esa-sceva/satcom-chunk-collection huggingface dataset:
# Handling Skewed Data: A Comparison of Two Popular Methods
## 3 Results
### Application to Real-Life Data
#### 3.2.3 The Pharmaceutical and Computer Companies Data  
We have a data table of 32 registered companies and some information like the sales, profits, assets, and number of employees in each company. There are two types of companies, computer companies and pharmaceutical companies. We wanted to check if the assets variable is significantly different between the two types. So, we tested the mean difference between the computer and pharmaceutical groups regarding the assets variable. The data was found to be skewed and fitted to an exponential distribution according to AICc values. The Q-Q plot and the frequency histogram of the companies' untransformed and log-transformed data are presented in Figure 7.  
We tested the data using both methods. The resulted \(t\)-value is \(t_{29,99}=1.97\) with \(p\)-value \(=\ 0.0292\), which indicates that there is a significant difference between the two groups' means and that the pharmaceutical companies have a significantly higher assets mean than the computer companies. For the second method, we applied GLM to the original data with a log link function. The resulting  
Figure 6: The normal Q–Q plots (up) for the weight variable before and after transformation and the frequency histogram of the weight variable on the left (down) fitted to a gamma distribution (red).  
\(p\)-value \(=0.4908\) indicates that there is no significant difference between the assets' means of the two types of companies. So, the two methods gave contradicting results and this issue puts us on a crossroads. We need to decide which result to adopt.
## 4 Results  
### Results of the Kinematics  
Figure 4 shows the kinematic differences in the side-cutting task. The ankle plantar flexion and inversion angles of the subjects decreased significantly as the external rotation angle of the foot at initial touchdown increased. Significant differences (\(p<0.001\)) were found between all three-foot progression angles, especially between TI and TO during the full phase of side cutting. Hip results showed a significant difference between the tripod  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & & **Toe-In** & **Toe-Neutral** & **Toe-Out** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Ground Reaction \\ Force (N) \\ \end{tabular} } & Vertical & 1831 & 1800 & 2047 \\  & Medial & 571 & 458 & 520 \\  & Posterior & 662 & 602 & 589 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Knee Angle (\(\circ\)) \\ \end{tabular} } & Flexion & 50.94 & 54.18 & 56.68 \\  & Abduction & 11.82 & 9.82 & 8.90 \\  & Adduction & 4.91 & 4.38 & 6.02 \\ \hline \multirow{6}{*}{Muscle Force (N)} & Semitendinosus & 295.1 & 296.9 & 294.8 \\  & Solens & 297.4 & 295.2 & 290.0 \\  & Biceps Femoris & 305.5 & 352.0 & 316.3 \\ \cline{1-1}  & Rectus Femoris & 897.3 & 762.7 & 810.3 \\ \cline{1-1}  & Vastus Lateralis & 307.2 & 266.7 & 277.9 \\ \cline{1-1}  & Vastus Medialis & 245.2 & 258.2 & 231.1 \\ \cline{1-1}  & Medial Gastrocnemius & 697.9 & 746.8 & 834.8 \\ \cline{1-1}  & Tibialis Anterior & 762.0 & 721.9 & 725.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Finite element model outputs for knee joint biomechanics under different foot positions.  
advancement angles during the stance phase (32-100%; \(p<0.001\)), with the TI position having a smaller range of hip flexion angles than the TO position. The knee results showed that TN did not differ significantly from TI and TO in knee flexion, whereas there was a significant difference between TI and TO in the stance phase (0-18%, \(p=0.036\); 23-59%, \(p=0.005\)). In the frontal plane, the results showed significant differences between all three-foot progression angles, with TI having a significantly greater knee valgus angle than TO, when, on the contrary, in the horizontal plane, the knee external rotation angle was significantly less for the TI position than for TO position as the foot external rotation angle increased.
# An Overview of CubeSat Missions and Applications
## 3 CubeSats as Earth Observation and Earth Science Platforms  
Earth observation satellite missions can be defined as missions that have the objective of collecting data related to various aspects of the Earth, such as the condition of the Earth's terrestrial surface, its bodies of water, its atmosphere, its magnetic field, etc. The data gathered by Earth observation satellite platforms are then analyzed in order to extract useful information which can be used for a multitude of purposes and applications including meteorology, climate change monitoring, environmental protection, precision agriculture, forestry and urban area monitoring, scientific research, local and regional planning, as well as civil protection through real-time natural disaster and wildfire monitoring [43]. Furthermore, the acquisition of Earth observation data has been of extreme importance for military intelligence applications since the dawn of the space age.  
