esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application
### Analysis of Underwater Distributary Channel Width  
Using the method described in Section 1, each candidate model was sampled 100 times. Every sample had a tag to identify the candidate model to which it belongs. All the training results were trained together in deep learning to obtain the fitted deep convolutional network model. The candidate models of different channel widths were compared with the conditional data to determine the underwater distributary channel widths in the study area. The results show that the model with a channel width of 160 m is the most consistent with the data in the study area.  
The combined prediction of well earthquakes in the study area was studied from a geological perspective. According to the combination characteristics of lithology and electricity, the type of sedimentary cycle of the second stage, and the reservoir's oil content rate, the target interval was subdivided step by step, and the isochronous relationship of each well point and all levels of the formation in the whole area was established under the guidance of sedimentological theory, so as to realize the unified division and correlation in the field. Through the extraction of well earthquake and prediction results in this block, the single sand body thickness was mainly concentrated in the range of 2-4 m, indicating that the width of the channel in this research area is roughly between 130 and 200 m, with 160 m of channel width in the majority. This is consistent with the prediction results in this paper, so it can be determined that the width of the channel in this research area is 160 m.
## 8 Security and Privacy Risk Assessment
### Security and Privacy Risk Assessment at the System Architecture Phase
#### 8.4.1 Review System Architecture  
To review the system architecture an organization needs to consider the following steps:  
* Review the system architecture for compliance with security and privacy design principles. To review system architecture, organizations should take the following security and privacy design principles into consideration:
* Identify whether each component of the application will interface externally or internally or both.
* Identify how the user will access each component of the application and define the trust boundary.
* Use least privilege principle while accessing and interfacing with any component. Take the threats and vulnerabilities identified in the requirement analysis phase into consideration while designing the security and privacy requirements.
* Identify the use of any third-party components and their security and privacy capabilities.
* Keep the system architecture as simple as possible.
* Ensure that all security and privacy requirements identified in Section 8.3.2.6 are implemented.
* If any security and privacy requirements or design principles are not implemented, then implement the missing one and iterate the review process.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 3 Methodology
### Template Caching  
The introduced algorithm uses the whole point cloud for each localization pass, but between localization passes, only parts of the point cloud are changed as the environment is observed. Sections of the point cloud that are not observed in an update step are also left unchanged. Since convolutions are expensive, we can take advantage of caching to improve performance. We split up the rasterized grid \(g\) from Section 3.3 into multiple two-dimensional blocks \(G_{kl}\), such that we have the following:  
\[g=\sum_{k}\sum_{l}G_{kl} \tag{5}\]  
Since the convolution is distributive, we can compute the convolution of the template matching step for every block individually and sum up the results:  
\[g*\hat{d}=\left(\sum_{k}\sum_{l}G_{kl}\right)*\hat{d}, \tag{6}\]  
Figure 3: The rasterization process of an axis-aligned point cloud: (**a**) The original layout in the BIM model; (**b**) the axis-aligned floor plan \(c_{i,j}\) of a recorded point cloud, where the walls and floors are clearly visible while unobserved locations are in black; (**c**) the floor plan \(c_{i,j}\) with applied thresholding, where observed walls have a value of 1, observed floors have a value of \(-1\), and unobserved parts have a value of 0; and (**d**) the combined floor plan \(g_{i,j}\).  
\[g*\hat{d}=\sum_{k}\sum_{l}(G_{kl}*\hat{d}). \tag{7}\]  
This allows us to only recalculate convolutions in areas which have been updated in the last step. When the recorded point cloud increases in size, the computation time of the convolution, thus, stays unaffected.
