esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Acoustic Mapping in Automotive Wind Tunnel: Algorithm and Problem Analysis on Simulated Data
## 2 Volumetric Acoustic Inverse Problem Formulation and Data Preprocessing  
### Acoustic Direct and Inverse Problems  
In frequency domain, the discrete acoustic direct problem can be described, for each frequency, by the following linear relationship:  
\[\mathbf{G}\mathbf{q}=\mathbf{p}\,, \tag{1}\]  
where \(\mathbf{q}\) is the vector of complex source strengths of \(S\) elementary sources in assumed positions, \(\mathbf{p}\) is the vector containing the complex pressures measured at \(M\) receiver locations, and the complex matrix \(\mathbf{G}\) represents the acoustic propagation matrix. The direct acoustic problem identifies the calculation of \(\mathbf{p}\) for given \(\mathbf{q}\) and \(\mathbf{G}\). This is a well-determined problem having a unique solution. Conversely, the calculation of \(\mathbf{q}\) for given \(\mathbf{G}\) and \(\mathbf{p}\) is the inverse acoustic problem, which results to be ill-posed in Hadamard sense, i.e., existence, uniqueness, and stability of solution are not guaranteed [19]. For this reason, the estimated source coefficients \(\mathbf{\hat{q}}(\mathbf{T})\) depend on the assumptions and a priori information about the source distribution. A detailed review about different inverse operators is provided by [PERSON] et al. [20; 21]. In the application of inverse problems for volumetric acoustic map reconstruction, three critical issues emerge: (i) the influence of potential sources located at very different distances from the array center, (ii) limited spatial resolution in the radial direction from the array center, and (iii) increase in the number of potential sources notwithstanding that a few of them significantly contribute to the acoustic field. These issues must be taken into account and must be properly considered in conjunction with all other challenges involved in acoustic mapping techniques. Note that these may be present in any acoustic imaging context, but they are severely enhanced in volumetric applications.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
### Contributions  
In this paper, to address the challenges mentioned above in multicast QoS routing, we propose a distributed delay-constrained multicast routing construction approach based on ICN. Our routing scheme is applicable to networks with a static topology. The primary contributions of this paper are as follows:  
To construct delay-constrained multicast trees, we propose a QoS multicast routing architecture based on ICN, consisting of three essential components: delay-constrained tree construction algorithm, neighborhood state awareness mechanism, and tree node selection mechanism. Leveraging the ICN system based on name-based lookup, our research employs globally unique names to identify multicast services, and the NRS maintains the mapping between multicast service names and Network Addresses (NA) of multicast tree nodes. The receiving node interacts with NRS to obtain tree nodes and select the optimal node to send joining requests. Join messages collect path state information during propagation to the selected tree node. Routers construct paths from the multicast tree to receivers hop-by-hop based on routing tables, neighborhood state information, and path state information. To circumvent acquiring accurate information regarding the status of the complete network, we design a path construction algorithm that makes decisions based on precise path state information and imprecise routing information. We have designed a tree node registration and resolution mechanism based on NRS. This allows receivers to access a viable set of nodes within the multicast tree through the NRS to select the optimal tree node for joining requests. Additionally, we have introduced a cost-effective neighborhood state awareness mechanism for network nodes to acquire relatively precise neighborhood state information. We have developed and implemented the proposed multicast routing method in NS-3 [30] and compared it with existing methods. Experimental results show that our approach has a significantly higher success ratio than other algorithms and has stable and low message-processing overhead.  
The remaining structure of this research is as follows. Section 2 provides a summary of related work. We present the details of the proposed approach in Section 3. In Section 4, we conduct comparative experiments with some other methods to assess the effectiveness of the proposed method. Section 5 concludes and discusses future directions for this work.
