esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Impact of the Number of STAs  
Figure 4b shows the saturation energy efficiency of the evaluated mechanisms with \(\alpha=1\) and \(\alpha=3\) as a function of the number of STAs up to 100 STAs. The energy efficiency of DCF and PSM as well as that of TXOP PSM with \(\alpha=1\) are shown by a single line and marker. A different line and marker are used to plot the energy efficiency of TXOP PSM with \(\alpha=3\). In addition, in line with the PHY data transmission rate, results for the saturation throughput as a function of the number of STAs are omitted. DCF, PSM, and TXOP PSM using \(\alpha=3\) achieve a high stable throughput improvement that ranges from 28% to 39% when compared to DCF, PSM, and TXOP PSM with \(\alpha=1\) as the number of STAs increases up to 100 STAs.  
As shown in Figure 4b, the energy efficiency of all the mechanisms decreases as the number of STAs in the network increases. The reason is that more STAs consume energy for overhearing frames sent to the AP or other STAs. More specifically, DCF and PSM with \(\alpha=3\) attain an increase in energy efficiency that ranges from 25% to 35% as compared to when they employ \(\alpha=1\) with larger numbers of STAs up to 100 STAs. This is due to the reduction of the overall channel access overheard enabled by the use of \(\alpha=3\).  
In addition, it can be seen in Figure 4b that the energy efficiency of TXOP PSM using \(\alpha=1\) is the same as that of DCF and PSM. However, TXOP PSM with \(\alpha=3\) achieves the highest energy efficiency for numbers of STAs above 1 STA as compared to DCF and PSM. No improvement over DCF and PSM is obtained when the network is composed of the AP and an STA, since this STA can never go to sleep. For larger numbers of STAs from 2 STAs to 100 STAs, TXOP PSM provides a significant increase in energy efficiency between 23% and 122%, since more STAs can sleep and consequently save energy.
## 4 Engineering Case
### Calculation of Frictional Resistance and Grouting Adjustment  
Based on the equipment, soil parameters (shown in Table 1) at the construction site, and pressure sensor data, the jacking construction resistance value f can be calculated using the calculation formula shown in Equation (4). Based on general experience from the construction site, the resistance warning value is initially set at 20 kN. The monitored resistance values during the construction process are shown in Figure 9.  
As presented in Figure 9, the analysis of the resistance values reveals that the design and implementation of the automated pipe jacking friction resistance identification and warning system can effectively monitor resistance in real time during the jacking process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Quantity** & **Equipment Name** & \
\begin{tabular}{c} **Model and** \\ **Specifications** \\ \end{tabular} &
\begin{tabular}{c} **Construction** \\ **Location** \\ \end{tabular} \\ \hline
1 & 4 & Resistance Sensor & High-strength steel & Pipe Jacking \\
2 & 1 & Resistance Collector & & Pipe Jacking \\
3 & 1 & Grouting Pipe & Outlet diameter & Grouting \\
4 & 1 & Grout Buffer Tank & 50 L capacity & Grouting \\
5 & 1 & Resistance Display & & Control Console \\
6 & 1 & Monitoring Host & & Control Console \\
7 & 1 & Slurry Balance Pipe Jacking Machine & NPD1000 & Pipe Jacking \\
8 & 1 & Spiral Grouting Machine & & Grouting \\
9 & 1 & Wheeled Crane & 25 T & Pipe Jacking \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key equipment and technical parameters table.  
Figure 9: Resistance values.  
The frictional resistance gradually increases from the starting point to a point 24 m away. The situation in which this resistance continuously increases at the beginning of the construction phase is consistent with reality. When the resistance value reaches the preset warning value of 20 kN, an alarm is triggered to indicate increased resistance. At this point, it is necessary to timely adjust field construction conditions, such as the rheological properties of the slurry mix. As seen from the graph above, after adjusting the rheological properties of the slurry, the resistance value during pipe jacking decreases continuously, effectively promoting long-distance jacking.
