esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Ablation Experiment  
To assess the contribution of each module to the overall performance of RSNC-YOLO, individual modules were removed from the enhanced YOLOv8-seg model, and the impact on performance was evaluated. Table 4 presents the results of the tuna identification task after the removal of various modules, highlighting their significance for the algorithm's effectiveness.  
The results in Table 4 indicate that removing the RepC3 module results in a significant drop in model accuracy and a slight decrease in detection speed. This decline is due to RepC3 utilizing more complex structures during the training phase, which are reparametrized into simpler forms during prediction, thereby accelerating inference. When SCDown modules were replaced with standard convolutional blocks, the overall computational cost of the model increased, resulting in a drop in Average Precision of approximately 3% compared to the proposed algorithm. This outcome suggests that SCDown is better  
Figure 8: Comparison of the RSNC-YOLO’s and the original model’s recognition results.  
suited to the improved algorithm, even though its computational complexity is only half that of the Conv blocks. After eliminating the NAMAttention module, the model's accuracy decreased, while the detection speed and the weight remained essentially unchanged, indicating that the NAMAttention module is lightweight and adds minimal complexity to the model. Additionally, this attention mechanism helps suppress insignificant features that could otherwise interfere with model predictions. Finally, removing the C2f-DCNv3-DLKA module led to a slight decline in accuracy but a significant increase in detection speed. This finding suggests that the combination of DCN and large convolutional kernels aids in fine-grained tuna recognition. The improved detection speed can be attributed to RepC3 being faster during inference compared to C2f-DCNv3-DLKA. Replacing all modules with C2f-DCNv3-DLKA was avoided due to its high-computation-density large convolutional kernels, which would significantly reduce the detection speed and increase memory consumption, thus complicating the model's training process.
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 4 Collision Risk Assessment
### Collision Risk Assessment Model
Fuzzy rule and fuzzy inference  
The basis of fuzzy inference is presented as output fuzzy variables, which are inferred from input fuzzy variables based on a set of fuzzy inference rules in linguistic terms [33], which are extracted from expert knowledge. The fuzzy rule is expressed in the form of \"If-Then\", and two components make up the \"If-Then\" rule: an antecedent that is compared to the input and a consequent. A fuzzy rule base contains a set of fuzzy \"If-Then\" rules, and these fuzzy \"If-Then\" rules are employed by fuzzy inference to define a mapping from input fuzzy sets to output fuzzy. In this paper, the fuzzy \"If-Then\" rules designed according to the expert experience were expressed as a language rule system with three inputs and one output:  
_R1: if \"T\" is NB and \"V\" is NB and \"D\" is PB, then Safety degree K is NB;_  
_R2: if \"T\" is NB and \"V\" is NB and \"D\" is PS, then Safety degree K is NB;_  
_......... Ren: if \"T\" is NB and \"V\" is PB and \"D\" is PB, then Safety degree K is PB;_  
The fuzzy reference rule tables, in the case when the delay time is NB, NS, PS, and PB, can be expressed in the forms of Tables 1-4. Mamdani [34] and Sugeno [35] are two frequently employed methods for fuzzy inference systems. Researchers rely heavily on Mamdani fuzzy inference method due to its practicality and efficiency. In light of this, the max-criterion Mamdani inference mechanism was implemented in this study for collision risk inference. 3. Defuzzification  
Defuzzification is used to the convert fuzzy outputs into clear outputs. There are several common defuzzification methods such as the center of the area (COA), the bisector of the area (BOA), the mean of the maximum(MOM), the smallest of the maximum (SOM) and the largest of the maximum (LOM) [25]. In the defuzzification layer, the output fuzzy numbers are converted to crisp numbers through the center of the area (COA) defuzzification method. \[\text{Center of area}:\ Z_{\text{COA}}=\frac{\int\limits_{Z}\mu_{A}(Z)Zdz}{ \int\limits_{Z}\mu_{A}(Z)dz} \tag{7}\]
## 5 Discussion
### AVI as a Sovereign Gateway for Enterprise and Public Sector LLM Deployment  
The AVI framework offers a pathway for enterprises and public sector organizations to harness the power of LLMs responsibly. Instead of relying solely on the opaque and often generic safety measures of external LLM providers, or risking data leakage by sending sensitive information to third-party APIs, organizations can deploy AVI as a sovereign gateway. This approach allows for (1) fine-grained control over content policies tailored to specific industry regulations, ethical guidelines, or national interests; (2) integration with internal knowledge bases, enhancing responses with proprietary, verified information while keeping such data secure within the organization's perimeter; (3) auditable and transparent governance over LLM interactions. Thus, AVI can empower organizations to adopt LLM technologies without coding control over their data or the information narrative, moving beyond simply blocking access to \"potentially dangerous LLMs\" towards a model of controlled and customized utilization [61]. This capability for tailored governance and auditable control is particularly relevant in light of emerging AI regulations, such as the European Union's AI Act [62], and similar legislative efforts globally, which emphasize risk management, transparency, and accountability in AI systems.
