esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper proposed the Bow-Tie-QCA-Bayesian Neural Network-Consequence Simulation model to conduct a quantified analysis on the poisoning and suffocation risk of a pre-treatment pool workshop. The main conclusions were as follows:  
1. Bow-tie was applied to find out the causes of biogas poisoning and suffocation accidents in the pre-treatment pool workshop.
2. The QCA method explored the accident cause combination paths in management.
3. The Bayesian neural network model predicted the distribution of accident frequency and provides model accuracy, thereby improving the reliability of the prediction results.
4. The ANSYS Fluent simulation quantified the biogas diffusion consequences in three different ventilation types.
5. The importance and necessity of ventilation and the reasons why the methane alarm was installed unreasonably in pre-treatment pool workshop were discussed. The reasons why carbon dioxide should not be ignored as a harmful factor in poisoning and suffocation accidents were explored. A suggestion on the installation of carbon dioxide alarms was also proposed.  
This study aims to provide references for risk analysis and a basis for improving biogas-related standards and specifications in the ventilation types and the deployment of gas alarms, so as to enable a safe and effective working environment for biogas production.  
In future work, we will integrate Bow-Tie analysis, QCA, Bayesian neural network (BNN) and computational fluid dynamics (CFD) simulation more tightly and intelligently to form a closed-loop system in which data-driven and physical simulations verify and reinforce each other. Additionally, we will replicate, validate and adapt the model in a variety of plant and regional environments, with the ultimate goal of developing practical tools to improve the overall safety level of the biogas industry.  
Conceptualization, B.F.; methodology, B.F.; software, B.F.; validation, B.F.; formal analysis, B.F.; investigation, B.F. and J.C.; resources, K.X.; data curation, B.F.; writing--original draft preparation, B.F.; writing--review and editing, J.C. and Z.Y.; visualization, B.F.; supervision, K.X.; project administration, K.X.; funding acquisition, K.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the National Key Research and Development Program of China (grant number 2018 YFC0808406) and the National Natural Science Foundation of China (grant numbers 52074066, 52004055).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 4 BCSO Application to UC
### Constraints Processing  
The particle will undergo constraint checks after the initialization. According to Formula (6), the state of particle is judged whether it has met the minimum up/down-time limit constraint. Once violation has occurred, a corresponding technique [32] will be conducted to fix the solution and make it meet the limit. Next, the particles are checked for the spinning reserve limit constraint according to (7). A heuristic-based handling approach [30] is adopted to handle this. Specifically, if the accumulated generation output power is larger than the sum of load and spinning reserve, some units should be turned off. Otherwise, it should take measures to ensure more units are on-line. The process of power demand balance constraint is embedded in the lambda iteration method where a tiny lambda value is tuned. The handling of the generation limit constraint (5) is also embedded in the lambda iteration method acting as the upper and lower boundaries.
## 4 Implementation Scenario and Conceptual Framework
### Implementation Conceptual Framework for MANA-NMS Using NTCA
Jobs arrive at the server's queue at independent exponentially distributed time intervals with mean \(1/\lambda\), they join the queue, and once in the head of the queue, they get served by the server whose service time per job is independent and exponentially distributed with parameter \(1/\upmu\). A task is scheduled into the appropriate queue when the task manager/event handler releases it. We assumed three different queues that are associated with a particular service agent chain, which indicates the three classes of services to be scheduled in the appropriate agent chain. 5. Performance Evaluation  
This section presents the performance evaluation results of NTPA and NTCA equipped with different DL and ML models, respectively. We used metrics such as prediction and classification accuracy, training and predicting latency, and memory and CPU utilization to evaluate the performance. Therefore, at the end of this section, we will able to identify appropriate DL/ML-based NTPA and NTCA for different prediction/classification network applications with respect to their time and resource constraints. Table 2 shows the DL models parameters used in the experiments.
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 2 Related Work  
Deep learning technologies have been extensively applied to medical image segmentation problems, along with the U-Network architecture, which is specifically designed for biomedical image segmentation, introduced in 2015 by [PERSON] et al. [10]. U-Networks exhibit robust performance in precise pixel-level classification. The U-Network is a U-shaped architecture designed for tasks such as image segmentation. They consist of four main layers organized into distinct stages: downsampling, bottleneck, upsampling, and skip connections. The downsampling stage progressively reduces the spatial dimensions of the input feature maps while increasing the number of feature channels, enabling the extraction of high-level features. At the core of the architecture, the bottleneck acts as a transition point between the encoder (downsampling) and decoder (upsampling), serving as a bridge to preserve critical feature information. The upsampling stage then restores the spatial dimensions of the feature maps, recovering the resolution of the input data. To enhance the reconstruction process, skip connections link corresponding layers in the encoder and decoder, appending feature embeddings from the encoder to the outputs of the decoder. This structure ensures the retention of both high-level and fine-grained spatial details, making the U-Network highly effective for applications requiring precise feature localization and reconstruction [10]. U-Net has achieved notable success in the BraTS 2018 segmentation challenge. This network was originally developed for handling and segmenting 2-dimensional images. Later, it was adapted into 3D to be used for training on the BraTS 2018 dataset. Many researches have leveraged the U-Net model for segmentation in BraTS 2018 [11; 12; 13; 14]. By looking at the development of the U-Net architecture, we can infer that the potential for developing this architecture is substantial. Its performance has also improved  
Figure 1: Magnetic resonance imaging scans showing T1, T1 ce, T2, and T2-FLAIR modalities.  
