esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimized Vector Tracking Architecture for Pseudo-Random Pulsing CDMA Signals
## 4 The Proposed VTL Architecture  
Based on the analysis of Section 3, it can be concluded that, if the VTL is used to track the PRPS with the architecture of a continuous CDMA signal, the performance of the VTL will degrade due to the signal structure difference. To solve the problems in tracking PRPS, an optimized VTL architecture that includes irregular update periods (IUP) pre-filters and a navigation filter based on predicted measurements (PM) is proposed. This section first presents the architecture of the IUPPM-VTL and then discusses the discrete-time models of the pre-filter and the navigation filter.
## 3 Challenges for Routing Protocols in FANETs  
The primary issues for routing protocols on FANETs are explained in this section, including high mobility, dynamic topology, low latency and QoS, remaining energy, and communication standards and various links. Every issue is briefly covered. Figure 19 depicts the main challenges for the FANETs routing protocols.
## 4 Derivation of Energy Efficiency Expression
### Network Energy Efficiency  
The network EE can be defined as the ratio of the achievable network throughput to the network power consumption [37]. For the convenience of derivation, we set \(\sigma^{2}~{}=~{}0\) and \(\alpha~{}=~{}4\). Based on Equations (10) and (13), we can get the closed-form expression of network EE in the following:  
\[\begin{split} EE&=\frac{R^{total}}{R^{total}}=\frac{ \lambda_{a}\left(R_{pm}A_{pm}A_{pm}+R_{pp}A_{ap}+R_{mm}A_{am}+R_{up}A_{up} \right)}{p^{total}}\\ &=\frac{\lambda_{a}}{p^{total}}\int\limits_{0}^{\theta W/N_{pw}} \frac{\theta W/N_{pw}}{\lambda_{p,m}\left(\theta\tau/\rho_{p}B_{p}P_{p}/\rho \right)+Q\left(\tau,1\right)}\\ &+\frac{\theta W/N_{pp}}{\lambda_{p,m}-1}\frac{\theta W/N_{pp}} {\lambda_{p,m}\left(\rho_{p}^{*},\pi_{p}B_{p}^{*}-1P_{p}\right)+Q\left(\tau,1 \right)}-\frac{\theta W/N_{pp}}{\lambda_{p,m}\left[Q\left(\rho_{p}\tau,B_{p} ^{*}-1P_{p}\right)-k^{2}+k_{p}^{2}\right]+Q\left(\tau,1\right)}\\ &+\frac{(1-\theta)W/N_{mm}}{\lambda_{p,m}\left(\Theta\left(P_{p} \tau,B_{p}P_{p}\right)+Q\left(\tau,1\right)\right)}dt\\ \end{split} \tag{14}\]
## 4 Conclusions  
The present study performed a numerical analysis of the close-contact melting characteristics of electro-thermal drilling probe (ETDP) based on CFD simulations. From the numerical results, the influence of the temperature of heated probe tip on the formation of the molten thin liquid layer, and, subsequently, the rate of penetration (ROP) of ETDP were analyzed. In particular, the present study mimicked the transient movement of ETDP  
Figure 8: The evolution of liquid layer thickness for different heated probe tip temperatures of 348, 358 and 368 K.  
Figure 7: Comparison of rate of penetration (ROP) for different heated probe tip temperature of (**a**) \(T_{\text{tip}}\) = 348 K, (**b**) \(T_{\text{tip}}\) = 358 K and (**c**) \(T_{\text{tip}}\) = 368 K.  
in a vertical direction using a simple model for predicting ROP. The distribution of temperature and liquid fraction were investigated during the drilling process. The following conclusions were drawn:  
1. The mushy zone, where the melting process occurs, exists near the heated probe tip, and its temperature remains at a melting temperature of 273.15 K. Although the temperature of the mushy zone has a constant value of 273.15 K, its enthalpy increases during the melting process.
2. A thin liquid layer forms during the melting process, and the electro-thermal drilling probe can penetrate the ice by pushing and squeezing the molten thin liquid layer. The penetration depth linearly increases with time, and it implies that the ROP has a nearly constant value. The time-averaged ROP increases with the temperature of the heated probe tip, and has the values of 0.38, 0.43 and 0.49 mm/s for the different heated probe tip temperatures of 348, 358 and 368 K, respectively.
