esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 5 Conclusions  
Our work provided an OMC algorithm to fuse multibox with semantic segmentation. In the OMC algorithm, semantic segmentation and multibox were processed in parallel, and the text detection results were mutually corrected. The mutual correction process had two stages. In the first stage, the pixel-level classification results of the semantic segmentation were adopted to correct the multibox  
Figure 11: Some detection results and their ground truth from the SVT dataset.  
Figure 10: Some detection results and their ground truth from the ICDAR2013 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{3}{c}{**IC13 Standard**} & \multicolumn{3}{c}{**DetEval Standard**} \\ \cline{2-7}  & **R** & **P** & **F** & **R** & **P** & **F** \\ \hline FCN & 50.78\% & 54.94\% & **52.78\%** & 55.13\% & 54.94\% & **55.03\%** \\ SSD & 48.61\% & 73.24\% & **58.43\%** & 48.17\% & 75.12\% & **58.70\%** \\ SSD-OMC & 69.91\% & 73.57\% & **71.69\%** & 66.01\% & 77.97\% & **71.48\%** \\ SSTD & 78.60\% & 73.35\% & **75.89\%** & 78.60\% & 73.35\% & **75.89\%** \\ SSTD-OMC & 85.13\% & 71.68\% & **77.83\%** & 81.65\% & 79.78\% & **80.71\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Improved algorithm compared with original algorithm (on SVT).  
bounding boxes. In the second stage, the CRF algorithm was used to precisely adjust the boundaries of the semantic segmentation results. Then the NMS was introduced to merge the text bounding boxes generated by multibox and semantic segmentation. The experimental results showed that the proposed OMC algorithm had better performance than the original IFE algorithm. The F-measure score increased by a maximum of 13.62% and the highest F-measure score was 81.38%. Future work will focus on more powerful and faster detection structures, as well as on rotating text detection research.  
Conceptualization: H.Q. and H.Z.; formal analysis: H.W. and Y.Y.; investigation: H.Q. and H.Z.; methodology: H.Q. and M.Z.; Writing--Original Draft: H.W. and W.Z.; Writing--Review & Editing: H.Q. and H.Z.  
This research was funded by National Natural Science Foundation of China, grant number 61801357.  
The authors declare no conflict of interest.
## 2 Theoretical Model
### Distribution of the Strain of a jet  
According to the quasi-steady collapse theory of jets, in the Lagrangian coordinate system in Figure 1, the coordinates of the point at which element \(N\) moves to collide with the axis at time \(t_{c}\) can be expressed as follows:  
\[z(x)=x+y\tan\Bigl{(}\alpha^{N}+\delta^{N}\Bigr{)}. \tag{1}\]  
where \(\alpha\) is half of the cone angle of the liner and \(\delta\) is the Taylor angle. Because there is no interaction between the elements, the part of the elements on the axis that forms a jet moves forward at a velocity of \(V_{j}\). The position of the element in the jet at time \(t\) is given below:  
\[\xi(x,t)=z(x)+(t-t_{c})V_{j}\quad\ t\geq t_{c}. \tag{2}\]  
Let \(\Delta\xi_{0}\) and \(\Delta\xi\) denote the distance between two points at time \(t_{1}\) and their increased distance at time \(t_{2}\), respectively. The 1D tensile strain \(\varepsilon_{s}\) of a jet is defined as the ratio of the increase in the length of the element to its initial length. For any \(x\), the following relationship holds:  
\[\varepsilon_{s}(x,t)=\left[\frac{\partial\xi}{\partial x}(x,t)/\frac{ \partial\xi}{\partial x}(x,t_{0})\right]-1. \tag{3}\]  
Differentiating Equation (2) with respect to \(x\) and substituting the result into Equation (3) provides the following equation:  
\[\varepsilon_{s}(x,t)=\frac{(t-t_{c}(x))V_{j}^{\prime}}{z(x)^{\prime}-t_{c}(x) ^{\prime}V_{j}}. \tag{4}\]  
In addition, based on the definition of the equivalent strain, the strain \(\varepsilon_{c}\) of a liner element during the collapse stage can be expressed as follows:  
\[\varepsilon_{c}(x,t_{c})=\frac{\xi(x,t_{c}(x))-\xi(x-\Delta x,t_{c}(x-\Delta x ))}{\Delta x/\cos\alpha}(x\geq\Delta x). \tag{5}\]  
Therefore, the plastic strain at the time when any jet element collapses on the axis can be determined using Equation (5). Before a fracture, the total strain of any jet element is the sum of Equations (4) and (5).
