esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel 4 \(\times\) 1 MISO-VLC System with FBMC-OQAM Downlink Signals
## 2 Material and Methods
In OQAM modulation, it is also necessary to first perform QAM modulation of the same order on the data. Subsequently, the data will be divided into two channels, and each \(M\)-point data will be mapped to the virtual space. This operation requires \(M\) multiplications in the simulation implementation. In the hardware implementation, since the real and imaginary parts of the signal are represented by two channels of data, only a mapping is required. And, no additional operational structure is necessary. However, it results in a doubling of the data volume, which, in turn, doubles the computational load of the subsequent IFFT module and PPN structure. Therefore, the difference in the computational complexity of the two systems can be attributed solely to the FFT module and the PPN structure. The FFT module requires \(M\) log\({}_{2}\)\(M\) / 2 point multiplications and \(M\) log\({}_{2}\)\(M\) point additions, while the PPN structure requires \(KM\) multiplications and \(KM\) additions. The OFDM system only needs \(M\) log\({}_{2}\)\(M\) / 2 point multiplications and \(M\) log\({}_{2}\)\(M\) point additions. The FBMC/OQAM system requires \(M\) log\({}_{2}\)\(M\) + 2\(KM\) point multiplication and 2\(M\) log\({}_{2}\)\(M\) + 2\(KM\) point addition. In this system, where \(M\) = 512 and \(K\) = 4, OFDM requires 2304 multiplications and 4608 additions. FBMC/OQAM requires 8704 multiplications and 13,312 additions. From the above, it can be seen that the implementation complexity of FBMC/OQAM is much larger than that of OFDM.
## 7 Conclusions  
In this study, an autonomous landing method for FPLIR was proposed based on prior structure data, and an FPLIR landing system was built that enabled the FPLIR to land autonomously on a power line. The RGB images were segmented using a real-time semantic segmentation network and combined with depth images to obtain the spatial coordinate information of the power line. Prior structure data was then used to determine the SLA. Finally, a geometrically constrained trajectory planning method was used by the MPC controller to generate a feasible dynamic trajectory and a tracking trajectory to achieve accurate landing.  
Simulation experiments were carried out to evaluate the performance of the system. The effectiveness and reliability of the algorithm were analyzed in terms of various aspects such as power line detection, SLA determination, and the landing process. In addition, outdoor landing line experiments were conducted in a non-GPS environment. The results showed that the position and velocity errors in FPLIR landing were \(6.18\times 10^{-2}\) m and \(2.16\times 10^{-2}\) m/s, respectively, meeting actual requirements. The multi-aspect validation showed that the method is able to control autonomous landing of the FPLIR efficiently and robustly, in a manner that is suitable for the real environment. In addition, the advantages and disadvantages of the system were discussed at the level of both the processor hardware and the algorithm design, and corresponding suggestions were made to address different  
Figure 21: Width of a 35 mm power line in an image versus distance.  
problems. In the future, on the one hand, we will use a combination of solid-state LiDAR and a camera to estimate the relative positions of power lines and UAVs. On the other hand, we will adopt a learning-based MPC control method to avoid model inaccuracy having an effect on the system.  
Conceptualization, Y.Z., B.L. and J.L.; methodology, Y.Z., B.L., J.L. and X.Q.; software, B.L., Y.Z. and X.Q.; validation, B.L., X.Q., Y.Z., Y.W. and J.Z.; formal analysis, Y.Z. and T.F.; investigation, B.L., J.L., Y.Z. and T.F.; resources, B.L., Y.Z. and J.Z.; data curation, Y.Z., B.L., J.L. and J.Z.; writing--original draft preparation, Y.Z., B.L. and J.L.; writing--review and editing, Y.Z. and J.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (grant nos. 62163032 and 62063030), the Financial Science and Technology Program of the XPCC (grant nos. 2021 DB003, 2022 CB011 and 2022 CB002-07), and the Science and Technology Special the ninth division (grant no. 2021 JS008).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Tokenized KAN Block (Tok-KAN)
It uses learnable edge activation functions and parameterized activation functions as weights, eliminating the need for a linear weight matrix:  
\[\text{KAN}(\mathbf{Z})= (\mathbf{\Phi}_{K-1}\circ\mathbf{\Phi}_{K-2}\circ\cdots\circ \mathbf{\Phi}_{1}\circ\mathbf{\Phi}_{0})\mathbf{Z}, \tag{2}\]  
Figure 2: Schematic diagram of Tok-KAN and KAN layer structure in Tok-KAN.  
