esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation
### Impact of the Cross-Chain Transaction Ratio  
First we discuss the impact of the proportion of cross-chain transactions to the total number of transactions on the cross-chain blockchain system. In a traditional blockchain system, where all transactions are local, we set the proportion of cross-chain transactions to 0. We conducted a total of five groups of experiments for comparison. The read/write ratios of these 5 groups of experiments were set as 0, 0.3, 0.5, 0.8, and 1. In each group of experiments, the proportion of cross-chain transactions was set as all local transactions (CrossRate = 0), cross-chain transactions accounted for a minority (CrossRate = 0.3), cross-chain and local transactions were balanced (CrossRate = 0.5), and cross-chain transactions accounted for a majority (CrossRate = 0.8). All cross-chain transactions was indicated by a CrossRate of 1. The number of sub-transactions for all cross-chain transactions was set to 2, because a traditional cross-chain operation can be thought of as consisting of two cross-chain transactions. We statistically compared TPS, SuccRate, and cross-chain conflict rate (because the status code of cross-chain conflict was set to 401, here referred to as 401 Rate) for each group of experiments. Experimental statistical results are shown in Figure 5.  
As we can see, the results of these 10 groups of experiments had the same variation trend, so it can be seen that CrossRate had the same influence on system performance under different RWRate conditions. In the case of fixed RWRate, as the proportion of cross-chain transactions increased, that is, the CrossRate gradually increased, the number of successfully processed transactions per second (TPS) of the system gradually decreased, the success rate of transaction processing of the system gradually decreased from nearly 100% to about 80%, and the proportion of cross-chain conflicts gradually increased from 0 to nearly 100%.
## 5 Simulation Studies  
### Robot and Environment Parameters  
The previously described robot model and control system were implemented in the MATLAB/Simulink package in order to carry out simulation studies. The ode4 fixed-step solver was used in the simulations, which implements the classic Runge-Kutta method. A step time of \(dt=0.01\) ms was assumed, which is a compromise between the simulation accuracy and the simulation time.  
For the simulation studies, the following basic parameters of the robot are assumed:  
* Dimensions: \(L_{f}=L_{b}=L=0.250\) m, \(W=0.520\) m, and \(r_{i}=r=0.0965\) m;
* Masses: \(m_{R}=45.4\) kg, \(m_{i}=1\) kg;
* Mass center co-ordinates: \({}^{R}X_{CM}=\) -0.02 m, \({}^{R}y_{CM}=0\) m, and \({}^{R}z_{CM}=0.04\) m;
* Mass moments of inertia: \(I_{Wy}=0.01\) kg\(\cdot\)m\({}^{2}\), and \(I_{Rz}=3.1\) kg\(\cdot\)m\({}^{2}\);
* Drive units: \(L_{d}=0.0823\) mH, \(R_{d}=0.317\)\(\Omega\), \(k_{e}=0.0301\) Vs/rad, \(n_{d}=53\), \(\eta_{d}=0.8\), \(k_{m}=0.0302\) Nm / A, \(U_{max}=32\) V, and \(\omega_{max}=15.8\) rad / s.  
As for the track width \(W\) (m), in accordance with the topic of the article, it is also the subject of further analyses in which the impact of its different values on the accuracy of tracking control and energy efficiency is analyzed.  
The robot's motion on concrete ground is analyzed and the following tire-ground contact parameters are adopted: \(\mu_{p}=0.85\), \(\mu_{k}=0.75\), \(f_{r}=0.03\), and \(\lambda_{p}=16.5\%\). Moreover, the value of gravitational acceleration \(g=9.81\) m/s\({}^{2}\) and the coefficient \(\beta=2\) s\({}^{-1}\) for the function \(\tan(\beta\omega_{i})\) approximating the function sgn(\(\omega_{i}\)) are assumed.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration
### Drone Hardware Details
#### 3.1.1 Mainboard (Raspberry Pi 3)  
This study uses Raspberry Pi 3 Model B+ as the mainboard for the drone system. It is mainly responsible for capturing/saving images and for saving the flight information for later use. Raspberry Pi is a credit-card sized computer developed by the Raspberry Pi Foundation in the United Kingdom. Raspberry Pi 3 Model B+ is equipped with four Cortex-A53 CPUs clocked at 1.4 GHz. A total of 1 GB of RAM along with four USB 2.0 ports, HDMI output, LAN port, Wi-Fi, and a MicroSD card port are available onboard the Raspberry Pi 3 Model B+. Raspberry Pi was selected as the mainboard due to its compact size, low weight, and cost efficiency.