## 2 Numerical Simulation of LCO  
### Governing Equations  
The simulation of the incompressible, two-dimensional (2D) wind field crossing the bridge deck was based on the unsteady Reynold-averaged Navier-Stokes (RANS) equations. Considering the deck section motion, the dynamic mesh is used to accommodate the moving boundaries. Accordingly, the governing equations of the wind-bridge interaction system nested in an arbitrary Lagrange-Euler framework can be expressed as:  
\[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial\rho\Big{(}u_{j}-u_{j}^{b }\Big{)}u_{i}}{\partial z_{j}}+\frac{\partial\rho u_{i}u_{i}}{\partial z_{i}} =-\frac{\partial p}{\partial z_{i}}+\frac{\partial}{\partial z_{j}}\Bigg{(} \mu_{eff}\frac{\partial u_{i}}{\partial z_{j}}\Bigg{)}+S_{i} \tag{1}\]  
\[\frac{\partial u_{i}}{\partial z_{i}}+\frac{\partial\Big{(}u_{j}-u_{j}^{b} \Big{)}}{\partial z_{j}}=0 \tag{2}\]  
where the \(u_{i}\) and \(u_{j}\) represent the wind velocity components in the along-wind and cross-wind directions; \(z_{i}\) and \(z_{j}\) denote the Cartesian spatial coordinates; \(\rho\) is the air density; \(p\) is the wind pressure; \(u_{j}^{b}\) is the grid velocity; \(S_{i}\) is the additional source contributions; and \(\mu_{\textit{eff}}\) is the effective viscosity including both laminar and turbulent effects. The turbulence was modeled using the shear-stress transport (SST) \(k\)-\(\omega\) scheme, where \(k\) is the turbulence kinetic energy and \(\omega\) is the specific dissipation. In this study, the commercial CFD software Fluent 2020R2 was employed to simulate the wind-bridge interactions.
# Bridge Displacements Monitoring Method Based on Pixel Sequence
###### Abstract  
In light of the challenges posed by intricate algorithms, subpar recognition accuracy, and prolonged recognition duration in current machine vision for bridge structure monitoring, this paper presents an innovative method for recognizing and extracting structural edges based on the Gaussian difference method. Initially, grayscale processing enhances the image's information content. Subsequently, a Region of Interest (ROI) is identified to streamline further processing steps. Following this, Gaussian check images at different scales are processed, capitalizing on the observation that edges show reduced correspondence to the Gaussian kernel. Then, the structure image's edges are derived using the difference algorithm. Lastly, employing the scale factor, the algorithm translates the detected edge displacement within the image into the precise physical displacement of the structure. This method enables continuous monitoring of the structure and facilitates the assessment of its safety status. The experimental results affirm that the proposed algorithm adeply identifies and extracts the structural edge's geometric characteristics with precision. Furthermore, the displacement information derived from the scale factor closely aligns with the actual displacement, validating the algorithm's effectiveness.  
G 2024 14 11901. [[https://doi.org/10.3390/appl42411901](https://doi.org/10.3390/appl42411901)]([https://doi.org/10.3390/appl42411901](https://doi.org/10.3390/appl42411901))  
1
## 2 Theoretical Framework
### Rectifier  
The PMSG in SWTs typically delivers a three-phase AC voltage that must be converted to DC before further processing, due to its variable frequency. In the proposed system, this conversion is achieved using a passive three-phase full-bridge rectifier composed of six diodes.  
