esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Tomato Disease and Pest Detection Method
## 5 Experiment and Result
### Model Evaluation Metrics and Training Parameter Settings  
The experimental part of this paper uses mean average precision (mAP) of 50 and 50:95 as metrics for model detection accuracy; FLOPs are used as a measure of model computation; the higher the FLOPs, the higher the computational complexity of the model, which requires more computational resources to complete training and inference. Parameters is the number of parameters to be learned in the model, and in general, the higher the number of parameters, the higher the complexity of the model, which requires more data and computational resources for training and inference. mAP50 and mAP50:95 were chosen as metrics to assess detection accuracy because they can simultaneously assess the model's ability to locate and classify targets for detection, which is more reflective of the model's detection capability than accuracy and recall. AP50 is the average accuracy of each category at an IoU threshold of 0.5, and similarly, AP95 is the average accuracy of each category at an IoU threshold of 0.95. mAP is the average of the APs of all categories at that IoU threshold, calculated as follows:  
\[mAP=\frac{1}{n}{\sum_{j=1}^{n}}AP(j) \tag{8}\]  
\[AP50=\frac{1}{n}{\sum_{i=1}^{n}}P_{i}^{IoU=0.5}\big{(}R_{i}^{IoU=0.5}\big{)} \tag{9}\]  
\[AP50:95=\frac{1}{10}(AP50+AP55+\ldots+AP95) \tag{10}\]  
In the formula, \(n\) is the number of categories, \(R\) is the recall rate, which is the ratio of the number of true positive and predicted positive samples to the total number of true positive samples, and \(P\) is the precision rate, which is the ratio of the number of samples that are true positive and predicted positive samples to the total number of predicted positive samples.  
For the experimental part, the official YOLOv5 recommended hyperparameters were used for the model training parameter settings. These hyperparameters were selected based on experiments conducted on the MS COCO dataset.  
Figure 9: Tomato pest and disease example diagram.
## 4 Experiment and Results
### Computational Complexity Analysis  
In this section, we compare the computational complexity of the model proposed in this study with popular sEMG-based hand gesture recognition methods to highlight the advantages of our approach in managing complexity. Drawing on the previously mentioned dataset, we selected the widely used NinaPro dataset as a benchmark and compared models with window lengths of up to 300 ms. Given the lack of a unified standard for window lengths in prior research, we opted for a comparison using a 250 ms window length.  
In Table 6, we present the recognition performance and parameter size of current popular gesture recognition models on the NinaPro DB4 and DB5 datasets. Compared to the existing optimal model, EMGHandNet, which boasts an accuracy of 89.5%, our proposed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database/** & **NinaPro** & **NinaPro** & **BioPatRec** & **BioPatRec** & **BioPatRec** \\
**Methodology** & **DB4** & **DB5** & **DB1** & **DB2** & **DB3** \\ \hline Multi-View CNN [26] & 54.3 & **90.0** & 78.2 & **94.0** & 50.5 \\ HVPN [36] & 67.0 & 87.1 & - & - & - \\ Attention sEMG [37] & 73.0 & 87.0 & - & - & - \\ EMGHandNet [38] & **89.5** & - & - & 83.9 & - \\ TDCT [39] & - & 72.8 & - & - & - \\ SE-CNN [40] & 77.6 & 87.4 & - & - & - \\ Ours & 83.0 & 88.3 & **91.5** & 91.6 & **90.9** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy (%) with different models. Bold indicates the best results under different models for the same dataset.  
model is slightly behind by 6.5% in accuracy. However, in terms of parameter size, our model accounts for only 3.8% of EMGHandNet. This significant difference demonstrates that our model substantially reduces parameter size while maintaining relatively high accuracy, thereby greatly saving on the resource consumption of the model. Apart from the optimal model, compared to other models, our model exhibits clear advantages in both accuracy and parameter size.  
In comparison to the HVPN and Multi-View CNN models, which employ a multi-feature approach on the NinaPro DB5 dataset, our proposed model exhibits a slight decrease in accuracy--approximately 1.7% lower than that of the Multi-View CNN model. However, our model achieves this performance with only 5% of its parameter scale. Unlike methods focusing on the fusion of multiple features, our approach, centered on channel feature selection and compression, appears to be better-suited for relevant recognition tasks. Despite a minor performance loss compared to the optimal model, the reductions in parameter scale and computational complexity contribute to an overall enhancement of our model's performance. Additionally, when contrasted with attention mechanism-based models such as Attention sEMG and TDCT, our model outperforms them both in terms of accuracy and parameter count. This suggests that in recognition tasks, improving model performance through local feature attention and the selection of key features, as opposed to a global focus on sEMG signal features, may be pivotal.
