esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 3 Application of the Methodology
### Determining Contributions of REs to GDS Accident  
Identifying the most critical REs based on their contributions to the occurrence of the GDS accidents is of paramount importance for the formulation of effective prevention measures. To achieve this, the total weighting index method outlined in Section 2.3.4 is employed to determine the criticality of REs within the BN accident model. The outcomes of this analysis, illustrating the contributions of REs to GDS leakage, are presented in Figure 6 (specifically, the blue bars represent the prior contribution weight values of the REs). As evident from the figure, X\({}_{4}\) (non-compliance with operating procedure) emerges as the most significant RE contributing to the occurrence of leakage at the station, closely followed by X\({}_{16}\) (equipment manufacturing defect). Furthermore, Figure 7 details the measurement of RE contributions to a specific accident scenario, taking OE\({}_{6}\) (long-duration jet fire) as an illustrative example, under the condition of GDS leakage (again, the blue bars indicate the contributions). According to the criticality ranking presented in Figure 7, X\({}_{60}\) (automatic emergency cut-off controller failure) holds the highest contribution to the occurrence of the major jet-fire accident, followed by X\({}_{62}\) (no response to manual emergency valve).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Undesired Event** & **Description** & **Prior Probability** \\ \hline CE & Natural gas release from GDS & 5.23 \(\times\) 10\({}^{-2}\) \\ OE\({}_{1}\) & Minor material losses & 4.54 \(\times\) 10\({}^{-2}\) \\ OE\({}_{2}\) & Flash-fire & 6.98 \(\times\) 10\({}^{-7}\) \\ OE\({}_{3}\) & Short-duration jet fire & 5.77 \(\times\) 10\({}^{-3}\) \\ OE\({}_{4}\) & Major material losses & 9.93 \(\times\) 10\({}^{-4}\) \\ OE\({}_{5}\) & Vapor cloud explosion & 1.53 \(\times\) 10\({}^{-8}\) \\ OE\({}_{6}\) & Long-duration jet fire & 1.26 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The prior probabilities of occurrence of CE and OEs.  
Figure 6: The importance measure of the REs for the occurrence of CE.  
Figure 7: The importance measure of the REs for the occurrence of OE\({}_{6}\).
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 3 Proposed Methodology  
This section illustrates the architecture of our presented method employed for brain tumor detection. Figure 1 shows the workflow of the proposed methodology. The presented DL technique is DenseNet-41-based Mask-RCNN, which aims to perform the accurate localization, segmentation, and classification of brain tumors. Given an MRI image, our aim is to instinctively detect the brain tumor from a complex background without requiring any manual intrusion. First, the input images are preprocessed to remove noise and artifacts added during MRI acquisition. Then, the ground truth segmentation masks are generated that are utilized for model training. Next, custom Mask-RCNN is applied for tumor localization, classification, and segmentation. The custom Mask-RCNN model performs the following steps: (1) keypoints extraction using DenseNet-41, (2) region of interest (RoI) creation, (3) RoI classification and bounding box regression, and (4) segmentation mask acquisition. First, a backbone network based on CLs computes the deep features from preprocessed input MRI images. The obtained features are then used by the region proposal network (RPN)to generate RoIs by mapping each point on the feature map into the original image. Next, the RoIAlign layer is employed to select features from the feature map corresponding to the RoIs obtained from the RPN network and distribute them with correlated layers to segment and classify the RoI.
# Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting
## 5 Discussion
### Limitations and Future Work  
The proposed combined weighting-nonlinear FAHP method for risk assessment of drilling and blasting tunnel construction demonstrates advantages in weight calculation and risk level determination, yet several limitations require improvement. Firstly, although the risk indicator system was established through the WBS-RBS coupling matrix, the coverage of risk factors for special geological conditions (e.g., karst development zones) may be incomplete. Moreover, the classification criteria for some secondary indicators primarily rely on expert experience, whose scientific validity and generalizability need further verification with more field measurement data. The model shows limited adaptability to dynamic risks, particularly in real-time response to sudden geological hazards, mainly due to the current assessment framework's reliance on static weight calculation. Additionally, while nonlinear operators were introduced to address fuzziness, the determination of prominent influence coefficients (\(\Lambda\)) remains based on empirical scaling, which may compromise assessment accuracy under extreme working conditions.  
Although case studies have validated the model's applicability in the Daliangshan No.1 Tunnel, its effectiveness for super-large cross-section tunnels or complex urban environments requires further verification. Future research will integrate construction process numerical simulation with real-time monitoring data to develop dynamic weight updating algorithms and expand the case database to enhance the model's engineering applicability.
