esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm
### Genetic Algorithms  
Genetic Algorithms (GA) were proposed to explain the adaptive processes of natural systems, as well as the design of artificial software systems that keep important mechanisms of the natural evolution. These algorithms consider a population of candidate solutions called chromosomes, and use an objective function to evaluate the life expectancy of these population members (i.e., the chromosomes). In our case, the objective function takes as an argument the vector that indicates the allocation order of nodes to the frame, computes the frame length according to the Algorithm 1, and returns the frame length as the quality of the solution found.  
The GA operates through an iterative method, where in each iteration the population undergoes a series of transformations that lead to a new generation. The evolutionary process of each generation consists of a series of steps, also called probabilistic transition rules, which are briefly explained next.
## 2 Materials and Methods
### Deep Learning Model
#### 2.4.2 Gradient-Weighted Class Activation Mapping (Grad-CAM++)  
Gradient-based explanation or interpretation constitutes one of the most efficient and lightweight methods for explaining deep networks' decisions. These methods use gradients to understand how a slight change in the input would affect the output. For example, in CNNs for image classification, local areas of the image are inspected to determine their significance based on the latter conception.  
The Grad-CAM algorithm is a technique used for visualising the regions of an image that contribute most to the output of a neural network. The Grad-CAM algorithm generates a class activation map, highlighting the regions of an image that are most relevant for predicting a particular class. First, the network's output gradients for the final convolutional layer are calculated to create this map. These gradients are then global-average-pooled to obtain the importance weights for each feature map in the last convolutional layer. Finally, the feature maps are weighted by their corresponding importance weights and summed to obtain the class activation map.  
The Grad-CAM++ algorithm [21] is an improved version of Grad-CAM [17] that improves the localisation of the object of interest in the image. It achieves this by incorporating the second-order gradients of the output for the final convolutional layer, which is done using a Taylor series approximation to estimate the second-order gradients and is then used to refine the importance weights obtained in the first step of the Grad-CAM algorithm.
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
###### Abstract  
Aiming at providing a high-performance terrestrial network for edge computing in satellite networks, we experimentally demonstrate a high bandwidth and low latency decomposed optical computing architecture based on distributed Nanoseconds Optical Switches (NOS). Experimental validation of the decomposed computing network prototype employs a four-port NOS to interconnect four processor/memory cubes. The SOA-based optical gates provide an ON/OFF ratio greater than 60 dB, enabling none-error transmission at a Bit Error Rate (BER) of \(1\times 10^{-9}\). An end-to-end access latency of 122.3 ns and zero packet loss are obtained in the experimental assessment. Scalability and physical performance considering signal impairments when increasing the NOS port count are also investigated. An output OSNR of up to 30.5 dB and an none-error transmission with 1.5 dB penalty is obtained when scaling the NOS port count to 64. Moreover, exploiting the experimentally measured parameters, the network performance of NOS-based decomposed computing architecture is numerically assessed under larger network scales. The results indicate that, under a 4096-cube network scale, the NOS-based decomposed computing architecture achieves 148.5 ns end-to-end latency inside the same rack and zero packet loss at a link bandwidth of 40 Gb/s.  
satellite-terrestrial network; hardware decomposition; nanoseconds optical switches 2022102515[[https://doi.org/10.3390/math10142515](https://doi.org/10.3390/math10142515)]([https://doi.org/10.3390/math10142515](https://doi.org/10.3390/math10142515))
# Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems
## 4 Simulation Results  
In this section a numerical simulation for a group of \(N=48\) mobile robots is presented. The initial coordinates (both positions and orientations) were random. The goal of the formation was to follow a circular reference trajectory at the same time avoiding collisions between agents. The formation had a shape of a circle. The assignments of robots to particular goal points were also random.  
The following settings of the algorithm were used: \(k_{1}=0.5\), \(k_{2}=0.5\), \(k_{3}=1.0\), \(t_{u}=1\) s, \(r=0.3\) m, \(R=1.2\) m.  
Figure 5a shows paths of robots on the \((x,y)\) plane. To make the presentation clearer in Figure 5a-g signals of 45 robots are grey while the 3 selected ones are highlighted in black. In Figure 5h the three selected inter-agent distances are highlighted in black. Figure 5b,c present graphs of \(x\) and \(y\) coordinates as a function of time. The robots converged to the desired values in 115 s. Figure 5d shows a time graph of the orientations. In Figure 5e,f linear and angular controls, respectively, are shown. Initially and in the transient state, their values were high, exceeding the maximum values of a typical mobile platform. In practical implementation, they should be scaled down to realizable values. Figure 5g presents a time graph of the freeze procedure (refer to Assumption 2) of all robots. Although the drawings are not easily readable (because they include the 'freeze' signal of all robots), one can interpret that the last collision avoidance interaction ends in 108 s. In Figure 5h relative distances between robots are shown. Important information that can be read from this drawing is that no pair of robots reaches the inter-agent distance lower than or equal to \(r=0.3\) m (dashed line). This means that no collision has occurred.  