# Application of Deep Neural Networks in Recognition of Selected Types of Objects in Digital Images
## 2 Results of Research
### GoogLeNet
#### 2.5.3 Experimental Results for the Transfer Learning Technique  
The results in Figure 26 show a detailed learning process for the YOLOv8 model using the transfer learning technique.  
* Training loss graph (train/loss): This graph shows that training loss decreases steadily with each successive epoch, suggesting that the model adjusts effectively to the training data.
* Validation loss chart (val/loss): Despite initial fluctuations, validation losses are also decreasing, indicating an improvement in the model's ability to generalize on new data.
* Accuracy of top-1 and top-5 classifications:
* Top-1 Accuracy: A measure that indicates how often the correct label (class) is the highest rated by the model among all possible classes. In other words, it is the percentage of cases in which the model correctly predicts the most likely class.
* Top-5 Accuracy: This metric measures how often the correct label is among the five highest rated classes by the model. Top-5 accuracy is used in tasks where there are many classes to choose from and it is possible that the model will identify several classes as potentially correct. This metric is more forgiving than top-1 because it allows for some margin of error.  
Figure 25: Results for the model obtained using the SimSiam technique. (**a**) Loss—Negative Cosine Similarity for model SimSiam. (**b**) Learning process—loss metric (transfer learning with frozen weights). (**c**) Learning process—accuracy metrics (transfer learning with frozen weights).  
These graphs show a gradual increase in classification accuracy, both for top-1 and top-5. Top-1 accuracy in particular shows significant improvement, confirming that the model is becoming more accurate in predicting first classes.
###### Abstract
The risks associated with space exploration span technical, human, launch, space environment, mission design, budgetary, and political dimensions. Sustainability principles emphasize the need to minimize environmental impact, optimize resource utilization, consider ethical considerations, engage in long-term planning, encourage international cooperation, and promote public outreach and education. The supply chain of required resources for components for space mission tasks involves a collection of dependable and resilient vendors who can deliver on time, with the requested quality and quantity. As a result, the research flow is as follows: we begin by evaluating relevant articles to find possible overlaps with existing material. As a result, Section 2 comprises a survey of the literature, including the most essential references on the issue. This covers problem contextualization, approaches, and conclusions that may help to improve the position of this work paper. A summary of space mission risk is in Section 3. In Section 4 description of space mission sustainability is explained. The definition of the main aspects related to space mission planning is shown in Section 5. In Section 6 an explanation of the required materials and methods for space mission planning is provided. Section 7 discusses the main elements and optimization framework for the space mission supply chain. A multi-objective conceptual optimization model's definition and design with risk, sustainability, and supply chain for space mission is the following step, which can be shown within Section 8. A space mission practical approach with the use of a conceptual multi-objective optimization model is explained in Section 9. Finally, Sections 10-13 present the application and analysis of the proposed approach, an analysis of the key findings and outcomes, in addition to an explanation of boundaries, potential future study areas, and findings. The presented work is needed at the time, because optimization technique in space mission planning is vital to maximize resource efficiency, enhance safety, accomplish scientific objectives, reduce mission durations, permit interplanetary exploration, and facilitate collaboration. As space missions become increasingly ambitious and diversified, optimization techniques serve a critical role in maximizing outcomes and advancing our understanding of the universe.
## 4 The Proposed Key Distribution Framework
### A Multipath-Multi-Key Distribution Algorithm
#### 4.2.5 Inspection/Test Phase  
After selecting optimal paths with key distribution, the CP initiates a testing phase to identify compromised nodes, as follows:  
Penalty Assignment Mechanism  
Assigning penalties \(\pm W\), where \(W\) is adjusted based on node behavior, to nodes based on their proximity to potential attacks is a strategic method to mitigate risks [47].  
Recognizing that attackers can move in space, making a compromised link more likely than a compromised node, the system assigns a smaller penalty (\(W\)) to nodes close to compromised nodes [22]. By imposing higher penalties on nodes closer to detected threats, the system can dynamically adjust routing decisions to avoid compromised areas. Implementing such a penalty system can improve the resilience of the QKD network against attacks [38].  