The present section is divided into two subsections. The first one covers the role of CubeSats in the field of traditional Earth remote sensing applications, with satellite missions dedicated to Earth imaging, atmospheric condition, and weather monitoring being discussed, while the second subsection is dedicated towards Earth science missions, a subcategory of Earth observation missions which are dedicated to the scientific study of the Earth as a system and the understanding of its processes and dynamics.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 2 Experimental Section  
### Measurement Site  
These campaigns aimed the characterization of the underwater EM link at carrying out measurements in real conditions. Thus, experiments were performed in Taliarte's harbour near PLOCAN's facilities in Gran Canaria (Spain). Measurement area had dimensions of 40 m \(\times\) 5 m and was separated 15 m from a concrete floating pier that was in line with it (Figure 1). Monitoring and control of the instruments were performed on this pier.  
The measurement site is surrounded by boats and other typical harbour objects. Moreover, there are concrete blocks lying on the seabed. The effect of these obstacles was found negligible for the measurements and only seabed composition and water depth have been studied and introduced as parameters in simulation tools. Water depth varied from 4 to 5.5 m depending on tides, and there was a sandy seabed.
## 5 Discussion
### Limitations of Satellite Altimetry Height Estimation  
It is important to keep in mind that even if a lake or reservoir is located beneath a ground track of a satellite altimetry mission, it is not always possible to derive valid height information. The ability to reliably generate water level time series strongly depends on the size of a water body, especially on the length of the satellite's track over the water; however, this is not the only influence. The quality of the measurements are also affected by the surrounding topography, the sensor type on board of the satellite and other factors. Thus, no generally applicable size limit can be defined. While [PERSON] et al. (2019) proposed a limit of 100 km\({}^{2}\) referencing work from 2002 and 2006, [PERSON] et al. (2019) showed results for a 0.52 km\({}^{2}\) small lake in France and [PERSON] et al. (2019) used satellite altimetry to monitor the River Garonne, which is only 200 m wide.  
For the MRB, using the DAHITI processing approach ([PERSON] et al., 2019), reliable long-term water level time series for 54 lakes and reservoirs can be derived, from which 41 targets can be seen to be part of WGHM. This is about 58% of the WGHM targets mapped by the past altimetry configuration. The minimal size of the observed WGHM lakes is 10 km\({}^{2}\). The smallest lake for which water level time series can be derived and which is not part of WGHM is 3.4 km\({}^{2}\) (with a 50% occurrence threshold). For the WGHM targets that are missing in DAHITI, no accurate long-term water level time series can be derived. This is mostly due to the shape of the lakes (very narrow) or due to satellite tracks being close to the lakes' shorelines. However, it is important to keep in mind that these numbers are not easily transferable to other basins, since the local characteristics strongly influence the quality of altimetry-derived water level time series.  
Another point worthy of mention here is the temporal resolution of the altimetry-derived time series. As already indicated in Section 2.2, this strongly depends on the repeat cycle of the satellite as well as on the number of tracks covering a lake or reservoir. In this study, a water body is counted as monitored when at least four overflights per year are identified (see Section 3.4). Thus, time series with temporal resolutions of three months up to a few days can be generated depending on the lakes' size and location. This ensures that seasonal variations are captured but will not guarantee the monitoring of short-term extreme events, especially for smaller lakes.
## 4 Experiments and Analysis
### Model Performance Index Analysis  
In order to further analyze the prediction effect of the GA-LSTM model on the two typical trajectories, this paper adopts MSE and MAE to evaluate the performance of the models. The index analysis results of the three methods are shown in Table 2.  