## 5 Numerical Analysis
### Probability Distribution and Received Probability  
Figure 3 shows the transformation of received probability of the OAM signal and crosstalk modes carried by POVs with wavelength \(\lambda=470\) nm (\(n_{I}=0.4487\times 10^{-9}\)) at transmission \(z=150\) m in seawater. In Figure 3a the diameter of receiver is 0.02 m, while in Figure 3b the diameter of is 0.005 m. By comparing the bar charts in Figure 3a,b, it can be found that (1) the optical communication link adopting the receiver of small aperture will cause serious loss of OAM signal owning high order topological charge, and it will cause a large loss of all OAM signals; (2) the receiving probability of the OAM signal is independent of topological charge when the communication link adopts a larger aperture to receive the OAM signal. This result is very beneficial to the mode division multiplexing of communication link and is the purpose of designing the POV. The curves in Figure 3a show that even though the channel has reached its experimental length 150 m for \(D=0.02\) m, OAM crosstalk produced by seawater turbulence is still very small. This result indicates that POVs transmitted in turbulent absorbing seawater still have the characteristics of their  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Symbol** & **Parameters** & **Value** \\ \hline Pr\({}_{T}\) & Prandtl number of the temperature & 0.72 \\ Pr\({}_{S}\) & Prandtl number of the salinity & 700 \\ \(Q\) & Non-dimensional constant & 2.5 \\ \(B\) & Bandwidth & 0.05 GHZ \\ \(C_{RX}^{2}\) & Turbulence strength & \(10^{-13}\) K\({}^{2}\)/m\({}^{2/3}\) \\ \(\varepsilon\) & Rate of dissipation of kinetic energy per unit mass of fluid & \(10^{-3}\) m\({}^{2}\)/s\({}^{3}\) \\ \(\chi_{T}\) & Dissipation rate of the mean-squared temperature & \(10^{-7}\) K\({}^{2}\)/s \\ \(\overline{\omega}\) & Ratio of temperature and salinity & \(-4.5\) \\ \(k_{0}\) & Wave number & \(2\pi/\lambda\) \\ \(w_{eff}\) & Effective radius & \(\left(\frac{2}{\overline{w}^{(2)}(z)}+\frac{2}{\overline{\rho}_{z}^{2}}+\frac{k _{0}n_{I}z}{R(z)}\right)^{-1/2}\) \\ \(w(z)\) & Half beam-width & \(\sqrt{1+z^{2}/z_{oR}^{2}}\) \\ \(r_{0}\) & Ring radius & 0.03 m \\ \(\eta\) & Inner scale & 0.01 m \\ \(L_{0}\) & Outer scale & 10 m \\ \(m_{0}\) & OAM topological charge & 1 \\ \(D\) & Receiver diameter & 0.05 m \\ \(\lambda\) & Wavelength & 470 nm \\ \(n_{I}\) & Imaginary part of the \(n_{oc}\) & \(0.4487\times 10^{-9}\) \\ \(z\) & Propagation distance & 150 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major system parameters.  
topological charges transmitted in free space [15; 16], that is, intensity is independent of their topological charges.
## 4 Conclusions  
The benefits of optical wireless communication systems make this technology very promising for novel wireless communications. However, some limitations have been defined by the light losses of the free space propagation determined by air compounds (absorption), weather conditions (scattering), or air turbulence (scintillation). Nowadays, there are several available systems operated in the near-infrared spectrum. However, compared to the commercially 1.55 \(\upmu\)m-data link, the described analysis determinates a \"better transparency\" of 4.0 \(\upmu\)m optical link for different weather conditions. However, it was also shown that higher laser power could provide a more extended data link but is not so practical for compensating the scintillation effects at short distances.  
The practical study of interband cascade laser application in optical data transmission is different from the previous approaches because it is the first link setup in which an \"on-shelf\" interband cascade laser operated at room temperature was tested. For return-to-zero format coding, the influence of the \"data frame\" shape on pulse fluctuations and amplitudes was observed. It is crucial considering the dynamic response of the receiving units. The maximum modulation rates were 155 MHz and 70 MHz for return-to-zero and non-return-to-zero format coding and defined a practical validation of the interband cascade laser-based technology level. The calculated data link range of the described setup for low-level scintillations and low visibility was about 2 km. These results indicate opportunities for future optical wireless communications development.  