Figure 5: Numerical simulation 1: trajectory tracking for \(N=48\) robots. (**a**) locations of robots in \(xy\)-plane, (**b**) \(x\) coordinates as a function of time, (**c**) \(y\) coordinates as a function of time, (**d**) robot orientations as a function of time, (**e**) linear velocity controls, (**f**) angular velocity controls, (**g**) ‘freeze’ signals, (**h**) distances between robots.
## 2 Materials and Methods
### Synthesis  
The synthesis was conducted according to the literature [22].  
2.1 Synthesis of 2-(3-Cyano-4-(4-(dibutylamino)phenyl)-5-oxo-1,5-dihydro-2H-pyrrol-2-ylidene)malononitrile, BAPCP  
A solution of disodium 3-cyano-2-(dicyanomethylene)-2H-pyrrole-4,5-bis(olate) (4.5 g, 0.02 mol) and \(N\),\(N\)-dibutylaniline (5.1 g, 0.02 mol) in 20 mL of dimethylformamide (DMF) was prepared. Methane sulfonyl chloride (4.5 g, 0.04 mol) was then added gradually over 30 min, maintaining the temperature at room temperature. Following the addition, the reaction mixture was heated to 40 \({}^{\circ}\)C and stirred continuously for 3 h. Once the reaction was complete, the mixture was cooled to 5 \({}^{\circ}\)C, and 40 mL of methanol was added slowly to promote precipitation, with additional stirring for 1 h. The precipitate was filtered from the cooled mixture, and the resulting solid was dried, yielding 4.5 g of a blue solid (yield: 49%). \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) 8.30 (d, \(J\) = 9.1 Hz, 2H), 6.94 (d, \(J\) = 9.3 Hz, 2H), 3.46 (t, \(J\) = 7.7 Hz, 4H), 1.53 (p, \(J\) = 7.9 Hz, 4H), 1.32 (p, \(J\) = 7.4 Hz, 4H), 0.89 (t, \(J\) = 7.3 Hz, 6H). \({}^{13}\)C NMR (100 MHz, DMSO-\(d_{6}\)) \(\delta\) 168.95, 159.35, 153.50, 144.46, 133.52, 115.73, 114.68, 113.69, 113.51, 112.70, 57.37, 50.87, 29.81, 20.04, 14.30. HRMS (FAB-MS, m/\(z\)): calcd. for C\({}_{22}\)H\({}_{23}\)N\({}_{5}\)O, 373.1903; found: 374.1983 [M+H]\({}^{+}\).  
2.2 Synthesis of 2-(3-Cyano-4-(4-(dihexylamino)phenyl)-5-oxo-1,5-dihydro-2H-pyrrol-2-ylidene)malononitrile, HAPCP  
Disodium 3-cyano-2-(dicyanomethylene)-2H-pyrrole-4,5-bis(olate) (3.4 g, 0.015 mol) and \(N\),\(N\)-dihexylaniline (3.9 g, 0.015 mol) were dissolved in 20 mL of DMF. Methane sulfonyl chloride (3.4 g, 0.03 mol) was then slowly added dropwise over 30 min while maintaining the temperature at room temperature. After the addition was complete, the temperature of the reaction vessel was increased to 40 \({}^{\circ}\)C, and the mixture was stirred for 3 h. Upon completion of the reaction, the temperature of the reaction vessel was reduced to 5 \({}^{\circ}\)C, and 40 mL of methanol was slowly added dropwise to induce precipitation, with stirring continued for 1 h. The precipitate was then filtered from the cooled mixture, and the collected solid was dried, yielding 4.1 g of a blue solid (yield: 53%). \({}^{1}\)H NMR (400 MHz, Chloroform-\(d\)) \(\delta\) 8.56 (d, \(J\) = 7.5 Hz, 2H), 8.28 (s, 1H), 6.75 (d, \(J\) = 7.5 Hz, 2H), 3.46 (t, \(J\) = 7.1 Hz, 4H), 1.68 (p, \(J\) = 7.0 Hz, 4H), 1.40-1.26 (m, 12H), 0.95-0.88 (t, \(J\) = 8.1 Hz, 6H). \({}^{13}\)C NMR (100 MHz, DMSO-\(d_{6}\)) \(\delta\) 167.91, 158.26, 152.49, 143.40, 132.63, 115.64, 114.64, 113.79, 112.36, 50.65, 34.01, 29.82, 25.55, 20.08, 14.25. HRMS (FAB-MS), m/\(z\)): calcd. For C\({}_{26}\)H\({}_{31}\)N\({}_{5}\)O, 429.2529; found: 430.2530 [M+H]\({}^{+}\).