## Physical Layer Network Coding Based on Integer Forcing  
Precoded Compute and Forward  
**[PERSON] * and [PERSON]**  
Department of Telecommunications and Systems Engineering, Autonomous University of Barcelona,  
Bellaterra, Barcelona 08193, Spain; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-935-868-585; Fax: +34-935-814-031.  
_Received: 2 July 2013; in revised form: 19 July 2013 / Accepted: 5 August 2013 / Published: 28 August 2013_  
**Abstract:** In this paper, we address the implementation of physical layer network coding (PNC) based on compute and forward (CF) in relay networks. It is known that the maximum achievable rates in CF-based transmission is limited due to the channel approximations at the relay. In this work, we propose the integer forcing precoder (IFP), which bypasses this maximum rate achievability limitation. Our precoder requires channel state information (CSI) at the transmitter, but only that of the channel between the transmitter and the relay, which is a feasible assumption. The overall contributions of this paper are three-fold. Firstly, we propose an implementation of CF using IFP and prove that this implementation achieves higher rates as compared to traditional relaying schemes. Further, the probability of error from the proposed scheme is shown to have up to 2 dB of gain over the existent lattice network coding-based implementation of CF. Secondly, we analyze the two phases of transmission in the CF scheme, thereby characterizing the end-to-end behavior of the CF and not only one-phase behavior, as in previous proposals. Finally, we develop decoders for both the relay and the destination. We use a generalization of [PERSON]'s theorem to justify the construction of these decoders. Further, we make an analytical derivation of the end-to-end probability of error for cubic lattices using the proposed scheme.  
**Keywords:** compute and forward; lattices; relaying; probability of error; precoding; physical-layer network coding; signal-to-noise ratio
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 4 Discussion and Analysis
### Future Work  
In this research, we transform the input model into distance field by using the eikonal equation. It is possible to deduce advanced encryption methods for volume models, based on the distance field computation. For example, varying the propagation speed, \(f\), in some voxels in the AABB will distort the distance field, making it harder to segment the model from the volume data. Besides, the initial distance field value on the model surface can be kept in secret such that comprehending the data set would become more difficult.  
In this research, the SOM procedure helps us to embed watermarks. It is capable of creating cavities inside models and generating engraved and embossed patterns on model surfaces. We plan to further exploit this technology and build editing tools for volume models such that we can carve and reshape simple models to create complex objects that are difficult to create by means of polygonal-based or parametric-mesh modelling methods. Therefore, we can create a geometric modeler for voxel models.  
Recently, some researchers proposed to build compliant mechanisms and 4D-printing models using AM [37,38]. The resultant objects can change shapes, produce motions, transport energies, and bear workloads when encountering external influences, such as water, forces, electricity, magnet, heat, chemical solutions, etc. They possess great applications in industries. Material and mechanic properties differentiate from one position to another inside these types of structures. These parameters are hard to specify in polygonal representations. However, by converting geometric models into volume data sets and encoding these characteristics into the voxels, we can create objects with non-uniform internal properties. We plan to extend the proposed watermark scheme, especially the SOM routine, to adjust structural, material, and mechanic properties inside geometric models to accomplish this functionality.  
Some researchers proposed to design models of anisotropic materials [39, 40, 41]. The SOM embedding module of our watermarking program can be used to define material properties and printing directions for individual voxels. Hence, this functionality can be achieved in the modelling stage. However, we need to revise the G-code generating method to realize anisotropic printing. New experiments also have to be carried out so that a robust manufacturing procedure can be built.
## 3 Results and Discussion  
### The Interactions between MPs and Fermentation-Sitinky Compounds  
Previous studies showed that indole, isovaleric acid, dimethyl disulfide, and dibutyl phthalate were identified as the key stinky compounds in fermented mandarin fish [2; 7; 8]. These four stinky compounds were used to explore the interactions between MPs and fermentation-stinky compounds, whose structures are shown in Figure 1. Figure 2A observed the percentage of free fermentation-stinky compounds at different concentrations. The percentage of free fermentation-stinky compound solution (without MPs) was set to 100% (Figure 2). If the percentage is less than 100%, the interaction between the compound and MPs occurs. Lower free percentages of fermentation-stinky compounds reflected stronger binding affinities. The binding affinities of all the free fermentation-stinky compounds significantly (\(p<0.05\)) increased as the additional concentration increased from 2 to 8 mg/L, which could be attributed to the presence of more binding sites [19]. Binding to MPs increased first, then decreased as isovaleric acid and dimethyl disulfide concentrations significantly increased (\(p<0.05\)). Yet both the free percentages of isovaleric acid and dimethyl disulfide reached a low at 5 mg/L, indicating the binding sites had been saturated on MPs. In particular, dimethyl disulfide could bind to higher compound concentrations and present better binding capacity compared to other compounds. Whereas dibutyl phthalate and indole had the strongest interactions with MPs at 2 mg/L, their binding capacity was gradually reduced, reaching site saturation. Dibutyl phthalate and MPs could occur through the action of hydrogen bonds and van der Waals. Moreover, hydrophobic interactions between small molecules and proteins can also affect the binding capacity of MPs [10]. The order of binding capacity to MPs at the optimal concentration was dibutyl phthalate = dimethyl disulfide > indole = isovaleric acid (\(p<0.05\)), with a percentage of 85.7%, 82.8%, 51.1%, and 49.1%, respectively.  
