esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation of the Key Assumption of the Study
#### Investigation of Multi-Collinearity  
The collinearity-matrix in Figure 14 gives the correlation coefficients between the predictor (X) variable pairs together with a proposed interpretation scheme.  
From the collinearity matrix in Figure 14, the correlation between vegetation input variables is between moderate to very high correlations (min = 0.53, max = 0.87). This is as opposed to the relationship between the pairings between vegetation and precipitation datasets, which is between no linear relationship to moderate (min = 0.0, max = 0.54). The assumption to  
Figure 14: Collinearity-matrix for the input (X) variables. Absolute correlation coefficient between the pairs in X is provided together with a proposed interpretation of the correlations.  
Figure 13: Distribution of non-selected models ANN performance on training, validation and testing. No model is noted to post an \(R^{2}\) of at least 0.5 in testing.  
precipitation and vegetation datasets will therefore generally result in pairings of weak to barely moderate correlations.  
In addition to the collinearity matrix, we investigated the problem of multi-collinearity between the independent variables in a two-step process--first for a model of all variables and second for the pairing of precipitation and vegetation variables. For each approach, we obtained the variable inflation factor (VIF) with the rule of thumb that a VIF \(>\) 5 indicates high multi-collinearity, while a VIF \(>\) 10 indicates multi-collinearity that has to be handled in the modeling process. The results for the investigation of VIF for all the model variables is presented in Table 7.  
The full model (with all variables) indicates the presence of multi-collinearity with VIF \(>\) 10 for 2 of the predictor variables. A further analysis for the models fed in to the GAM process obtained the results provided in Figure 15.  
The results in Figure 15 confirm that using the vegetation-precipitation variable pairs (corresponding to low correlation portions of the correlation matrix in Figure 14) ensures models that are not affected by multi-collinearity.  
Concurvity, which has similar effects to those posed by multi-collinearity, was not a major limitation in the approach to GAM modeling employed by this study since only one smooth term was used in the development of the GAMs across all the models. In fact, an investigation with smoothing on all the terms (not presented), resulted in model overfitting that limited the smoothing of the dependent variables.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Variable Inflation Factor (VIF)** \\ \hline VCI3M\_lag1 & 6.14 \\ NDVIDekad\_lag1 & 1.41 \\ VCI1M\_lag1 & 976.21 \\ VCIDekad\_lag1 & 1057.46 \\ RCI1M\_lag1 & 4.41 \\ RCI3M\_lag1 & 5.90 \\ RFE1M\_lag1 & 2.63 \\ RFE3M\_lag1 & 2.88 \\ SPI1M\_lag1 & 3.34 \\ SPI3M\_lag1 & 5.24 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Variable inflation factor (VIF) for single model with all 1-month lag variables.  
Figure 15: Variable inflation factor (VIF) for GAM models.
# Characteristics of Deep Groundwater Flow and Temperature in the Tertiary Pohang Area, South Korea
## 4 Results
### Geophysical Log Profiles  
The geophysical logging was conducted under steady-state conditions (without the influence of drilling and pumping) in October 2010 and December 2014, after the drilling of BH-1, BH-2, BH-3, and BH-4 from January 2003 to November 2006. Temperature, caliper, EC, and natural gamma-ray loggings were conducted to interpret the lithology, aquifer, and flow characteristics of the four boreholes (Figures 5-8). Owing to the difficulty in acquiring highly reliable water temperature data immediately after drilling, the geophysical logging was executed at the stabilization of the groundwater temperature. In BH-1, only the temperature log data were available for the entire depth; the caliper and natural gamma-ray logs were measured up to 359 m, owing to a borehole collapse. The accuracy of the geophysical logging for BH-3 was uncertain with respect to the EC and temperature data, owing to the influence of drilling mud. In BH-4, geophysical logging was conducted to a depth of 2000 m, owing to temperature and pressure sensor limits [24].  
An accurate temperature log is critical in determining the thermal gradient and thermal conductivity; however, this was difficult to achieve, owing to the high specific weight of the drilling mud used to maintain the borehole wall during excavation and because of the casing installed after the excavation. The temperature log data for BH-3 have low accuracy because the data were obtained during drilling and because of the effect of the drilling mud. The thermal gradients of the boreholes (as well as the groundwater flow rate and direction) were estimated from the temperature logs. The decreasing tendency of the temperature from the surface to a depth of 100 m indicates the influence of the surface air temperature on the underground temperature [49; 50; 51].  
