esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Segmentation of the Lung Anatomical Structures
#### 3.1.7.  
Fissure Detection and Lung Lobe Segmentation  
Pulmonary fissures are double layers of visceral-pleura and appear as bright lines spanning the parenchyma on the CT scan (Figure 7a). In order to segment the fissures correctly, prior anatomical knowledge is crucial. As we discussed in Section 3.1.1, fissures divide the lungs into five separate lobes. A fissure is treated as a boundary between the two lung lobes; thus, the bronchial airways and vessels cannot overpass it to enter the adjacent lobe. Based on this fact, the location of a fissure can be assumed to be a gap between the two lung lobes where the airways and vessels are clear. As with vessel segmentation, we began fissure segmentation by detecting all of the lesions inside the lungs, as depicted in Figure 7b,c. Subsequently, we enhanced the tubular lesions using Hessian-based filtering, as shown in Figure 7d. Among these tubular lesions, we found the longest continuous and non-branching lines across the lungs, as represented in Figure 7e. These lines were regarded as the possible fissure regions, and all fissure regions from 2D CT slices were segmented slice-by-slice. After achieving all 2D fissure segments, the isotropic structure of possible fissure lines was created, as in Figure 7f. From that figure, it is evident that the isotropic structure can give a more comprehensive view of fissure lines. The stacked fissure lines became plate-like structures in the isotropic view. However, there was some noise and incomplete parts in the fissure planes. Hence, we performed 3D morphological operations to remove the noise and enclose the incomplete parts. Finally, we successfully segmented the fissures by extracting three biggest planes, as illustrated in Figure 7g. To highlight the segmented fissures, we also provide transparent views of the isotropic images. Figure 7h shows a transparent view of the oblique and horizontal fissures, dividing the right lung into three lobes. Similarly, Figure 7i shows a transparent view of the oblique fissure dividing the left lung into two lobes. Subtracting these fissure planes from the segmented lung volumes (Section 3.1.4) gave five different lobes, as illustrated in Figure 7j.  
Figure 7: Segmentation of the pulmonary fissures and lung lobes: (**a**) input 2D CT slice, (**b**) segments of lung regions, (**c**) segments of lung internal structures, (**d**) enhanced tube-like structures, (**e**) possible 2D fissure lines, (**f**) isotropic view of possible fissures plane, (**g**) successfully segmented fissure planes, (**h**) transparent view of oblique and horizontal fissures in the right lung, (**i**) transparent view of oblique fissure in the left lung, (**j**) Successfully segmented lung lobes.
## 1 Introduction  
Abdominal aortic aneurysms (AAA) are defined as an aortic diameter over 3.0 cm, they are often asymptomatic prior to potentially fatal complications such as dissection or rupture [1]. Reported AAA prevalence varies depending on country and patient demographics, affecting up to 2.2% and 2.5% of the American and European populations, respectively [2]. The main risk factors for AAA include age, male gender, family history, hypertension, hypercholesterolemia and smoking [3; 4; 5]. Risk of rupture is increased in patients with a vessel diameter greater than 5.5 cm, a growth rate above 1 cm/year and female gender [6; 7; 8; 9].  
Advanced age and male sex have led to the adoption of a national screening programme for men over 65 in the UK and US Preventive Services Task Force (USPSTF) recommending one-time ultrasound screening in men aged 65 to 75 years who have ever smoked [10; 11]. Current AAA management guidelines advocate the use of diameter as a metric for risk stratification. AAAs are monitored with ultrasound until they reach the intervention threshold of 5.5 cm. In the UK, AAAs between 3.0-4.5 cm and 4.5-5.5 cm are monitored annually and every 3 months, respectively. Ultrasound provides anatomical information at fixed time points but does not predict potential growth rate. A biomarker to predict AAA growth and personalise surveillance regimens or intervention thresholds is lacking in the field.  
Previous studies have evaluated AAA metabolic activity using fluorine-18-2-deoxy-D-glucose ([18F]FDG) positron emission tomography-computed tomography (PET-CT) as a marker of pathological wall weakening [12; 13]. There are conflicting data on the utility of semi-quantitative analysis using standardised uptake value (SUV) metrics for prediction of future AAA growth and rupture [12; 13; 14; 15; 16; 17]. More sophisticated imaging feature analysis may have greater potential to predict AAA growth. Radiomics is a process involving extraction of high dimensional features from medical imaging allowing quantitative analysis of the distribution and relationship of pixel grey levels [18]. This technique has been extensively studied in the oncology setting for outcome prediction modelling [19; 20; 21; 22; 23; 24]. There is currently a paucity of data on the potential use of radiomic analysis for predictive modelling in non-oncological settings. In particular, the use of radiomic features derived from [18F]FDG PET-CT for prediction of AAA growth has not yet been reported to the best of our knowledge.  