Conceptualization, J.M. and M.M.; methodology, J.M.; software, J.M.; validation, J.M., R.W. and M.M.; formal analysis, J.M.; investigation, J.M.; resources, J.M. and R.W.; writing--original draft preparation, J.M.; writing--review and editing, M.M.; visualization, M.M.; supervision, J.M.; project administration, J.M.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Research carried out in the Institute of Optoelectronics MUT laboratory and supported by Narodowe Centrum Badan i Rozwoju, grant No. MAZOWSEZ/0196/19-00. We would like to acknowledge a fruitful consultation with [PERSON] from the Wroclaw University of Science and Technology.  
The authors declare no conflict of interest.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 6 Results and Discussion
### Generalization to Other Topologies and Reconfiguration Algorithms  
These findings highlight the effectiveness of ML and DL techniques in improving the reliability of SGs and offer valuable insights for the future development of intelligent grid management systems.  
It is important to clarify that our developed models are not intended as tailor-made solutions for a specific, fixed topology. Rather, they are optimized by employing randomized topologies that vary systematically in node numbers, connectivity degrees, and probabilistic topological models, applying the IEEE 123 Node Test Feeder parameters to achieve this diversity. While it is true that our model may not deliver peak performance for any particular topology, we emphasize that it achieves optimal average performance across a broad range of topologies, representing a trade-off approach that generalizes effectively across diverse network conditions.  
Accordingly, additional investigation with different reconfiguration algorithms, or with AI techniques specifically tailored for certain topologies, would be desirable in the long term to compare to what extent the customization of models would yield better results. In any case, this initial study serves as a foundation for future analysis and proves its ability to enhance the DEN2 DE algorithm, which was our main objective.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 7 Routing for Multi-hop WSNs with a Single Immobile Sink
### Solution Methods
#### 7.4.1 A partially Distributed Algorithm  
In which the Lagrangian for the objective function is obtained. The dual function is the minimum Lagrangian function where the minimizers are the primal variables of the problem \(q\) and all \(r_{ij}\). The primal decision variables \(q\) and \(r_{ij}\) appear in separate additive terms in the Lagrangian and hence the dual function can be evaluated separately in \(\mathbf{r}\) (the vector of flow variables) and \(q\).  
The primal objective function, \(q\), is modified to an equivalent strictly convex function \(q^{2}\) plus a strictly convex regularization term \(\epsilon\sum_{i\in V}\sum_{j\in N_{i}}r_{ij}^{2}\). Also a loose upper bound is imposed on the variable so that all decision variables havethe subgradient algorithm with guarantee that it converges to the solution of the strictly convexified primal problem. The dual function for the strictly convexified problem is still separable in the primal variables \(q\) and each of \(r_{ij}\). In each iteration \(k\) of the subgradient algorithm, \(q^{k}\) and \(r_{ij}^{k}\) are obtained by solving box constrained convex quadratic single variable optimization problems. The obtained values of the primal variables are used to calculate the values of the dual variables from the next iteration by the subgradient formulas.  
At a given iteration \(k\) in this algorithm, the values of the dual variables needed to solve for the flow variables in each iteration \(k\) are locally available to a node \(i\). This means that optimum rate decision variables can be obtained separately at each node without any communication overhead between the nodes. The calculation of \(q\) however, needs all the values dual variable values in iteration \(k\)\(,\lambda_{k}\), from all the nodes in the network to be transmitted to the node responsible for the computation of \(q^{k}\) in every iteration \(k\). The values of \(q^{k}\) need to be broadcasted to all the nodes as they are needed for the subgradient calculation.  
For the \(i\)th element of the subgradient to be computed at node \(i\) for iteration \(k\), it needs the rate variables \(r_{ji}^{k}\) for all its neighbors in the set \(N_{i}\) to be transmitted to it. Therefore, each node has to broadcast its \(r_{ij}^{k}\) values to its neighbors. Then, using those, the value of \(q^{k}\) received from a broadcast and the locally calculated \(r_{ij}^{k}\), the \(i\)th element of the subgradient gets calculated at node \(i\). The values of the new dual variables are calculated locally at each node.  
Since every node contributes in the computation of the primal variables, dual variables and the subgradient at each iteration, the algorithm is therefore like a distributed one. However, the algorithm is not fully distributed since at iteration \(k\), node \(i\) still needs other calculated variables from other nodes. Another algorithm was proposed in [2] that is fully distributed.
