esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Quality, InN-Based, Saturable Absorbers for Ultrafast Laser Development
## 4 Discussion
The observed modulation depth and saturation intensities of the InN-based saturable absorbers are comparable to those reported for graphene or MXene in the high-power regime. Furthermore, compared to 2D materials, InN obtained the highest value of output power, and hence, the energy per optical pulse with a value of 28.2 kW and a 5.4 nJ. Compared with previous work [29], the pulse duration of the modified SA has decreased considerably, while the nonlinear saturable absorption increased two times. Therefore, these materials have demonstrated huge advantages in the high-power, ultrafast regime in comparison to other saturable absorbers.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 5 Performance Evaluation  
### Simulation Setup  
In this work, we simulate the proposed scheme using the OPNET modeler version 18.7. The simulation parameters are given in Table 1. We conducted the simulation 100 times with a 95% confidence interval. Specifically, we run the simulations for 3600 s and set the network size to 5000 m \(\times\) 5000 m. To build a large-scale network, we set the number of sensor nodes to 100 and the number of sink nodes to 5. To build a tree topology, the sink node periodically broadcasts a build message every 20 s. After receiving the build message, each node updates the number of hops to the sink node in the neighbor table and start to choose a parent node. In addition, all nodes periodically send a hello message to its neighbors every 5 s. We set the sizes of the build and hello messages to 192 and 128 bits, respectively. To verify that the proposed scheme provides a reasonable trade-off between multiple objectives by adaptively changing the parent node according to changes in network conditions, we observe the performance metrics by varying the traffic bit rate and bit error rate. To simulate congestion, we change the traffic bit rate from 1000 to 5000 bits/s. In addition, we change the bit error rate from \(10^{-2}\) to \(10^{-6}\) in order to make the link condition dynamic.  
We compare the performance of the proposed scheme with the linear weighted sum-based parent selection algorithm [17] and the MCMD-based parent selection algorithm. The linear weighted sum-based scheme considers the number of hops, buffer occupancy ratio, link quality, and residual energy as decision metrics. Here, the weight of each metric is fixed at the same ratio. On the other hand, the MCDM-based parent selection scheme logically determines the relative importance between the decision metrics based on the preferences or background knowledges of the decision maker. Specifically, in our previous work, we (i.e., the decision maker) set the relative importance of each metric based on the following rules. When the network is unstable, we increase the weight of the received signal strength, leading each node to choose a parent node with good link conditions. If the link conditions of the candidate nodes are not significantly different, each node changes another node with a low buffer occupancy and high residual energy to a parent node.
# Multi-Means Comprehensive Forecasting Technology for Water Inrush in Long Tunnels
## 3 Comprehensive Geophysical Exploration Techniques
### Integrated Detection and Prediction Strategy  
This study develops a hierarchical detection system integrating long-, medium-, and short-range geophysical methods through multi-source data fusion. The methodology progressively refines geological interpretations across spatial scales, ensuring high-precision forecasting through complementary technical advantages and systematic uncertainty reduction.  
(1) Long-range Detection (100-120 m): The seismic elastic wave reflection method is integrated with seismoelectric coupling analysis to capitalize on complementary advantages: high spatial resolution from seismic waves and enhanced sensitivity from seismoelectric effects. This combined approach enables comprehensive geological characterization and aquifer identification in extended zones ahead of the workface. Sectional processing of seismic and seismoelectric data facilitates rock mass quality classification (Grades I-V) and hydrostratigraphic segmentation based on water-bearing characteristics.  
(2) Mid-range Detection (50-60 m): Transient electromagnetic methodology is deployed for its operational efficiency, deep penetration capability (up to 60 m), and adaptability to complex geological conditions. Multi-channel decay curve analysis and 3D conductivity inversion algorithms yield detailed geological profiles and quantitative assessment of water-bearing structures, refining predictions of formation water distribution in intermediate zones.  
(3) Short-range Detection (20-30 m): The ground penetrating radar with 100-200 MHz antennas achieves millimeter-scale resolution for near-face investigations. High-frequency electromagnetic pulses enable non-destructive detection of fractures and moisture anomalies, providing real-time profiling of geological conditions and localized water features within immediate excavation zones.  
This tiered framework progressively reduces geological uncertainty from 40% to 8% through multi-physics data fusion, demonstrating 95% accuracy in water-bearing structure identification during Fuzhou tunnel applications.  
Figure 4: Schematic diagram of the transient electromagnetic method.  
Figure 3: Equivalent induced current diagram in half-space.
## 2 Methodology  
### Dynamic PCM Wall Concept  
The proposed dynamic PCM system relies on the ability of the PCM layer to change its position inside the building envelope (vertical or horizontal). The Boolean variable POS describes the position of the PCM layer with respect to the insulation layer at each time. Therefore, the system is able to place PCM facing outdoors during sunny hours to store incident solar energy and moves back PCM to face indoor space and release stored solar energy to the indoor environment when space heating is required (usually at night time).  