Figure 1: The 3D structures and partition coefficient-P (log P) values of the fermentation-stinky compounds (indole, isovaleric acid, dimethyl disulfide, and dibutyl phthalate) were identified from fermented foods, such as fermented mandarin fish.
## 3 Results and Discussion  
### Fuel Properties  
The results of the basic properties are presented in Table 2. FADR and HFO have similar properties in terms of calorific value and density. The calorific value (i.e., lower heating value, LHV) of FADR is around 86.7% of the value for HFO. A similar tendency was observed by [PERSON] et al., who concluded that the higher heating value, HHV, of liquefied oil from swine manure was 36.05 MJ/kg, which is about 90% of that of heavy fuel oil (40 MJ/kg) [11]. Nevertheless, due to the higher density of FADR (i.e., 882 kg/m\({}^{3}\) at 60 \({}^{\circ}\)C) compared with HFO (i.e., 864 kg/m\({}^{3}\) at 60 \({}^{\circ}\)C), the energy density of FADR (i.e., 32.298 GJ/m\({}^{3}\) (from LHV)) is 88.5% of the value for HFO (i.e., 36.505 GJ/m\({}^{3}\) (from LHV)). The comparison of the energy density (GJ/m\({}^{3}\)) based on HHV for different fuels is presented in Figure 2. The value of FADR energy density is similar to the values for diesel, gasoline, micro wax, and crude oil. The lower viscosity of FADR (i.e., at 60 \({}^{\circ}\)C 0.0255 Pa-s) was observed compared with HFO (i.e., at 60 \({}^{\circ}\)C 0.057 Pa-s). In this case, lower viscosity is an added value in terms of lower energy demand on the compression. It is known that there is a recommended limit of viscosity for nozzle application. Too-high viscosity causes the creation of liquid filaments instead of a spray at the nozzle outlet and increases fuel pump consumption [25]. In this research, the recommended limit of viscosity is 100 mm\({}^{2}\)/s. Nevertheless, according to our experimental observations, the good-enough atomization of liquid fuel was observed for kinematic viscosity lower than 20 mm\({}^{2}\)/s (calculated from dynamic viscosity and estimated density). At this value of viscosity (for FADR temperature in the range of 70 \({}^{\circ}\)C) and proper pressure, efficient atomization was observed at a nozzle diameter of 0.5 mm (see Table 3). [PERSON] and [PERSON] [16] mentioned that the recommended nozzle viscosity for atomization of liquid fuels (i.e., animal fat and HFO) was in the range of 7-10 mm\({}^{2}\)/s. [PERSON] et al. [12] reported the maximum viscosity at the nozzle in the range of 50-118 mm\({}^{2}\)/s. A comparison of the kinematic viscosity of selected liquid fuels (i.e., HFO and fat-delivered fuels) as a function of temperature is presented in Figure 3. It can be noticed that HFOs have significantly higher values of kinematic viscosity; thus, this fuel should be heated up to a certain temperature (usually 100 \({}^{\circ}\)C) to obtain a proper value of viscosity. It is known that the viscosity (at 20 \({}^{\circ}\)C) of commercial liquid fuels (i.e., biodiesel, diesel, E15, and gasoline) was in the range of 0.79-8.34 mm\({}^{2}\)/s [26]. [PERSON] et al. [12] noticed that the kinematic viscosity of animal by-products not intended for human consumption (ABNIHC) was 51.97 mm\({}^{2}\)/s and 9.06 mm\({}^{2}\)/s for 40 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, respectively. Figure 2: The comparison of energy density, GJ/m\({}^{3}\), based on the HHV, data from this study, and [22; 27].