# A Novel Dual-Wavelength Method for Evaluating Temperature Effect in Fiber-Optic SPR Sensors
## 2 Material and Methods
### Multi-Layer Matrix Method  
In general, the fiber sensor based on the SPR can be considered as an N-layer model [32; 33; 34]. To simplify the model, all the layers are assumed to be uniform, isotropic, non-magnetic, and stacked along the z-axis, as shown in Figure 2. Here, we use the matrix method to obtain the expression for the reflected light intensity of p-polarized incident light.  
For the \(n\)th medium layer, the thickness, dielectric constant, and permeability are defined as \(d_{n},\varepsilon_{n}\), and \(\mu_{n}\). The tangential fields at the first boundary (\(Z=Z_{1}=0\)) and the final boundary (\(Z=Z_{N-1}\)) are related by the relation of Equation (10):  
\[\left[\begin{array}{c}E_{1}\\ H_{1}\end{array}\right]=M\left[\begin{array}{c}E_{N-1}\\ H_{N-1}\end{array}\right] \tag{10}\]  
where, \(E_{1}\), \(H_{1}\), and \(E_{N-1}\), \(H_{N-1}\) are the tangential components of electric and magnetic fields at the boundary of the first layer and the \(N\)th layer, respectively. Here, \(M\) is the characteristic matrix for the multilayer structure and can be given by Equation (11):  
\[M=\prod_{n=2}^{N-1}M_{n} \tag{11}\]  
Herein,  
\[M_{n}=\left[\begin{array}{cc}\cos\beta_{n}&\frac{-i\sin\beta_{n}}{q_{n}}\\ -iq_{n}\sin\beta_{n}&\cos\beta_{n}\end{array}\right] \tag{12}\]  
with the coefficients:  
\[\beta_{n}=\frac{1}{\lambda}2\pi d_{n}\sqrt{\varepsilon_{n}-n_{1}^{2}{\sin^{2} \theta}} \tag{13}\]  
where \(\lambda\) is the free space wavelength and \(n_{1}\) is the RI of the first layer.  
In transverse-electric (TE, s-polarized) models,  
\[q_{n}=\sqrt{\varepsilon_{n}-n_{1}^{2}{\sin^{2}\theta}} \tag{14}\]  
In transverse-magnetic (TM, p-polarized) models,  
\[q_{n}=\frac{q_{n}}{\varepsilon_{n}} \tag{15}\]  
The reflection amplitude for polarized incident light can be calculated by Equation (16):  
\[r=\frac{(M_{11}+M_{12}q_{N})q_{1}-(M_{21}+M_{22}q_{N})}{(M_{11}+M_{12}q_{N})q_ {1}+(M_{21}+M_{22}q_{N})} \tag{16}\]  
Therefore, the final reflection intensity of unpolarized wave containing TE and TM modes can be given by Equation (17):  
\[R=\frac{1}{2}\Big{(}|r_{TE}|^{2}+|r_{TM}|^{2}\Big{)} \tag{17}\]  
Figure 2: Multi-layer model configuration for the calculation of reflected light intensity.
# The First Seismic Imaging of the Holy Cross Fault in the Lysogory Region, Poland
## 6 Conclusions  
This study demonstrates the effectiveness of seismic surveying for imaging near-surface geological structures within the Holy Cross Mountains' fold belt and its complex fault systems. Our findings confirm that small-scale, academic-funded seismic profiles can significantly enhance the interpretation of intricate fault networks, providing valuable insights into the subsurface structure of regions like the Lysogory Unit and the broader Holy Cross Fault zone, as well as other similar fault areas. The study confirms a complex fault zone with multiple intersecting strands and varied geometries typical for strike-slip faults. By integrating proper field acquisition, precise positioning, and advanced seismic processing, we achieved significant improvements in subsurface clarity and signal-to-noise ratio, enabling accurate detection of near-vertical faults and better resolution in shallow fault zones. These findings underscore the potential of high-resolution, low-cost seismic profiling as an essential tool in academic geophysical research, particularly for refining structural models of complex fault systems in fold belts. Future research would benefit from even denser geophone arrays and expanded profiling to further enhance resolution, deepening our understanding of fault geometries within the HCM and geological settings. The better results could be obtained with a much larger 3D survey that would cover a larger area of the HCM. We also suggest the continuation of 2D and 3D seismic imaging in various areas of the HCM to understand the detailed subsurface structures of this complicated and interesting area.  
