esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
## 3 Sampling Class Imbalance Approaches
### Under-Sampling Methods  
Random Under-sampling (RUS), randomly eliminates samples from the majority class to compensate the class imbalance [60].  
Editing Nearest Neighbor (ENN) uses the \(k-NN\)\((k>1)\) classifier to estimate the class identifier of every sample in the dataset and eliminates samples whose class identifier disagrees with the class associated to most of the \(k\) neighbors [27].  
Tomek Links (TL) are pairwise sets of samples \(a\) and \(b\) from different classes of the dataset such that a sample does not exist \(c\) where \(d(a,c)<d(a,b)\) or \(d(b,c)<d(a,b)\), \(d\) being the distance between the pairwise of samples [26]. When two samples (\(a\) and \(b\)) form a TL, both samples are removed [64].  
One Side Selection (OSS) is an under-sampling method that uses TL to reduce the majority class. When two samples generate a TL, OSS removes only the samples from majority class, unlike the TL method removing both samples from the majority and minority classes [19].  
Condensed Nearest Neighbor rule (CNN) is a technique that seeks as much as possible reduce the size of the training dataset; i.e., it does not a cleaning strategy as TL and ENN do. In CNN, every member of **X** (original dataset) must be closer to a member of **S** (the pruned dataset) of the same class than any other member of **S** from a different class [28].
## 4 Traffic Simulation Modeling
### Performing Process  
The main steps to perform the PTV Visum macro-simulation software were: (i) defining the road network geometry (i.e., nodes, arcs, and turning maneuvers); (ii) identifying the speed limit for each vehicle category; (iii) defining the capacity of each road segment of the transport network; (iv) specifying the traffic assignment procedure; (v) defining the simulation time interval.  
The road network geometry (see Figure 2) was imported into PTV Visum as a shapefile, and through the graphic interface contained in the code, the speed limit of passenger cars and HGVs, as well as the capacity, were defined for each road segment of the investigated system. The traffic demand was modeled by specifying the hourly traffic volume of passenger cars and HGVs going from a point of origin to one of destination.  
Figure 5: Flow chart of the methodology.  
The traffic assignment procedure can be static or dynamic. The former considers both the demand and transport supply constant over time; whereas they might be assumed time-dependent using dynamic traffic assignment procedures. The latter, in fact, considers the traffic flow as a continuous fluid for which specific variables such as the traffic flow, vehicle speed, and density, can be defined at each point of space and time. In this way, it is possible to model not only the variability of traffic demand (e.g., peak traffic flow) but also that of the transport supply (e.g., the closure of a lane and/or a link), so that specific dynamic phenomena that characterize a congested network, as well as the delay time and the formation of the queue can be considered, too. In the present paper, the dynamic user equilibrium (DUE) traffic assignment procedure, integrated into PTV Visum, was more especially implemented to have several potentialities with respect to the following aspects: (i) simulation of congested network in which over-saturation conditions occur for several hours; (ii) simulation of transportation network with congestion effects resulting in different route choices; (iii) simulation of the effects of accidents and their management (for more details see PTV Visum 17 User's Manual [42]).  
With reference to the simulation time interval, according to the PTV Visum 17 User's Manual, it should be included in the range of 5 and 15 min. In this study, we set a simulation time interval equal to 5 min: this means that the code provides the output (i.e., average vehicles' speed and traffic flow) every 5 min of simulation for each road segment.  
The proposed macro-simulation model was then resolved, and the results are shown in the subsequent paragraphs.  
However, it is to be stressed that the convergence criterion adopted in simulation foresees the runs to be stopped when the difference in the results between two consecutive runs is less than 5%. In this regard, 10 runs were found to be sufficient in our case.
# Design of a Lung Lesion Target Detection Algorithm Based on a Domain-Adaptive Neural Network Model
## 4 Experiment Results and Analysis
### Ablation Experiment and Model Analysis  
After completing the model comparison tests, an ablation experiment is conducted to evaluate the contributions of individual modules within the model. The decomposition and naming of each variant are described in detail in Section 4.1. The results of these ablation experiments on both the training and test sets are presented in Figure 6.  
Figure 5: The 10-fold validation for CNDAD—Net on LUNA16 datasets.  
