phases: initialization phase and authentication phase. Table 1 presents the parameter list.  
Figure 7: Blockchain-based spatio-temporal big data security authentication scheme.
###### Abstract  
Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as'mean', and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.  
climate strategies; air pollution; urban planning; air pollution control; mobility; spatial data; smart city; geospatial analysis; air quality; geographic information systems +
Footnote †: journal:  
0000-0001-999-2291-3880][PERSON]
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 3 Materials and Methods  
### Methodology  
The CRoss Industry Standard Process for Data Mining (CRISP-DM) methodology was followed during this research. The first steps are described in the present section, while the analysis of the results is described in Section 4. The method's deployment is left for future work.  
The first step in the project was the study of the problem, which consisted of a visit to the company and a literature review. The second step, \"Data understanding\", consisted of a statistical analysis of the variables involved, as well as visualization experiments. The third step, \"Data preparation\", consisted of data selection and cleaning. After that, seasonal decomposition and a stationarity analysis were performed in the modeling phase. The analysis of the results consisted of the evaluation of the \(p\)-value over different time windows.
###### Abstract
[18] proposed an improved method of conditional boundary integration method for solving reliability problems with high accuracy requirements by using the direct integration method, which adopts a simple modification of binary integration based on the probability addition rule, and the computational accuracy and efficiency of the method were verified by the error analysis of arithmetic cases. Although scholars have improved and proposed many new methods to address the shortcomings of the traditional reliability solution methods, the following shortcomings still exist: the applicability and solution accuracy of some reliability calculation methods are limited due to the complexity of actual structural engineering problems and the existence of highly nonlinear failure surfaces. The limit state functions of concrete gravity dams are often highly nonlinear when considering the randomness of external loads, material parameters, and other factors. Therefore, it is difficult to express them accurately with explicit functions. When solving such problems using the above reliability solution methods, it is usually difficult to ensure the accuracy and efficiency of the calculation results. The development of machine learning and intelligent algorithms provides effective tools for solving practical engineering problems, especially in solving engineering reliability problems [19; 20]. Least squares support vector machine (LSSVM) is a new generation of machine learning method. LSSVM has advantages of rigorous mathematical foundation, strong small-sample learning ability, low over-fitting risk, fast solving speed, etc. However, in practical application, it is difficult to determine hyper-parameters of LSSVM. For example, when the radial basis function is adopted as kernel function, it is hard to determine its penalty parameter \(\gamma\) and kernel width parameter \(\sigma\). At present, optimizing the search algorithm is the main approach to solving the above problem. The particle swarm optimization (PSO) algorithm [21] is widely used to tune the hyper-parameters of the LSSVM because of its simple form and easy understanding [22; 23; 24; 25]. However, the PSO algorithm has the drawback of easily falling into the local optimum, so this paper improves the PSO algorithm for the hyper-parameters tuning. To improve the calculation accuracy and efficiency, this paper proposed a reliability analysis method based on least squares support vector machines with an improved particle swarm optimization algorithm (IPSO-LSSVM). The IPSO-LSSVM is adopted to establish the response surface to approximate the limit state function based on the samples generated by computer experiments. Subsequently, the proposed response surface is utilized in conjunction with the Monte Carlo (MC) method to obtain the desired reliability estimation.