esa-sceva/satcom-chunk-collection huggingface dataset:
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 5 Simulation Results and Performance Evaluation
### Simulation Results
#### 5.2.4 Prr  
It can be seen from Figure 12 that PRR of LMPC in scenario one is less than in both proposed schemes. Initially, it is higher and it gradually decreases as the time increases. Because establishment of binary tree from source node increases duplicate packets in dense region which results in high collision. This collision increases packet drop. Moreover, one hop information in LMPC may lead data packet to void region which also affects PRR. In scenario two of LMPC, initially PRR is less because of less number of copies reached to gateway. However, it improves as the time increases because of increasing active node density. As active nodes increases, number of possible paths increases.  
PRR of DBR in both scenarios is less as compared to both FLMPC schemes and LMPC. The reason for low performance in term of packet reception is greedy approach for at every data packet is broadcasted to find out a shortest path. The broadcast mechanism increases the collision of the data packets. Moreover, with the increase in number of active nodes, the chances of collision also increases. In addition, the factor of holding time also contributes in the reduction of PRR at each hop. Hence, from the depicted results it can be concluded that high collisions and inefficient holding time of the data packet by the neighbors node result in less PRR.  
In the proposed schemes, unlike LMPC, every source node unicasts the data packet. When this data packet reaches the the cross node of nearest layer, this cross node generates a binary tree. This mechanism results in a lower number of duplicate packets which reduces collision as compared to LMPC. In a sparse region FLMPC-One's two hops and FLMPC-Two's three hops neighbor information avoid void holes. In void holes avoidance, proposed schemes awake most suitable sensor nodes to  
Figure 11: Energy Consumption with Different Data Rate and Packet Size. (**a**) Scenario-1; (**b**) Scenario-2.  
lead data packet towards destination. There are less copies generated in both proposed schemes as compared to LMPC. However, reliable advancement of data packets increases PRR. PRR of FLMPC-One in scenario one is higher than PRR of scenario two because it has less active nodes in scenario one which ultimately leads to less collision.
## 2 Methods
### Accuracy Measures  
A straightforward and common measure in sentiment analysis is the _accuracy_ of sentiment classification with regard to a dataset of positive and negative tweets, which is--roughly speaking--the proportion of correct classifications. It is defined as \"Accuracy = (TP + TN)/(TP + TN + FP + FN)\" [28] (p. 11) where TP (true positives) and TN (true negatives) refer to the number of tweets correctly classified (i.e., in line with the manual evaluation) as positive or negative by the algorithmic evaluation. FP (false positives) and FN (false negatives) refer to the number of tweets classified as positive or negative in contradiction to the manual evaluation. We excluded neutral and nonsense tweets from the test dataset and balanced the dataset in order to have the same number of positive and negative tweets [26]. The advantage of a balanced dataset is that the lower benchmark of accuracy is exactly 50% with reference to a random classification.  
This approach of creating a validation dataset [26] and assessing the accuracy [28; 29; 36] is common in the literature and the natural choice when sentiment analysis techniques are the main subject of investigation as its accuracy can be expressed in a single straightforward value. However, it cannot be considered sufficient with regard to testing the applicability of sentiment analysis in the sports domain for two reasons: First, the exclusion of neutral and nonsense tweets creates an artificial validation dataset that does not correspond exactly to real-world datasets. Second, in real-world applications, there is no need to correctly classify the sentiment of a single tweet, but to classify the general sentiment of a set of hundreds or potentially thousands of tweets with a reasonable degree of accuracy. With regard to the aims of this study, more insights into the accuracy of classifying realistic sets of tweets as well as knowledge on how the accuracy depends on the structure of the tweet sets is needed.  
In line with the objectives of this study, an alternative and more suitable accuracy measure denoted as _set accuracy_ is introduced and calculated. Test sets of n tweets with a predefined number of tweets from each category are created. The proportion of neutral and nonsense tweets in the test sets equals the proportion in the validation dataset. For the remaining tweets in the test set, a predefined proportion p has the same polarity (either positive or negative). The tweets in the test dataset are randomly chosen from the validation dataset, taking into account the predefined distribution of tweets from the different categories, resulting in test sets with a predominantly positive or predominantly negative sentiment. Then, these test sets are classified as either positive or negative, based on the average sentiment of all tweets and this procedure is repeated m times. The calculation of the set accuracy follows the same formula as for the accuracy; however, TP and TN now refer to the number of test sets correctly classified by the algorithmic evaluation, and FP and FN refer to the number of test sets of tweets classified incorrectly.
