esa-sceva/satcom-chunk-collection huggingface dataset:
# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 3 Results and Discussion
### Evaluation Metrics  
This experiment employed commonly used evaluation metrics in deep learning, including precision (P), recall (R), average precision (AP), and mean average precision (mAP).  
P and R are widely used measures in detection and classification tasks. They are calculated as follows:  
\[\text{Precision}=\frac{\text{k}}{\text{N}}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
\[\text{Recall}=\frac{\text{k}}{\text{M}}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
where TP represents true positives (correctly detected positive samples), FP represents false positives (incorrectly detected positive samples), and FN represents false negatives (missed positive samples).  
AP and mAP are important metrics for evaluating model performance in object detection tasks. AP represents the average precision for a single object category, while mAP takes into account the average precision across all object categories. The calculation formulas are as follows:  
\[\text{AP}=\int\limits_{0}^{1}\text{P}(\text{R})\text{d}(\text{R}) \tag{12}\]  
\[\text{mAP}=\frac{1}{\text{M}}\sum\limits_{\text{j}=1}^{\text{M}}\text{AP}_{ \text{j}} \tag{13}\]  
where M represents the total number of object categories.  
FPS [31] represents the number of image frames a detection model can process per second and is a crucial metric for evaluation. FPS directly influences the real-time capability and response speed of a detection. The calculation formulas are as follows:  
\[\text{FPS}=\frac{\text{N}_{\text{i}}}{\text{T}_{\text{t}}} \tag{14}\]  
where N\({}_{\text{i}}\) represents the number of image frames processed in a certain period of time; T\({}_{\text{t}}\) represents the measurement interval.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Sum** & **Training Set** & **Validation Set** & **Testing Set** \\ \hline Number of images & 4950 & 3465 & 990 & 495 \\ Number of annotations for ’Human’ & 3351 & 2384 & 676 & 291 \\ Number of annotations for ’Hat’ & 2786 & 2005 & 541 & 240 \\ Number of annotations for ’Safetbelt’ & 1478 & 1044 & 238 & 196 \\ Number of annotations for ’Seine’ & 2618 & 1820 & 512 & 286 \\ Number of annotations for ’Fence’ & 1663 & 1184 & 312 & 167 \\ Number of annotations for ’Noneckline’ & 1358 & 997 & 229 & 132 \\ Number of annotations for ’Notiff’ & 1245 & 895 & 213 & 137 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Annotations for each category of the power operation scenario data set.
# Linked Data Aware Agent Development Framework for Mobile Devices
## 4 Case Study and Discussion
### Using the LDAF-M Framework  
#### 4.2.1 Constructing a New Agent  
Using LDAF-M, developers are able to build agent based software which is mainly composed of agents running on mobile devices in various application domains.  
When a developer needs to construct a new agent, he/she should extend the \"Agent\" class which serves as the main component for defining agents in LDAF-M. For example, the code fragment that is going to be used for building a client agent of the auction application is shown in Listing 1.  
```
01 public class clientAgent extends Agent {
02 public void start(){...}
03 public void finish(){...}
04}
```  
Listing 1: Template for building a client agent.  
The BDI related components of an agent, such as belief base, plan base, and goal base together with the other components, such as message base and agent life cycle related data, are held in the Agent class. The definitions of these components are made in the start() method. On the other hand, operations related with the termination of an agent are defined in the finish() method.  
Agents constructed using LDAF-M framework communicate with each other using FIPA Agent Communication Language messages. An agent may receive many messages and to give the agent the ability to select the meaningful ones for itself among the incoming messages, a message filter class (e.g., FactoryMessageFilter) is defined in the framework. Using the methods in that class, developers can define patterns for indicating the significant messages that an agent expects. These pattern definitions are also made in the start() method of the agent class.
## 2 Motivation and Related Works
### Contributions  
The main contributions that this work addresses, which have not been included in previous studies, are as follows:  
* An IHS architecture for emergency scenarios is proposed for the first time, which consists of three segments. The architecture is explained in detail, and we conclude that the space segment, near space segment and ground segment will work closely together to provide information transfer services for remote sensor devices.