It can be seen from Table 2 that the GA-LSTM model is the lowest in both evaluation indicators, its accuracy is relatively the highest, and its model prediction performance is better. For ship-1, when the optimal parameter combination is (11, 0.0165), the LAT MSE and MAE predicted by this method are 1.6393 \(\times\) 10\({}^{-6}\) and 0.0014, respectively; meanwhile, when the optimal parameter combination for LON prediction is (7, 0.023), the MSE and MAE are 4.3188 \(\times\) 10\({}^{-6}\) and 0.0024, respectively. For ship-2, the LAT MSE and MAE predicted with the optimal parameter combination (13, 0.0163) are 3.0375 \(\times\) 10\({}^{-6}\) and 0.0017, respectively; the LON MSE and MAE predicted by the optimal parameter combination (14, 0.0105) are 1.8304 \(\times\) 10\({}^{-6}\) and 0.0012, respectively. Both indicators are the lowest. It can be seen that firstly adopting the GA to optimize the key hyperparameters of the LSTM network model and then using the optimal parameter combination to construct  
Figure 10: Comparison of experimental results of ship-2 trajectory prediction.  
Figure 9: Prediction results of LON and LAT of ship-2.  
the GA-LSTM trajectory prediction model can effectively improve the performance and accuracy of prediction.
# _photonics_
## 4 Simulation Results
### QKD Link Performance of an LEO Satellite Constellation over Greek QCI
#### 4.3.2 Satellite Full Constellation Pass over a Single OGS  
Every satellite passes over a ground station approximately twice a day (24 h). Therefore, 10 constellations are visible around 20 times a day by every station. A single station can establish 10 different quantum links, each one with a different satellite. In Figure 6, the passes of the constellation over the observatory of Helmos over a period of one month and over two days are shown. Again, the time instances when the sun is up were filtered out, as it can be clearly seen in Figure 6b.  
The total daily time contact for a single pass of the constellation over the OGS of Helmos can be found to be 2010 s or 33.5 min, thereby allowing for a longer quantum communication window. Aside from the increase of the overall key rates, the LEO satellite constellation increases the QKD link availability, since it is more likely that an OGS establishes a link to at least one satellite. Since the weather conditions and specifically clouds may interrupt the satellite downlink, the employment more satellites can increase the QKD link availability within the day. It is worth mentioning that the constellation satellites are placed in such a way to achieve the maximum availability. This means that no more than one satellite is visible by an OGS at the same time.
## 4 Proposed Non-Linear Diffusion Method
### SAR Data  
In the experiment analysis, the two different SAR images have been taken of different background nature to verify the reliability of the proposed algorithm. The original SAR image dataset is considered from the public access database [48; 49], showing agricultural landscapes with distinct plots of fields in various stages of cultivation, ranging from light green newly planted areas to darker green mature crops. The speckled SAR images are named SAR-1 and SAR-2 at a noise level of \(\sigma\) = 20, as displayed in Figure 3a,b.
## Materials and Methods
### Membrane Fabrication and Casting  
Casting solutions were prepared using DMA solvent and 16 wt. % PES polymer. Then, 35 mL of dope solutions were aliquoted into 150 mL capacity glass beakers. Next, 1 wt. % AG and various loadings of GO were added to cast the nine membranes that are detailed in Table 1. The casting solutions were subject to a period of 1 h sonication (the ultrasonic processor with a power input of 300 W and a 20 kHz frequency was set to a 30% amplitude and a pulse of 1 s on and 2 s off program) using a Q500 sonicator probe (Thomas Scientific, USA) that was equipped with a tip diameter of 12 mm whilst being stirred using a labForce digital hotplate stirrer (Thomas Scientific, USA) to allow for efficient dispersion of the nanofiller throughout the solution. The solution was degassed for 1 h in order to eliminate air bubbles. The membranes were cast at room temperature according to standard phase inversion techniques using a Labcoat Master casting system (PHILOS, Korea). The casting speed was set at 3 m/min and a knife gap height of 200 nm. The glass was immersed into a coagulation bath containing DW where the solvent (DMA)/non-solvent (DW) exchange takes place, causing the polymeric membrane to precipitate off the glass. The cast membranes were washed and stored in DW.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Membrane** & **AG (Arabic Gum) wt.\%** & **GO (Graphene Oxide) wt.\%** \\ \hline M1 & - & - \\ M2 & 1 & - \\ M3 & 1 & 0.1 \\ M4 & 1 & 0.3 \\ M5 & 1 & 0.5 \\ M6 & 1 & 1 \\ M7 & 1 & 3 \\ M8 & 1 & 5 \\ M9 & - & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of PES (polyethersulfone) membranes synthesized. M1 is the neat membrane. All membranes were composed of 16% PES dissolved in DMA (N,N-Dimethylacetamide) solvent.