## 3 Results
### Variations of Kivirfruit Orchard Carbon Fluxes
#### 3.2.2 Daily Variations of GPP, NEE, and R\({}_{\rm e}\)  
The seasonal variations in carbon fluxes during the growing season of 2018 and 2019 are displayed in Figure 3b,c. Daily GPP and R\({}_{\rm e}\) increased gradually, peaking in July or August, and then decreased at the end of November. The value of daily R\({}_{\rm e}\) was compared to that of GPP at the beginning and end of the growing seasons. The trend in NEE was opposite to the GPP and R\({}_{\rm e}\), and the value of NEE was negative almost throughout the growing seasons, but was negligible or slightly positive occasionally on cloudy or rainy days. The monthly average daily NEE peaked at \(-4.61\) g C m\({}^{-2}\) d\({}^{-1}\) in June 2018 and \(-2.96\) g C m\({}^{-2}\) d\({}^{-1}\) in August 2019, and the maximum monthly mean GPP was \(10.25\) g C m\({}^{-2}\) d\({}^{-1}\) in 2018 and \(9.46\) g C m\({}^{-2}\) d\({}^{-1}\) in 2019, respectively (Table 2). As indicated by the positive NEE in Table 2, the kiwifruit orchard ecosystem acted as a carbon source in March and November, and as a carbon sink in most of the days throughout the growing seasons. The total phenological GPP was \(1684.66\) g C m\({}^{-2}\) and \(1526.27\) g C m\({}^{-2}\) in 2018 and 2019, respectively, with NEE accounting for \(26.4\%\) and \(18.6\%\) of the total GPP in each year.  
Figure 3: Variations in (**a**) monthly average diurnal gross primary productivity (GPP), ecosystem respiration (R\({}_{\rm e}\)), and net ecosystem exchange (NEE) during the kiwifruit growth period in 2018 and 2019; and seasonal GPP, R\({}_{\rm e}\), and NEE in (**b**) 2018 and (**c**) 2019.
Notes: V1 and V2 are the measured voltages for different gradients at two positions in distance \(\Delta\)x using the ion-selective microelectrode (ISME), mV, and \(\Delta\)x is the distance between two positions where the ISME vibrated (\(\upmu\)m). E = k \(\pm\) slgC, where E is the measured voltage between the microelectrode and the reference electrode (mV), C is the ion concentration (mol/L), s is Nernstian slope (mV/dec); k is Nernstian intercept (mV). In fact, in many applications, manipulation tools are not permitted to touch the measured object. Therefore, we can only choose a non-contact positioning method such as visual positioning the most commonly used methods. In addition, in microscopic imaging, small noise is exponentially magnified. When the detection environment is complex, the efficiency of the noise-sensitive template matching algorithm is considerably reduced. Accordingly, tip location in a microscopic image is a very hard task [18]. The contribution of this paper is the proposition of an object recognition method with better robustness, real-time performance and good generalization under the micro scale. In addition, the proposed method can be used to automatically and accurately calculate the relative distance between two micro-objects, e.g., the ISME and the measured object in the microscopic image. Moreover, we provide a method for microscopic object recognition with a relatively small data set by selecting suitable features to train the recognition model. The remainder of the paper is organized as follows. In Section 2, we introduce the experimental material and algorithm implementation methods. In Section 3, we introduce the key testing results of the algorithm. We discussed the crucial parts of algorithm are discussed in Section 4. Finally, Section 5 presents the conclusion and significance of this paper, as well as a brief introduction of the future work.
# A DDPG-Based USV Path-Planning Algorithm
## 3 Simulation
### USV State Space  
While navigating on open water, an unmanned boat, or drone boat, is programmed to autonomously refine its path by leveraging the available map information to steer clear of hindrances. Throughout this procedure, it is presumed that the vessel advances at a uniform distance during each move, resulting in the movement space of the drone boat corresponding to the steering angle for each individual motion.  
Ordinary intelligent entities, such as indoor robots, often travel at angles of 45 or even multiples of 90 degrees. This type of action space is prevalent, but it can result in elongated path trajectories and large-angle turns, including complete about-faces of 180 degrees. These movements are inconsistent with the typical maneuvering characteristics of unmanned vessels, prompting the need for consideration of the specific steering capabilities of the boat.  
Therefore, to tailor the maneuvering space for an unmanned boat, it becomes essential to ensure that its heading remains as steady as feasible or that large-angle turns are set as few as possible during navigation and in the process of obstacle avoidance. Such considerations contribute to a more seamless route, enhancing not only the safety of the unmanned boat's journey but also minimizing power depletion.  
