esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Novel Proposed Method Application for Planetary Gearbox Fault Diagnosis
### Planet Gear Fault Experiment  
For this experiment, we analyzed one planet gear wear fault mode to test the proposed method while the remaining gears were all in good condition, undamaged. Similar to the case study in Section 4, in order to ensure a stable signal, the data acquisition system is adjusted to use the data sample rate based on the tachometer signal. The adjusted sampling rate is a synchronous sampling rate and donates the resampled vibration signal. During the experiment, the mean rotating frequency of the input shaft connecting the sun gear of the planetary gearbox was 20.0111 Hz, while a load of 405 Nm was applied to the output shaft connecting the planet carrier. An accelerometer was mounted on the top of the gearbox casing (as shown in Figure 19), and the vibration signals were collected from the accelerometer at position S3. According to the planetary gearbox configuration and its running speed, the characteristic frequencies are calculated and listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Meshing Frequency**} & \multicolumn{3}{c}{**Rotating Frequency**} & \multicolumn{3}{c}{**Local Damage**} \\ \cline{2-7}  & **Sun** & **Carrier** & **Planet** & **Sun** & **Ring** \\ \hline
238.8748 & 20.0111 & 1.6361 & 3.7324 & 7.4648 & 55.1249 & 4.9083 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristic frequencies (Hz).  
Figure 23: (**a**) Time-frequency contour of the squared envelope signal using the S transform; (**b**) local amplification of (**a**).  
Figure 24: (**a**) Time-frequency contour of the original envelope signal using the S transform; (**b**) local amplification of (**a**).  
Similarly, we can apply the DC offset cancellation operation to this signal. The CK value shows that the signal only after the first DC offset cancellation iteration has the strongest PIS. The CK value variation vs. the iteration number can be seen in Figure 25. Though the CK achieves highest value at the 10 th iteration, after Iteration 6, the envelope signal is corrupted seriously. Therefore, the first iteration is optimal. The comparison of the squared envelope spectrum and original envelope spectrum is shown in Figure 26. It can be seen that there is no difference in essence, except the amplitude. Because of the very small planet gear fault frequencies, it is very difficult to display the time-frequency contour using the S transform.
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 4 Formulation of Social-Aware D2D Peer Selection Algorithm and Performance Indicators
### Performance Indicators
#### 4.2.4 Energy Efficiency  
We calculate the \(\varepsilon\varepsilon\) of D2D communications between \(Tx\) and \(Rx\) (i.e., \(\varepsilon\varepsilon_{(Tx,Rx)}\)) in small- and macro-cells using the following equation [21]  
\[\varepsilon\varepsilon_{(Tx,Rx)}=\ \frac{R_{(Tx,Rx)}}{P_{(Tx)}+P_{ckt}}- \rho_{(Tx,Rx)}\varepsilon\varepsilon_{Init^{\prime}} \tag{18}\]  
where \(P_{ckt}\) is the circuit power consumption, \(\varepsilon\varepsilon_{Init}\) represents the initial energy efficiency of potential D2D peers which has a constant value given in Table 3, and \(\rho_{(Tx,Rx)}\) is the power consumption co-efficient defined in (19). As in social-aware D2D communications, the transmit power depends on the social relationship strength between the two DUEs, the power consumption coefficient between \(Tx\) and \(Rx\) is modeled as an exponentially decaying function as given by [21]  
\[\rho_{(Tx,Rx)}=\exp(-\delta_{s(Tx,Rx)}SCI_{(Tx,Rx)}) \tag{19}\]  
where \(\delta_{s(Tx,Rx)}\) is the social closeness coefficient between \(Tx\) and \(Rx\), which is generically defined in (14) for both the social and physical parameters.
# Securing SDN-Based IoT Group Communication
## 4 Implementation and Evaluation  
### Implementation  
We implemented our solution using Eclipse's Thingweb node-wot ([[https://github.com/eclipse/thingweb.node-wot](https://github.com/eclipse/thingweb.node-wot)]([https://github.com/eclipse/thingweb.node-wot](https://github.com/eclipse/thingweb.node-wot)) accessed on 5 August 2021) as an IoT endpoint, and libcoap library ([[https://libcoap.net/](https://libcoap.net/)]([https://libcoap.net/](https://libcoap.net/)) accessed on 5 August 2021) for emulating CoAP clients. For the SDN underlay, we relied on the tools presented in [15], i.e., we used the Open vSwitch [16] SDN switch, the POX [17] SDN controller, and we emulated the network using the mininet network emulator [18]. Finally, we used the JSON-LD library ([[https://github.com/digitalbazaar/jsonld-signatures](https://github.com/digitalbazaar/jsonld-signatures)]([https://github.com/digitalbazaar/jsonld-signatures](https://github.com/digitalbazaar/jsonld-signatures)) accessed on 5 August 2021) to generate and verify LDPs.  
