esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Pseudo-Doppler Principles
### Using a Quadrature RF Oscillator and Mixers  
Without belaboring the details, a way of implementing the relative dynamic weighting of the two receiving antenna signals comes to mind using orthogonal sinusoidal modulations, visualized as in Figure 6. It is even simpler to implement than an image-rejecting mixer in the front end of many common microwave radio receivers. Note that the sinusoidal wave generator can have a very high rate, as microwave oscillators are very common and straight-forward to implement. (This high rate will be necessary to separate the OAM signals in frequency domain, thereby facilitating their signal recovery. A real Doppler shift of such a frequency would require physical motion at speeds approaching the speed of light.)  
Figure 6: An alternative implementation of pseudo-Doppler OAM receiving front end.  
By strategically working through the mathematics of the output equation in Figure 6, the result for the output during selected time-gated intervals can be obtained approximately as  
\[\begin{split}& Z_{1,k}(t)\approx\sqrt{2}S_{k}(t)e^{i(\varphi_{1}(t)- \frac{M}{2R})}\times\ldots\\ &\cdots\times(\cos(\Omega t+\pi/4))e^{i(\frac{M}{2R})(\Omega t+ \pi/4)}\end{split} \tag{10}\]  
with the understanding that \(\Omega=2\pi F\) is the radian pseudo-Doppler frequency, \(F\) being the corresponding frequency in Hz. Note that the bottom factor is due purely to the effect of the pseudo-Doppler modulator on the OAM incident wave, and the scaled pseudo-Doppler radian frequency shift \(k\Omega d/(2R)\) is independent of the RF carrier radian frequency \(\omega\), which is implicit in \(S(t)\), which in turn is the transmitted signal on the \(k\)-th OAM mode,  
\[S_{k}(t)=m_{k}(t)e^{i\omega t} \tag{11}\]  
with \(m(t)\) being the modulating signal and \(\omega\) being the radian carrier frequency.  
Before developing the models for the necessary time-gating and demodulation functions, it is instructive to estimate the potential performance of this method of OAM reception in terms of link and antenna geometry.
## 4 Conclusions  
Solar-assisted heat pump heating units are promising choices for providing the space heating demand with reasonable electricity consumption. The present work examines such a configuration with a high-temperature heat pump aiming to provide high-temperature heat for feeding radiative terminal units. The analysis is conducted for a typical case study of a building in Athens (Greece). The most valuable conclusions of the present investigation are the following:  
* It was found that an increase in the solar field area and of the thermal tank volume leads to higher useful heat produced by the exploitation of the sun and consequently to a greater performance in the system.
* The auxiliary heater aids the mean tank temperature to be kept at the desired levels and it is activated only for a small time period which is only 6.2% of the heating time.
* According to the results, the economic optimization indicates that the optimal design includes 35 m\({}^{2}\) of solar thermal collectors connected to a storage tank of 2 m\({}^{3}\) for facing the total heating demand of 6785 kWh. In this case, the life cycle cost was calculated at 22,694 EUR, the seasonal system coefficient of performance at 2.947 and the mean solar thermal efficiency at 31.60%.
* The multi-objective optimization with both energetic and economic criteria indicates that the optimum design is the selection of 50 m\({}^{2}\) of solar thermal collectors connected to a storage tank of 3 m\({}^{3}\). In this case, the life cycle cost was calculated at 24,084 EUR, the seasonal system coefficient of performance at 4.066 and the mean solar thermal efficiency at 25.33%.
* It can be concluded that the rise in the solar field area leads to significant enhancements to the system performance by leading to electricity savings, while the additional cost is reasonable, according to the life cycle cost analysis.  
In the future, it would be interesting to examine a system with both solar and geothermal inputs in a high-temperature heat pump. Moreover, the present system can be examined in other locations with different heating demands and solar potential values. An extra interesting idea is to couple the present heat pump with a thermal photovoltaic collector
## 3 Methodology
### Data Preprocessing  
The data preprocessing session of the proposed watermarking mainly contains feature point extraction and data blocking. Feature point extraction refers to the extraction of some of the most stable vertices in a vector map by a specific algorithm, and feature points are required to be the key nodes that make up the vector map. If these feature points are corrupted, the data quality will be drastically reduced. This property protects the data and merely provides a theoretical foundation for the security and robustness of the zero watermarking scheme based on feature points. The Douglas-Peuker algorithm [PERSON] (1984), which is a common way to simplify polylines, was used in this research to extract feature points. The larger the threshold value set for the Douglas-Peuker algorithm in the feature point extraction process, the fewer feature points are obtained, and the less information is used to construct the zero watermark, but the watermarking scheme is more resistant to compression attacks as well as coordinate point attacks. However, collecting fewer feature points results in fewer data being used to generate the zero watermark. Consequently, a suitable threshold value must be selected via trial and error prior to watermark construction to guarantee there are adequate and stable feature points for zero watermark creation.  