3. The molten liquid layer maintains its thickness within the 1 mm around the heated probe tip. The thin liquid layer does not become thicker; instead, it stays at about 0.6 mm during the melting process. This is because the molten liquid layer is squeezed by the ETDP and pushed radially outward.  
Conceptualization, S.H.L. and K.K.; validation, K.K.; investigation U.-J.P., H.J.L. and C.H.J.; methodology H.S.K. and C.H.J.; simulation U.-J.P. and C.H.J.; supervision, S.H.L.; writing of the original draft U.-J.P. and C.H.J.; project administration, J.-Y.P. and S.H.L.; funding acquisition, J.-Y.P.; writing--review and editing S.H.L. and C.H.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the endowment project of \"Development of core technologies of underwater robot ICT for polar under-ice-shelf exploration and remote monitoring\" funded by Korea Research Institute of Ships and Ocean Engineering (PES4010). This research was also supported by the Chung-Ang University Research Scholarship Grants in 2020.  
The authors declare no conflict of interest.
## 3 Platform Subsystem Design  
This section gives an overview of all the bus subsystems, highlighting the hardware components and functions of each. Since the payload detectors will require a volume of approximately 2U, a 3U size was apt for the Light-1 mission, with the bus components occupying approximately 1.3 U of the CubeSat. A 3U configuration is a stack of three 1U CubeSats with dimensions of 100 \(\times\) 100 \(\times\) 340 mm. While the payload detectors are fully designed, developed, and tested by the students, the majority of the bus components of the satellite were purchased off-the-shelf from the vendor, NanoAvionics, each of which has flight heritage and hence is space qualified. A detailed assembly is included in Section 3.1. The Light-1 bus, similar to any other CubeSat bus, consists of mechanical, power, command and data handling, attitude determination, and control and communication subsystem--each of which have been described in thorough detail in Sections 3.1-3.5. The overall system-level block diagram of Light-1 is shown in Figure 3, while a detailed pin-level diagram has been included as Figure 4.  
Figure 3: System architecture of Light-1.  
Figure 4: Pin level diagram of Light-1.
## 5 Discussion and Future Research Directions
### Forecasting Water Levels  
The water level forecasting topic presents research opportunities in developing and comparing machine learning algorithms for predicting groundwater levels, water distribution, and potential maps. Studies demonstrate the use of support vector regression, convolution neural networks, extreme gradient boosting, and other machine learning techniques to analyze potential groundwater maps, groundwater depths, and lake levels.  
The second intriguing research line relates to utilizing machine learning algorithms to improve water resource management, such as forecasting lake water levels, predicting irrigation water demand, and estimating groundwater levels in aquifers. Various machine learning models, like Minimax Probability Machine Regression, Gaussian Process Regression, Extreme Learning Machine, and artificial neural networks, are applied to forecast lake levels, estimate irrigation water demand, and predict groundwater levels for better water management strategies.  
A third line of research is concerned with the noteworthy observation of the existence of numerous data sources which are often not integrated. For the enhancement of forecasting accuracy, it is proposed that these diverse data sources be amalgamated. This would enable the subsequent progression of machine learning models to predict floods and evaluate their severity. This unification would ease the analysis of various factors, encompassing weather patterns, soil moisture levels, river flow rates, and historical flood data, utilizing machine learning algorithms like Decision Trees, Random Forests, or deep learning networks. The paramount objective continues to be the improvement of the precision of flood forecasts, thereby providing communities with the tools necessary to better prepare for, and potentially alleviate, the impacts of such occurrences.  
Finally, a four research line involves applying machine learning and remote sensing techniques to monitor and assess water quality, pollution, and eutrophication. This research aims to provide better insights into the sources and composition of dissolved organic matter in water bodies and evaluate the effectiveness of water protection measures or human disturbance effects on water quality and lake ecosystems. Studies develop models to derive parameters, such as the humification index, using machine learning methods like XGBoost, in combination with other remote sensing data.
