esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Signal Processing Method for Time-of-Flight and Center Frequency Estimation
## 2 Methodology for Estimation of Time-of-Flight and Center Frequency of Signals
### The Influence of Noise on the Accuracy of the Measured Parameters  
In order to analyze the influence of noise on the measured parameters, we performed simulations by adding white Gaussian noise to the original signal (Equation (1)). We used the standard MATLAB R2024b function _augn_ for this [24]. We examined the effects of nine types of noise intensity (in the range of 10-50 dB). The original signal was modeled as a harmonic signal with three periods and a Gaussian envelope (Figure 3a). In the simulation, we used signals with four center frequencies: 100, 300, 500, and 1000 kHz.  
The purpose of the simulation was to determine how much the signal-to-noise ratio influences the deviations of measured propagation times ToF (\(t\)), and frequencies \(f_{\mathrm{c}}\). The relative error was obtained by calculating the investigated parameters of the noisy signal and comparing them with the parameters of the signal without noise  
\[\Delta_{t}=100\%.\frac{\left|t_{f0}-t_{\mathrm{SNR}}\right|}{t_{f0}},\Delta_{ f}=100\%.\frac{\left|f_{\mathrm{c0}}-t_{\mathrm{SNR}}\right|}{f_{\mathrm{c0}}}, \tag{19}\]  
where \(t_{f0}\) is the ToF of the signal without noise, \(t_{\mathrm{SNR}}\) is the ToF of the signal with white Gaussian noise, \(f_{\mathrm{c0}}\) is the frequency of the signal without noise, and \(f_{\mathrm{SNR}}\) is the frequency of the signal with white Gaussian noise. The obtained results of the calculation of the relative error are presented in Tables 1 and 2. In the calculations, the signal-to-noise ratio varied from 50 dB to 10 dB for every 5 dB in the direction of decrease. In Table 2, next to the propagation times in parentheses, it is noted which frequency signal these times correspond to.  
As we can see from the tables, the calculation results of the proposed algorithm are influenced only by a low SNR. It should be noted that the parameters are not calculated in the tables due to the fact that the frequency response of the signal is no longer automatically determined at low signal-to-noise ratio. In this case, this should be done manually by observing the frequency response.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Frequency SNR** & **0 dB** & **50 dB** & **45 dB** & **40 dB** & **35 dB** & **30 dB** & **25 dB** & **20 dB** & **15 dB** & **10 dB** \\ \hline
100 kHz & 0.002 & 0.005 & 0.009 & 0.017 & 0.050 & 0.052 & 0.054 & 0.159 & 0.580 & 1.460 \\
300 kHz & 0.003 & 0.005 & 0.013 & 0.015 & 0.030 & 0.083 & 0.259 & 0.850 & - & - \\
500 kHz & 0.004 & 0.006 & 0.027 & 0.091 & 0.134 & 0.296 & 0.419 & - & - & - \\
1000 kHz & 0.006 & 0.007 & 0.052 & 0.225 & 0.238 & 0.417 & - & - & - & - \\ \hline \end{tabular}
\end{table}
Table 1: The relative error of frequency detection obtained for different SNRs.  
Figure 3: 300 kHz original signal (**a**) and the same signal with white Gaussian noise (signal-to-noise ratio SNR = 20 dB) (**b**).
# Real-Time Behaviour of Dredged Slurry Treated by Air-Booster Vacuum Consolidation
## 2 Brief Introduction to Air-Booster Vacuum Preloading  
The air-booster vacuum preloading method introduces an air-booster system to a conventional vacuum system. Conductors (i.e., booster tubes or plates) are inserted between the prefabricated vertical drains (PVDs) and then connected to the air compressor to produce pressurised air in the slurry. The conductors are generally made of flexible brackets coated with filter cloth, which allows air to flow into the soil. The air pressurisation is generally \(<\)40 kPa and lasts for 2-4 h per day for 5-7 consecutive days. The magnitude of the air pressure distinguishes the air-booster vacuum preloading method from the combined method of vacuum preloading and pneumatic fracturing for soft ground improvement [16], which commonly adopts a much higher air pressure (e.g., 500 kPa).  
