esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Challenges of MAC Protocol for IBSN
### Requirements of MAC Protocol Design for IBSN
#### 3.1.4 Throughput  
Network throughput is measured by average number of received data bits per second. Packets can be lost due to loss of connectivity or collisions, among others. In the MICS band, the radio channel has been shown to have an essentially slow data-rate. Although flat-fading and inter-symbol interference from multi-path are insignificant in the MICS frequency band, low power transmission, and dynamic link quality due to human activities deteriorate the effective throughput of the network. Heterogeneous medical devices in IBSN demand dynamic throughput for different applications. For example, an EEG sensor requires a much larger throughput (i.e., in the orders of 150 Kbps), whereas glucose sensors require a much lower throughput (e.g., in the orders of 30 Kbps). MAC protocols should be able to operate efficiently in spite of the restrictions set by the spectrum regulations and at the same time meet the dynamic requirements of heterogeneous sensor nodes. Existing MAC protocols are not specifically designed to address such complex application scenarios. Our analysis in Section 4 describes why existing MAC protocols do not meet the requirements of IBSN.
## 3 Estimation and Reliability Calculation of Road Travel Time Distribution after Earthquake
### Impact Factors of Earthquake Damage of Path Travel Time  
#### 3.2.1 Evaluation of Road Accessibility after Earthquake  
After the earthquake, the accessibility probability \(P_{t}\) of the road is expressed as exponential distribution:  
\[P_{t}=P\left\{x<c\right\}=\begin{cases}\int_{0}^{c}\frac{1}{\theta}e^{-\frac{ 1}{\theta}}dx,&c>0\\ 0,&c\leq 0\end{cases} \tag{12}\]  
Among them, the value of \(\theta\) is:  
\[\theta=(1-ind_{k}{}^{2})\cdot c_{k}^{r}/1.5 \tag{13}\]  
Among them, \(ind_{k}\in\) (0,1) is the seismic damage index of road section \(k\), and \(C_{k}^{r}\) is the ideal capacity of road section \(k\) under normal conditions, in \(\text{pcu}/\text{h}/\text{lane}\).  
Under the premise of a certain normal traffic capacity, the \(Q_{3}\) values of post-earthquake road sections under different earthquake damage indexes are shown in Figure 3.  
Find the 3/4 quantile of post-earthquake capacity \(C\) by the distribution function defined by Formula (12):  
\[Q_{3}=-ln0.25\times(1-ind_{k}{}^{2})\times c_{k}^{r}/1.5 \tag{14}\]  
The traffic \(Q_{3}\) values of sections with different traffic capacity after the earthquake are given in Formula (14). At the same time, the road capacity is analyzed, respectively, from the normal and post-earthquake conditions. The results of the above two examples show that the exponential distribution model is reasonable. According to Figure 3a, when the earthquake damage index increases, the road capacity gradually decreases. According to Figure 3b, under the same earthquake damage index, the greater the basic road capacity of a road, the higher the road capacity is.
## 5 Numerical Simulations
### Discussion  
As a result of the three simulations, we observed the following three facts.  
First, the model was switched appropriately by the Luenberger observer. Figures 14, 26, and 38 show that the estimation error of the velocities in the axial direction converged immediately; in particular, \"converging the only slipping link\" was one of the main characteristics of the designed observer. This means that the designed Luenberger observer worked as expected. Otherwise, the estimated state diverged, or the estimated velocity for other links was non-zero, as the normal Luenberger observer converges the estimation by adjusting all elements impartially. On the other hand, the observer designed in this paper changes its weighting matrices depending on the discrimination result for individual slipping links. This idea gives the observer inequality, and a specific state (i.e., the velocity of the slipping link) was estimated preferentially over others. This characteristic resulted in the simulation results.  
Second, the designed control system was able to converge the side-slipping immediately, as was found in Figures 13, 25, and 37. This means that the proposed tuning method worked as expected. Otherwise, the robot continued locomotion with side-slipping longer than that observed in the results. Such a controller might be able to stabilize the system; however, this decreases energy efficiency.  
Third, the system generated serpentine locomotion, similar to a real snake. Serpentine locomotion comprises the very basic kinematics of a real snake. Figures 9, 21, and 33 show that the relative angles varied sinusoidally with almost the same phase difference. This is an important characteristic of serpentine locomotion; that is, the control system based on SDRE was able to generate a snake-like morphology without any trajectory generation.