## 4 Electron Beam Modulation and Undulator Emission
Figure 6: Emission by undulator–radiator: modulated case: (**a**) phase-space; and (**b**) power spectrum. We may now ask whether there is any optimisation procedure to further increase the power radiated by the undulator. The most relevant tool is the distance between the Cerenkov electromagnetic resonator and the entrance of the magnetic undulator. This is because the energy modulation acquired by the electrons in the cavity transforms into a velocity modulation, and consequently bunches before entering the undulator interaction region. This \"drift space\" allows a ballistic process that correlates the velocity variation of each electron, with its position, and therefore phase, in the bunch. In Equation (11), we report the phase term that should be inserted in the set of Equation (10) that takes into account the phase variation, after the velocity modulation expressed by \((\
u_{2})_{j}\) and for a specific drift space \(L_{Drift}\). The index \"\(j\)\" indicates the specific electron and \(\
u_{ref}\) is the velocity of the \"reference electron\":  
\[\left(\Psi_{Drift}\right)_{j} = \omega_{RF}L_{Drift}\left(\frac{1}{(\
u_{2})_{j}}-\frac{1}{\
u_{ ref}}\right)+\psi_{j} \tag{11}\]  
Considering a series of values for \(L_{Drift}\) ranging from 0 to 3.5 metres, the power spectrum was calculated and reported in Figure 7. As can be appreciated, the peak power, starting from a specific value, tends to reduce, while increasing \(L_{Drift}\), and the spectrum itself tends to broaden with a couple of peaks appearing. A further increase in the modulator radiator distance produces a spectral band reduction and a peak power increase up to the final value. Figure 7: _Cont._
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 6 Performance Evaluation  
Each simulation sequence performed the data collection to guarantee a 95% confidence interval for the estimated parameters. For each simulation run, the metric of interest was the message delivery probability, average delay, and overhead.  
We applied statistical tests to estimate the efficiency of our mechanism regarding the related proposals. We divided the analysis into two subsections for each compared scenario.
## 5 Simulation Results
### Enhancing the Algorithm  
As described in the previous section, the weight of vehicles' speed in the cost formula had a small impact in the overall CO2 emission results. This is somehow counter-intuitive, so this issue was further investigated. The real-time observation of the results of the cost formula with the traffic flow and vehicle behaviour gave an insight into the reasons for this unexpected result. As a matter of fact, the problem is that both traffic flows use the same cost formula, but a moving vehicle in a blocked lane should not have the same cost as a moving vehicle in an allowed lane, as the former will almost certainly have to stop, because switching the traffic lights takes some time.  
Therefore, the cost formula was changed to increase the weight of vehicles' speed, but only in the traffic flow that is allowed at that time. This was carried out by changing the K constant from 1 to 30; this value was considered as the optimal one, after experimenting with several values. After this tweak, the results for CO2 emissions improved by 2.8% more,  
Figure 4: Effect of constant J on results.  
the awaiting time by 1.3% more, and average speed by 5.8% more. These results are shown in Table 5.  
Another potential improvement that was investigated is related to the safeguard periods, the small periods of time with both traffic flows blocked (all the lights red), designed to prevent collisions should a vehicle not comply with the yellow light. In passive systems, this safeguard is crucial to avoid accidents, but active systems can be aware of the vehicles' behaviour at the intersection; thus, this safeguard can be safely bypassed if the traffic flow, which is being blocked, is already stopped at the yellow light or if there are no vehicles near the intersection in the lanes being blocked.  
Thus, the algorithm has been tweaked to bypass the safeguard periods when it is safe do it, and the results are substantial: CO2 emissions improved by 3.8% more, the waiting time by 3.4%, and the average speed by 10.4%. Table 5 shows the overall results for each stage of the algorithm.  
The CO2 emissions' improvement is significant, if we consider that the maximum improvement possible for this traffic profile is 39%, and only if the traffic flows were nonconcurrent, something that is highly unrealistic.
# Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
## 3 Results and Discussion
### UV-Visible Spectral Analysis  
The UV-visible spectrum analysis of the dye sample without plasma irradiation shows two prominent absorption peaks at 276 and 496 nm, as depicted by the dark blue curve in Figure 3. It is observed that the intensity of these peaks diminishes as the dye samples are subjected to plasma treatment for varying durations of irradiation. The data reveal that  
\begin{table}
\begin{tabular}{l l l} \hline
**Treatment Time** & **Nitrates** & **Nitrites** \\ \hline
0 min & 0.892 & 0.058 \\
10 min & 2.32 & 0.122 \\ \hline \end{tabular}
\end{table}
Table 1: Concentration (mL) of nitrates and nitrites from spectrophotometry method.  
there is a substantial decrease in intensity during the initial 2 min, which then slows down between 2 and 10 min.  