* The transmission power requirements of the terminal end and HAP end are investigated in a slow flat Rician fading channel. The gradient descent and differential equation methods are used to obtain the optimal transmitted power while maintaining a minimum bit error rate (BER) level.
* Using the concept of link-state advertisement (LSA), a novel energy-efficient transmission strategy for the energy-efficient path selection is designed. The strategy can significantly reduce the energy consumption, both of the terminal and HAP, which is demonstrated through in-depth numerical simulations.  
The remainder of this paper is organized as follows. Section 3 gives the system model, including three segments and the channel modeling. Section 4 gives the mathematical formulation of the optimization problem. The transmission power requirements are derived by the gradient descent and differential equation methods. In Section 5, we design the energy-efficient transmission strategies, which use the LSA concept. The energy consumption at the source and system level are modeled, and an adaptive algorithm for the energy-efficient path selection is given. Section 6 shows numerical simulations and results. Finally, Section 7 concludes the paper with a summary and a discussion of future research directions. Table 1 lists some key notations used in the paper.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 4 Experiment Analysis and Results  
Based on the real data sets collected during the flood, the influence of flood on L4 combination observation of GPS and GLONASS satellites is analyzed. In addition, considering that the characteristic of L4 is related to the surrounding environment of the observation station, the azimuth and elevation should keep consistent during the comparison. Thus, the satellite ground track repeat period is presented in the view of the sky plot. Both the influence of rainfall and hardware device are investigated to further demonstrate that the influence of flood on L4 is mainly caused by the flood but not by other elements.
## 3 Results  
### Validation of NmF2/hmF2 observed by FY3C/GNOS with COSMIC  
#### 3.1.1 Error Analysis of NmF2 between FY3C/GNOS and COSMIC  
The linear fit and statistical error distribution of 346 NmF2 data pairs between FY3C/GNOS and COSMIC during 2016.035 and 2017.035 are presented in Figure 2a,b, respectively. The correlation coefficient of NmF2 data pairs, bias and std of relative errors of NmF2 data pairs are shown in Figure 2b, which are 0.95, \(-\)2.19%, and 17.48%, respectively, we can also see that the relative errors of NmF2 between FY3C/GNOS and COSMIC are most concentrated between \(\pm\)20%, showing a symmetric normal distribution with a mean value around 0. Figure 3a,b demonstrates the linear fit of 245 and 101 NmF2 data pairs between FY3C/GNOS and COSMIC at daytime (0600 LT-1800 LT) and nighttime (1800 LT-0600 LT), respectively. During daytime, the correlation coefficient, bias and std of NmF2 between FY3C/GNOS and COSMIC are 0.93, \(-\)1.20%, and 16.54%, respectively, during nighttime, the correlation coefficient, bias and std of NmF2 data pairs are 0.93, \(-\)4.60%, and 19.37%, respectively.  
Figure 2: Linear fit and statistical error distribution of NmF2 between FengYun-3C (FY3C)/Global Navigation Satellite System occultation sounder (GNOS) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) over a whole day during 2016.035 and 2017.035: (**a**) the linear fit of NmF2 between FY3C/GNOS (horizontal axis) and COSMIC (vertical axis) over a whole day, in which the blue dots represent the matched data pairs of NmF2 and the red line depicts the linear regression fit of NmF2 data pairs; (**b**) the number (vertical axis) of NmF2 relative errors (horizontal axis) between FY3C/GNOS and COSMIC, in which the red curve represents the distribution fit of the relative errors. The correlation coefficient of NmF2 data pairs, bias, and std of relative errors of matched NmF2 data pairs are also labelled in the right side of (**b**).  
