esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Analysis
### Ablation Study
#### 3.3.2 LS Module: Large-Kernel Size Effectiveness
Beyond the optimal sizes, further increases in \(k\) lead to diminished performance improvements relative to the additional computational load. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(k\) & **S2 Looking F1** & **LEVIR-CD+ F1** & **Params (M)** & **FLOPs (G)** \\ \hline
1 & 66.34 & 87.05 & 49.61 & 17.46 \\
3 & 66.41 & 87.24 & 49.65 & 17.48 \\
5 & **67.00** & **87.28** & 49.72 & 17.52 \\
7 & 66.61 & 87.36 & 49.82 & 17.57 \\
9 & 66.43 & 87.17 & 49.96 & 17.64 \\
11 & 66.97 & 87.50 & 50.13 & 17.73 \\
13 & 66.66 & 87.05 & 50.34 & 17.84 \\
15 & **67.13** & 87.20 & 50.58 & 17.96 \\
17 & 66.73 & 87.22 & 50.86 & 18.10 \\
19 & 67.02 & 87.06 & 51.17 & 18.26 \\
21 & 66.71 & 87.02 & 51.52 & 18.44 \\
23 & 66.91 & 87.04 & 51.90 & 18.63 \\
25 & 66.42 & 87.11 & 52.31 & 18.84 \\
27 & 66.87 & 87.12 & 52.76 & 19.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Impact of LS module kernel size (\(k\)) on S2 Looking and LEVIR-CD+, encompassing Parameters (Params) count, FLOating-Point OPerations (FLOPs), and F1-score comparisons in RCCD training. Color convention: best, 2 nd-best, and **3 rd-best**. Figure 7: Impact of kernel size (\(k\)) and loss weights (LS \(\lambda_{1}\) and GS \(\lambda_{2}\)) on F1.
## 3 Research Methodology
### Data Preprocessing  
In natural language processing tasks, data cleaning is a crucial step to ensure data quality and enhance model performance [41; 42]. Given the characteristics of requirement documents, this study designs and implements three data cleaning strategies to ensure data consistency and usability: (1) Removal of irrelevant numbering and markings: Requirement documents are typically organized by chapters and often contain irrelevant noise, such as chapter numbers, section markers, and other formatting symbols. If this noise is included in the input, it could interfere with the subsequent text parsing per  
Figure 1: The schematic diagram of the proposed framework.  
formed by the large language model. (2) Text format normalization: The raw documents frequently contain extra spaces, line breaks, and other special characters, which may cause errors in text segmentation or parsing difficulties. (3) Maintaining consistency in requirement text context: Given that requirement documents often have multi-level nested structures, ensuring the coherence and integrity of the requirement descriptions is crucial. For sub-items or specific functionality entries that follow a main requirement description, their content is concatenated with the primary description to ensure consistency in the subject matter. This approach ensures that the requirement subject remains consistent throughout.  
After completing the data cleaning process, this paper annotated the requirement document data according to the five types of requirement labels defined in SysML: functional requirements, interface requirements, performance requirements, physical requirements, and design constraints. These labels comprehensively cover the main types of requirements in complex system design and provide clear standards for the classification and analysis of requirement documents. The document descriptions corresponding to these five requirement labels are collectively referred to as system requirements (SRs). By classifying the scattered SRs into categories, this process not only improves the organization of information but also lays the foundation for the subsequent construction of information extraction templates. The specific definitions and examples of the five requirement labels are provided in Table 1. This study strictly adheres to the definition standards for each requirement label and systematically annotates the SRs to ensure the accuracy and consistency of the annotation results, thus providing a high-quality corpus for subsequent research.
# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 1 Introduction
Researchers [30] enhanced a YOLOv5 model for precise plant detection, which significantly improved the accuracy and efficiency of a precision spraying robot. Integrating an attention mechanism and the C3-Ghost-bottleneck module boosted performance, increasing the mean average precision (_mAP_) by 3.2%. The work presented in [31] introduces a robotic weeding system that minimizes herbicide usage through precise application. It featured a stereo camera, an inertial measurement unit, and spray nozzles controlled by a binary linear programming-based algorithm for optimal coverage. A study [32] presented a deep learning-based detection model that distinguished weeds from cotton seedlings with high accuracy by using a convolutional block attention module (CBAM), a Bidirectional Feature Pyramid Network structure (BiFPN), and a bilinear interpolation algorithm. Existing systems, while advanced, primarily focus on agricultural settings with structured environments and often lack the capability to navigate the complex, dynamic, and GPS-denied environments typical of urban landscapes. A comprehensive review of the ex  
Figure 1: Common mosquito breeding sites in urban landscapes. isting literature (Table 1) shows a notable deficit in real-time autonomous dynamic hotspot mapping and fumigating robots. This absence underscores the novelty and importance of the proposed contribution, which introduces an innovative system specifically tailored for urban settings. The proposed fumigation robot aims to fill this gap by utilizing advanced sensor fusion and AI-based detection algorithms [33], ensuring precise and effective pest control in challenging urban terrains. Furthermore, integrating AI-based detection in precision fumigation enhances the robotic system's ability to perform targeted actions effectively. The system ensures that interventions are precise and efficient by leveraging advanced AI algorithms for detection and identification. This approach is particularly beneficial in densely populated urban areas where mosquito control must be meticulously managed to maximize impact while minimizing chemical usage, highlighting the robot's potential to transform urban pest management practices. From the literature survey, the following highlights are identified:  
The main contributions of this paper are as follows:  
1. The development of a precision fumigation robot for urban landscapes: This is an original contribution, as no existing autonomous robots navigate urban environments for precision fumigation applications. This novel development addresses the need for precise, automated solutions in urban pest control. 2. The development of a LiDAR-Vision-IMU fusion algorithm: Inspired by existing research [34; 35], this contribution enhances traditional sensor fusion techniques to fit an autonomous fumigation robot, improving its ability to identify and map mosquito
# An Improved FFIP Method Based on Mathematical Logic and SysML
## 5 Improved FFIP Method  
According to the above improvements, the proposed FFIP method is mainly divided into the following five steps:  
Step 1: Establish structure model and function model  
Use the BDD of SysML to build the structure model and function model in FFIP. These two models describe the structural configuration and functional relationship between system components. In addition to the two models, information flow and function flow of a component could be built into one graph more explicitly, making the model more coherent.  
Step 2: Introduce deviation of key process variables  
Select the key process variables that can reflect the behavior state of components. Then, calculate the deviation with formula (1). The deviation value can reflect the change of component state.  
\[D=\left|\frac{RV-DV}{DV}\right|\times 100\% \tag{1}\]  
where \(D\) is the deviation; \(RV\) is the present value; \(DV\) is the design value.  
Step 3: Build behavior model  
The behavior model includes behavior rules and failure logic. Behavior rules represent the activities of system components, as well as the inputs and outputs that lead to the activities. The SD in SysML is used to build the behavior rules in FFIP, which can better emphasize the occurrence order of behaviors and help to clarify the logic. Failure logic describes the relationship between the cause, process, and consequences of component failures. The state and state transformation conditions of the component also need to be analyzed. According to Table 2, using mathematical logic, we express the transformation conditions of states. Then, we describe the state and transformation relationships of the components, in combination with the improved state machine diagrams in SysML.  
Step 4: Establish the simulation model and simulate the failure  
According to the established FFIP models, the nominal model and failure model are established in Simulink. The failure injection method is used to simulate the failure.  
Step 5: Analyze the simulation results and output the fault tree.  
Using the fault propagation paths obtained by simulation, the fault tree is generated, and the minimum cut set is calculated to obtain the key failure components.