In order to not modify libcoap to support the used SDN-based group communication, we developed a CoAP proxy that implements the related protocols; CoAP clients wishing to send a request to a group simply forward their request to the proxy using plain CoAP (see Section 5.7 of [8]). Using this approach, group communication is implemented transparently from the used CoAP library. This is a useful property since it allows the use of our solution even with constrained IoT devices, acting as a CoAP client, although using CoAP libraries with limited functionality.  
We measured the time required to generate and verify an LDP in a desktop PC equipped with an Intel-i5 CPU and 4 GB RAM, running Xubuntu, and a Raspberry Pi 2 Model B Rev 1.1 with a 900 MHz quad-core ARM Cortex-A7 CPU and 1 GB RAM, running Raspberry Pi OS. Table 1 shows the results. The size of the corresponding base64-encoded LDP is 508 bytes.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 6 Conclusions and Future Work  
This paper introduces a novel approach, MCE-EO, designed to determine an optimal set of threshold values for effectively segmenting prostate MRI images. The method is  
Figure 8: _FSIM_ comparison across methodologies. This figure compares _FSIM_ values for different segmentation methods, with each group representing a method and various threshold settings. Higher _FSIM_ values indicate better feature preservation during segmentation. The the best is marked by an asterisk (*).  
Figure 7: Comparative study of _SSIM_ across different methodologies. This figure presents a comparison of _SSIM_ values for various segmentation methodologies. Each group along the horizontal axis represents different methods, with _SSIM_ values evaluated over multiple thresholds. The results highlight the structural similarity performance of each approach, with higher _SSIM_ values indicating better preservation of image structure during segmentation. The the best is marked by an asterisk (*).  
based on the equilibrium optimizer (EO), a stochastic optimization algorithm, and employs minimum cross-entropy as a non-parametric criterion. The efficacy of MCE-EO is assessed through two experiments. The first experiment evaluates the performance of EO in segmenting general-purpose images, while the second focuses on prostate MRI segmentation. In both cases, MCE-EO is compared against several other stochastic optimization algorithms, including the sunflower optimization (SFO) algorithm, sine cosine algorithm (SCA), particle swarm optimization (PSO), differential evolution (DE), the genetic algorithm (GA), and the Hirschberg--Sinclair algorithm (HS). Additionally, statistical significance was assessed using a post-hoc test. The segmentation quality for MRI images was evaluated using key objective quality metrics such as peak signal-to-noise ratio (_PSNR_), structural similarity index Measure (_SSIM_), and feature similarity index (_FSIM_).  
In conclusion, the proposed MCE-EO method for prostate MRI segmentation outperformed several other metaheuristic algorithms, including SFO, SCA, and PSO. In experiments involving standard datasets and transaxial-cut prostate MRIs, MCE-EO achieved superior segmentation accuracy, with higher values of _PSNR_, _SSIM_, and _FSIM_, demonstrating its robustness and effectiveness in handling complex medical images. These results confirm the potential of MCE-EO in improving the early detection of prostate cancer, where accurate image segmentation is critical for diagnosis and treatment planning.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 5 Applications
### Bioanalytics  
Semi-quantitative element analyses of biological samples of tea leaf standard, Laminaria japonica, and pigskin were demonstrated in earlier studies conducted using the ns-LI-O-TOFMS instrument [188]. The detection limit of ug/g levels could be achieved for elemental determination with a solving power of about 5000. Low sensitivities for light bio-elements, such as C, N, H, and O, were observed due to the mass discrimination of cooling cell. Nevertheless, the signals of K and Ca were observed to saturate due to the high concentrations of these elements. Further, in addition to metallic elements, P and S were measured. Trace elements Sr and Ba were detected using their isotopes: \({}^{86}\)Sr and \({}^{135,136,137}\) Ba. However, the RSCs of most other elements were acceptable and the studies can be considered as semi-quantitative. The samples required a simple preparation; the pulverised tea leaf was dried and then pressed to produce a disk, the Laminaria japonica and pigskin were washed by deionised water and dried in a vacuum oven before the measurements.  