Figure 6: The ablation experiment result for different modules.  
In Figure 6, it is evident that the proposed model outperforms the CND-Net model without domain adaptation, achieving nearly a 10% improvement in overall performance. Furthermore, the detection performance significantly declines when using either 2D or 3D data alone, underscoring the importance of incorporating domain adaptation through transfer learning and leveraging multi-dimensional feature fusion. This approach, which integrates domain adaptation and multi-scale feature fusion, substantially enhances the detection accuracy of lung lesions.  
Additionally, we conducted a detailed analysis of each evaluation metric under a 10-fold validation scheme, assessing the model's performance across different test stages. The results, visualized through box plots, are presented in Figure 7.  
From Figure 7, it is evident that the proposed CNDAD--Net achieves outstanding detection performance, demonstrating robust results across all four evaluation metrics. In the figure, the box is given with the mean value by the line and square. From observation, it is clear that the positioning of quartiles as well as the mean values of each indicator are ahead of those observed in other methods. It is further obvious that the incorporation of domain adaptation and the fusion of multi-level data have significantly added to the robustness and precision. These observations increase the model's potential in practical applications developed for the reliable and accurate detection of lung lesion.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Set14**} \\ \hline \hline
**Method** & **Metric** & **QF = 10** & **QF = 20** & **QF = 30** & **QF = 40** & **QF = 50** & **QF = 60** & **QF = 70** & **QF = 80** & **QF = 90** \\ \hline JPEG & \(SSIM\) & 0.7257 & 0.8008 & 0.8346 & 0.8536 & 0.8674 & 0.8795 & 0.8933 & 0.9102 & 0.9346 \\  & \(PSNR\) & 25.57 & 27.76 & 28.95 & 29.76 & 30.42 & 31.10 & 32.04 & 33.39 & **36.19** \\ \hline EPIIC & \(SSIM\) & 0.7379 & 0.8046 & 0.8378 & 0.8564 & 0.8701 & **0.8819** & **0.8956** & **0.9120** & **0.9355** \\  & \(PSNR\) & 25.88 & 27.94 & 29.14 & 29.94 & 30.61 & 31.28 & 32.21 & 33.49 & 35.79 \\ \hline FBCNN & \(SSIM\) & **0.7921** & **0.8481** & **0.8729** & **0.8868** & **0.8970** & **0.9055** & **0.9155** & **0.9277** & **0.9448** \\  & \(PSNR\) & **27.89** & **29.98** & **31.12** & **31.85** & **32.45** & **33.03** & **33.82** & **34.90** & **36.67** \\ \hline Gaussian Filter & \(SSIM\) & 0.7370 & 0.8045 & 0.8377 & 0.8564 & 0.8700 & **0.8819** & 0.8955 & 0.9119 & 0.9354 \\  & \(PSNR\) & 25.82 & 27.93 & 29.14 & 29.94 & 30.60 & 31.28 & 32.21 & 33.50 & 35.80 \\ \hline Guided Filter & \(SSIM\) & **0.7445** & **0.8122** & **0.8423** & **0.8583** & **0.8703** & **0.803** & **0.8922** & 0.9060 & 0.9250 \\  & \(PSNR\) & **26.08** & **28.18** & **29.32** & **30.09** & **30.74** & **31.37** & **32.24** & 33.36 & 35.13 \\ \hline Average Filter & \(SSIM\) & 0.7225 & 0.7725 & 0.7912 & 0.7996 & 0.8056 & 0.8098 & 0.8139 & 0.8179 & 0.8228 \\  & \(PSNR\) & 25.39 & 26.64 & 27.08 & 27.29 & 27.43 & 27.54 & 27.65 & 27.76 & 27.90 \\ \hline Median Filter & \(SSIM\) & 0.7229 & 0.7806 & 0.8026 & 0.8128 & 0.8200 & 0.8254 & 0.8305 & 0.8356 & 0.8420 \\  & \(PSNR\) & 25.60 & 27.17 & 27.80 & 28.12 & 28.36 & 28.56 & 28.77 & 28.97 & 29.25 \\ \hline Wiener Filter & \(SSIM\) & 0.7380 & 0.7961 & 0.8198 & 0.8314 & 0.8398 & 0.8462 & 0.8526 & 0.8591 & 0.8662 \\  & \(PSNR\) & 25.96 & 27.89 & 28.81 & 29.34 & 29.75 & 30.13 & 30.55 & 31.00 & 31.55 \\ \hline Non-local Means Filter & \(SSIM\) & 0.7257 & 0.8009 & 0.8347 & 0.8538 & 0.8676 & 0.8797 & 0.8936 & 0.9105 & 0.9347 \\  & \(PSNR\) & 25.57 & 27.76 & 28.95 & 29.76 & 30.43 & 31.10 & 32.04 & 33.40 & **36.19** \\ \hline Median