A booster tube was adopted in the original version of the air-booster vacuum preloading method [1]. The tube top needs to be buried at a certain distance below the ground surface (see Figure 1) to prevent the pressurised air from entering the membrane sealing (and losing vacuum pressure). Since the booster tube has a circular cross-section, it cannot be driven by the commonly available machines designed for inserting PVDs (with rectangular cross-sections). Recently, an improved version was designed with the booster tube replaced by a PVD plate [13], which can be readily driven by the machine to the desired depth. The booster plate is designed to have dual functions; that is, it is initially connected to a vacuum pump to facilitate vacuum consolidation of the slurry. Later, the connection is shifted to the air compressor to conduct the pressurised air. The effectiveness of the booster plate in promoting vacuum consolidation of the dredged slurry has been confirmed by field tests [13].
## 2 Data Specifications and Preprocessing
For the MR images (Landsat Enhanced Thematic Mapper (ETM), Landsat Thematic Mapper (TM) and Landsat Multi-Spectral Scanner (MSS)), three landcover classes (\"built-up areas\", \"vegetation\" and \"other not built-up areas\") were organized in the two groups and reference datasets were created in the same way through manual labeling of image segments.
# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
## 1 Introduction  
As a cornerstone of global energy production, the oil and gas industry has historically faced challenges in accurately predicting and optimising drilling costs [1]. As exploration and production activities expand into more complex geological formations and extreme environments, the need for sophisticated cost prediction and optimisation techniques becomes increasingly important [2].  
Drilling operations represent a significant proportion of the total expenditure incurred in oil and gas exploration and production. Industry reports indicate that drilling costs can account for up to 50% of the total cost of well development, with this percentage often increasing in challenging environments, such as ultra-deepwater or unconventional reservoirs [3]. The ability to accurately predict and optimise these costs is not only a matter of financial prudence, but also a critical factor in project feasibility, risk management andstrategic decision making [4]. With volatile oil prices and increased environmental scrutiny, the need for cost-effective and efficient drilling operations has never been more pressing.  
Traditionally, the estimation of drilling costs has relied on empirical models and expert judgement [5]. While these methods have proven valuable, they often fail to capture the full complexity of drilling operations, particularly in new or challenging environments. The inherent uncertainty of subsurface conditions, coupled with the dynamic nature of the drilling process, often results in significant discrepancies between estimated and actual costs [6]. Such inaccuracies can lead to project overruns, sub-optimal resource allocation and, in some cases, the abandonment of otherwise viable prospects.  
The advent of big data analytics, machine learning and artificial intelligence has ushered in a transformative era in drilling cost forecasting and optimisation [7]. These technologies can process vast amounts of historical and real-time data, identify complex patterns, and generate insights that were previously unattainable [8]. In the context of shale gas development and exploitation, understanding the cost structure and making dynamic predictions about cost changes is critical [9]. This enables the formulation of effective development strategies and informed decision making.  
The rest of the paper is organised as follows: Section 2 presents the related work on machine learning, respectively; Section 3 analyses the data characteristics of shale gas drilling costs and the existing related machine learning models and proposes a stacked integrated learning scheme based on SVM + LSTM; Section 4 shows the experimental results and analyses them, and finally summarises the work and proposes a further optimisation scheme.
## 2 Measurement System  
Figure 1 shows the schematic of the LD-based system for detecting the geometric errors of machine tools, including a horizontal straightness error \(\delta x\), a vertical straightness error \(\delta y\), a pitch error \(\theta x\) and a yaw error \(\theta y\). The measurement system is composed of a sensor head and a detector.  