## 1 Introduction
Currently, the European Space Agency (ESA) is evaluating this new RS approach in the framework of the ESA Earth Explorer 8 program through the Fluorescence Explorer (FLEX) mission ([[http://www.esa.int/ESA](http://www.esa.int/ESA)]([http://www.esa.int/ESA](http://www.esa.int/ESA))). Several studies are underway to evaluate the technical feasibility of measuring the two-peak fluorescence signals at 687 nm and 760 nm (F\({}_{687}\) and F\({}_{760}\)) from a spaceborne platform. Additionally, it became recently possible to use spectrometers on board the GOSAT (TANSO-FTS) and the MetOp (GOME-2) satellites to derive F at a single waveband in coarse spatial and temporal resolution [16; 17; 18; 19]. In the last decade several studies have investigated the potential of F\({}_{760}\) as an estimate of GPP. For example, diurnal GPP estimation of a corn field was improved when including F as a proxy for LUE [20]; however, no relationship between PRI and LUE was found. Additional studies in corn, however, showed that GPP estimation could be significantly improved by incorporating both PRI and F [21], which is in agreement with studies from different agricultural crops and grassland [22; 23]. In this paper, we exploit the capability to measure diurnal courses of F at 760 nm (F\({}_{760}\)) and PRIusing ground and airborne based spectroscopy. We investigate spatial heterogeneity of F and the PRI within crop fields, in-between field of two different agricultural species, winter wheat and sugar beet. Further, we evaluate the improvement of diurnal GPP modeling using F and PRI in combination with [PERSON]'s LUE approach for the test case of those two species.
# Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges
## 2 Fog and Edge Computing: Modelling and Simulation Challenges
### Infrastructure and Network Level Modelling  
Due to cloud communication dependency and large volumes of data generated by fog and edge applications, network connectivity and capacity can be be a significant limitation, especially in the case of real-time delay-sensitive applications. This is particularly the case at the mobile edge. Mobility management is critical in mobile edge computing (MEC), especially in highly dynamic environments. To manage demand in MEC scenarios, massive numbers of small cells are deployed. In this scenario, the user range can be very limited and therefore handovers are more frequent, resulting in a heavy burden on the network [24].  
Fog and edge devices make use of a wide variety of communication technologies, ranging from traditional low cost protocols, such as IEEE 802.11 to energy efficient protocols, such as IEEE 802.15.4 (ZigBee/6 LoWPAN) and Bluetooth Low Energy (LE). Each of these technologies has an impact on the end point performance directly whether data processing, service time, data transfer delay etc. Arriving at the optimal network access technology, typically involves a trade-off between performance and cost and is often outside of the control of the service provider [17].  
Fog and edge system models can extend to thousands of distributed site locations creating a network of resources spanning multiple countries. Each site can be comprised of computing and network equipment hosting multiple applications that can be accessed by edge service users. Creating such a model by hand, even at a higher abstraction level, is no longer practical from a time and effort perspective. To solve the problem an automated approach is required for model building. Integration with a monitoring data collection system can partially address the challenge by taking snap-shot of an existing infrastructure state [25]. However, in order to build meaningful system behaviour models the monitoring data has to undergo additional processing to extract behaviour trends of workload and application resource demands. Such a process brings big data management and processing challenges into play that require further development within the scope of the simulation domain.
# Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions
## 2 Methodology  
This review conducted a thorough analysis of the existing harvesting technologies for various fruits and vegetables, with a particular focus on the automated systems relevant to button mushroom harvesting. The keywords used to find the relevant works included automation in agriculture, mushroom harvesting, computer vision in agriculture, AI in agriculture, Agaricus bisporus, button mushroom production and life cycle, automated harvesting of button mushroom, etc. To focus our work on button mushroom harvesting, only the works that were directly related to button mushroom harvesting were considered for Sections 3 and 4. The study covered work from 1990 to 2024, drawing data and information from a variety of sources, including Web of Science, academic theses, scientific reports, online articles, and videos, with most referenced from Google Scholar. Out of 95 cited sources, 77 were recent works published between 2018-2024. The number of references used for mechanical systems and computer vision systems in Sections 3 and 4 are 42 and 33, respectively, with some common sources. Additionally, the analyses in the Discussion and Potential and Challenges sections are informed by our visits to existing mushroom farms. The information from the mushroom farm visit includes the performance of human picking, the mushroom growing environment, the mushroom life cycle, and the sizes of Dutch shelves. These data directly influence the performance of the harvesters; therefore, they have been used in the Discussion, Potential and Challenges, and Conclusion sections.  
The review is divided into two main sections: the Mechanical System and the Computer Vision System. The Mechanical System section explores the physical components of automated harvesters, such as robotic arms, end-effectors, and conveyors. The Computer Vision System section delves into the visual sensing technologies used in these systems, including cameras and the programming that enables precise mushroom identification and selection. This approach provides a thorough understanding of both the mechanical and sensory aspects of automated mushroom harvesting systems, offering a comprehensive analysis of the current technologies and their future potential.
## 3 Relational Extraction Model PTKRE
### Entity Location Prediction Layer  
In the SemEval-2010 Task 8 dataset, relationship categories, except for \"other\", are corresponded in pairs, such as \"Cause-Effect (e1, e2)\" and \"Cause-Effect (e2, e1)\". Hence, distinguishing the relative positions of two entities in this dataset becomes crucial. To assist the model in the final relation extraction task, we first designed three categories: \"head entity-tail entity\", \"tail entity-head entity\", and \"other\", as shown in Table 2. We utilized the vector representations of the \"
\" token, the head entity, and the tail entity to construct nodes in an undirected graph. Edges link every pair of nodes among the three, and each node has self-connections. We then applied GCN to capture the topological features of the graph by computing new representations for each node. For multi-layer GCNs, the propagation rule is as follows:  
\[H^{(l+1)}=\sigma(\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}}H^{( l)}W^{(l)}) \tag{5}\]  
where \(\tilde{A}=A+I_{N}\) is the graph's adjacency matrix with the addition of self-connections for nodes, and where \(I_{N}\) is the identity matrix. \(\tilde{D}_{ii}=\sum_{j}\tilde{A}_{ij}\) and \(W^{(l)}\) is a learnable weight matrix. \(\sigma(\cdot)\) represents the activation function. Finally, the updated features of the three nodes in the graph are spliced and used for the final prediction of the relative positions of the head and tail entities, and we obtain \(Loss_{3}\) using the cross-entropy loss function.
## 4 Discussion  
### The Influence of the Degree of Deterioration on Pore Volume and Specific Surface Area  
Volatile matter reflects the degree of coal metamorphism, and the smaller the volatile matter, the higher the degree of coal metamorphism. To analyze the influence of metamorphic degree on the pore structure of coal, scatter plots of volatile matter and pore volume and specific surface area at various stages such as micropores, mesopores, and macropores were plotted, and the curves were fitted (as shown in Figure 6).  
Figure 6: Relationship between volatile matter, pore volume, and specific surface area. (**a**) Pore volume, (**b**) surface area.  
Figure 6 shows that both the micropore volume and specific surface area decrease with the increase in volatile matter, showing a nonlinear decreasing trend. Due to the metamorphism of coal, coal molecules undergo condensation and polymerization reactions, thus generating high polymers. The microstructure of the coal surface changes markedly, and the micropore volume and specific surface area increase with the degree of metamorphism. Moreover, the metamorphism of coal promotes the development of micropores. The pore volume and specific surface area of mesopores and macropores decrease first and then increase with the increase in volatile matter, and the curve is U-shaped, with the minimum value occurring at approximately 23% of volatile matter. Noticeably, the microporous, mesoporous, and macroporous structures of coal differ markedly with the degree of metamorphism.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 2 Principles and Methods
### An Overview of the Evolutionary Algorithms  
When seeking the optimal LEO configuration as a GNSS RO mission, the direct way is the exhaustive search method. Considering the large size of search space for both 2D-LFC and 3D-LFC, an exhaustive search is computationally unfeasible. Additionally, when seeking the Geometric Dilution Of Precision (GDOP)-optimal flower constellations for globe coverage problems, the optimized constellation configuration found is responsive to the optimization algorithm used, and the solutions obtained by two evolutionary algorithms, GA and PSO, are generally better than those obtained by an exhaustive algorithm [27]. Therefore, both of these two evolutionary algorithms are used here to seek the best constellation parameters for 2D-LFC and 3D-LFC with the optimal GNSS RO performance over the globe.
