esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Effect and MAI Impact on SAC-OCDMA System Based on 2D Multi-Diagonal Code and Laser Array
## 5 Results and Discussion  
Table 4 exhibits the simulation parameters deployed for the numerical simulation results of the Q-Factor calculation performed using a computer simulator tool, in particular for continuous wave (CW) Laser Array transmitter, Standard SMF (SSMF) specification G.652 [32] and PIN specification [33], respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \(\lambda\) & Wavelength & 1550 nm \\ \(\text{P}_{\text{T}}\) & CW laser optical power & \(-5\) dBm \\ \(\Delta\lambda\) & DFB spectral width & 0.5 nm \\ \(L\) & SMF length & (50–600) km \\ \(A\) & SMF attenuation loss & 0.2 dB/km \\ \(\text{D}_{\text{c}}\) & SMF Chromatic dispersion coefficient & 16 ps/(nm\(\cdot\)km) \\ R & PIN Responsivity & 1 A/W \\ B & PIN bandwidth & 20 GHz \\ \(\text{I}_{\text{dc}}\) & PIN dark current & 10 nA \\ Er & MZM extinction ratio & 20 dB \\ \(K\) & Simultaneous users & 4 \\ D & Bit rate & (10–100) Gb/s \\ \(\Delta\lambda_{i}\) & WDM multiplexer/demultiplexer & 0.8 nm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical simulations parameters.
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 3 Materials and Methods
### Enhanced Incremental SFM Sparse Reconstruction Algorithm for Similarity Measurement  
Feature matching establishes 3D point correspondences across multi-view images, and incremental Structure from Motion (SfM) achieves sparse reconstruction by progressively optimizing camera poses and 3D coordinates. However, traditional methods face two critical limitations: exhaustive pairwise matching leads to exponential computational complexity in large-scale scenarios; repetitive textures and similar regions generate mismatches, degrading point cloud quality.  
Traditional \"Matching Views\" construction relies on full-image feature matching and geometric validation (e.g., RANSAC), resulting in redundant matches. Although existing SfM frameworks attempt error filtering through bundle adjustment, reconstruction accuracy remains suboptimal. The improved algorithm in this study achieves transitive image connectivity with fewer matching instances, producing sparser yet geometrically more consistent matching relationships, as shown in Figure 6. Comparative experiments demonstrate that this method generates denser point clouds and more complete models.  
To achieve high-precision and comprehensive scene reconstruction, this study proposes a view construction framework integrating similarity metrics and temporal continuity constraints. By employing adaptive matching strategies, the algorithm significantly reduces redundant matching requirements, enabling robust sparse reconstruction with minimal matching effort and generating richer point clouds and more accurate models (as illustrated in the flowchart in Figure 6.  
Figure 5: Flowchart of the Enhanced RANSAC Matching Optimization Algorithm with Automatic Threshold Adjustment.
## 3 Materials and Methods
### Remote Sensing (RS)  
One of the efficient and simplest methods of RS is band ratio, in order to enhance the spectral features, and it can be used in geological mapping and mineral exploration [36]. Certain features can be highlighted on the basis of the spectral absorption and reflectance characteristics of rocks and minerals, generating a specific ratio for mineral and rock detection.  
