esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response of a Heavy-Haul Railway Tunnel's Bottom Structures in Hard Rock
## 5 Discussion  
In the research on the dynamic response of railway tunnels, the research mainly focuses on the tunnel bottom structure in poor surrounding rock, whereas the dynamic response analysis of rock mass at the bottom of the tunnel is very limited.  
Compared to the literature [27, 31, 35, 49], the numerical results suggested that the dynamic response of a hard-rock tunnel is obviously greater than that of a soft-rock tunnel, as the static stress caused by the surrounding rock pressure in hard rock is significantly smaller than that in the soft-rock tunnel.  
This paper mainly studied the dynamic response of the hard-rock tunnel bottom structure and basement rock. The dynamic-response distribution characteristics and time-history characteristics of the bottom structure and surrounding rock were obtained, and the coupling effects of the surrounding rock and train dynamic load and axle load were considered. When considering the rock pressure and train-load coupling, the dynamic responses of the tunnel bottom structure and base rock were all greater than that without considering the rock pressure. The maximum principal stress amplitudes of the tunnel structure and base rock increased by 28.4% and 56.4%, respectively. It is suggested that the rock pressure is not negligible in the dynamic analysis of a railway tunnel [50, 51, 52, 53].  
Furthermore, it is important to consider that the hard-rock tunnel structure is much thinner and vulnerable compared to the soft-rock tunnel structure; thus, in hard-rock tunnels, the probability of basement diseases under the rail may be higher than that in soft-rock tunnels. The research results can provide a useful reference for the research on the mechanism and prevention of basement diseases in hard-rock tunnels.  
Through numerical simulation, this paper studied the dynamic response characteristics of the tunnel under a heavy-haul train load. There are still some deficiencies that need further investigation.  
1. The elastic-plastic damage model of the surrounding rock and concrete structure can be introduced into a later numerical analysis to study.
2. The infinite element boundary can be used to completely eliminate the boundary effect.
3. Defects and irregularities on the rail surface should be considered in a further study.
4. Combined with the numerical simulation results, the progressive damage of the tunnel bottom structure and surrounding rock should be further studied through a fatigue test.  
Figure 24: Influence of train axle load on vertical displacement: (**a**) time–history curve; (**b**) \(\Delta\)U\({}_{2}\)-axle load.
## 3 Results and Discussion
### Effect of the Stabilization Process
An asterisk highlights the most discriminative compounds selected during a variable reduction procedure (i.e., potential markers related with the stabilization method). A variable reduction procedure was performed to determine the esters most impacted by the stabilization process, i.e., the markers of this process. This procedure was repeated 100 times, and the compounds selected in at least the 70% of the iteration are highlighted in Figure 4 with an asterisk. Ethyl esters of long chain fatty acids were among the most impacted esters. The ester compounds selected as markers of room temperature stabilization were isoamyl octanoate, isoamyl hexanoate, ethyl isovalerate, ethyl isobutyrate, isobutyl hexanoate, isoamyl butanoate, propyl acetate, ethyl butyrate, ethyl palmitate, ethyl iserate, and ethyl myristate. As previously addressed, changes in these compounds could not be properly controlled; thus, this stabilization method may not be adequate for obtaining brandies with high quality standards. Meanwhile, none of the esters were selected as markers for the brandies stabilized at cold temperature, providing evidence of the minor impact of this process on the volatile profile of the brandies.
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 1 Introduction
Experimental results show that the proposed method can improve the overall prediction ability of the model. * We conduct extensive empirical validation on real datasets based on realistic highways to determine the effectiveness of the proposed method. In addition, we conduct a rich comparative analysis with the existing various prediction methods, which reflects the superior performance of the proposed model. The remainder of the paper is organized as follows: Section 2 defines the trajectory prediction problem. Section 3 details the processing of the data collected by the visual sensors. Section 4 describes the algorithm of the network structure. Section 5 discusses experimental results, ablation experiments, and comparisons to existing methods. Finally, a conclusion is reached in Section 6.