Node and Link Compromise Detection  
In QKD networks, the security of intermediate nodes often referred to as trusted nodes is paramount. If a node is compromised, it can undermine the entire key distribution process. Therefore, detecting compromised nodes or links is essential. The proposed method involves sending pre-known quantum keys to all nodes and monitoring their responses to identify any anomalies that may indicate a compromise. This approach is consistent with strategies discussed in recent studies, which emphasize the importance of monitoring node behavior to detect potential security breaches [7, 48].  
Incorporating time as a correction factor where the absence of attacks detected over a significant period suggests the correctness of CP selections [49].
## 2 Materials and Methods  
### Cardiac Arrest and CPR Setup  
Ten female pigs (6-9 weeks old, 33-39 kg) were selected for this study to eliminate the influence of sex differences in susceptibility to ventricular fibrillation (VF) and because female pigs typically exhibit a higher rate of return of spontaneous circulation (ROSC). The animals were fasted overnight before being sedated with an intramuscular injection of ketamine (20 mg/kg; \"Ketalean,\" Bimeda-MTC Animal Health, Cambridge, ON, Canada). The level of anesthesia was continually assessed and the rate of the anesthetic agent adjusted to ensure no discomfort. After sedation, the pigs were intubated and maintained under anesthesia with continuous isoflurane (1-3% mixed with oxygen). Ventilation was supported using an Ohmeda ventilator (Ohio Medical Products, Madison, WI, USA) with the settings adjusted to stabilize physiological parameters such as the pH (7.35-7.45), \(PCO_{2}\) (35-45 mm Hg), and \(PO_{2}\) (>100 mm Hg). To prevent hypovolemia, normal saline (NS) was administered intravenously through a cannulated ear vein at a rate of 2-4 mL/kg/h. Electrocardiogram (ECG) leads were attached, and defibrillation patches (Zoll Medical, Inc., Chelmsford, MA, USA) were applied to monitor the cardiac activity. The aortic and right atrial pressures were continuously monitored via femoral artery catheters (Mikro-Tip Transducer; Millar Instruments, Houston, TX, USA), providing real-time data to detect the onset of cardiac arrest (VF) [13]. Figure 1 shows the experimental setup, including the positioning of the automatic compression device on the left and the hNIRS system on the right.  
All pigs were euthanized with an anesthetic overdose with 5% isoflurane followed by T61 (0.3 mL/kg) IV or by inducing VF at the end of the experiment. All experimental protocols adhered to the guidelines for reporting in vivo experiments [14]. Ethical approval was obtained from the Animal Care Committee of St. Michael's Hospital (Toronto, ON, Canada), and all procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals as outlined by the U.S. National Institutes of Health (NIH publication number 85-23, revised in 1996).
## 6 Evaluation
### Efficiency Evaluation (RQ2)
However, its performance on Class-2 and Class-3 targets reveals limitations in the transferability of its detection capabilities, reflecting the difficulty of broadly applying its SQLI detection knowledge gained on the initial training set to new scenarios. By comparing SqliGPT with Sqlmap, we observe the significant improvement in detection efficiency that LLMs provide for the SQLI black-box scanner. For Class-1 targets, the average number of HTTP requests required by SqliGPT is only 17% of that required by Sqlmap. Notably, for Class-2 targets, this ratio is further reduced to 8%. These results highlight the significant enhancements in detection efficiency that LLMs contribute to the design of SqliGPT.
# Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
## 3 Improved Eagle Strategy
### Service Selection and Dynamic Composition  
While the above algorithmic details provide a deep dive into the IES's functionality, the essence lies in its ability to dynamically adapt to changing service demands, ensuring optimal performance for end-users.  
Figure 9 illustrates dynamic WS selection. The IES automatically selects a service; if any fail during the invocation process, the substitution technique replaces the current workflow service [13; 37]. The symbol X in the diagram at the send email service illustration indicates the service failed to respond. Therefore, the IES will dynamically select the other service available with a high response value from the clustered service repository without human interaction. This is to maintain the dynamism and continuity of the workflow.  