In the Vigonzano area, in situ samples are available (Figure 3). As is shown in Figure 3, the sample distribution is directional, with more density in the central part of the mining wastes. In addition, the Sentinel-2 image (sending date: 8 October 2020) was used. The Sentinel-2 mission orbit is sun-synchronous with 98.62\({}^{\circ}\) inclination, and the mean local solar time (MLST) at the descending node is 10:30 (a.m.). The multispectral instrument (MIS) on board Sentinel-2 ensures large swath high geometrical and spectral performance of the measurements. The MSI measures the Earth's reflected radiance in 13 spectral bands from VNIR to SWIR [37]. In the pre-processing step, all the Sentinel-2 bands were stacked and resampled at the 10 \(\times\) 10 m spatial resolution using the nearest neighbor method. The time variable is not the target in this study, and it is considered constant. Then, several band ratios, defined for Sentinel-2 images, were calculated for data analysis (on the basis of the iron presence) such as iron oxides, ferrous iron oxides, ferric iron, and ferric oxides [17]. The function of the chosen band ratios is to detect any iron bearing features on the surface. On the basis of the iron contents and its correlation with other selected elements, one can use the most representative band ratio (with higher correlation coefficient). The band ratios are selected on the basis of the previous studies for iron mineral detections [17]. Because of the high density of vegetation in the area, the band ratios were used indirectly with the in situ samples.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 2 Background
### A Primer on the GNSS
However, the F-1 satellite that services Australasia has had several outages since its launch in 2005, including in March 2023 when two-thirds of the Australian winter wheat seeding program was reported to be impacted [16]. Precise point positioning (PPP) uses the 'direct observables' of dual frequencies broadcast by satellites (L1 and L2 in the case of GPS) to estimate ionospheric change and 'ephemerides' data, which are precise estimates of satellite orbits estimated using data from a global network of ground stations. Post-processing solutions have been available to implement corrections based on such estimates, but recently improved ephemerides data have become available with low latency over the internet. For example, an open-source toolkit ('Ginan') has recently been released for the creation of precise point positioning (PPP) positions and analysis products in Australia [17]. This free resource can be used in local applications with 4 or 5G service or an internet connection to deliver position correction data, providing positioning accuracy to 3 to 5 cm across Australia [17]. It is anticipated that in the future, consumer-level devices will access this service to deliver a highly accurate positioning capability. However, PPP requires more processing power than conventional methods and connectivity for access to an ephemeris correction stream, and it can take longer (minutes to hour) to converge to full accuracy. The recent development of the Starlink communication satellite constellation (Starlink, Redmond, Washington, USA) is dramatically changing connectivity, offering (Australian) continent-wide coverage in a reliable, low-cost solution. This resource currently consists of 3580, of a planned 12,000, low-orbit satellites. This capacity can underpin the delivery of SBAS, RTK, or PPP services.
# Feasibility of LED-Assisted CMOS Camera: Contrast Estimation for Laser Tattoo Treatment
## 3 Results
The overall contrast values from the red tatto0 were significantly lower than those from the black tatto0 (Figure 3b). Unlike the black tatto case, the contrast in the red tatto0 was dependent on the LED colors. In the case of the red LED, the contrast values between the pre and the post treatment were almost comparable (i.e., Contrastpre = 0.07 \(\pm\) 0.02 vs. Contrastpost = 0.06 \(\pm\) 0.02; \(p\) = 0.42). Meanwhile, under the green and the blue LEDs, Contrastpost became 30% and 26% lower than Contrastpre, respectively (i.e., Contrastpre = 0.55 \(\pm\) 0.02 vs. Contrastpost = 0.39 \(\pm\) 0.02 for green; \(p\) = 0.001 and Contrastpre = 0.45 \(\pm\) 0.03 vs. Contrastpost = 0.33 \(\pm\) 0.02 for blue; \(p\) = 0.007). Based upon the contrast estimation in Figures 3 and 4, the relative contrast reduction in tatto after the laser treatment was quantified for both the black and red tatto0 (Table 2). In the case of the black tatto0, the contrast reduction was almost invariant for all the LEDs, which ranged from 71.5% to 72.8% (\(p\) = 0.74-0.78). However, the red tatto0 showed no contrast reduction under the red LED but both the green and the blue LEDs yielded a comparable contrast decrease after the treatment (23.2-28.7%; \(p\) = 0.33-0.70). Figure 4: Comparison of red tatto0 from pre and post treatment under three LED colors: (**a**) intensity value and (**b**) contrast (N = 10; * \(p\) < 0.01). Figure 3: Comparison of black tatto from pre and post treatment under three LED colors: (**a**) intensity value and (**b**) contrast (N = 10; * \(p\) < 0.001). Figure 5 compares the cross-sectional distribution of the tattoos in mouse skin: untreated (upper) and treated (below) tattoo areas. Both the treated black and red tattoos became fainter, in comparison with the non-treated tattoos. According to Figure 5a, the area distribution of the black tattoo significantly decreased after the laser treatment. Meanwhile, the red tattoo was partially removed from the surface (epidermal layer). Both the tattoos still remained at the deeper position (\(-400\)\(\upmu\)m) of the skin. Figure 5b quantifies the degree of relative reduction in the tattoo areas after the treatment. The treated black tattoo area was reduced by 89%, compared with the untreated area (\(p<0.01\)). Meanwhile, the treated red tattoo area was removed by 27% more than the untreated red tattoo area (\(p<0.01\)), which was three-fold less than the reduction in the black tattoo area (\(p<0.001\)).
