esa-sceva/satcom-chunk-collection huggingface dataset:
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 2 Related Work
### Discussion  
The existing FANET routing scheme relies on sending Hello packets at fixed intervals for neighbor discovery, which not only increases communication overhead but may also affect the accuracy of routing decisions due to delayed topology updates. In addition, there is still room for optimization in energy consumption control and routing stability for these methods. Therefore, we adopt a combination of mobile prediction and intelligent learning algorithms and extend the Kalman filter model to accurately predict the position of nodes at the next moment in order to cope with the frequent changes in node positions. Then, a mechanism was designed to dynamically adjust the Hello packet sending gap and optimize the neighbor discovery process. Finally, considering factors such as communication distance, link stability, and energy balance, Q-learning was adopted for routing decisions to maximize the target reward and further optimize link stability and communication energy consumption.  
In the existing FANET routing protocol, QGeo uses the Q-learning method to dynamically select the next hop node based on geographic routing, but does not fully consider the energy status of nodes, which may result in some nodes consuming energy too quickly. QMR further introduces a multi-objective optimization mechanism that balances latency and energy consumption but still uses a fixed Hello packet interval, which cannot dynamically adapt to topology changes. As a classic geographic routing protocol, GPSR's greedy forwarding strategy can easily lead to routing rollback problems in high dynamic scenarios. In contrast, MP-QGRD uses EKF for mobile prediction, which improves the perception accuracy of link status and adopts an adaptive Hello packet adjustment strategy to reduce communication overhead. In addition, MP-QGRD takes into account link stability, energy consumption, and communication distance in the reward function design of reinforcement learning.
## 1 Introduction  
Soil salinization is a land degradation phenomenon under the joint action of nature and human activities [1; 2; 3]. As a global ecological environment problem, it seriously restricts the sustainable development of the agricultural economy and the stability of the ecosystem [2; 4]. The Alar Reclamation Area is located in the arid area of southern Xinjiang, where the problem of soil salinization is serious [5]. Although the use of water-saving irrigation technology has effectively reduced farmland water use, the problem of the secondary salinization of the soil has increased in recent years. How to quickly and effectively extract the spatial information of soil salinization is the key to sustainable land development and agricultural scientific management in the Alar Reclamation Area [6].  
The traditional field sampling survey method is characterized by consuming substantial human sources, and it is mostly limited to small-scale research obtaining point-like information [1]. On the contrary, remote sensing (RS) technology can quickly obtain large-scale ground object information in a repeat manner, which is helpful for long-term soil salinization research. Benefiting from the advantages of macroscopic coverage, comprehensive analysis, dynamical processing, and the low cost of RS technology, it has been widely used in the (near) real-time monitoring of soil salinization, the quantitative estimation of soil salinity, etc. [7; 8; 9; 10].  
According to the RS-based soil salinization analysis model based on the NDVI-SI feature space, the salinity index 1 (salinity index 1, SI1) and four types of vegetation indices that are sensitive to crop growth monitoring were selected, including the normalized difference vegetation index in a green-red band (GRNDVI), normalized vegetation index of greenness (GNDVI) [8], normalized difference vegetation index (normalized difference vegetation index, NDVI), and difference environmental vegetation index (difference environmental vegetation index, DVI). Then, based on the Sentinel-2 remote sensing image data and the field measured data in the Alar reclamation area, combined with the above five spectral indices, four types of remote sensing salinity monitoring index models are constructed: S1 DI1 (SI1-GRNDVI), S1 DI2 (SI1-GNDVI), S1 DI3 (SI1-NDVI), and SDI4 (SI1-DVI), and the inversion of cotton field soil salinity in the Alar reclamation area is explored in this study. The optimal remote sensing salinity monitoring index model for waterlogging in this study aims to provide a scientific basis for the precise irrigation of cotton fields in the reclamation area and the good growth and development of cotton.
## 4 Results and Discussion
### Spinal Oscillation  
The spinal oscillation is an important characteristic of quadruped bounding with a flexible-articulated spine. It is defined as the positive difference between the maximum and minimum spinal pitch angles. It has been known that MCPSS influences greatly on gait characteristics such as stride period, stride length and duty factor. The spinal motion is coupled with the gait characteristics, so the spinal oscillation should be influenced obviously by MCPSS.  
