esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
## 3 Results
### Statistical Study
#### 3.1.2 Hypothesis Testing Analysis  
To test RQ1, a one-factor fixed effects ANCOVA was applied for the participant type \"expert vs. novice\" considering the covariate (participant type \"student vs. teacher\") with respect to the dependent variable crossword result. No significant differences were found, but a mean effect value was found (F = 1.91, \(p\) = 0.40, \(\eta^{2}\) = 0.66). Additionally, no effect of the covariate was found (F = 0.03, \(p\) = 0.90, \(\eta^{2}\) = 0.03), and in this case, the effect value was low.  
To test RQ2, a one-factor fixed effects ANCOVA (participant type \"over 50 vs. under 50\") was applied considering the covariate (participant type \"student vs. teacher\"). No significant differences were found in the metrics of fixations, saccades, blinks and scan path length. A covariate effect was only found in the metrics of saccade amplitude minimum (F = 5.19, \(p\) = 0.03, \(\eta^{2}\) = 0.13) and saccade velocity minimum (F = 5.18, \(p\) = 0.03, \(\eta^{2}\) = 0.13), in both cases with a low effect value. All results can be found in Table 1 in Appendix A.  
Regarding test RQ3, a one-factor fixed effects ANCOVA (participant type \"novice vs. expert\") was applied considering the covariate (participant type \"student vs. teacher\"). No significant differences were found in the metrics of fixations, saccades, blinks and scan path length. The effect of the covariate was only found in the metrics of saccade amplitude minimum (F = 6.90, \(p\) = 0.01, \(\eta^{2}\) = 0.16) and saccade velocity minimum (F = 7.67, \(p\) = 0.01, \(\eta^{2}\) = 0.18), and in both cases, the effect value was medium. All results can be found in Table 1 in Appendix A.
## 4 Discussion
### Microbial Mineralization  
In comparing its aerobic counterpart, the anaerobic soil conditions induced higher mineralization of \({}^{14}\)C-metolachlor irrespective of the soil type used, as evident from the increasing \({}^{14}\)CO\({}_{2}\) measured from the soil microcosms. The mineralization level of the \({}^{14}\)C-metolachlor from the aerobic microcosms in the present study agreed with previous studies [28], where a \(\leq\)8% cumulative mineralization of \({}^{14}\)C-metolachlor was reported in cultivated soils. [PERSON] et al. [29] reported a higher level of cumulative mineralization at 17.6% for metolachlor in cultivated soils. Nevertheless, contrary to the observation made by this article, [PERSON] et al. [8] noted that the mineralization of \({}^{14}\)C-metolachlor was minimal (\(<\)2%), regardless of the soil being saturated or not. Similarly, low levels of mineralization have also been reported for metolachlor herbicide in other studies [30].  
Metolachlor is not readily mineralized in soil and requires a lag period before the initiation of this process [31; 32]. It has also been agreed upon that the repeated application of herbicides could inhibit such lag periods from occurring during their mineralization [33]. Sites with long histories of herbicide usage could lead to better survival and acclimation of the herbicide degrading microbial populations in the soil, assuring faster mineralization following its application [34]. The observations on the evolution of \({}^{14}\)CO\({}_{2}\) during the mineralization of \({}^{14}\)C-metolachlor confirmed the cleavage of the labeled phenyl ring in the metolachlor molecule.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 4 Discussion
### Practical Insights and Future Trends  
There are clear differences in the presence of weeds at the sampling points in relation to the weed control management used and whether herbicide was applied.  
Although there is no direct relationship between % weed and productivity, as with the presence of weeds in the different management approaches, the DM productivity of the forage also responded to the application of the herbicide.  
While VRT may incur higher initial costs due to the need for digital imagery, data storage, and analysis infrastructure, it is designed to optimize herbicide use over time, potentially leading to long-term cost savings and huge environmental benefits. These facts are highlighted by [PERSON] et al. [31], regarding various types of variable-rate technologies.  