## 2 Materials and Methods
### Methodology  
#### 2.3.1 Drought Severity Index (DSI)  
The drought severity index (DSI) is calculated based on the MODIS data for the NDVI, ETa, and PET. It combines agricultural drought and meteorological drought [38], and has been shown to capture major global drought events [39; 40; 41]. Therefore, the DSI was used in this paper to monitor drought conditions in the MRYR during 2001-2022. The DSI was calculated as a standardized value with the following equation:  
\[A_{NDVI}=\frac{NDVI-\overline{NDVI}}{\delta_{NDVI}} \tag{1}\]  
\[A_{EVA}=\frac{(ETa/PET)-(\overline{ETa/PET})}{\delta_{(ETa/PET)}} \tag{2}\]  
\[A=A_{NDVI}+A_{EVA} \tag{3}\]  
\[DSI=\frac{A-\overline{A}}{\delta_{A}} \tag{4}\]  
where \(A_{NDVI}\) is the standardized anomaly for the NDVI, calculated from the mean (\(\overline{NDVI}\)) and standard deviation (\(\delta_{NDVI}\)) of the NDVI from 2001 to 2022. \(A_{EVA}\) is the standardized anomaly for the ratio of ETa to PET (_ETa/PET_). A is the sum of \(A_{NDVI}\) and \(A_{EVA}\). \(DSI\) is the standardized anomaly for A. The DSI drought categories are shown in Table 2[38].
## 2 Calculation Methods of Surrounding Rock Pressure  
### [PERSON]'s Theory  
In the [PERSON] theory [25], it is assumed that the rock mass is a loose medium, while containing certain cohesiveness. Tunnel excavation causes the surrounding soil to move down, and moving soil is subjected to resistance action on both sides. Overburdened pressure on the support structure originates in the moving soil, as shown in Figure 1.  
Figure 1: Calculation sketch based on [PERSON]’s theory.  
[PERSON] expressed that tunnel pressure is caused by the gravity of all upper strata of the tunnel and that the pressure is correlated with the gravity of the rock, the friction coefficient, and the cohesion of rock masses on both sides. The calculation formula for surrounding rock pressure is shown in Equation (1):  
\[q=\frac{\gamma q_{1}-c}{K_{0}tan\varphi}\bigg{(}1-e^{-\frac{K_{0}tan\varphi}{a_{ 1}}H}\bigg{)} \tag{1}\]  
\[a_{1}=\frac{B}{2}+h\ tan\Big{(}45^{\circ}-\frac{\varphi}{2}\Big{)} \tag{2}\]  
where \(q\) is the vertical uniform load acting on the supporting structure, \(\gamma\) is the surrounding rock density, \(H\) represents the depth of the tunnel, \(a_{1}\) is the half-span of the arch, \(B\) is the span of the tunnel, \(h\) denotes height of the tunnel, \(\varphi\) denotes the real internal friction angle of the rock mass, \(c\) is the cohesion of the rock mass, and \(K_{0}\) is the lateral pressure coefficient of the rock mass.  
When [PERSON]'s theory is used to calculate soil pressure, the soil pressure tends to be constant when the soil cover depth, \(H\), is not less than \(5a_{1}\) (\(H\geq 5a_{1}\)).
# Investigation of Load Environment and Bending Load Capacities of Aged Prestressed Concrete Sleepers
## 3 Evaluation of Bending Load Capacities of Aged PC Sleepers
### Test Results  
Figures 14 and 15 show the bending test results, including the flexural proof load and the flexural fracture load. The figures indicate that the crack generation load and the ultimate load decrease gradually with increases in age and passing tonnage. The crack generation load and the ultimate loads of all PC sleepers satisfied the JIS standard values of \(P_{\text{cr}}\) and \(P_{\text{u}}\). In addition, the right axes of these figures show the bending moments  
Figure 13: Outline of JIS E 1201 bending test.  
generated in the PC sleepers. As shown in Figure 7, the bending moments generated in the PC sleepers during actual train passage were quite small compared with the crack generation loads and ultimate loads during the bending tests. Furthermore, the bending moment obtained from the numerical analysis (Figure 11), except for the analysis results involving the rail surface roughness at the rail joints (Figure 12), was smaller than the crack generation load in the bending tests.  