The effectiveness of atmospheric-pressure plasma discharge (APP) in degrading cochineal dye was evaluated using Equation (2) to measure the rate of color removal during APPJ discharge irradiation. The results demonstrated that approximately 90% of the dye was degraded after 10 min of exposure (Figure 4). This finding is consistent with a similar study reported by [PERSON] et al. [34] and [PERSON] et al. [35]. Indeed, [PERSON] reported that the interaction of cochineal dye with a plasma-treated bimessite layer leads to 95% degradation after 24 h. These results support the hypothesis that plasma treatment can be an effective method for dye removal, making plasma irradiation a promising technique for eliminating dyes. However, it is important to recognize the limitations of this research, such as the specific experimental conditions used. Further research is needed to optimize the process and investigate the effects of different plasma parameters on dye removal efficiency. Additionally, future studies could explore the potential use of plasma treatment for other types of dyes and examine the long-term stability and environmental impact of plasma-treated dye samples.  
Figure 4: Percentage degradation of cochineal dye by APPJ discharge.  
Figure 3: UV-vis spectra measured for different plasma irradiation times.
## 2 Modelling and Methods
### ANN Development Protocol
#### 2.5.5 Model Calibration  
Through training, the ANN converges to the optimum values of the initial random weights assigned to its neurons; this enables an accurate prediction of the dependent variable to eventually be made. One of the most computationally efficient algorithms for achieving such optimisation is backpropagation [39]. A loss function is used at the end of every epoch to calculate the difference between the ANN prediction and the ground truth. This study utilises the Mean Squared Error (MSE) loss function, since the physical phenomenon under consideration was not linear and the final predictions were not distinct categories (i.e., aiming for the regression of a range of values). With the level of inaccuracy now quantified, an optimisation function, in this instance Gradient Descent, calculates the local minimum (optimum) and feeds back to the network, updating the neuron weights accordingly to reduce the difference between the prediction and the truth (loss). Although other methodologies for optimising the performance of the model are available [40], they are beyond the scope of this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper Parameter** & **Value** & **Comments** \\ \hline Number of epochs & 50 & Following ad hoc experimentation with various values. \\ Learning rate & 0.001 & Default value of Adam optimiser. \\ Batch size & 10 & To allow more efficient processing of the large dataset. \\ Activation function & ReLU & Used the default values of the rectified linear unit activation function. \\ Optimiser & Adam & Default values of Adam [38] optimiser were used. \\ Loss function & MSE & Mean squared error loss function. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyperparameters used for the development of the ANN.  
Figure 6: Graphical representation of the employed ANN architecture.
## 2 Theoretical Modeling
### Middle Constraint Matrix Model  
Middle constraint is the indispensable configuration in the pipe system; the modeling method and application of the simple middle constraint were investigated in [40; 41; 42; 43]. The middle constraint matrix, including the concentrated mass and additional stiffness, is presented by using the point transfer matrix in this subsection.  
Based on the force balance and motion continuity, the relation between state vectors at either end of the middle constraint node could be expressed as:  
\[\boldsymbol{\varphi}_{i+1}(0,t_{i})=\mathbf{N}_{i}(t_{i})\boldsymbol{\varphi} _{i}(L_{i},t_{i}) \tag{24}\]\(\mathbf{N}_{i}\) is the constraint matrix of the middle constraint, which represents the transitive relation at the discontinuity of the series connection pipe. If there are no constraints at the connection location and the diameter and material of the pipe are unchanged, \(\mathbf{N}_{i}\) is unit matrix.  
\[\mathbf{N}_{i}=\left[\begin{array}{cccccccccccccccc}1&0&0&0&0&0&0&0&0&0&0&0&0&0 \\ 0&1&0&0&0&0&0&0&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0&0&0&0&0&0&0\\ 0&0&K_{zi}&1&0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&T_{yi}&1&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&K_{xi}&1&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&1&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&1&0&0&0&0\\ 0&0&0&0&0&0&0&0&T_{xi}&1&0&0&0\\ 0&0&0&0&0&0&0&0&0&0&K_{yi}&1&0&0\\ 0&0&0&0&0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&0&0&0&T_{zi}&1\end{array}\right] \tag{25}\]  
where \(K_{(i)}\) and \(T_{(i)}\) are translational stiffness and rotational stiffness, respectively.  
The field transfer matrix (see Equation (41)) of the pipe can be expressed as:  
\[\mathbf{U}_{\text{all}}(t_{i})=\mathbf{U}_{N}(L_{N},t_{i})\cdots\mathbf{U}_{i +1}(L_{i+1},t_{i})\mathbf{N}_{i}(t_{i})\mathbf{U}_{i}(L_{i},t_{i})\cdots \mathbf{U}_{1}(L_{1},t_{i}) \tag{26}\]  
The application methods of \(\mathbf{N}_{i}\) and \(\mathbf{U}_{\text{all}}(t_{i})\) in solving the FSI fourteen-equation model are specified in the next section.
