esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 3 Results
### Experiment on rEl-World Image Sequence  
To demonstrate the application of the proposed uncertainty analysis in line reconstruction of real-world scenarios, we also conducted experiments on the Merton I dataset (data available www.robots.ox.ac.uk/~vgg/data/). The Merton I dataset is a real-world dataset which contains three images of an Oxford University building and provides line segment matching across views. The proposed uncertainty analysis is used to detect those poorly localized lines with small reprojection errors. Firstly, the camera poses and sparse point reconstruction is computed by COLMAP [45; 46], a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. Secondly, camera covariance matrixes are estimated by the method proposed in [30]. Then, a 3D line map is generated and the associated uncertainty is estimated by the proposed uncertainty fusion method. The camera poses with uncertainty and line tracks across images are used as input. Finally, the line map is culled. In map culling, we present results with and without uncertainty based rejection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Outlier Rejection Method** & **Mean Directional Error (deg)** & **Mean Distance Error (m)** & **Good Line Ratio** \\ \hline Reprojection error & 21.73 & 9.05 & 57.69\% \\ Reprojection error + Uncertainty & 0.76 & 0.01 & 93.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: 3D line map quality after outlier rejection with different methods.  
Figure 10: Outlier rejection with and without uncertainty. The first row shows reconstructed lines only with reprojection error based outlier rejection: (**a**) reconstructed line segments on the reference frame; (**b**) front view of reconstructed 3D lines. (**c**) Side view of reconstructed 3D lines. The second row shows reconstructed lines that pass reprojection error and uncertainty tests: (**d**) reconstructed line segments on the reference frame; (**e**) front view of reconstructed 3D lines. (**f**) Side view of reconstructed 3D lines.
## 5 Discussion  
Our approach involved continuously monitoring ice melt using different analytical techniques to provide specific visual, thermal, and chemical information. The collected information is consistent and allows us to quantify the effectiveness and kinetics of applied RDP, data that are valuable for product developers. Our tests also provide information on the concentration of RDP brine, which, if too diluted, depending on the temperature, may cause the system to freeze again, posing a skidding risk to aircraft. The use of a portable Raman spectrometer would help with taking data directly from the runway to be more effective in RDP analysis with a lot of parameters, such as wind.  
The integration of advanced technological tools such as thermal cameras, Raman spectrometry, and visual cameras has great potential for enhancing the efficiency and safety of winter runway operations. When combined with artificial intelligence (AI) programs, these tools significantly improve decision-making processes [38]. By applying data-mining techniques to analyze data from thermal cameras, Raman spectrometry, and visual cameras, a comprehensive understanding of RDP behavior in dynamic environments can be achieved. Neural networks can process these data along with inputs such as air and ground temperatures, temperature variations, and precipitation intensity (e.g., freezing rain or snow). Operators can then make well-informed decisions regarding the optimal timing of RDP replacement. Moreover, AI-driven systems can forecast runway conditions and slippage probabilities [39], offering valuable support to runway operations teams. This sophisticated approach represents a notable advancement in runway management, particularly considering the rapid changes in weather conditions that require prompt and accurate decision-making [40]. By leveraging AI and integrating data-mining techniques, runway operations can be carried out with greater precision, efficiency, and safety to ensure smooth air traffic flow, even in challenging winter environments.
# P4-KBR: A Key-Based Routing System for P4-Programmable Networks
## 7 Conclusions  
In this paper, we propose, implement and evaluate an in-network key-based routing protocol called P4-KBR. This non-IP-based routing protocol extends SDN networks to the non-host centric paradigm, implementing all the forwarding tasks at network level without the need of any additional infrastructure.  
An efficient implementation of this solution, in terms of requirements of device resources, was possible thanks to the potential and flexibility offered by data plane programmability. P4 and P4 Runtime opened the data plane programmability, paving the way for new protocols, solutions and applications without restrictions linked to standardized interfaces.  
