esa-sceva/satcom-chunk-collection huggingface dataset:
# Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements
## 4 Discussion
However, at the Swarm altitude, to which the conductivity maps we have reconstructed refer, the auroral electrojet currents cannot be observed because they flow at significantly lower altitudes. At around 450 km altitude, however, their effect on the magnetic field is measurable. At these altitudes, we can certainly observe the effects of FACs, particle precipitation, and large-scale plasma motions driven by the solar and magnetospheric electric fields typical of this altitude. Our findings reveal that at high latitudes, both Pedersen and Hall conductivities exhibit an enhancement on the dayside, particularly in the region associated with the polar cusp, as can be seen in the spatial-temporal distributions of conductivity in the Northern Hemisphere (see Figures 7 and 8). The increase in conductivities around the polar cusp is due to the continuous flux of charged particlesfrom the solar wind, which can penetrate to lower altitudes in the ionosphere because the magnetic field lines in this region are open. The charged particles ionise the region, enhancing its conductivity. Another interesting region at high latitudes characterised by an enhancement of both Pedersen and Hall conductivity values is primarily concentrated in the duskside sector of the Northern Hemisphere and in the nightside sector of the Southern Hemisphere. This pattern is consistently observed in the distributions of conductivities across different seasons and levels of solar activity. This enhancement could be the result of convective currents associated with global movements of ionospheric plasma driven by solar and magnetospheric electric fields.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 2 Data and Methods
### Analytical Methods
#### 2.3.5. Correlation Analysis  
The Pearson coefficient is used to analyze the correlations between WUE and climate factors as follows:  
\[R=\frac{\sum(x-\overline{x})(y-\overline{y})}{\sqrt{\sum(x-\overline{x})^{2}} \sqrt{\sum(y-\overline{y})^{2}}}, \tag{14}\]  
where \(R\) is the Pearson coefficient, \(x\) is the climate factor (precipitation or temperature), and \(y\) represents the WUE. To calculate the significance level of correlation, a \(t\)-test is adopted as follows:  
\[t=\frac{R\sqrt{n-1}}{\sqrt{1-R^{2}}}, \tag{15}\]where \(n\) is the number of samples. For a given significance level \(\alpha 1\) (\(p<0.05\)) and \(\alpha 2\) (\(p<0.01\)), if \(t>t_{\alpha 1}\), the correlation is significant; if \(t>t_{\alpha 2}\), the correlation is extremely significant. Otherwise, the correlation is not significant.
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 3 Results and Discussion  
### Exploratory Data Analysis and Visualization  
Figure 2 depicts the spatial distribution of the cumulative precipitation captured by both the radar and GPM IMERG products during Tropical Storm Imelda. Cumulative rainfall estimates show variability in spatial patterns between the Stage-IV gridded product and the three IMERG satellite products for the same grids during the study period (Figure 2). Both the Early- and Late-run IMERG products showed similar spatial patterns in the coastal and inland areas in the southeast coastal region receiving a high amount of precipitation during the study period (7 days). The products showed the area of highly hit areas (areas receiving more than 250 mm) as \(\sim\)15,000 km\({}^{2}\) and \(\sim\)14,000 km\({}^{2}\) for Early- and Late-run respectively. The Final-run product showed a different pattern, with a more expanded distribution of the tropical storm, with a \(\sim\)21,500 km\({}^{2}\) area getting more than 250 mm during the seven-day event (Figure 2C). The Stage-IV radar product (Figure 2D) showed a different spatial distribution in the cumulative rainfall estimation, showing two hot spots along the southeast coastal region. However, when considering the extreme peak that is about the limit of the NWS rain gauges (\(\sim\)500 mm), the pattern reverses. The Early- and Late-run reported areas about 3700 km\({}^{2}\) and 3500 km\({}^{2}\) to have received precipitation more than 500 mm, respectively. While, the Final-run showed only a 900 km\({}^{2}\) area with precipitation higher than 500 mm. The most extreme rainfall area was observed on the Stage-IV radar product, with almost 6000 km\({}^{2}\) getting precipitation higher than 500 mm.
