esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
## 4 Results
### Evaluation by Pre-Trained Model Used as Backbone  
How much does the pre-trained network selected as the backbone influence the F1-score? To answer this question, the 864 models are grouped according to the backbone  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Type** & **Hyperparameter** & **Number of Options** & **Values** \\ \hline \multirow{4}{*}{Architecture} & \multirow{4}{*}{Pre-trained model} & \multirow{4}{*}{8} & VGG19, ResNet50, InceptionV3 \\  & & & InceptionResNetV2, MobileNetV2 \\  & & & DenseNet201, EfficientNetB5, Xception \\  & Network Depth & 4 & L1 (deeper), L2, L3, L4 (shallower) \\  & Neurons (FC layer) & 3 & 256, 512 and 1024 \\ \hline \multirow{2}{*}{Training} & Optimizer & 3 & SGD, Nadam, RMSprop \\  & Learning Rate (LR) & 3 & \(1\times 10^{-4},1\times 10^{-5},1\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stage 2: Hyperparameters and values selected in this study.  
used, i.e., 108 models per case in this study, and their results are plotted as mean, first, and third quartiles using box plots (see Figure 4).  
According to Figure 4, an inappropriate choice of pre-training model will provide F1-score below 20%, while an appropriate choice will provide values above 90%. Specifically, EfficientNetB5 is the least appropriate pre-trained network of the set of options defined in this study, while DenseNet201 is the best option. Other backbones have similar behavior to DenseNet such as: Xception, InceptionV3, MobileNetV2, VGG19, and InceptionResNetV2 (from best to worst). Finally, ResNet50 is not a competitive pre-trained model for this classification problem.  
In summary, this hyperparameter has a very high impact on model performance (with variations of up to 70% among them).
## 1 Introduction  
Gears play an important role in power transmission mechanisms and are widely used across various industries. With the growing demand for high-volume production, there is a need to manufacture transmission gears that are both highly efficient and low in noise. The properties of gear tooth flanks generally affect not only the strength but also the efficiency and noise level of the gears in power transmissions [1; 2; 3; 4; 5]. While high precision grade gear pairs are believed to produce low-noise and low-vibration transmission systems, there are instances where even with the use of such gears, quiet gear power transmissions cannot be achieved [6; 7; 8]. This suggests that the current gear accuracy evaluation system may still be insufficient. The existing gear inspection parameters only account for tooth profile deviation and tooth helix deviation of individual teeth, without considering the relative evaluation among the deviations that may cause unnecessary vibration during power transmission [9; 10].  
Network theory is an analytical approach that is commonly employed to analyze complex system structures, such as railway networks, airline networks, and the network of spreading of COVID-19, as well as social network analysis of attitudes towards immigrants [11; 12; 13]. In addition, the method can be utilized to understand and elucidate the deviation of manufactured gears, for which [PERSON] et al. proposed a novel approach based on network theory for expressing the relationship between tooth helix deviation curves in helical gears [14; 15]. The study is accomplished by calculating the inner product between two helix deviations, which generates correlation coefficients which is an indicator capable of acquiring the phase difference between the deviations in the direction of the helix. Based on this approach, a network image of the gear is constructed, with each tooth of the gear representing a node and the correlation coefficient representing a link, respectively. Consequently, a comprehensive overview of the entire helix deviation curve of a gear is obtained, a feat that was previously challenging to achieve using conventional chart diagrams of helix deviation. However, to date, no detailed studies have created a tooth helix deviation network using deviation data of plastic gear manufactured through injection molding, nor have any studies investigated the impact of the manufacturing method on the phase difference network of the gear.  
In this paper, this study aims to develop an evaluation method to assess the relative relationship between shape deviations on each tooth of an injection-molded gear by utilizing network theory. Firstly, the gear tooth shape deviation on each tooth of the injection-molded plastic gear was measured, and the data acquired were used to construct a helix deviation network. Subsequently, the adjacency matrix of the network was output as an image in the form of a pixel plot. The periodic pattern that formed in the pixel plot of the helix deviation network was then meticulously investigated by generating network images consisting of various fixed correlation coefficient ranges.