## 1 Introduction
For the electrochemical precipitation process, some works proposed changing composition and pH value of the electrolytes: (i) [PERSON] demonstrated that ammonium perchlorate or sulfate (1 M) and a very acidic pH value exhibited more suitable electrolytes, prompting almost quantitative electro-precipitation up to 99.8% efficiency for various radionuclides \({}^{230}\)Th, \({}^{232}\)U, \({}^{239}\)Pu, and \({}^{241}\)Am using 2 h of deposition duration onto a cathode area of 2 cm\({}^{2}\)[30]; (ii) another approach resulted from simple sodium sulfate or sodium sulfate/hydrogen sulfate mixture, which exhibited robust electrolytes enabling the stabilization of pH value even when using relatively high current densities of several 100s mA.cm\({}^{-2}\) and thus obtaining high efficiencies of more than 90% for different radionuclides \({}^{230}\)Th, \({}^{237}\)Np, \({}^{239}\)Pu, \({}^{241}\)Am, and \({}^{244}\)Cm and thus pH values could be tolerant from 1.5-4.5 [33; 34]. Still, one limitation is arising, and is necessary for the process of using conductive materials with high corrosion resilience. Several metals offer these properties, several may be noble metals, and thus expensive. Furthermore, they always exhibit a high atomic number, and for some applications in nuclear facilities the metal gets activated and becomes radioactive waste. Inspired by these electro-precipitation methods, our group has developed an innovative device called \"ActiFind\" combining the electrochemistry of a thin synthetic layer of conductive boron-doped diamond (BDD), coupled with the radiometric detector made e.g., of silicon that now allows the direct monitoring of alpha-particles in aqueous media using actinide deposition at a low current of several mA.cm\({}^{-2}\)[35]. Such innovative device presents a high benefit for fast and on-field monitoring alpha-activity in nuclear and environmental samples due to direct deposition of radionuclide onto radiometric detector through a diamond thin layer. Furthermore, this work demonstratedthat multi-actinides including \({}^{239}\)Pu, \({}^{241}\)Am, and \({}^{244}\)Cm could be successfully deposited onto the diamond/silicon sensor at high electro-precipitation yields [36]. Here, the diamond layers used were fabricated using MicroWave Plasma-Enhanced Chemical Vapor Deposition (MWPECVD), where thin diamond layers, so-called nanocrystalline diamond (NCD), can be synthesized using methane at low cost. It is to be noted that diamond is generally insulating, but can be easily doped during growth by the addition of boron impurities, to become BDD. In the current work, we extend the use of diamond as a substrate for electro-precipitating actinide elements for the preparation of spectroscopic alpha-particle sources based on BDD, a substrate that exhibits high corrosion hardness as well as a low atomic number.
## 4 Results and Discussion
### Cross-Cultural Implications and Industry 5.0 Context  
We analyzed the data for each country separately and all data cumulatively. The data analysis of the three datasets indicates that performance expectancy, social influence, and perceived sociability have a significant statistical relationship with behavioral intention to use the robot as a co-worker. In addition, effort expectancy has a significant relationship with performance expectancy, similar to the findings of previous related studies [8; 9; 18]. The data analysis indicates that effort expectancy does not have a significant relationship with behavioral intention to use the robot as a co-worker. [PERSON] [69] discusses how, as technology evolves, the significance of effort expectancy on behavioral intention will  
Figure 5: Structural model of behavioral intention to use the robot as a co-worker.  
be brought into question. Our finding related to the non-significance of effort expectancy in terms of behavioral intention to use the robot as a co-worker is an empirical answer to such questioning in the UTAUT model. Our result is also consistent with the findings of [11], indicating that the effect of effort expectancy on behavioral intentions becomes less significant as robots operate autonomously.  
Our findings from all three datasets are similar. As a result, we can predict that robots will likely be accepted in workplaces, even in different countries with different cultures. Naturally, confirmation of universal robot acceptance may require evidence from a much broader dataset including many countries. On the other hand, modernization and globalization have an impact on national culture, whereby standardization in industrial, cultural, and educational systems results in a homogeneous transnational culture [70].  