# Optimizing Multidimensional Pooling for Variational Quantum Algorithms
## 5 Experimental Work
### Experimental Setup  
The efficacy of the two proposed pooling methods was examined through tests using real-world, high-resolution data of varying dimensions and data sizes. One-dimensional pooling was performed on selected publicly available sound quality assessment material  
Figure 12: Multilevel decomposition \(d\)-dimensional Euclidean pooling circuit.  
published by the European Broadcasting Union, which was pre-processed into a single channel with data sizes ranging from \(2^{8}\) data points to \(2^{20}\) data points when sampled at 44.1 kHz [32]. Two-dimensional pooling was evaluated on black-and-white (B/W) and color (RGB) images of Jayhawks [33], as shown in Figure 13, sized from \((8\times 8)\) pixels to \((512\times 512\times 3)\) pixels. Additionally, 3-D pooling was performed on hyperspectral images from the Kennedy Space Center (KSC) dataset (see [34]) after pre-processing and resizing, with sizes ranging from \((8\times 8\times 8)\) pixels to \((128\times 128\times 128)\) pixels.  
To validate the accuracy of the proposed pooling techniques, fidelity was measured over multiple levels of decomposition. The metric of data fidelity (see (36)) is used to measure the similarity of the quantum-pooled data \(\mathbf{X}\) compared to the classically pooled data \(\mathbf{Y}\)[36]. As expressed during testing, pooling was performed on all tested dimensions until one dimension could not be further decomposed. For example, for a hyperspectral image of \((128\times 128\times 128)\) pixels, \(\ell\) was varied from 1 to \(\lceil\log_{2}\min(128,128,128)\rceil=7\), i.e., \(\ell=1,2,3,4,5,6,\text{ and }7\).  
Classical average and Euclidean pooling were performed using the PyWavelets library [37]. Using the Qiskit SDK (v0.45.0) from IBM Quantum [19], simulations were run with the quantum average and Euclidean pooling circuits over the given data in both noise-free and noisy (with 32,000 and 1,000,000 circuit samples/shots) environments to display the effect of quantum statistical noise on the fidelity of the results. The experiments were performed at the University of Kansas on a computer cluster node [38] populated with a 48-Core Intel Xeon Gold 6342 CPU, 3\(\times\)NVIDIA A100 80 GB GPUs (CUDA version 11.7), 256 GB of 3200 MHz DDR4 RAM, and PCIe 4.0 connectivity.  
\[\text{Fidelity}(\mathbf{X},\mathbf{Y})=\frac{\langle\mathbf{X},\mathbf{Y} \rangle}{\|\mathbf{X}\|_{F}\|\mathbf{Y}\|_{F}} \tag{36}\]
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
###### Abstract  
In this paper, we propose a new protocol called LoRaCog to introduce the concept of Cognitive Radio (CR) in the LoRa network. LoRaCog will enable access to a wider spectrum than that of LoRaWAN by using the unutilized spectrum and thus has better efficiency without impacting the end devices' battery consumption. LoRa networks are managed by LoRaWAN protocol and operate on the unlicensed Industrial, Scientific and Medical (ISM) band. LoRaWAN is one of thriving protocols for Low-Power Wide-Area Networks (LPWAN) implemented for the Internet of Things (IoT). With the growing demand for IoT, the unlicensed spectrum is expected to be congested, unlike the licensed spectrum, which is not fully utilized. This can be fairly balanced by applying CR to the LoRa network, where the End Devices (EDs) may change the operating channel opportunistically over the free/available licensed spectrum. Spectrum sensing, channel selection and channel availability relevance become essential features to be respected by the proposed protocol. The main objective of adding CR to LoRaWAN is reducing the congestion and maintaining LoRaWAN's suitability for battery-operated devices. This is achieved by modifying LoRaWAN components such as the ED receive window RX2 rearrangement, spectrum sensing functionality by gateway (GW) for identifying unused channels, and reaching a decision on the unused channels by network server (NS). These changes will create LoRaCog meeting spectrum efficiency and maintain the same level of battery consumption as in LoRaWAN. Numerical simulations show a significant decrease in the rejected packet rate (more than 50%) with LoRaCog when more EDs use cognitive channels. As the results prove, LoRaWAN can reach above 50% rejected packets for the simulated environment versus 24% rejection for LoRaCog using only one additional channel (means total two channels). This means that the system can eliminate rejected packets almost completely when operating over the possible many channels. As well, these results show the flexibility in the system to utilize the available frequencies in an efficient and fair way. The results also reveal that a lower number of GWs is needed for LoRaCog from LoRaWAN to cover the same area.  