Figure 5: Geophysical and lithological profiles of BH-1: (**a**) Caliper log; (**b**) temperature log; (**c**) delta temperature log; (**d**) natural gamma-ray log; (**e**) a simplified lithological log; and (**f**) a casing installation. The blue triangle indicates upward ground flow, and the red upside-down triangle indicates downward ground flow. Estimated depth intervals where groundwater flows occurred are marked with dotted lines.  
Figure 6: Geophysical and lithological profiles of BH-2: (**a**) Caliper log; (**b**) temperature log; (**c**) delta temperature log; (**d**) electrical conductivity (EC) log; (**e**) natural gamma-ray log; (**f**) a simplified lithological log; and (**g**) a casing installation. The blue triangle indicates upward ground flow and the red upside-down triangle indicates downward ground flow. Estimated depth intervals where groundwater flows occurred are marked with dotted lines.
# Deep Learning Cluster Structures for Management Decisions: The Digital CEO +
## 5 Experimental Results
### Quality of Service Deep Learning Cluster Results (3 \(\times\) 3 Nodes)  
The 3 \(\times\) 3 CPN is simulated with a continuous 160 Cognitive Packet stream. The first 20 packets are used to initialize the CPN network. Goal changes after 20 packets whereas QoS metric changes 2 packets after the new Goal is selected following \(T_{l}=0.9T_{l-1}+0.1R\) where \(T_{l}\) is the Threshold at decision packet \(l\) and \(R\) is the Reward. The QoS DL clusters have been validated with seven different variable Goals for the same Cognitive Packet stream (Table 3). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dimension** & \(\Delta\) **= 0.0** & \(\Delta\) **= 0.1** & \(\Delta\) **= 0.2** & \(\Delta\) **= 0.3** & \(\Delta\) **= 0.4** \\ \hline
1 & 9.7500 \(\times\) 10\({}^{-11}\) & 0.0102 & 0.0409 & 0.0921 & 0.1638 \\
2 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.0213 & 0.0851 & 0.1915 & 0.3406 \\
3 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.0326 & 0.1305 & 0.2938 & 0.5226 \\
4 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.0451 & 0.1806 & 0.4067 & 0.7238 \\
5 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.0576 & 0.2306 & 0.5195 & 0.9249 \\
6 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.0715 & 0.2867 & 0.6465 & 1.1519 \\
7 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.0851 & 0.3414 & 0.7703 & 1.3732 \\
8 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.1006 & 0.4038 & 0.9119 & 1.6273 \\
9 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.1153 & 0.4633 & 1.0470 & 1.8698 \\
10 & 9.7537 \(\times\) 10\({}^{-11}\) & 0.1323 & 0.5321 & 1.2038 & 2.1526 \\ \hline \end{tabular}
\end{table} 
Table 2: Cyber Deep Learning Cluster Validation. The average error and learning algorithm iteration values for the QoS and Cyber DL clusters is shown in Table 4. The learning error of the QoS and Cyber DL Clusters is very reduced. The number of updates in the network weights, or routing table, for the DL cluster and the RNN Reinforcement Learning is represented in Table 5.  
The RNN Reinforcement Learning algorithm continuously updates its network weighs whereas the DL Cluster route only refreshes when a better route is found, however, the number of required iterations to update RNN-RL is only one whereas QoS DL clusters require approximately 160 iterations as shown in Table 4. The route decision taken by the CEO Management Cluster when the Cyber management cluster has authorized the different Cyber keys is shown in Tables 6-8 for only three different Goals as the simulation includes seven. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cyber DL Cluster** & **Error** & **Iteration** & **QoS DL Cluster** & **Error** & **Iteration** \\ \hline Cyber User & 6.96 \(\times\)\(10^{-10}\) & 58 & QoS Delay & 9.59 \(\times\)\(10^{-10}\) & 163.67 \\ Cyber Packet & 7.34 \(\times\)\(10^{-10}\) & 108 & QoS Loss & 9.16 \(\times\)\(10^{-10}\) & 163.14 \\ Cyber Node & 9.94 \(\times\)\(10^{-10}\) & 1162.33 & QoS Bandwidth & 9.16 \(\times\)\(10^{-10}\) & 135.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Deep Learning Cluster Validation (3 \(\times\) 3 Nodes).
## 4 Experiments  
### Excitation System  
An excitation system was used for the experiment as shown in Figure 10. The generated specified vibration waveform signal by the computer was then put into the power amplifier. After that, the amplified signal actuated the vibration exciter to output the expected force which was used to excite the frame structure. Here, a vibration exciter, Modal Shop 2100E11, was adopted in the experiment. It is a lightweight electrodynamic modal exciter, and is capable of providing 440 N of peak force excitation in a small footprint weighing just 15 kg.