## 3 Results
### Effects of the Background on LAI Estimation
#### 3.2.3 Estimation of LAI by Regression Analysis of CIs  
Similar to Section 3.2.3, we also opted to use three CIs to construct a multiple linear model and assess the impact on image segmentation for LAI evaluation. The performance of the models is shown in Table 2. Across all growth stages, the model based on the segmented CIs exhibited the highest R\({}^{2}\) values compared to the model based on the original CIs and original images and the model based on Retinex-corrected CIs. This indicated that image segmentation effectively enhanced the accuracy of LAI simulation and highlighted the significant impact of background elements on LAI estimation. Compared to the Retinex-corrected CI models, the R\({}^{2}\) value of segmented CI models in the tillering and heading stages showed the greatest improvement, with an increase of 0.11. The segmented CI models for the jointing stage showed the least increase at only 0.04. Overall, image segmentation enhanced the estimation accuracy of LAI. Background elements can have significant effects on the estimation of LAI by CIs.  
Figure 5: Boxplot of Retinex-corrected CIs of rice and image backgrounds. T, E, B, H, and G represent tillering stage, elongation stage, booting stage, heading stage, and grouting stage, respectively.
## 5 Plant Propagation Algorithm-Based Controller Performance Analysis
### Performance under Varied Conditions  
During the optimization process, the controller was tuned under only a certain condition, but to be a successful controller it must run optimally irrespective of the environmental (irradiance and temperature) conditions. Hence the controller was tested under varied irradiance conditions. A properly tuned controller should be able to deliver real and reactive power as per the reference values. For the testing purpose, the irradiance of the system varied quickly with a wide range of values. It started with 200 W/m\({}^{2}\), then suddenly changed to 800 W/m\({}^{2}\) at 3 s, followed by 600, 700, and 400 W/m\({}^{2}\) at 4, 5, and 6 s respectively. The temperature was fixed at 25 \({}^{\circ}\)C, although it can also be varied. The corresponding reference and simulated power outputs can be seen in Figure 15c. Reference reactive power was also varied during this period in such a way that it does not violate the \(Q\leq\sqrt{S^{2}-P^{2}}\) condition. The DC link voltage controller was found to track the reference value very efficiently.  
The performance of the proposed controllers was also tested under voltage sag condition. With a symmetrical voltage sag of 50% with 0.15 s duration, the controllers were still able to reach the steady-state comfortably. The sudden rises in responses at 0.2 and 0.35 s from Figure 16b,c represent the changes in voltage level.  
In Figures 15 and 16, red lines belong to references and blue lines belongs to response values.  
To validate the robustness of the PPA-based proposed controller, performance under varied conditions of the system parameters such as filter and transformer resistances and inducances were also conducted. Parameters are varied between \(-10\%\) to \(10\%\) of their nominal values, which can be found in Table 1. The corresponding fitness values are given in Table 6.  
Figure 14: Total harmonic distribution (THD) analysis of inverter-injected currentFigure 16: Controller performance under symmetrical 50% voltage sag.  
Figure 15: Performance of the proposed controller under different irradiance conditions.  
From Table 6 it can be seen that although system parameters varied widely between \(-10\%\) to \(+10\%\), the objective function values did not vary much and stayed very near to the tested condition objective function value (0.2918).
# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 1 Introduction
[PERSON] and [PERSON] [34] proposed a new adaptive sparrow search algorithm for optimal model parameter identification of proton exchange membrane fuel cell stacks. [PERSON] et al. [35] proposed an improved sparrow search algorithm for distributed maximum power point tracking. [PERSON] et al. [36] proposed an adaptive spiral flying sparrow search algorithm, which reduces the probability of getting stuck into local optimum and has stronger optimization ability than other algorithms. [PERSON] et al. [37] combined the PSO algorithm with SSA algorithm to speed up the convergence speed before the SSA individual update. [PERSON] et al. [38] proposed an enhanced SSA algorithm based on reverse learning and work function mechanism to improve the search conditions. [PERSON] et al. [39] used the SSA algorithm to perform multi-component fault diagnosis on wheelset bearings. [PERSON] et al. [40] proposed a dynamic adaptive SSA algorithm based on Bernoulli chaotic mapping, the SSA algorithm of weighting, Cauchy mutation and reverse learning to improve the efficiency of microgrid clusters. Compared with other algorithms, the SSA has higher stability and better convergence accuracy. It can escape falling into the local optimum to a certain extent. However, due to the fast convergence rate, there is still a probability that no feasible solution can be obtained. In order to solve these existing problems, a novel adaptive SSA algorithm based on the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy is proposed in this paper. The sine chaotic mapping strategy is employed to initialize the population in order to reduce the impact of population randomness, and then the adaptive weighting strategy is used to better perform the global search. The t-distribution mutation strategy is used to update the position of the individual sparrow in order to find a more reliable and feasible solution and improve the optimization performance of the SSA algorithm. The innovations and main contributions of this paper are described as follows. * A novel CWTSSA based on the strategies of chaotic mapping, adaptive weighting and t-distribution mutation is developed to effectively solve the complex optimization problem. * The sine chaotic mapping strategy is used to initialize the population in order to enhance the diversity of the population. * The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. * The adaptive t-distribution mutation operator is designed to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. * Comprehensive experiments are designed and executed to comprehensively prove the effectiveness of the CWTSSA by 15 standard test functions.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 5 Proposed Future Security Mechanisms
### Artificial Intelligence for 6G
#### 5.3.1 RAN Intelligent Controller  
5G networks are incredibly complex from a protocol and software reliance perspective but even more so from a system management perspective. This is especially true for the complex, highly time-dependent and demanding task of radio resource management. To allow more responsive system operations, and to widen interoperability opportunities, O-RAN presents a significant change to the RAN organizational and operational structure. In addition, it created the opportunity to improve the monitoring and management of the RAN's most crucial tasks in the form of the RAN Intelligent Controller (RIC) defined by 3 GPP. The RIC comes in two variants, Non-Real Time and Real Time. Both of these  
Figure 12: Results from [134] for popular PQC algorithms key generation, encapsulation, and decapsulation performance on a Raspberry Pi 4.  
mechanisms are provided through virtualized network functions (VNFs) and serve to optimize RAN operations including improving radio unit efficiency, network slicing, high-bandwidth and low-latency provisioning, as well as for beamforming and radio resource management [138]. Researchers in [139] created their own ML workflow and executed the model on O-RAN compliant hardware. Through their study, they present suggestions on making the RIC more efficient by way of hardware accelerators, federated learning, and ML model packaging, to name a few. The RIC presents numerous opportunities for AI/ML-assisted 5G operations, which are expected to become central in the development of 6G as well.