# Inter-UAV Routing Scheme Testbeds
## 5 Conclusions and Future Work  
This paper has discussed industrial, military and agricultural use-cases for FANETs, surveyed directly applicable mobile ad hoc routing protocol to be used in such deployments, as well as existing testbed/simulation frameworks which can be used to evaluate the efficiency of routing protocols. Additionally, matters of network topology and mobility modeling are addressed in the context of scheme simulation. This work is concluded with the presentation of:  
* A standardized hardware prototype capable of supporting all routing protocols currently implemented in the Linux kernel
* Component interface modes as a function of desired evaluation framework  
Figure 8 constitutes the result of the proposed testbed module's evaluation using an ESP-Now link and the embedded PCB antenna of the ESP. Even when using the PCB  
Figure 7: Supplementary aerial mode components.  
antenna, power consumption was kept below 2.5 Watts, while the link did not fail to maintain maximum bandwidth at 85 m with a direct and constant LOS. The module's evaluation consisted of:  
* Link establishment between two modules
* Formation of ESP-Now packets using dummy data harvested from the MPU6050 gyroscope/accelerometer
* Communication between a constantly increasing distance (5 m-85 m)
* Measurement of the transmitting module's power consumption at 5-m intervals  
Future work shall be constituted of test results originating from the usage of the proposed testbed with OLSRd and BATMAN. A comparison between said routing schemes in terms of: (a) scalability and (b) mobility shall be part of said future work. NS-3 simulations shall be used to validate the performance of the proposed testbed under the mobility models mentioned in Section 2.2. Due to the current lack of functional open-source flight controllers meant to be used with the ESP-series, such firmware shall be developed and made openly available, as is the case with the proposed module's hardware.  
Conceptualization, T.L., PS, V.V, P.F. and G.A.; methodology, G.A.; software, G.A.; validation, T.L., G.A. and P.S.; formal analysis, G.A., T.L., P.S.; investigation, G.A.; resources, T.L., P.S., V.V.; data curation, T.L., P.F., V.V.; writing--original draft preparation, G.A.; writing--review and editing, T.L., G.A.; visualization, G.A.; supervision, T.L. and P.S.; project administration, T.L. and P.S. All authors have read and agreed to the published version of the manuscript.  
This research was cofunded by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, grant number T1 EDK-04759.  
Not applicable.  
**Data Availability Statement**: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Figure 8: Communication power of an ESP-Now direct link between two modules.
## 6 Conclusions  
To conclude this paper, we summarize the results of the research. The paper deals with the analysis of the level of Industry 4.0 implementation in SMEs. Based on cluster analysis, we developed a categorization of the enterprises which consists of four groups, namely top I4 technological enterprises, advances I4 enterprises, I4 starting enterprises, and I4 noobs enterprises. The largest group is formed by SMEs, which are just-introduced Industry 4.0 technologies. Depending on their classification, enterprises differ in the use of preferred technologies. Generally, SMEs are more used technologies that can be found at lower levels of the VPi4 index (data collection, analysis, Cloud, IS, IT, and mobile platforms). At the same time, SMEs with a higher level of implementation of Industry 4.0 reported enough skilled human resources.  
The research examined hypotheses about the differences between SMEs and large enterprises in the implementation of Industry 4.0. Results of comparisons show that SMEs have, so far, a lower level of Industry 4.0 implementation. This confirms the assumption that large enterprises have greater opportunities to use new technologies and transform them into smart factories. However, this situation may change in the future if new technologies become more accessible (for example, using leasing financing) and more appropriate for investment of SMEs.  
As part of future research, we plan to conduct a deeper analysis of the implementation of Industry 4.0 in SMEs, focusing on the main barriers. Case studies on specific enterprises will be appropriate. These case studies could explain why SMEs are not yet implementing Industry 4.0 to a greater extent. We plan to show the procedure of how to rank the implementation steps or technologies in a single enterprise. It may be interesting to compare the current and required (or expected) level of Industry 4.0 in SMEs. Other challenges for future research include developing a methodology (step-by-step roadmap) to implement Industry 4.0 in enterprises. SMEs often do not have much information on how they can turn their business into a digital form; however, in the current situation (pandemic of coronavirus), they are forced to use home office and reduced working hours. New Industry 4.0 technologies can help them with this current challenge.  