Modified Wiener Filter & \(SSIM\) & 0.7338 & 0.7979 & 0.8240 & 0.8369 & 0.8460 & 0.8531 & 0.8602 & 0.8675 & 0.8759 \\  & \(PSNR\) & 25.89 & 27.93 & 28.93 & 29.53 & 29.98 & 30.40 & 30.89 & 31.44 & 32.13 \\ \hline Anisotropic Diffusion Filter & \(SSIM\) & 0.7148 & 0.7625 & 0.7832 & 0.7849 & 0.7965 & 0.8059 & 0.8066 & 0.8171 & 0.8247 \\  & \(PSNR\) & 25.72 & 27.39 & 28.12 & 28.43 & 28.88 & 29.19 & 29.46 & 30.14 & 30.44 \\ \hline Gaussian Bilateral Filter & \(SSIM\) & 0.7257 & 0.8008 & 0.8346 & 0.8536 & 0.8674 & 0.8795 & 0.8933 & 0.9102 & 0.9346 \\  & \(PSNR\) & 25.57 & 27.76 & 28.95 & 29.76 & 30.42 & 31.10 & 32.04 & 33.39 & **36.19** \\ \hline \end{tabular}
\end{table} 
Table 4: \(SSIM\) and \(PSNR\) scores for the Set14 dataset. The **first**, the **second**, and the third best results are highlighted.
## 4 Economic and Application Discussion
### Typical Case Analysis  
A three-span hollow slab girder bridge was selected. The bridge consists of eight hollow slab girders. Two of the spans are planned to be monitored. Two sets of health  
Figure 10: Displacement curve of the whole process from loading to unloading.  
monitoring programs were compared by selecting different monitoring items for their costs. Option 1 adopts only the displacement response to establish a plan for bridge health monitoring; option 2 adopts the common plan for the health monitoring of large-span bridges.  
Option 1: Referring to the arrangement of deformation sensors of conventional hollow slab girder bridges, the bridge needs to be arranged with eight displacement monitors.  
Option 2: The bridge needs to be equipped with eight displacement gauges and eight strain gauges, and according to the current state of the bridge, eight crack gauges should be placed, one thermometer in the middle of each lane of the two spans with one acceleration sensor and a weighing system on one side of the bridge are required, and for a total of eight displacement gauges, eight strain gauges, eight crack gauges, four acceleration sensors, four thermometers and a weighing system are needed.  
1. The use of the plan developed in this paper for short and medium-span bridge health monitoring systems saves 43,500 Yuan RMB compared to the plan configured by commercial products, which has certain economic advantages.
2. Commercial sensor manufacturers in the current market generally only provide sensor equipment, installation and data receiving software, while the design of the bridge health monitoring system, the selection of measuring points, and the further analysis and structural evaluation of the data all need to be carried out by bridge professionals. Completed by a technical unit, only the cost of data analysis software is included in the commercialized system, and the cost incurred in actual operation may not be limited to this. The whole system independently developed by the research group has fully been integrated into the health monitoring system from the initial system design to the final structural state analysis with other methods. The bridge management unit no longer needs the participation of a third party, which can save communication costs and labor costs.
3. Since the whole system adopts the method of independent research and development, if there is any problem during the operation period of the system, it can be solved by a set of system maintenance controls. Therefore, in actual operation, it is possible to avoid the complicated problems of communication and coordination between the owner, the system design and executing company, and the hardware equipment manufacturer in the current bridge health monitoring system, and it is possible to reduce three-party communication to two-party communication, which significantly improves work efficiency.