The above results indicate that at present, even aged PC sleepers that are approximately 50 years old satisfy the standard \(P_{\mathrm{cr}}\) and \(P_{\mathrm{u}}\) values specified by the JIS, and safety is not immediately compromised. However, the bending load-bearing capacity decreases with age and an increase in passing tonnage. Sudden, continuous, non-satisfying bending load-bearing performance should be avoided at any given time. In the future, a quantitative service life should be established, and a shift to a systematic maintenance management system is necessary.
# Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition
## 1 Introduction  
### Problem Statement  
According to the World Health Organization (WHO), approximately 466 million people worldwide suffer from hearing loss, of whom 34 million are children. Moreover, an estimated 900 million people will suffer from hearing loss by 2050 [1]. Sign language is the most effective bridge between the hearing-impaired community and the outside world. Although hearing-impaired people also use written language to communicate, this is inefficient and inconvenient. Sign language uses gestures to articulate meaning in text or speech [2] and sign language recognition uses techniques to recognize these gestures. The emergence of sign language recognition has made the lives of hearing-impaired people more convenient and has eased communication, but most available solutions for sign language recognition are imperfect and inaccurate [3].  
Expressions in American Sign Language (ASL) are simple, rich, and diverse [4], but the complex background recognition influence and similarity between gestures are very high. Building a reliable ASL letter recognition model is essential for improving communication as a tool for hearing-impaired people to help spell names and book titles and correct letters. However, complex backgrounds and similarities between gestures make recognition challenging.  
Deep learning allows computational models with multiple processing layers to learn and represent data with multiple levels of abstraction, imitating the human brain mechanism and implicitly capturing the complex structures of large-scale data [5]. Deep learning has also enabled better solutions to hundreds of practical problems and has been widely used in natural language processing, human-computer interaction, and other fields. Withthe growth of data and the improvement of computing power, the problem of the lack of data and difficulties in training deep neural networks are gradually being solved. At present, computer vision based on deep learning has brought significant progress in image classification [6] and face recognition [7], and the appearance of convolutional neural networks (CNNs) [8] has greatly improved deep learning.  
CNNs have different feature extraction structures but a single CNN has difficulty eliminating the influence of the background, so an ensemble of different CNNs is useful for accurate recognition by decreasing the background influence.
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 4 Practicality Validation of Polyimide FBGs
### Reinforced Concrete Specim Internal Explosion Measurement
The strain transfer loss is little and adhesive after solidifying can provide enough strength to make sensors have the same response frequency as concrete specimen. As to say, the dynamic coupling of the bare polyimide FBG and substrate is perfect enough. The os-3200 and JFBGS-200 are all encapsulated FBG sensors. There are more strain transfer layers in their strain measurement system. More strain transfer layer can reduce strain transfer ratio and lose some details. Moreover, FBG in JFBGS-200 is encapsulated by a metal tube which has an empty cavity in it. That will decrease response frequency apparently. The results of the explosion measurement experiment clearly show that the bare polyimide FBG is more sensitive and accurate than the encapsulated optical fiber sensors. Polyimide FBGs are practical and suitable for surface strain measurement even in some strict and difficult experiments.
# Overflow Energy Loss Recovery System Based on Hydraulic Motor-Electric Generator
###### Abstract  
Overflow loss is one of the main reasons for the inefficiency of the hydraulic system. Aiming at the overflow loss in the hydraulic system, an energy recovery system based on a hydraulic motor and generator is proposed. Further, a pressure monitoring and control strategy based on variable speed control for electric generator is adopted, which can control the pressure differential between the inlet and outlet of the proportional relief valve as a small value, and achieve the purpose of energy recovery by reducing the loss of the pressure differential between the inlet and outlet of the relief valve. Through establishing a simulation system model and test platform, the stability, step response characteristics and energy saving of the system are studied. The results show that the energy recovery unit at the outlet of the relief valve does not affect the normal operation of the relief valve, but also greatly reduces the pressure regulation deviation. Besides, the efficiency of overflow loss recovery system about is 67%.  
hydraulic transmission; energy saving; energy regeneration; overflow loss; electric generator