cognitive radio; IoT; LPWAN; LoRaWAN; LoRaCog; licensed spectrum; unlicensed spectrum; rejected packet rate; primary users; secondary users +
Footnote †: journal: Sensors
## 4 Discussion
### Future Perspectives  
Future studies could reinforce the current findings by enhancing the approach with the inclusion of performance indicators derived from laboratory or in-field tests, trackingchanges over time and across categories, and evaluating their predictive power for future success in comparison with main competition results, scores, and rankings. The present investigation highlights the benefits of integrating qualitative variables, such as in-field expert opinions, with quantitative variables. Replicating the current approach in different contexts could be considered in future studies. Additionally, exploring more qualitative variables, such as the country distribution, concentration of velodromes, and training infrastructures, and a more in-depth analysis of talent selection and support systems used by national and international federations, may shed further light on the factors influencing success in track cycling.
## 5 Results
### Heterogeneous Network Scenarios  
For practical applications in wireless sensor networks, not all of the sensors have the same initial energy. In order to compare the performance of DCK-LEACH with the other four algorithms in terms of energy consumption, one heterogeneous network with 300 nodes is considered as Scenario 4, as a 100 m \(\times\) 100 m network, consisting of two types of nodes:  
(a) 20% of all nodes are normal nodes, where the initial energy of nodes is set at 0.1 J;  
(b) 80% of all nodes are advanced nodes, where the initial energy of nodes is set at 0.2 J.  
All nodes are randomly deployed, as shown in Figure 8, where the red nodes represent the node (a), and the black nodes represent the node (b).  
Figure 8: A 100 m \(\times\) 100 m heterogeneous network with 300 nodes randomly deployed. “+” denotes a node with initial energy of 0.2 J, “red star” denotes a node with initial energy of 0.1 J.  
In Figure 9, the FND, HND, and LND of five algorithms in heterogeneous networks are compared. The FND of DCK-LEACH algorithm is 244 rounds, which is 408%, 559%, 293%, and 306% longer than LEACH (48 rounds), K-LEACH (37 rounds), IEECHS-WSN (62 rounds), and RCH-LEACH (60 rounds), respectively. For HND and LND, DCK-LEACH is also the best among the five algorithms.  
In Scenario 4, nodes in heterogeneous networks differ in initial energy. Due to frequent re-clustering and different electing of the cluster-head schemes, the performance of other algorithms is degraded to some extent. However, the proposed algorithm does not degrade the performance in terms of FND, HND, and LND in heterogeneous networks, but also improves the performance. It further shows that DCK-LEACH could balance the energy consumption of nodes in the network effectively, and prolong the network lifetime.  
In Scenario 4, Figure 10 shows the performance of the five algorithms in terms of energy consumption. In Figure 10, compared with that of the other four algorithms, the remaining energy of the DCK-LEACH algorithm decreases the slowest, which indicates that DCK-LEACH could save energy more effectively.  
Figure 10: Comparison residual energy of the network.  
Figure 9: Comparison FND, HND, LND of DCK-LEACH and four other algorithms.  
Figure 11 shows the number of packets received by the base station during the network is operational for the five algorithms. The number of data packets received by the DCK-LEACH algorithm at the base station is significantly higher than that of LEACH, K-LEACH, IEECHS-WSN, and RCH-LEACH. This is because DCK-LEACH abandons LEACH's random selection of cluster-heads and adopts the double cluster-head mechanism. At the same time, with the change of residual network energy, the energy and distance factors are used to dynamically adjust the weight to select the best double cluster-heads, in order to make energy consumption of the network more uniform. When the network cluster structure is more uniform, the network transmission is stable for a long time. As a result, more data packets are sent to the base station.