with other developments [15]. U-Net has also been deployed for image segmentation to predict the segmentation mask for every input image. This approach was very successful for all the segmentation tasks. The state of the art is nnU-Network, where the architecture and the hyperparameters are automatically chosen by each dataset [16]. In the realm of medical image segmentation, several advanced models have been developed to address the challenges posed by the complex anatomical structures of the brain. Among these, U-Net++, DeepLabV3, and Mask R-CNN have gained significant attention due to their advanced architectures, capabilities, and efficiency.
## 3 System Model
### Energy Model  
The energy model considers several attributes such as transmission energy, reception energy, sleep time energy (energy spent when the node sleeps), and listening energy. The quantity of energy consumed by the sensor node \(N_{CE}\) is determined as follows:\[\sum_{k=0}^{l}N_{CE}^{k}\ =\ \sum_{k=0}^{l}N_{TXE}^{k}\times t_{RX}^{k}+\sum_{k=0}^{l}N _{RXE}^{k}\times t_{TX}^{k}+\sum_{k=0}^{l}N_{LXe}^{k}\times t_{LX}^{k}+\sum_{k=0}^ {l}N_{SXe}^{k}\times t_{SX}^{k} \tag{7}\]  
\(N_{RE}^{k_{0}}\) and \(N_{RE}^{k_{1}}\) represent the available energy of the sensor node \(N_{k}\) and \(Energy\_res(N_{i},t_{1})\) represent the available energy of the node \(N_{i}\) at time '\(t_{0}\)' and '\(t_{1}\),' respectively. The quantity of energy consumed during transmission as well as the reception of data are denoted as '\(N_{TXE}\)' and '\(N_{RXE}\),' respectively. In cases where there is no data transmission, then the node will be moved to a sleep or listening state, denoted as '\(N_{LXe}\)' and '\(N_{SXe}\),' respectively.  
The total residual energy can be calculated based on the initial energy '\(N_{IE}\)' and the amount of energy consumed '\(N_{CE}\)' as follows:  
\[\sum_{k=0}^{l}N_{RE}^{k}=\sum_{k=0}^{l}N_{IE}^{k}-\sum_{k=0}^{l}N_{CE}^{k} \tag{8}\]
## 5 Conclusions  
QoE is now widely accepted as providing a numerical proxy for actual user experience, and the literature has reported many mathematical mappings between QoS and QoE. Additionally, some papers have reported an evaluation of the statistical errors that arise when estimating QoS by sampling. However, no papers have reported on how error in sampling the QoS metrics propagates statistical error into the estimated QoE, which is vital for understanding how reliable are these estimated values of QoE.  
In this paper we use state-of-the-art commercially measured loss and jitter values. From these we determine how to set up delay distributions in our network emulator, NetEm. We analysed the uncertainty in QoS due to suboptimal performance of NetEm and determined its effect on the QoE evaluation. Our results for this show that the errors due to NetEm do not significantly affect the QoE evaluation--for instance, the uncertainty in PLR increases at higher PLR configurations but at higher configuration QoE is closely consistent to MOS = 1. This shows that NetEm inaccuracies have a relatively trivial effect on QoE evaluation.  
Our analysis has shown that the statistical error in the QoE is almost entirely the result of sampling error in PLR and not the sampling error in jitter. We evaluated the sampling error that arises during QoS measurements and determined the correlation between PLR and jitter. We used the theory of error propagation to evaluate the error that arises in the estimated subjective QoE and (in line with normal scientific practice) represented this error as 95% confidence interval width. To align our results with actual recommended networking practice we employed UK Government guidelines for the number of packet samples that are to be taken in a busy hour of network operation.  