## 3 System Model and Problem Formulation
### System Model
As per 3 GPP TS 28.211, the periodicity \(T_{\text{PRS\,per}}\) is selected from a set defined by \(2\mu\) times a series of values, and the offset \(T_{\text{PRS\,off}}\) is an integer within the range from 0 to one less than the periodicity itself.
## 3 Implementation of Constellation Dynamic Selection in ARAIM  
The computational complexity and performance uncertainty have been analyzed in the above section. Instead, ARAIM with two constellations is easy to implementation. However, its robustness is not good enough because the availability is seriously affected by constellation difference and satellite outages [24]. In this section, a practical ARAIM method with constellation dynamic selection is proposed. What we want to do is to select the best two constellations for ARAIM evaluation anywhere at any time. The feasibility of constellation dynamic selection is talked about first, then the particular implementation is given in details.
# Survey on Security Threats in Agricultural IoT and Smart Farming
## 4 Mitigation Measures and Strategies for Security Threats in Agriculture

Table 3: Overview of security threats in agriculture across different IoT layers. space, fault tolerance, trusted origin and accountability, access control, removal of third-party risks, prevention of illegal use of personal (private) data, as well as protection of the sharing of IoT network information [112; 113]. In summary, prior research for the enhancement of security and privacy in IoT and ICT applications in the agricultural sector falls under six main categories according to [PERSON] et al. [79]: (i) privacy-preserving solutions; (ii) data integrity solutions; (iii) authentication solutions; (iv) access control solutions; (v) data confidentially solutions; and (vi) blockchain-based solutions and consensus algorithms. Cybersecurity and mitigation measures and strategies is a very wide, open and rapidly evolving research field. Every day new threats and risks are emerging and thus new countermeasures become available. Table 4 features a list of countermeasures that can be taken against security aspect threats and IoT layer threats as those described in Tables 2 and 3. Lastly, regarding agricultural cyber-security, researchers intensify their efforts in designing Machine Learning (ML) and Deep Learning (DL) methods for anomaly behavior detection and network analysis of intrusion detection systems (IDS) [124]. Traditional Machine and Deep Learning applications applied for cybersecurity in agriculture include [125; 126]:  
* Support Vector Machine (SVM);
* K-Nearest Neighbor;
* Decision Trees;
* Deep Belief Network (DBN);
* Recurrent Neural Networks (RNN);
* Convolutional Neural Networks (CNN);
* Artificial Neural Networks (ANN);
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 4 Discussion
### Analysis of the Factors Affecting the Scallop Height of the Grinding Surface
#### 4.2.2 Effect of Step Pitch on Scallop Height  
The scallop height \(h\) is a function of the step pitch \(l\), and the partial derivative of the step pitch \(l\) occurs on both sides of Equation (6):  
\[\frac{\partial h}{\partial l}=\frac{\left(R-r\right)^{2}\sin(l/2R)\cos(l/2R)} {R\sqrt{r^{2}-\left(R-r\right)^{2}\sin^{2}(l/2R)}}+\frac{\left(R-r\right)}{2R }\sin(l/2R) \tag{11}\]  
According to Equation (11):  
\[\frac{\partial h}{\partial l}>0 \tag{12}\]  
The scallop height \(h\) is positively correlated with the step pitch \(l\). The simulation calculation is shown in Figure 9. It can be seen that the larger the step pitch, the greater the scallop height. Therefore, in the grinding process, to obtain a lower scallop height, a smaller step pitch should be selected as far as possible when the processing efficiency allows.  
Figure 8: Effect of grinding wheel radius \(r\) on scallop height.