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 3 Secrecy Performance Analysis
### SOP Analysis  
According to [23], the secrecy capacity for one realization of the SNR pair \((\gamma_{\text{B}},\gamma_{\text{E}})\) is given by  
\[C=\left\{\begin{array}{ll}\ln(1+\gamma_{\text{B}})-\ln(1+\gamma_{\text{E}}),&\text{if }\gamma_{\text{B}}>\gamma_{\text{E}}\\ 0,&\text{if }\gamma_{\text{B}}\leq\gamma_{\text{E}}\end{array}\right. \tag{8}\]  
Referring to (8), the SOP is defined as the probability that the instantaneous secrecy capacity falls below a target secrecy rate, which can be written as  
\[P_{\text{SOP}}=\text{Pr}(C\leq c) \tag{9}\]  
where \(c\) denotes the target secrecy rate, and it can be re-expressed using the SNR threshold \(\gamma_{\text{th}}\) as \(c=\ln(1+\gamma_{\text{th}})\). Therefore, Equation (9) can be further written as  
\[P_{\text{SOP}} = \text{Pr}\left[\ln\left(\frac{1+\gamma_{\text{B}}}{1+\gamma_{ \text{E}}}\right)\leq\ln(1+\gamma_{\text{th}})\right] \tag{10}\] \[= \text{Pr}\left[\gamma_{\text{B}}\leq(1+\gamma_{\text{th}})(1+ \gamma_{\text{E}})-1\right]\]  
According to (5), Equation (10) can be further written as  
\[P_{\text{SOP}}=\int_{0}^{\infty}\int_{0}^{(1+\gamma_{\text{th}})(1+w)-1}f_{ \gamma_{\text{E}}}(w)f_{\gamma_{\text{B}}}(u)\mathrm{d}u\mathrm{d}w \tag{11}\]  
By analyzing (11), the double integrals can be simplified to a single one, which is shown in the following theorem.  
According to (14), the SOP can be written as  
\[P_{\text{SOP}} = \Pr\left[C\leq\ln(1+\gamma_{\text{th}})\right] \tag{15}\] \[= \Pr\left(C_{\text{Asy}}-\delta\leq\ln(1+\gamma_{\text{th}})\right)\] \[\geq \Pr\left(C_{\text{Asy}}\leq\ln(1+\gamma_{\text{th}})\right)\] \[= \Pr\left[\gamma_{\text{B}}\leq(1+\gamma_{\text{th}})\gamma_{\text {E}}\right]\overset{\Delta}{=}P_{\text{SOP}}^{\text{L}}\]  
Note that (15) provides a lower bound \(P_{\text{SOP}}^{\text{L}}\) for the SOP. After some analysis, the following theorem is derived.  
Theorem 2: _For the M-distributed fading channels (i.e., \(H_{n}\sim\mathcal{M}(\alpha_{n},\beta_{n},\rho_{n},\Omega_{n},\xi_{n},\phi_{1,n},\phi_{2,n})\), a closed-form expression for the lower bound of SOP can be derived as_  
\[P_{\text{SOP}}^{\text{L}} = 1-\frac{A_{\text{B}
# Wide-Aperture Bimorph Deformable Mirror for Beam Focusing in 4.2 PW Ti:Sa Laser
## 2 Materials and Methods (Design of a 320 mm Bimorph Deformable Mirror)
The total surface distortions arising from the manufacturing errors, internal stresses in the dielectric layers of the coating, the effect of \"printing\" of the piezoelectric plates and fixing the substrate in the mount can significantly exceed the WF aberrations, for which DM is intended to compensate. In this case, most of the full voltage range should be spent to correct for initial distortions that makes DM useless in the adaptive systems. To correct for the beam aberrations in the CoReLS multipetawatt Ti:Sa laser [3], we developed the bimorph DM containing the glass substrate (BK7) with a diameter of 320 mm and thickness of 7 mm (Figure 1). A one-piece 240 mm piezoceramic disk was glued in the center of the substrate. The insulating paths were etched by the photolithography, and the grid of 91 electrodes was formed on the silver-covered disc surface. Nine piezoceramic plates in the form of the sectors were arranged around the big disk and filled the entire area of the substrate (Figure 2a). Each small plate contained four electrodes and, thus, the total number of the control electrodes was 127 (Figure 2b). Such a configuration of the arrangement of piezoceramic elements almost illuminated the \"print through\" effect of the glued piezoelectric plates. The surface of the DM glass substrate was polished to a roughness of 2 nm (RMS). The flatness of the mirror after polishing was about 2 \(\upmu\)m (P-V) including the overall concave curvature with a stroke of 1.8 \(\upmu\)m. It should be noted that, at this stage, the curvature of the surface was not critical since it depends on the temperature. The significant change of the curvature as well as mirror profile is expected after the coating deposition. The multilayer dielectric coating provided a reflectivity of not less than 99.9% for a wavelength range from 740 to 860 nm, with a damage threshold of not less than 0.6 J/cm\({}^{2}\) at a pulse duration of 20 fs. The coated mirror was installed in a metal mount, where it was fixed along the border through elastic spacers made of the porous silicone cord. Figure 3 shows the front and the back views of the mounted DM. Sixteen aligning screws located along the circle outside the mirror substrate were placed for adjusting the tension of the elastic spacers. Therefore, it was possible to push or pull the local sections of the mirror substrate to reduce the initial aberrations. Obviously, such a design allowed to compensate mechanically for astigmatism and a bit of coma aberrations that, as a rule, prevail in the initial surface distortions and require applying of the significant electrical voltage to correct for them. However, these screws cannot reduce mirror curvature and spherical aberration. Figure 1: Design of the bimorph deformable mirror (##—numbers of the electrodes). Figure 2: Configuration of DM electrodes: (**a**) piezoceramic plates and their arrangement; (**b**) the map of the control electrodes.