## 3 Results
### General Characteristics and Energy Flux
#### 3.1.2 Energy Density and Flux  
The distribution of the surface amplitude induced by the largest \(M_{2}\) internal tides in three waves is shown in Figure 5a. The locations with a significant amplitude are almost consistent with the hotspots revealed in the SSH data (Section 3.1), with the maximum amplitude reaching up to 1.13 cm in the northeastern Arabian Sea. In addition, amplitudes near the Maldive Islands and the Carlsberg Ridge are around 0.5 cm, which is relatively higher than their surroundings. As the energy is proportional to the square of the amplitude, the vertically integrated energy flux is illustrated in Figure 5b. This value was calculated via the plane-wave-fitting method using the first three largest waves. Those three waves can propagate along different, even opposite, directions, hence the unorganized pattern in Figure 5b. The locations of the sources of the internal tides are distinct, and the magnitude and directions of the energy fluxes generally agree with the findings from the hourly snapshots (Section 3.1), revealing a reasonable consistency between the SSH and the energy flux. Moreover, the maximum energy flux reaches up to 10 kW/m in the northeastern Arabian Sea, and this value is comparable with that recorded around the islands of Hawaii [18], where internal tides are significant on a global scale.  
In the northern Red Sea, the energy flux generally propagates in an easterly direction at around 2 kW/m, which is much weaker than the energy flux in the hotspot locations of the Arabian Sea. In the southern Red Sea, the baroclinic waves are generated between the Farasan Islands and the Dahlak Islands, which then propagate northeast until reaching the coastline. Further, southeastward signals are observed to the south of the Farasan Islands, probably associated with the topography: when internal tides reach the Farasan Islands, the signals are split in two by the topography, with one movi  
Figure 5: (**a**) Spatial distribution of \(M_{2}\) internal tides’ sea surface amplitude. (**b**) The vertically integrated energy flux of the mode-1 \(M_{2}\) internal tides. Gray contouring in the background illustrates the bathymetry.  
and the other moving toward the southeast. Similar phenomena were reported in the southern Red Sea using numerical simulations [41; 42] and satellite synthetic aperture radar (SAR) imagery [39]. Some common features of internal tides can be extracted from both Figures 3 and 5. The source locations of baroclinic tides have a high-gradient topography, which favors the generation of internal tides [1].
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 5 Simulation Tests and Results
### Effect of Chunk Size  
One question that can be considered is if there is an optimal value of the chunk size: on the one hand, a small size is expected to reduce the loss probability of a frame; on the other hand, a big size will reduce the overhead caused by LoRa headers. A battery of simulations is carried out in order to see whether this optimum exists. The simulations are run in a scenario with 10 nodes, using two values for the maximum radius: 400 m and 2 km. The header of the frames is 23 bytes long, and the payload ranges between 16 and 192 bytes, considering that it is ciphered with AES in blocks of 16 bytes. Each test is run 40 times. The 95% confidence intervals are also included.  
In the small scenario (400 m, Figure 15), the three methods show similar behavior: the best option is always to use the longest payload (192 bytes plus the 23-byte header). Again, the _only unicast_ method is the slowest one and, in this case, the two other methods present very similar results.  
Things are different in the big scenario (2 km, Figure 16): as far as the _only unicast_ method is concerned, the graph shows that bigger payloads (bigger chunks of firmware) are always beneficial for the update time. In this case, the best option is always to maximize the payload size.  
Figure 15: Effect of payload size on update time. Maximum radius 400 m: (**a**) _Only unicast_ method; (**b**) _Broadcast_ + _unicast_ method; and (**c**) _Only broadcast_ method. Note that the \(Y\) axis is in logarithmic scale.  
However, in the case of the _broadcast + unicast_ method, the tendency is just the opposite: if 39-byte frames are used, the update time is 80.4% of the time required if 215-byte frames are employed. This is caused by the high loss probability: if a frame is too long, the probability of not receiving it correctly is higher.  
Something similar happens with the _only broadcast_ method: in this case, it is also better to use small frames. In addition, there is a value of 135 bytes, which is the worst of all by a small difference (roughly 4.5%).
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 3 Algorithm Design
### Genetic Operator Design  
The chromosomes are guided by genetic operators to find an optimal solution for the problem. The genetic operators has been customized regarding the problem, consisting of crossover, mutation and selection, which are combined to find the perfect solution for the problem. The chromosomes are modified by operators toward a better solution in every iteration.
# Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones
## 3 Methodology
### Multilinear Regression Models  
Once the meteorological indices with high influence on the _PAR_ component were determined, taking into account all the sky types and the three sky types (clear, partial, and overcast), four MLR models were developed with which to estimate _PAR_: one MLR model for all sky types and one for each sky type. The indices selected with [PERSON]'s correlation coefficient (\(r\)) were taken as input variables in all models.  
The working method to obtain the MLR models was as follows: the experimental dataset was divided into two groups. The first group contained 85% of the data and was used to fit the models. The other group, with the remaining 15% of the data, was used to validate the models using the conventional statistics corresponding to the coefficient of determination (\(R^{2}\)), the normalized Mean Bias Error (_nMBE_), and the normalized Root Mean Square Error (_nRMSE_), calculated from Equations (1) and (2):  
\[nMBE=\frac{1}{\frac{\sum_{i=1}^{n}\left(\frac{PAR_{mod}-PAR_{exp}}{n}\right)} }\times 100\ (\%) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline \(k_{t}\)**Sky Type** & **[1-0.9]** & **(0.9–0.7]** & **(0.7–0.5]** & **(0.5–0.3]** & **(0.3,0)** \\ \hline All sky conditions & _RaGH_ & _cosZ_, \(k_{t}\) & \(k_{d}\), \(\varepsilon\) & \(T\) & \(\Delta\), \(P\), \(T_{d}\) \\ \hline Clear & _RaGH_, _cosZ_ & & \(k_{t}\), \(\varepsilon\) & \(T\) & \(k_{d}\), \(\Delta\), \(P\), \(T_{d}\) \\ \hline Partial & _RaGH_, _cosZ_ & & \(T\) & \(k_{t}\), \(k_{d}\), \(\Delta\), \(\varepsilon\), \(P\), \(T_{d}\) \\ \hline Overcast & _RaGH_ & _cosZ_ & \(k_{t}\), \(\Delta\) & & \(k_{d}\), \(\varepsilon\), \(P\), \(T\), \(T_{d}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pearson’s \(r\) (_PAR_, \(Ml_{j}\)) based on the sky conditions according to the \(k_{l}\) sky classification (clear, partial, and overcast).  
\[nRMSE=\frac{1}{\mathit{PAR}_{exp}}\sqrt{\frac{\sum_{i=1}^{n}\bigl{(} PAR_{mod}-\mathit{PAR}_{exp}\bigr{)}^{2}}{n}}\times 100\ (\%) \tag{2}\]  
where \(n\) represents the amount of experimental data used for fitting the models; \(\mathit{PAR}_{exp}\) is the experimental value of \(\mathit{PAR}\), and \(\mathit{PAR}_{mod}\) is the modelled \(\mathit{PAR}\) value. Table 3 shows the mathematical expressions of the four regression models and the fit of each one with their mean square error. As Table 3 shows, the model fitting results presented good correlations with the experimental data, given \(R^{2}>0.97\) and \(nRMSE\) lower than 8% The small and negative values of \(nMBE\) indicated that the models presented a good fit although they tended to underestimate the \(\mathit{PAR}\) values. It can be seen that the model for clear skies showed the highest accuracy with an \(R^{2}\) of 0.99 and had the lowest errors, for \(nRMSE\) and for \(nMBE\).