# Rough-Terrain Path Planning Based on Deep Reinforcement Learning
## 2 Model Construction
### Energy Consumption Model  
The main energy consumption of the vehicle during driving comes from the air resistance, tire rolling resistance, gravity, acceleration energy consumption, and braking energy consumption. Assuming that the vehicle travels at a constant speed, the vehicle force analysis is shown in Figure 1; then, we calculate the following:  
\[\begin{array}{l}F=F_{r}+mg\sin\theta+F_{a}\\ =f_{rr}(s)mg\cos\theta+mg\sin\theta+\frac{1}{2}\rho Ac_{D}v^{2}\end{array} \tag{5}\]  
where \(F_{r}\) is the tire rolling resistance; \(F_{a}\) is the air resistance; \(m\) is the mass of the car; \(f_{rr}(s)\) is the rolling resistance coefficient, and the value in this paper is 0.01; \(g\) is the acceleration of gravity; \(\theta\) is the angle of the gradient; \(\rho\) is the air density; \(A\) is the area of the car's front surface; \(c_{D}\) is the coefficient of resistance; and \(v\) is the average speed between nodes. Its energy consumption is finally converted into work performed to overcome tire resistance (\(E_{r}\)), work performed to overcome gravity (\(E_{g}\)), and work performed to overcome air resistance (\(E_{a}\)). Then, the energy consumption of a car when traveling a distance of \(d\) uphill and downhill can be expressed as follows:  
\[E_{ud}=E_{udr}+E_{uda}+E_{g}=f_{rr}(s)mg\cos\theta\cdot d+\frac{1}{2}\rho Ac_{D }v^{2}\cdot d+mg\sin\theta\cdot d \tag{6}\]  
where \(E_{udr}\) and \(E_{uda}\) represent the work performed to overcome tire resistance and air resistance when going uphill and downhill, respectively. The energy consumption of a car when traveling at a constant speed on a flat road can be expressed as follows:  
\[E_{f}=E_{fr}+E_{fa}=f_{rr}(s)mg\cdot d+mg\sin\theta\cdot d+\frac{1}{2}\rho Ac_{ D}v^{2}\cdot d \tag{7}\]where \(E_{fu}\) and \(E_{fu}\) represent the work performed to overcome air resistance and tire resistance when driving on a flat road. To eliminate the effect of different vehicles, this paper defines the uphill and downhill unit energy consumption as follows:  
\[E_{s}=\frac{E_{ud}}{E_{f}} \tag{8}\]  
Its unit is u/m, where 'u' is a standardized energy unit defined as multiples of joules, and the unit energy consumption varies for different vehicles going up- and downhill. When traveling at 45 km/h, the work performed to overcome air resistance is about one-quarter of the work performed to overcome tire resistance; i.e., \(E_{uda}=E_{fa}=\frac{1}{4}E_{fr}\). Therefore, the unit energy consumption of the uphill and downhill is as follows:  
\[E_{s}=0.8\cos\theta+80\sin\theta+0.2 \tag{9}\]  
Therefore, the energy consumption between two neighboring nodes (\(i,j\)) can be expressed as follows:  
\[E(i,j)=\max(D_{3}(i,j)\cdot E_{s},0) \tag{10}\]  
The total path energy consumption when the whole path has stable \(n\) sampling points is as follows:  
\[E_{total}=\sum_{i=1}^{n-1}E(i,i+1) \tag{11}\]
# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 2 Materials and Methods
### Computational Framework
* Recall Score: In mathematical terms, it may be expressed as the ratio of properly predicted positive instances to the total number of true positive cases, as reported in the following Equation (12) \[RS=\frac{TP_{k}}{TP_{k}+FN_{k}}\] (12) where \(FN_{k}\) represents the number of false negatives for the class \(c_{k}\). * F1-Score: It is the harmonic mean of Precision and Recall, thus emphasizing both high precision and recall. Its mathematical formulation is given by the next Equation (13) \[F1=\frac{2 TP_{k}}{2 TP_{k}+FP_{k}+FN_{k}}\] (13) where \(FP_{k}\) is the total number of false positives for the class \(c_{k}\). * Jaccard Score: It is computed as the ratio of the intersection size between the predicted and actual label sets to the size of their union. Such metric is calculated according to the following Equation (14) \[JS=\frac{|y_{i}\cap\hat{y}_{i}|}{|y_{i}\cup\hat{y}_{i}|}\] (14) \begin{table}
\begin{tabular}{c c l l} \hline \hline