Conceptualization, M.P. and J.V.; data curation, M.P. and J.V.; formal analysis, M.P. and J.V.; funding acquisition, M.P. and J.V.; investigation, M.P. and J.V.; methodology, M.P. and J.V.; project administration, M.P. and J.V.; resources, M.P. and J.V.; software M.P. and J.V.; supervision, M.P.; validation, M.P. and J.V.; visualization, M.P.; writing--original draft, M.P.; writing--review and editing, M.P. and J.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"EF-150-GAJU 047/2019/S\".  
The authors thank the enterprises taking part in the research.  
The authors declare no conflict of interest.
## 6 Advantages of Including 3D Printing in the Manufacture of Prostheses  
The inclusion of 3D printing in prosthetic manufacturing has revolutionized the field of medicine and engineering, providing more accessible, personalized, and cost-effective solutions for amputees. This technology allows for the creation of customized devices adapted to the individual needs of each patient, significantly reducing production times and costs compared to traditional methods. In addition, the ability to experiment with new materials and innovative designs expands the possibilities of improving the functionality, comfort, and aesthetics of prostheses, offering a better quality of life to users.  
The integration of 3D printing into robotic prosthetic manufacturing has been a disruptive breakthrough that has radically transformed the prosthetic field in terms of accessibility, customization, production time, and cost reduction. Traditionally, prosthetic manufacturing involves long, expensive, and inflexible processes, making it difficult for many people to access high-quality devices. However, the advent of 3D printing has enabled a revolutionary approach to the design and production of robotic prosthetics, democratizing access and enabling significant advances in terms of customization and functionality.
## 3 Objectives of the Proposed Access Control and Data Sharing System  
In this section, the objectives of the proposed system for access control and data sharing among IoT devices' users are presented. Such objectives are given below.  
* **Trustfulness:** The trust of IoT devices' users is maintained through a misbehavior-judging method, which is implemented by JC. The users who misbehave are known as untrusted users and are penalized. Only the trusted users are allowed to access their required services.
* **Authentication:** In this system, authentication of users is done by RC. Any user, who becomes part of the system is registered by RC. Furthermore, a record is maintained in the user registration table.
* **Authorized access:** In this system, only authorized users are allowed to access their required services. The users' authorization is maintained through permission levels.
* **Efficient data sharing:** The data sharing among IoT devices' users is maintained through the ACC. Which maintains the sharing of services among users. Furthermore, the permission-based sharing is also achieved through the permission levels.
* **Understanding of user behavior [33]:** In this system, the behavior of users is checked by implementing behavior judging method in JC. Misbehavior is conducted by subjects by sending too many requests. After identifying misbehavior, the requests of subjects are halted for a particular time. That is why, only the users who behave well are provided with the requested services.
* **Less cost consumption:** The system consists of three smart contracts, as in [1]. In the previous scheme, ACC is generated by users, for every transaction. It will increase the overall cost of the system. However, in this system, single ACC is used to control the access of the system. It also reduces the cost in terms of gas consumption of smart contracts, which is given in the comparison section.
# Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model
## 4 Experiments  
### Experimental Data  
The experimental data in this paper were obtained from the hourly meteorological data (weather conditions, temperature, body temperature, barometric pressure, humidity, rainfall, wind direction, wind force, wind speed) and hourly air quality monitoring data  
Figure 1: Basic framework of the AGA–LSTM prediction model.  