It can be seen that the correlation coefficients of NmF2 are all above 0.9 during the whole day, daytime and nighttime, showing a good correlation of NmF2 between FY3C/GNOS and COSMIC. Besides, the bias in range of \(-\)5% to \(-\)1% is accompanied by the std between 16% and 20%, which eliminates the systematic offsets between FY3C/GNOS and COSMIC. The overall std of 17.48% and the bias of \(-\)2.19% show the good precision consistency of two different IRO products between FY3C/GNOS and COSMIC. Besides, the precision consistency of NmF2 deteriorated at nighttime compared to daytime and whole day.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 3 Overview of Previous Studies on IRS-Assisted UAV Communications
### Improvement of Spectral Efficiency  
In this subsection, we describe the system models, specific technologies adopted in the system, and optimization algorithms proposed in previous studies for improving the performance related to the spectral efficiency. In particular, it is important to incorporate appropriate communication technologies, such as mmWave, THz, NOMA, and simultaneous wireless information and power transfer (SWIPT), into IRS-assisted communications systems to maximize the spectral efficiency. NOMA transmission is distinguished from conventional orthogonal multiple access in that devices can be allocated to the same time/frequency resources. The key feature of the NOMA technique is based on interference management to share the resources, which enables enhancing the spectral efficiency. SWIPT, which exploits the same radio signals to transfer both information and energy using time switching or power splitting of signals, can prolong UAVs' battery lifetime and improve the spectral efficiency [57]. Table 1 briefly summarizes the main features of the previous works that will be introduced in this subsection. A scenario, in Figure 2, associated with each work is also indicated in the table. Figure 2: Five representative scenarios of IRS-assisted UAV communications. The authors of [58, 59] aimed to maximize the sum rate by applying the NOMA technique to IRS-assisted UAV communications systems. Numerical results verified the effect of IRS on the performance of UAV communications, as well as on the performance of NOMA algorithms. In [58], NOMA decoding scheduling was considered for different user groups to maximize the sum rate, and an interference elimination algorithm was proposed. Reference [59] adopted deep reinforcement learning (DRL) to optimize the UAV trajectory while avoiding environmental obstacles. Reference [60] attempted to maximize the data rate of strong users while ensuring a target rate of weak users by optimizing the IRS phase shift and horizontal position of the UAV in a multiple-input single-output NOMA system. References [56, 61] aimed to improve the average rate of an IRS-assisted UAV system in an urban environment. Both the LoS and NLoS channel models were considered for each link. In particular, ref. [56] considered an NLoS channel between the UAV and a user, and [61] considered multiple channels between the UAV and users.
# Theoretical Framework (Module) for Short-Sea Shipping System Evaluation
## 3 Theoretical Basis of the Evaluation of Short-Sea Shipping Processes
### Mathematical SSS System Model
\[P_{L}=\frac{1}{\eta p}(k_{P}P_{S}N_{T}+k_{R}P_{R}N_{T}+k_{T}P_{T}N_{T}+\cdots) \tag{2}\]  
where \(\eta_{P}\) denotes the correlation coefficient for a similar process (this could be between 0.98 and 1.0 (in case of one transport mode, correlation coefficient will be 1.0--typical for transport systems)); \(k_{P}\) denotes the discount coefficient, which depends on the quantity of transportation cargo or units; \(P_{S}\) denotes the price (tariff) of sea transportation for one cargo or transport unit; \(N_{T}\) denotes the number of cargo or units transported by the SSS system; \(k_{R}\) denotes the discount coefficient, which depends of the quantity of goods or units transported; \(P_{R}\) denotes the price (tariff) of road transportation for one cargo unit; \(k_{T}\) denotes the discount coefficient, which depends on the quantity of the cargo or units transported by railways; \(P_{T}\) denotes the price (tariff) of railway transportation for one cargo unit; and \(+\ldots\) denotes other possible transportation systems' price elements, like payments for emissions trading and so on, as well as prices of services performed using additional ports or terminals. The price of transportation by ship, in general, of one cargo unit or of a ship's party, for example by RORO ship, (\(P_{S}\)), can be calculated as follows [10,11]:  
\[P_{OTHC}+P_{BAF}+P_{CAF}+P_{ISPS}+P_{WRS}+P_{CONG}+P_{PORT}+P_{THC}+P_{DOC}+P_{ IMP/EXP}+P_{S/P}+P_{RF}=P_{S}, \tag{3}\]  
where \(P_{OTHC}\) denotes the sea freight factor; \(P_{BAF}\) denotes the bunker adjustment factor; \(P_{CAF}\) denotes the currency correction factor; \(P_{ISPS}\) denotes the International Ship and Port Facility Security factor; \(P_{WRS}\) denotes the war risk factor; \(P_{CONG}\) denotes the factor for the cost of transshipment in ports; \(P_{PORT}\) denotes the port dues factor; \(P_{THC}\) denotes the terminal handling fee factor; \(P_{DOC}\) denotes the factor for the fee for the preparation of document; \(P_{IMP/EXP}\) denotes the export/import tax factor; \(P_{S/P}\) denotes the Panama/Suez Canal fee factor; and \(P_{RF}\) denotes the fee for connecting the referring container and other factors.