The relation between the spinal oscillation with MCPSS is shown in Figure 7 at the low, medium, and high horizontal speeds, respectively. It can be seen that the spinal oscillation decreases monotonously as MCPSS moves from the spinal joint towards the hip joint at all speeds. In particular, for quadruped bounding at low and medium speeds, the decreasing trend of spinal oscillation becomes slower gradually, especially after MCPSS crosses over the geometric center of spinal segment. For quadruped bounding at high speed, the decreasing trend maintains almost constantly. Based on these finds, it can be obtained that the spinal oscillation with respect to MCPSS being at the spinal joint and the hip joint is about 1.07 and 0.20, respectively, at high speed. The latter is 81% less than the former. Similarly, the corresponding ratio is about 58% and 61% at the low and medium speeds, respectively. Therefore, the effect of MCPSS on spinal oscillation is very obvious at any speed. Significantly, the decreased spinal oscillation can provide enough margin of spinal oscillation for faster and more efficient quadruped bounding. So, adjusting MCPSS from the spinal joint towards the hip joint is greatly meaningful, especially when the quadruped bounding moves fast relatively.
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 6 Experimental Results
### Discussion
This also shows that the traditional way of solving the VRP cannot be directly applied to the solution of the ERRP. The solution \begin{table}
\begin{tabular}{l l c} \hline \hline The number of patients is 20 & Deprivation cost 1 & 37,143.0729 \\  & Deprivation cost 2 & 1892.0069 \\ The number of patients is 30 & Deprivation cost 3 & 60,618.7343 \\  & Deprivation cost 4 & 5419.0027 \\ The number of patients is 50 & Deprivation cost 5 & 122,708.478 \\  & Deprivation cost 6 & 8870.4518 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of whether to include deprivation cost in the objective function. Figure 17: Comparison of whether to include deprivation cost in the objective function. model that includes the deprivation cost is more in line with the fundamentals of providing humanitarian relief in post-disaster situations. Algorithm complexity analysis is a crucial aspect of algorithm design and evaluation. In this study, we analyzed the time and space complexity of the proposed hybrid algorithm, which combines three different algorithms, namely BSO, LNS, and SA, to achieve superior performance. Our analysis reveals that there is no significant difference in complexity between BSO, BSO+LNS, and HBSO. Specifically, the time complexity of these algorithms is \(O\left(n^{2}\right)\), and their space complexity is \(O\left(log_{n}\right)\). In summary, our findings indicate a significant improvement in the synthesis ability of the HBSO algorithm as compared to the other two methods. The incorporation of LNS enhances the algorithm's ability to exploit the solution space and discover superior individuals. By selectively retaining non-optimal individuals based on probability, the algorithm achieves a more comprehensive exploration of the solution space and a stronger global search capability. Additionally, our model, which incorporates deprivation cost, significantly reduces patient suffering, resulting in a higher likelihood of successful rescue and a more compassionate approach to healthcare.
## 4 Results and Discussion
### Tracking and Passenger Comfort  
Passenger comfort, evaluated based on acceleration smoothness, and performance tracking of MPC-based controllers were initially examined using the UDDS driving cycle, as illustrated in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **UDDS** & **ARDC** & **AUDC** & **RD1** & **RD2** \\ \hline Leader (kg) & 1.1102 & 1.5676 & 0.5518 & 0.2288 & 0.2479 \\ \hline MPC\({}_{\text{noFC}}\) (kg) & 1.0727 & 1.5127 & 0.4576 & 0.2017 & 0.2147 \\ \hline MPC\({}_{\text{FC}}\) (kg) & 1.0692 & 1.5085 & 0.4563 & 0.2006 & 0.2142 \\ \hline DP (kg) & 1.0141 & 1.4590 & 0.4293 & 0.1900 & 0.1953 \\ \hline \end{tabular}
\end{table}
Table 2: Fuel consumption in Kg for each tested driving cycle and controller.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **UDDS** & **ARDC** & **AUDC** & **RD1** & **RD2** \\ \hline MPC\({}_{\text{noFC}}\) & 3.4\% & 3.5\% & 17.1\% & 11.8\% & 13.4\% \\ \hline MPC\({}_{\text{FC}}\) & 3.7\% & 3.8\% & 17.3\% & 12.3\% & 13.6\% \\ \hline DP & 8.6\% & 6.9\% & 22.2\% & 16.9\% & 21.2\% \\ \hline \end{tabular}
\end{table}
Table 3: Percentage fuel savings for each tested driving cycle and controller.  