## 3 State-of-the-Art MIMO-SDR Systems
### MIMO Systems
#### 3.1.1 Educational Platforms and Testbeds
[26] focused on designing a framework for a massive MIMO testbed by taking into consideration some basic requirements, mainly the system complexity, the signal waveform mode, and the frame format. The design was validated within the Lund University massive MIMO hardware [23]. The suggested design supports up to 100 antennas and more than 50 FPGAs. It is allowable to have up to 12 users at the same time/frequency slot with an OFDM modulation in a time-division duplex (TDD) (LTE-like TDD) strategy [26]. The tests were performed in indoor and outdoor environments. Finally, a mm-wave MIMO testbed [27] was developed at the Institute of Digital Signal Processing (DSV) at the University of Duisburg-Essen to operate, in the beginning, for sub-6 GHz frequencies. It was implemented in a heterogenous environment, over the FPGA, DSP, and GPU. The platform could be reached remotely through VPN by academic and industrial researchers. Small-Scale MIMO Systems  
Several additional studies could be classified as being in the educational platform and testbed category, which is mainly designated for a general and a fast prototyping of MIMO systems with more simple architectures (maximin 4 \(\times\) 4 systems) and high performance. Table 2.2. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Testbed 1** & **Year** & **Tx \(\times\) Rx 2** & \multicolumn{4}{c}{**Hardware Implementation**} & **Software 3** & **BW 4** & \ \begin{tabular}{c} **Opering** \\ **Frequency** \\ \end{tabular}  & **Waveform** \\ \hline \multirow{3}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{3}{*}{2019} & \multirow{3}{*}  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
Many of those testbeds have memory buffers accessible in real time, and both their analogue digital converters (ADCs) and their digital analogue converters (DACs) offer a 12-bit resolution and a sampling rate of more than 50 Msamples/s. In addition to MathWorks, the TI Code Composer is used to develop some of the described testbeds. Additionally, the FPGA code is created within some Xilinx FPGA tools such as ISE or System Generator. The platforms built with Sundance hardware (Sundance Multiprocessor Technology Ltd., Chesha, UK) [36] adopt 3L Diamond.
# Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming
## 3 A Constraint Programming Model  
The \(PDSTSP\) can be formally described by the following CP model. The variables are defined as follows: A binary variable \(y_{ij}\), with \(i,j\in V\), takes value 1 if node \(i\) is visited right before node \(j\) by the truck during its tour, and value 0 otherwise. Note that, in case a customer \(i\in C^{D}\) is visited by a drone, then \(y_{ii}\) is set to 1, 0 otherwise. A binary variable \(x_{di}\), with \(d\in D\) and \(i\in C^{D}\), takes value 1 if the drone-eligible customer \(i\) is visited by the drone \(d\), and value 0 otherwise. Finally, a variable \(\alpha\) is used to capture the objective function value, which is the time taken by the last vehicle to go back to the depot after completion of its tasks.  
Figure 1: An example of PDSTSP is provided on the left; we assume that two drones are available (travel times are omitted for the sake of simplicity). A solution is provided on the right. The black arcs represent the tour of the truck that visits nodes 1 and 3 before going back to the depot. The red arcs depict the missions of the first drone (that serves nodes 2 and 6), while the blue arcs depict the missions of the first drone (that serves nodes 4 and 5).  