Embedding watermarks in sub-blocks is a crucial method of the watermarking scheme against cropping attacks, as data blocking is to divide the original data into a number of sub-blocks, which is similar to cropping attacks. In this study, we chose the point-constrained blocking method to block the original vector maps, which is based on the average area blocking method, and the blocks with a number of points greater than the threshold continued to be divided until the number of points was less than the threshold [PERSON] and [PERSON] (1984). The initial blocking area a and the threshold points count t must be adjusted accordingly. After reasonable debugging, the zero watermarks constructed on this basis were more uniformly and reasonably distributed in the data, as the number of points within each block was similar and the area difference was as small as possible.
## 5 Case Study and Analysis
### The Detailed DM Steps  
#### 5.2.1 Determine DMs' Weights  
For solving the above-mentioned case, the evaluation information matrix in the form of q-ROFV provided by the three DMs is depicted as Tables 1-6 (\(q=3\)), which is shown in Appendix A.  
**Step 1:** Determine the mean evaluation value \(\left(\overline{\mu}_{\zeta}^{\tau},\overline{\
u}_{\zeta}^{\tau}\right)\) based on Equation (14).  
\[\begin{array}{l}\left(\overline{\mu}_{1}^{1},\overline{\  
\left(0.6966,0.25646\right).\end{array}\]  
**Step 2:** Based on Equation (6) and Equation (14), the similarity degrees \(z_{\kappa_{\zeta}}^{\tau}\)\((k=1,2,3\) and \(\varsigma=1,2,3,4\)) are depicted in Tables 12-12, which are shown in Appendix B.  
**Step 3:** Calculate the overall similarity degree of each DM concerning the criteria by Equation (16).  
\[\begin{array}{l}\delta_{1}^{1}=3.1416\;\delta_{2}^{1}=3.0944\;\delta_{3}^{1}= 2.9193\;\delta_{1}^{2}=2.8273\;\delta_{2}^{2}=2.8637\;\delta_{2}^{2}=3.1952\\ \delta_{1}^{3}=3.2130\;\delta_{2}^{3}=3.1638\;\delta_{3}^{3}=3.2932\;\delta_{1}^ {4}=2.7397\;\delta_{2}^{4}=3.2373\;\delta_{3}^{4}=2.5080\\ \delta_{1}^{5}=3.2104\;\delta_{2}^{5}=2.6719\;\delta_{3}^{5}=2.6907\;\delta_{1}^ {6}=2.9828\;\delta_{2}^{6}=2.9625\;\delta_{3}^{6}=3.1707\end{array}\]  
**Step 4:** Based on Equation (17), the weight of each DM can be obtained.  
\[\begin{array}{l}\omega_{1}^{1}=\frac{\delta_{1}^{1}+\delta_{2}^{1}+\delta_{3 }^{1}}{\delta_{1}^{2}+\delta_{1}^{1}+\delta_{3}^{1}}=0.3431\;\alpha_{2}^{1}= \frac{\delta_{1}^{1}}{\delta_{1}^{1}+\delta_{1}^{1}+\delta_{3}^{1}}=0.3380 \omega_{3}^{1}=\frac{\delta_{1}^{1}}{\delta_{1}^{1}+\delta_{2}^{1}+\delta_{3}^ {1}}=0.3189\\ \omega_{1}^{2}=\frac{\delta_{1}^{2}}{\delta_{1}^{2}+\delta_{2}^{2}+\delta_{3}^ {2}}=0.3182\omega_{2}^{2}=\frac{\delta_{2}^{2}}{\delta_{1}^{2}+\delta_{2}^{2}+ \delta_{3}^{2}}=0.3223\;\alpha_{3}^{2}=\frac{\delta_{3}^{2}}{\delta_{1}^{2}+ \delta_{2}^{2}+\delta_{3}^{2}}=0.3596\\ \omega_{1}^{3}=\frac{\delta_{1}^{3}}{\delta_{1}^{3}+\delta_{2}^{3}+\delta_{3}^ {3}}=0.3323\;\alpha_{3}^{2}=\frac{\delta_{3}^{3}}{\delta_{1}^{3}+\delta_{2}^{2}+ \delta_{3}^{2}}=0.3272\alpha_{3}^{3}=\frac{\delta_{3}^{3}}{\delta_{1}^{3}+ \delta_{2}^{2}+\delta_{3}^{3}}=0.3406\\ \omega_{1}^{4}=\frac{\delta_{1}^{1}}{\delta_{1}^{4}+\delta_{2}^{4}+\delta_{3}^ {4}}=0.3229\omega_{2}^{4}=\frac{\delta_{2}^{2}}{\delta_{1}^{2}+\delta_{1}^{2}+ \delta_{3}^{4}}=0.3815\;\alpha_{3}^{4}=\frac{\delta_{3}^{2}}{\delta_{1}^{4}+ \delta_{2}^{4}+\delta_{3}^{4}}=0.2956\\ \omega_{1}^{5}=\frac{\delta_{1}^{5}}{\delta_{1}^{5}+\delta_{2}^{5}+\delta_{3}^ {5}}=0.3745\;\alpha_{2}^{5}=\frac{\delta_{3}^{5}}{\delta_{1}^{5}+\delta_{2}^{ 5}+\delta_{3}^{2}}=0.3117\alpha_{3}^{5}=\frac{\delta_{3}^{2}}{\delta_{1}^{5}+ \delta_{2}^{5}+\delta_{3}^{5}}=0.3139\\ \omega_{1}^{6}=\frac{\delta_{1}^{6}}{\delta_{1}^{6}+\delta_{2}^{6}+\delta_{3}^ {6}}=0.3272\alpha_{2}^{6}=\frac{\delta_{2}^{6}}{\delta_{1}^{6}+\delta_{2}^{6} +\delta_{3}^{6}}=0.3250\;\delta_{3}^{6}=\frac{\delta_{3}^{2}}{\delta_{1}^{6}+ \delta_{2}^{6}+\delta_{3}^{6}}=0.3478\end{array}\]
## 4 Methodology
### UdpBasicApp  
As described above, all nodes in the simulation are equipped with an application called UdpBasicApp. The application can send UDP datagrams to a set of preconfigured IP addresses and port numbers. The datagram size and the send rate can be configured either as fixed values, or as random values drawn from a specific probability distribution. In addition, UdpBasicApp listens on a configurable port for incoming UDP datagrams and uses these to compute basic statistics for metrics such as throughput and packet lifetime.  
We have configured UdpBasicApp to draw the datagram size from a discrete uniform distribution over the interval 100 bytes to 1000 bytes. Similarly, the datagram send rate follows a discrete uniform distribution over the interval 100 ms to 200 ms. This is intended as a rough approximation of low bitrate voice-over-IP communication between the planes, such as that produced by the Mixed Excitation Linear Prediction Enhanced (MELPe) codec, including real-time transport protocol (RTP/RTCP) headers [40; 41]. Each node in the simulation is configured to send UdpBasicApp datagrams only to all other nodes belonging to the same team, but none to nodes belonging to other teams.  
The random numbers are generated using an implementation of the Mersenne Twister [42] pseudo-random number generator (PRNG) that is included in the OMNEST distribution. In OMNEST, multiple PRNG instances can run simultaneously to create independent streams of random numbers. We have dedicated a PRNG instance to the UdpBasicApp. This way, the random events in UdpBasicApp are not affected by random events in the reminder of the simulator (e.g., scheduled updates in the routing protocol that include a random delay component). The result is that the transmission time and size for UdpBasicApp datagrams remain the same when we change the routing protocol. In effect, this becomes the workload against which we evaluate the MANET protocols in this study.
# Research on Path Planning of Mobile Robot Based on Improved Theta* Algorithm
## 2 Algorithm Design
### Trajectory Optimization  
The output of the Theta* algorithm is a global optimal path point based on a 2D grid map, and these path points may be sparse and unsmooth, which can lead to problems such as spinning in place and poor movement of the robot during motion. Therefore, the generated paths need to be optimized so that the robot can achieve smooth steering during operation.  