A laser beam emitted from an LD is divided into two beams by a beam splitter (BS) after being bent by a mirror (M1). The reflected beam is projected onto a quadrant photodetector (QD1) for measuring \(\delta x\) and \(\delta y\). The transmitted beam is bent by a mirror M2 and projected onto QD2 after being focused by a focus lens (FL) for measuring \(\theta x\) and \(\theta y\). When the stage has error motions in \(\delta x\) and \(\theta x\), the center of QD1 and QD2 will be shifted (\(\Delta x_{1}\) and \(\Delta x_{2}\)) with respect to the laser beam. Therefore, \(\delta x\) and \(\theta x\) can be obtained by:  
\[\left\{\begin{array}{l}\delta x=\Delta x_{1}=K_{\delta x}\frac{(I_{A-1}+I_{D -1})-(I_{B-1}+I_{C-1})}{I_{A-1}+I_{B-1}+I_{C-1}+I_{D-1}}\\ \theta x=\frac{\Delta x_{2}}{f}=K_{\theta x}\frac{(I_{A-2}+I_{D-2})-(I_{B-2}+I_ {C-2})}{(I_{A-2}+I_{B-2}+I_{C-2}+I_{C-2})\cdot f}\end{array}\right., \tag{1}\]  
where \(K_{\delta x}\) and \(K_{\theta x}\) represent the \(X\)-directional sensitivities of QD1 and QD2, respectively, which can be obtained by the calibrations. \(I_{m\cdot i}\) (\(m=A\), \(B\), \(C\) and \(D\); \(I=1\), \(2\)) represents the output current of each quadrant of QD1 and QD2, respectively, and \(f\) is the focal length of the FL.  
Figure 1: Schematic of the LD-based system.
# An Experimental Framework for 5G Wireless System Integration into Industry 4.0 Applications
## 3 Experimental Framework for 5G Integration  
The proposed experimental framework is aimed at providing services to industries, and is expected to be technology-unbiased. In this respect, the main actors of the framework can be, in a first moment, academic institutions interested in cross-disciplinary research on wireless and manufacturing. Initiating a dialogue with the manufacturing sector, and bringing wireless solution to factories, can indeed be mutually beneficial. The manufacturing sector can obtain an unbiased view on the wireless technologies and installation types that would better suit their specific needs. Besides, the manufacturing sector can enrich their vision for a wireless factory by leveraging the knowledge of the scientific trends brought by the academic institutions. Our dialogue with Danish factories has revealed that such unbiased view on wireless technologies is of great value for them. On the other hand, academic institutions can acquire extra knowledge on real factories setups, data traffic and production needs, besides identifying shortcomings of current wireless solutions or manufacturing concepts in addressing the factory demands. This can pave the way of future research activities on both wireless and manufacturing domains, and their integration. In the long term, we foresee opportunities where new business entities can act as middlemen between the manufacturing sector and the wireless sector (vendors and operators) for providing adequate recommendations on solutions and installations which are not tight to specific technologies or equipment/service providers and, thus, this framework can be of relevance for them.  
The proposed framework is composed of: an operational flow that describes the overall sequential actions to be taken to achieve a successful operation of a given I4.0 application over 5G, an industrial research lab environment where the experimentation will take place, and the 5G hardware and software prototypes tools that are used to integrate the 5G system and the machinery associated with the selected industrial use case.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 2 Taxonomy
### IoT Health-Care Services
Embedded Gateway Configuration (EGC): EGC is a compositional facility that is responsible for the connection of nodes (that are specifically associated with patients) in the network, the inter-network (that attaches the essential servers and customers), and more required systems for medical. By a service point of view, the gateway might be emerging with various characteristics, which requires a few common integration features, depending on the particular reason of the conveyed gateway. A good model for an EGC facility is investigated in [42] because of being a component of a universal health-care system. The service permits for computerised along-with intelligent supervising. Embedded Context Prediction (ECP): for the fabrication of context-aware (CA) medical health-care applications on IoT network, third-party designers require bland systems with appropriate components which are known as ECP service. A similar arrangement is produced in [43] with regards to pervasive health-care. Several challenges in CA health-care system are investigated in [44]. Similar challenges for the mentioned system's applications when they are overlaid on IoT-based network are thoroughly discussed, a context predictor is used for remote health monitoring that is based on IoT in [45].