## 2 Materials and Methods
### Methods for Solving the Optimization Problem
#### 2.3.3. PSO Approach  
In PSO, each particle's coordinates represent a possible solution that is associated with two vectors, the position (\(x_{i}\)) and velocity (\(v_{i}\)) vectors [27,28]. The size of vectors \(x_{i}\) and \(v_{i}\) is equal to the number of particles. A swarm consists of a number of particles or \"possible solutions\" that proceed (fly) through the feasible solution space to explore optimal solutions. Each particle's position is updated based on its own best exploration, overall swarm experience, and previous velocity vector. The update rules with inertial weight for particle \(i\) at iteration k + 1 are given by (9) and (10).  
\[v_{i}^{k+1}=wv_{i}^{k}+c_{1}r_{1}\left(p_{i}^{k}-x_{i}^{k}\right)+c_{2}r_{2} \left(g_{i}^{k}-x_{i}^{k}\right) \tag{9}\]  
\[x_{i}^{k+1}=x_{i}^{k}+v_{i}^{k+1} \tag{10}\]  
where \(v_{i}^{k},x_{i}^{k}\) are the velocity and position of particle \(i\), respectively; \(w\) is the inertia weight; \(c_{1}\) and \(c_{2}\) are the acceleration coefficients; \(r_{1}\) and \(r_{2}\) are two random numbers between 0 and 1; \(p_{i}^{k}\) is the best position found so far for the \(i\)th particle, \(g_{i}^{k}\) is the best position found so far by the entire swarm. Figure 4 shows the flowchart of the PSO approach.
# Design and Testing of Torveastro: An Outer Space Service Robot
## 8 Discussion
The investigation will focus on the selection of materials that offer both durability and protection against ionizing radiation and thermal stress. The architecture of the three-tiered control system, which is distinguished hierarchically by the complexity of the activities dedicated to each level, will be the topic of a further key future design. The first (higher) level specifies the more intricate steps necessary to execute a job. The second (intermediate) level analyzes these activities to deconstruct them into reasonably complicated procedures and discovers effective solutions. This level is responsible for computing the parameters of the recognized operations and the relative trajectories to begiven to the digital controllers of the servomotors, using the data supplied by the motor sensors and other robot sensors as feedback. The third (lower) level will be responsible for the collection of sensor data, the transmission of instructions to the servomotors of each limb, the management of communication channels between the different hardware components, and any other health-related function. After the introduced assembly of the first terrestrial demonstrator prototype, the next step will be to validate the general and specific design solutions to achieve a prototype for outer space operations.
## 4 Experimentation and Results
### Experimental Setup
#### 4.1.3 Model Training  
In order to assess the compression performance of the model and obtain the optimally performing compression model, we conducted a total of three sets of experiments. In the first set of experiments, we compressed single-channel and three-channel data separately, testing whether compressing multi-channel data together could better represent data features and validating the rationality of multi-channel compression. In the second set of experiments, we controlled the compression quality of JPEG to manage the compression ratio of the data, comparing the data reconstruction capabilities of the model under different compression ratios. In the third set of experiments, we compared our proposed data compression method with other compression methods to evaluate its effectiveness.  
We choose mean squared error (MSE) as the loss function for model training to evaluate the error between the reconstructed data and the original data. We use the AdamW algorithm to update the model parameters to minimize the MSE loss and set the number of epochs to 200, resulting in a training duration of approximately four hours. It is well known that the choice of learning rate has a significant impact on model adjustment and performance. A larger learning rate can accelerate the model training process but may lead to instability and even loss explosion. On the other hand, a smaller learning rate helps reduce the risk of overfitting and allows the model to learn a set of optimal weights, but at the cost of longer training time. In this paper, we manually try several learning rate values and select the optimal one. As shown in Figure 4, if the learning rate is equal to 0.0001, it is easier to ensure the robustness of our training.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 6 Conclusions  
In this study, we propose a novel Multi-Granularity Dilated Transformer system to address the challenging problem of lung nodule malignancy classification. The lung nodules have wide diameter variation, and each can range between 3 and 30 mm. We thus propose a Local Focus Scheme to mine the fine-grained local features to reduce the ambiguity of highly similar lung nodules. Moreover, we design a Deformable Dilated Transformer that consists of two parts: deformable attention and dilated layer, and the frontier can only reference points to guide the model to learn long-range global dependencies and avoid the overfitting problem. Meanwhile, the dilated layer is used to improve local spatial receptive fields. The combination of them can help us obtain a more discriminative global representation. Finally, to further improve the performance of hard samples, we adopt the multi-granularity structure to capture relations at different levels. The proposed model achieves state-of-the-art results on the LIDC-LDRI dataset, and the ablation studies also demonstrate significant improvements in terms of all the metrics on all scope nodules, especially the nodules with diameters between 5 and 12 mm. Although MGDFormer has a clear performance gain compared with state-of-the-art methods, it needs more time for convergence. In future work, we will modify MGDFormer principles to accelerate the convergence of the model and use the modified structure to design an end-to-end early-stage lung cancer detection and classification model, examining its performance on bigger, more diverse datasets.  