We found that the magnitude of the sampling error (expressed as QoE CI) changes significantly at different operating points. For instance, at very high or very low PLR operating points the propagated uncertainty is very small. However, at the key operating points around PLR = 0.001 and PLR = 0.01 the propagated uncertainty is significantly larger. A very similar observation was made when studying the variation in perception using SOS. The SOS was higher in the key range of operating points of PLR and diminishes rapidly on both sides. Most commercial SLAs specify a target PLR of 0.001 (on average) [35], so our discovery that this is a critical point around which QoE uncertainty is largest is potentially of great significance.  
To date we have limited our attention to VoD, and it is for VoD that we have derived all our numerical results. However, sampling error is not application dependent, and so we anticipate that, whatever the application, error in sampling QoS metrics will result in sampling error being propagated to the estimated QoE values. Sizing the significance of this propagated error for different application types is a very interesting and significant area for future work.
## 6 Conclusions  
By simulating and evaluating adversarial FGSM, data-poisoning and adversarial evasion attacks, this study has demonstrated the varying degrees to which these threats compromise a model's performance, which can result in significant accuracy drops and increased false classification rates. The introduction of the HAVA with the PAVI provides a comprehensive framework for quantifying and assessing the impact of multiple attack vectors on DL models.  
The PAVI contextualizes the severity of performance degradation, and provides a standardized means to quantify a model's vulnerabilities relative to its baseline accuracy. For example, the adversarial FGSM and evasion attacks, characterized by high PAVI values, emphasize the need for defenses such as adversarial training and noise-resistant preprocessing. Conversely, with its relatively low PAVI, data poisoning suggests resource-efficient measures such as federated learning. These insights transform post-attack impacts into a foundation for targeted mitigation efforts to ensure continuous improvement of AI models in dynamic healthcare environments.  
Our findings have significant implications for healthcare applications, where errors induced by attacks can lead to delayed treatments, unnecessary medical interventions, or the erosion of trust in AI systems. By emphasising diagnostic accuracy and cybersecurity, the HAVA serves as a bridge that translates theoretical vulnerabilities into actionable defense strategies to ensure that AI models in healthcare are high-performing and resilient to evolving threats.  
**Author Contributions:** [PERSON] conceptualized the study; [PERSON]-u.-a.M. handled the data collection and preprocessing tasks; data analysis and result interpretation were performed by both [PERSON] and [PERSON]-u.-a.M.; data visualization was led by [PERSON]; [PERSON]-u.-a.M. reviewed related works and both authors identified the research gaps; [PERSON] designed and implemented the HAVA; [PERSON] and [PERSON]-u.-a.M. drafted the manuscript; experimentation was carried out by [PERSON] and [PERSON]-u.-a.M.; administrative coordination and documentation support were provided by [PERSON], with [PERSON] also serving as the corresponding author. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The dataset analyzed in this study is publicly available and can be retrieved from the source in reference [29].
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 3 Continuation of Existing in Services and Replacement Possibilities  
Nowadays, we are witnessing rapid changes in telecommunications. Some Communication Service Providers are just introducing Voice over LTE (VoLTE) on an IP Multimedia Subsystem (IMS) architecture besides their existing 2G and/or 3G networks [3, 34, 47]. Meanwhile, in several countries, the sunset of 2G and/or 3G networks has already started, forcing MNOs to provide end-user services purely on an IMS and to shut down their 2G and/or 3G networks, where previously legacy IN-based services were provided. In both cases the legacy 2G/3G Intelligent Networks services need continuation for subscribers in the IMS domain. Figure 4 shows the IN-based service evolution, reflecting the continuation possibilities of legacy IN-based services, and the replacement possibilities to enable the ramp-down of the service control point (SCP) network elements.  
Figure 4: Alternatives to provide CS domain services in the IMS domain (**A**: continue with the existing IN service, **B**: implement the service natively as an IMS AS, **C**: invoke the service over API).
## 2 Methodologies and System Description
### Signal Preprocessing
#### 2.3.3 Bandpass Filter  
The main purpose of the Bandpass filter is to eliminate waveform noise and reduce feature space. The heart changes its frequency almost every heartbeat, which is reflected as a carrier frequency variation. This paper uses the Bandpass filter to isolate the carrier frequency of the heartbeat. According to reference [19], [PERSON] proposed that the typical frequency component range of the QRS complex is approximately 0.4 Hz to 10 Hz. Therefore, in this study, the filter is set at 0.4-10 Hz, and two frequency bands of HRV are defined, (1) the LF band is from 0.045 to 0.15 Hz and (2) the HF band is from 0.15 to 0.4 Hz.  