## Appendix B Canonical Transformation Absorbing the In-Plane Zonal Harmonic Terms  
It is shown in Section 3.1 that the in-plane and out-of-plane dynamics are not coupled and can be analyzed separately. Indicating as \(H_{xy}\) the in-plane terms of the Hamiltonian function, the ones depending on the perturbation coefficients of order one (\(J_{n}\)) can be absorbed by means of a canonical transformation defined through a type-3 generating function \(S(x,y,P_{x},P_{y})\) such that  
\[\begin{cases}p_{x}=P_{x}+f_{1}\\ p_{y}=P_{y}+f_{2}\\ Q_{x}=x-g_{1}\\ Q_{y}=y-g_{2}\end{cases}\] (A29)  
and  
\[\tilde{H}_{xy}=H_{xy}^{(0)}(\mathbf{Q},\mathbf{P})+o(\mathbf{Q},\mathbf{P},f_ {n}^{k\geq 2})\] (A30)  
Introducing Equations (A29) into \(H_{xy}\) results in the following expression:  
\[\tilde{H}_{xy}=H_{xy}^{(0)}(\mathbf{Q},\mathbf{P})+f(\mathbf{Q},\mathbf{P},f_ {n}^{k})\] (A31)  
Then, making the function \(f(\mathbf{Q},\mathbf{P},f_{n}^{k})\) explicit in \(\mathbf{Q}\) and \(\mathbf{P}\), Equation (A31) is rearranged as follows:  
\[\tilde{H}_{xy}=H_{xy}^{(0)}(\mathbf{Q},\mathbf{P})+(3g_{1}-2g_{1}K_{11}+K_{10 }-f_{2})Q_{x}+(-g_{2}-2g_{2}K_{33}+K_{20}+f_{1})Q_{y}+\]  
\[+\Big{(}-g_{2}-v_{x}^{(1)}+f_{1}\Big{)}P_{x}+\Big{(}g_{1}-v_{y}^{(1)}+f_{2} \Big{)}P_{y}+K_{11}Q_{x}^{2}+K_{22}Q_{y}^{2}+K_{33}Q_{z}^{2}\] (A32)  
Finally, to obtain the desired form for \(\tilde{H}\), the following identities must be verified:  
\[\begin{cases}3g_{1}-2g_{1}K_{11}+K_{10}-f_{2}=0\\ f_{1}-g_{2}-2g_{2}K_{33}+K_{20}=0\\ f_{1}-g_{2}-v_{x}^{(1)}=0\\ g_{1}-v_{y}^{(1)}+f_{2}=0\end{cases}\] (A33)  
The solutions of the system are given by Equation (21), which provides a complete definition of the generating function and, therefore, of the canonical transformation.
[PERSON] [4] showed that the decrease in global radiation from 1961-1990 is attributable to the increases in cloud optical thickness and the direct effects of aerosols, thereby reducing solar radiation by 18 and 8 W m\({}^{-2}\), respectively. [PERSON], et al. [14] argued that the decline in surface solar radiation in South Asia is attributable to the increase in the amount of anthropogenic aerosols from 1979 to 2004. This conclusion is supported by the agreement between the observed decadal changes in anthropogenic aerosol emissions and the trends in global solar radiation [6; 15]. Solar dimming and brightening may be of a local or regional nature and are unavoidably influenced by regional sources and meteorology [9]. Here, we focus mainly on the dimming and brightening caused by changes in aerosols, water vapor, and clouds, which are the main factors that alter atmospheric transparency, and thus, regulate solar radiation incidences to the surface [6; 9]. South Asia is experiencing a levelling-off trend in solar radiation and even a transition from dimming to brightening, but the related reasons are not determined. Therefore, this study used observations from multiple observations of the A-Train satellite constellation to evaluate the impacts of three-dimensional (3D) aerosol, cloud, and water vapor variations on the recent changes in surface solar radiation during the monsoon season (June-September) in South Asia from 2006 to 2015. The observations used mainly include CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Moderate Resolution Imaging Spectroradiometer (MODIS), and Clouds and the Earth's Radiant Energy System (CERES). Our study mainly aims to assess the connection between variations in aerosols, clouds, and water vapor with respect to surface solar radiation, and identify the possible reasons for the recent brightening in South Asia.