The proposal was implemented and evaluated using Mininet, an ONOS controller and a network composed of bmv2 P4 switches. In addition, this work includes a detailed review and description of the integration process of the ONOS controller and a P4-based networking solution.  
Conceptualization, [PERSON], [PERSON] and [PERSON]; methodology, [PERSON], [PERSON] and [PERSON]; software, [PERSON]-L.; validation, [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON] and [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Spanish State Research Agency (AEI), under project grant AriSe2: FINE, (Ref. PID2020-116329 GB-C22 / AEI / 10.13039/501100011033).  
Figure 12: OFC-KBR versus P4-KBR. Occupancy of the device tables: number of entries in the _key_routing_ and _mpls_forward_ tables.  
Data Availability Statement: All data included in this study are available upon request by contact with the corresponding author.
## 4 Discussion
recommended to register as many different positions of the target chosen for calibrating the cameras as possible (minimum 15);
* a target with a lower manufacturing accuracy should be used for the calibration of the cameras only in case of necessity--however--it has to be taken into account that the measurement results will be obtained with a higher measurement uncertainty than in the case of targets manufactured with a guaranteed higher manufacturing accuracy. The determination of appropriate image brightness is another factor that can influence the results of strain/stress analyses performed by DIC. The performed measurements show that as long as the surface of the analysed specimen is illuminated uniformly and the lighting conditions are unchanged throughout the experimental period, there is a relatively wide range of image brightness (expressed by the mean grey value MGV from 56 up to 171) at which a nearly constant relative difference in the compared von Mises stresses is achieved for each loading level. Subsequent analysis has shown that the levels of these differences only slightly exceed the mean relative measurement errors evaluated on the analysed region of the specimen with the hole. On the contrary, images acquired with smaller or larger MGV cause significant differences in the obtained results or do not allow for evaluation of the realised measurement at all. The reasons can be found in that the darkest and brightest images thus obtained are either represented by a narrow range of intensity values (Figure 15a) or a significant representation of one of the intensities (Figure 15b). Figure 15c shows the intensity histogram obtained for images with MGV = 122 and significantly better intensity distribution of individual image points. The classification of image quality into five categories, i.e., unsuitable too dark images, underexposed images, suitable image brightness, overexposed images, and unsuitable too bright images, determined from the analysis of the differences in the evaluated equivalent stress fields, is shown in Table 5. \begin{table}
\begin{tabular}{c c c} \hline
**Image Quality** & **MGV** & **Consequence** \\ \hline unsuitable too dark images & less than 22 & measurement evaluation issues \\ \hline underexposed images & from 22 up to 55 & increase in deviation in the evaluated stresses \\ \hline suitable image brightness & from 56 up to 171 & deviation in the evaluated stresses approximately at the measurement error level \\ \hline overexposed images & from 172 up to 194 & increase in deviation in the evaluated stresses \\ \hline unsuitable too bright images & more than 194 & measurement evaluation issues \\ \hline \end{tabular}
\end{table} 
Table 5: Classification of images quality based on their mean grey value (MGV). Figure 15: The intensity histograms obtained for: (**a**) darkest image (MGV = 22), (**b**) brightest image (MGV = 194), (**c**) image with MGV = 122. Note: 0 corresponds to a black colour, 255 corresponds to a white colour.
## 3 Acoustic Simulation
### Boundary Conditions
#### 3.2.1 Comparison of Different Designs of Ultrasonic Cleaner  
Six different ultrasonic cleaner designs were simulated to find the optimum design. According to the acoustic simulation results, a design developing the best results (both in terms of pressure distribution and electrical energy consumption) was selected.  