where \(\mathbf{\Phi}_{i}\) denotes the \(i\)th mapping function, \(K\) represents the total number of layers, and the input \(\mathbf{Z}\) is iteratively transformed through each mapping function \(\mathbf{\Phi}_{i}\) from \(i=0\) to \(i=K-1\). The output of each layer serves as the input for the next one, with the symbol \(\circ\) representing the composition of functions, indicating that the functions \(\mathbf{\Phi}_{i}\) are applied sequentially, starting from \(\mathbf{\Phi}_{0}\) and ending with \(\mathbf{\Phi}_{K-1}\). This multilayer composition enables the network to progressively capture and model complex, nonlinear relationships in the data. Each layer of the KAN consists of an input dimension \(n_{\text{in}}\), an output dimension \(n_{\text{out}}\), and a learnable activation function \(\phi\) as shown in Equation (3):  
\[\mathbf{\Phi}=\big{(}\phi_{q,p}\big{)},\quad p=1,2,3,\cdots,n_{in},\quad q=1,2,3, \cdots,n_{out} \tag{3}\]  
The computations from layer \(k\) to layer \(k+1\) in the KAN can be expressed in matrix form as \(\mathbf{Z}_{k+1}=\mathbf{\Phi}_{k}\mathbf{Z}_{k}\), where  
\[\mathbf{\Phi}_{k}=\left( \begin{array}{ccccc}\phi_{k,1,1}(\cdot)&\phi_{k,1,2}( \cdot)&\phi_{k,1,3}(\cdot)&\cdots&\phi_{k,1,n_{k}}(\cdot)\\ \phi_{k,2,1}(\cdot)&\phi_{k,2,2}(\cdot)&\phi_{k,2,3}(\cdot)&\cdots&\phi_{k,2,n _{k}}(\cdot)\\ \phi_{k,3,1}(\cdot)&\phi_{k,3,2}(\cdot)&\phi_{k,3,3}(\cdot)&\cdots&\phi_{k,3,n _{k}}(\cdot)\\ \vdots&\vdots&\vdots&&\vdots\\ \phi_{k,n_{k}+1,1}(\cdot)&\phi_{k,n_{k}+1,2}(\cdot)&\phi_{k,n_{k+1},3}(\cdot)& \cdots&\phi_{k,n_{k+1},n_{k}}(\cdot)\\ \end{array} \right) \tag{4}\]  
Finally, after processing at each KAN layer, the features are further optimized by an efficient deep convolutional layer (DwConv) [49] and batch normalization (BN) and ReLU activation functions. To enhance training stability and the network's representational power, residual connections are introduced in the KAN layers. Specifically, the original tokenized sequence is added as a residual to the output of the current layer, followed by layer normalization (LN) [50] for feature standardization. Formally, the output of the kth tokenized KAN module can be expressed as follows:  
\[\mathbf{Z}_{k}=LN(\mathbf{Z}_{k-1}+\text{DwConv}(\text{KAN}(\mathbf{Z}_{k-1}) )), \tag{5}\]  
where \(\mathbf{Z}_{k}\in\mathbb{R}^{H_{k}\times W_{k}\times D_{k}}\) is the output feature map of the \(k\)th layer.
## 3 Results and Discussion
### Antimicrobial Activity of Gaseous Citral in Food Matrix  
Aiming at scouting and validating the optimal citral concentration to be assayed in post-harvest trials, 0.0125 and 0.05 uL/cm\({}^{3}\) of citral amounts (corresponding to MIC and 4 MIC) were preliminary tested on table grape berries infected with _R. oryzae_ ITEM 18876 under conditions favoring fungal growth; the 4 MIC value was selected considering the food matrix that could affect the antimicrobial effects. Citral is a low toxic molecule and these concentrations are considered safe for human health [50].  
These results confirmed the efficacy of the selected citral concentration in controlling ITEM 18876 soft rot appearance on inoculated berries. Indeed, after seven days of incubation at 25 \({}^{\circ}\)C, the treated berries did not show mycelium occurrence and spoilage symptoms (Figure 1a), except for a slight the browning registered in the untreated samples and presumably due to the increased temperature during the assay. As shown in Figure 1b, differences in the berries' structure were observed in both samples; a slight softening in the treated berries was also observed.  
Figure 1: Table grapes artificially contaminated with _R. oryzae_ ITEM 18876 (10\({}^{6}\) spores/mL) and incubated under citral exposure (0.0125 uL/cm\({}^{3}\)) for seven days at 25 \({}^{\circ}\)C. (**a**) Comparison between the inoculated treated (**left**) and not-treated (**right**) grape berries. (**b**) A section of the grape berries showing structure changes between the inoculated treated (**left**) and not-treated (**right**) samples.