To align the turning angle of the unmanned boat with its actual navigation characteristics, the angle is crafted according to the principle of minimal-angle steering. The action space is defined to allow a single unit's distance of advancement in a specified direction, with angles fixed at plus or minus 15 degrees, plus or minus 30 degrees, or 0 degrees.  
Figure 3 illustrates this concept further, with the left diagram showing the action space as it is depicted in traditional reinforcement learning algorithms. The right diagram displays an optimized action space designed specifically to accommodate the operational attributes of the unmanned boat.
## 5 Results
### Positioning Performance Analysis
As can be seen from Figure 10, overall, jamming has a lower impact on GPS L5, Galileo E5a, and Galileo E5b than it does on GPS L1 C/A, Galileo E1, and BeiDou B1. Furthermore, in the nominal situation, signals in the L5/E5 band provide better 3D-positioning performance than signals in the L1/E1/B1 band. Figure 10 also demonstrates that single-frequency multi-constellation diversity improves receiver resiliency by leveraging diverse characteristics of contemporary multi-constellation signals. Finally, when it comes to mitigation of jamming impact and enhancing the robustness of the GNSS receivers, a resilient MFMC solution with a jamming event detector is always viable. As an example of how the resilient MFMC receiver performed in a maritime environment for the whole test duration (i.e., \(\sim\)200 s), Figure 11 offers a comparison of instantaneous horizontal positioning accuracy for Scenario II. FGI-GSRx can generate a PVT solution with reference to the beginning of the first subframe of each signal, which caused different initial delays for different signals in obtaining the positioning solution. These initial delays of not having PVT solution can be seen in Figure 11. It can be observed that the proposed resilient MFMC receiver first utilized all the available signals and all constellations until jamming was detected on L1/E1/B1 signals; after this, it switched from traditional MFMC to single-frequency, multi-constellation solution utilizing jamming-free L5/E5a/E5b signals for the duration of jamming on L1/E1/B1; then, later it again switched back to normal  
Figure 10: 3D-RMS comparison of different GNSS signals under four scenarios. MFMC-based position solution, once there was no more jamming detected on L1/E1/B1 bands. The proposed resilient MFMC receiver provided an uninterrupted positioning solution in the case of a single-frequency jamming incident. A similar finding was also observed for Scenario III.
## 3 Results
### Drop Size Parameters  
Table 2 summarizes the characteristic diameters of the sprays as affected by nozzle type and working pressure.  
Results were consistent with expectations: in fact, drop pulverization was higher for the turbulence nozzle than for the two air induction nozzles. As an example, at the pressure of 0.5 MPa, VMD was 147 \(\upmu\)m for the ATR 80 orange, 624 \(\upmu\)m for the TVI 8002, and 449 \(\upmu\)m for the AVI 11003 nozzle. In addition, all diameters were affected by pressure: an increase in the spraying pressure caused an increase in the drop pulverization, regardless of the type of nozzle. When the pressure increased from 0.3 to 1.5 MPa, VMD decreased by 21.8% for the ATR 80 (from 174 to 136 \(\upmu\)m) and by 51.8% for the TVI 8002 (from 861 to 415 \(\upmu\)m). The reduction in VMD for AVI 11003 was 11.3% (from 506 to 449 \(\upmu\)m) when the pressure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nozzle Type** & **Measured Pressure, MPa** & **Measured Flow Rate, L/min** & **Reference Pressure, MPa** & **Reference Flow Rate, L/min** \\ \hline  & 0.311 & 0.78 & 0.3 & \\ ATR 80 & 0.517 & 0.98 & 0.5 & 0.99 \\ orange & 0.988 & 1.37 & 1.0 & 1.39 \\  & 1.445 & 1.64 & 1.5 & 1.69 \\ \hline  & 0.306 & 0.80 & 0.3 & \\ TVI 8002 & 0.518 & 1.05 & 0.5 & 1.03 \\  & 1.031 & 1.47 & 1.0 & 1.46 \\  & 1.484 & 1.76 & 1.5 & 1.79 \\ \hline  & 0.296 & 1.18 & 0.3 & 1.20 \\  & 0.500 & 1.53 & 0.5 & 1.55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nozzle flow rate values in function of spraying pressure.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pressure, MPa** & \(D_{10}\) & \(D_{20}\) & \(D_{30}\) & \(D_{32}\) & \(D_{\
u 10}\) & \(D_{\
u 50}\) & **RSF** \\ \hline  & & \multicolumn{8}{c}{ATR 80 orange} \\ \hline
0.3 & 81 & 97 & 112 & 150 & 94 & 174 & 263 & 97 \\
0.5 & 70 & 83 & 95 & 126 & 79 & 147 & 236 & 107 \\
1.0 & 57 & 69 & 82 & 115 & 70 & 138 & 227 & 114 \\
1.5 & 54 & 67 & 79 & 112 & 66 & 136 & 22 increased from 0.3 to 0.5 MPa. Relative Span Factor values generally increased when the pressure increased for all nozzles, a sign of a lower uniformity in drop size distribution.  