Regarding the concern about weed recognition accuracy and the potential risk of herbicide application failures, we acknowledge that this is an important limitation of the proposed methodology. While the reported recognition accuracy of the proposed method is lower than those of the RF and KNN methods, this methodology prioritizes rapid and low-cost processing, which could be advantageous for real-time field applications. However, we agree that improving recognition accuracy is crucial in minimizing the risk of untreated weed patches. These problems associated with the increasing weed resistance highlighted by [PERSON] [32] require the close attention of many countries, especially those which are already on a trajectory for future weed resistance increases but have limited capability to address this problem.  
The specific threshold values we used (e.g., 50 and 120 for the ExG Index) were calibrated for the Xiaomi Redmi 8 camera used in this study. If a different camera were used, these parameters might indeed require recalibration. To minimize the need for recalibration, future work could investigate the use of standardized color calibration techniques or normalization algorithms to reduce variability between cameras. By standardizing the input images regardless of the camera used, the same parameters could potentially be applied across devices, reducing the need for extensive software adjustments.  
In future research, it would be very interesting to investigate the difference between applying or not applying herbicide, in terms of the response of weeds to this management approach, productivity, environmental and economic benefits, and how much this management approach would reduce the use of these plant protection products. Future studies should explore strategies to enhance weed recognition accuracy while maintaining cost-effectiveness.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 5 FedOpt Evaluation  
In this section, we conduct the experimental evaluation of the proposed FedOpt in terms of model accuracy, communication efficiency and computational overhead. We conduct our experiments on the server with an Intel(R) Core(TM) CPU i7-4980 HQ (2.80 GHz) and 16 GB of RAM. The compression and privacy-preserving algorithms are simulated by TensorFlow in Python. For evaluation, we consider baseline configuration of FL, Federated Averaging (FedAvg) [23] and Privacy Preserving Deep Learning (PPDL) [24]. In particular, we evaluate the performance of FedOpt on MNIST dataset where the gradient consists of 60,000 training examples and each example consists of 28 \(\times\) 28 size images. Then, similar to MNIST dataset, we assess the performance of FedOpt on CIFAR-10 dataset where the gradient consists of 50,000 training examples and 10,000 testing examples and each example consists of 32 \(\times\) 32 size images with three different RGB channels. The baseline configuration setup is given in Table 1.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Parameters** & **Number of Users** & **Participation Ratio** & **Mini-Batch Size** & **Classes per User** & **Gradient Size** & **Number of Epochs** & **Privacy Budget** \\ \hline Value & Various & 10\% & 20 & 10 & 32-bits & Various & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline Configuration.
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 3 Proposed FLDHDT  
### System Architecture  
The 5G UDN we consider in this paper is heterogeneously composed of 4G BSs (i.e., evolved NodeB, eNB) and 5G BSs (i.e., next-generation NodeB, gNB) overlaid on the coverage of 4G BSs. Option 3 of the non-standalone (NSA) networking mode is used, in which the control functions rely on the control plane of the existing 4G networks while the 5G network focuses on the user plane [10]. The NSA networking mode--specifically the coexistence of 5G and 4G--is apt for accelerating the commercialization of 5G. By using Option 3 of the NSA networking mode, UEs are configured with dual connectivity technology, meaning that they are connected to both eNB and gNB. Control signalling is transmitted from the eNB to the evolved packet core (EPC) network of 4G, and user data are preferentially transmitted from the gNB to the EPC through the eNB, as depicted in Figure 3.  
Figure 3: Option 3 of the NSA networking mode.