The addition of PCM in the movable layer can be achieved by means of different techniques [23], such as impregnation, tubes filled with PCM attached to continuous layers, or honeycomb systems. A rotatable electrical servomotor can be implemented on top and bottom of the insulation layer to guide the movement of the PCM layer from one face to the other. The use of timing belts and/or gears are expected in this sort of system to ensure appropriate control of movement without slip. Moreover, the use of endless screw might be appropriate to link electrical servomotor with the rotatable gears. More details about the possible design and integration of PCM can be found in de Gracia [22]. Figure 2 shows in detail a possible mechanical design of the system, including the position of the servomotor and the endless screw connected to the rotatable gear.
## 6 Conclusions  
This paper proposes a prediction method for ionospheric TEC based on SVR. The following conclusions can be drawn by comparing this model with the IRI and SML models: (1) The SVR model has the highest prediction accuracy. Monthly, it outperforms the IRI model in all cases and the SML model in 75% of cases. However, the SVR model has a prediction step size of one month, while the IRI and SML models do not have this limitation. (2) Using the SML method for predicting TEC may result in negative predictions, which are outliers and unsuitable for engineering applications. (3) The training process of the SVR and SML models indicates that the number of solar activity indices used as input does not necessarily correlate with better performance. The optimal solar activity indices may differ depending on the model.  
Overall, using the SVR model for long-term prediction with a one-month forecast interval demonstrates a higher accuracy and more minor prediction errors than the IRI and SML models. This indicates that the kernel function's introduction positively impacts ionospheric parameter prediction. Future research can extend this study by (1) investigating the impact of other kernel functions on the accuracy of ionospheric parameter modeling, (2) exploring the effectiveness of the SVR method in modeling at other stations or regions, (3) examining the effectiveness of the SVR method in modeling other ionospheric parameters, and (4) validating this model with updated data. These studies will improve  
Figure 11: MAE and RMSE between the predictions by the four models and observations: (**a**) MAE; (**b**) RMSE.  
Figure 10: MAE and RMSE between the four models’ predictions and observations by month: (**a**) MAE; (**b**) RMSE. Numbers 1 to 12 correspond to April 2006 to March 2007.  
communication system performance, optimize engineering design and operations, enhance satellite navigation accuracy, and support space weather forecasting.  
Conceptualization, Q.Y., X.M. and J.W.; methodology, Q.Y., X.M. and J.W.; software, Q.Y., X.M. and J.W.; validation, Q.Y., X.M. and J.W.; formal analysis, Q.Y., X.M. and J.W.; investigation, Q.Y. and X.M.; resources, J.W.; data curation, Q.Y., X.M. and J.W.; writing--original draft preparation, [PERSON] and X.M.; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information Systems (No. CEMEE2022G0201).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Acoustic Pressure Amplification through In-Duct Sonic Black Holes
## 3 Simulation Analysis  
Analytical, TMM and FEM simulations have been performed on axi-symmetric SBHs of radius \(R=0.047\,\text{m}\) and length \(L=0.15\,\text{m}\), up to the first duct cut-on frequency \(f_{c}=2142\,\text{Hz}\). The graded cavities of the SBHs have a maximum depth \(D_{N}=R\) at \(x=L\). Particle swarm optimization [21; 27] has been used to maximize the total power dissipated by opened SBH silencers between 20 Hz and \(f_{c}\). The optimal design parameters are \(N=20\) annular cavities of axial width \(w=3.5\,\text{mm}\) separated by ring walls of thickness \(w_{p}=4\,\text{mm}\), such that \(w=L/N-w_{p}\), and an axial rate \(m=2\) at which the cavity depths increase. The MPP-SBH is coated by a micro-perforated skin with thickness \(t_{h}=1\,\text{mm}\) and perforation ratio \(\sigma=2\,\%\), which is able to achieve high dissipation performance [24] as well as moderate drag under a low-speed flow [28]. The SBHs are insonsified by an incident plane wave. It is of interest to assess the sensing properties of the optimal SBH silencers and, conversely, to evaluate the silencing performance of the optimized SBH sensors. A key point is to assess if similar or different sets of parameters contribute to the axial silencing or lateral sensing performance.  
Figure 2: Sketch of the axi-symmetric computational domain considered in the Thermoviscous Acoustics FEM with the sub-domains related to the PMLs, a visco-thermal fluid domain \(\Omega_{\text{VT}}\) with imposed incident plane wave in the background acoustic field (\(x\leq 0\)), and no-slip isothermal boundary conditions over the cavity rings and duct walls \(\text{S}_{\text{VT}}\); \(\text{S}_{\text{sym}}\) is the axis of symmetry.
