esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Image Processing Procedure
### Image Segmentation  
To characterise the defects in a CT image, e.g., sizes, locations and shapes, they need to be segmented from the background. One simple way to do this is by image thresholding. Thresholding can be regarded as partitioning pixels in the images into foreground object and background based on the comparison between the grey level of a pixel and a threshold. Because of their simplicity in theory and efficiency in computation speed, thresholding techniques have been widely employed in image data segmentation and a variety of algorithms have been proposed [26; 27]. These algorithms automatically compute a threshold based on a given distribution or histogram of grey levels. However, CT scan images have very specific grey level histograms, i.e., they can show a very unbalanced distribution due to many more background pixels than foreground pixels.  
Among thresholding methods, the entropic method by [PERSON] et al. [28] seems well adapted to our application. It gives accurate results in the case of an unbalanced histogram as observed in this study. In the entropic method [28], the two classes in an image (i.e., foreground object and background) are regarded as two different signal sources. An optimal threshold \(T_{opt}\) can be resolved through the maximisation of the sum of the two class entropies. It is mathematically expressed as:  
\[T_{opt}=\text{arg max}\left\{Hb(T)+Hf(T)\right\}\]  
with  
\[Hb(T)=-\sum\!Ti=1\;pb(i)\;\times\;\log\;(pb(i))\;\text{and}\;Hf(T)=-\sum\!Li=T \!+\!1\;pf(i)\;\times\;\log\;(pf(i)) \tag{1}\]  
where \(Hb(T)\) and \(Hf(T)\) are the background and foreground entropies respectively, \(p(i)=f_{i}/N\) is the probability of grey level \(i\) with \(f_{i}\) and \(N\) being the number of pixels of level \(i\) and total number of pixels in the image, respectively, and L is the maximum intensity value in the image, i.e., 255 for an 8-bit grey image.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 5 Classification of Efforts for Evolving the Control Plane
### The Criteria
#### 5.1.3 Control Plane Placement  
Other criteria adopted in this survey are the control plane placement [51; 52], which can be as follows:  
* Distributed: Each control plane element is uniquely responsible for composing the network state and performing the routing computation. Thus, those elements perform independent computation about routes and are capable of managing the portion of the network that is directly connected to it.
* Centralized: This approach is based on a single control plane that manages all the network devices.
* Logical centralized: Although multiple elements to manage the network can exist, one layer of abstraction aggregates all those elements into a seamless solution.
# Second-Order Text Matching Algorithm for Agricultural Text
## 5 Experiments
### Summary  
Through a series of controlled experiments, it can be found that when the candidate set size \(s\) is 10, the P@1, P@3, and P@5 values of SOTM algorithm are 88.34%, 94.56%, and 94.56%, respectively, and retrieving an average query takes 2.02 s.  
The percentage change in retrieval accuracy can be compared by subtracting the retrieval accuracies of the two algorithms or models. Additionally, the percentage change in velocity can be calculated by employing Equation (5).  
\[W=(\frac{V_{old}-V_{new}}{V_{old}})\times 100\% \tag{5}\]  
where \(W\) is the percentage velocity increase. If \(W\) is negative, it represents the percentage decrease in velocity. \(V_{old}\) is the velocity of the old model, \(V_{new}\) is the velocity of the new model. The comparison results of SOTM with BM25, BERT respectively at different parameters are given in Table 4. Positive numbers indicate the percentage of improvement and negative numbers indicate the percentage of decrement.  
Compared to using only BM25 algorithm, this is a 13.73% improvement in P@1, a 6.48% improvement in P@3 and a 3.11% improvement in P@5, despite being 100.00% slower in velocity. Compared to using only BERT model, it is 96.47% faster in velocity and 8.81% improvement in P@1, 2.59% improvement in P@3 and 0.78% deterioration in P@5. This demonstrates that SOTM algorithm balances accuracy and efficiency compared to using only a single text matching model. It has high retrieval efficiency along with high retrieval accuracy, especially when the number of returned matching texts \(K\) is small.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **P@1** & **P@3** & **P@5** & **Velocity** \\ \hline BM25 & 13.73 & 6.48 & 3.11 & \(-\)100.00 \\ BERT & 8.81 & 2.59 & \(-\)0.78 & 96.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of SOTM with BM25 and BERT under different parameters.