Methodology, K.W.; Writing--original draft, K.W.; Writing--review & editing, B.P. and D.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Key Research and Development Program of Sichuan Province Grant No. 23 ZDYF0090.  
Not applicable.  
The data that support the findings of this study are openly available at [[https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)]([https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)).  
The authors declare no conflict of interest.
## 2 The Evolution and Trends of 6G Network Architecture
### Opportunities for Fundamental Change
#### 2.2.3 Native Open Source Support  
Driven by economic, security, and innovation cycle considerations, open source becomes a growing element in the 6G ecosystem. Remarkably, tier-1 operators are adopting open source for portions of the radio access network in addition to their core network. This presents opportunities for contributions at scale from the communications and computer science community. Open data, in addition to open source, become crucial in unlocking 6G's potential. Given that many decisions in 6G will involve algorithms, which require substantial training data, the traditionally conservative telco ecosystem needs to create automated mechanisms that grant access to critical data without compromising network security or customer privacy.
## 4 Simulation and Results  
### Simulation Environment  
The simulation environment that is validated in this study is a component of a production line that produces battery management systems. Figure 17 displays the production system design in which we compared the performance indicators of conventional (FIFO) and RL approaches for task distribution based on the model to optimize storage capacity. The production system was modeled using FlexSim simulation software for validation [30]. To implement RL, we used the PPO algorithm from the Stable-Baseline3 library [31], which is known for its effectiveness in solving numerous problems. PPO is a policy gradient method that directly optimizes an agent's policy function in an RL setting. The objective of the agent is to acquire a policy that maximizes the total reward that it obtains over certain time [32]. PPO controls the difference between the old and new policies while enforcing the \"closeness\" of policy updates during the policy renewal phase. This increases the stability of learning by preventing large updates or unstable policy updates. PPO uses a \"clipping\" technique to limit the size of policy updates, which prevents updates from becoming large, improving convergence speed and stability. It is used in a variety of applications for its stability, new policy learning, parallelism and scalability, and low parameter tuning. Factory simulation and reinforcement learning were applied using the Flexsim simulator. In the simulation with RL, the variables of task assignment were defined based on the process time of the production line. RL mainly determines the behavior of the agent in the state space, and the average waiting time is determined based on the average production. We defined the actions that the RL agent could perform within the simulation. By iteratively learning the lead time and average waiting time, it continuously checks for reward signals to find a solution that optimizes the average waiting time. To use reinforcement learning, one needs to set the observation and action parameters to drive the learning. FlexSim simulation Version FlexSim 2022 was used to set the parameters to be observed for learning. In this paper, the type of recently processed items of Routers 1\(\sim\)3 was set as the observation parameter. The state to be taken by the agent through the observation parameter was changed through the action parameter. In this paper, the type of item to be pulled by Routers 1\(\sim\)3 was set as the action parameter.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.6 Reliability  
The primary reliability challenge associated with KDN lies in the reliability of the data. If the data collected by sensors or devices are inaccurate or incomplete, the KDN system may make incorrect decisions, which can lead to poor network performance and increased security risks. Another reliability issue is the reliability of the machine learning or rule-based model for knowledge generation. For example, a machine learning model for traffic classification may produce subpar classification results (low accuracy, F1 score, etc.) even after being trained on comprehensive and reliable data, decreasing the system's dependability. Furthermore, data may not be able to be obtained from all the devices owing to environmental variables such as device failure, system congestion, etc. In such instances, the decisions made by the KDN systems may be inaccurate or incomplete.  
To overcome these challenges, high-quality devices and sensors can be utilized in the network to ensure that correct data are received and to use machine learning algorithms trained very well for accuracy and reliability that can detect errors in data and correct themselves. Before deploying knowledge generation models in a KDN, the network administrators must make sure that the performance of the knowledge generation model is satisfactory for a test dataset in terms of performance evaluation metrics such as accuracy, F1-score, precision, etc. Furthermore, KDN systems should have additional fail-over systems to function in the case of device failures or other environmental factors.
## 2 Materials and Methods
### Model Description
#### 2.4.2 Soil Water Balance  
A simple daily soil water balance model was used in the current version of the model, which kept track of the available soil water (AW--mm):  
\[AW=min\Big{\{}\max\{AW^{*}+P-(REW^{*}PET^{*}),AW_{r}\},AW_{fc}\Big{\}} \tag{5}\]  
where AW* is the available water the previous day, P (mm) is the amount of precipitation of the current day, REW* is the relative extractable water REW (mm) of the previous day and PET* (mm) is the potential evapotranspiration of the day before. AW\({}_{\rm r}\) and AW\({}_{\rm fc}\) refer to the residual available water and field capacity available water, respectively. Residual water is a certain amount of water that cannot be drained from the soil, even at high tension, due to it being retained in disconnected pores and immobile films [53]. The residual soil water content (AW\({}_{\rm r}\)) and water content at field capacity (AW\({}_{\rm fc}\)) were calculated based on the soil texture classification and the parameters provided in [54; 55] for European soils (Appendix A, Table A1).  