## 3 Results and Discussion
### Optimization of UE
#### 3.1.2 Effect of the Extraction Solvent Volume  
The extraction solvent volumes of 25 mL, 50 mL and 75 mL were used in the investigation of their efficiency. The percentage recovery increased with an increase in solvent volume from 25 mL to 50 mL for most of the PAHs, which can be attributed to the fact that more solids were dissolved in a larger amount of the solvent. The statistical evaluation (Student's _t_-test) in Table S2 showed that there was a significant difference between the recoveries of these volumes with a \(p\) value of 0.02. When the solvent volume was increased from 50 mL to 75 mL, the recoveries obtained showed a slight decrease with a significant difference (_p_ = 0.005) in recoveries. This could mean that the solution was saturated; a point of equilibrium where no more solute could dissolve in the solvent. Therefore, 50 mL was chosen as the extraction solvent volume as it gave the highest recoveries (86.7-123%), (Figure 3). The recoveries obtained in this study were comparable to the recoveries obtained by [PERSON] et al. (2007) of 48-100% with 25 mL, 67-100% (50 mL), 70-112% (75 mL) and 76-114% (100 mL) [26].  
Figure 2: Effect of extraction solvent on PAHs %recovery using UE (_n_ = 5).
## 4 Discussion
In Egypt, lower levels of AMR to AMP (66.7%) were reported, but higher levels of SXT (73.3%) were discovered in a goat slaughterhouse [64]. In Mexico, a study detected 92% and 75% of AMR to AMP and TET, respectively, in bovines. This contrasts with our study where both antibiotics showed a lower level of AMR. Another study in this country found AMR to cephalosporporins in STEC isolates from bovines. Interestingly, we did not find any AMR to these antibiotics. Despite this, both studies showed AMR to TET and AMP [16; 65]. In the case of a-EPEC, a lower resistance rate in comparison with this study was found in adult sheep in Spain with a 1.9%, 0, and 1% for GEN, TET, and SXT, respectively [66]. Conversely, a study from Brazil detected higher rates of resistance against CIP (22%), AMK (4%), GEN (9%) and cephalosporporins (72%) in a sheep abattoir [67]. In the particular case of ETEC, we found higher resistance levels for AMP (100%) and TET (50%) in our work compared to [PERSON] et al. (2013) [46] with goat isolates in Kenya. Multi-drug resistance (MDR) was found against three or four antibiotic classes in 11 STEC, 1 EPEC, 1 ETEC and 1 EIEC isolates. The presence of MDR _E. coli_ in the gut microbiota of the analyzed sheep could further disseminate to other microorganisms due to horizontal gene transfer [68]. In the present study, it was possible to detect resistance genes such as _tetA_, _tetB_, _suI1_, _suI2_ within isolates resistant to tetracycline and trimethoprim-sulfamethoxazole. Research studies around the world also reported finding some of these genes. Portugal [69] informed the presence of _tetA_, _tetB_ and, in a smaller number, _suI2_, in sheep samples processed in a slaughterhouse. [PERSON] et al. (2011) [66], working with live sheep in Spain, reported the presence of these same genes with _tetA_ the most frequent. Finally, in France, bovine isolates harbored _tetA and sul2_ genes [15].
## 3 Results and Discussion
### Tearing Resistance  
The tearing test was performed in two directions--longitudinal (along the wales) and transversal (along the courses). It was found that values of the tearing force vary in the ranges of 217-227 N (with a 5% relative error of measurements), irrespective of the tearing direction, knitting structure and difference in the loop length. Images of the specimens after the tearing test are presented in Figure 4. The character of knitted samples deformation under the action of the tearing load is different from the woven fabrics and has to be taken into account. During the first stage of the tearing along the wales, unknitting of the loops at the point of maximum stress was observed. As a result of this process, straight segments of the unknitted yarn appears in several courses. During the next stage, these straight segments start to counteract against the tearing load, while the parts of the specimen fixed in the top and bottom clamps--\"legs\"--curl to the technical-back side along the courses. Finally, the sample breaks in the area of the curled \"legs\" of the specimen, however, the main part of the specimen remains unimpaired.  
A similar situation was observed when a tearing load acts along the courses (Figure 4). When a tearing force is applied along the courses, at the initial stage, unknitting of stitches along the courses occurs in the \"legs\" of the specimen at the point of maximum stress. The \"legs\", fixed in the top and bottom clamps, curl to the technical-face side along the wales. In this case the sample finally breaks also in the area of the curled \"legs\". The unknitted parts of the yarn counteract against the tearing load; therefore, the specimen is not torn completely but only the \"leg\" breaks. The principal behaviour during the tearing test of the WP and WPE knits is similar, however, the elastomeric fleece yarn used in the WPE knitted fabrics prevents unknitting of stitches under the influence of tearing force applied both along the wales, and along courses.  