Conceptualization, R.S.; methodology, A.M., M.M. and E.R.; software, E.R. and M.M.; validation, E.R. and M.M.; formal analysis, E.R. and M.M.; investigation, E.R., M.M. and A.M.; resources, M.M.; data curation, A.M. and E.R.; writing--original draft preparation, E.R. and M.M.; writing--review and editing, E.R., M.M., A.M. and R.S.; visualization, E.R. and M.M.;  
Figure 7: The final reflection seismic image with marked recognized faults.  
supervision, M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Centre Poland (NCN) grant number 2022/45/B/ST10/00658.  
Not applicable.  
The data is publicly available in the data portal of the Institute of Geophysics, Polish Academy of Sciences ([[https://dataportal.igf.edu.pl/dataset?organization=geophysical-imaging](https://dataportal.igf.edu.pl/dataset?organization=geophysical-imaging)]([https://dataportal.igf.edu.pl/dataset?organization=geophysical-imaging](https://dataportal.igf.edu.pl/dataset?organization=geophysical-imaging))) on 1 February 2025. During the analysis the commercial software Globe Claritas developed by PetroSys was used and geodetic conversions were performed in QGIS software.  
We would like to thank [PERSON] for useful discussions about the geological interpretation of our results.  
The authors declare no conflicts of interest.
# An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture
## 5 Numerical and Simulation Results
### Simulation Topology  
In this section, we evaluate the performance of the proposed model by using the LENA-based NS-3 simulator. LENA NS-3 is open-source code and is available for building a network system. NS-3 has already developed a 4G network topology [48] including two main components: the LTE model and the EPC model. LTE framework is divided into two parts: lower radio protocol stack, which includes particular PHY, MAC layer as well as Scheduler, and the second one is upper radio stack which includes RRC, PDCP, and RLC protocols. These entities within the U.E. and eNB nodes are unmodified because the proposed 5G architecture keeps the access layer of 4G intact. While the EPC framework  
Figure 12: The impact of \(\delta\) on the handover delay.  
Figure 13: The impact of \(\epsilon\) on the handover delay.  
is modified as follows. First, The S-GW and P-GW are implemented within a single node. Thus, the gateway relocation mobility concept and S5/S8 interface are not supported. Second, the use of a single S-PGW causes incorrect signaling message procedures. Third, the GTP tunneling protocol (GTPv1) was replaced by GTPv2. Finally, socket transmission is used for the data plane (GTP-U) only, and logical connections are used to overcome inaccurate results. We create one more node acted as S-GW to overcome these challenges, then attach mobility functions from the original S-PGW node. Next, we fulfill the GTP protocol by adding GTPv2 features. Then, we make the S5/S8 GTP connection between S-GW and P-GW and implement fully signaling messages. The last step is creating socket transmission for both the control plane (GTP-C) and the data plane (GTP-U), Now the 4G model in NS3 gives more accurate results.  
This paper used LENA NS-3 version 3.22 in a Linux environment to build the proposed network topology. The proposed architecture programming consists of, first, building MCE, C.E., M-GW nodes. Second, implementing control and data plane into the corresponding interface. This research focuses on mobility simulations; Intra and Inter-Handover gateway mobility. We compare the results in terms of latency while U.E. moves with different speeds and moves across several domains. The flow monitor module [49] is used to gather and store data from the simulated network. We created a Remote host node that acts as an Internet server that can send packets to other nodes. Additional parameters in the simulation model are shown in Table 2[50].
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Experiments Performed  
The purpose of this simulation model is to evaluate certain aspects of MANETs as applied to this scenario. In order to demonstrate that the model is fit for this purpose, a set of experiments has been performed using this model to find the minimum communication range required for a MANET to be established that would connect ships to each of the four VTSs using ranges from 5 km to 30 km in 5-km steps. The simulations are run using the DSDV ([PERSON] and [PERSON], 1994, [24]), AODV ([PERSON], 2003, [165]), and DSR ([PERSON] and [PERSON], 1996, [10]) protocol implementations in ns-2 supplied by the CMU Monarch Project (CMU Monarch Project, 1999, [185]). The same scenario with the same set of trace data has been used for each of the protocols. T different lengths of sample time and data from different days, which show that there is no significant difference in the results between a one-hour sample and a one-day sample or between samples taken from different times or different days. Therefore, the same one-hour period has been used in each case. To provide a basis for comparison, a RWP model was also built using the basic parameters from real trace data gathered from AIS, including area covered and number of nodes. The initial version of the RWP model has all the nodes moving, but in order to get a more relevant comparison, a second RWP model was also used. It had four fixed nodes at the locations of the VTS stations in the real model with all the other nodes randomly placed and randomly moving according to the RWP algorithm. This provided a direct comparison between the results for random movement and for the real movement of the mobile nodes as shown by the trace data from AIS whilst also giving a valid target for data sinks in the RWP model. The results obtained from running this shipping model are discussed in the subsequent section.