## Appendix B Formulae of Key Performance Indicators (KPIs)  
KPIs are presented below.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**No.** & \multicolumn{2}{c}{**Key Indicator**} & \multicolumn{1}{c}{**Formula**} \\ \hline
1 & \(\sum\) Demand (products) & \(\sum\limits_{t=1}^{n}\)_Demand at time period_\(t\) \\ \hline
2 & On-time delivery (\%) & \(\frac{\sum_{t=1}^{n}}{\sum_{t=1}^{n}Total\)_products delivered_\(\times\) 100\% \\ \hline
3 & Service level (\%) & \(\frac{\sum_{t=1}^{n}}{\sum_{t=1}^{n}}\)_Weoks without product hacklog_\(\times\) 100\% \\ \hline
4 & \(\mathcal{O}\) Customer backlog (products) & \(\frac{\sum_{t=1}^{n}\text{Customer\ backlog\ (products)}}{\sum_{t=1}^{n}t}\). \\ \hline
5 & \(\sum\) Weeks with customer backlog (weeks) & \(\sum\limits_{t=1}^{n}\)_Customer backlog_\((weeks)\). \\ \hline
6 & \(\mathcal{O}\) Stock (products) & \(\sum\limits_{t=1}^{n}\)_Stock at the warehouses at time_\(t\) \\
7 & \(\sum\) Procurement orders (orders) & \(\sum\limits_{t=1}^{n}\)_Procurement orders at time period_\(t\) \\ \hline
8 & \(\sum\) Inventory costs (mil. USD) & \(\sum\limits_{t=1}^{n}\)_Procurement costs_\(+\)_Warehouse Storage costs_\(+\)_Stockout costs._ \\ \hline
9 & \(\sum\) Procurement management costs (mil. USD) & \(\sum\limits_{t=1}^{n}\)_Orders Cost per Order_\(+\)_Warehouse Storage costs_\(+\)_Stockout costs._ \\ \hline
10 & \(\sum\) Procurement costs (USD) & \(\sum\limits_{t=1}^{n}\)_Units_\(\times\)_Cost per Unit_\(+\)_Orders_\(\times\)_Cost per Order_ \\ \hline
11 & \(\sum\) Warehouse storage costs (USD) & \(\sum\limits_{t=1}^{n}\)_Warehouse Storage Cost per Unit_ \\ \hline
12 & \(\sum\) Capital commitment costs (USD) & \(\sum\limits_{t=1}^{n}\)_Inventory value_\(\times\)_Storage time_\(\times\)_Interest rate_ \\ \hline
13 & \(\sum\) Storage costs (USD) & \(\sum\limits_{t=1}^{n}\)_Units_\(x\)_Cost per unit_ \\ \hline
14 & \(\sum\) Stockout costs (USD) & \(\sum\limits_{t=1}^{n}\)_Penalty per unit backlogged_\(\times\)_units backlogged_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Target system: KPIs and formulae.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 3 Energy Management Strategy and Power Distribution Method
### Model Predictive Control  
MPC is an advanced control method that utilizes process models to predict the future dynamic behavior of controlled systems and solves constrained control optimization problems through rolling solutions. Due to its reliability, robustness, and practicality, MPC is widely used in complex industrial processes [38].  
Generally, the state equation and relationship between input and output variables can be expressed as Equation (30):  
\[\begin{array}{l}\dot{x}=Ax+Bu\\ y=Cx+Du\end{array} \tag{30}\]  
where \(A\) and \(B\) stand for the state space matrix and input matrix, and \(C\) and \(D\) are the output matrix and direct transfer matrix, respectively.  
\[\begin{array}{l}\min\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\  
where \(Q\) and \(R\) are the weighting matrices, \(f\left(\cdot\right)\) and \(g\left(\cdot\right)\) stand for the mapping relationship of \(x\), \(u\), \(y\), \(x_{\text{min}}\) and \(x_{\text{max}}\) are the lower and upper boundaries of state variants, \(u_{\text{min}}\) and \(u_{\text{max}}\) are the lower and upper boundaries of input variants, and \(y_{\text{min}}\) and \(y_{\text{max}}\) are the lower and upper boundaries of output variants, respectively.  