Figure 3: Cut resistance.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 6 Future Work  
This study focuses on addressing the theoretical problem of maximizing physical-layer security through UAV trajectory optimization in the presence of multiple eavesdroppers. The primary contribution of this work lies in the design of the optimization algorithm. In future work, we plan to extend the proposed algorithm to real-world scenarios, where we will consider the design of the UAV security system's network architecture, the integration of hardware and software, and the deployment of the algorithm within this system.  
**Author Contributions:** Supervision, L.S.; project administration, L.S. and G.W.; methodology, L.S. and X.H.; funding acquisition, L.S., G.W. and X.H.; Writing--original draft, L.S. and J.N.; writing--review and editing, J.N. and G.W.; visualization, J.N. and M.L.; validation, J.N.; software, L.S. and J.N.; data curation, M.L. and Q.Z.; formal analysis, G.W. and X.H.; conceptualization, X.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the National Natural Science Foundation of China (nos. 92467103 and 92367302), the Henan Provincial Department of Transportation (no.2023-3-2), the Henan Provincial Science and Technology Research Project (nos. 252102210176 and 252102210232), and the Foundation of Henan Educational Committee (nos. 25A510015 and 25A520008).  
Figure 5: The secrecy rate versus UAV maximum speed.
## 4 Methods for Improved Reliability and Robustness
### Model Improvement Techniques
#### 4.2.1 Overfitting  
As discussed in Section 3.2.1, the necessary complexity of DL-based models to guarantee a high-performance accuracy has been established. Therefore, MI techniques to reduce overfitting firstly consist of strategies to enlarge the available datasets, when further data collection is not possible. [PERSON] et al. [91] apply geometrical operations to the source training data in order to simulate various possible data distributions across different domains. This data augmentation strategy was also adopted by [PERSON] et al. [45] in the context of the M&Ms Challenge.  
Other MI techniques assume it is not possible to sufficiently increase (artificially or through further data collection) the size of the training set that it avoids overfitting and propose to control the complexity of the highly complex models through regularization. Among them, [PERSON] et al. [21] present a DenseNet-based FCN architecture with long skip and short-cut connections to increase parameter efficiency. [PERSON] et al. [92] integrate continuous kernel cut and bound optimization into a CNN, building a unified max-flow framework with improved generalization capabilities.
## 2 Related Work
### Mobile Sink Nodes and Delay  
The primary focus of [34] is to address the issue of delay when implementing a MSN. In particular, when considering delay-sensitive data which should not be subject to the same restrictions placed upon other data. In this case the authors developed a delay-intolerant routing scheme (DRS), implementing a deadline by which data should be received by the sink. When a sensor has emergency data to transmit it will either wait until the sink is within its grid or send immediately to an awake sensor node. Again, this is a study which takes place at the network layer, with regard to the routing scheme implemented. However, reference is made to the probable effectiveness of controlling the wake-up pattern of nodes alongside this scheme.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 2 Related Work
### Forest Fire Detection Using the Machine-Learning Approach  
[PERSON] et al. [9] developed a new method to detect the geometrical characteristics of a fire depending on its position, surface, and length. In this study, the fire color was categorized into pixels. Moreover, the pixels were classified based on the average intensity of the non-refractory images. [PERSON] et al. [10] introduced an upgraded boundary-detection operator, and their model used a multistep operation. However, the abstraction of the model was only applied to simple and stable fire and flame images. Researchers worldwide have used a new algorithm based on FFT to detect fires. [PERSON] [11] developed a real-time fire detector that combined background and foreground color frames. However, the real-time color-based program did not provide a better output because of the smoke and shadow. In [12], based on the dynamic textures of smoke and flame, the fire was detected using dynamic systems (LDSs).
## 5 Experimental Validation and Benchmarks  
To demonstrate the practical applicability of the proposed solutions, I conducted several experiments to validate the performance of the AI models in communication networks. These experiments focus on key metrics such as accuracy, latency, energy consumption, and scalability. The following subsections detail the experimental setup, results, and comparisons with existing approaches.