In the recent studies, the LI-O-TOFMS system equipped with IR fs-laser ablation ion source was used to study the elemental composition of about 100 \(\upmu\)m long, 40 \(\upmu\)m in width, and few \(\upmu\)m thick single Paramecium cells [189]. ICP-MS studies were undertaken to reference the LIMS results. The mass spectra were measured with the resolving power of 2200, hence little isobaric interference occurred due to clusters. Limits of detections were determined at the femtogram level. Before measurements, Paramecium cells were incubated in the solution for 24 h at 25 \({}^{\circ}\)C. Subsequently, living cells of paramecium were separated from large particles and debris by filtration using two mesh nylon sifters (pore size, approximately 30 and 150 \(\upmu\)m) and gently and quickly washed with ultrapure water. The isolated cells were transferred and concentrated from the mesh onto a clean quartz plate. Replicate analyses showed that signal variations are 15-35% for metallic elements and 25-50% for non-metallic elements. The studies demonstrated that the instrument can conduct fast cell chemical analysis at the semi-quantitative performance.
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
## 5 Energy Entropy and Energy Capacity
### Entropy  
Given a BPSK signalling, the entropy of the binary source \(H_{B}(X)\) is well known [30]  
\[H_{B}(X)=p(s_{1})\log p(s_{1})+p(s_{2})\log p(s_{2}) \tag{22}\]  
where \(X\) is a discrete random variable with possible values \(\{s_{1},s_{2}\}\). The values \(s_{1}\) and \(s_{2}\) can be interpreted as the symbols emitted by the informative source.  
In the proposed Z-BPSK, the zero symbol \(s_{0}\) has to be added to the binary symbols \(s_{1}\) and \(s_{2}\). Thus, the entropy of the Z-BPSK \(H_{z}(X)\) can be defined as  
\[H_{Z}(X)=-P_{0}\log P_{0}-(1-P_{0})\log\frac{1-P_{0}}{2} \tag{23}\]  
where \(P_{0}=P(s_{0})\) is the probability of generation of the zero symbol. The probability of emitting a non-zero symbol is supposed equiprobable \(p(s_{1})=p(s_{2})=\frac{1-P_{0}}{2}\).  
Defining the energy entropy as the number of symbols that a source can emit per energy unit  
\[H_{1}(X)\triangleq\frac{H(X)}{E_{s}}, \tag{24}\]  
we can compare the bit-per-joule curves for the Z-BPSK and the BPSK. The energy per symbol \(E_{s}\) is different for the Z-BPSK and the BPSK (Equation (1))  
\[E_{s}^{[BPSK]} = E_{1} \tag{25}\] \[E_{s}^{[Z-BPSK]} = (1-P_{0})E_{1} \tag{26}\]The result of the comparison is shown in Figure 3. If the probability of emission of the zero symbol increases, the entropy of the Z-BPSK grows significantly over the entropy of the BPSK.