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 3 Experimentation  
In this section, we evaluate our method based on the common metrics and protocols used for prior methods and compare them with existing semantic segmentation approaches. Since this paper mainly addresses the semantic segmentation of outdoor scenes, we used the publicly available Cityscapes dataset [27] as a benchmark to train our model. The dataset is commonly used to evaluate semantic segmentation performance for outdoor scenes. In addition, we conducted relevant experiments in real forest scenes to further demonstrate the effectiveness of our approach. Our method was implemented using the PyTorch [36] framework, which is a machine learning toolkit released by Facebook that can run on GPUs to achieve acceleration.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 3 Results
### Base Station Antenna Position Determination  
Using the previous positioning methods, the position of antenna RF2, from here on also designated as the base station, was measured for the entire survey. The obtained results are presented in Table 2.  
From these results, it is clear that both EKF and UKF present a much more precise and accurate position solution, with an error below the meter level. The Hatch Filter also results in a lower spread for the LS and WLS results but produces a slightly higher mean error, while for the EKF and UKF, the effect is opposite.
## 2 Comparative Method
### Operational Improvements to Be Compared  
With the overall project goal to develop harmonized implementation strategies for different operational climate mitigation measures, a set of promising operational improvements (OIs) for the aviation sector is identified from the current state of literature. In an expert assessment in course of the ClimOP project, candidate OIs have been rated according to the following criteria [43]:  
* **Mitigation potential** of the OI to significantly reduce the climate impact of aviation in terms of CO\({}_{2}\) and non-CO\({}_{2}\) emissions. * **Scientific relevance**, i.e., the OI is discussed and considered promising in the specialized literature on climate change and aviation. * **Technological readiness**, which directly affects the timescale of implementation in everyday operations and a **low cost/benefit ratio** of the implementation to facilitate the measures realization. * Coverage of all **areas of operations**, namely the airport operations, the aircraft ground operations, and the aircraft operations at network and trajectory levels. * **Modeling feasibility, capability and expertise** of involved parties of the project including the ability to model changes of the relevant operations, and to quantify the impact on the climate and the stakeholders. Based on a long list of 25 operational climate mitigation measures, the expert rating led to the selection of 9 OIs of different types, which are investigated individually (selection details in [43; 44]), before being compared to each other on a set of defined climate and non-climate key performance indicators (KPIs). The types of OIs, the specifically selected OIs, and their respective assessment approach are displayed in Table 1.  
Trajectory-related improvements are performed with different degrees of freedom, including reduced cruise altitudes and speeds (LOSL), free routing (FREE), wind-optimal flight planning (WIND) incorporating different objective functions, and climate-optimized trajectories (CLIM) integrating a four-dimensional optimization of trajectories. As these improvements significantly depend on day-specific mission characteristics and meteorological boundary conditions, a sample of representative days is investigated. Network-related improvements comprise strategic network optimization (NETW) with different objective functions and climate-optimized Intermediate stop operations (ISOC). As these OIs do not focus on individual flight missions, an aggregated approach is selected, where a yearly European network is analyzed based on climatological mean values. Among ground-related OIs, we consider sustainable taxi operations (SETX), electrification of ground vehicles (ELEC), and an upgrade of airport infrastructure (INFR) assessed for European airports for the reference year. Details on the OI assessments, the included operational changes, and the detailed results are provided in Appendix A.
## 3 Results and Discussion
### Fast Transient Charging Effect with Pulse I-V
Even when the PW is continuously increased up to 200 \(\upmu\)s, in the case of LM-HEMTs, the drain current value saturates after around 50 \(\upmu\)s and does not significantly decrease. On the other hand, MHEMT shows a phenomenon in which the current continuously decreases until a PW of 200 \(\upmu\)s is reached. It can be said that deep inside the semiconductor, many traps are continuously created. This  
Figure 3: Single-pulse \(I_{D}V_{G}\) measurement and rapid drain current degradation as a function of time corresponding to a pulsed \(I_{D}\)–\(V_{G}\) sweep for (**a**) LM-HEMTs and (**b**) MHEMT. (**c**) \(I_{D}\)–time sweeps for both types of samples as a function of the PW (10, 50, 100, 150, 200 \(\upmu\)s), with \(t_{r}\) and \(t_{f}\) maintained constant (50 ns). trapping phenomenon leads to a more significant reduction in drain current in MHEMT, highlighting the impact of the fast transient charging effect on their performance.
# Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
## 3 Materials and Methods
### Design of the Model
#### 3.1.1 Description of the System  
The distribution network to be monitored is assumed to have a graph-like structure with connections and some interconnecting nodes, following our previous experience in assessing pipeline systems. The model represents a monitoring process of such an object by drones. Each UAV performs continuous monitoring of a part of the overall network graph. The speed, range, identification number and number of UAVs are assumed to be known for the modelling step. The monitoring object has at least two checkpoints, one at the beginning, one at the end. An object can have several intermediate checkpoints as well as intersection points. The distance between the closest checkpoints is known. At any checkpoint, the UAV always executes preflight preparations and charges the batteries before take-off.  
We assume that the monitoring process is performed in the following order: The UAV takes off at one point, flies while monitoring and lands at another point when the battery is low. The UAV may not land if it has enough range to reach the next checkpoint. When flying forward, the UAV monitors an object and its speed may be limited by the payload capability. When flying to the start of the object, the speed of the UAV is limited only by the specifications of the UAV. The UAV is maintained between flights according to the manufacturer's recommendations. Figure 1 illustrates the monitoring process described above.  
The goal of the later performance evaluation of the model is to determine the highest possible frequency to monitor the whole object when the frequency is understood as the number of flights in a given period (e.g., per day, week or month) similar to typical requirements. Each performance evaluation of a certain model setup thus tells the designer if the given configuration is balanced and fulfils the requirements (see later on).
# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Generative Adversarial Network (GAN)  
In their paper titled \"GAN-Based Anomaly Detection for Imbalance Problems\", [PERSON]. [32] explored the use of generative adversarial networks (GANs) for improved anomaly detection. Anomaly detection involves identifying data points that deviate from the normal distribution. GANs, known for their ability to handle complex and high-dimensional data, are considered a cutting-edge technology in this field. However, the conventional GAN loss function is not designed for anomaly detection and fails to produce effective anomaly detectors. To address this limitation, the researchers proposed enhancements to the GAN loss function. These improvements aim to generate samples that align better with the boundaries of the true data distribution. They introduced a novel approach called Fence GAN (FGAN), wherein the discriminator score is directly utilized as an anomalous threshold for anomaly detection. Through their experiments on the MNIST, CIFAR10, and KDD99 datasets, the researchers demonstrated that FGAN outperforms existing methods in terms of accuracy in anomaly classification. This research highlights the potential of FGAN for more effective and accurate anomaly detection [31].  
In their study, Ref. [33] developed a novel method called Adversarially Learnt Anomaly Detection (ALAD) to address the challenge of anomaly detection. ALAD utilizes bi-directional Generative Adversarial Networks (GANs) to generate adversarially learned features that capture anomalies in the data. These learned features are then used to assess the abnormality of data samples based on their reconstruction errors. ALAD incorporates recent advancements in dataspace and latent-space cycle consistency, as well as GAN training stability, to significantly enhance the performance of anomaly detection. The researchers found that ALAD outperforms the sole publicly available GAN-based solution by a factor of a hundred on various image and tabular datasets. Additionally, ALAD demonstrates remarkable speed in anomaly detection. The proposed ALAD approach demonstrates the potential of utilizing bi-directional GANs and adversarially learned features to improve the accuracy and efficiency of anomaly detection. By leveraging reconstruction errors and incorporating advanced techniques, ALAD offers a promising solution for detecting anomalies in diverse datasets.
# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 4 Experiments and Results
### Backbone Network Selection
#### 4.2.1 Analysis of Computational Cost and Accuracy  
In describing deep learning models, in addition to the accuracy, the number of floating-point operations (_FLOPs_) and the number of parameters are often used to illustrate the computational cost of the model. The number of Flops is calculated as follows:  
\[FLOP_{S}=2 HW\Big{(}C_{in}K^{2}+1\Big{)}C_{out}\;\;. \tag{11}\]  
Figure 8: Predicted mask images based on different backbone networks of DeepLabv3+.  
where \(H\), \(W\) and \(C_{in}\) are the height, width and number of the input feature map of the channel, k is the kernel width, and \(C_{out}\) is the number of output channels. We discuss the computational cost of the experiments through six metrics, as shown in Table 2, and record the evaluation metrics for each model, as shown in Table 3.  
From the data in Tables 2 and 3, we can see that DeepLabv3+-ResNet101 shows significant advantages in the landslide detection task when considering both the computational cost and the model accuracy. Although the training time, GPU memory consumption and parameter quantity of the proposed model are higher than those of some lightweight models, it performs well in terms of the evaluation metrics: the MIoU reaches 0.752, the F1 score is 0.9220, the recall rate is 0.9160, and the precision rate is 0.9283. In contrast, although MobileNet and ResNet18 are more computationally efficient, their MIoU is reduced by 18.9% and 2.8%, respectively, which will significantly affect the localization accuracy of the landslide boundaries.  