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
## 3 Results
### The Pulse Width Penalty  
The penalty from ISI alone has been determined by simulating a SiPM with the same parameters as a J30020, except that the recovery time was set to 1 ps. This recovery time is significantly shorter than the minimum timestep of the simulations. Consequently, all microcells fully charged at each simulated timestep and SiPMs' responses are therefore linear. For each simulation the data rate was set. Then, the BER after DFE was evaluated for count rates that achieved BERs between \(10^{-3}\) and \(10^{-2}\). Linear interpolation and this data were then used to estimate the count rate that would give a BER of 3.8 \(\times\)\(10^{-3}\). These results were then expressed as a count rate penalty, which is the count rate needed to achieve a BER of 3.8 \(\times 10^{-3}\) divided by 5.2 times the data rate, which is the count rate that is expected to achieve this BER. To include the effect of SiPMs' non-linearity this process was then repeated for a SiPM with the same recovery time as a J30020.  
The results in Figure 6a show that, for data rates less than 2 Gbps, the non-linearity has no impact on the count rate penalty; however, the results in Figure 6b show that the non-linearity increases the irradiance required to obtain the same count rate at 2 Gbps. Furthermore, by 2 Gbps the count rate penalty is approximately 16. This means that the required number of photons per bit when a one is transmitted has increased from 6 at 200 Mbps to 82 at 2 Gbps.  
Figure 5: The BER achieved when 5.2 photons are detected per bit when a one is transmitted. (**a**) shows the results at different data rates compared to the ideal result. (**b**) shows that the important parameter is the product of the pulse width and the data rate.  
Some results for a SiPM with the same parameters as a J30020 are missing from Figure 6 because it was not possible to achieve a BER of 3.8 \(\times 10^{-3}\) at higher data rates. The results in Figure 7 show that at 2.5 Gbps the BER starts to increase as the count rate increases. This creates a minimum BER above the required BER. A minimum BER as the irradiance, and hence count rate, increases has been observed experimentally previously [10]. This previously unexpected behavior was found to be due to a new form of ISI caused by SiPMs' non-linearity. A comparison between the count rates of these simulations and the maximum count rate of the simulated SiPM suggests that this phenomenon has become important once the count rate is 40% of the maximum count rate.
# Resistance of Concretes to External Chlorides in the Presence and Absence of Sulfphates: A Review
## 3 Resistance of Concrete to External Chloride in the Presence of Sulphate
### Effect of sulphate on Chloride Binding Capacity  
The effect of sulphate on chloride resistance is less controversial than the converse situation discussed above. Sulphates affect the ingress of chloride into concrete at early-age exposure due to the preferential formation of ettringite crystals over Friedel's salt, leading to a compacted microstructure, thereby decreasing chloride ingress [126]. However, at later ages, the trend is reversed, possibly due to excessive formation of expansive ettringite crystals, resulting in cracks that provide channels for rapid ingress of chlorides into concretes [128; 138]. It has also been reported that chloride ions penetrate cement matrices faster than sulphate ions do. However, Friedel's salt formation is depleted by incoming sulphates, thereby increasing free chloride levels [139].  
The impact of magnesium sulphate on chloride binding in Portland cement paste was also investigated by [PERSON] et al. [140]. They found similar chloride binding in sea water and a NaCl solution, with chloride binding by both C-S-H and AFm phases reducing due to the presence of sulphate in sea water. Similar findings were reported by [PERSON] [72]. However, chloride binding capacity increased with partial replacement of Portland cement by GGBS.  
Combined sulphate-chloride exposure has been found to exacerbate reinforcement corrosion. [PERSON] and [PERSON] [141] showed that steel corrosion was greater in combined chloride-sulphate solutions than in either pure chloride or sulphate solutions.  
Figure 7: Effects of combined chloride–sulphate exposure on compressive strength of concrete [18].  
Similarly, [PERSON] et al. [142] concluded that long-term corrosion current density in chloride solutions increased in the presence of sulphate.  
However, although there appears to be a consensus on the effect of sulphate on chloride attack of reinforced concrete, more work must be conducted to better understand the effects of changes in binder composition, effects of SCMs, temperature and exposure to solutions containing multiple compounds.