## 6 Simulation of Meshing
### Helical Gear Pair with In-Plane Misalignment  
As with the spur gear model, the TCA of misaligned helical gears was simulated by using a novel type of fundamental three-dimensional tooth surface contact equations presented in this work. To assess the effect of each type of crowning, the TE function, the path of contact, and the area \(S_{c,p,l}\) of the TE functions were determined. A range of parameters was also set to meet certain criteria, just as was the case for the spur gears examined in the previous sections. Table 4 displays the simulation parameters of the helical gears. The axes of rotation of the pinion and gear were parallel during the simulation, and the modification was used only for the involute pinion, while the involute gear remained unchanged. For the helical gear pair with parameters shown in Table 4, an evaluation on the level of crowning required to obtain a continuous TE function for all three crowning types is conducted and presented in Figure 17. Again, it can be observed that an insufficient amount of crowning will lead to a discontinuous TE function and hence should be optimized to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Gear** & **Pinion** \\ \hline Number of teeth & 40 & 37 \\ Module, mm & 1 & \\ Pressure angle, degree & 20 & \\ Helix angle, degree & 14 & \\ Face width, mm & 20 & \\ Center distance, mm & 39.67 & \\ Poisson’s ratio & 0.3 & \\ Modulus of elasticity, GPa & 206 & \\ Total load, N\({}^{\dagger}\) & 200 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation parameters of helical gears. Figure 16: The MPR of crowned spur gears with a minimum amount of circular crowning and tip relief for an out-of-plane misalignment of 0.01\({}^{\circ}\) (0.17 mrad). obtain a contact path close to the center of tooth surfaces that results in a continuous TE function. The findings that led to a continuous TE function for the modification parameter for all three types of crowning are listed in Table 5. It can be seen that the level of crowning required for continuous TE function decreases with an increased level of misalignment. Furthermore, for every modification, the maximum TE (\(\Delta\varphi_{max}\)) is obtained. By comparing the values of the \(\Delta\varphi_{max}\) for every modification and misalignment angle, the logarithmic modification gives the smallest value, followed by the circular and then a parabolic one. As with the model of the spur gear with the in-plane misalignment, the helical gear with in-plane misalignment shows small differences between the TE function of different modifications (Figure 18). To determine the exact difference, the area \(S_{c,p,l}\) of the TE functions was calculated and resulted in the following:  
\[S_{c}=-0.220082;\ S_{p}=-0.223297;\ S_{l}=-0.218083\]
## 1 Introduction
The method proposed here has its foundation in extensive field studies we conducted in the lower basin of the Senegal River (SRB) between 2016 and 2018, where we identified specific vegetation types (Figure 2a) strongly associated with snail distribution and abundance and, ultimately, with increased transmission risk of _Schistosoma haematobium_ (urinary schistosomiasisomiasis) in the SRB [7; 10]. In these studies, we also found that aquatic vegetation serving as suitable snail habitat can be visually identified using high-resolution drone imagery (Figure 2b). Unmanned aerial vehicles, however, have limited autonomy and cannot be operated over large geographical areas, i.e., at the scale at which information on potential transmission hot spots will be most valuable to prioritize intervention strategies. In this paper, we investigated whether, by associating snail habitat data to spectral signatures from drone and satellite imagery (Figure 2c), it is possible to use U-Net, a deep learning algorithm constructed with convolutional neural networks (CNNs) [11], trained on a small set of satellite imagery, to identify snail habitat over regional scales. We also tested whether a more widely used machine learning (not deep learning) approach, i.e., random forest, performed better, equally, or worse than the deep learning approach, with respect to prediction accuracy on hold-out datasets from nearby regions not used in training. Using the SRB as a case study, we show that a system integrating drone and satellite imagery, with deep learning algorithms and information on habitat suitability for the obligate host snails of schistomae parasites, can provide a rapid, accurate, and cost-effective assessment of aquatic vegetation indicative of potential transmission hotspots for urinary schistosomiasisomiasis. This system can be scaled up to generate seasonally updated information on the risk of urinary schistosomiasisomiasis transmission and might be applicable to other sub-Saharan African regions with endemic urinary schistosomiasisomiasis, similar snail-parasite ecologies, and associations among snails and their habitat.