## 3 Results and Discussion
### Effects of Biogas Composition  
With a given operating condition, equivalence ratio, and DME content, the biogas compositions only slightly affected the indicative engine cycle work and gas temperature. Simulation calculations showed that as the engine operated at a speed of 1800 rpm with biogas M6C4, M7C3, M8C2 enriched by 20% DME with \(\phi\) 0.77/0.98, the indicative engine cycle work varied from 957 J/cyc to 985 J/cyc, i.e., it only increased by 3% when shifting from biogas M6C4 to biogas M8C2. The maximum gas temperature increased by 50 \({}^{\circ}\)K for the same variation of biogas compositions, as shown in Figure 7a. This can be attributed to the fact that the heat value of the fuel increased with the increase of \(\mathrm{CH_{4}}\)  
Figure 6: Variation of \(\mathrm{W_{i}}\), CO concentration (**a**); \(\mathrm{NO_{x}}\) concentration and soot volume fraction (**b**) with DME content in biogas M7C3 under effects of biogas compositions (\(n=1200\) rpm, \(\phi\) 0.77/0.97).  
content in the biogas. As shown in Figure 7a, with the same advanced ignition angle, the combustion in the case of M8C2 started earlier due to higher laminar flame speed. Because the maximum temperature occurred near TDC, the peak of pressure was higher. However, the indicative engine cycle work was slightly changed due to the increase of negative work at the end of the compression process resulting from early combustion.  
Since the NO\({}_{\text{x}}\) formation is greatly affected by the combustion temperature, the concentration of NO\({}_{\text{x}}\) increases with the increase of the CH\({}_{4}\) component in biogas, as shown in Figure 7b. When the CH\({}_{4}\) content in biogas rises from 60% to 80%, the NO\({}_{\text{x}}\) concentration increases by 50%. The increase in CH\({}_{4}\) concentration in biogas improves the combustion rate. Thus, combustion is complete, resulting in a decrease in CO concentration in combustion products. It can be seen in Figure 7c that the concentration of CO in the exhaust gas dropped by 20% when the CH\({}_{4}\) content in biogas increased from 60% to 80%. Soot formation depends mainly on the diffusion combustion of diesel jet, so it was slightly affected by the biogas composition (Figure 7d). The results show that the soot volume fraction was about 0.02 ppm as the engine was fueled with biogas containing 60%-80% CH\({}_{4}\) enriched by 40% DME.  
Figure 7: Effects of biogas compositions on combustion characteristics and pollutants formation (20% DME, \(n=1800\) rpm, \(\phi\) 0.77/0.97). Variation with the crank angle of pressure (**a**), temperature and NO\({}_{\text{x}}\) concentration (**b**), diesel concentration and soot volume fraction (**c**), and CO concentration (**d**).
