esa-sceva/satcom-chunk-collection huggingface dataset:
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 2 Related Works  
### Overlay Node Placement  
The overlay nodes can re-route traffic on alternative paths to avoid congestion or failures. The selection of overlay nodes affects the performance of an overlay network. Reference [8] proposed the topology-aware node placement heuristics. The method classifies nodes with similar path diversity and latency into clusters through network measurements. The overlay nodes in a single ISP are randomly selected from every cluster, while the overlay nodes across multiple ISPs are also studied to maximize topology diversity between ISPs. Although the measurement-based method is simple to deploy, it only considers single-hop overlay routing. Reference [10] introduced a genetic algorithm (GA) that maximizes path diversity while minimizing the number of relays. This method defines the end-to-end path diversity of source-destination pairs and optimizes the network implementation by maximizing the minimum path diversity. The path diversity defined in this method is based on end-to-end paths, which lack scalability. Reference [12] defines the Overlay Routing Resource Allocation (ORRA) problem, which selects the minimum number of overlay nodes to meet confined transmission costs. Ref. [9] proposed an overlay node placement algorithm based on the shortest path tree, which can cover all the underlay paths with the minimum number of nodes. While this method can utilize all the underlay links, it does not consider link overlap in multipath transmission, and only fits source-based routing methods. Another approach to network node placement is based on centrality, which does not consider routing characteristics and path overlap [15].  
In this study, overlay nodes not only record routing information and perform traffic weight distribution, but also make routing decisions. Moreover, instead of serving specific transmission pairs, this study addresses a more general scenario, all-to-all multipathtransmission. This requires considering the positional relationships between all nodes, rather than between specific node pairs.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 3 Methodology
### Model Specification
#### 3.4.2 Decision Tree (DT)  
The DT model recursively partitions the input space by choosing features and thresholds that minimize the mean squared error (MSE) within each split [18]. The maximum tree depth and minimum samples per leaf node were optimized via 5-fold cross-validation to prevent overfitting. Grid search was used to test the following ranges:  
max_depth: [3; 5; 10; 15]  
min_samples_leaf: [1; 2; 4; 6]  
The best-performing configuration was: max_depth = 10, min_samples_leaf = 2.
## 3 Results
### Kinematic PPP Validation
Figure 10: Comparison of positioning accuracy and convergence time with different ratio values on DOY 031. Table 5 shows the mean positioning errors of all the stations in one week after two hours of convergence. For the Undifferenced solution, the RMSs of the positioning errors are 6.61, 4.49 and 12.14 cm in east, north and up directions, while the RMSs of the Undifferenced-SCB solution can reduced by 43.0, 23.4 and 35.9 %, respectively. Figure 11: Time series of the positioning errors using different satellite clocks for station ABPO on DOY 031. Figure 12: RMS of positioning errors for different PPP solutions when compared to IGS weekly solutions at 57 stations from DOY 031 to 037. In addition to positioning accuracy, the convergence speed of different solutions has also been assessed. Figure 13 shows the mean convergence time of all the stations in a single day. Obviously, significant convergence improvements can be found in the Undifferenced-SCB solutions with respect to the Undifferenced solutions, and the convergence time of the former is half that of the latter. Considering that clock estimation starts from 0 h in each day, the convergence time gap between the Undifferenced-SCB and the IGS solutions is reasonable.
# Automated Measurement of Grid Cell Firing Characteristics
## 2 Design of Algorithms
Although grid fields are not exactly circular, we consider them close enough to circles to use the formula for finding the diameter of a circle from its area. As a default option, GridMet computes grid field orientation from the autocorrelogram as the minimum angle relative to a wall (in the case of a rectangle environment) from the most-central field detected to the field closest to it. Specifically, GridMet calculates the angles between two sides of a triangle (Figure 4): one parallel to the x- or \(y\)-axis and the other connecting the center field's centroid to the other field's centroid. The x- or \(y\)-axis assignment depends on which axis the non-center field centroid is closest to in terms of Euclidean distance. All angle measurements are reported in absolute values. Alternatively, GridMet offers the option to compute the grid map orientation angle by adopting the same method used in Opexebo, which leverages code from the CircStat MATLAB toolbox [11]. That process reports the average of the angles from the center of the plot to its three nearest fields, each relative to the closest of the three major axes of the hexagonal grid [12]. GridMet, including its field detection algorithm, can be applied to multiple areas of work. For instance, this software can be used to quantitatively compare simulated grid fields with fields present in animal recordings (as in [10]). Further research could investigate how grid cell activity changes in different environments and conditions. Another application of GridMet can be to quantify the ratio of grid field spacing to grid field size across experiments to determine if there is a linear or non-linear relationship between those properties (as performed by [7]). Future work can include studying a larger population of cells, other animal species, or novel experimental designs. GridMet is expressly designed to be user-friendly and flexible for animal experimentalists and computational scientists alike. It requires no programming knowledge to be used, and its basic operation can be run with default configuration options. At the same time, interested users can modify the multiple available options depending on their needs for more advanced applications. GridMet is released as a runtime file, which broadens the potential user base by not requiring the purchase of a MATLAB license. This also aids in batch scripting to integrate the software into automated experiment pipelines coded in any languages, including Python. The code is released in an open-source form to allow and encourage the research community to modify or expand on it [13].