4. The corresponding data receiving and analysis software can be shared by multiple bridges; if the displacement monitor and data acquisition instrument in the monitoring system can be mass-produced, the cost can be further reduced.
## 2 Materials and Methods
### Setting of Computational Model and Boundary Conditions
The bottom boundary was configured as a no-slip wall. Due to the large volume of the ancestral hall building, the surface roughness of the roof and exterior walls has minimal impact on the results. Therefore, in the study, the surface of the building model was set to be a no-slip wall. Coupling pressure and velocity was accomplished using the SIMPLE method, a common and effective coupling algorithm. To enhance the numerical accuracy, the least squares method was employed for discretization. Pressure gradients were managed using a second-order scheme to ensure numerical stability and accuracy. For convective terms, the second-order upwind differencing method was utilized, known for its validation and reliability in accurately capturing turbulent structures and flow characteristics in the flow field.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 5 Evaluation
### Test Data
#### 5.2.1 Bluetooth Trace  
The first trace that we generated simulates communication via Bluetooth. The input and output parameters for this trace are displayed in Tables 2 and 3. The number of nodes is among the output parameters because it is determined by the number of grid cells and the crowd's density at different points on the map. For this scenario, Bluetooth was the only protocol used as means of exchanging data during contacts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Spray Phase** & **Wait Phase** \\ \hline SWv1 & SWb & FC1 \\ SWv2 & SWb & FC2 \\ SWv3 & SWb & FC3 \\ SWv4 & if FC3 [SWb] else [SWs] & SW wait \\ SWv5 & if FC2 [SWb] else [SWs] & SW wait \\ SSW1 & if (FC2 or FC3) [SWb] else [SWs] & SW wait \\ SSW2 & if (FC2 or FC3) [SWb] elseif FC4 [SWb] else [SWs] & SW wait \\ SWv6 & if (FC2 and FC3) [SWb] else if FC1 [SWs] & SW wait \\ SWv7 & if FC3 [SWb] else if FC2 [SWs] & SW wait \\ \hline \hline \end{tabular}
\end{table}
Table 1: Opportunistic algorithm versions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Area & 30 \(\times\) 20 m\({}^{2}\) \\ Protocol & Bluetooth \\ Bluetooth range & 5 m \\ Wi-Fi Direct range & 30 m \\ Message size & 100 KB \\ Data memory & 5000 messages \\ Trace duration & 1 h \\ Group size & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input parameters for the Bluetooth and Wi-Fi traces.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Area & 30 \(\times\) 20 m\({}^{2}\) \\ Protocol & Bluetooth \\ Bluetooth range & 5 m \\ Wi-Fi Direct range & 30 m \\ Message size & 100 KB \\ Data memory & 5000 messages \\ Trace duration & 1 h \\ Group size & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bluetooth trace output parameters.  
The contact duration distribution for the Bluetooth trace is displayed in Figure 5. The vast majority of contacts, slightly under 350,000, lasted less than 1 min. This trend is due to the fact that, at a concert, the crowd is very dense, so a node will encounter many people as it moves towards a target. The contacts with a duration of more than 55 min correspond to the nodes which have remained static for the entire trace time. The other values can be explained by the nature of the movement. To make this clear, we illustrate an example in Figure 6. As node A moves away from its community to a cell on the edge of the grid (assumed to be the food court and drinks area), it breaks the long contacts maintained with its community, then it creates very short-lived contacts with the nodes on its way to the edge of the map. After a while, it goes back to its community, creating other short-lived contacts as it makes its way back through the crowd.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 2 Related Work
### Routing Protocol for VANETs  
Different from the Mobile Ad Hoc Networks (MANETs), VANETs require a more stable path to deliver the packet. A wealth of work has been performed on routing for VANETs. For the unicast routing, existing routing protocols can be roughly divided into four categories, namely location-based, topology-based, cluster-based, and opportunity-based protocol.  