## 3 Results
### Mechanical Properties
#### 3.1.1 MOE and MOR  
For the MUF bonded boards, the walnut shell ones (B) performed better in terms of MOE (1.51 GPa) and a MOR of 5.20 N/mm\({}^{2}\) compared to boards made from hazelnut shells (A) with an MOE of 1.30 GPa and a MOR of 4.25 N/mm\({}^{2}\) (Table 4). Although ANOVA showed that the material had a statistically significant influence in terms of MOE, it did not have an influence on the MOR. When PUR was used as an adhesive, it did not make a difference whether the boards were made of hazelnut (D) or walnut (E) shells. MOE was roughly 1.3 GPa for both, MOR 7.43 and 8.86 N/mm\({}^{2}\) for hazelnut and walnut shell boards, respectively. Furthermore, ANOVA showed that for walnut shell boards, it did not make a difference if MUF or PUR (both at a concentration of 10 w.t. %) was used as an adhesive when it came to MOE, while it did influence the MOR. When hazelnut shells were used as a raw material, the adhesive had a statistically significant influence on both MOE and MOR. The reference boards made from spruce and MUF (C) achieved the highest values for MOE and MOR (2.57 GPa and 13.26 N/mm\({}^{2}\), respectively). Surprisingly, the PUR bonded spruce boards (F) performed weaker than the nutshell panels (0.87 GPa for MOE and 5.47 N/mm\({}^{2}\) for MOR). The results for both hazelnut and walnut panels bonded with PUR were similar to the values obtained by [34], namely a MOR between 6 and 9 N/mm\({}^{2}\) and an MOE between 0.8 and 1.3 GPa. When the same amount of hazelnut shells was mixed with wood particles (50% + 50%), the values of the MOE and MOR increased significantly [32].  
In terms of MOE (Table 4), the walnut shell boards showed higher values than the ones produced by [46], which reached 1.15 GPa on average compared to 1.51 GPa and might have been caused by the adhesive used. The study by [46] applied UF, while for these boards was utilized a less brittle MUF [47]. Another reason might be that [46] produced boards with a density of 700 kg/m\({}^{3}\) while those boards had a target density of 1000 kg/m\({}^{3}\)[48].  
The results for MOE and MOR of the nutshell boards were expected to range below the results of spruce particleboards. This can be traced back to the fact that the mechanical properties are strongly influenced by different properties of the raw material like density, chemical composition, and particle size. A low-density raw material allows a higher compression in the panel, leading to improved performance in bending tests [49]. Given that the density of the used nutshells was very high (between 700 and 1030 kg/m\({}^{3}\) depending on the fraction) and spruce particles have a density of 450 kg/m\({}^{3}\), the lower performance of the MUF bonded nutshell boards can be traced back to a lower compression rate [50]. The weak behavior of the PUR bonded spruce boards can be traced back to unsatisfying bonding, caused by an insufficient moisture content of the raw material (3%) for the adhesive to react [51].
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 5 Conclusions  
Using newly chosen wave periods and wave heights, an ANN model was used to anticipate the responses of a spar platform. Based on structural and environmental limitations and parameters, this attempt forecasted the heave, surge, and pitch response of a spar hull as well as the top tension of mooring lines.  
