esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward a Trust Evaluation Mechanism in the Social Internet of Things
## 4 REK Trust Evaluation Model in the SIoT
### Experience TI Evaluation Model
* _Experience Decrease (in case of an uncooperative interaction occurs)_: The mathematical model for the Experience Decrease is as following: \[Experience_{t+1}=Max\langle min_{Experience},\;Experience_{t}-\beta\times \Delta Experience_{t+1}\rangle\] (8) where \(\Delta Experience_{t+1}\) is specified as in Equation (2); and \(\beta\) is as a damping factor controlling the rate of the decrease. The \(\beta\) parameter can be fixed or dynamic depending on situations, but it should be always greater than 1 because the experience relationship is hard to gain but easy to lose. \(min_{Experience}\) is a parameter indicating the minimum value of the experience (i.e., 0), which guarantees that the experience value cannot go lower than that. * _Experience Decay (in case of no interaction)_: Experience TI decreases if there is no interaction during a period of time. However the rate of the decrease may vary according to the level of current status of the relationship (i.e., the current experience value). If the current status is high (meaning that there is a strong tie between two entities) then the decrease is not much; but if current status is low (i.e., a weak tie between the two) then the decrease is much. Hence, experience is assumed to require periodic maintenance but strong ties tend to persist longer even without reinforcing cooperative interactions. Decay is assumed to be inversely proportional to the current experience value; thus, experience with a high value will exhibit less decay than experience with a low value. Then, the mathematical model for the Experience Decay is proposed as following:  
\[Experience_{t+1}=Max\big{<}min_{Experience},\ Experience_{t}-\Delta decay_{t+1} \big{>} \tag{9}\]  
\[\text{where }\Delta decay_{t+1}=\delta\times\left(1+\gamma-\frac{Experience_{t-1} }{max_{Experience}}\right) \tag{10}\]  
The \(\delta\) is a parameter indicating the _minimal decay value_ of Experience which guarantees that even strong ties still get decreased if experience is not maintained. \(\gamma\) is a parameter indicating the _rate of decay_ which can be fixed or dynamic depending on particular situations. According to the Experience TI model, in order to obtain a high experience value (i.e., a strong tie between a trustor and a trustee), it is required to have many cooperative interactions in a short duration of time. And when it gets high, it is not easy to decay as time goes by. However, uncooperative interactions can highly damage the experience relationship, especially when the current state is not strong. This is similar to what happens in the real human world, thus, we believe the proposed Experience TI model can effectively migrate the experience relationship from human sociology environment to entities in the SIoT.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 5 Results
### Value Distribution  
The Shapley value has been evaluated considering the producers and the group of consumers as actors. The producers receive only a part of the profit of the investment, since part of it goes to the consumers. In Figure 12, the global value of the investment for the community is depicted in blue, while the cash flow of the producers is shown in red. It is possible to see that the initial investment of the community is exactly the investment of the producers, but then the community obtains higher profits that are only partially shared with the producers. This means the payback time is higher than the one that could be achieved allocating to the producers the entire value generated by the community (+44.4%). In a similar way, the net present value of the investment for the producers is lower than the global one achieved by the community (37,204 EUR, \(-\)56.7%), with a profitability index equal to 1.36 (\(-\)0.48). The difference between the value of the community and the value allocated to the producers is distributed to the consumers. The distribution amongst the group of users is obtained via the Shapley value, then it is divided proportionally between the number of users within the group. In Figure 13, the yearly revenue obtained by each consumer that is taking part to the community is depicted. The revenues depend on the amount of energy consumed but also on the way to consume it and, on average, each user achieves a saving of 32.1 EUR per year. It is important to notice that users can obtain this value without any economical effort (not considering the yearly contribution to cover administrative expenses, set in this case to 5 EUR). This is aligned with one of the primary purposes of the energy community as characterised in the aforementioned REC definition; that is \"to provide economic or social community benefits for its shareholders or members or for the local areas where it operates\". Furthermore, this could be a way to fight energy poverty \"assessing the possibility to enable participation by households that might otherwise not be able to participate, including vulnerable consumers and tenants\" [1].  