## 1 Introduction
### Contribution and Findings  
Our first contribution is a concept for a (semi-automated) on-boarding service, which enables institutions to provide data endpoints in a DA infrastructure--the so-called Personal Health Train. Using a central registry, we make these data nodes, equipped with metadata, visible for researchers. We have applied the on-boarding process to an already existing Personal Health Train infrastructure and evaluated our work with six (healthcare) institutions across Germany in a sample data analysis use case, i.e., breast cancer detection, with open-source data, which represents the second contribution of our work.  
We have found several shortcomings of the state-of-the-art DA ecosystems that can be categorised into three groups: _FAIRification_, _automation_, and _installation_. Our evaluation has shown that our on-boarding process indeed poses a solution for these aspects by reducing the deployment time for an institution to participate in the network, by simultaneously requiring less technical expertise for the deployment process, and by contributing to the FAIRification of DA. Further, we experienced less one-to-one support during the deployment of the data endpoint, which increases the scalability of such a network. The data use case shows that our solution is an enabler for the collaboration between different data-sharing institutions, which supports clinical research.
## 5 Conclusions  
We proposed and demonstrated an ONN to regenerate PAM4 signal with high ISI generated experimentally by a SiMRM. As the SiMRM has a 3-dB modulation bandwidth of \(\sim\)67 GHz, the expected PAM4 data rate is \(\sim\)134 Gbit/s (i.e., 2 bit/symbol \(\times\) 67 Gbaud). When the data rate is operated at \(>\)200 Gbit/s, the generated PAM4 optical signal suffers from severe ISI. The proposed ONN has a multiple MZI configuration achieving a transmission matrix that resembled a fully connected layer in a neural network. The PAM4 signals at data rates from 160 Gbit/s to 240 Gbit/s (i.e., 80 Gbaud to 120 Gbaud) were experimentally generated using a SiMRM with limited modulation bandwidth of \(\sim\)67 GHz. The proposed ONN is performed via Neuroptica, which is a customized ONN simulator programmed in Python. Results showed that SD-FEC requirement (i.e., BER \(<\) 2.4 \(\times\) 10\({}^{-2}\)) can be achieved at 200 Gbit/s transmission, and the proposed ONN has nearly the same performance with ANN implemented using traditional computer simulation. Moreover, we also discussed the effect of electro-optic nonlinear activation function on the ONN model. By comparing the ONN model with and without electro-optic nonlinear activation function in different input signal amplitudes, it can be observed that the accuracy and loss can be significantly improved at low input signal amplitudes. Even at the normalized input signal amplitude of 0.1, the accuracy can still achieve 99.7%. Furthermore, we analyzed the impact of the phase shift error of MZI to the ONN model. Both ONN model with and without electro-optic nonlinear activation function can still achieve SD-FEC threshold under a 1\({}^{\circ}\) phase shift error.  
**Author Contributions:** All authors contributed to the study conception and design. Data collection and analysis were performed by [PERSON][PERSON], [PERSON] and [PERSON]. The first draft of the manuscript was written by [PERSON][PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by National Science and Technology Council, Taiwan, under Grant NSTC-112-2221-EA49-102-MY3, NSTC-112-2218-E-011-006, NSC-112-3111-E-A49-001, NSTC-110-2221-E-A49-057-MY3.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 13: BER performance under various standard deviation phase errors at a data rate of 160 Gbit/s.
## 6 Conclusions  
In this work, initially, we have analyzed and compared five passive monitoring systems proposed for WSN: SNTS, SNIF, Pimoto, LiveNet and PMSW. However, none of these systems aims to reduce the energy consumption of the monitoring network. Within this context, in this work we propose an energy-efficient passive monitoring system for WSN named EPMOSt that reduces the energy consumption of the monitoring network and provides monitoring information using an SNMP agent.  