In the location-based routing, the location of nodes are taken into consideration in route discovery. Greedy Perimeter Stateless Routing (GPSR) is a representative of position-based routing. According to the idea of GPSR, a source node makes greedy forwarding decision based on location data of neighbors. [PERSON] et al. [14] proposed a location privacy-based secure routing protocol. However, this routing protocol only focuses on the privacy-preserving location. A velocity and position-based message transmission scheme is proposed in [15]. The velocity and position information are used to alleviate rebroadcast message collision. Additionally, in [16], authors discussed the position-based routing, and proposed FoG-oriented VANETs structure in support of location-based routing.  
In the topology-based routing protocols, each vehicle needs to establish a routing tables based on the topology information. [PERSON] et al. [17] proposed a Stackelberg-game-model-based routing protocol. The relay nodes are selected by street topology. [PERSON] et al. [18] proposed a network connectivity-based low-latency routing protocol. They took the most representative features of vehicles into consideration during the routing discovery. A novel geographic routing is proposed in [19]. In the routing protocol, some feasible paths to the destination are selected according to the network topology.  
Based on the idea of cluster-based routing, the nodes in the network are divided into different clusters by a certain principle [20, 21]. In [22], a chain-branch-leaf cluster-based routing protocol is proposed and analyzed. In addition, for the sake of improving performance, vehicles with similar trajectory features are grouped in [23]. In [24], a connectivity prediction-based dynamic clustering model is proposed, and the proposed model is used to realize stable communications.  
The opportunistic routing generally combines with the geographic routing and forms a Geographical Opportunistic Routing (GOR) protocol. In the GOR protocol, a source node trends to choose a relay node with closer to the destination. [PERSON] et al. [25] proposed an adaptive geographic routing to transmit video data. In the routing protocol, a number of independent path is discovered. However, multiple paths result in high complexity. In [26], authors proposed a trajectory-based opportunistic routing. The Global Position System (GPS) information of vehicles is used to transmit data, and a relay node is selected based on the proximity to the trajectory.
## 4 QoS Provisioning Techniques for Future FiWi Networks  
### Enabling FiWi Technologies  
Various optical RF generation techniques have been proposed to avoid the electronic bottleneck, such as all-optical wavelength up-conversion by means of cross-absorption modulation (XAM) in an electroabsorption modulator (EAM) or four-wave mixing (FWM) in nonlinear dispersion-shifted fiber. According to [39], external intensity and phase modulation (PM) schemes are currently the most practical solutions for all-optical RF generation due to their low cost, simplicity, and long-distance transmission performance.  
For multi-user modulation support, different multiplexing techniques, such as wavelength division multiplexing (WDM) and sub-carrier multiplexing (SCM), have been considered. For instance, the joint distribution of ultra wideband (UWB) and WiMAX radio signals over PON using single- and orthogonal polarization multiplexing was proposed in [40]. In this approach, both UWB and WiMAX signals are modulated by an external orthogonal frequency division multiplexing (OFDM) modulator, while they are jointly transmitted by means of polarization multiplexing. The reported results illustrate the feasibility of polarization multiplexing over a 25 km single-mode fiber (SMF) PON with tolerable error vector magnitude (EVM) penalties.  
To provide a low-cost flexible optical backhaul for FiWi networks, remote modulation techniques might be used, where a central light source is used at the central office (CO) to generate a downlink wavelength that is reused at RAUs for upstream transmission by means of remote modulation. It is important to note that by using remote modulation techniques, there is no need for an additional light source at each RAU. Different remodulation schemes were experimentally studied in [39], such as PM for downstream/directly modulated semiconductor optical amplifier (SOA) for upstream, where an optical carrier is combined with an RF signal, generated by means of PM, and sent downstream using a fiber Bragg grating (FBG) at the RAU to reflect the optical carrier and pass the RF signal. The reflected optical carrier is amplified and directly modulated with upstream data using an SOA.  
In FiWi networks, different enabling optical and wireless technologies may be used. Among others, RPR and EPON are expected to play an important role in the design of a flexible and cost-effective optical backhaul. Other key FiWi technologies are tunable transceivers, burst-mode laser drivers and receivers, and colorless ONUs [41]. Moreover, emerging broadband wireless access technologies (_i.e._, LTE, WiMAX, and WiFi) might be considered for triple-play QoS support of cellular and wireless end-users over a flexible and user-friendly platform.