In this study, SOC is used as the state references with the input being the vehicle's required power and the output being the fuel cell power. SOC reference is set to 0.5. In order to maintain SOC stability, the \(V_{\alpha,lu}\) can be considered constant when the SOC fluctuates within a small range.  
\[\begin{split}& e_{SOC}(k+1|k)=SOC(k+1|k)-SOC_{ref}\\ &=e_{SOC}(k|k)+\frac{\tau_{d}(P_{FC}(k|k)-P_{load})}{3600c_{lu} \sqrt{V_{\alpha,lu}^{2}-4R_{lu}(P_{load}-P_{FC}(k|k))}}\end{split} \tag{32}\]  
where \(\tau_{d}\) is the time interval (1 s), \(e_{SOC}(k+1|k)\) and \(e_{SOC}(k|k)\) refer to the deviation between the SOC at time \(k\) and \(k+1\) and the reference value, \(SOC_{ref}\) is the SOC reference, and \(SOC(k+1|k)\) represents the SOC value at time \(k+1\), based on the state at time \(k\).  
\[\begin{split}\min\!J=\sum\limits_{i=1}^{N_{p}}\|p_{i}e_{SOC}(k+i )\|^{2}+\sum\limits_{i=1}^{N_{p}}\|q_{i}P_{FC}(k+i)\|^{2}\\ &\qquad\qquad\qquad\qquad\qquad s.t.-0.3\leq e_{SOC}\leq 0.3\\ P_{load}(k)-\delta_{FC}\leq P_{FC}(k+i|k)\leq P_{load}(k)+\delta_ {FC}\end{split} \tag{33}\]  
where \(p\) and \(q\) stand for the weight factor of SOC deviation and \(P_{FC}\) respectively, and \(\delta_{FC}\) is the floating range of \(P_{FC}\) related to \(P_{load}\).  
The battery management system of the lithium-ion battery limits the SOC to remain between 0.2 and 0.85.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 5 Security Analysis
### Security Proof
#### 5.1.2 Privacy-Preserving Negotiation  
**Definition 2** (Atomic Exchange Ideal Functionality).: _The ideal functionality \(\mathcal{F}_{\mathrm{atomic}}\) receives encrypted inputs \(\big{(}\mathsf{enc}(D_{A}),\mathsf{enc}(D_{B})\big{)}\) and reveals \((D_{A},D_{B})\) only upon receiving mutual agreement signatures from both parties._  
**Theorem 2** (UC-Secure Atomic Exchange).: _The staged atomic exchange protocol \(\Pi_{\mathrm{atomic}}\) UC realizes the ideal functionality \(\mathcal{F}_{\mathrm{atomic}}\) against semi-honest adversaries._  
Proof.: Construct a simulator \(\mathcal{S}\) such that for all PPT environments \(\mathcal{E}\), we have the following:  
\[\big{|}\Pr[\mathcal{E}\;\mathrm{outputs}\;1\;\mathrm{in}\;\mathrm{IDEAL}_{ \mathcal{F}_{\mathrm{atomic}},\mathcal{S}}]-\Pr[\mathcal{E}\;\mathrm{outputs}\;1\;\mathrm{in}\;\mathrm{HYBRID}_{ \Pi_{\mathrm{atomic}}}]\big{|}\leq\mathsf{negl}(\lambda).\]  
The simulator \(\mathcal{S}\) achieves this by performing the following:  
* Simulating commitments as \(\mathrm{Com}(0^{\lambda};r)\) to preserve the hiding property.
* Extracting inputs only after receiving valid agree signatures, ensuring the binding property.
# Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
## 3 Materials and Methods
### Wavelet Analysis
#### 3.1.1 2D Discrete Wavelet Transform  
Given an image \(x\), we can use 2D Discrete Wavelet Transform (2D-DWT) with four convolution filters, i.e., low-pass filter \(f_{LL}\) and high-pass filters \(f_{LL}\), \(f_{HL}\), and \(f_{HH}\), to decompose \(x\) into four sub-images, i.e., \(x_{LL}\), \(x_{LH}\), \(x_{HL}\), and \(x_{HH}\)[24]. Note that the four filters have fixed parameters with convolutional stride 2 during the transformation [13; 18]. Taking the Haar wavelet as an example, these filters are defined in Equation (1).  