Fast Fourier Transform, FFT  
FFT is a method for calculating the Discrete Fourier Transform (DFT) faster, and it is usually used for frequency analysis. FFT analysis transforms the signal from the time domain to the frequency domain. According to reference [20], the difference between FFT and DFT lies in time complexity, which is O(Nlog\({}_{2}\)(N)) iterations. In Equation (15), \(N\) represents the number of samples in the sequence.  
\[x_{n}=\frac{1}{N}\sum_{k=0}^{N-1}X_{k}e^{12\pi k\frac{\pi}{N}}\ n=0,\ldots,N-1 \tag{15}\]  
The FFT is used in this paper for frequency analysis. It converts a signal from the time domain to the frequency domain. According to reference [20], the difference between FFT  
Figure 8: Trapezoidal rule illustration.  
and Discrete Fourier Transform (DFT) lies in their time complexity, with FFT having an iterative time complexity of Nlog\({}_{2}\)(N) times. In Equation (15), \(N\) represents the number of samples in the sequence. To achieve visualization, the Hanning window method is used to estimate the FFT, as shown in Figure 9. The Hanning window function is defined as W(\(t\)) (16) and is used to reduce spectral energy leakage. In this paper, the sequence is subjected to a Fourier transform at a frequency of 10 Hz with a uniformly distributed rate. The PSD is integrated into the very-low-frequency (VLF: 0 to 0.2 Hz), low-frequency (LF: 0.20 to 0.75 Hz), and high-frequency (HF: 0.75 to 3.0 Hz) bands, with a spectrum estimated by the Hanning window for each band. Finally, the area is calculated using the trapezoidal formula, and the LF/HF ratio is calculated to evaluate the balance of the sympathetic and parasympathetic nervous systems.
## 4 Analysis of Results
#### 4.2.3 Analysis of Spatial Panel Measurement Results Based on Economic Matrix
If financial support for agriculture in a province increases by 1%, the ratio of agricultural green development change in neighboring provinces to agricultural green development change in its own province is about 1:-0.85, indicating that with the application of agricultural technology, financial support for agriculture will produce large negative spillover to its neighboring provinces. In fact, the government implements a fiscal support agriculture policy to provide certain \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Direct Effect** & **Indirect Effect** & **Total Effect** \\ \hline Inata & 0.0120 *** & 0.0314 *** & 0.0434 *** \\ Infsa & 0.0276 *** & \(-0.0326\) *** & \(-0.0049\) \\ Inmi & 0.0302 *** & 0.0965 *** & 0.1266 *** \\ Inadr & \(-0.0115\) *** & 0.0381 *** & 0.0266 ** \\ Incps & 0.0022 & \(-0.0277\) ** & \(-0.0255\) * \\ Inmci & 0.0109 & 0.0422 & 0.0531 * \\ \hline \hline \end{tabular} Note: ***, *, and * represent significance levels of 1%, 5% and 10% in turn.  
\end{table} 
Table 13: Decomposition results of the spatial effect of agricultural technology application on agricultural green development under the economic matrix. subsidies to farmers. While promoting agricultural green development in the province, it will also distort market price factors, which will affect farmers' production behavior (resource utilization, resource input, etc.). This results in the waste of resources, ecological damage and, due to the large gap in the fiscal policy of supporting agriculture between provinces, further restricted agricultural green development in neighboring provinces. Regardless of whether the core explanatory variable is agricultural technology research and development or agricultural technology application, the spatial spillover effect of agricultural machinery input is positive, and it passed the 1% significance level test, which shows that the input of agricultural machinery will produce considerable positive spillover to its neighboring provinces. This is because the improvement of agricultural mechanization will bring about the improvement of green agricultural production efficiency while freeing up the labor force. When the province raises the production efficiency through agricultural machinery input, due to its large effect, neighboring provinces will adopt the mode of \"watching and learning\" to promote the improvement of their own green agricultural production efficiency.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 4 Fundamental Measurement Limits  
The aim of this section is to describe fundamental measurement limits, which apply for every flow velocimetry technique based on Mie scattering. The general effects of using seeding particles are discussed in Section 4.1. In the next Section 4.2, the measurement error due to the motion of the seeding particles not originating from the fluid motion is described. Finally, the physical limit of the measurement uncertainty due to photon shot noise is compared for Doppler and time-of-flight measurement principles in Section 4.3.  
Note that each effect (discussed here, as well as others) propagates to a measurement uncertainty of the flow velocity. The computation of the uncertainty propagation and the combination of all uncertainty contributions is possible by means of analytic propagation calculations or Monte Carlo simulations, which is described in the international guide to the expression of uncertainty in measurements (GUM) [38; 39]. For uncorrelated uncertainty contributions, the combined measurement uncertainty is the square root of the sum of all squared uncertainty contributions. As a result, the largest uncertainty contribution dominates and thus limits the total measurement uncertainty. However, the aim of the section is not to derive a complete measurement uncertainty budget, but to review fundamental measurement limits, which always occur in the measurement uncertainty analysis of flow velocimetry techniques based on Mie scattering.