The aim of this study was to explore the utility of a machine learning (ML)-based regression model utilising radiomic features extracted from [18F]FDG PET-CT to predict future AAA growth rate.
## 3 The Multi-Agent Framework
### Complexity Analysis of the Framework  
This framework is proposed as an assistant tool to enhance the performance of existing routing approaches, but meanwhile, the framework incurs extra complexity, which is independent of the complexities of specific underlying routing approaches. Such complexity is incurred during the creation and use of cooperative neighbour sets. As described above, a node, e.g., \(v_{i}\), first estimates the reward of adding another node, e.g., \(v_{j}\), to \(v_{i}\)'s cooperative neighbour set. Then, node \(v_{i}\) uses Algorithm 1 to decide whether or not to add \(v_{j}\) as one of its cooperative neighbours. In the future, when \(v_{i}\) has a packet to send out, it will choose one of its cooperative neighbours to relay the packet.  
The estimation of reward does not incur much extra complexity, as there is only the computation of simple equations (Equations (1)-(5)). Then, for the decision making of a node, in Algorithm 1, there is a nesting loop (Lines 2-7). The number of iterations of the loop, Lines 2-7, is \(30\). The number of iterations of the loop, Lines 4-6, is two, as there are two available actions: adding a node as a cooperative neighbour or not. Because the number of iterations of both of the loops is constant, the complexity of Algorithm 1 is \(\mathcal{O}(1)\). For the selection of cooperative neighbours, in Algorithm 2, there are two loops. The number of iterations of each loop equals the number of cooperative neighbours of a sensor node. In the worst case, the number of cooperative neighbours of a sensor node is the number of sensor nodes in the WSN. As the number of sensor nodes in the WSN is \(n\), the complexity of Algorithm 2 is \(\mathcal{O}(n)\). It should be noted that to alleviate the potential worst case, sensor nodes are allowed to remove inefficient cooperative neighbours (recall Section 3.4.1).  
In addition, there is also storage and communication consumption. Every time a node adds another node as a cooperative neighbour, the focal node has to use some storage space to store such a neighbouring relationship. In the worst case, a node adds all of the \(n\) nodes in the WSN to be its cooperative neighbours. Thus, the storage complexity is \(\mathcal{O}(n)\). Furthermore, before a node adds another node as a cooperative neighbour, a message has to be sent. Similarly, in the worst case, a node adds all of the \(n\) nodes in the WSN to be its cooperative neighbours, which means that \(n\) messages have to be sent. Thus, the communication complexity is \(\mathcal{O}(n)\), as well. Certainly, the potential worst case can be alleviated by allowing sensor nodes to remove inefficient cooperative neighbours.
## 3 End-to-End SNR Statistics  
The end-to-end instantaneous SNR under the DF relay is given by:  
\[\gamma_{SRD}=\frac{\gamma_{SR}\gamma_{RD}}{\gamma_{SR}+\gamma_{RD}+1}\approx \min(\gamma_{SR},\gamma_{RD}) \tag{18}\]  
The joint channel end-to-end CDF is as follows:  
\[F_{\gamma_{SRD}}(\gamma)=\Pr\{\min(\gamma_{SR},\gamma_{RD})<\gamma\}=F_{\gamma_ {SR}}(\gamma)+F_{\gamma_{RD}}(\gamma)-F_{\gamma_{SR}}(\gamma)F_{\gamma_{RD}}(\gamma) \tag{19}\]  
Substituting Equations (6) and (14) into (17) yields:  
\[F_{\gamma_{SRD}}(\gamma)=1-\exp\biggl{(}-\frac{m_{SR}}{\Omega_{SR}}\gamma \biggr{)}\sum_{t=0}^{m_{SR}N_{SR}-1}\frac{1}{t!}\biggl{(}\frac{m_{SR}}{\Omega_{ SR}}\gamma\biggr{)}^{t}\biggl{(}1-\frac{\xi^{2}A}{2}\sum_{m^{\prime}=1}^{\beta}b_{m^{ \prime}}G_{2A}^{3,1}\biggl{[}\frac{B\gamma}{\tau_{RD}}\big{|}_{\xi^{2},\alpha,m^{\prime},0}\biggr{]}\biggr{)} \tag{20}\]
# Stabilized Sewage Sludge as Fertilizer: Risks Related to the Presence of Microplastics
## 3 Results and Discussion
### The Qualitative Analysis of Microplastics  
The qualitative analysis of microplastics focused on identifying particles using Raman confocal microscopy. Polyethylene, a thermoplastic resin, can exist in either a transparent, solid, amorphous form or as a white crystalline material. Excellent insulating properties and high chemical stability characterize this material. Polyethylene is an incredibly versatile material and one of the most affordable plastics available.  