## 4 UAV Navigation and Control
### Path Planning and Trajectory Design
#### 4.2.2 Two-Dimensional Path-Plus-Speed  
Along with the UAV 2D path, the UAV speed is optimized based on the UAV energy consumption for propulsion which consumes a major part of the overhead energy. The propulsion energy consumption of a rotary-wing UAV is obtained based on the UAV speed [131]:  
\[\mathcal{E}^{UAV}=\delta\left(P_{0}\left(1+\frac{3\|v\|^{2}}{r^{2}\varpi^{2}} \right)+\frac{d_{0}\rho sA_{r}}{2}\|v\|^{3}+P_{1}\left(\sqrt{1+\frac{\|v\|^{4}} {4v_{0}}-\frac{\|v\|^{2}}{2v_{0}}}\right)^{\frac{1}{2}}\right), \tag{23}\]  
where \(\mathcal{E}^{UAV}\) denotes propulsion energy consumption at sample time \(\delta\) as a function of the UAV linear speed \(v\). Physical parameters of the quadrotor impacting the energy consumption are given in Table 2. The propulsion energy consumption formula given in (24) has been used as the objective function to optimize the UAV speed [87], and some other works that studied 2D path-plus-speed (or acceleration [132]), are given in Table 1.  
However, there are several critical issues with this model. First, it imposes significant computational overhead due to the need to solve the optimization function, including the non-convex formulation in (24). Additionally, using this model for optimizing the UAV's 3D trajectory in dense urban areas with obstacles is computationally prohibitive and inefficient. In environments with mobile vehicles and IoVs, the UAV's speed must be adaptable to maintain LoS paths for vehicles, as a fixed speed is impractical. Furthermore,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Unit** \\ \hline \(v_{0}\) & The mean rotor velocity in & m/s \\ \(P_{0}\) & Blade profile power in & W \\ \(P_{i}\) & hovering & W \\ \(\rho\) & Induced power in hovering & W \\ \(\varpi\) & Rotor angular velocity & Radian/seconds \\ \(d_{0}\) & Fuselage drag ratio & \(-\) \\ \(A_{r}\) & Rotor disk area & m\({}^{2}\) \\ \(r\) & Rotor radius & m \\ \(s\) & Rotor solidity & m\({}^{3}\) \\ \(\rho\) & Air density & Kg/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rotary-wing physical parameters affect the propulsion energy consumption.  
when the UAV hovers, its speed and energy consumption are fixed and known. Therefore, this model may be useful for obstacle-free long-distance flight at fixed speed (optimal speed) but is not practical in dynamic urban areas with mobile UEs.
## 1 Introduction
### Threat Models  
Threat models for authentication schemes in smart mobile devices are divided into the following five main categories according to the security attributes that the attack attempts to compromise [2].  
1. Identity-based attacks: This attack targets authentication and attempts to forge identities to gain access to the system posing as an authorized user.
2. Eavesdropping-based attacks: This attack targets confidentiality and is based on eavesdropping on the communication channel between the user and the server to obtain some secret information and break the confidentiality of the system.
3. Combined eavesdropping and identity-based attacks: This attack targets confidentiality and authentication, and combines eavesdropping and identity-based techniques to compromise systems.
4. Manipulation-based attacks: This attack targets data integrity and involves an unauthorized party accessing and changing sensitive data.
5. Service-based attacks: This attack targets availability and attempts to make the authentication service unavailable. After that, legitimate users cannot log in to the server.
# Grafting of (3-Chloropropyl)-Trimethoxy Silane on Halloysite Nanotubes Surface
## 3 Results and Discussion
### Effect of HNTs:CPTMS:H\({}_{2}\)O Molar Ratio  
Even though an increasing concentration of reactants results in enhancing the chance of collisions between the reactant molecules, hence speeding the rate of reaction, increasing the number of moles of CPTMS results in decreasing the degree of grafting of CPTMS onto the HNT surface. This observation has no clear explanation [31; 32; 33; 34]. The molar ratio HNTs/CPTMS/H\({}_{2}\)O has been varied: 1:1:0, 1:2:0, 1:1:33:0, and 1:1:3. According to the data obtained from elemental analysis, the greatest degree of grafting was achieved by using the molar ratio 1:1:3. As reported in our recent work, the effect of HNT/silane molar ratio on silanization of HNTs using (3-aminopropyl) triethoxy silane and (3-mercapopropyl) trimethoxy silane was studied, and the results reveal that the degree of grafting of silane onto the HNT surface decreased as the number of moles of silane increased [2; 18; 35]. In the present work, it was found that for a HNTs:CPTMS:H\({}_{2}\)O molar ratio equal to 1:1:3, a larger degree of grafting is obtained (Table 1).