The three objectives of path length, steering smoothness and steering safety need to be considered when smoothing the path. The optimization method proposed in this paper represents the trajectory by nth order polynomial as shown in Equation (6).  
\[f(t)=p_{0}+p_{1}t+p_{2}t^{2}+\ldots+p_{n}t^{n}=\sum_{i=0}^{n}p_{i}t^{i} \tag{5}\]  
where \(p_{0}\sim p_{n}\) are the trajectory parameters, which can be set as a parameter vector as shown in Equation (7).  
\[p=\left[p_{0},p_{1},\ldots,p_{n}\right]^{T} \tag{6}\]Thus, with Equations (6) and (7), the trajectory function f can be expressed in vector form as:  
\[f(t)=[1,t,t^{2},\ldots,t^{n}]\cdot p \tag{7}\]  
By deriving the trajectory function, the position, velocity, acceleration, jerk, snap, etc. of the trajectory at any moment can be obtained, and the specific function expressions are as follows.  
\[v(t)=f^{\prime}(t)=[0,1,2t,3t^{2},4t^{3},\ldots,nt^{n-1}]\cdot p \tag{8}\]  
\[a(t)=f^{\prime\prime}(t)=[0,0,2,6t,12t^{2},\ldots,n(n-1)t^{n-2}]\cdot p \tag{9}\]  
\[jerk(t)=f^{(3)}(t)=[0,0,0,6,24t,\ldots,\frac{n!}{(n-3!)}t^{n-3}]\cdot p \tag{10}\]  
\[snap(t)=f^{(4)}(t)=[0,0,0,0,24,\ldots,\frac{n!}{(n-4!)}t^{n-4}]\cdot p \tag{11}\]  
Since a complex trajectory cannot be represented by a polynomial, it is necessary to divide the trajectory into multiple local trajectories in time, and then represent the local trajectories by a polynomial each.  
\[f(t)=\left\{\begin{array}{ll}[1,t,t^{2},\ldots,t^{n}]p_{1}&t_{0}\leq t<t_{1 }\\ [1,t,t^{2},\ldots,t^{n}]p_{2}&t_{1}\leq t<t_{2}\\ \ldots\ldots&\\ [1,t,t^{2},\ldots,t^{n}]p_{k}&t_{k-1}\leq t<t_{k}\end{array}\right. \tag{12}\]  
where \(k\) is the number of segments of the trajectory and \(p_{i}\) is the parameter vector of the \(i\)th segment of the trajectory.  
The objective function of the minimum jerk is to solve the parameter vector of each trajectory to minimize the value of the jerk function, and it also needs to satisfy the constraints. The objective function of minimum jerk can be obtained from the above:  
\[\min(jerk(t))=\min\!\int_{0}^{T}{(f^{(3)}(t))}^{2}dt=\min\!\sum_{i=1}^{k}\int_ {t_{i-1}}^{t_{i}}{(f^{(3)}(t))}^{2}dt \tag{13}\]  
According to the optimization function to add constraints, there are mainly two kinds of constraints without considering obstacles, one is equation constraint, which mainly constrains the initial state and termination state of the trajectory, as well as the start and end position of each section of the trajectory; the other is continuity constraint, which can make the adjacent trajectories smoothly transition. The optimized effect is shown in Figure 4.  
Figure 4: Trajectory optimization effect figure.
## 3 Project Overview and Data Preprocessing
### Optimal Feature Subset Selection  
In machine learning tasks, the input features of a model play a crucial role in determining its upper limit. Moreover, the selection of input feature sets also impacts the entire model's training time and generalization ability. Therefore, careful consideration is necessary when selecting input features for a model. This study employs RFECV [36; 37] to determine the optimal combination of model features and uses it to evaluate reserved model feature performance. RFECV achieves an adaptive determination of feature combinations through cross-validation, providing more accurate and objective performance evaluations under different feature combinations. Thus, the researcher utilizes RFECV to select the necessary input features that can be used to identify surrounding rock grades.
# Failure and Deformation Behavior of Underground Geo-Structures: Advances in Geomechanics
###### Abstract  
In this paper, we present a new method for the development of energy-aware energy  
storage facility, including geological features, overburden pressure, and changes in fluid pressure [9; 10].  
Geothermal energy is a renewable energy source derived from the heat stored beneath the Earth's surface. Geothermal energy is considered a clean and sustainable energy source because it produces minimal greenhouse gas emissions and has a small environmental footprint compared to fossil fuels. Despite its environmental benefits, geothermal energy faces some challenges, including potential environmental impacts such as land subsidence and induced seismicity. Ongoing research and technological advancements are addressing these challenges and expanding the potential for geothermal energy development [11].  
Drilling wellbores for carbon dioxide (CO\({}_{2}\)) and acid gas storage in saline aquifers and depleted reservoirs is a crucial aspect of initiatives aimed at mitigating climate change. Saline aquifers and depleted reservoirs are considered viable storage sites due to their geological characteristics, such as porosity and permeability, which allow for the safe and secure containment of CO\({}_{2}\) and acid gas underground. The process typically involves drilling a wellbore deep into the subsurface rock formations, often thousands of meters below the Earth's surface, which serves as a structure for gas injection. Key considerations in the drilling and operation of these wellbores include ensuring the integrity and stability of the wellbore and preventing gas leakage into the atmosphere or groundwater. Advanced geomechanical models and technologies play a crucial role in addressing the challenges associated with ensuring wellbore integrity in these storage operations [12; 13].  