All modules of the EPMOSt have been described and validated. Experiments were performed using the MicaZ platform and their results show that the election mechanism used in our system reduces, by up to 69.3% (11 sniffers), the energy consumed by the sniffers for transmitting captured packets. When the election mechanism and the aggregation of headers are used, the energy consumption decreases by 78.5% (11 sniffers). However, although the proposed election mechanism to achieve a rate of captured packets is slightly smaller, this rate increases with the number of sniffers and is reduced by only 0.62% when the monitoring network has 11 sniffers. Therefore, the results of the experiments demonstrate the viability of using the EPMOSt to monitor WSN in real scenarios, because the reduction of energy consumption of the monitoring network contributes to prolong the lifetime of this network. It was also shown that the SNMP agent developed in this work enables the integration of EPMOSt with any management tool which supports the SNMP protocol, facilitating the management of the target network.  
Figure 15: Packets captured by the monitoring network _vs_. number of sniffers.  
The contributions presented in this work bring up interesting perspectives for future research. We highlight three main directions. Firstly, we intend to change the election mechanism to take into account RSSI, LQI (Link Quality Indicator), level of battery of the sniffers, and the number of target nodes monitored by each sniffer, aiming to balance the energy consumption of the sniffers and prevent some sniffers from having their energy depleted long before others. Secondly, we intend to conduct new experiments to evaluate the lifetimes of the monitoring network and the target network. Finally, we will use a network simulator to simulate the operation of the EPMOSt in a network with higher density in order to evaluate its scalability.
## 2 Related Works
Packet Success Rate (PSR), Throughput (TP), End-to-End (E2E) Delay, and Packet Drop Ratio (PDR) are the main factors that were taken into account when evaluating the efficiency of the FANET. The CLEA-AODV protocol was implemented, and the network efficiency was evaluated using the NS-2. The simulated findings demonstrated that the CLEA-AODV outperforms the AODV and SOC-GSO protocols in terms of PSR, TP, E2E delay, and PDR. In [18], the effectiveness of various proactive, reactive, and hybrid routing algorithms, including DSR, ZRP, AODV, and OLSR, were analyzed in two disaster scenarios in order to evaluate FANET performance. In the first situation, when the infrastructure network is expected to be harmed, UAVs link mobile ground stations. In the second situation, drones send video from search and rescue operations to GCS. Simulation-based testing was performed using the Netsim emulator, and it was demonstrated that OLSR and AODV perform well in disaster cases in the dynamic environments of FANETs. In both situations, DSR routing algorithms have the lowest routing overhead. The OLSR protocol provides a routing table prior to the routing process by exchanging a message about the link's state, whereas AODV is reactive to route requests and begins route discovery, which increases latency [19]. Consequently, OLSR satisfies the critical requirement for real-time UAV communication, such as in SAR operations, which have specific requirements such as reliable data delivery and low latency. Table 1 presents a comparison of related studies and explains how our study differs from these prior works. OLSR-based routing protocols adopt the OLSR mechanism that can support SAR operations by delivering transmission packets with minimum delay. As mentioned earlier, this paper evaluates and compares the performance of OLSR-based routing algorithms (OLSR, D-OLSR, ML-OLSR, and P-OLSR) in a UAV ad hoc network. We evaluated these routing algorithms in three basic SAR simulation scenarios (increasing mobility, increasing density, and increasing the functional/allowed space of UAVs) that mimic the actual world of UAVs. We used NS-3 to simulate the above OSLR-based routing protocols and replicate these SAR scenarios.
# A Hybrid Optimization Algorithm for the Synthesis of Sparse Array Pattern Diagrams
## 1 Introduction
To overcome the above limitations, this paper proposes a hybrid particle swarm optimization algorithm based on quantum behavior and genetic variation strategies, namely, the QPSO (quantum behavior particle swarm optimization algorithm). This algorithm enhances the global search ability by introducing the quantum potential well model and suppresses premature convergence by combining adaptive mutation mechanisms. Moreover, the introduction of quantum behavior further optimizes the convergence characteristics of the particle swarm, thereby improving computational efficiency while ensuring optimization accuracy. The simulation results show that in the array synthesis optimization algorithms for three types of arrays, namely, asymmetric sparse linear arrays, planar arrays, and concentric circular arrays, its robustness and peak sidelobe level reduction effects are superior to those of classical particle swarm optimization, the grey wolf algorithm, and the whale algorithm.