The ANN model remains effective and can significantly reduce the prediction time of spar hull and mooring line response time histories.  
The spar platform responses predicted by the ANN technique align with FEM-derived outcomes.  
The required time to calculate the response of a spar platform by means of the ANN technique is 10 s with 4.0 GB of RAM (i3-2100 CPU @ 3.10 GHz) on a normal desktop PC, while the FEM model on a Dell Workstation (Intel Xeon E5620 @ 2.40 GHz and 8 GB of RAM) takes 23 h. Rapid forecasting using the ANN will assist in making decisions related to whether to stop or continue oil and gas production in the event of adverse weather conditions. Furthermore, the practice of ANN does not necessarily require the on-site attendance of a skilled technical person to obtain the platform responses.  
The accuracy of predicting the spar platform responses within the range of the training data was 90.0%.  
The accuracy of predicting responses outside the range of the training data was 85.35%. Thus, accurate results can be obtained if more data are used for training.  
An artificial neural network (ANN) can be used to monitor the responses of spar platforms in the face of extreme storms, unintentional collisions with supply vessels, and more. For safety analysis and design, an ANN can be employed to investigate the reliability of the spar platform.  
Conceptualization, M.A.U. and M.J.; methodology, M.A.U. and M.J.; software, M.A.U; validation, M.A.U. and M.J.; formal analysis, M.A.U. and M.J.; investigation, M.A.U., M.T.R.B. and M.J.; resources, M.A.U., M.T.R.B. and M.J.; data curation, M.A.U. and M.J.; writing--original draft preparation, M.A.U., M.A. and M.J.; writing--review and editing, M.A.U., M.T.R.B. and M.J.; visualization, M.A.U. and M.A.; supervision, M.A. and M.T.R.B.; project administration, M.A. and M.J; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Deanship of Scientific Research (Ambitious Research Track) in the King Faisal University, Saudi Arabia, grant number: GRANT329. And the APC also funded by the same grant.  
Not applicable.  
The data that support the findings of this study are available from Md. [PERSON], upon reasonable request.  
The authors extend their appreciation to the Deanship of Scientific Research (DSR) at King Faisal University (KFU) for funding this work through research grant number GRANT329.  
The authors declare no conflict of interest.
## 4 Application of the New Method in Fault Diagnosis of Rolling Bearing
### Experimental Verification
Figure 9: The vibration signal (**a**), the synchronous error (**b**), the power spectral density of outer ring damage using the measured signal (**c**) and the synchronization error (**d**). In this figure, the motor speed is 1796 rpm, and the double rotation frequency of bearing is 60 Hz. Combined with the coefficients in Table 3, the characteristic frequency of the inner ring fault is 141.0908 Hz. Comparing the peak power spectral density of the two subgraphs, it can be seen that the double rotation frequency of bearing and the fault characteristic signal is magnified significantly by the coupled Lorenz system, and it can be more easily identified. As the slight bearing fault in the experiment, the multiple harmonics of the fault frequency are not obvious.
## 5 Experiments and Results
### Monaco  
In addition to grid environments, we also validated the robustness and scalability of our algorithm in real-world environments. As the Figure 4 shows, we sampled thirty controllable traffic lights in real Monaco city traffic and marked them in blue. We can also see that there are different types of intersections in this traffic network, which are not symmetrical like the grid traffic environment. An intersection may be connected by three roads, four roads, or even five roads. Therefore, different intersections have specific environmental states and action-space configurations. According to the positional relationship of these 30 traffic lights, we divided them into four regions, including 10, 8, 6, and 6 traffic lights, respectively.  
Different types of traffic lights and different sizes of regional settings reflect the scalability and adaptability of our algorithm. In this environment, we set up four sets of flows, F1-F4. Its origin and destination are rectangular areas located on the edge of the map, and each pair of OD passes through the center of the map.  
The figures show the performance of our algorithm in the Monaco urban traffic scene, respectively. In the average reward, we can see that the GMHM performs the best, with fast convergence and a high final reward level. Figure 5 shows the average queue length and waiting time for GMHM in Monaco cities. The performance advantage of GMHM over other algorithms can also be seen.  
Figure 4: Monaco transport network with various intersections.