## 3 Results  
### GPP Simulation Results and Temporal and Spatial Evolution  
#### 3.1.1 GPP Simulation Accuracy Evaluation  
In order to compare the performance of the RF model, we also construct a multiple regression model to simulate GPP using environmental factors, with its results compared to those of the RF model. Specifically, the GPP predicted by the RF model was denoted as RF_GPP, the GPP from the GLASS product was referred to as GLASS_GPP, and the GPP derived from the multiple regression model was labeled as regGPP. Spatial pattern maps of annual averages and interannual variation trends derived from the regression model are included in the Supplementary Information. A comparative analysis against flux station observations indicated that RF_GPP (R\({}^{2}\) = 0.611, RMSE = 69.162 gC-m\({}^{-2}\)-month\({}^{-1}\)) achieves significantly higher accuracy than both GLASS_GPP (R\({}^{2}\) = 0.360, RMSE = 91.764 gC-m\({}^{-2}\)-month\({}^{-1}\)) and regGPP (R\({}^{2}\) = 0.429, RMSE = 86.578 gC-m\({}^{-2}\)-month\({}^{-1}\)). These results, illustrated in Table 2, highlight the superiority of the RF model in capturing GPP variability.  
The analysis shows that the accuracy of the GLASS_GPP product on the QTP is relatively limited. The regGPP product obtained through the multiple regression model demonstrates improved accuracy, but the RF_GPP product achieves the highest accuracy. Furthermore, the spatial patterns derived from the RF simulation are more consistent and reasonable [72; 73].
# Research on Dynamic Modeling and Transition Flight Strategy of VTOL UAV
## 2 Aerodynamic Expression
### Induced Wing Aerodynamic Model  
The induced wing surface is connected to the airframe, and its aerodynamic force is mainly determined by the airflow velocity at the outlet of the lift duct. The velocity at the outlet of the lift duct is taken as the free flow velocity of the induced wing surface, so as to simplify the establishment of the induced wing aerodynamic model.  
As shown in Figure 7, the aerodynamic force and moment of the induced wing surface can be obtained. In the airflow coordinate system, namely the coordinate system of the lift duct, the calculation is as follows [21]:  
\[\begin{array}{c}F_{X,iw}=-(C_{D,0}+C_{D,i})\cdot q\cdot S\cdot\xi_{D}\\ F_{Z,iw}=-[C_{L,0}+C_{L\alpha}\cdot(\theta_{iw}-\theta_{d})]\cdot q\cdot S \cdot\xi_{L}\,\\ M_{Yiw}=C_{M(\theta_{iw}-\theta_{d})}\cdot q\cdot S\cdot c\cdot\xi_{M}\end{array} \tag{41}\]  
where \(C_{D,0}\) is the zero-lift drag coefficient, \(C_{D,i}\) is the induced drag coefficient, \(C_{L,0}\) is the zero Angle of attack lift coefficient, \(C_{L\alpha}\) is the slope of the lift line, \(\theta_{iw}\) is the induced wing inclination angle, and \(\theta_{d}\) is the raised culvert inclination angle, \(\xi\) is the correction factor.  
The transformation matrix from the lift duct coordinate system to the body coordinate system is \(R_{bd}\), and its aerodynamic force and torque are expressed as follows under the body coordinate system: \(\mathbf{F}_{b,iw,i}=\mathbf{R}_{bd}\cdot\mathbf{F}_{iw}\), \(\mathbf{M}_{b,iw\Lambda,i}=\mathbf{R}_{bd}\cdot\mathbf{M}_{iw,i}\).  
The calculation of the moment generated by the induced aerodynamic force of the wing on the airframe is based on the cross product of the position vector and force of the stress point under the body coordinate, that is \(\mathbf{M}=\mathbf{L}\times\mathbf{F}\), and the specific expression is as follows: \(\mathbf{M}_{b,iw\Gamma,i}=(x_{iw\_,i},y_{iw\_,i},z_{iw\_,j})\times\mathbf{F}_ {b,iw\_,j}\), where \(x\), \(y\), \(z\) is the coordinate position of the induced wing, and \(i\) is the imaginary number of the induced wing.