The rectifier outputs a pulsating DC voltage whose average value depends on the generator's rotational speed. Assuming a balanced and sinusoidal generator voltage with peak line-to-line voltage \(\mathrm{V_{LL}^{peak}}\), the ideal output voltage of the rectifier can be approximated as:  
\[V_{DC}=\frac{3\sqrt{3}}{\pi}\mathrm{V_{LL}^{peak}}\approx 1.654\mathrm{V_{ LL}^{peak}} \tag{7}\]  
This rectified voltage is directly proportional to the rotor speed, given the nature of the PMSG. As wind speed varies, so does the output voltage, which in turn defines the operating point of the subsequent boost converter.  
Although passive rectification does not allow for power factor correction or bidirectional power flow, it offers high efficiency, low cost, and reliability--features that are well suited for low-to-medium power small wind applications. Additionally, the simplicity of the passive rectifier ensures compatibility with open-source hardware controllers and reduces the complexity of the overall system.  
To smooth the voltage ripple at the rectifier output and ensure stable operation of the DC-DC converter, a high-value electrolytic capacitor is placed at the rectifier's output. The capacitor value is selected based on the expected current draw, the line frequency, and the acceptable voltage ripple. For a three-phase full-wave rectifier with capacitive filtering, the peak-to-peak output voltage ripple \(\Delta V\) can be approximated by the following equation:  
\[\Delta V=\frac{\text{I}_{\text{load}}}{\text{fC}} \tag{8}\]  
where I\({}_{\text{load}}\) is the output (load) current, f is the line frequency (for full-wave rectification, the ripple frequency is \(2f\), and C is the capacitance value.  
This equation shows that increasing the capacitance or ripple frequency reduces the ripple voltage, which is crucial for ensuring stable downstream DC-DC conversion.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Summary of the Statistical Model Performance
#### 6.5.2 Performance on Normal Traffic Datasets  
The statistical model consistently achieved a 100% detection rate for normal traffic across all scenarios, with no false positives for malicious or attack-specific packets. This highlights its precision in handling benign datasets, making it a reliable tool for minimizing disruptions in normal operations.
## 3 Results
### Comparisons Results of RMSE, MAD, MAE Methods Evaluation  
For evaluation of the results of the application of nonlinear regression to measurements of air pollutant concentration from low-cost sensors, the methods of Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Error (MAE) were applied to both weekly and monthly measurement data. Data are reported for the primary and corrected (nonlinear regression) measurements for each low-cost sensor (ozone and nitrogen dioxide) relative to reference measurements.  
Tables 5 and 6 show the RMSE, MAD, and MAE results of both primary and corrected measurements for the three low-cost ozone (O\({}_{3}\)) sensors, in monthly and weekly measurements, respectively.  
Tables 7 and 8 show the RMSE, MAD, and MAE results of both primary and corrected measurements for the three low-cost nitrogen dioxide (NO\({}_{2}\)) sensors, in monthly and weekly  
RMSE, MAD and MAE methods is shown for ozone, the monthly measurements (Table 5), the RMSE of 20% to 60%, the MAD of 10% to 24% and the MAE of 7% to 15%. The weekly measurements (Table 6), the RMSE of 17% to 86%, the MAD of 9% to 14% and the MAE of 3% to 9%. For nitrogen dioxide, the monthly measurements (Table 7) show RMSE of 3% to 28%, MAD of 9% to 60% and MAE of 4% to 25%. The weekly measurements (Table 8) show RMSE of 1% to 3%, MAD of 1% to 3% and MAE of 3% to 4%.
## 4 MILP Model Formulation  
To analyze the integrated scheduling problem mathematically, a MILP model is developed on the basis of the adjacent sequence modeling idea (for more details on the idea, please refer to Section 2.3). The reasons for selecting the adjacent sequence-based modeling approach can be attributed to two main aspects.  