## 3 Materials and Methods
### Equipment  
During this experimental investigation, the cutting operations were conducted using the precise AWJC equipment, OMAX 2626 Jet Machining(r) Center. This state-of-the-art system integrates pivotal components, including a high-pressure pump, a precision CNC system, and a robust 3-axis motion system (see Figure 3a). A custom clamping system was developed for clamping the CFRP specimens, as depicted in Figure 3b.  
The monitoring system developed in this research is presented in Figure 4.  
The monitoring system (Figure 4b) was set up with the utilization of the PCI-6110 National Instruments data acquisition board (DAQ). This board is proficient in capturing Acoustic Emission (AE) signals at a rate of 5 million samples per second across four channels. The PCI-6110, equipped with 4 analog input ports (12-Bit, 5 MS/s/ch), 2 analog output ports, and 8 digital input-output ports. It is a simultaneous sampling, multifunction DAQ device designed for various data acquisition applications.  
The AE sensors were interfaced with the analog input of the DAQ through the NI 2110 connection box, establishing a seamless connection for signal acquisition and analysis. They were seamlessly connected to the analog input of the DAQ using the NI 2110 connection box, facilitating a streamlined and efficient data acquisition process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Process Parameters** & **Values** \\ \hline Water pressure, MPa (\(P\)) & 100; 162.5; 225; 287.5; 350 \\ Feed rate, mm/min (\(V\)) & 50; 1162.5; 2275; 3387.5; 4500 \\ Abrasive mass flow, kg/min (\(Ma\)) & 0.1; 0.225; 0.35; 0.475; 0.6 \\ Material thickness, mm (\(t\)) & 1; 2; 3; 4; 5 \\ Abrasive type and size & Garnet-mesh 80 \\ Standoff distance, mm (\(SOD\)) & 2 \\ Orifice diameter, mm & 0.35 \\ Focusing tube dimensions, mm & df = 0.76/lf = 101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The process parameters used in the experiment.  
To measure Acoustic Emission, the VS900-M sensor manufactured by Vallen was employed. This passive piezoelectric AE sensor boasts a broad frequency response. The sensor features a Frequency Range (fPeak) of 100 to 900 kHz and a capacity of 540 pF. It  
Figure 4: The AE signal acquisition setup: (**a**) the AE sensor installation, (**b**) the AE signal acquisition system architecture.  
Figure 3: The experiment setup: (**a**) the Omax 2626 AWJ equipment; (**b**) the clamping system.  
does not come equipped with an integrated preamplifier. The sensors were glued and mechanically secured using manual clamping devices (Figure 4a).  
Preamplifiers 2/4/6, provided by Physical Acoustics, were utilized to enhance the Acoustic Emission (AE) signal. Adjustable gain settings, with options of 20/40/60 dB, and internal bandpass filters covering the range of 0.1 to 1.2 MHz were incorporated. Specifically, a gain of 20 dB was chosen for signal amplification in this instance. To ensure the reliability and consistency of the results, each experiment was repeated five times.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 2 5G Technologies beyond Release 15
### Multi-Radio Dual Connectivity  
The road infrastructure may provide connectivity coverage by means of different radio access technologies (RATs), including LTE and 5G NR, operated in different bands and provided by a common operator. Heterogeneous V2X communications can help addressing the bandwidth and scalability requirements that future vehicular networks will face [13]. This may provide an opportunity for a vehicle to utilise simultaneous connections for increased capacity through bonding or enhance reliability through the inherent redundancy. To that end, multi-radio dual (MR-DC) schemes, initially specified in the final phase (\"late drop\") of Rel-15, include LTE-NR dual connectivity and NR-NR dual connectivity. In Rel-16, asynchronous NR-NR dual connectivity is specified to avoid the need to collocate gNodeBs (gNBs), providing dual connectivity and reduced dual connectivity setup delays [14]. Furthermore, the space industry is joining in to develop a stack configuration which inter-operates within a 5G architecture [15; 16].
## 3 Materials and Methods
### Obtain the Sampling Point and Symmetry Axis  
The sampling points are selected as shown in Figure 4. Since the DT transformation value is the distance between the point and the nearest edge point to itself, most of the sampled points are selected on the canny edges and a few points are selected randomly to increase the robustness. Under this strategy, the minimum function we solve has a clear meaning--since symmetric objects have symmetric edges, we want the symmetric points at the edge points of the image to remain on the edge, or at least as close to the edge as possible.  