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 7 Experiments
### Evaluation--Drone Authentication Protocol  
We also evaluated the performance of the authentication protocol in SENTINEL and compared it with the \"TLS for IoT\" protocol with client authentication. We assumed that the size of the message (\(msg_{d}\)) to be authenticated was 40 bytes. In the case of the \"TLS for IoT\" protocol with client authentication, the handshaking procedure was first performed between the drone \(d\) and the ground station \(gs\), and the drone \(d\) then creates a message for authentication by signing the message with its private key \(sk_{d}\), while in SENTINEL, the drone \(d\) just computes the message authentication code using the flight session key without the handshaking procedure. Under this assumption, we performed measurements on the authentication protocol of both SENTINEL (described in Figure 4) and the conventional system. As shown in Table 4, the average execution time of SENTINEL was 178.57 milliseconds with the standard deviation of 35.07 milliseconds, and the message sizes were 72 bytes in total. For the \"TLS for IoT\" protocol, on the other hand, the average execution time was 549.75 milliseconds, with the standard deviation of 84.27 milliseconds, and the message size was 1644 bytes in total. In comparison, the execution time of SENTINEL (178.57 milliseconds) was about 3.1 times faster than the \"TLS for IoT\" protocol (549.75 milliseconds) on average. Furthermore, the total size of the messages used in SENTINEL was 1572 bytes smaller than the \"TLS for IoT\" protocol. In addition, Figure (b)b shows the distributions of the execution times for the drone authentication protocols, respectively, in SENTINEL and \"TLS for IoT.\"  
\begin{table}
\begin{tabular}{r r r r} \hline \hline  & \multicolumn{2}{c}{**SENTINEL**} & \multicolumn{1}{c}{**TLS for IoT**} \\ \hline
**Time (ms)** & **Avg.** & 178.57 & 549.75 \\  & **Std.** & 35.07 & 84.27 \\ \hline
**Message size (bytes)** & **Total** & 72 & 1644 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results of drone authentication protocol.  
Figure 8: Execution time comparison between SENTINEL and “TLS for IoT.”
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design
### Adaptive Q-Learning Parameters  
In Q-learning, the learning rate controls the step size at which the RL algorithm updates its value function or policy. It determines the extent to which newly acquired information supersedes old information. A higher learning rate leads to faster updates of Q values. The learning rate typically falls within the range of 0 to 1. Most existing Q-learning-based routing protocols have fixed learning rates. However, in VANETs, the links between vehicles can be highly unstable. Therefore, this study dynamically adjusts the learning rate with relative velocity adaptively. In this study, the adaptive learning rate (\(\alpha\)) is controlled by a linear function of the stability of the link from node \(v\) to node \(w\) and is defined as follows:  
\[\alpha=\alpha_{min}+(\alpha_{max}-\alpha_{min})\times LSF(I), \tag{13}\]  
where \(\alpha_{min}\) serves as the lower threshold for the learning rate, thereby ensuring a foundational update rate, while \(\alpha_{max}\) functions as the upper threshold for the learning rate, thereby offering a degree of constraint.  
The discount factor controls the extent to which a reinforcement-learning algorithm prioritizes future rewards. The range of values for the discount factor lies between 0 and 1. When \(\gamma=0\), it implies a focus solely on immediate rewards, while \(\gamma=1\) indicates the full consideration of future rewards. The discount factor plays a crucial role in balancing immediate rewards and future rewards.  
The adjustment of the discount factor should be balanced according to the specific problem at hand. If future rewards significantly influence current decisions in the task, then the discount factor should be set to a higher value. Conversely, it should be reduced to a lower value if future rewards have less impact. In the context of VANET routing decisions, having reliable neighbors for data-packet forwarding represents a higher level of influence on current decision making. Therefore, it is necessary to increase the discount factor. The discount factor is defined as follows:  
\[\gamma=LDF(l)\times RSSF(l)\times ABWF(v)\times\gamma_{max}, \tag{14}\]  
where \(LDF\) is the link duration factor defined by Equation (8), \(RSSF\) is the received signal-strength factor defined by Equation (9), and \(ABWF\) is the available bandwidth factor defined by Equation (12). \(\gamma_{max}\) indicates the upper limit constraint of discount factor. The value ranges from 0 to 1. It is evident from Equation (14) that to ensure stable data transmission, all three factors, namely LDF, RSSF, and ABWF, should be taken into account. A longer link duration factor ensures that communication remains within a reliable range, a larger received signal-strength factor minimizes environmental interference, and a higher available-bandwidth factor guarantees a better communication environment.