On the one hand, the adjacent sequence-based modeling approach has several advantages. (1) It can represent job sequences more naturally and efficiently by focusing on the relative positions of adjacent jobs, which is crucial for accurately modeling the constraints and objective functions of scheduling problems. (2) It generally leads to lower model complexity, requiring fewer variables and constraints to represent the same scheduling problem, thus improving solution efficiency. This advantage is especially significant for large-scale scheduling problems, where computational resources and time are critical considerations. (3) It shows high flexibility and adaptability in handling various scheduling scenarios, including those with complex constraints and multiple objectives. It can effectively deal with different types of scheduling problems, such as those in flexible flow shops with multi-variety and small-batch production. (4) The experimental results from related studies indicate that adjacent-sequence-based MILP models often outperform other types of MILP models in characterizing and solving scheduling problems. For example, in [4], this model was more effective than position-based MILP models in solving the three-stage remanufacturing system scheduling problem (3T-RSSP) due to its ability to more accurately capture the intrinsic characteristics of scheduling problems, leading to more optimal solutions.  
On the other hand, compared with other modeling methods, (1) sequence-based approaches can represent job sequences and their precedence relationships, but they usually require more variables and constraints, leading to higher model complexity and longer solution times, especially for large-scale problems. (2) Position-based approaches focus on the specific positions of jobs in the schedule but are less flexible and more complex to implement than adjacent-sequence-based approaches. They may be inefficient in handling certain scheduling constraints and objectives. (3) Time-based approaches model scheduling problems by considering the time intervals of job processing. Although they provide detailed timing information, they often result in complex models with numerous variables and constraints, making them computationally challenging to solve, particularly for problems with many jobs and resources.  
Figure 1: The investigated FFSP-AGV.  
As noted above, the parallel machines in each processing stage are unrelated, but the AGVs in the shop are identical, and therefore, we can model the problem without explicitly considering the AGVs. In other words, we can avoid the utilization of an additional index for AGVs, which can lead to a remarkable reduction in the number of decision variables and constraints. Next, we introduce some of the notations used, followed by the MILP model.
###### Abstract  
Measurement of beach heights in the intertidal zone has great importance for dynamic geomorphology research, coastal zone management, and the protection of ecological resources. Based on satellite images, the waterline method based on satellite images is one of the most effective methods for constructing digital elevation models (DEMs) for large-scale tidal flats. However, for fast-changing areas, such as Tiaozini in the Jiangsu coast, timely and detailed topographical data are difficult to obtain due to the insufficient images over a short period of time. In this study, as a supplement to the waterline method, an artificial neural network (ANN) model with the multi-layer feed-forward back propagation algorithm was developed to simulate the topography of variable Tiaozini tidal flats. The \"7-15-15-1\" double hidden layers with optimized training structures were confirmed via continuous training and comparisons. The input parameters included spectral bands (HJ-1 images B1-B4), geographical coordinates (X, Y), and the distance (D) to waterlines, and the output parameter was the elevation. The model training data were the HJ-1 image for 21 March 2014, and the corresponding topographic data obtained from the waterline method. Then, this ANN model was used to simulate synchronous DEMs corresponding to remote sensing images on 11 February 2012, and 11 July 2013, under low tide conditions. The height accuracy (root mean square error) of the two DEMs was about 0.3-0.4 m based on three transects of the in-situ measured data, and the horizontal accuracy was 30 m--the same as the spatial resolution of the HJ-1 image. Although its vertical accuracy is not very high, this ANN model can quickly provide the basic geomorphological framework for tidal flats based on only one image. This model, therefore, provides an effective way to monitor rapidly changing tidal flats.  
tidal flats; topography; remote sensing; artificial neural networks; waterline method +
Footnote †: journal: Applied Sciences
# Numerical Investigation on Handling Stability of a Heavy Tractor Semi-Trailer under Crosswind
## 5 Effect of the Wind Deflector on Crosswind Stability  
In current research, the crosswind stability of tractor semi-trailer with and without a wind deflector has been investigated. The aerodynamic coefficients of the tractor semi-trailer with wind deflector under different yaw angles are obtained based on the above computational process. The variation tendency of the rolling moment coefficient and yawing moment coefficient is drawn in Figures 16 and 17. It is obvious that the rolling moment coefficient decreased when the wind deflector was added, which exerted great improvement on the performance of the vehicle. The wind deflector is also beneficial to reduce the yawing moment coefficient under crosswind, especially to the large relative angles, and that will have a positive impact on the handling stability of the vehicle. Obviously, this wind deflector could improve the aerodynamic characteristics of the vehicle in the crosswind circumstance. Meanwhile, the improvement of the aerodynamic characteristics would have large effects on the handling stability of vehicle. To investigate the improvement effects of the aerodynamic characteristics on handling stability under crosswind, the fitting formulas of aerodynamic characteristics of the vehicle with a wind deflector are also applied to the multi-body dynamic model.  