Figure 2: Distance error, MPC\(-\)equipped ego, and lead velocities and accelerations along UDDS driving cycle, driving cycle.  
For improved clarity, Figure 3 provides a zoomed-in view of the velocity and acceleration profiles, highlighting the controller's ability to produce smoother profiles compared to the lead vehicle. A comprehensive comparison, incorporating the lead vehicle, MPC, and baseline DP velocity and acceleration profiles, is presented in Figure 4 within the same zoomed window. Additionally, Figure 5 extends this comparison to the AUDC driving cycle.  
Figure 4: Comparison of velocity and acceleration profiles of ego equipped with fuel consumption MPC and DP controllers with lead along UDDS driving cycle, [0, 450 s] zoom.  
Figure 3: MPC\(-\)equipped ego and lead velocities and accelerations along UDDS driving cycle, [0, 450 s] zoom.  
Based on the preliminary findings discussed in Section 4.1, only the MPCFC results are displayed, as both MPC controllers produced nearly identical outcomes. Furthermore, Figures 4 and 5 showcase that while DP achieved greater smoothness in velocity due to its access to the entire drive cycle, its acceleration profile occasionally exhibited sharper transitions than MPC in dynamic scenarios. Table 4 further corroborates these findings, emphasizing the minimal differences between the results of the two MPC controllers.
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 4 Case Study
### Pca  
From the matrix built above, we made a feature extraction and reduced the size of the matrix caused by feature generation using the Principal Component Analysis (PCA) tool for the study of the principal components. From the matrix 4558 \(\times\) 60, a reduction was made, starting to have a matrix with only ten main components, 4558 \(\times\) 10. As already described, the first dimensions are those that contain the greatest variation of the data. In Table 3, taken from the MatLab PCA function, we can see the percentage variability of each of the main components and how it decreases in each PCA.  
Therefore, the top ten components were chosen as this number of components preserves \(\sim\)82% of the data. For a graphical analysis of the variability of the data on each PCA, we used a Pareto diagram (Figure 4).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Principal Components** & **\% of Preserved Data** \\ \hline
1 & 26.8299362662924 \\
2 & 11.3444018431582 \\
3 & 10.5209646496898 \\
4 & 7.43400606597570 \\
5 & 6.75048105033166 \\ (\(\dots\) ) & (\(\dots\) ) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentage variability explained by the main components.  
Figure 3: All features for each one of the signals sensors.  
In addition to the feature extraction through PCA, a study can still be done where we can identify which features contributed most to each of the main components, through the coefficients of the main component. This can be very well represented using a heat map represented in Figure 5.  
In the coefficient matrix, each coefficient column contains coefficients for a principal component and the columns are in descending order of component variance. Therefore, we represented in the lines the 60 variables extracted for each one of the sensors and, the 10 Main Components were represented in the columns. Therefore, the first 10 lines will  
Figure 4: Pareto diagram to represent the percentage of data preserved in each Principal Component.  
Figure 5: Contribution of each of the features in each of the Principal Components.  
represent all the features extracted for the first sensor and so on, respecting the order of sensors and features described above. In this way, it is possible to verify which variables most contribute to each of the PCA, and the ones that contribute the most are the darker blue ones. As PCA number one is the one with the most variability of data, we can deduce that the features that most influence this component are the most important.