The highest Flops of ResNet101 is 16.008 G, and its reasoning time of 0.0190 s still maintains the millisecond response ability, which can meet the real-time requirements in actual deployment. For high-precision tasks such as geological disaster detection, sacrificing part of training resources for more than a 10% improvement in key indicators has significant engineering value, especially in the case of false detection/missed detection, which may cause serious consequences, so selecting ResNet101 as the backbone network can more reliably balance performance and efficiency.
# Leather Defect Detection Based on Improved YOLOv8 Model
## 4 Improved YOLOv8-AGE Algorithm
### VoV-GSCSP Module  
Given that the detection of leather defects is typically performed in real time, it is crucial to lighten the model. In this study, the original C2f modules were replaced by the VoV-GSCSP module, which was constructed using GSConv. Compared to Sconv (Standard Convolution), DSC (Depthwise Separable Convolution) significantly reduces the number of parameters and floating point operations. However, the separation of channel information in the input features during DSC computation can lead to information loss in deeper networks. Therefore, a new convolution module, GSConv, which incorporates both SConv and DSC, is introduced. The structure of GSConv is shown in Figure 6.  
Figure 5: Schematic diagram of the ASFF structure.  
Figure 6: Structure of GSConv.  
GSConv integrates SC-processed information into DSC-processed information through shuffling, retaining the high performance of SC while maintaining the lightweight advantage of DSC. The structures of the GSBottleneck module and VoV-GSCSP, designed based on GSConv, are shown in Figure 7.  
In the neck section, the VoV-GSCSP module replaces the Block module in the AFPN architecture. This results in the construction of an AFPN-VoV neck structure, which offers improved multi-scale detection performance while using fewer parameters. The progressive feature fusion strategy of AFPN adaptively aggregates detailed cues from features at various scales, enabling more precise feature representation. For small defects, whose characteristics are often subtle, features at different scales may encompass distinct detail information. Through progressive feature fusion, these details from various scales can be effectively integrated, thereby more accurately representing the features of small defects and enhancing detection accuracy. The robust feature extraction capability of the VoV-GSCSP module adeptly captures the features of small defects, providing enhanced feature representation for AFPN's feature fusion. Consequently, the combination of these two components significantly strengthens the detection capability for small defects. This allows the model to achieve superior multi-scale detection performance with a more parameter-efficient approach.
## 2.3. Screening Process  
The literature selection followed a three-stage filtering approach aligned with PRISMA: identification, screening, and eligibility phases. In the identification phase, the initial search yielded 219 documents. At this stage, non-English publications, duplicate studies, publications before 2010, and conference proceedings and reviews were excluded, resulting in the removal of 82 documents. The remaining 137 studies, including articles, reviews, conference papers, and book chapters, were retained for further screening, as shown in Figure 2.  
During the screening phase, the titles, abstracts, and conclusions were reviewed to assess the relevance of each study to the research focus. This step aimed to identify literature situated within the context of emerging advanced digital and immersive technologies, including DT and IM to enhance worker safety in the manufacturing sector. As a result, 43 studies that were not focused on manufacturing or relevant technologies were excluded, leaving 94 studies for full-text evaluation.  
In the second step of the screening phase, that is, the eligibility process, the remaining studies were thoroughly reviewed to determine their alignment with the scope of this study. Research documents that addressed interconnected immersive technologies aimed at improving OHS were included. However, studies focusing solely on policies and regulations, algorithm optimization, and not addressing OHS were excluded. This process resulted in 75 articles being selected, ranging from 2016 to 2025, and eligible for this study.  
To ensure the relevance of the findings, it was necessary to establish eligibility criteria definitions, as automatic search results could yield research items that are not directly related to the research questions or database search criteria. Thus, it was deemed important to set both exclusion and inclusion criteria. The eligibility criteria for including the studies are summarized below:  
* Published in peer-reviewed international journals, conferences, or book chapters.
* Focused on the use of immersive or digital technologies (e.g., DTs, IM, XR, VR, AR, and CPS) within the manufacturing sector.
* Explicitly addressing the implications for OHS in industrial settings. Similarly, the studies that were excluded were based on the following exclusion criteria:  
Figure 1. Search string used for the collection of data using SCOPUS and Web of Science databases.  
* Focused solely on technical algorithm development or optimization.
* Discussed regulatory policies and regulations.
* Pertained to unrelated sectors such as healthcare, construction, and education.
* Duplicate documents.
* Conference proceedings.  
Figure 2: PRISMA flow chart showing the SLR approach.