Figure 4 presents the Raman spectrum of polyethylene, which shows peaks corresponding to different types of vibrations: the stretching of C-H bonds (around 3000 cm\({}^{-1}\)), the bending and twisting of these bonds (around 1300 and 1400 cm\({}^{-1}\)), and the stretching of C-C bonds (within the range of 1000-1200 cm\({}^{-1}\)).  
The qualitative findings of this study are consistent with those previously published in the literature. [PERSON] et al. analyzed environmental samples and identified polyethylene as the dominant contaminant among microplastic particles, followed by polystyrene and polypropylene [22]. Similarly, another Raman spectroscopic study revealed that approximately 89.5% of the detected particles were plastic polymers, including polyesters, polyamide (PA), polyethylene teerephthalate (PET), and polyethylene (PE). This polymeric composition suggests that the microplastic particles may originate from laundering processes and personal care products [20].  
The qualitative results obtained in this study align closely with findings reported in the existing literature. [PERSON] et al. identified polyethylene (PE) as the most prevalent microplastic polymer in their samples, followed by polystyrene (PS) and polypropylene (PP) [22]. Similarly, Raman spectroscopic analysis in another study showed that approximately 89.5% of the particles were plastic polymers such as polyesters, polyamide (PA),  
Figure 4: Microplastic spectrum classified as LDPE.  
polyethylene terephthalate (PET), and polyethylene (PE), suggesting that these microplastics may originate from laundry activities and personal care products [20; 26; 27; 28; 29].  
In a separate investigation, polyethylene was again reported as the most commonly detected polymer type across all microplastic samples, highlighting its widespread occurrence in environmental matrices [24]. Furthermore, in an analysis of sewage sludge, the distribution of microplastics was dominated by PE (67.9%), followed by PET (16.1%) and polyester (PES) (10.7%), underscoring the predominance of polyethylene in wastewater treatment residues [25].
## 3 Key Findings
### RQ2: What Kinds of Learning Algorithms Are Being Deployed Towards the Security of IIoT?  
In recourse to the identification of the main loopholes of IIoTsec in Section 3.1, techniques such as ML and DL have become an important approach to tame the security risks, in recognition of their successful efficiency in similar studies. And for the fact that the security challenges are in two modes--active and asleep, the corresponding approach should be detective and preventive, accordingly [49]. Thus, IDSs are, in general, helpful to signal alert at the potential attempt of an intruder into network data flow [45], though an intrusion prevention system (IPS) proves better as it preempts the attacks and subsequently prevents their occurrence [1]. Thus, both are essentially cybersecurity approaches, especially for the guard of the IIoT ecosystem. For instance, ML-based IDSs were formerly challenged by the passive mode of intrusion, in which the discovery of assault awareness is likely to remain obscure. Then, the accuracy and precision rate at which ML algorithms detect such anomalies distinguish it from other IDSs. Such a feat has thus made ML algorithms deployable in many fields of cybersecurity [30; 66; 67; 68; 69]. Interestingly, ML algorithms have attracted the attention of the research community in IIoT for their sensitivity and sustenance to the living economy [30; 31; 32; 48]. Based on this background, we present three techniques mostly deployed toward IIoTsec in the literature as RQ2-1.
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 4 Results and Discussion
### Seismic Multi-Attribute Prediction Result
#### 4.2.2 BP Neural Network Prediction Result  
Utilizing the BP neural network from machine learning for nonlinear problem determination, we trained the model using revealed point coal thickness data and validated it against predictions from nearby working face boreholes. This approach achieved function approximation from input to output and integrated multiple seismic attributes, revealing complex relationships with coal thickness.  
In selecting other network parameters, the transfer function from the input layer to the hidden layers was chosen as the logsig function, known for its good differential properties and belonging to the sigmoid function family. This choice ensures neurons produce outputs even with weak sample attribute values and prevent overflow with large values. The transfer function from the hidden layers to the output layer was purelin, a linear transfer function that retains and scales values from any previous range for comparison with predicted sample values.  