## 5 Results  
The findings pertaining to centralized learning are delineated in Table 6.  
The performance metrics associated with federated learning are listed in Table 7, with the evaluation executed using the model that exhibited the optimal performance across all iterations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} \multirow{2}{*}{Gil Hospital} \\ \hline Accuracy \\ \end{tabular} & \
\begin{tabular}{c} Christ Hospital \\ DeltaPR\_HIGH \\ \end{tabular} &
\begin{tabular}{c} Christ Hospital \\ DeltaPR\_LOW \\ \end{tabular} \\ \hline ROC-AUC & 0.8846 & 0.8305 & 0.8269 \\ \hline ROC-AUC & 0.9417 & 0.6408 & 0.8902 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Centralized learning performance results.  
Upon juxtaposing the outcomes of the two learning paradigms, Gil Hospital's accuracy was enhanced by approximately 4.34% and the ROC-AUC value exhibited an improvement of approximately 5.30% in relation to centralized learning, whereas Chris Hospital DeltaPR_HIGH demonstrated an accuracy increase of approximately 6.12% and a corresponding ROC-AUC value augmentation of approximately 8.66%.  
For Chris Hospital DeltaPR_LOW, the accuracy increased by approximately 16.28%, although the ROC-AUC value witnessed a slight decrease of approximately 0.29%, thereby corroborating that the performance was effectively sustained.  
This analysis corroborates that both the accuracy and ROC-AUC generally exhibit improvement within the framework of federated learning, signifying enhanced performance for both Gil Hospital and Chris Hospital. These findings suggest that federated learning has a greater capacity to encapsulate diverse data patterns than centralized learning, thereby addressing the issue of data imbalance prevalent in each hospital. These  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} Gil Hospital \\ \end{tabular} & \
\begin{tabular}{c} Christ Hospital \\ DeltaPR_HIGH \\ \end{tabular} &
\begin{tabular}{c} Christ Hospital \\ DeltaPR_LOW \\ \end{tabular} \\ \hline Accuracy & 0.9230 & 0.8813 & 0.9615 \\ \hline ROC-AUC & 0.9916 & 0.6963 & 0.8876 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Federated learning performance results.  
Figure 5: Federated learning round-by-round performance: Each graph illustrates the test accuracy and AUC performance across rounds of federated learning. The Y-axis represents accuracy and AUC values, with a maximum of 1, while the X-axis denotes the learning rounds.  
results highlight the potential for greater impact when multiple institutions collaborate on artificial intelligence solutions. They demonstrate that, even if individual datasets are insufficient, federated learning enables a more comprehensive understanding of the overall data distribution, thereby improving the model's generalization performance. In the long term, this approach is expected to support more reliable decision-making and contribute to enhancing the quality of patient care.
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
## 3 Optimal Relay Deployment and Resource Allocation  
In this section, we formulate the optimal tradeoff among the latency, normalized energy, and error probability. For this purpose, we first present the overall error probability in terms of the blocklength \(n_{t}\) and signal-to-noise ratio \(\gamma_{t}\) of each node \(R_{t}\). Then, the optimization problem is formulated to minimize the error probability with constraints on the overall blocklength and normalized energy.