Daily soil water content (\(\theta\)--m\({}^{3}\) water m\({}^{-3}\) soil) was calculated based on:  
\[\theta=\Big{(}AW\,\rho_{W})\,/(\rho_{soil}\ SD) \tag{6}\]  
with \(\rho_{\rm Soil}\) (g cm\({}^{-3}\)) the soil bulk density, \(\rho_{W}\) (g cm\({}^{-3}\)) the density of the water and SD the soil depth (mm).  
The [PERSON] [56] water retention model was used to convert between soil water content and water potential:  
\[\Psi=-\Psi_{e}*(\theta/\theta_{s})^{-b} \tag{7}\]  
where \(\Psi_{e}\) (MPa) is the air-entry potential, \(\theta_{s}\) the maximum volumetric soil water content at saturation and b the soil water retention parameter related to soil texture (see Appendix A, Table A1), with values per soil texture class taken from [54]. In order to account for the species differences in their ability to extract water from relatively dry soils, a species-specific minimum water potential was used when estimating REW:  
\[REW=\big{(}\theta-\theta_{Spmn}\big{)}/\Big{(}\theta_{fc}-\theta_{Spmn}\Big{)} \tag{8}\]  
with \(\theta_{\rm fc}\) the soil water content at field capacity and \(\theta_{\rm Spmn}\) the soil water content corresponding to the minimum species-specific water potential \(\Psi_{\rm min}\). For the study species, we used a \(\Psi_{\rm min}\) value of 3.3 (MPa) [57].  
The daily potential evapotranspiration rate was estimated using an empirical equation for Greece [58]:  
\[PET=(0.06257R_{ex})/(1-0.0234T_{mean}) \tag{9}\]  
with R\({}_{\rm ex}\) the extraterrestrial radiation (MJ m\({}^{-2}\) day\({}^{-1}\)), calculated according to the FAO equation [59] and the average air temperature (in \({}^{\circ}\)C).
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 4 Binary Graph Operations
### Disjoint Union  
The _disjoint union_ of two vertex-disjoint graphs \(G_{1}\) and \(G_{2}\), denoted by \(G_{1}\oplus G_{2}\), is defined as the graph with vertex set \(V(G_{1})\cup V(G_{2})\) and edge set \(E(G_{1})\cup E(G_{2})\).  
[PERSON] and [PERSON] [28] proved the following theorem with respect to the tree-width and path-width of a graph that is the disjoint union of two vertex-disjoint graphs.  
Theorem 4.1: _Let \(G_{1}\) and \(G_{2}\) be two vertex-disjoint graphs. Then, it holds that_  
\[tw(G_{1}\oplus G_{2})=\max(tw(G_{1}),tw(G_{2}))\]  
_and_  
\[pw(G_{1}\oplus G_{2})=\max(pw(G_{1}),pw(G_{2})).\]These bounds imply that the tree-width and path-width of a graph can be derived from the tree-width and path-width of its connected components.  
Corollary 8: _Let \(G\) be a graph. It holds that the tree-width of \(G\) is equal to the maximum tree-width, and the path-width of \(G\) is equal to the maximum path-width of its connected components._
## 2 Materials and Methods
### Evaluation Metrics
#### 2.3.5 F\({}_{1}\)-Score  
The metric \(F_{1}\)-score is the harmonic mean of the sensitivity and precision, as in Equation (4).  
\[F_{1}\text{-score}=2\cdot\frac{\text{precision}\cdot\text{recall}}{\text{ precision}+\text{recall}}. \tag{4}\]  
When sensitivity and precision are disparate, the metric \(F_{1}\)-score tends to a smaller value. Therefore, the model will perform better the closer \(F_{1}\)-score is to 1.
###### Abstract  
This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude, longitude, altitude, heading, speed, and time. The model's foundation is rooted in the Generative Adversarial Network (GAN) framework, known for its inherent generative capabilities, rendering it remarkably resilient against Adversarial Attacks. To enhance its credibility, the Blockchain is employed as a Ledger Technology (LT) to securely store legitimate predicted values utilized in subsequent trajectory predictions. The Blockchain ensures that only authorized and non-adversarial samples are stored in the blocks, rejecting any adversarial predictions. In the validation process, trajectory data for training the GAN model were generated through the UAS-S4 Elecatl simulation model. The performance evaluation relies on the model's resistance to adversarial attacks, measured by fooling rates. The results acquired affirm the excellent efficacy of the GAN model, Secured by Blockchain, approaching against adversarial attacks.  
robustness; aircraft trajectory prediction; generative adversarial networks; blockchain Article