## 6 SAR Payload Performance Analysis
### Noise Equivalent Sigma Zero  
The NESZ is an index of the radiometric sensitivity of the system and reflects a quantity directly related to the SAR imaging performance, which is defined as the target radar cross-section if the signal-to-noise ratio of the image is equal to one [2; 3; 4]. The NESZ of the \(n\)-th antenna beam corresponding to the \(n\)-th swath can be expressed as [35; 45]  
\[\sigma_{NE,n}=\frac{4(4\pi)^{3}R_{s,n}^{3}L_{s}kT_{s}B_{t,n}\sin\theta_{i,n}v_{ s}}{c\cdot G_{t,n}(\theta_{e})\cdot G_{r,n}(\theta_{e})\lambda^{3}P_{t}\tau_{p,n}f_{p,n}}, \tag{5}\]  
where \(B_{t,n}\) represents the transmitted bandwidth, \(\tau_{p,n}\) indicates the pulse width, \(G_{t,n}\) and \(G_{r,n}\) represent the Tx and Rx antenna pattern of the \(n\)-th antenna beam, \(f_{p,n}\) reflects the PRF, \(R_{s,n}\) denotes the slant range to the scene center of the \(n\)-th antenna beam, \(\theta_{i,n}\) denotes the incidence angle of the \(n\)-th antenna beam. Based on the system parameters listed in Tables 3-5, the NESZ for the S-STEP SAR in the high-resolution and wide-swath modes are evaluated over a range of incidence angles, as displayed in Figure 24a,b, respectively.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{3}{c}{**Incidence Angle**} \\  & \(\mathbf{15}^{\circ}\) & \(\mathbf{25}^{\circ}\) & \(\mathbf{35}^{\circ}\) \\ \hline Beam steering angle & \(-8.02^{\circ}\) & \(1.41^{\circ}\) & \(10.29^{\circ}\) \\ Pulse width & \(32\)\(\mathrm{\SIUnitSymbolMicro s}\) & \(32\)\(\mathrm{\SIUnitSymbolMicro s}\) & \(32\)\(\mathrm{\SIUnitSymbolMicro s}\) \\ Transmitted & \(156.65\)\(\mathrm{MHz}\) & \(94.07\)\(\mathrm{MHz}\) & \(69.55\)\(\mathrm{MHz}\) \\ Sampling frequency & \(187.98\)\(\mathrm{MHz}\) & \(112.88\)\(\mathrm{MHz}\) & \(83.47\)\(\mathrm{MHz}\) \\ PRF & \(4033\)\(\mathrm{Hz}\) & \(4068\)\(\mathrm{Hz}\) & \(4000\)\(\mathrm{Hz}\) \\ Sampling window & \(40.72\)\(\mathrm{\SIUnitSymbolMicro s}\) & \(46.38\)\(\mathrm{\SIUnitSymbolMicro s}\) & \(51.37\)\(\mathrm{\SIUnitSymbolMicro s}\) \\ length & \(14\) & \(15\) & \(16\) \\ Sampling window & \(37.38\)\(\mathrm{\SIUnitSymbolMicro s}\) & \(37.52\)\(\mathrm{\SIUnitSymbolMicro s}\) & \(75.63\)\(\mathrm{\SIUnitSymbolMicro s}\) \\ Synthetic aperture & \(0.25\)\(\mathrm{s}\) & \(0.26\)\(\mathrm{s}\) & \(0.29\)\(\mathrm{s}\) \\ Number of pulses & \(994\) & \(1065\) & \(1146\) \\ acquired & \(994\) & \(1065\) & \(1146\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Operational parameters of the S-STEP SAR payload in the wide-swath mode.
## 6 Experiment  
### Experimental Steps  
To verify the simulation results of the MMW fuze echo signal based on the Monte Carlo method, three detectors were tested in a real rainfall environment. The parameters of these detectors are listed in Table 3. The antennas of the three detectors were designed with a circular shape using microstrip antenna technology, with their gain characteristics closely approximating a Gaussian distribution.  
During the experiment, the MMW fuze operating in frequencies of 35 and 60 GHz was placed outside the window on the second floor of the experimental building by hand to detect the outer plane of another experimental building. At this time, the outer plane of the second experimental building served as the surface target, which was closer to the detection scene during the ground detection process of the fuze flight. The distance between the two experimental buildings was measured to be \(\sim\)13.15 m by using a laser rangefinder, with an open space in the middle. The detection scene is shown in Figure 9a.  
In high-frequency detectors, the issue of signal leakage between the transmitting and receiving antennas is significant. To avoid this problem, an analog high-pass filter is incorporated into the hardware systems of the 35 and 60 GHz detectors to eliminate low-frequency leakage signals. However, this also filters out the low-frequency rain backscatter noise. To experimentally prove the existence of low-frequency rain backscatter noise, a 24 GHz detector without an analog high-pass filter was used to test a wall target 6 m away. The detection scenario is shown in Figure 9b.  
Figure 8: Beat signal spectrum at distance of 9 m, rainfall rate of 50 mm/h, and operating frequency of 60 GHz for 3 dB beam widths of **(a)** 120\({}^{\circ}\), **(b)** 60\({}^{\circ}\), and **(c)** 30\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **24 GHz** & **35 GHz** & **60 GHz** \\ \hline Modulation bandwidth & 250 MHz & 150 MHz & 150 MHz \\ Horizontal 3 dB beam width & 78\({}^{\circ}\) & 120\({}^{\circ}\) & 120\({}^{\circ}\) \\ Vertical 3 dB beam width & 23\({}^{\circ}\) & 120\({}^{\circ}\) & 120\({}^{\circ}\) \\ Farthest range & 10 m & 20 m & 20 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of three detectors.