# Fracture Threshold Analysis and Parameter Matching of Cut-Out Induced Bolts for Subway Couplers
## 2 Experiment and Simulation Analysis
### Simulation Analysis
#### 2.2.3 Validation  
After the simulation calculation, the calculated force-time curve was compared with the force-time curve collected from the test. As can be seen from the curves in Figure 14, the time history trend of the coupler contact force is roughly similar. Additionally, as described in Section 2.1.3, due to installation errors and other reasons, there is a certain amount of free travel between the buffer and the internal components of the buffer. Therefore, during the model calibration process, a certain amount of free travel was also given to the buffer beam's force-displacement curve to synchronize the contact force time history changes between the test and simulation.  
In the simulation, the coupler heads did not fully couple at the beginning of the calculation, leaving a certain gap. Therefore, the moment when the contact force starts to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Density (g/cm\({}^{3}\))** & **Young’s Modulus (GPa)** & **Poisson Ratio** & **Yield Stress (MPa)** \\ \hline
7.82 & 206.08 & 0.3 & 835.27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic mechanical property parameters of bolts and other components of coupler [29].  
Figure 14: The comparison between simulation calculation and real vehicle test data.  
rise was set as T = 0 ms, as shown in Figure 14. When T = 1.19 ms, the initial peak force F = 2032 kN was reached; when T = 2.35 ms, the contact force dropped to zero, entering the free travel stage; when T = 6.9 ms, the free travel ended, and the contact force began to rise again; when T = 9.76 ms, the expansion tube entered the stable crushing stage, with an average crushing force F\({}_{\text{mean}}\) = 1200 kN; when T = 20.4 ms, the bolt broke, and the contact force started to decrease.  
As shown in Figure 15, the deformation of the bolts in the simulation and test is as follows: when T = 0 ms, the coupler heads are fully coupled, about to generate contact force, and the bolts are in the state of being preloaded with no deformation; when T = 10 ms, under the simulation calculation, the expansion tube enters the stable crushing stage, and the bolts at the cut-out zone begin to show slight deformation; when T = 20 ms, under continuous force, the bolts at the cut-out zone continue to deform, the cross-section area decreases, and a more obvious necking phenomenon can be observed, with the bolts at the brink of breaking; at T = 22 ms, the bolts have broken, and the two parts fly apart. In summary, the bolt breaking time in the test is T = 21.2 ms, and the bolt breaking time obtained from the simulation calculation is T = 20.4 ms, with a difference of approximately 3.77%, proving the model's validity.
## 3 Proposed Method
### Acceleration by Down-Sampling  
In the haze-removal method, it is necessary to calculate the transmission map \(t(\mathbf{x})\) for each pixel. Therefore, the calculation time of haze removal depends on the image size, and thus larger image sizes have higher associated calculation costs. Fortunately, observation of the transmission map \(t(\mathbf{x})\) indicates that it is characterised by a relatively low frequency except edges between objects, particularly at different depths. Therefore, we reduced the computation time greatly by down-sampling the input image. It estimated the transmission map \(t(\mathbf{x})\) and atmospheric light \(\mathbf{A}\) using down-sampled image, and then the haze-removal image \(\mathbf{J}\) was estimated by the up-sampled transmission map \(t(\mathbf{x})\) using Equation (6). Figure 4 shows the haze-removal results with different down-sampling ratios. The down-sampling ratio set to 1/4 achieved visually acceptable results, but when it was set to 1/8 or 1/16, halo effects were generated along edges such as along the sides of trees and leaves within the dashed red rectangles (second row of Figure 4e,f). Also, significant aliasing occurred along edges of the bench within the yellow dotted rectangle (third row of Figure 4e,f), and uneven colour occurred in the enhancement results in second row of Figure 4e,f. We therefore set the down-sampling ratio to 1/4 in all further experiments. Also, we used box filtering in down-sampling and bicubic interpolation in up-sampling. Too, acceleration by the down-sampling approach helps with noise suppression by spatial smoothing.