In Figure 4, six different design models and pressure distribution results are shown. These results show positive-negative pressure change (or pressure-tension change) in the tank along with their intensity. From the simulations, the following conclusions were drawn for optimum design selection.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Boundary Condition** & **Selected at** & **Reason/Comment** \\ \hline Sound Hard Boundary & All of the boundaries except the top of the container (which is open), and the contact point of the container and the PZT Disc & To avoid any external interference of waves. \\ Plane Wave Radiation & Selected on top of the tank only. & To allow the waves to leave the system with a minimal angle of reflection. \\ Exterior Field Calculations & Selected on top of the tank only & Selected for scattered boundaries and waves. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary Conditions for Acoustic Simulation.  
Figure 4: Six Different design models with FEM Acoustic Simulation.  
1. Models c and e cannot be selected, as the intensity of pressure distribution values are very low;
2. In model d, the pressure is dense near the transducers and lower when away from transducers;
3. In models a and f, four transducers were used, which resulted in higher electrical energy consumption than in model b, which used only two transducers but produced the same pressure distribution and acoustic response;
4. Model b was selected and replicated for this research work.
5. For efficient cleaning, there is also a requirement of height distance from the transducer to the bottom tip of the cleaning object; therefore, a basket was used in an ultrasonic cleaner to provide the optimum height. By achieving this optimum height, better cleaning can be achieved.
## 3 Proposed Broadcast Event-Triggered Consensus Control Scheme  
The feedback system shown in Figure 3 has been designed using an optimization algorithm known as the simultaneous perturbation stochastic algorithm (SPSA). The local controller was designed by adopting a stochastic and deterministic element of SPSA,which will determine the linear dynamic agent's next state position \(x_{i}(t+1)\) at time \(t\). At every movement of the agent, a global controller will update the information of its state position to determine the scalar value based on the objective function calculation, which represents agent achievement. As long as the local controller receives the scalar value, it means that the agent has yet to reach the desired target. Therefore, the process will be continuous until consensus is achieved, when the termination criteria is satisfied, that is, when the agent reaches the rendezvous at the desired target point. The uniqueness of this hybrid consensus controller is that an event-triggered function (ETF) is embedded in the distributed-agent consensus controller. The ETF is specifically designed with a certain threshold value, which will limit the number of data transmissions and control updates. With event-triggered control (ETC), the value of gradient approximation of the SPSA is affected by the communication error, which has an effect on the agent's state position in the next iteration. Details of the design are explained in Section 3.1 for the global controller, in Section 3.2 for the local controller, in Section 3.3 for the event-triggered controller and in Section 3.4 for related theorems.
## 2 Related Work
### Optimal Resource Scheduling on General NOMA  
Effective resource scheduling is a critical aspect of NOMA systems, aimed at maximizing spectral efficiency and supporting massive connectivity. Unlike orthogonal multiple access (OMA), which assigns distinct frequency resources to each user, NOMA allows multiple users to share the same frequency band by differentiating power levels. This unique feature requires careful management of user pairing and power allocation to optimize system performance. An analytical performance comparison between NOMA and OMA is carried out by [PERSON] et al. [12], and validated the superiority of NOMA with respect to sum rate and power consumption. [PERSON] et al. [11] highlight NOMA as a superior alternative to traditional OMA techniques like OFDMA, offering higher spectral efficiency and support for more users through power-domain signal overlap and SIC. NOMA meets the key demands of 5G and beyond, such as higher data rates and lower latency, outperforming OMA in system throughput and user fairness via optimized power allocation and pairing strategies.
# A Survey of Machine Learning Methods for Time Series Prediction
## 3 Tree-Based Machine Learning Architectures
### Random Forests  
Random Forests (RF) is an ensemble learning method that constructs multiple decision trees and combines their outputs through averaging for regression tasks or majority voting for classification tasks. RF uses bootstrapping to train individual decision trees on a random subset of the data at each split. This randomization, coupled with its ensemble nature, enhances the robustness of RF compared to single decision trees, significantly reducing the risk of overfitting.  
Figure 1: Architectural differences between RF and GBDT, where attributes unique to RF are labeled in green and those specific to GBDT are labeled in blue. Solid arrows represent connections consistent across all architectures, and dashed arrows represent deviations for specific architectures.