# Behavior of Fiber-Reinforced and Lime-Stabilized Clayey Soil in Triaxial Tests
## 3 Results and Discussion
### Shear Strength Parameters  
The shear strength parameters (i.e., cohesion and angle of internal friction) of the treated soil varied depending on the type and content of additives. Figure 3 shows a comparison of the shear strength parameters at different contents of wheat straw fiber and lime. It is shown that until reaching a fiber content of 0.4%, both the cohesion and the angle of internal friction increased nonlinearly with increasing fiber content. After that, a slight reduction was observed. The maximum cohesion of the fiber-reinforced soil, achieved at a fiber content of 0.4%, was 1.13 times the cohesion of untreated soil. That value for the angle of internal friction was 1.17. When lime was introduced to soil samples reinforced with 0.4% fiber, the maximum cohesions and angles of internal friction increased observably. The maximum cohesions were, respectively, 1.18, 1.28, and 1.16 times that of untreated soil for lime contents of 2%, 4%, and 6%. These values for the maximum angles of internal friction were, respectively, 1.27, 1.26, and 1.30. In addition, it was indicated that the effect of lime content on cohesion was slightly different to that on the angle of internal friction. At a fixed fiber content, the cohesion decreased with a further increase in lime content above 4%. However, the angle of internal friction increased with increasing lime content from 4% to 6%. Generally, a combination of fiber reinforcement and lime stabilization led to shear strength parameters much greater than those achieved when fiber and lime were used separately.
# Research on Classification of Tibetan Medical Syndrome in Chronic Atrophic Gastritis
## 5 Experiment and Analysis
### Comparison with CBA Algorithm  
In order to compare with the CBA algorithm, the minimum support minSup is set to be 0.05 and the minimum confidence minConf is set to be 0.8, and with the same experimental environment. Firstly, construct the training set and the test set with All, Random, Order, and 5-Folds-Validation four ways, and the ACAR are mined. Then, sort the priority of the rules based on the Lift optimal sorting method, and 40, 80, 200, 400, 800, 1000 rule sets of different sizes are selected according to the priority order to establish the corresponding classification models. Finally, validate the accuracy of the classification model with the CBA algorithm, as is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Accuracy (\%)**} \\ \hline  & \multicolumn{3}{c}{**First Partial Classification**} & \multicolumn{3}{c}{**Second Partial Classification**} \\ \hline
**Rules Sorting Methods;** & \multirow{2}{*}{**Conf1 Sup2**} & \multirow{2}{*}{**Corr**} & \multirow{2}{*}{**Lift**} & \multirow{2}{*}{**Conf1 Sup2**} & \multirow{2}{*}{**Corr**} & \multirow{2}{*}{**Lift**} \\
**Data Set Building Ways** & & & & & & \\ \hline All & 87.5 & 89.4 & 90.4 & 87.5 & 89.4 & 90.4 \\ \hline Random & 88.4 & 91.3 & 91.3 & 88.4 & 91.3 & 91.3 \\ \hline Order & 88.4 & 89.9 & 92.8 & 88.4 & 89.9 & 92.8 \\ \hline
5-Folds-Validation & 86.5 & 89.4 & 90.4 & 87.5 & 89.4 & 90.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Partial classification results of the proposed method.  
Figure 4: Classification accuracy of the proposed method.  
It can be seen that the accuracy of the classification model is the highest when the number of the ACAR is 400, and the training set and test set are constructed based on the Random method. Under this condition, the performance of classification model is compared with the proposed method from the following three aspects: The number of atomic classification association rules generated, the time consumption to build the classification model and predict new instances and accuracy, and the result as is shown in Table 2.  
It is obvious that the classification model based on the proposed method can classify all samples in the data set in a shorter time, which requires only 27 strong atomic class association rules, and two times of partial classification, and the accuracy is 92.8%, which is higher than the CBA algorithm.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 6 Security Analysis
### Internal Threats
#### 6.1.2 Log Modification  
DPTracer provides tamper-evident logging, ensuring forward integrity [22], where loggers are presumed honest but may be subject to Byzantine failures by node compromises. While traditional forward integrity prevents tampering only before a logger is compromised, DPTracer extends this protection by maintaining tamper evidence throughout the data provisioning process via mutual auditing.  
If attackers compromise the logger to modify the log entries during the process, this manipulation is detected within the audit window. When a node validates the interval proof, it recalculates the commitments for all the inputs and outputs from the received interval proof and compares them to the received commitments. Any discrepancies in this comparison reveal modifications to the log entries.