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 5 Simulation Results
### Optimal Power Ratio and SE Performance  
Figure 4 shows the optimal power ratio \(\beta_{\mathrm{opt}}\) under different frame structures. In this subsection, the channel power levels satisfy the normalization condition \(\sum_{i=1}^{p}\sigma_{h_{i}}^{2}=1\). The results show that \(\beta_{\mathrm{opt}}\) is proportional to SNR. This is because under low-SNR conditions, the main interference term in the proposed method is the noise term, and the system needs to reduce the power allocated to superimposed symbols to improve the noise resistance of data symbols; however, under high-SNR conditions, to ensure the channel estimation ability, the system should allocate more power to superimposed symbols. Figure 4 also shows that \(\beta_{\mathrm{opt}}\) is inversely proportional to the frame structure size. The main reason is that as the frame structure size increases, the structure size of the superimposed PBAs also increases, and when performing the correlation operation (12), less energy needs to be allocated to PBAs to produce significant correlation peaks. Thus, \(\beta_{\mathrm{opt}}\) can be reduced accordingly.  
Figure 5 shows the effects of \(\beta_{\mathrm{opt}}\) on SINR and BER with ideal MMSE estimation. It can be seen from Figure 5a that around \(\beta_{\mathrm{opt}}\), the SINR will achieve its maximum value, while the BER will simultaneously achieve its minimum value as shown in Figure 5b. This verifies the rationality of adapting the optimal power ratio.  
In Figure 6, we compare the SE performance of the proposed SPBA method with that of the EP method from [13]. As a reference, we also show the ideal SE performance, corresponding to the case in which channel information is already known by the system, meaning that no pilot is needed. It can be seen that the proposed SPBA method can achieve nearly ideal SE performance even at the lower bound. Compared with the EP design, because the pilot is superimposed onto the data matrix rather than embedded in it along with the protected symbols, the proposed method can achieve significantly improved SE performance. This improvement reaches 23.84% when SNR = 15 dB, when compared to the upper bound of EP design.  
Figure 4: Optimal power ratio versus SNR.
# Review of Photodetectors for Space Lidars
## 2 Space Lidar Detectors from UV to near Infrared  
Most of the space lidars to date used diode-pumped Q-switched Nd:YAG laser at the 1064 nm fundamental lasing wavelength and/or the 532, 355, or 266 nm frequency harmonics. These lasers give high-output pulse energy and can operate in space for multiple years without being serviced. The photodetectors used in these space lidars were PMTs for the 266, 355, and 532 nm wavelengths [4] and silicon APDs for the 532 and 1064 nm wavelengths [2].
## 2 Materials and Methods
### External Conditions During the Monitoring
#### 2.2.1 Radiative Conditions  
In July 2023, the total surface incoming direct radiation (SID) in the Lublin area reached 360.15 MJ\(\cdot\)m\({}^{-2}\), exceeding the multi-year average by 29.10 MJ\(\cdot\)m\({}^{-2}\) and placing it at the 73 rd percentile, near the upper quartile of recorded SID values. The linear trend for July indicated a decrease of 2.2 MJ\(\cdot\)m\({}^{-2}\) per year in SID totals, though this trend was not statistically significant (\(p\) = 0.792) (Figure 1). In August 2023, the SID total in the Lublin area was 287.67 MJ\(\cdot\)m\({}^{-2}\), representing an anomaly of \(-\)15.36 MJ\(\cdot\)m\({}^{-2}\) and placing it at the 42 nd percentile, below the multi-year average. This positioned August 2023 closer to the lower quartile for SID totals. The trend for August showed a minor increase in SID totals, approximately 6.9 MJ\(\cdot\)m\({}^{-2}\) per decade, though this trend lacked statistical significance (\(p\) = 0.429). In September 2023, the recorded SID total in the Lublin area was 258.30 MJ\(\cdot\)m\({}^{-2}\), with a positive anomaly of +66.65 MJ\(\cdot\)m\({}^{-2}\) above the long-term average of 191.65 MJ\(\cdot\)m\({}^{-2}\). This value ranked at the 87 th percentile, placing September 2023 near the upper quartile for SID totals. The linear trend for September indicated a gradual increase of 11.97 MJ\(\cdot\)m\({}^{-2}\) per decade in SID totals, yet this trend did not reach statistical significance (\(p\) = 0.271) (Figure 1).  