# In-Field Quantum-Protected Control-Based Key Distribution with a Lossy Urban Fiber Link
## 2 Protocol Description
With that, the optimal strategy for both [PERSON] and [PERSON] to measure the transmitted bits is photon-number detection. However, because photon-number fluctuations scale as \(1/\sqrt{N}\) relative to the mean photon number \(N\), there is a fundamental limit to how well bits 0 and 1 can be distinguished. For [PERSON], this is more problematic, since the signals she intercepts are weaker than [PERSON]'s--which is verified by the protocol's loss control--and therefore exhibit larger relative fluctuations. This limitation, combined with the consequent postselection, acts as the quantum protection mechanism, guaranteeing users' information advantage over [PERSON]. In our experiment, Step (i) is repeated once every 500 cycles of the Transmission and Control stage (Steps 1-3). During these 500 cycles, any interceptable local losses are updated via transmitometry, which remains reliable over this interval (see the next section for details). Step (ii) is executed every 20 cycles, at which point the postselection parameters are adjusted to optimize the precision of bit identification. Postprocessing (Steps 4-5) can, in turn, be applied to the raw bit sequence after the end of the active Transmission & Control phase. The advantage distillation used in Step 5 effectively amplifies the correlation between [PERSON]'s and [PERSON]'s bit sequences at the price of discarding part of the bits. In this procedure the users split their raw bit strings into blocks of length \(M\). For each block \(a\), [PERSON] announces a pair of \(a\) and its inversion \(\bar{a}\). If the corresponding [PERSON]'s block matches with either-one, \(a\) now represents a \"distilled\" 0 bit, and \(\bar{a}\) \"distilled\" 1 bit. Otherwise, the corresponding block is discarded. As a result, the effective BER becomes \(\bar{p}_{\text{err}}=p_{\text{err}}^{M}/(p_{\text{err}}^{M}+(1-p_{\text{err}}) ^{M})\), where \(p_{\text{err}}\) is the BER before the distillation. We note that the protocol's security relies on additional assumptions regarding the used equipment, namely that eavesdropper is not able to substitute the equipment at the sender and receiver sides. This makes the protocol device-dependent but allows us to overcome the PLOB bound. This approach is physically justified not less then the traditional device-dependent scenario. Besides, we do not make any compromises in security while enhancing the system performance along with the correct channel work ensured by the tomography methods. For more details on these assumptions and a comprehensive security analysis, we refer the reader to Ref. [19].
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Analysis of Fiber Bragg Gratings
#### 2.2.2 Multilayer-Based Analysis Method  
The algorithm of multi-layer (ML) represents an accurate solution to analyze any arbitrary FBG. ML-based algorithm also leverages the transfer-matrix method [31; 33]. In this case, ML-TMM sections the whole grating into layers whose length \(\delta l_{i}\) is sufficiently short so that the refractive index variation within the layer can be regarded as constant. Typically, \(\delta l_{i}\ll\Lambda_{\min}\), with \(\Lambda_{\min}\) being the shorter period in the grating [29; 30] (see Figure 3).  
The resulting arrangement can be seen as a multi-layer structure that alternates two elements, namely, a dielectric medium of constant refractive index and the interface between two dielectric media of different refractive indexes. Considering the propagation of a plane wave, the transfer matrix that characterizes a lossless medium with constant refractive index \(n_{i}\) and length \(\delta l_{i}\) is  
\[[M_{M,i}]=\left[\begin{array}{cc}\exp(jk_{0}n_{i}\cdot\delta l_{i})&0\\ 0&\exp(-jk_{0}n_{i}\cdot\delta l_{i})\end{array}\right] \tag{18}\]  
where \(k_{0}=2\pi f/c\) is the wavenumber in a vacuum.  