# Wind Load Characteristics of Wind Barriers Induced by High-Speed Trains Based on Field Measurements
## 1 Introduction
However, the reliability of simulation results, especially the fluctuating characteristics of the wind load, should be further verified owing to the limited accuracy of the computer. Moreover, time is another obvious drawback of CFD simulation. Although there are some problems related to its difficult implementation, for example the time-consumption and high cost, field measurements are an indispensable way to accurately determinate the characteristics of train-induced wind loads. However, field measurements are sometimes not allowed by railway departments for safety reasons, especially for high-speed railways [16]. Thus, there are limited field measurements regarding the fluctuating wind pressure, induced by high-speed trains, on wind barriers. By considering the effect of train types, train speeds, and barrier types, a systematical measurement about fluctuating wind pressures on barriers was conducted at the Nuremberg-Inoglstadt high-speed railway line in Germany [23]. [PERSON] et al. [24] obtained the fluctuating wind pressures on a barrier, as well as the dynamic responses of the barrier, by field measurement, and proposed two design methods to evaluate the dynamic responses of the barrier. [PERSON] et al. [25] conducted a full field test to obtain the fluctuating wind pressures on a 2.15 m high bridge noise barrier induced by a CRH380A EMU high-speed train at the speed of 250 km/h to 380 km/h. However, the major concerns of this previous research are related to fluctuating pressure time history curves and the peak-to-peak pressure. In addition, the published literature is mainly focused on barriers installed on embankments or small span bridges. As result of field measurements and numerical simulations, it was found that the dynamic response of the barriers, induced by the passage of high-speed trains, can be explained by the resonance effect. The cyclic loads acting on the barriers due to the repeated passage of high-speed trains will lead to the fatigue of the barriers [26]. So, in the present study, we took field measurements and performed an analysis of pressure distribution in different frequency bands of train-generated wind loads on wind barriers for different running speeds of the CRH380A EMU vehicle. The measured wind barriers were installed on the Xijiang Bridge of the Nanning-Guangzhou high-speed railway. The wind pressure features of the wind barrier were analyzed using multi-resolution analyses based on wavelet transform. The results in this paper can directly provide a scientific basis for the dynamic wind-resistant design of wind barriers. In addition, the obtained characteristics of the train-induced wind loads could be a reference to validate the accuracy of related wind tunnel and CFD results.
## 3 Methodology
### Experimental Setup  
To ensure that our analysis accurately reflects real-world conditions and in the absence of an experimental framework for Starlink measurement campaigns, we relied on the \"Starlink-On-the-Road-Dataset\", previously used in [11]. This dataset serves as a reliable benchmark for assessing the performance of various handover strategies in LEO satellite networks, providing valuable insight into the challenges and behaviors observed in satellite communication systems.  
The dataset, openly available through the Starlink-on-the-Road platform (Starlink-on-the-Road dataset, available at: [[https://github.com/sys-uos/Starlink-on-the-Road](https://github.com/sys-uos/Starlink-on-the-Road)]([https://github.com/sys-uos/Starlink-on-the-Road](https://github.com/sys-uos/Starlink-on-the-Road)) (accessed on 29 March 2025)), provides a rich and diverse source of real-world high-fidelity measurements, covering critical performance indicators such as upload speed (Mbps), download speed (Mbps), ping (ms), and packet loss (%).  
Our algorithms were executed on a Lenovo ThinkPad E15 Gen 4 laptop equipped with an Intel Core i5-1235U processor (12 th generation, up to 4.4 GHz), 8 GB of DDR4 RAM, and a 512 GB NVMe SSD. The system runs on Windows 10. This hardware configuration provided sufficient computational resources for simulating the algorithms and performing the required data processing tasks efficiently.
## 4 Results and Discussion
### Comparative Performance Between Datasets
#### 4.2.4 RMSE  
Regarding the RMSE, the oZm v3 has a mean of 22,855 and a standard deviation of 4799, indicating a moderate variability. At the same time, the oZm v1 shows a more significant variability with a mean of 30,566 and a standard deviation of 7003. The OMPM has the most considerable variability, with a mean of 38,896 and a standard deviation of 82,267. Despite some outlier measurements, all three meters show similar results, highlighting that the OMPM can offer the best value in some instances. However, concerning the RMSE metric, the oZm v3 shows a poor performance with a mean of 297.678 and a high variability. At the same time, the OMPM offers values closer to the ideal (mean of 18.936 and standard deviation of 7.312). Figure 14 shows the heat map.  
In the lower part corresponding to the measurements taken with the OMPM, the results are much higher than those obtained with the oZm (yellow tones). It also highlights how the data received with the oZm v3 is much worse than with the oZm v1 (tones tending to red). Although the heat map gives us a complete overview of all the measurements, it also clarifies the importance of combining all the measurements obtained from the appliances by the meter/dataset, as shown in Figure 15.  
Figure 14: The heat map for the RMSE.  
In summary, when analyzing the RMSE data, the performance of each meter varies significantly depending on the appliance. The oZm v3 seems more accurate for the TV and fan, but less accurate for the incandescent lamp and vacuum. The oZm v1 performs well for the freezer but has higher errors in other devices. The OMPM stands out for its low RMSE in the incandescent lamp but performs less impressively on the TV and fan.