The findings emphasize the role of human-robot collaboration in realizing the goals of Industry 5.0, particularly in promoting human-centric and creative work environments. Robots designed for office settings should not only address functional requirements but also consider cultural adaptability to foster acceptance across diverse workplace contexts. Organizations adopting robotic technologies should implement training programs and create supportive environments that encourage collaboration between humans and robots. Additionally, policies promoting the ethical and sustainable integration of robots can further align with the principles of Industry 5.0, ensuring that technological advancements contribute positively to both productivity and employee well-being.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**BaTiO\({}_{3}\) Condition**} & \multicolumn{2}{c}{**1000 \({}^{\circ}\)C Calcination**} & \multicolumn{2}{c}{**1100 \({}^{\circ}\)C Calcination**} \\ \cline{2-5}  & **Tetragonality (\(\epsilon\)/a)** & **Permititivity (\(\epsilon\)/a)** & **Tetragonality (\(\epsilon\)/a)** & **Permititivity (\(\epsilon\)/a)** \\ \hline BT\#1 & 1.0094 & 2195 & 1.0102 & 2446 \\ BT\#2 & 1.0097 & 2162 & 1.0104 & 2357 \\ BT\#3 & 1.0094 & 2093 & 1.0104 & 2334 \\ BT\#4 & 1.0100 & 2129 & 1.0102 & 2408 \\ BT\#5 & 1.0048 & 3494 & 1.0087 & 3584 \\ BT\#6 & 1.0076 & 2380 & 1.0085 & 2561 \\ BT\#7 & 1.0060 & 1993 & 1.0083 & 1980 \\ BT\#8 & 1.0060 & 1891 & 1.0077 & 1721 \\ BT(A) & 1.0070 & 4042 & 1.0098 & 16,780 \\ BT(R) & 1.0065 & 1782 & 1.0094 & 1867 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of tetragonality and permittivity according to BaTiO\({}_{3}\) conditions (calcination condition: 1000 \({}^{\circ}\)C \(\times\) 2 h, 1100 \({}^{\circ}\)C \(\times\) 2 h). Figure 11: Evaluation of dielectric properties of sintered specimens obtained using solid-state-synthesized BaTiO\({}_{3}\) with composite TiO\({}_{2}\) and mixed TiO\({}_{2}\) (solid-state synthesis: 1100 \({}^{\circ}\)C \(\times\) 2 h; sintering condition: 1300 \({}^{\circ}\)C \(\times\) 2 h). temperature (\(T_{c}\)). However, for the powders prepared using simply mixed TiO\({}_{2}\), relatively low dielectric constant values were confirmed at \(T_{c}\). BaTiO\({}_{3}\) exhibits a tetragonal crystal structure in the temperature range of ambient temperature-\(T_{c}\), and when the temperature increases above \(T_{c}\), the BaTiO\({}_{3}\) crystal structure changes to a cubic phase. Therefore, BaTiO\({}_{3}\) prepared using composite TiO\({}_{2}\) requires high activation energy for transformation from a tetragonal structure to a cubic structure, whereas in the case of BaTiO\({}_{3}\) formed using mixed TiO\({}_{2}\), the crystal structure can be changed with relatively low activation energy.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 2 Overview of Marine Internet
### Typical Applications
#### 2.2.3. Backbone for Internet of Things in Oceans  
The terrestrial Internet of Things (IoT) is mainly composed of wireless sensor networks and the terrestrial Internet. The former is used to connect objects usually via radio wave, while the latter is used to connect wireless sensor networks distributed in different locations to interconnect everything. Similarly, the IoT in oceans also requires marine Internet to connect different sensor networks located on and under water in oceans.  
The IoT in oceans can be roughly divided into two parts: the IoT on water surface and the Internet of underwater things (IoUT) [2]. The former is mainly composed of various sensors installed on ships, buoys and other platforms, such as sensors for temperature, humidity, wind speed and wind direction, rainfall and location [22; 23]. An unmanned automata ship further should be equipped with more sensors to collect data related to unmanned ship-steering operation.  
The IoUT is composed of various underwater sensor networks for environmental monitoring, underwater detection, disaster prediction and port security as well as national defense. These sensors include those sensing water temperature, salinity, current speed, sonar and a variety of biological and chemical sensors. However, as discussed in Section 3.5.2, most underwater networks are acoustic networks able to cover only small areas at high speed, and many such networks are distributed in different sea areas. Marine Internet can be used to interconnect them to construct the IoUT.