**Model** & \(\mathbf{\theta}\) & **Description** & **Range/Set** \\ \hline SVC & \(\theta_{1}\) & Kernel, \(\phi\) & 0: linear, 1: rbf, 2: sigmoid \\  & \(\theta_{2}\) & Bandwidth parameter, \(\gamma\) & [\(10^{-5}\), 1000] \\  & \(\theta_{3}\) & Regularization parameter, \(C\) & [\(10\), 10,000] \\ \hline MLP & \(\theta_{1}\) & \(L_{2}\) regularization term, (\(\alpha\)) & [0.0001, 1] \\  & \(\theta_{2}\) & Number of hidden layers (NL) & [1, 3] \\  & \(\theta_{3}\) & No. neurons in the layers (NN) & [1, 50] \\  & \(\theta_{4}\) & Activation function (\(\varphi\)) & 0: Identity, 1: Logistic, 2: Tanh, and 3: ReLU \\  & \(\theta_{5}\) & Solver & 0: Aadam, 1: lbfgs, 2: sgd \\ \hline XGB & \(\theta_{1}\) & L1 regularization on weights (\(\alpha\)) & [0, 1] \\  & \(\theta_{2}\) & L2 regularization on weights (\(\lambda\)) & [0, 1] \\  & \(\theta_{3}\) & Learning rate & [\(10^{-6}\), 1] \\  & \(\theta_{4}\) & No. estimators & [\(10\), 300] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The below table reports the encoding of candidate solutions for hyperparameters where, for each ML model parameter, it is respectively reported the search range or set. where the actual label set is represented by \(y_{i}\) while \(\hat{y}_{i}\) stands as the predicted label set.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 3 Proposed Update Methods
### Broadcast + Unicast Method  
This version of the update protocol (see Figure 2) makes use of broadcast LoRa frames. However, two unicast stages are also required, as we will next explain.  
Figure 1: Stages of the _only unicast_ method.  
Each node stores a variable with its current firmware version number, and a bitmap in which each bit is set if the corresponding chunk has been received.  
These are the stages:  
(a) _Initial stage (unicast)_. The gateway sends a unicast message _OTA BROADCAST START REQUEST_ to each of the nodes. The message includes the number of the new firmware version, the number of chunks, and the size of each chunk (note that the last chunk may be smaller than the rest). When a node receives this message, it sets all the bits of the bitmap to 0 and answers with an _OTA BROADCAST START CONFIRMATION_. This message includes the number of the current firmware version running in the node.  
(b) _Sending of the binary (broadcast)_. The gateway starts sending broadcast _OTA BINARY FRAGMENT_ frames, which also include the identifier of the chunk. When a node receives one of these messages, it stores the received bytes in the corresponding position, calculated from the initial memory position, and the identifier is multiplied by the size of the chunk. It also sets the corresponding bit of the bitmap to 1. There is no acknowledgement. The gateway may perform one or more broadcast rounds. If a node has already received a chunk, it just discards new messages containing the same chunk. After this stage, while  
Figure 2: Detail of stages (a), (b), and (c) of the _broadcast + unicast_ method. The red crosses correspond to lost frames.  
some nodes may have received all the chunks, others may have only received a portion of them.  
(c) _Sending of the pending chunks (unicast)_. The gateway starts with the first node. It sends a message _SEND ME THE BITMAP_. The node answers with a _BITMAP_ frame, which specifies the identifier of the missing chunks. The gateway sends all these chunks in unicast frames, following the _only unicast_ method, but this only applies to the pending chunks.  
After this, the method is equal to the three last stages of the unicast mode (not detailed in Figure 2), namely _Sending of the checksum_, _Checksum validation and firmware flashing_, and _Final confirmation_.
# Reconfigurable Antennas: Switching Techniques--A Survey
## 2 Electrical Reconfiguration  
In this type of reconfiguration method, the antenna characteristics are changed using electronic switching components such as PIN diodes, varactors or MEMS. Using these switches, the antenna structure can be reconfigured, which causes the redistribution of the surface current and alters the antenna's fundamental characteristics in terms of frequency, radiation pattern and polarization. The implementation of such a reconfigurable antenna with switching elements is easy and has received lots of attention in research [29; 30; 31; 32]. Next, different methods along with some examples of electrically reconfigurable antennas to obtain the corresponding reconfigurability function with their own advantages and disadvantages using PIN diodes, varactors or MEMS switches are described.