# Incremental Algorithm for Association Rule Mining under Dynamic Threshold
## 1 Introduction
The discovered large itemsets, also known as frequent itemsets, have more occurrences in the database than a pre-defined confidence threshold. 2. The frequent itemset generates strong association rules. A rule is considered strong and serves as output list if it satisfies the pre-defined confidence threshold. Despite the existence of numerous techniques, ARM is the most widely used method for data mining. The analysis of market basket appears to be the most popular case study for association rules. The items in a market database are saved in a single record (transaction), along with customer ID, transaction time, and other imminent details. The discovering ARM includes rules, such as follows: let us assume a customer takes products x and y, but he is highly expected to purchase product w; where x, y, and w are initially unknown. The customized promotional marketing program can be designed based on useful discovery rules. Apart from devising effective marketing plans, the data mining methods can be applied to both control and minimize consumption, such as energy and carbon dioxide (see [15; 27]. The Apriori algorithm can effectively extract a frequent itemset from a massive dataset. Based on the discovered itemset on certain step (\(n\)), the algorithm generates an itemset called candidate itemset in the next step (\(n+1\)) [2]. In each step, the algorithm checks the support counting for the candidate itemset using a vital scan database. The itemset generation process in a level-by-level manner leads to level-wise problem. In fact, many algorithms have been introduced to address the multiple defects found in Apriori algorithm [10; 24]. These algorithms are aimed at minimizing mining costs, apart from generating consistent and stable outcomes. In order to meet this goal, vast amount of data should be gathered and analyzed. One of the many mining issues refers to database size, as new transactions are dynamically updated and removed frequently. The already discovered rule sets need to be updated, or the rule set would not reflect the database. As such, maintaining ARM result incrementally has been examined in this study by placing focus on vertical mining. The proposed algorithm can overcome several existing drawbacks by reducing mining time and incorporating all manipulative operations. Minimizing database scan frequency is one of the proposed algorithm objectives. First, the algorithm builds a complete itemset, and later updates the set without rebuilding it to hinder level-wise issue. The paper is organized as follows. In the following section, the related terms are listed and defined. Section 3 presents the review of some past studies. In Section 4, the incremental Apriori (IApriori) is elaborated, and Section 5 describes the dataset. Section 6 presents the empirical outcomes and the comparison of the 7 findings is depicted in Section 7.
## 3 Experimental Activities
### Advanced Shielding for Spacecraft  
Among the experimental activities, test campaigns were dedicated to the investigation of the shielding capability of advanced configurations or novel materials. With respect to standard sandwich panels, currently used as structural elements with a certain shielding capability, new materials and configurations may show enhanced performances. Hypervelocity impact testing allows the determination of the ballistic limit of these new configurations, as well as the ability to fragment impacting bodies to further reduce the vulnerability of internal components to perforation.
# Research on Seismic Signal Analysis Based on Machine Learning
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Category**} & **Standard** & **Standard** & **Standard** \\  & & **Deviation of** & **Deviation of** & **Deviation of F1** \\  & & **Accuracy** & **Recall** & **Deviation of F1** \\ \hline Inception10 & Supervised & 0.0762 & 0.0487 & 0.0644 \\ VGG16 & Supervised & 0.1114 & 0.1107 & 0.1370 \\ ResNet-18 & Supervised & 0.0592 & 0.1162 & 0.1386 \\ MiniRocket & Supervised & **0.0207** & **0.0203** & **0.0207** \\ K-means & Unsupervised & **0.0021** & **0.0023** & **0.0023** \\ MiniRocket + & Collaborative & 0.0514 & 0.0407 & 0.0427 \\ K-means & learning & & & \\ LCL-SSS (ours) & Collaborative & **0.0097** & **0.0181** & **0.0187** \\  & learning & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Standard deviation of accuracy, recall, and F1 values. The results of this paper showed that the supervised classification method was significantly better than the unsupervised method. Theoretically, the objective function of supervised classification task optimization is \(\mathrm{E}_{(x,y)\sim\mathrm{D}}[\log\mathrm{p}_{\theta}(y|x)]\), and the optimization process is the process of extracting label-related information from data, while the unsupervised task is mainly used for maximum likelihood estimation \(\mathrm{E}_{\mathrm{x}\sim\mathrm{D}}[\log\mathrm{p}_{\theta}(x)]\). The lack of category information makes it difficult to correctly estimate the distribution based on category, which may be the reason why unsupervised classification methods are rarely used in the current research on identifying earthquake types based on seismic full waveform. Compared with the unsupervised clustering method, supervised classification can find the event discrimination threshold more accurately, thus yielding better performance.