Figure 10 shows the dynamic flowchart for WS selection. Dynamic service substitution is performed under two distinct conditions. First, it occurs when a service is unavailable. Second, substitution is initiated when the response time of a service falls below the specified threshold value.  
Table 3 shows the algorithm for the IES. The IES surpasses other bio-inspired optimization techniques such as PSO and Artificial Bee Colony (ABC) through its unique combination of real-time adaptability and reduced computational complexity in large-scale cloud environments. While traditional methods like PSO excel in convergence speed, they suffer from high computation times when dealing with large datasets, and techniques like ABC face limitations in handling multi-objective service selection, especially in dynamic IoT environments. In contrast, the IES leverages a hybrid approach that dynamically adapts to varying user requirements and optimizes both functional and non-functional attributes. This not only reduces overall computation time but also enhances service accuracy in large-scale, distributed environments. The clustering-based reduction of search space further distinguishes the IES, making it significantly more efficient and scalable than traditional methods, particularly in cloud and IoT ecosystems.  
Figure 9: Dynamic Web Service Composition Scenario with IES.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 5 Evaluation and Discussion of the Results
### Simulation Results
#### 5.2.1 Changing the Priority of WBAN Applications  
Selecting the best network based on the QoS application requirements helps to enhance the effectiveness of our algorithm in WBAN, especially for emergency traffic.  
Therefore, to evaluate this aspect, we assumed that the initial application running on patients is a normal traffic type, and the default is to connect to WiFi AP with the 802.11b standard. These patients move (0.5 m/s speed) far away from their default AP. Moreover, this normal traffic type will exceed a normal threshold after the simulation period (e.g., blood pressure \(\geq\) 140 mmHg), thus moving to an emergency traffic type. To keep the best connection for the patient, the device will switch to other networks that satisfy the QoS of the running application. In this case, the best network is the highest-ranking network (LTE) in the ranking list, as shown in Table 6. This result demonstrates the effectiveness of our approach in the selection of the best network for each application type, even when the QoS requirements change over time.
## 4 Defense Against False Data Injection  
As discussed in the previous section, FDI attacks significantly threaten the performance of RL-based charging coordination systems and benefit malicious consumers at the expense of honest ones. To mitigate these negative effects, a robust defense mechanism is needed to detect and counteract FDI attacks. This section presents the proposed defense strategy, which employs machine learning models to learn and distinguish the patterns in SoC values reported by honest consumers and malicious consumers. By identifying these patterns,  
Figure 6: Total reward under Attack #1 for two and four malicious consumers. (**a**) Total reward with two malicious consumers. (**b**) Total reward with four malicious consumers.  
the model can detect false SoC values injected by attackers. The section also details the experiments conducted to evaluate the effectiveness of this defense strategy.
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### Fuzzy Adaptive Selection of Data Forwarding Routes
#### 3.6.4 Fitness Evaluation and Path Selection
\right.\]  
Here, \(d_{\rm o}\) is the number of hops between the e-CH and the BS and \(d_{i}\) is the number of hops between the CH\({}_{i}\) and the BS. Since the verification nodes are initially chosen probabilistically in our proposed scheme, and their proximity to the e-CH improves with time, the probability of CH\({}_{i}\) detecting a false vote is given by \(\frac{s-1}{L}\frac{d_{\rm o}-d_{i}}{L}\geq\ p_{CH_{i}}\geq\frac{(s-1)}{L}\frac{ d_{i}}{d_{\rm o}}\). Our proposed scheme uses the Mamdani model for fuzzy inferencing [55]. The time and space complexities of the proposed fuzzy inferencing technique are given by O \(\left(n_{rules}\times n_{input\_dim}\right)\) where \(n_{rules}\) is the number of fuzzy if-then rules and \(n_{input\_dim}\) is the number of input dimensions (fuzzy membership functions) [56].