## 2 Results and Discussion
### Algorithm
If this graph comprises all nodes of the query \(A\), then we have found a maximum common graph, or in the terminology introduced above, a full match. The algorithm then continues with Step 4. If a full match has not yet been achieved after merging, the cliques can possibly be extended towards a full match by relaxing the distance tolerances \(\delta\) in Inequalities 2 and 3. Therefore, we loop back to Step 1, and increment the distance tolerance \(\delta_{1}\), e.g. by 0.1 A. It may be that even after several iterations over Steps 1, 2, and 3 no full match can be found. In this case the iteration is stopped after \(\delta_{1}\) or \(\delta_{2}\) have reached a threshold preset by the user. The output to Step 4 is then the largest common subgraph found so far. A nice feature of the iterative approach is that it is possible to define an intuitive measure for the certainty with which a node pair \(a_{i}b_{j}\) is matched. To this end we store for each matched node pair the sum \(S\left(a_{i}b_{j}\right)\) of the number of all cliques in all iterations in which the respective node pair participates. The probability-like score \(P\left(a_{i}b_{j}\right)=\frac{S\left(a_{i}b_{j}\right)}{S_{max}}\) (with \(S_{max}\) the maximum of all \(S\left(a_{i}b_{j}\right)\)) then indicates how certain this pair of nodes is matched. If a match \(a_{i}b_{j}\) occurs already at low distance tolerance \(\delta_{2}\) and is member in many cliques, then \(P\left(a_{i}b_{j}\right)\) will be higher (up to 1), whereas matches \(a_{i}b_{j}\) that turn out only at high distance tolerances or in few cliques will haver lower \(P\)-scores (down to \(\frac{1}{S_{max}}\)). The score \(P\) is also used to eliminate ambiguous or incompatible matches. E.g. if an atom \(a_{i}\) is matched with two different atoms \(b_{j}\) and \(b_{k}\) in two different largest common subgraphs, then the two \(P\)-scores \(P(a_{i}b_{j})\) and \(P(a_{i}b_{k})\) are compared, and the pair with the lowest \(P\)-score is dropped. If several ambiguities occur, then all relevant \(P\)-scores are compared in an analogous way. Figure 2: Schematic of situation in Step 2 after identification of matching cliques. The distance differences not yet investigated in the previous step are tested for fulfillment of inequality 3. Here, these are \(\left|D_{A,23}-D_{B,45}\right|\), \(\left|D_{A,34}-D_{B,52}\right|\), \(\left|D_{A,24}-D_{B,42}\right|\), the differences of the lengths of the corresponding bold edges. After having passed this test it is guaranteed that all corresponding edges of the two complete graphs (cliques) have distance differences of \(\delta_{2}\) or less. The stars around \(a_{6}\) and \(b_{7}\) in Figure 1 have not passed the test and have been dropped.
# Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds
## Appendix A Preliminaries on Fuzzy Sets  
The basic concepts of fuzzy numbers and their mathematical operations are presented in the following. [PERSON] defined the fundamental concepts of fuzzy logic and sets in [32]. A fuzzy number \(A\) is a fuzzy set, and its corresponding membership function \(\mu_{A}(x)\) must hold the following properties,  
* \(\mu_{A}(x):\mathbb{R}\rightarrow[0,1]\), which means that it is a continuous and normalized fuzzy set.
* For exactly one element \(x_{0}\), \(\mu_{A}(x_{0})=1\).
* \(\mu_{A}(x)\) is a convex fuzzy set.  
In modified fuzzy VIKOR, we use positive Triangular Membership Functions (TMF), as shown in Figure 11, which are defined as,  
\[\mu_{A}(x)=\begin{cases}\frac{x-l}{\dot{m}-l}&\text{if }x\in[l,m]\\ \frac{u-x}{\dot{m}}&\text{if }x\in[m,u]\\ 0&\text{otherwise}\end{cases} \tag{11}\]  
Figure 11: Triangular membership functions.  
Assuming that \(l\leq m\leq u\), a fuzzy number is denoted as the triplet \(A=(l_{A},m_{A},u_{A})\). The following mathematical operation between fuzzy numbers is defined according to [28],  
\[A\oplus B =(l_{A}+l_{B},m_{A}+m_{B},u_{A}+u_{B}), \tag{11}\] \[A\ominus B =(l_{A}-u_{B},m_{A}-m_{B},u_{A}-l_{B}),\] (12) \[A\otimes B =(l_{A}*l_{B},m_{A}*m_{B},u_{A}*u_{B}),\] (13) \[A\odot B =(l_{A}/u_{B},m_{A}/m_{B},u_{A}*l_{B}). \tag{14}\]  
The comparison of fuzzy numbers is not straightforward. The most common comparison method is the defuzzification of these numbers; converting them into crisp values. Many defuzzification methods have been proposed in the literature. Adopting the defuzzification approach of [25], the crisp value \(\hat{A}\) of the fuzzy number A is defined as,  
\[\hat{A}=\frac{l+4m+u}{6} \tag{15}\]
## 4 Experimental Evaluation and Analysis
### Quantitative Analysis

Table 1: RMSE values for vehicle trajectory prediction using I-80 and US-101 datasets. Data are converted into meters. The performance of models denoted by \({}^{*}\) is published in [21; 22]. All results except ours are extracted from previous studies. The smaller the RMSE value, the better the prediction accuracy. Figure 6: Prediction errors of different models.