The effects of spray pressure on cumulative volumetric curves are shown in Figure 6 in function of drop diameters and nozzle types. The graph was obtained by considering all the drops of all repetitions. The effect of pressure on the hollow cone ATR 80 nozzle was more evident when the pressure increased from 0.3 to 0.5 MPa (VMD decreased from 174 to 147 ), whereas above 0.5 MPa the cumulative volumetric curves were almost superimposed (VMD decreasing from 147 to 136 ). Instead, the cumulative volumetric curves were clearly distinct for the air induction nozzles, as well as the volumetric diameters.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 4 Experiments
### Model Complexity Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Error Rate (\%)**} & \multicolumn{6}{c}{**PSNR**} & \multicolumn{6}{c}{**SSIM**} \\ \cline{2-13}  & **1 bit** & **2 bit** & **3 bit** & **4 bit** & **1 bit** & **2 bit** & **3 bit** & **4 bit** & **1 bit** & **2 bit** & **3 bit** & **4 bit** \\ \hline SteganoGAN [17] & 3.94 & 7.36 & 8.84 & 10.00 & 25.98 & 25.53 & 25.70 & 25.08 & 0.85 & 0.86 & 0.85 & 0.82 \\ FNNS-R [31] & 0.14 & 1.80 & 5.28 & 15.17 & 39.79 & 35.12 & 33.40 & 32.53 & 0.96 & 0.89 & 0.84 & 0.79 \\ FNNS-D [31] & 0.00 & 0.00 & 0.00 & 3.17 & 36.06 & 34.43 & 30.05 & 33.92 & 0.87 & 0.86 & 0.71 & 0.84 \\ FNNS-DE [31] & 0.00 & 0.00 & 0.00 & 2.58 & 21.16 & 20.85 & 20.67 & 21.03 & 0.71 & 0.68 & 0.63 & 0.64 \\ LISO [19] & \(4\times 10^{-4}\) & \(1\times 10^{-3}\) & \(4\times 10^{-3}\) & \(1\times 10^{-1}\) & 35.62 & 36.02 & 32.25 & 30.07 & 0.89 & 0.90 & 0.82 & 0.79 \\ Ours & 0.00 & 0.00 & 0.00 & 0.04 & 40.81 & 38.46 & 35.27 & 31.21 & 0.97 & 0.93 & 0.93 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with other state-of-the-art steganography algorithms with different payloads on CelebA dataset.
###### Abstract  
As a key technology in smart healthcare monitoring systems, wireless body area networks (WBANs) can pre-embed sensors and sinks on body surface or inside bodies for collecting different vital signs parameters, such as human Electrocardiograph (ECG), Electroencephalograph (EEG), Electromyogram (EMG), body temperature, blood pressure, blood sugar, blood oxygen, etc. Using real-time online healthcare, patients can be tracked and monitored in normal or emergency conditions at their homes, hospital rooms, and in Intensive Care Units (ICUs). In particular, the reliability and effectiveness of the packets transmission will be directly related to the timely rescue of critically ill patients with life-threatening injuries. However, traditional fault-tolerant schemes either have the deficiency of underutilised resources or react too slowly to failures. In future healthcare systems, the medical Internet of Things (IoT) for real-time monitoring can integrate sensor networks, cloud computing, and big data techniques to address these problems. It can collect and send patient's vital parameter signal and safety monitoring information to intelligent terminals and enhance transmission reliability and efficiency. Therefore, this paper presents a design in healthcare monitoring systems for a proactive reliable data transmission mechanism with resilience requirements in a many-to-one stream model. This Network Coding-based Fault-tolerant Mechanism (NCFM) first proposes a greedy grouping algorithm to divide the topology into small logical units; it then constructs a spanning tree based on random linear network coding to generate linearly independent coding combinations. Numerical results indicate that this transmission scheme works better than traditional methods in reducing the probability of packet loss, the resource redundant rate, and average delay, and can increase the effective throughput rate.  