The need for efficient transportation in urban areas has led to innovative solutions such as constructing tunnels at narrow distances underground. The shallow depth of underground transportation systems in urban areas can present unique challenges and considerations, particularly regarding their interaction with buildings, facilities, and roads. It is crucial to consider the impact of tunnel construction on surrounding rock formations and ensure the safety and stability of underground spaces. An appropriate analysis of rock/soil damage is also essential for the stability of the opening and safety during service [14; 15].  
Ultra-long and ultra-deep mountain tunnels present significant challenges; they also offer promising opportunities for enhancing connectivity, accessing resources, and promoting sustainable and resilient infrastructure development. Effective planning, engineering expertise, and collaboration are essential for realizing the full potential of these ambitious projects [16].  
This Special Issue highlights the variety of studies within the field of underground geostatures and emphasizes the exploration of analytical methods and emerging technologies. A brief overview is presented to encourage readers to delve deeper into the contents of the articles.
# Energy-Aware Security Adaptation for Low-Power IoT Applications
## 4 Dynamic Security and Energy Adaptation
### Analytical Evaluation in the Context of LoRaWAN
#### 5.2.2 Results and Analysis
In addition, the preference threshold is chosen to be 20%, since in this case, data security is preferred over savings in energy. This results in choosing a higher security level and, in consequence, increasing the energy required to support the new configuration. The different values obtained for the same scenario result from the application of different conditions and algorithms, reinforcing the existence of a differentiation in the treatment of security and energy savings, which depends on the controller's input values. The larger gain is always recorded with a preference threshold factor of 80% and a minimum security level of 1, which means that the algorithm chooses to save battery over security and can decide to lower or even turn security off, according to what is most appropriate given the current conditions of the network and the energy remaining in the battery of the device. On the other hand, the loss (or the lowest gain) is recorded when the preference threshold is equal to 20% and the minimum level of security is 2, which represents a controller approach focused on security instead of saving energy. In this situation, a security scheme is employed even if no attacks on the system are detected. There are some factors that, in practice, can influence the gains obtained by using the controller, such as the size of each message, the frequency of message transmission and the available energy in the device's battery.
## 5 Computational Experiments
### Discussion  
SFS was used in feature selection from the extracted features. As a result, in all cases, the accuracy was higher than estimating the weighting factors using only the objective function values. In addition, the selected features were different for each case. We found that even a small number of features can be estimated with good accuracy if they are valid features. The feature of variance of completion time was effective for small-scale problems. Especially in case I, it was judged to be more effective in improving accuracy than the objective function values other than the setup cost sum.  
For the large-scale problem, the accuracy was particularly better compared to the other cases. This is because the objective function values are more variable than in the small-scale case. In the inverse optimization, the weighting factors were estimated almost exactly by repeatedly solving the scheduling problem. The approximate solution method was also highly accurate. In addition to the objective function value, the sum of completion time of each machine and the variance of delivery time setting for each machine were found to be effective in machine learning for large-scale problems. However, the improvement in accuracy from estimating only the objective function value was smaller than that for small-scale problems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Machine Learning** & **Inverse** \\ \hline \hline  & **Only Objective Function Values** & **SFS** & **Optimization** \\ \hline MSE & \(5.020\times 10^{-2}\) & \(4.796\times 10^{-2}\) & \(2.345\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: MSE results of machine learning and inverse optimization.  
Figure 7: Weighting factor is (\(w_{1}\), \(w_{2}\)) = (0.9, 0.1).  
Experiments on near-realistic data confirmed the same trend as for randomly generated problems. However, the estimation accuracy was low, partly because there were problems in which the objective function value of the sum of setup costs did not change much.  
The following insights obtained from our study are as follows.  
1. In randomly generated problems, the estimation accuracy of the large-scale problem was higher than that of the small-scale problem because the solution changed continuously in response to the change of the weighting factor.
2. In the case of machine learning, the feature of the variance of completion time for each machine was effective for small-scale problems, and the features of the sum of completion time of each machine and the variance of delivery time setting for each machine were effective for large-scale problems.
3. When using the input/output data of the same scheduling model, if a solution close to the exact solution is used, the inverse optimization has higher estimation accuracy than machine learning because the gradient was recalculated many times during the estimation process.