Figure 18 compares the lateral displacement, lateral acceleration and yaw rate of the heavy tractor semi-trailer with and without a wind deflector. It is shown in the results that the tendencies of these parameters were quite similar, but the lateral displacement, lateral acceleration and yaw rate were reduced accordingly with the wind deflector added, that is, the handling stability was improved. Therefore, it could be concluded that the wind deflector not only could improve the aerodynamic characteristics, but also could enhance the handling stability under crosswind. Comparing the  
Figure 16: Comparison of rolling moment coefficient.  
Figure 17: Comparison of yawing moment coefficient.  
two results, the lateral acceleration and yaw rate were decreased by a maximum of 14.6% and 16.5%, respectively.
## 6 Experimental Evaluation
### Results on Alerts Forecasting
ARIMA needed at least 10 samples. ITALTELECO argues that an acceptable error should fall within three months. The reader can observe that the obtained error is much higher than the desired one (3 months corresponding to 90 days). As several components had a few number of samples to be used as training set, the predictions in such cases are usually very far from the real values. Therefore, we counted the number of samples for each component, and Table 14 indicates the related distribution and the corresponding errors averaged per each model and the components with a number of notices fixed in the first column. The reader notices that the average error for ARIMA for the first six rows is empty because the model requires at least 10 samples, as previously mentioned. Therefore, we analysed the performances with respect to the sample size and identified the cases that could generate an acceptable error. We defined the global error \(E_{c}^{M}\) as the errors of the model \(M\) computed, taking all the errors corresponding to the components whose number of samples was greater than \(c\). For example, \(E_{10}^{LR}\) corresponds to the average error of the linear regression for all the components with number of samples greater than 10 (from sample length 11 on). Table 15 illustrates the global error \(E_{c}\) for different values of \(c\). As indicated in Table 14, by choosing \(c\) = 43, we obtained the minimum average error of 84.11 days. From this, it can be deduced that the model that outperforms the others is ARIMA, and that for a sample size greater than 43 elements, both ARIMA and PI satisfy the constraint related to the maximum acceptable error. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Average Error LR** & **Average Error ARIMA** & **Average Error PI** \\ \hline
647.66 & 418.56 & 476.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Average error of each regression model.
# Soliton Content of Fiber-Optic Light Pulses
## 4 Methods to Verify Soliton Content
### Soliton Radiation Beat Analysis
A comparison of DST and SRBA reveals the advantages and weaknesses of both. SRBA requires more input data. These can only be available from numerical simulations; it is hard to imagine how suitable data could be obtained from an experiment. On the other hand, SRBA can treat cases that are inaccessible to DST. Its limit is reached when for a growing number of solitons involved, the number of traces becomes too large to disentangle them all. DST can be applied to many-soliton cases [94],  
Figure 12: Full-frequency SRBA chart to determine the soliton content of an \(N=2\) soliton. Grey scale corresponds to the log of the Fourier transform of the spatial evolution of the spectral power density. Arrows labeled \(S_{1}\) and \(S_{2}\) mark traces pertaining to the first and second soliton; their beat note is also highlighted (from [93]). Figure 13: (Left) SRBA chart for an energy scan of a DM soliton. For an explanation of the procedure, and the apparent white vertical stripes in particular, see the text. (Right) Extract of the left panel, with standardized line types. The fundamentals pertaining to three constituent solitons are labeled \(S_{1}\)–\(S_{3}\). The dashed line labeled ‘sol’ is for reference; it represents the spatial frequency for a non-DM soliton in a fiber with \(\beta_{2}=\overline{\beta}_{2}\) (from [93]). but only in strictly integrable systems, or with the limitations in 'nearly integrable' systems as pointed out in Section 4.1.3.