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 5 Conclusions and Future Work  
This paper has presented the performance analysis of VANETs propagation models and routing protocols. In this work, SUMO and NS-3.29 are used for simulation. The results are presented and discussed in detail. Results show that the OLSR has a large throughput, end-to-end delay, packet delivery ratio, and MAC/PHY overhead over AODV and DSDV, as analysed using a Two-Ray Ground Propagation model. Whereas, taking OLSR with a different propagation model proved that the performance of FRIIS and Two-Ray Ground are nearly the same, and the Log-Distance propagation model outperforms the others with varying simulation time. With the varying number of nodes (vehicles), the packet delivery ratio and throughput of FRIIS and Two-Ray Ground proved to be better than Log-Distance but the most stable model is Nakagami. Therefore, the most appropriate model for a real-time VANET scenario is Log-Distance and Nakagami rather than FRISS and Two-Ray. Analysis and results may provide guidelines for future implementation and design of traffic control mechanisms for safety applications and faster data dissemination. The analysis presented proved that the propagation model is an essential factor for the performance of a network.  
Future work focuses on testing other mobility models like Random-Way Point, Manhattan, Gaussian, with some contemporary routing protocols. Furthermore, a model enhances the MAC layer 802.11p for delay-tolerant delivery of safety messages and control congestion schemes.  
**Author Contributions:** Conceptualization, L.H. and A.K.; methodology, L.H. and A.K.; software, L.H., A.K. and B.P.N.; validation, L.H., A.K. and B.P.N.; formal analysis, L.H., A.K., B.P.N., G.G.M.N.A. and B.S.; investigation, L.H., A.K., B.P.N. and B.S.; writing--original draft preparation, L.H. and A.K.; writing--review and editing, L.H., A.K., B.P.N., G.G.M.N.A. and B.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data generated and analyzed during this study are included in this published article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**List of Acronyms**  
**Acronym Description**  
AODV Ad-hoc On-demand Multipath Distance Vector Routing protocol  
BSM Basic Safety Message  
CSMA/CA Carrier-Sense Multiple Access with Collision Avoidance  
CCH Control Channel  
DSDV Destination Sequenced Distance Vector  
DSR Dynamic source routing protocol  
DSRC Dedicated Short-Range Communication
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
## 4 Simulations and Remarks
### Tradeoff between Reliability and Latency under the Mini-Slot Retransmission Mechanism  
It is well known that the retransmission mechanism can improve the reliability, but prolong the latency in turn. The tradeoff between these two metrics of the OFDM/FHMA system based on the mini-slot retransmission mechanism is investigated in Table 2. In this analysis, the parameters are the same as those in Figure 6 and \(N_{u}=10\). It can be seen that, from the table, as the channel quality is poor (for example, the SNR approaches to 0 dB), more retransmissions times are required (\(N\) increases) so as to achieve higher reliability. The exact value of the latency in this table is determined by the parameter selections of the number of OFDM symbols in a mini-slot (\(L\)) and the OFDM numerology. For example, for the OFDM numerology with 15 kHz and \(L=7\), the interval of the mini-slot is \(T_{\text{mini-slot}}=0.5\) ms; thus, the exact latency is equal to \(NT_{\text{mini-slot}}\). It is easy to obtain that the latency requirements of various services listed in Table 1 can be perfectly satisfied even for the case of a larger \(N_{u}\), smaller SNR, and less \(N_{h}\).  
Figure 6: The effects of system capacity \((N_{u})\) and the number of FH frequency slots \((N_{h})\) on the reliability of the proposed OFDM/FHMA (**(a)**\(N_{h}=20\); **(b)**\(N_{h}=80\)).
## 7 Results and Discussion  
In our simulation, we have employed the OPNET 14.5 simulator. We use OPNET as a simulation environment with the AODV protocol. Simulation time was 1000 seconds, speed was 128, update interval as 500,000 events, and number of network nodes was seven. Because of the autonomous nature of MANET technology, we recommend connecting the SAR and UAV computing intelligence to accomplish disaster management effectively andefficiently in a short time. The AODV routing protocol is used in the proposed network. The simulation takes 1000 s to complete. One UAV is travelling and moving closer to the activities.  