## 4 Experiments  
### Experimental Setup  
#### 4.1.1 Datasets  
To thoroughly assess our model's performance, we employ two widely recognized stance detection datasets: SEM16 [1] and P-Stance [16]. These datasets are chosen to encompass both broad and politically focused stance detection scenarios, ensuring robustness and generalizability. The SEM16 dataset originates from the SemEval-2016 Task 6 competition and is a standard benchmark for stance detection. It consists of 4870 tweets expressing stances toward six targets: _Atheism_ (AT), _Climate Change is a Real Concern_ (CC), _Feminism_ (FM), _[PERSON]_ (HC), _Legalization of Abortion_ (LA), and _[PERSON]_ (DT). Notably, the DT target lacks training data, making supervised learning infeasible for this category. The P-Stance dataset is a large-scale stance detection dataset collected from social media, specifically designed for political stance detection. It contains 21,574 tweets related to three major political figures: _[PERSON]_, _[PERSON]_, and _[PERSON]_, with a balanced distribution across stance categories to reduce label bias. Table 1 provides a summary of dataset  
Figure 4: Stance classification using LLM and HSN. The input consists of a tweet and a classification request toward atheism. Instead of directly classifying the stance, an LLM leverages an HSN to retrieve indirect stance clues by analyzing relationships between entities.  
statistics. Both datasets adopt three labels: **favor**, **against**, and **none**. In our implementation, we reinterpret **none** as **neutral** to better align with our stance representation.
## 4 Results
### Variants of Proposed Approach  
'PA_B-J48' is also compared with its variants, including Boosting with Naive Bayes (BNB), Adaptive Boosting (B Ada), Boosting with Neural Network (BNN), and Boosting with Support Vector Machine (BSVM), respectively. The variants uses the same set of features but differ in classifiers. Table 6 shows that the PA_B-J48 outperforms its variants on the DrivFace, BU, FT-UMT, and Pointing'04 datasets. The features of the J48 classifier that result in better accuracy are its ability to handle missing values, continuous attribute value ranges, and derivation of rules.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **EM-Stat** & **TA** & **AM-Mouth** & **Ali** & **Lee** & **Frid** & **PA\_B-J48** \\ \hline A (\%) & 95.25 & 90.10 & 92.68 & 95.25 & 94.36 & 95.05 & **96.63** \\ \hline Se (\%) & 95.00 & 90.00 & 92.00 & 95.00 & 94.00 & 95.00 & **96.00** \\ \hline P (\%) & 95.00 & 91.00 & 92.00 & 95.00 & 94.00 & 95.00 & **96.00** \\ \hline F & 0.95 & 0.88 & 0.92 & 0.95 & 0.94 & 0.95 & **0.96** \\ \hline TP & 0.95 & 0.90 & 0.92 & 0.95 & 0.94 & 0.95 & **0.96** \\ \hline FP & 0.16 & **0.50** & 0.33 & 0.13 & 0.22 & 0.14 & 0.08 \\ \hline ROC & 0.87 & 0.68 & 0.83 & 0.88 & 0.86 & 0.89 & **0.97** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the existing approaches, EM-Stat, TA, AM-Mouth, Ali, Lee, and Frid with the proposed approach, PA_B-J48, for the FT-UMT dataset.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **EM-Stat** & **TA** & **AM-Mouth** & **Ali** & **Lee** & **Frid** & **PA\_B-J48** \\ \hline A (\%) & 86.33 & 71.58 & 55.03 & 89.20 & 78.05 & 82.37 & **92.08** \\ \hline Se (\%) & 86.00 & 71.00 & 55.00 & 89.00 & 78.00 & 82.00 & **92.00** \\ \hline P (\%) & 86.00 & 71.00 & 53.00 & 89.00 & 78.00 & 82.00 & **92.00** \\ \hline F & 0.86 & 0.71 & 0.54 & 0.89 & 0.78 & 0.82 & **0.92** \\ \hline TP & 0.86 & 0.71 & 0.55 & 0.89 & 0.78 & 0.82 & **0.92** \\ \hline FP & 0.13 & 0.31 & **0.50** & 0.11 & 0.21 & 0.18 & 0.08 \\ \hline ROC & 0.92 & 0.74 & 0.59 & 0.92 & 0.81 & 0.81 & **0.96** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the existing approaches, EM-Stat, TA, AM-Mouth, [PERSON], Lee, and Frid with the proposed approach, PA_B-J48, for the Boston University dataset.