## 4 Co-Seismic Ground Deformation
### Interferometric Data and Results
The horizontal (east-west) displacement map reveals an eastward motion up to 6 cm in the area west of the epicenter and a westward movement up to 7.6 cm for the area east of Arckalochori (Figure 11b). The latter is in satisfactory agreement with [21] where the displacement is associated with the horizontal motion of the strike-slip component of the mean event focal mechanism. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Ascending Image Pair**} \\ \hline
**Satellite** & **Ref./Repeat** & **Acquisition** & **Track** & **Orbit** & **Bperp (m)** & **Btemp (days)** \\ \hline S1B & Reference & 23 September 2021 & 102 & 28,828 & \multirow{2}{*}{\(-111.36\)} & \multirow{2}{*}{6} \\ \cline{1-1} S1A & Repeat & 29 September 2021 & 102 & 39,899 & & \\ \hline \multicolumn{4}{c}{**Descending Image Pair**} \\ \hline
**Satellite** & **Ref./Repeat** & **Acquisition** & **Track** & **Orbit** & **Bperp (m)** & **Btemp (days)** \\ \hline S1A & Reference & 25 September 2021 & 36 & 39,833 & \multirow{2}{*}{\(-28.08\)} & \multirow{2}{*}{6} \\ \cline{1-1} S1B & Repeat & 1 October 2021 & 36 & 28,937 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The main characteristics of the SAR images used in this study. Figure 11: Displacement maps for (**a**) the vertical (up–down) and (**b**) the E–W displacement components for the Arkalochori earthquake overlain by the earthquakes with magnitude greater than 3 and the active faults of the broader area. Positive values on the E–W component indicate eastward motion, while the negative ones describe westward motion. Figure 10: Upper maps: wrapped ascending (**left**) and descending (**right**) co-seismic interferograms over the Arkalochori area. The interferograms are draped over shaded relief. Lower maps: co-seismic displacement maps generated using the ascending and the descending image pairs and draped over shaded relief.
## 3 Results and Discussion  
### Optimization of Soft Soil Stabilization Material Mix Proportions  
This study, based on the requirements of stabilized soft soil foundations using mixing piles, used SWC to replace cement and formulated the optimal ratio of soft soil stabilization materials. As described in Section 2.2, the proportions of the main components of the cementitious materials are variable, while the proportions of the additives, water-to-cement ratio, and C/S are constant. Table 5 lists the compressive strengths of the stabilized soil (G1\(\sim\)G7) and cement soil (GC).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Compressive Strength**} \\ \cline{2-4}  & **3 d/MPa** & **7 d/MPa** & **28 d/MPa** \\ \hline G1 & 1.07 & 3.06 & 4.18 \\ G2 & 2.01 & 4.03 & 5.75 \\ G3 & 0.48 & 2.93 & 5.50 \\ G4 & 3.96 & 4.92 & 5.91 \\ G5 & 2.68 & 4.38 & 4.79 \\ G6 & 1.25 & 2.85 & 4.22 \\ G7 & 2.71 & 4.61 & 5.97 \\ GC & 0.89 & 1.68 & 2.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compressive strength test results of the samples.  
As seen from Table 5, the early and late compressive strengths of stabilized soil G4 and G7 were greater than those of other samples and cement soil GC. The early strength of G4 increased rapidly, with the 7 d compressive strength reaching 4.92 MPa. Although the early strength of G7 was lower than that of G4, the late strength was higher, reaching 5.97 MPa at 28 d. The experimental results show that the prepared SWC can stabilize soft soil well and is much stronger than cement soil in both the early and late stages. The 7 d compressive strength of G4 and G7 exceeded that of GC by 192.9% and 174.4%, respectively, while the 28 d compressive strength of G4 and G7 surpassed that of GC by 123.9% and 126.1%.