Specific parameters included: the initial learning rate set to 0.01; the target function error reduced to 0.01; and the momentum factor set to 0.9 to effectively enhance learning efficiency, reduce errors and prevent the network from converging to local minima.  
The actual observation data from the No. 2 coal seam in the 24 Mining Area of the Lu'an coalmine were filtered to select nine borehole seismic attributes as learning samples for network training. To ensure robustness with a moderate model complexity, we constructed an initial BP artificial neural network model with four layers. Four selected seismic attributes were chosen as input layer nodes, followed by two hidden layers with three and two nodes, respectively. The output layer predicted coal thickness. Training with samples established from arc length, percentile frequency (2), maximum amplitude, and total energy resulted in learned connection weights W\({}_{1}\) between the input and first hidden layer; W\({}_{2}\) between the first and the second hidden layer; and W\({}_{3}\) between the second hidden layer and output layers. The first hidden layer contained three nodes, while the second had two nodes.  
\[W_{1}=\begin{bmatrix}5.252644441&-4.233421812&3.957612198&-3.207962213\\ 3.36081903&-1.123996543&4.813339729&-0.89108932\\ 9.408939889&2.654392006&3.112900161&-14.91667746\\ \end{bmatrix} \tag{8}\]  
Figure 11: Cross-plot analysis of effectiveness for attributes of the No. 2 coal seam.  
\[W_{2}=\begin{bmatrix}4.533082705&2.492960496&0.877679381&-3.252873526\\ 1.470133801&-3.082943086&12.57992379&-1.66106168\end{bmatrix} \tag{9}\]  
\[W_{3}=\begin{bmatrix}8.026619943&7.672284883&8.12632643\end{bmatrix} \tag{10}\]
## 3 Fast Channel Information Prediction Based Random Forest
### RB and Power Allocation Algorithm for Energy-Efficient Optimization
The constructed Lagrangian dual function is:  
\[g(\lambda,\mu)=\max_{b,\hat{p}}\mathcal{L}(b,\hat{p},\lambda,\mu) \tag{18}\]  
The dual optimization problem of the original optimization problem \(P_{4-1}\) is:  
\[\min g(\lambda,\mu)=\min_{\lambda>0,\mu>0}\max_{b,\hat{p}}\mathcal{L}(b,\hat{ p},\lambda,\mu)=\min_{\lambda>0,\mu>0}\sum_{n,m}g_{k}(\lambda)+\lambda P_{ \rm T}+\mu \tag{19}\]  
where  
\[g_{k}(\lambda,\mu)=\max(n_{n,m}^{kj}B_{\rm RB}\log_{2}\Bigg{(}1+\frac{S_{n,m} ^{kj}}{n_{n,m}^{kj}}\gamma_{n,m}^{k}\Bigg{)}-\rho\Big{(}\zeta S_{n,m}^{kj}-P_{ \rm C}\Big{)}-\lambda S_{n,m}^{kj}-\mu\eta_{n,m}^{kj}\]  
To solve the minimum value of \(g_{k}(\lambda,\mu)\), according to the KKT conditions, using the method of finding partial derivatives of \(S_{n,m}^{kj}\) and \(\eta_{n,m}^{kj}\), we can obtain:  
\[\sum_{n=1}^{N}\sum_{m=1}^{M}\sum_{k\in U_{n}}\sum_{j\in J_{m}}S^{kj}_{n,m} \leq P_{T}\]0\[\sum_{n=1}^{N}\sum_{m=1}^{M}\sum_{k\in U_{n}}\sum_{j\in J_{m}}S^{kj}_{n,m} \leq P_{T}\]1 \[\sum_{n=1}^{N}\sum_{m=1}^{M}\sum_{k\in U_{n}}\sum_{j\in J_{m}}S^{kj}_{n,m} \leq P_{T}\]2  
where \(\left[\mathrm{x}\right]^{+}=\max(0,\mathrm{x})\), we obtain that the optimal solution of RB is \(\eta^{*\mathrm{k},\mathrm{j}}_{\mathrm{n},\mathrm{m}}\),  
\[\sum_{n=1}^{N}\sum_{m=1}^{M}\sum_{k\in U_{n}}\sum_{j\in J_{m}}S^{kj}_{n,m} \leq P_{T}\]3  
As the value of \(\eta^{\mathrm{k},\mathrm{j}}_{\mathrm{n},\mathrm{m}}\) in the original optimization problem \(\mathrm{P}_{2}\) is an integer \"0\" or \"1\", with \(\mathrm{\mu}\) known, the RB identifier \(\eta^{\mathrm{k},\mathrm{j}}_{\mathrm{n},\mathrm{m}}\) assigned to user \(\mathrm{k}\) is obtained by the following formula:  
\[\eta^{\mathrm{k},\mathrm{j}}_{\mathrm{n},\mathrm{m}}=\left\{ \begin{array}{c} 1,\mathrm{k}\in\mathbb{U}_{\mathrm{n}}=\mathrm{argmax}\left(\mathrm{r}^{ \mathrm{k},\mathrm{j}}_{\mathrm{n},\mathrm{m}}/\mathrm{P}^{\mathrm{k},\mathrm{ j}}_{\mathrm{n},\mathrm{m}}\right),\;\forall\mathrm{n}\in\mathbb{C}_{\mathrm{k}}, \forall\mathrm{m}\\ 0,\;\mathrm{otherwise}\end{array} \right. \tag{23}\]  