Figures 8-12 illustrate the performance of the UAV computing and SAR network in terms of network load, traffic data between sender and receiver, throughput, and E2E delay, respectively. In the figures, the \(x\)-axis refers to each node in the network, while the y-axis indicates the specific network load, traffic sent and received, throughput, and E2E delay. The simulation scenario was created in OPNET with an area of \(1\times 1\) km [52], with two UAVs, four SARs, and a disaster center unit (i.e., server). Table 2 illustrates the rest parameters and network configuration with the respective values. From our proposed scenario results, we observed that the desired delay, throughput, traffic sent, and received path loss and network load are met by the proposed method and significantly improve network performance.  
Figure 11: Network delay of different nodes.  
Figure 12: Network load for different nodes.  
Figure 10: Network throughput of different nodes.  
This UAV acquires photographs of the occurrence and communicates them with the SAR team in the disaster area, allowing the team to take required action based on the information provided. \(Drone_{2}\) delivers supplies to rescuers and individuals in a specific crisis region. \(SAR_{1}\), \(SAR_{2}\), and \(SAR_{3}\) are assisted by \(Drone_{1}\)and \(Drone_{2}\).  
As demonstrated in Figures 8-12, the network's performance is evaluated using a variety of measures, including delay, load, routing traffic transmitted and received, and throughput. During the catastrophe, all numbers depict performance indicators for UAVs and responders. In addition, the routing traffic transmitted and received for each device and UAV is measured, as illustrated in Figures 8 and  
distance from SAR, as shown in Figure 13. As a result, the UAV altitude has a dual effect on SAR distances.
# Qutrit Control for Bucket Brigade RAM Using Transmon Systems
## 1 Introduction
At each level of the tree, a value of 0(1) means a routing to the left (right) sub-tree. \(\bullet\) states denote the wait state. \(d_{i}\) represents the data register at address \(i\). In this example, the data of the register with address 101 are requested. Blue color indicates traversed nodes. In such a scheme, the address register is coupled to the routing qutrits and changes the state from \"wait\" to the value of the \(i\)th qubit, where \(i\) denotes the index of the qubit in the index register. For the example of Figure 1, the address register is \(|101\rangle\), which sets the states of the corresponding qutrits accordingly. Although a single address is depicted in the example, in general, the QRAM should be able to query a superposition of data registers, according to  
\[\sum_{j=0}^{2^{n}}\alpha_{j}|j\rangle|0\rangle\rightarrow\sum_{j=0}^{2^{n}} \alpha_{j}|j\rangle|d_{j}\rangle \tag{1}\]  
where \(j\) denotes the addresses of the data registers we want to obtain, \(\alpha_{j}\) is the amplitude of the superposition of addresses, and \(d_{j}\) are the contents of the data register indexed at \(j\). For the 8-qubit RAM considered in the above example, and assuming 1-qubit data registers for simplicity, if we wanted to obtain an equal superposition of the registers located at positions indexed at 2 and 7, the QRAM would reply with the superposition:  
\[\frac{1}{\sqrt{2}}(|2\rangle)d_{2}+|7\rangle|d_{7}\rangle) \tag{2}\]  
where Equation (1) is instantiated with \(j=2,7\) and with \(\alpha_{j}=\frac{1}{\sqrt{2}}\) for \(j=2,7\), and \(\alpha_{j}=0\) otherwise. As the operations that are performed on qutrits in the BBA are of the form of single-qutrit rotations to obtain the desired states and controlled operations to change the state based on the incoming state (i.e., to change a qutrit value from waiting to zero or one), the following architecture can be considered a good candidate architecture for implementing a BBA circuit:  
* It provides three-level systems with each state having a sufficiently high relaxation \(T1\) and dephasing time \(T2\) relative to the execution time of a quantum gate;
* It inherently supports gates for the arbitrary initialization of a qutrit state. * It allows the execution of single- and two-qutrit gates as the ones required by the BBA algorithm. [PERSON] et al. presented a possible implementation of BBA using photons propagating along a network of coupled cavities that contain trapped atoms [14]. In the regime of transmon systems, [PERSON] et al. [20] demonstrated that transmons can be excited with sequential pulses \(\pi\) up to the fourth excited state, with relaxation times that allow the possibility of using these extra energy states for encoding quantum information. The main aim of the work presented in this paper is to demonstrate that the necessary operations needed for implementing a QRAM based on the BBA can be realized in transmon systems and provide a framework for dynamically finding pulse parameters that realize the necessary transitions.
# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 3 Results
### Flowchart for Early Warning Systems  
Based on Table 2, the primary and secondary causes influencing the occurrence of natural hazards have been identified. Table 3 further categorises the manifestations of these hazards.  
When designing a comprehensive monitoring and early warning system, it is essential to consider not only the causes of these events but also their manifestations, as these are the direct sources of risk. In EWS, sensors should be used to identify both the causes and consequences of hazards. Therefore, a data flow analysis is necessary.  
The diagram follows the initial impact of primary causes, which are continuously monitored by sensors. These data are constantly transmitted to a data centre, where they are analysed. The same applies to secondary causes.  
The manifestations of natural hazards represent state variables, they exist at all times, but their values fluctuate. When a hazard occurs, these values change, creating anomalies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Natural Hazard** & **Manifestation** & **Group** \\ \hline \multirow{2}{*}{Floods} & Water level & Hydrological \\ \cline{2-3}  & Flow rate & Hydrological \\ \hline \multirow{2}{*}{Droughs} & Soil moisture (saturation) & Hydrological \\ \cline{2-3}  & Water level & Hydrological \\ \hline Extreme Temperatures & Temperature & Meteorological \\ \hline \multirow{2}{*}{Wildfires} & Temperature & Meteorological \\ \cline{2-3}  & Air quality & Meteorological \\ \hline Wind & Wind & Meteorological \\ \hline Landslides & Ground displacement & Geophysical \\ \hline Earthquakes & Ground motion & Geophysical \\ \hline \hline \end{tabular}
\end{table}
Table 3: Manifestations of the natural hazards.  
Throughout the event, their development is monitored by sensors that collect real-time data, which are then sent to the data centre or database.  
Modern EWSs often integrate automated processes, artificial intelligence, and machine learning [71; 72; 73]. However, human input is still used for data monitoring and analysis in many cases. If an anomaly meets the criteria for activating a warning system, the system is triggered, and an alert or warning is issued. In this case, the final step in the model is the distribution of this information to the affected population. A flowchart of such a system is illustrated in Figure 2.  
However, some data remain static or are difficult to measure--primarily spatial data. This includes factors such as substrate composition, terrain slope, orientation, and land use and land cover, which should be available to crisis management teams for risk assessment [35].
## 5 Results
### Qualitative Analysis  
In addition to the quantitative evaluation, we also conducted an extensive qualitative assessment to provide a more visually indicative evaluation of the segmentation performance. Referring to the method in [49], we compared our approach's performance with other typical prominent retinal vessel segmentation models, including R2U-Net [38], AttU-Net [50], and LadderNet [51]. Figure 4 shows the visualization segmentation results of the comparison models, providing local images to better illustrate the details. R2U-Net exhibited the poorest segmentation results, with a substantial number of vascular pixels inaccurately identified, including both thick and thin vascular pixels. AttU-Net included an attention mechanism to better capture important information, demonstrating slightly better segmentation performance than R2U-Net. LadderNet, with multiple pairs of encoder-decoder branches, allowed for more information flow between layers, leading to a significant improvement over R2U-Net and AttU-Net. However, LadderNet failed to accurately capture thin blood vessels. Our proposed network utilizes the MSFE to extract multi-scale information and employs an auxiliary branch to further improve segmentation performance. Compared with other networks, our network achieved the most accurate prediction results.  
Furthermore, we also carried out a qualitative analysis of the ablation experiments. Figure 5 provides the visualization results of the ablation experiments. Figure 6 displays some local image patches, demonstrating the segmentation results more clearly and emphatically. As observed in Figure 5, the baseline with the MSFE captured more thin vessels compared to the baseline network, effectively enhancing the network's ability to identify vessel pixels. Similarly, the baseline with DR also accurately identified more vascular pixels and correctly classified more non-vessel pixels. When both components were integrated into the network, our proposed network demonstrated stronger segmentation capability, leading to more continuous segmentation results and achieving superior segmentation performance compared to the baseline network or the network with only one additional component. Detailed observations can be made from Figure 6, which indicates that the addition of the MSFE resulted in the correct classification of more vessel pixels, enhancing vessel connectivity for both coarse and fine vessels. Similar effects were observed with the addition of the DR reconstruction branch.  
Figure 4: Visualization of comparative experimental results.  
Figure 5: Visualization of the ablation experiments.  
The visualization results from the comparison and ablation experiments intuitively demonstrate that our model outperforms other models and prove that our designed MSFE and DR reconstruction branch can effectively improve segmentation performance.