We can see that the object detection box of the original image is no longer a rectangle after the projective transformation \(\mathbb{P}\), but we can still get the value of \(c_{x}\) in \(\mathbb{S}\) by the center of the four corner points of the detection box \(\{b_{i}\}\) after the transformation.  
\[\left[\begin{array}{c}c_{x}\\ c_{y}\\ 1\end{array}\right]=\sum_{i=1}^{4}\frac{\mathbb{P}\cdot b_{i}}{4} \tag{7}\]  
We sample the rotation angles \(\theta\) at 5-degree intervals, calculate the value of the cost function \(\{f_{i}(\theta)|i=1,2\cdots 18\}\) in Equation (6) and note the angle that minimizes the cost function as the object orientation. In addition, the observation is considered valid only if the minimum value is less than \(0.1\sum_{i}f_{i}/18\).
# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 3 Short-Term Traffic Load Prediction Based on DBO-BiGRU
### DBO-BiGRU Model  
The demand for prediction models in the preset scenario of this paper is accurate and fast. BiGRU, on the other hand, has bi-directionality, which can simultaneously and comprehensively mine the features in the bi-directional time-series information to improve the prediction accuracy; on the other hand, it has the feature of high parameter efficiency, and the training is efficient. In general, the hyperparameter tuning of BiGRU prediction models relies on expert experience and intuition. This process contains a subjective element, i.e., it relies on individual expertise. This expert-driven adjustment has the potential to lead to sub-optimal configurations that are more likely to fall into local optima, thus affecting the final predictive performance of the model. To address this problem, this paper introduces the DBO algorithm to adjust and optimise the key parameters of the BiGRU prediction model in a more automated and objective way, and to seek the global optimal parameters of the model. To ensure security, we set the available traffic information to include only the cumulative traffic size for each time period with a time-series feature.
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 1 Introduction
### Contribution  
Based on the challenges mentioned above, this paper proposed a mobility, battery capacity, link quality status, and contention window size-aware optimal routing (MBLCR) to enhance the QoS performance of D2D communication over an IoT-based 5G network. The proposed MBLCR aims to balance battery consumption among the devices and select the optimal path amongst the multiple paths by taking advantage of the MCDM technique. The contributions of this paper are as follows:  
1. In the route computational procedure, evaluate the mobility, battery, link quality status, and contention window size of the relay devices from the source to the destination devices.
2. Assign individual weights to the relay devices according to the evaluated value by exploiting multiple criteria of the decision-making technique.  
Figure 1: The application of a D2D communication scenario over IoT-based 5G networks.  
3. Compare the performance results of various QoS metrics of the MBLCR routing scheme with the conventional MP-OLSRv2, MEQSA-OLSRv2, and EMBLR routing scheme using over different device speeds. This paper is organized as follows. Section 2 presents existing works on routing schemes in wireless networks. Section 3 explains the mathematical model of the proposed MBLCR routing approach. Section 4 shows the results and discussion of the routing schemes. Section 5 concludes the paper with possible extensions and direction for future research.
# Modeling and Simulation Tools for Fog Computing--A Comprehensive Survey from a Cost Perspective
## 3 Modeling and Simulation Challenges
### Fog Computing Infrastructure
#### 3.1.3 Communication  
As mentioned above, a fog computing system cooperates with the cloud layer and the end-devices layer. It works in a distributed manner and connects geographically distributed devices--mainly end-devices--with fog nodes and clouds. It usually exploits existing access and metro network infrastructure [4] in order to provide data transfer facilities. The communication channels can be wired or wireless, supported by different networking technologies (e.g., WLAN for wireless and part of IP networks for wired). Wired connections can be found between fog nodes and cloud data centers or among fog nodes, while wireless connections are typically used to connect end devices and fog nodes [6]. It is common for fog devices to maintain three different connection types [30]:  
* For communication between fog and mobile users
* For communication between cloud and fog
* For communication between fogs  
For each connection type, the links may be heterogeneous, may have different capacities and may utilize different technologies. In the first connection type, basic fog elements such as fog servers provide a local infrastructure where communication services take place between fog and end-users. The fog layer is connected with end-devices using technologies such as WiFi, Bluetooth and NFC [14]. Here, a key feature is the prediction of the specific location of mobile users, which can then be used to handle localized issues and provide specialized services. Regarding the second connection type, fog and cloud communications are mostly based on IP core networks. In some cases, they exploit software-defined networking (SDN) technologies [6; 27], disassociating the traffic routing task from the data forwarding process. Finally, the communication between fog nodes could take place in a centralized or distributed manner. The centralized approach utilizes SDN facilities, while distributed solutions take place through the traditional routing process using wired or wireless transmission. More recent approaches propose the use of Network Function Virtualization (NFV) for communication [6]. NVF utilizes virtualization technology to provide networking fully independent of hardware.  
Throughput, average round trip time, average response time and transmission delay are among the issues that concern researchers [12]. Fog systems have to undergo additional investigation and evaluation to extract cost and performance trends. Modeling and simulation are flexible, scalable and low-cost tools to evaluate the performance of these systems, for every communication type.