# Computational Study on Rogue Wave and Its Application to a Floating Body
## 2 Computational Methods
### Numerical Methods  
The time derivative term was discretized using a second-order accurate backward implicit scheme, with the constant time step size of \(0.001\) s used. The solution gradients at the cell centers were evaluated using [PERSON]'s method. The convection terms were discretized using a total variation diminishing (TVD) scheme with the van Leer limiter, and for the diffusion terms, a central differencing scheme was used. The velocity-pressure coupling, and overall solution procedure were based on the PIMPLE algorithm, which combines the semi-implicit method for pressure linked equation (SIMPLE) algorithm and the pressure-implicit with splitting of operators (PISO) algorithm. SST \(k\)-\(\upomega\) turbulence model [20] with the wall function [17] was used for the turbulence closure. The discretized algebraic equations were solved using a Gauss-Seidel iterative algorithm, while an algebraic multi-grid method [21] was employed to accelerate solution convergence.
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 4 Assisting Drivers around Stop Sign Intersection
### Driving Assistance Protocol at Stop Sign Intersections
#### 4.2.1 Configuring the Intersection's Topology and Stop Signs' Distribution Case  
The digital maps installed on the traveling vehicles notify the drivers early regarding the approaching stop sign intersections. The topology of the targeted intersection is matched to one of the three declared typologies (i.e., T-junction, Y-junction, and X-junction) in Section 3. Then, the input traffic flows controlled by stop signs should be extracted to determine the traversed case of the signalized intersection.  
The installed GPS in each vehicle determines its relative location to the targeted intersection. Accurate location results are obtained by the alignment of visual data with the installed digital map [24; 25]. Vehicles arriving at the intersection from an input traffic flow free of a stop sign can pass directly through the intersection without stopping. This traffic flow is considered the main flow at the intersection. The main flow can be a unidirectional or two-directional flow. In T-junction and X-junction, when the arriving vehicle on the main two-directional flow aims to take the left exit on the intersection, it should stop and wait for a suitable gap on the other side. In case the main traffic flow is one direction, vehicles can pass through the intersection directly without stopping even for the left exit. However, in the Y-junction, the arriving vehicles on the main flow can proceed directly to the intersection's right and left.  
On the other hand, vehicles arriving at the intersection on a traffic flow controlled by a stop sign should come to a complete stop. This is regardless of the targeted direction of the vehicle on the investigated road intersection.
## 4 Results
### EASY Concept Design Details: System Functionality for Different Users
#### 4.4.1.1 Content Authoring and Packaging of Information  
A key requirement described by workshop stakeholders was the need to make information more integrated and focused upon specific extension delivery tasks. Though considerable research and other agricultural management information exists, this information was considered to be not easily accessible and not in a format useful for extension delivery activities. A clear role for EASY would be to support the grouping of spatial datasets, textual web-based content, modelled outputs, executable models, and spatial datasets as topic packages that include all of the information needed by extension officers, agricultural consultants and landholders to complete a specific agricultural extension activity. Where possible, EASY should be designed to enable non-technical users to create topic packages and publish information. Moreover, where there are existing information systems, EASY should integrate with those systems rather than duplicating their functionality.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 3 Results
### Modelling  
When modelling our data, Figure 6 shows a histogram of both the service and inter-arrival times of a normal period. We use this histogram to discern different partitioning and transformation techniques to support parametric and non-parametric modelling. We also model by file size. We do this to understand the range of files to be ingested into the queueing system, and we also use it to understand the required storage needed for simulation testing of these messages.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline \multicolumn{3}{c}{**Time**} \\ Hour & Model by each hour in the dataset \\ Busy Period & The number of messages queued was always bigger than zero and growing. \\ Scheduled & Different times where we observed a higher volume of messages, like a range of minutes around midnight, the 15 min, 30 min and 45-min mark. \\ Normal Period & Opposite of the busy period \\ \hline \multicolumn{3}{c}{**Data Partition**} \\ Tail & We partition the data and retain its tail (values \(>\) than some limit) \\ Head & We partition the data and retain its head (values \(<\) than some limit) \\ Remove Zeros & Remove all zero values from the dataset \\ \hline \multicolumn{3}{c}{**Batch**} \\ Batch By Category & We filter where “Category” contains “Flat Translation” and “Batch \\ XML Translation” & We count the number of times a message arrives. If the message only arrives once, we take this message. We then take any messages that arrive where the count is greater than one. If the count is greater than 1, we then take the first and last message and ignore all messages in between. \\ Batch By Split Count & Count the number of times a message arrives, and we count the number of \\ XML documents. The count of XML Documents should be greater than 1. \\ \hline \multicolumn{3}{c}{**Non-Batch**} \\ Non-Batch By Split Count & Count the number of times a message arrives, and we count the number of \\ XML documents. The count of XML Documents should be equal to 1. \\ We filter where “Category” does not contain “Flat Translation” and “Batch \\ XML Translation” & “Batch \\ \hline \hline \end{tabular}
\end{table}
Table 6: Message classification data dictionary.  