(PM2.5, PM10, SO2, NO2, CO, O3) of Beijing from 1 January 2016 to 31 December 2016 from Nanjing Yunchuang Big Data Technology Co., Ltd. The air quality monitoring data refer to the hourly monitoring data of 12 monitoring points. In order to make the meteorological data consistent with the air quality monitoring data, the data of one monitoring point were taken uniformly. The position of the monitoring point is 116.28 longitude and 39.89 dimension; so, there were 8760 data records each, but due to some uncontrollable factors, some data were missing, and the data records finally used were 6430. The Chinese descriptions of weather conditions, wind direction, and wind power are coded as shown in Tables 1-3.  
the top four with higher correlation with the first four that displayed greater correlation with PM2.5 as the main influencing factors, thus obtaining eight influencing factors. Again, the correlation between the eight main influencing factors and PM2.5 was analyzed by the method presented in this paper, and the estimated CE values were normalized as the weights of each main influencing factor and introduced into the prediction model to achieve pollutant prediction. The experimental results are shown in Figures 2 and 3. The horizontal coordinates 1-8 of Figure 2 indicate air temperature (TMP), body temperature (FEELST), air pressure (PRES) relative humidity (HUM), precipitation (RAIN), wind direction (WDIR), wind speed (WSC) and wind speed (WSPD), respectively. The horizontal coordinates 1-5 of Figure 3 indicate PM10, NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), and CO, respectively.  
In Figure 2, the top four influencing factors with stronger correlation are PRES, TMP, HUM and WDIR according to the calculated CE results of each meteorological factor with PM2.5. Similarly, the top four influencing factors with stronger correlation are PM10, NO\({}_{2}\), SO\({}_{2}\) and O\({}_{3}\), according to the calculated CE results of each pollutant factor with PM2.5 in Figure 3. It was also found that PM10, NO\({}_{2}\), SO\({}_{2}\) and O\({}_{3}\) had stronger correlations with PM2.5 than each meteorological influence factor. Therefore, the eight influencing factors PM10, NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), PRES, TMP, HUM and WDIR with stronger correlation with PM2.5 were selected; the CE results of the eight influencing factors and PM2.5 concentration were obtain to calculate the weight of each influencing factor \(\omega_{i}\). The attribute data of the eight impact factors \(x_{i}\) combined with the weights \(\omega_{i}\) were input into the model for prediction.
## 4 Distributed Hotspot Network Architecture
### Application Servers/Service Providers/Remote Users  
Application servers host applications and/or software to provide business applications to their subscribers or end users. Such servers may use the server framework service model through an application programming interface (API) to accomplish the desired task. In general, application servers are built to be fail-safe and can perform load balancing. Mobileapplication servers (MAS) can also be used to augment business logic with representative state transfer (REST) so that the bandwidth can be minimized. In addition, MAS can provide authentication services, offline support, security, and data orchestration. Service-oriented architecture (SOA)-oriented infrastructures are capable of connecting to dependent end users, but limited resources and broken connections can cause problems.
## 2 Materials and Methods
### Motion Configurations Tested and Design of the Simulations
#### 2.3.4 Third Set, S3: Speed of Motion Patterns and Sampling Frequency  
Since the number of time frames obtained for each cycle can affect the performance of the AoR estimation algorithms, due to different time discretization of a kinematic pattern, several sampling frequencies \(f_{\text{s}}\) were tested in the S3 set. These frequencies correspond to the target frequency of the downsampling applied to the original trajectory data produced by the model, which ran at 1000 Hz. The four values selected were equal to 100, 250, 500, and  
\begin{table}
\begin{tabular}{c c c} \hline
**Nomenclature** & **Weight \(w_{i}\)** & **References** \\ \hline IHA (w.1) & 1 & [38] \\ IHA (w.2) & \(\sin(\vartheta_{i}/2)\) & [23,32] \\ IHA (w.3) & \(\sin^{2}(\vartheta_{i}/2)\) & [34] \\ \hline \end{tabular}
\end{table}
Table 2: Helical axis weighting and corresponding nomenclature used in the simulations.  
1000 Hz. For a fixed sampling frequency, the discretization of joint motion also depends on its speed. For this reason, as considered in the S1 set, the speed of each motion pattern was varied by tuning of the gait cycle duration \(t_{\text{cyc}}\), yielding the same three increasing values of speed labeled as V1, V2, and V3. The simulations were performed for A and D configurations, with the default range of motion of flexion/extension equal to (+20, -30) deg.