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 4 Results
### Average Vehicle Gas Emissions and Fuel Consumption Results  
Figures 16-19 present the impact of different autonomous vehicle (AV) behaviors and penetration rates on carbon monoxide emissions (CO), nitrogen oxides emissions (NOx),  
Figure 15: Influence of AV behaviors on average vehicle delay at various traffic signal cycle times. Source: processed by authors.  
volatile organic compounds emissions (VOC), and fuel consumption across various traffic signal cycle times (60 to 204 s). Emissions and fuel consumption generally decrease with shorter cycle times across all driving behaviors and AV penetration rates.  
Figure 16: Influence of AV behaviors on average CO emissions at various traffic signal cycle times. Source: processed by authors.  
Figure 17: Influence of AV behaviors on average NOx emissions at various traffic signal cycle times. Source: processed by authors.  
Figure 19: Influence of AV behaviors on average fuel consumption at various traffic signal cycle times. Source: processed by authors.  
Figure 18: Influence of AV behaviors on average VOC emissions at various traffic signal cycle times. Source: processed by authors.  
Cautious AVs consistently exhibit the worst performance, with the highest emissions and fuel consumption across all cycle times. Although emissions and fuel consumption for cautious AVs start to decrease as cycle times increase, they remain the least efficient scenario compared to other AV behaviors.  
Aggressive AVs, especially at 100% penetration, demonstrate the lowest emissions and fuel consumption. While other AV behaviors show slight increases in emissions and fuel consumption at higher cycle times, aggressive AVs with 100% penetration achieve the best results by maintaining lower emissions and fuel consumption throughout all cycle times.  
Normal AVs with high penetration show improved emissions and fuel consumption compared to human-driven vehicles, particularly at shorter cycle times. However, as cycle times increase, emissions and fuel consumption for normal AVs begin to rise.  
Platooning AVs consistently exhibit low emissions and fuel consumption, with performance almost as efficient as aggressive AVs, particularly at higher penetration rates. In mixed AV environments, emissions and fuel consumption levels remain stable, balancing the various driving behaviors and leading to more efficient and predictable traffic flow.
## 1 Introduction
In Mode 3, the AVs communicate with existing infrastructure, like roadside units, for any service provisioning, while in Mode 4, the AVs use the wireless ad-hoc vehicle environment (WAVE) to communicate with each other in the absence of the infrastructure [15; 16]. The coexistence of Mode 3 and Mode 4 is the main motivation to provide two solutions that encounter ensemble learning for the Mode 3 situation, in which the RSU ensembles the locally trained model through a voting mechanism after acquiring the locally trained model of each associated AV in its range. For the Mode 4 scenario, we proposed a gossip learning (GL)-based solution, in which the AVs will share locally trained models, and the AV will decide to retain a specific model of future DoS attacks among all the shared models after testing the each received model using its testing data. The core contributions of this study are as follows. 1. Autonomous vehicle systems operating in Mode 3 and Mode 4 are considered for the development of distinct IDS solutions tailored to each mode. 2. An ensemble learning method is proposed for Mode 3, which is based on the voting mechanisms, and selects the prediction outcome based on the majority vote among the received prediction of each locally trained model at RSUs by the AVs. 3. A gossip learning scheme is proposed for Mode 4, in which the AVs build a pool of trained models by sharing their locally trained models and decide to choose the most suitable trained model based on the best accuracy of the trained models. Extensive simulations are performed using the UNSW-NB15 dataset, which contains both real normal traffic and synthesized attack traffic. The performance of the proposed schemes is evaluated based on classification accuracy (CA) and misclassification rate (MCR). Furthermore, various statistical parameters, including sensitivity, specificity, false positive rate, false negative rate, and precision, are also used to assess the effectiveness of the systems. The rest of the paper is structured as follows: Section 2 provides a comprehensive literature review of existing schemes in developing IDSs for AVs, Section 3 provides a detailed description of the proposed scheme, including SVM- and ANN-based DoS attack detection in an AV environment, Section 4 presents a description of the parameters utilized for the evaluation of the proposed scheme, Section 5 discusses the simulation results, and finally, our conclusions and future work are presented in Section 6.