# Artificial Flora (AF) Optimization Algorithm
## 2 Artificial Flora Algorithm
### The Proposed Algorithm Flow and Complexity Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Functions** & **Algorithm** & **Best** & **Mean** & **Worst** & **SD** & **Runtime** \\ \hline Sphere & PSO & 13,513,53237 & 29,913,32912 & 55,187,50413 & 9279,053897 & 0.515428 \\  & ABC & \(6.73535\times 10^{-8}\) & \(3.56859\times 10^{-7}\) & \(1.17148\times 10^{-6}\) & \(2.37263\times 10^{-7}\) & 3.548339 \\  & AF & 0 & \(4.22551\times 10^{-32}\) & \(2.11276\times 10^{-30}\) & \(2.95786\times 10^{-31}\) & 3.476036 \\ Rosenbrock & PSO & \(9,137,632,795\) & \(53,765,803.92\) & \(313,258,238.9\) & \(49,387,420.59\) & \(0.707141\) \\  & ABC & \(0.409359085\) & \(13.87990385\) & \(49,83380808\) & \(9.973291581\) & \(4.094523\) \\  & AF & \(47\,95457\) & \(48.50293\) & \(48,87977\) & \(0.246109\) & \(6.674920\) \\ Rastrigin & PSO & \(500,5355119\) & \(671,5528998\) & \(892.8727757\) & \(98.8516628\) & \(1.036802\) \\  & ABC & \(0.995796171\) & \(3.850881679\) & \(7.36921061\) & \(1.539235109\) & \(3.661335\) \\  & AF & 0 & \(0\) & \(0\) & \(0\) & \(3.753900\) \\ Schwefel & PSO & \(-1.6819\times 10^{127}\) & \(-5.9384\times 10^{125}\) & \(-25.216\times 10^{5}\) & \(2.7148\times 10^{126}\) & \(2.672258\) \\  & ABC & \(-20,111.1655\) & \(-19,720,51324\) & \(-19,318,44458\) & \(183.7240198\) & \(3.433517\) \\  & AF & \(-20,680.01223\) & \(-23,796.53666\) & \(-93,734.38905\) & \(16,356.3483\) & \(4.053411\) \\ Griewank & PSO & \(118.8833865\) & \(283.810608\) & \(524.5110849\) & \(101.3692096\) & \(1.609482\) \\  & ABC & \(1.63\times 10^{-6}\) & \(1.29\times 10^{-3}\) & \(3.30\times 10^{-2}\) & \(0.005265446\) & \(6.573567\) \\  & AF & 0 & \(0\) & \(0\) & \(0\) & \(3.564724\) \\ Ackley & PSO & \(20.169350\) & \(20.452818\) & \(21.151007\) & \(0.2030427\) & \(1.499010\) \\  & ABC & \(0.003030661\) & \(0.009076649\) & \(0.033812712\) & \(0.005609339\) & \(4.315255\) \\  & AF & \(8.88\times 10^{-16}\) & \(8.88\times 10^{-16}\) & \(8.88\times 10^{-16}\) & \(0\) & \(5.463588\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of statistical results obtained by AF, PSO, and ABC in 50-dimensional space.
## 2 Network Aspects and Survivability
### Network Survivability  
Due to the massive volumes of data transported by optical networks, ensuring network survivability is paramount. To achieve this, these networks must implement mechanisms to recover traffic in the event of failures. Such failures can occur at either the node or link level. The former are primarily caused by equipment failures, particularly transponders (see [1] for an explanation of their role), while the latter are mainly due to fiber cuts. To guarantee resilience against failures in the working transponder (i.e., the one used to support service traffic), redundant transponders can be used. These transponders can be provided in either a dedicated or shared manner [14]. In the dedicated solution, each working transponder is duplicated, while in the shared solution, a single redundant transponder can protect multiple working transponders.  
To protect against link failures, additional backup resources need to be provided. Similarly, these resources can also be dedicated or shared. In the first case, each working channel has a fully reserved backup channel, while in the second, multiple working channels share the same backup resources. Furthermore, dedicated protection can be applied at the path, or link level. In the first case, the entire end-to-end path (or connection) between the source and destination nodes is protected, while in the second case, only individual links in the path are considered. A requirement of dedicated path protection is that the working and the backup path must be node- and link-disjoint. Although shared protection schemes are more efficient in terms of resource utilization, they are not advisable in the context of backbone segments, as they involve complex coordination and may not guarantee the stringent recovery time requirements expected in these segments.  