Figure 6: Histogram: Normal Period: Service And Inter-arrival Times.
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 2 Functions of Heat Transfer and Mechanical Performances
### The Function of Heat Transfer Performance
In order to get \(q_{0}\), it is necessary to obtain heat flux \(q_{1}\) which enters internal fluid directly from the target surface:  
\[q_{1}=h_{1}\Big{[}T_{w}(n)-T_{f}(n)\Big{]}=\frac{h_{1}q_{0}}{k_{s}m}\cdot\frac {\cosh(ml)}{\sinh(ml)} \tag{9}\]  
where \(h_{1}\) is the heat transfer coefficient between air and target surface. In order to simplify the calculation, thermal resistance between member and target surface is ignored, so \(h_{1}\) and \(h_{\infty}\) are considered to be the same. Therefore, the heat flux \(q\) entering the \(n\)th row of LS core is:  
\[S_{x}\cdot S_{y}\cdot q=\bigg{(}S_{x}\cdot S_{y}-\frac{3\pi D^{2}}{4}\bigg{)} q_{1}+\frac{3\pi D^{2}q_{0}}{4} \tag{10}\]By combining Equations (9) and (10), the following equation can be obtained:  
\[q_{0}=\frac{S_{x}\cdot S_{y}\cdot q}{\left(S_{x}\cdot S_{y}\cdot\frac{3\pi D^{2}}{ 4}\right)\cdot\frac{h_{1}q_{0}}{k_{s}m}\cdot\frac{\cosh(ml)}{\sinh(ml)}+\frac{3 \pi D^{2}}{4}} \tag{11}\]  
In conclusion:  
Figure 2:0  
By combining Equations (7), (11), and (12), the final equations can be obtained. The formula of \(Nu\) for a single core of PLS is:  
Figure 2:1  
The formula of \(Nu\) for a single core of XLS is:  
Figure 2:2  
The function of average \(Nu\) for a whole LS channel is:  
Figure 2:3  
where \(N\) was 5, which meant that the whole LS channel had five LS cores. \(f_{1}(x)\) was the first objective. This function was validated as follows. Material used in the simulation was IN 718, as shown in Figure 2:4  
It can be observed from Figure 3 that with the increase of \(Re\), the trend for the growth of \(Nu\) was consistent between the experiment value of Kim [38] and the calculated value of PLS channel with the fin method. Meanwhile, the maximum error between the calculated value with the fin method and the experiment value of Kim [38] was approximately 17%. Therefore, the given function of \(Nu\) was reliable. Furthermore, compared with 5 LS cores, when 10 LS cores were set, the average \(Nu\) decreased about 23.1%; when 15 LS cores were set, the \(Nu\) decreased about 47.9%. Therefore, five LS cells were set in flow direction of LS channel to reduce the overall size and improve heat transfer performance of the cooling structure, as shown in Figure 2:5 Generally, the pressure drop of the cooling channel gradually increases with the increase of \(Re\). However, when \(Re\) reached up to 10,000, the pressure drop was only around 300 Pa. In addition, the changing \(D\) and \(\omega\) of LS had little effect on the changing of pressure drop of LS channel. Thus, pressure drop was not further investigated.