\[(PDSTSP) \min\alpha \tag{1}\] \[s.t. \text{Circuit}(y_{ij};i,j\in V)\] (2) \[\sum_{d\in D}x_{di}=y_{ii} i\in C^{D}\] (3) \[\alpha\geq\sum_{i\in V}\sum_{j\in V,i\
eq j}t_{ij}^{T}y_{ij}\] (4) \[\alpha\geq\sum_{i\in C^{D}}t_{i}^{D}x_{di} d\in D\] (5) \[y_{ij}\in\{0;1\} i,j\in V\] (6) \[x_{di}\in\{0;1\} d\in D,i\in C^{D} \tag{7}\]  
The objective function (1) minimizes \(\alpha\) that will be assigned the time of the longest tasks among the vehicles. Constraint (2) implements a _circuit_ that imposes \(y\) variables to take values in such a way as to form a feasible truck tour, and eventually self-loops for the variables associated with customers not visited by the truck. Constraint (3) requires that, if a customer \(i\in C^{D}\) is not visited by the truck (which means \(y_{ii}=1\)), then a drone \(d\) must visit it. Constraint (4) sets \(\alpha\) to be at least as large as the length of the truck tour. Constraint (5) requires \(\alpha\) to take a value larger than or equal to the time spent by each drone \(d\) to execute the tasks assigned to it. Finally, constraints (6) and (7) define the domain of the binary variables.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of DDoS Attack Detection Approaches on CC
#### 5.3.3 Hybrid Detection
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs** & **Approach Used** & **Advantages** & **Disadvantages** & **Note** \\ \hline
[120] & HTTP flood DDoS attacks & It uses the OpenStack Platform and real-time datasets for performance evaluation & HTTP Errors, Higher Response time & The suggested framework will be improved in the future to identify and mitigate HTTP flood attacks in the CC \\
[123] & Anomaly Detection & Reduce resource consumption & Low Detection Rate and Detection Time & The proposed approach has a performance average of (97.03%), with an average accuracy (98.05%), average sensitivity (93.34%), and average specificity (93.34%) \\
[124] & Architecture for mobile CC security & High accuracy & High overhead & NA \\
[125] & Correlation analysis model & Determines anomalies accurately with very few false alarms. High detection rate & Misidentified benign or malicious traffic in a few cases & Hybrid optimization strategies may be applied in the future to improve detection efficiency \\
[127] & Anomaly-based framework & High DDoS attack detection & High overhead & There is a future scope to enhance the attack detection approach \\
[128] & Execution Trace Analysis & High accuracy in attack detection & Low efficiency & There is a future scope to enhance the attack detection approach \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Cont. Tables 12 and 13 list some existing DDoS detection approaches on CC using hybrid detection techniques.
## 3 Results and Discussion
### Correlation Analysis  
A Pearson's correlation was performed to establish the correlation between all parameters analyzed (Figure 2).  
Concerning the correlation between phenolic and HMF contents and antioxidant and anti-aging capacities, it was possible to verify some significant positive correlations (\(p\leq 0.05\)).  
Regarding the phenolic content, there was a significant and positive correlation between total phenols and antioxidant assays (\(r_{\text{DPPH}}\) = 0.790, \(r_{\text{ABTS}}\) = 0.0.670, \(r_{\text{FRAP}}\) = 0.645), that corroborate the influence of phenolic compounds on the antioxidant capacity as mentioned previously [51]. _Ortho_-diphenyl also showed significant and positive correlations for anti-aging assays (\(r_{\text{tyrosinease}}\) = 0.880, \(r_{\text{elastase}}\) = 0.606) and flavonoids contents were significantly and positively correlated with ABTS, FRAP, and HMF (\(r_{\text{ABTS}}\) = 0.600, \(r_{\text{FRAP}}\) = 0.714, \(r_{\text{HMF}}\) = 0.498). In fact, concerning the HMF content, some studies reported a  
Figure 2: Pearson correlation between phenolic content, antioxidant capacity, anti-aging capacity, and HMF content. Significance: * significant at \(p<0.05\); ** significant at \(p<0.01\).  
relationship between the flavonoids and HMF components, associated with its production, i.e., compounds such as catechins can inhibit or reduce the production of HMF, probably due to less reactivity of the sugars [56]. In some cases, negative correlations were found, which can be explained by synergistic/antagonistic effects between compounds.