A commonly used path-dedicated protection technique operating in optical domain is designated as \(1+1\) OCh protection [13]. In this scheme, the source node transmits identical signals over two physically disjoint optical channels (working and backup), and the destination node receives both signals, selecting the one with the highest quality. This scheme is operationally simple and ensures fast protection switching because the decision is local and does not require signaling between nodes. However, since the working and backup paths use the same wavelength, this increases the complexity of wavelength assignment strategies and overall wavelength consumption. The determination of the additional wavelength consumption inherent in this scheme will be a central topic of this work.
## 5 Discussion
### Room Segmentation Evaluation  
We compare the experimental results with the relevant segmentation methods using the same dataset, and the results are shown in Table 3. The method of [PERSON] [18] et al. assigns different labeling information to each individual segmented region after segmenting the ceiling projection by an extended wall line, however, some regions may be assigned wrong labels due to the local ceiling missing. The morphology-based segmentation method is the more classic segmentation scheme among room segmentation methods, although it cannot clearly delineate the boundaries of rooms. The method proposed by [PERSON] [38] et al. can better divide the indoor space, but for the connecting transition space between rooms, which are directly classified as indoor space or eliminated. Compared with the above three segmentation schemes, the segmentation method in this paper can effectively segment the indoor space and has the following advantages: (i) our room segmentation method can effectively avoid the segmentation error caused by the missing local ceiling; (ii) The segmentation accuracy of each room boundary is ensured; (iii) The connecting transition space between rooms can be well extracted.  
Figure 15: Visualization of unsigned distance deviations on the left and histogram of deviation statistics for the data on the right. (**a**) represents the accuracy evaluation results of TUBI. (**b**) represents the accuracy evaluation results of TUB2. (**c**) represents the accuracy evaluation results of UOM.  
Figure 16: Visualization of unsigned distance deviations on the left and histogram of deviation statistics for the data on the right. (**a**) represents the accuracy evaluation results of Synth. (**b**) represents the accuracy evaluation results of Room. (**c**) represents the accuracy evaluation results of Office1. (**d**) represents the accuracy evaluation results of Office2.
## 5 Conclusions and Future Work  
The following conclusions can be drawn through simulation verification and experimental testing:  
Figure 5: Peak search results of XRD pattern of lithium in coal.  
* Adjusting parameters cannot correct the misjudgment problem generated by SZAC in specific scenarios. By introducing a comparison method, ISZAC has better accuracy of peak identification.
* For different types of spectral signals, SZAC needs to adjust the parameters to achieve the optimal recognition effect, otherwise there will be a phenomenon of missing judgment. ISZAC uses a group of conversion functions to give full play to the advantages of wide and narrow window functions to suppress noise and identify overlapping peaks, which enhances the applicability of the method.
* The experimental results of ISZAC basically meet the requirements of phase matching, which has a practical application prospect.  
The effect of the SZAC peak search is good, but its two limitations lead to the limited use of the method. Based on the symmetric zero-area conversion, ISZAC introduces a comparison method and data fusion, which improves the accuracy and applicability, and realizes the peak searching of different spectral data under the same set of parameters. In addition, the free combination of conversion functions has ISZAC has good expansibility. The form of the conversion function group can be modified to identify specific samples, so as to obtain higher recognition efficiency. It should be pointed out that the identification results of ISZAC may contain false peaks that meet the peak characteristics. How to adjust the fusion strategy or optimize the method combined with the peak area needs to be further explored in this field.  