\[f_{LL}=\begin{bmatrix}+1&+1\\ +1&+1\end{bmatrix},f_{LH}=\begin{bmatrix}-1&-1\\ +1&+1\end{bmatrix},f_{HL}=\begin{bmatrix}-1&-1\\ -1&-1\end{bmatrix},f_{HH}=\begin{bmatrix}+1&-1\\ -1&+1\end{bmatrix} \tag{1}\]  
Moreover, the operation of DWT is defined in Equation (2):  
\[\begin{array}{l}x_{LL}=\left(f_{LL}\otimes x\right)\downarrow_{2},\quad x_{ LH}=\left(f_{LH}\otimes x\right)\downarrow_{2}\\ x_{HL}=\left(f_{HL}\otimes x\right)\downarrow_{2},\quad x_{HH}=\left(f_{HH} \otimes x\right)\downarrow_{2}\end{array} \tag{2}\]  
where \(\otimes\) denotes convolution operator, and \(\downarrow_{2}\) means the standard downsampling operator with factor 2. In other words, DWT mathematically involves four fixed convolution filters with stride 2 to implement the downsampling operator. Moreover, according to the theory of Haar transform [24], the \((i,j)\)th value of \(x_{LL}\), \(x_{LH}\), \(x_{HL}\), and \(x_{HH}\) can be written in Equation (3).  
\[\begin{array}{l}x_{LL}(i,j)=x(2i-1,2j-1)+x(2i-1,2j)+x(2i,2j-1)+x(2i,2j)\\ x_{LH}(i,j)=-x(2i-1,2j-1)-x(2i-1,2j)+x(2i,2j-1)+x(2i,2j)\\ x_{HL}(i,j)=-x(2i-1,2j-1)+x(2i-1,2j)-x(2i,2j-1)+x(2i,2j)\\ x_{HH}(i,j)=x(2i-1,2j-1)-x(2i-1,2j)-x(2i,2j-1)+x(2i,2j)\end{array} \tag{3}\]  
Given that the derivability of the Haar transform is a good property for end-to-end backpropagation [25], Haar wavelet decomposition is used as a pooling layer in the proposed structure.
## 3 Materials and Methods  
### Chemicals  
The industry fatty acid esters (FAME) mix 37 standard for fatty acids analysis was purchased from Bellefonte, PA (USA). The purity of CO\({}_{2}\) used for SC-CO\({}_{2}\) extraction was 99.97% (\(w/w\)) (Messer, Osijek, Croatia). All other chemicals and reagents were of analytical reagent grade and obtained from Sigma-Aldrich (St. Louis, MO, USA) and 3-(\(N\)-morpholino)propanesulfonic acid was purchased from Sigma-Aldrich (Chemie GmbH, Taufkirchen, Germany).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Microorganism**} & \multicolumn{2}{c}{**MIC\({}_{\mathbf{50}}\)**} & \multicolumn{2}{c}{**GIC\({}_{\mathbf{50}}\)**} \\ \cline{2-5}  & **H\({}_{2}\)O Extract** & **DMSO Extract** & **H\({}_{2}\)O Extract** & **DMSO Extract** \\ \hline _Alternaria alternata_ & 500 & 500 & - & - \\ _Aspergillus flavus_ & - & 5000 & 5 & \(5,50\) \\ _Aspergillus ochracues_ & 500 & - & - & 5000 \\ _Fusarium graninearum_ & 5000 & - & - & - \\ _Fusarium verticillioides_ & 500 & - & - & - \\ _Penicillium expansum_ & - & 50 & 5000, 500 & - \\ _Rhizolus_ spp. & - & 5000 & 5000 & - \\ \hline \hline \end{tabular}  
MIC\({}_{50}\)—minimal inhibitory concentration reducing the optical growth for at least 50% of the tested fungi; GIC\({}_{50}\)—minimal growth inducing concentration resulting in at least 50% increase of growth for the tested fungi; all numbers in Table 3 are expressed as concentrations of used extracts in ug/mL; “-” is used when no significant change was observed.  
\end{table}
Table 3: Antifungal (MIC\({}_{\mathbf{50}}\)) and fungal growth inducing (GIC\({}_{\mathbf{50}}\)) effect of tested water (H\({}_{2}\)O) and dimethyl sulfoxide (DMSO) extracts of _C. bursa_ on selected fungal species.