# The Cube Surface Light Field for Interactive Free-Viewpoint Rendering
## 3 Cube Surface Light Field Representation
To facilitate the parametric representation of the light field, the cube surface, as shown in Figure 2(a) can be expanded along any of its twelve edges, producing different cube map layouts, such as vertical and horizontal cross layouts, as shown in Figure 2(b). We use the horizontal layout as shown in Figure 2(c) to present each sub-view of the cube surface light field. Intuitively, the texture coordinates can be used to further simplify Equation (2), resulting in the final representation of the cube surface light field denoted by Equation (3). Seeing from the dimension of \(u\) and \(v\), each coordinate (\(u\), \(v\)) represents a set of light rays from a fixed position, which can be described as a light field sub-view like in the traditional 2 PP light field representation. Meanwhile, seeing from the dimension of \(s\) and \(t\), each coordinate (\(s\), \(t\)) represents a set of light rays from a fixed direction, which can be described as a sub-aperture image like in the 2 PP light field representation. \[\mathrm{L}:\mathrm{I}(u,v)\times\mathrm{I}(s,t)\rightarrow\mathrm{RGB} \tag{3}\]  
One advantage of the cube surface parameterization is that it is easy to sample the light field. The two adjacent edges of each sub-plane are divided into several segments uniformly or non-uniformly. By connecting the endpoints of the two opposite segments, the sub-plane can be subdivided into several small square or rectangular blocks. The barycentric coordinates of each square or rectangular can be selected as the sampling points  
Figure 3: The different layouts of cube map. (**a**) six face are marked by +X to \(-\)Z, (**b**) the cross layout and (**c**) the horizontal layout. of the cube surface light field. This representation also helps to compress the light field images at each sampling point. The images generated by the sampling points on the same sub-plane have only translational transformation in both horizontal and vertical directions. If the equidistant sampling strategy is adopted in the sampling stage, the relationship between the image pixels of these viewpoints is easy to be quantitatively described by parallax, which facilitates the prediction and compression of adjacent views. It is also conducive to subsequent rendering, i.e., the light field rendering of an arbitrary scene only requires drawing a simple unit cube geometric. By intersecting the desired light rays with the cube surface, the light ray's parameters can be computed and used to query the color value of the novel viewpoint pixels. The computational consumption for testing intersection with the light rays and cube surface is relatively low, which makes the cube surface light field rendering possible to achieve real-time performance.
# A Complete EDA and DL Pipeline for Softwarized 5G Network Intrusion Detection
## 4 Dataset Description, Exploration, and Discussion
### Data Pre-Processing
#### 4.2.1 Data Cleaning  
An initial data cleaning process was conducted for the dataset, where duplicate entries were removed. Additionally, a few rows belonging to the benign class were dropped from the dataset, due to a significant number of missing values across various columns. Similarly, certain columns (sVid and dVid) were removed, as they were deemed extraneous to the analysis. Additionally, missing values in each row were replaced with the most frequent value (mode) within each group, as determined by the attack type column, using a custom function. The mode was selected as the imputation method because each of the columns had a low number of unique values (often only 6-7 different values). This indicated that the data were categorical or ordinal in nature. Utilizing the mode in this instance preserved the categorical nature of the data, while avoiding the introduction of additional values that may not be representative of the original data distribution. For a subset of columns, an iterative imputer (MICE) was employed to handle any remaining missing data. Figure 3 depicts the degree of correlation between columns with missing values. More specifically, the figure shows the relationship between features that have missing values, to determine how often they both have a missing value at the same time. For example, it can be seen from the figure that the feature sTos had a correlation value of 1 with sDSb, sTtl, and sHops. This means that every time sTos had a missing value, the features sDSb, sTtl, and sHops all had missing values as well. This was expected, since all these features are related to the data traffic source. Hence, if the system was unable to collect a value for one of them, it would not be able to collect a value for any of them. In a similar fashion, the feature dTos had a correlation value of 1 with dDSb, dTtl, and dHops. Again, this meant that every time dTos had a missing value, the features dDSb, dTtl, and dHops all had missing values. This gave us a better understanding of the relationship that existed between the different features collected and helped us better select features in the \"feature selection module\".