Conceptualization, X.J. and Y.L.; methodology, X.J., Y.L. and T.X.; software, X.J., Y.L. and T.X.; validation, X.J. and Y.L.; formal analysis, X.J. and T.X.; investigation, X.J., Y.L. and T.X.; resources, T.X.; data curation, T.X.; writing--original draft preparation, X.J.; writing--review and editing, X.J., Y.L. and T.X.; visualization, Y.L. and T.X.; supervision, X.J.; project administration, Y.L. and T.X.; funding acquisition, T.X. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Adaptive Hierarchical Sliding Mode Control for Double-Pendulum Gantry Crane Based on Neural Network
## 3 Controller Design
### Design of a Hierarchical Sliding Mode Controller Based on RBF
\begin{split}& u=u_{eq}+u_{sw}+u_{pd}+u_{a}\\ &=M\ddot{x}_{d}-f(z)-\eta sat(s)-k_{p}e-k_{d}\dot{e}-k_{a}(\theta _{1}+\theta_{2})\end{split}  \tag{23}\]  
According to the universal approximation theorem, the RBF neural network can approximate continuous functions with arbitrary precision, including the input layer, hidden layer, and output layer. Let the system's unknown dynamic function be \(f(z)\). For any continuous function \(f(z)\in U\), where U is a compact set in \(R_{n}\), for the control law designed in this section, the RBF neural network input vector is Z, and the input layer is a six-dimensional input vector:  
\[z= \begin{bmatrix}e&\dot{e}&\theta_{1}&\dot{\theta}_{1}&\theta_{2}&\dot{ \theta}_{2}\end{bmatrix} ^{T} \tag{24}\]  
The hidden layer uses 49 Gaussian basis function nodes, and each Gaussian basis function node is represented as  
\[\phi_{j}(z)=\exp(\frac{-\left\|Z-C_{j}\right\|^{2}}{2\sigma^{2}}),j=1,2,...,n \tag{25}\]  
where \(\sigma_{i}\) is the width parameter, and \(\left\|\cdot\right\|\) represents the Euclidean norm. The center of the j-th basis function is  
\[c_{j}=[c_{j1},c_{j2},...,c_{j6}] \tag{26}\]  
Let the RBF neural network input vector be \(f(z)\), and there exist ideal weights \(w^{*}\) for the RBF neural network, such that  
\[f(z)={w^{*}}^{T}h(z)+\varepsilon \tag{27}\]  
The error approximation satisfies \(|\varepsilon(z)|\leq\varepsilon\):  
\[\hat{f}(z)=\hat{w}^{T}\varphi(z) \tag{28}\]  
where \(\hat{w}\) is the estimate of \(w^{*}\). Define the weight estimation error:  
\[\widetilde{w}=\hat{w}-w^{*} \tag{29}\]  
Then, the error approximation can be expressed as  
\[ \begin{array}{l}f(z)-\hat{f}(z)={w^{*}}^{T}\varphi(z)+\varepsilon-w^{T}\varphi(z) \\ =-\widetilde{w}^{T}\varphi(z)+\varepsilon\end{array}  \tag{30}\]  
where \(\varepsilon\) is the approximation error. The differential equation for weight estimation is  
\[\dot{\hat{w}}=\Gamma[\varphi(z)s+\alpha_{1}\varphi(z)(\theta_{1}+\theta_{2})+ \alpha_{2}\varphi(z)(\hat{\theta}_{1}+\hat{\theta}_{2}) \tag{31}\]  
where s is the composite sliding mode surface, and \(\alpha_{1}\) and \(\alpha_{2}\) are positive design parameters, \(\alpha_{1}=0.1,\alpha_{2}=0.2\).
## 5 Experimental Evaluation
### Evaluation of the Autonomic Estimation Algorithm  
The _Autonomic Estimation Algorithm,_ aiming to estimate two network parameters, is activated right after the sensor nodes become operational. In each node, the algorithm converges if the estimated parameters' values between two consecutive cycles is less than 5%.  
Figure 9(a) shows the number of messages that are exchanged until the algorithm converges to the estimation of the density and the size parameter for various sizes and densities. Figure 9(b) shows how these messages are distributed between nodes. There is a linear relationship between the total number of messages exchanged and the network size. It derives that the algorithm convergences without imposing significant overhead since the average number of messages per node remains small and stable even for large-scale networks. The autonomic estimation process may be repeated periodically in predefined number of cycles, related with the dynamicity that is present in the WSN.  
Figure 10 presents the cycles that are necessary for the algorithm to converge. Stable behaviour is achieved for each network density independently from the network size. The algorithm converges in less than 20 cycles in sparse networks and in less than 10 cycles in dense networks, independently of the network size. In dense networks, convergence is faster since more messages are exchanged at each cycle.  
The _Autonomic Estimation Algorithm_ achieves adequate precision for the estimation of the network parameters, as presented in Figure 11(a) for the density estimation and Figure 11(b) for the size estimation. For the density estimation, the deviation from the real values is less than 9% in all cases  
Figure 10: Cycles for convergence of the estimated parameters.  
Figure 9: (a) Messages for Network Density and Size Estimation and (b) Messages for Network Density and Size Estimation per node.  
and remains approximately constant for a given network density. For the size estimation, the deviation is less than 20% for sparse networks and less than 10% in dense networks. In both cases, higher precision is noticed in dense networks since averaging is performed between multiple neighbours in each cycle.