Assuming normal incidence on an interface between two dielectric media of different refractive indexes \(n_{i}\) and \(n_{i+1}\), the transfer matrix of this interface is [29; 30]:  
\[[M_{I,i}]=\frac{1}{2n_{i}}\left[\begin{array}{cc}n_{i}+n_{i+1}&n_{i}-n_{i+1}\\ n_{i}-n_{i+1}&n_{i}+n_{i+1}\end{array}\right] \tag{19}\]  
Hence, the transfer matrix of the whole grating \(M_{FBG}\) can be obtained from the multiplication of the 2 \(\times\) 2-transfer matrices characterizing the sequence of those simpler elements, as  
\[[M_{FBG}]=[M_{I,1}]\cdot[M_{M,1}]\cdot\ldots\cdot[M_{I,i}]\cdot[M_{M,i}]\cdot \ldots\cdot[M_{I,N}]\cdot[M_{M,N}]\cdot[M_{I,N+1}] \tag{20}\]  
The first and last transfer matrices characterize the interface between the unperturbed fiber core and the first/last layer of the grating. Finally, the grating's reflection and transmission spectral responses \(H_{R}(f)\) and \(H_{T}(f)\) are readily obtained from the elements of \(M_{FBG}\) using the Equations (16) and (17). As previously stated, ML-TMM is a general algorithm useful for the characterization of any arbitrary grating profile, including a non-cosenoidal or even non-periodic refractive index perturbation. However, due to the required high sampling rate, the computation workload required in ML-TMM becomes extremely heavy for FBGs longer than a few cm.
# Investigating the Impact of Random Field Element Size on Soil Slope Reliability Analysis
## 4 Conclusions  
In this study, a reliability calculation program taking into account the spatial variability of soil parameters was presented. Based on the program, the impact of \(H/D_{e}\) on \(PF\) and \(F_{s}\) is analyzed. The following conclusions can be drawn:  
The \(H/D_{e}\) has significant influence on the distribution of \(F_{s}\). When \(H/De\) is less than 10, the distribution of \(F_{s}\) becomes more concentrated as \(H/D_{e}\) increases; when \(H/D_{e}\) is greater than 10, the distribution of \(F_{s}\) becomes very close as the grid size increases.  
The \(H/De\) has significant influence on \(PF\). The \(PF\) decreases with the increase in \(H/D_{e}\) and gradually tends to remain unchanged. When the value of \(H/D_{e}\) is small, it may lead to an overestimation of \(PF\). This phenomenon underscores the importance of selecting an appropriate value for \(H/D_{e}\) to ensure accurate assessment of slope stability and avoid misleading results.  
The results show that when the \(H/D_{e}\) exceeds 10 and 15 in Examples 1 and 2, respectively, \(PF\) remains essentially unchanged, which indicates that setting \(H/D_{e}\) to 20 can not only ensure the accuracy of calculations but also prevent the unnecessary consumption of computing resources.  
This study focuses on the influence of \(H/D_{e}\) on computational accuracy. In order to obtain relatively stable computation results, this study has adopted more conservative parameter settings for both the number of MCS and the iterations of PSO. Reducing the number of MCS while maintaining computational accuracy, as well as rapidly searching for the CSS, are two crucial research directions for optimizing the computational strategy of slope reliability, warranting further investigation.  
Conceptualization, J.S.; methodology, Y.W.; software, H.G.; validation, H.G. and B.S.; data curation, J.S.; writing--original draft preparation, J.S.; visualization, B.S.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by grants of the Key R&D Program Project of Ningxia (No. 2022 BEG03052), the Natural Science Foundation of Ningxia (2023 AAC03036), and Ningxia Teaching Reform Project (Reform and Practice of \"Photogrammetry and Remote Sensing\" Course Teaching Based on School-Enterprise Cooperation).  
Not applicable.  
Figure 9: Effect of the \(H/D_{e}\) ratio on the relative error of probability of failure \(PF\) in Example 2.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.