wireless body area network (WBAN); real-time monitoring; network coding; fault-tolerant; smart healthcare +
Footnote †: journal: Applied Soft. 2017
## 6 Optimization Scenarios for 6G MCNs
### Task Offloading to Edge Nodes in MCNs
Finally, the third (C3) guarantees that the task size \(d_{i}\) (in bits) transmitted to node \(n\) over a wireless link with bandwidth \(B_{i,n}\) (in bps) respects a maximum latency limit \(T_{max}\) (i.e., if this constraint is disrespected, it means that offloading is not feasible for that task-edge pair). **Potential Solution Approaches:** Multiple solvers can be used to provide a solution to problem (8). Below, we describe the major among them:  
1. _Mixed Integer Linear Programming (MILP)_: The offloading problem can be formulated as a MILP when all latency and energy models are linearized [19; 100]. MILP solvers (e.g., CPLEX, Gurobi [19]) can produce optimal solutions for small- to medium-scale networks. However, this approach is computationally intensive for large dynamic MCNs and not scalable when task arrival is continuous or unpredictable. 2. _Multi-Agent Reinforcement Learning (MARL)_: In a distributed MCN setting, task offloading can be modeled as a multi-agent RL problem, where each maritime node (ship, UAV, buoy) is an agent deciding whether to offload and to which edge server [98]. In the formulation of MARL, the agents' state can include local task queue, channel quality, residual energy, and/or nearby edge availability. The agents may select an edge node for offloading or choose local execution (i.e., action). The reward collected at each step can be defined as in the optimization function in problem (8) (i.e., weighted delay-energy cost), possibly by further penalizing the agents for task drops [39]. Note that although this section provided the formulation basis of three different optimization methods, there might also be other emerging resource allocation problems in 6G MCNs, including UAV trajectory planning for coverage optimization [101], RIS and beamforming optimization [102], FL across heterogeneous maritime devices [57; 69] and latency-constrained multi-hop routing [103].
## 2 Materials and Methods  
### Substrates and Inoculum  
Three independent batch experiments were conducted with four different materials as feedstock. The four different feedstocks were untreated WS, steam-exploded wheat straw (SE), dried distillers' grains with solub (DDGS) and rapeseed meal (RM). DDGS was supplied by a UK bioethanol plant (Vivergo, Yorkshire, UK), was ground into a fine powder using a coffee grinder (DeLonghi, Australia), sieved through sieve mesh No. 20 (particle size smaller than 0.85 mm) and stored at 4 \({}^{\circ}\)C prior to use. RM was kindly provided by Stainswick Farm (Oxfordshire, UK) and was generated via a cold pressing oil extraction process. RM samples were ground using a dry-grinder and sieved to obtain uniform sized particles (<0.85 mm). The remaining oil in the meal was removed using a supercritical CO\({}_{2}\) extraction rig (SciMed, UK) at 60 \({}^{\circ}\)C and 300 bar pressure for 1 h, with ethanol (10%, _vv_) as a co-solvent. The residual defatted meal was kept at 4 \({}^{\circ}\)C prior to use. WS was collected from fields in the wider area of Norfolk, UK. Part of this residue was steam-exploded with the use of an economiser (Economizer SE, Biogas Systems, Austria). The economiser was fed with raw WS and operated at high temperature (155 \({}^{\circ}\)C) and high pressure (5 bar) for 3 min with subsequent release of generated pressure by a valve, causing rapid depressurization of the substrate. Subsequently, both the non-steam-exploded straw (stated as untreated WS from now on) and the steam-exploded straw (stated as SE from now on) were stored in plastic bags at \(-20\)\({}^{\circ}\)C until further use.  
As an inoculum source, the effluent of four lab-scale mesophilic (42 \(\pm\) 1 \({}^{\circ}\)C) semi-continuous stirring tank reactors (CSTRs) with a working volume of 4 L, operating under a steady-state at an Organic Loading Rate (OLR) of 5 g volatile solids (VS)/L per day, was used. Two of the CSTRs were digesting untreated WS while the other pair of reactors were fed with steam-exploded WS. The reason for mixing two slightly different inocula was to minimise, as much as possible, the chances of having an inoculum acclimatised to one of the two substrates (untreated WS vs. SE). Equal amounts of the two effluents (2 L of each inoculum) were manually mixed and degassed at mesophilic conditions (\(42\pm 1\)\({}^{\circ}\)C) for one week before the beginning of the AD experiments. The reason for adding the degassing step was to minimise the inoculum's endogenous microbial activity and gas production.