## 3 Results
### An Accuracy Assessment of CHM-Based Height  
The average and standard deviation of inventory tree height were 27.5 and 5.9 m from 4845 forest plots. The corresponding statistics obtained by CHM were 28.2 and 5.6 m, respectively. It should be noted that above statistics are plot-wise maximum tree height, whereas the statistics in Section 3.1 were calculated from pixel-wise canopy height.
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture
### System Model
#### 3.1.1 Network Model  
The group of drones assumed in this study is shown in Figure 1. The registration of the drones and the access of the GS to the database are both considered to be carried out over secure channels. We assume that the base station and the database are resource-unconstrained, so communication between the database and the GS uses a recognized high level of security with authentication and encryption systems by default. The database is considered secure, and when the administrator assigns a task to the drone fleet, the GS can only access the information of the drone specified by the administrator. The drone does not store any information, while the database does store all the data. With such an architecture, the attacker's attack shifts to high-performance devices, which significantly increases the difficulty of the attack.  
The drones in the fleet establish a local area network using ad hoc protocol and do not have interfaces to open networks such as the Internet. The LAN is constructed through various types of frames for neighbor listening and link perception. Apart from network topology-related information being disseminated through flood broadcasting, specific data information is communicated in a P2P manner with the use of P2P secret keys. For example, Drone A needs to send a message to Drone B. If A uses a P2P secret key between A and B, then neither the other drone nor the GS can receive the message. The ad hoc protocol still retains the feature that allows arbitrary joining, permitting newly authorized drones to join the mission midway through. However, the drone communication channel is considered to be an open channel that can be freely monitored and potentially attacked. In addition, this LAN uses a distributed network with P2P communication, and gateway drones are introduced to become a clustered networking system when the number is high to reduce the storage overhead of P2P secret keys.  
The communication distances of the drones and the GS are all limited and subject to link instability, leading to packet loss. Therefore, all the drones are within the communication range of the GS when drones take off. The drones need to transmit status information back to the GS at regular intervals so that the GS can recall the drones that work abnormally in time. For the drones that broadcast the recall, the remaining drones update the communication secret key set.  
Figure 1: System model.
## 5 Experiments
### Collaborative Framework Performance Tests
#### 5.1.4 Anchor Transmission Latency  
Anchors are essential for the AR user experience, since they contain the environmental information necessary to determine the three-dimensional points where the virtual objects are. Therefore, it is interesting to measure anchor transmission latency.  
It must be noted that such a latency is mostly determined by the size of the anchor generated by the Microsoft HoloLens framework, which depends on the complexity of the shapes of the real-world environment and on the amount of detail with which the AR devices have been able to map their surroundings. Due to these factors, the size of an anchor ranges from a few to several hundreds of megabytes. Thus, the transfer times of the anchors are proportional to their size.  
In the performed tests, anchor transfer times ranged from 2 s for the smallest anchors to 30 s for the largest ones. These are large times, but it should be noted that the transfer of anchors is only necessary once when the AR application starts, in order to establish the same reference points for all the devices that share the AR experience. Once this initialization is performed, all the changes in coordinates are specified in relation to the initial reference points, so it is in general not necessary to transfer the anchor again.
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 4 Simulation Analysis
### Stimulation Analysis of the Pest and Disease Diagnosis Model
#### 4.2.3 Treatment Execution Stage  
In the simulation, keeping all other \(\lambda\) values constant, \(\lambda_{21}\) was varied from 1 to 20. The relationship between \(\lambda_{21}\) and the steady-state probabilities of each system state is shown in Figure 10. As \(\lambda_{21}\) increases, which indicates that the speed of the treatment effectiveness assessment process is accelerated, the probability of meeting the treatment effectiveness assessment, P(M\({}_{21}\)), significantly decreases. This results in an overall increase in the probabilities of other states, with the probabilities of severe infection P(M\({}_{11}\)) and recovery of health P(M\({}_{19}\)) increasing the most. This behavior occurs because the system has more opportunities to try different treatment methods until a viable and effective solution is found, thus improving the treatment speed. Additionally, the changes in treatment effectiveness assessment also affect data monitoring and subsequent control measure implementation through feedback. Since the system needs to continuously adjust control measures based on treatment effectiveness, this change leads to iterative and repeated adjustments in the treatment process. This, in turn, enhances the overall system's efficiency and resource allocation. Therefore, to improve treatment efficiency and effectiveness, the system needs to automatically adjust its treatment strategies based on real-time plant growth data and pest/disease conditions, ensuring a responsive and optimized treatment process.
# Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
## 3 Optimization of UAV Position and Power Allocation  
In this section, an iterative algorithm is proposed to solve P1. Firstly, we optimized the position of the UAV by transforming the non-convex optimization problem with a fixed power allocation into a convex problem by employing SCA and variable substitution. Secondly, we obtained the power allocation with a quadratic transform method effectively. Finally, we continuously updated the variable block \(\mathbf{A}\), \((x,y)\) and \(\mathbf{P}\) alternatively to obtain the solution of P1.
## 2 Materials and Methods
### Machine Learning Models
#### 2.3.6 Light Gradient Boosting Machine (LightGBM)  
The LightGBM model and the XGBoost model are both extended versions of GBDT. GBDT, XGBoost, and LightGBM belong to the boosting algorithms, which are characterized by the sequential training of learners, with each subsequent learner aiming to correct the prediction errors made by its predecessors. The difference is that LightGBM uses three innovative techniques, including histogram, gradient-based one-side sampling, and exclusive feature bundling, to improve the efficiency of data processing during training, thereby improving the training speed [54].
###### Abstract  
Herein, two diketopyrrolopyrrole (DPP)-based, small-molecule isomers, \(o\)- and \(p\)-DPP-PhCN, were introduced as acceptors in ternary organic solar cells (OSCs). The isomers have the same molecular backbone but differ in the positions of the cyanide (CN) substituents (ortho and para), which greatly affects their packing behavior. Ternary solar cells composed of poly(3-hexylthiophene) (P3 HT):DPP-PhCN:phenyl-\(C_{61}\)-butyric acid methyl ester (PCBM) were fabricated, and the effects of the different packing behaviors of the third component on the device performance and the working mechanism of the ternary cells were investigated. The addition of \(o\)-DPP-PhCN with a relatively high-lying lowest unoccupied molecular orbital energy level resulted in an increase in the open-circuit voltage (\(V_{\text{OC}}\)) in the ternary devices, demonstrating the alloy-like structure of the two acceptors (\(o\)-DPP-PhCN and PCBM) in the ternary system. However, the \(p\)-DPP-PhCN-based ternary cells exhibited \(V_{\text{OC}}\) values similar to that of a P3 HT:PCBM binary cell, irrespective of the addition of \(p\)-DPP-PhCN, indicating a cascade energy-level structure in the ternary system and an effective charge transfer from the P3 HT to the PCBM. Importantly, by increasing the addition of \(p\)-DPP-PhCN, the short-circuit current density increased substantially, resulting in pronounced shoulder peaks in the external quantum efficiency responses in the long-wavelength region, corresponding to the contribution of the photocurrent generated by the light absorption of \(p\)-DPP-PhCN. Despite sharing the same molecular backbone, the two DPP-PhCNs exhibited substantially different packing behaviors according to the position of their CN substituents, which also greatly affected the working mechanism of the ternary devices fabricated using the DPP-PhCNs as the third component.  
organic solar cell; organic photovoltaic cells; OSC; OPV; ternary cell; diketopyrrolopyrrole; DPP; nonfullerene; acceptor
## 2 Related Work
Smart devices such as smart appliances, smart sensors, smart storages, smart meters etc., at the users' premises as well as grid or community wide software and network infrastructure are essential requirements for implementing the ICT based energy management systems [21; 27; 28]. The ICT-based energy management system has been termed as Internet of Energy (IoE) by some researchers [27]. Prosumers are important stakeholders and contributors to the smart grid and it is imperative to manage prosumer energy production, sale and consumption in an intelligent manner. Using the AMI and Automatic Meter Reading (AMR) infrastructure, prosumer energy management not only enhances grid capability to create the demand-supply balance, and hence reduced power outages and improved power quality, but also helps mitigate the carbon footprints associated with the fossil fuel based large scale production. The latter comes from the fact that most of the prosumers use renewable clean energy resources such as solar and wind etc. [29; 30]. It is clear from the above literature review that numerous methods have been proposed for smart energy markets and prosumer community building/management. Prosumer interaction at an individual level within a community is hardly touched upon by researchers. In this paper we aim to fill this gap. Note that this work optimizes the energy sharing for a single community. The same mechanism may be used to optimize energy management within multiple communities separately. The results can then be used for inter-community energy sharing optimization. We present a novel Prosumer-based Energy Sharing and Management (PESM) methodology which optimizes energy sharing among prosumers in a community. The aim is to allocate maximum possible surplus energy (extra energy available to some users) to the users with a shortage. Furthermore, price of the surplus energy has also been taken into account. Binary Integer Programming (BIP) has been used to solve the optimization problem. The PESM gives priority to the prosumers with a shortage based on different criteria. Details of the proposed scheme are described in the subsequent section.