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 3 CPWES Modeling and Assumption
### Traffic Model for Meteorological Mast  
We considered a met-mast to measure the wind condition in the WF area. Based on Reference [28], we defined the number, types, and locations of the senor nodes and measuring instruments installed at the met-mast, as shown in Figure 6. The platform height was 10 m with respect to the mean sea level, and the top of the met-mast was about 97 m. Anemometers were installed at 97 m, 96 m, 86 m, 86 m, 76 m, 66 m, 56 m, 46 m and 26 m. Wind vanes were installed at 96 m, 76 m, 56 m and 46 m. Barometers and temperature and humidity sensors were installed at 94 m and 13 m. The details of the sensor types and their locations at the met-mast are shown in Table 6. The acoustic doppler current profile (ADCP) is used to measure the current direction and velocity at sea level.
# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
###### Abstract  
Short-term load forecasting is an important prerequisite for smart grid controls. The current methods are mainly based on the convolution neural network (CNN) or long short-term memory (LSTM) model to realize load forecasting. For the multi-factor input sequence, the existing methods cannot obtain multi-scale features of the time series and the important parameters of the multi-factor, resulting in low accuracy and robustness. To address these problems, a multi-scale feature attention hybrid network is proposed, which uses LSTM to extract the time correlation of the sequence and multi-scale CNN to automatically extract the multi-scale feature of the load. This work realizes the integration of features by constructing a circular network. In the proposed model, a two-branch attention mechanism is further constructed to capture the important parameters of different influencing factors to improve the model's robustness, which can make the network to obtain effective features at the curve changes. Comparative experiments on two open test sets show that the proposed multi-scale feature attention mixture network can achieve accurate short-term load forecasting and is superior to the existing methods.  
load forecasting; multi-scale; deep learning; two-branch attention +
Footnote †: journal: Applied sciences
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 2 Materials and Methods
### Positioning Performance of Low-Cost GNSS Devices in Urban Areas  
To evaluate the positioning performance quality of low-cost GNSS devices in urban areas, observations performed in ST 1, ST 2, ST 3, and ST 4 were used. The data were acquired in each station for 1 h, which presents one session. This interval was divided into independent sub-session of 30 min, 20 min, and 15 min. Short sessions are considered to analyze the accuracy that can be achieved if a certain position is measured for a short time interval, which is the case in most surveying projects. For each session, horizontal and vertical coordinates \((e_{i},n_{i},h_{i})\) were estimated in the Slovenian national coordinate system [29], while their differences from the true coordinates \((e_{t},n_{t},h_{t})\) were calculated as follows:  
\[\lambda e =e_{i}-e_{t} \tag{1}\] \[\lambda n =n_{i}-n_{t}\] (2) \[\lambda h =h_{i}-h_{t} \tag{3}\]  
Reference coordinates were obtained from observations with geodetic GNSS devices (GD\(-\)1). The absolute differences for horizontal and spatial positions are estimated as follows:  
\[d_{2D}=\sqrt{\left(e_{i}-e_{t}\right)^{2}+\left(n_{i}-n_{t}\right)^{2}} \tag{4}\]  
\[d_{3D}=\sqrt{\left(e_{i}-e_{t}\right)^{2}+\left(n_{i}-n_{t}\right)^{2}+\left( h_{i}-h_{t}\right)^{2}} \tag{5}\]  
To emphasize the differences between open-sky and urban locations, the ratio of ambiguities (fix or float) is shown. As a reference station, another low-cost GNSS device (as LC\(-\)1) was used, which was placed on point FGG4 with favorable conditions for the GNSS survey. The data processing was performed with open-source software RTKLIB (demo5_33b), and the processing settings are shown in Table 1[30].  
The RTK method is usually used for land surveying because it is not time-consuming and does not require post-processing. In this test, LC\(-\)1 was used as the rover, while the GSR1 station located 4 km away, which is part of the Slovenian CORS, served as the reference station for the corrections. Observations lasted 8 h at each station, and a total of 24 sessions were conducted. In each session, a triplet of coordinates was obtained, with a time difference of 20 min between sessions to account for changes in satellite geometry. First, the RMSE of the determined coordinates was estimated to determine the precision of the estimated coordinates. To analyze the positioning accuracy of low-cost receivers, the absolute differences for the horizontal and spatial positions were estimated similarly to the relative positioning explained above.