# Spin-Related Micro-Photoluminescence in Fe\({}^{3+}\) Doped ZnSe Nanoribbons
###### Abstract  
Spin-related emission properties have important applications in the future information technology; however, they involve microscopic ferromagnetic coupling, antiferromagnetic or ferrimagnetic coupling between transition metal ions and excitons, or \(d\) state coupling with phonons is not well understood in these diluted magnetic semiconductors (DMS). Fe\({}^{3+}\) doped ZnSe nanoribbons, as a DMS example, have been successfully prepared by a thermal evaporation method. Their power-dependent micro-photoluminescence (PL) spectra and temperature-dependent PL spectra of a single ZnSe:Fe nanoribbon have been obtained and demonstrated that alio-valence ion doping diminishes the exciton magnetic polaron (EMP) effect by introducing exceeded charges. The \(d\)-\(d\) transition emission peaks of Fe\({}^{3+}\) assigned to the \({}^{4}\)T\({}_{2}\) (G) \(\rightarrow\)\({}^{6}\)A\({}_{1}\) (S) transition at 553 nm and \({}^{4}\)T\({}_{1}\) (G) \(\rightarrow\)\({}^{6}\)A\({}_{1}\) (S) transition at 630 nm in the ZnSe lattice have been observed. The emission lifetimes and their temperature dependences have been obtained, which reflected different spin-phonon interactions. There exists a sharp decrease of PL lifetime at about 60 K, which hints at a magnetic phase transition. These spin-spin and spin-phonon interaction related PL phenomena are applicable in the future spin-related photonic nanodevices.  
spin-spin coupling; micro-photoluminescence; alio-valence doping; diluted magnetic semiconductor +
Footnote †: journal: Applied Physics Letters
# Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
## 3 Our Proposed Work
### Opportunistic Cooperation  
In Co-Hydrocast, fixed nodes are not involved in the minimization of the number of transmissions. However, in Co-Improved Hydrocast fixed nodes play a role in reducing the number of transmissions. The cooperation is performed in both schemes at each destination node to increase the SNR of the received signal. Details of the procedure are given below:  
When a source node transmits a signal towards a destination node, it is overheard by other neighboring nodes, and they keep a copy of the received data packet. Destination node after receiving the source node signal, checks if SNR meets the threshold or not. If the SNR is (according to Equation (17)) within the acceptable range, then the destination node accepts the received signal and broadcasts an acknowledgment (ACK), as shown in Figure 8. After receiving the ACK, the relay nodes discard the stored copy of the source node signal.  
In sparse networks, the probability of data packet drop ratio and the low SNR at the destination increases. Figure 9 shows when the SNR at the D is less than the pre-defined threshold. In that case, a negative ACK (NACK) is generated and broadcasted by D, which is received by R1 and R2. Receiving NACK, a relay node of highest priority forwards the copy of the S towards D. Both copies of the signal that are received directly from a source node and via relay node are combined at the destination node by MRC technique to avoid the re-transmission.  
Figure 8: Data packet transmission without cooperation.  
Figure 7: Cooperation.
# Accuracy Analysis of the Measurement of Centre of Gravity and Moment of Inertia with a Swing
## 5 Conclusions  
The quantification of the location of the centre of gravity (CG) as well as the moments of inertia (MI) are critical for many investigations. In the case of tank testing, the majority of investigated floating devices are downscaled models. Due to constraints connected with the model build as well as the ballasting, the precise realisation of the CG as well as the MI have a higher uncertainty connected. Hence, it is very important to measure those values very precisely at model scale to check and potentially correct the connected numerical simulations and empirical analysis. Different methods could be used and, in this paper, an experimental set-up for a swing is presented and investigated. The swing is balanced on two knife edges placed on a support structure, which reduces the potential motion to a single rotation around a pivot axis. It allows static investigations to measure the distance of the CG in the range of millimetres as well as dynamic investigations for the additional calculation of the MI in the range of 1%. Therefore, two different pivot points are available, and it could be shown that a multi distance check with the calibration weights are very important to ensure the quality of the measurement. The theoretical analyses are expanded with a constant offset to compensate imperfection in the zeroing of the swing. A removable bracing at the top, which provides stability but can be removed to ensure crane accessibility, was identified as a potential improvements of the design. All in all, the swing provides a flexible and standardised experimental set-up for static and dynamic investigations to quantify the centre of gravity and moments of inertia of typical models tested in FloWave.  
Conceptualization and measurement R.G.; methodology R.G., T.D., E.N. and D.M.I., formal analysis, R.G., T.D. and E.N.; writing--original draft preparation, R.G. and T.D.; writing--review and editing E.N. and D.M.I. All authors have read and agreed to the published version of the manuscript.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: C cases CG centre of gravity F fitted data FRO full range output M measurement M measurement M moment of inertia P pivot axis