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
## 5 Results
### Experiments
#### 5.3.3 Strong Magnetic Catastrophe Medium
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Sampling Methods**} & \multicolumn{3}{c}{**Rrms Error (\%)**} \\
**Spatial Domain** & **Wavenumber Domain** & \(B_{ax}\) & \(B_{ay}\) & \(B_{az}\) \\ \hline Uniform & Uniform & 1.91 & 1.82 & 1.83 \\ Uniform & Log-domain uniform & 0.95 & 1.08 & 0.95 \\ Non-uniform & Piecewise uniform & 0.19 & 0.24 & 0.25 \\ Non-uniform & Log-domain uniform & 0.17 & 0.17 & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy statistics of different sampling points between \(-\)0.25 and 0.25 in the wavenumber domain (z = 0 m, \(Nx\) = \(Ny\) = \(Nz\) = 101, \(Nx\) = \(Nky\) = 101). 0.17%, and 0.21%, respectively. Therefore, high accuracy can be achieved in the cases of non-uniform sampling in the spatial domain, piecewise uniform sampling, and log-domain sampling in the wavenumber domain. This shows that the AS-FT algorithm can realize arbitrary samples and has high computational efficiency and accuracy. The comparison between the numerical simulation results and analytical solutions of the non-uniform sample in the spatial domain and log-domain sample in the wavenumber domain is shown in Figure 17. The isolines of the two solutions in the figure are consistent, indicating that AS-FT is still very suitable for strong magnetic models. Based on the AS-FT algorithm, the 3D numerical simulation of the weak and strong magnetic field in the space-wavenumber domain has excellent performance. The AS-FT can obtain high-precision solutions and higher computational efficiency in fewer wavenumbers than Gauss-FFT and standard-FFT. Compared with the continuous model, the catastrophe model oscillates in the spectrum, and the spectrum distribution range is broader. Still, the simple selection of wavenumbers can also calculate the model accurately. The spectrum's energy is enhanced from a weak magnetic to a strong magnetic field, and the calculation accuracy can still be guaranteed by expanding the wavenumber selection interval. The energy distribution of the strong magnetic catastrophe model is the most complex, the  
Figure 16: Schematic diagram of horizontal non-uniform sectioning. (**a**) Spatial domain sample, (**b**) Wavenumber domain sample. Figure 17: Comparison between numerical solutions and analytical solutions of strong magnetic sphere. spectrum amplitude is the largest, the oscillation is violent, and the selection of wavenumber is more complex, but a high-precision solution is obtained by non-uniform sampling. In conclusion, AS-FT has good adaptability to the 3D numerical simulation of weak and strong magnetic fields.
## 7 Experimental Evaluation
### Streaming ML vs. Batch ML/DL
Thus, training once but never updating the model may cause the model to underperform significantly. However, periodically updating the model or retraining it from scratch can lead the model to better performance, as observed in the 2 nd and 3 rd scenarios. This is due to the nature of the traces, where the workload access patterns of a bucket often have similarities to the ones of the previous bucket. The 3 rd scenario has a better F1 score than the 2 nd scenario because it builds upon previous knowledge. However, the streaming model always exhibits a higher and stable F1 score because it is continuously updated at every data sample, while the model from the 3 rd scenario is only updated in batches. Hence, even a sudden change in the workload patterns (as observed clearly in bucket #7 where all batch models experience a sudden drop in prediction performance) can be learned quickly in a streaming environment (with a minor drop of 1% in bucket #7). **Key takeaways:** Periodically retraining a batch model (either from scratch or incrementally) yields higher overall predictive performance than using a fixed batch model, reducing MAE by 0.12 and increasing F1 score by 6% on average for _Next Offset_. Streaming ML further reduces MAE by 0.03 and increases F1 score by 1.8% compared to retraining the batch model incrementally (3 rd scenario). At the same time, streaming ML significantly reduces retraining costs compared to the batch-based 3 rd scenario, as it is an order of magnitude faster than DTs and three orders of magnitude faster than RNNs. Figure 11: Runtime for the three batch scenarios using RNN (LSTM) against streaming ML for the _Next Offset Class_. Figure 12: F1 score per test bucket for the three batch scenarios using DTs and RNNs as well as streaming ML for Trace 8 for the _Next File Hotness Class_.