In Lublin, the highest daily total of solar radiation (TSR) in July occurred on 9 July (29.7 MJ\(\cdot\)m\({}^{-2}\)), and the lowest occurred on 13 July (7.9 MJ\(\cdot\)m\({}^{-2}\)) (Figure 2). Notably, the highest value of direct radiation on a horizontal surface (SID) was recorded on 16 July (24.4 MJ\(\cdot\)m\({}^{-2}\)), coinciding with the day of the highest maximum air temperature. In August, the maximum daily TSR was observed on 12 August (25.2 MJ\(\cdot\)m\({}^{-2}\)), and the minimum was on 5 August (6.7 MJ\(\cdot\)m\({}^{-2}\)). The highest direct radiation (SID) also occurred on 12 August (21.5 MJ\(\cdot\)m\({}^{-2}\)), just before the onset of a multi-day heat wave. This case suggested that in summer, high air temperature values in Lublin were influenced not only by radiative factors but also by advection, i.e., the inflow of hot air masses from lower latitudes. In September, the highest daily TSR occurred on 5 September (20.7 MJ\(\cdot\)m\({}^{-2}\)), while the lowest was recorded on 20 September (2.7 MJ\(\cdot\)m\({}^{-2}\)) (Figure 2).  
Figure 1: Multi-year variability and anomalies of SID totals in Lublin in July, August, and September during the period 1991–2023.  
Figure 2: Course of daily total (TSR) and diffuse (DIF) radiation sums from July to September 2023 in Lublin.
## 3 Optimization, Considering the Cogging Torque and Torque Ripple  
The cogging torque is the torque generated by the interaction between the iron core and the magnet when current is not applied. For a heavy-load machine, the cogging torque mainly degrades the performance at low speed and high torque. In this paper, the variations of cogging torque and output electromagnetic torque under different conditions were studied by optimizing the shape of the rotor surface core, the topology of the rotor PM, and the shape of the iron bridge. The variation of cogging torque and output electromagnetic torque under different conditions are studied.  
First of all, the electromagnetic torque \(T_{\theta}(t)\) at any time \(t\) can be calculated by integrating the tangential stress \(\sigma\) (\(\theta\), \(t\)), on the rotor over the surface [27],  
\[(\theta,t)=\frac{B_{c}(\theta,t)B_{r}(\theta,t)}{\mu_{0}} \tag{2}\]  
\[T_{\theta}(t)=\frac{r^{2}}{\mu_{0}}\int_{0}^{l_{a}}\int_{0}^{2\pi}B_{c}(\theta,t)B_{r}(\theta,t)d\theta dt \tag{3}\]  
where \(B_{c}(\theta\), \(t)\) and \(B_{r}(\theta\), \(t)\) are the circumferential and radial flux density components around the air-gap circumference; \(l_{a}\) is the stack length; \(r\) is the rotor outer radius. And \(B_{c,r}(\theta,t)\) is obtained by,  
\[B_{c,r}(\theta,t)=\frac{1}{\mu_{0}}\frac{F(\theta,t)}{\delta(\theta,t)} \tag{4}\]  
where \(F(\theta,t)\) is the distributed magneto-motive-force, and \(\delta(\theta,t)\) is the effective air-gap length, which is related to the slots of the stator.  
Equations (2)-(4) indicate that the cogging torque and electromagnetic torque are calculated based on the flux density distributions in the air-gap, which are directly related to the shape of the rotor pole surface. Thus, the air-gap flux density distributions and rotor pole surface arc were studied, as follows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Main Specifications** & **Value** & **Key Parameters** & **Value** \\ \hline Rated power output & 130 kW & Number of poles & 12 \\ Rated speed & 1500 r/min & Number of slots & 72 \\ DC bus voltage & 540 V & Stator outer diameter & 190 mm \\ Rated current & 235 A & Stator inner diameter & 129.5 mm \\ Winding connection mode & Y & Core length & 240 mm \\ Number of phases & 3 & PM width & 9 mm \\ Rate torque output & 830 Nm & PM length per pole & 45 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key design specifications and parameters of the IPMSM.  
Figure 1: Finite element model of the 12-pole/72-slot internal permanent magnet synchronous machine (IPMSM).
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Case Studies: Non-Optimal Results
#### 3.1.1 Snohomish County PUD (SCPUD)  
SCPUD updated their entire distribution system for general improvements and to enable CVR. The improvements were made to 68 Substations with a total of 272 feeders and 290,000 customers. An overall energy savings of 53,856 MWh/yr (6.15 MW on avg.) were achieved. The average customer voltage reduction was 2.3% with a CVR factor of 0.7. A typical feeder has 1.61% energy Savings (198 MWh/yr). Distribution system losses were reduced by 11,226 MWh/yr and the average customer saved 1.32% (156 kWh/yr) [24].
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions
### Peer to Peer Internet  
Employing ML with future D2D networks might help in realizing newly decentralized peer to peer internet. Decentralized multiplayer MAB techniques can help on solving such problem. In addition, federated and distributive learning techniques will be effective solutions. Future 6G systems will be definitely depend on real time/responsible AI.