The RB and power allocation of the optimal energy efficiency is carried out on the premise that the Lagrangian multipliers \(\lambda\), \(\mathrm{\mu}\) are known. Therefore, the optimal value of the Lagrangian multipliers \(\lambda\), \(\mathrm{\mu}\) should also be obtained by searching. Since the dual problem Equation (19) of \(\mathrm{P}_{4-1}\) is an unconstrained convex optimization problem, we adopt the sub-gradient method in [33] to update \(\lambda\) and \(\mathrm{\mu}\). The update method is shown in Equations (24) and (25):  
\[\lambda^{\mathrm{i}+1}=\left[\lambda^{\mathrm{i}}-\frac{1}{\mathrm{i}}\left( \mathrm{P}_{\mathrm{T}}-\sum_{\mathrm{n}=1}^{\mathrm{N}}\sum_{\mathrm{m}=1}^{ \mathrm{M}}\sum_{\mathrm{k}\in\mathbb{U}_{\mathrm{n}}}\sum_{\mathrm{j}\in
# Topological and Fractal Analysis of Nanostructured Metal-Dielectric Films
## 1 Introduction  
The fast development of industry and technology demands the creation of increasingly novel functional materials with unique properties [1, 2, 3, 4, 5, 6]. A major area of emphasis for researchers is the development of more efficient light-emitting or light-absorbing materials [7, 8]. Metal-dielectric composites with phases the size of nanometers show promise in this regard [9, 10]. These materials' optical response is mostly determined by the metallic phase characteristics, which means that the response can be controlled by varying the corresponding parameters of metallic nanostructures [11, 12, 13]. It was demonstrated [14, 15] that the energy redistribution in such materials can lead to the amplification of local optical fields. This effect is linked to the nascency of surface plasmon resonance in metallic nanoparticles as a result of the conduction electrons oscillating inside them [16, 17, 18]. Significant local field enhancement occurs in stochastic metal-dielectric structures with randomly distributed metallic nanoparticles of different sizes. A model example of such metal-dielectricnanostructures is thin metal films produced by thermal evaporation, with thicknesses below the percolation threshold [19; 20; 21].  
The stochastic creation of metal islands takes place during the initial phases of deposition. The parameters of the structure are determined by the substrate's quality and temperature [22; 23; 24], its surfactant sublayers [25; 26], and other factors [27; 28; 29; 30]. From a practical standpoint, island films of silver (Ag) and gold (Au) are very intriguing since the frequency of the plasmon resonance in such structures falls within the visible and near-ultraviolet ranges [31; 32; 33; 34]. Due to the plasmonic effect, nanoparticles of these metals have been applied in various ways, including in solar energy [35; 36], in biomedical fields [37; 38], and in the creation of new materials [39; 40], among others. Consequently, studying the morphology and determining the integral structural parameters of ultrathin gold and silver films is crucial as it directly affects the characteristics of the materials.  
The purpose of this work is topological and fractal dimension analysis of ultrathin silver and gold films with thicknesses below the percolation threshold using atomic force microscopy (AFM). The study of surface evolution of films with increasing mass thickness is the basis for determining a set of various parameters, including classical ones such as roughness and grain size, as well as those based on correlation analysis. The probe's influence on the surface image's quality and reliability is also thoroughly analyzed. Various approaches are used in this work to determine the fractal dimensionality, among which the metal film is considered as either a 2D or 3D object.