Figure 11: Cash flow contributions.  
Figure 10: Optimal installed power for each generator’s availability.  
Figure 12: Investment cash flows obtained by the producers applying redistribution rules.  
Figure 13: Yearly revenue obtained by each consumer applying redistribution rules.
## 1 Introduction
Filler metals containing rare earth elements have been successful in directly bonding notoriously unweldable materials such as ceramics, composites, silicon, graphite and micro-arc oxidized titanium and aluminum alloys [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] in air. Sn-xZn-0.1 RE (x = 5, 10, 20 and 30 wt.%) fillers had the same solidus temperature of about 199 \({}^{\circ}\)C, corresponding to the eutectic temperature of Zn-Sn binary alloys. Endothermic peaks were found in DSC curves of Sn-5 Zn-0.1 RE, Sn-10 Zn-0.1 RE, Sn-20 Zn-0.1 RE, and Sn-30 Zn-0.1 RE alloys at 203.5, 202.4, 205.8 and 206.8 \({}^{\circ}\)C, respectively [32]. The Sn-10 Zn-0.1 RE alloy near the Sn-9 Zn eutectic composition exhibited the lowest melting point. The melting point of the hypoeutectic Sn-5 Zn-0.1 RE alloy and of hypereutectic Sn-20 Zn-0.1 RE and Sn-30 Zn-0.1 RE alloys increased with increasing zinc content. All of the microstructures of Sn-Zn-RE alloys consisted of \(\beta\)-Sn solid solution matrix phase and Zn rich phase. The microstructure of the hypoeutectic Sn-5 Zn-0.1 RE alloy mainly consisted of the \(\beta\)-Sn phase and Sn-Zn eutectics. The hypereutectic Sn-10 Zn-0.1 RE alloy had alternate distributions of Sn-Zn eutectic phase and needle-like Zn-rich phase. The microstructures of hypereutectic Sn-20 Zn-0.1 RE and Sn-30 Zn-0.1 RE alloys exhibited coarse clusters of Zn and needle-like Zn particles embedded in the \(\beta\)-Sn solid solution matrix phase. The microstructures of Sn-Zn solders were refined by adding rare earth elements. In comparison to the study of [PERSON] et al. [12], the size of the \(\beta\)-Sn solid solution matrix phase of hypoeutectic Sn-Zn alloy decreased from 70-100 \(\upmu\)m to 15-45 \(\upmu\)m with the addition of rare-earth elements. Furthermore, the number and size of coarse Zn-rich phase in hypereutectic Sn-Zn alloys decreased significantly with the addition of trace amounts of rare earth elements [32]. The present work investigates the interfacial reactions and solderability in air of AZ31 magnesium and 6061 aluminum alloy joints with Sn-Zn filler metals containing rare-earth elements. The influence of Zn content and thermal aging duration on the interfacial microstructures and mechanical properties of the joints were also studied in detail.
## 4 Results Analysis
### Surface Settlement Analysis  
Figure 8 shows the horizontal lateral surface settlement curve at Y = 40 m when the construction of pipe jackings I and II is completed, and this soil section is located in the range of pile foundation. It can be concluded that due to the influence of the viaduct piles and bearing platforms, the surface settlement curve of this section changes significantly, mainly in the bearing platform area, as there is a more obvious uneven settlement. The bearing platforms in pipe jackings I and II's construction completion have vertical maximum bulge amounts of 1.0 mm and 2.6 mm, respectively. When excavating in the pipe jacking excavation area, the maximum settlement position is at the center axis of pipe jacking I, and it gradually travels to the two tunnels. The maximum settlement position gradually moves to the central axis of the two tunnels during the building of pipe jacking II. The highest settlements at the conclusion of pipe jackings I and II's construction are 11.9 mm and 14.9 mm, respectively. The settling tank's width also expands from 4D to 5D, with a large rise in surface uplift and settlement.  
Figure 7: Differential deformation of pipe section under different cases.  
The ground surface's longitudinal settlement curve is depicted in Figure 9 after pipe jacking II's side penetration pile foundation is built. The surface settlement curve changes significantly near the interface between the reinforcement zone and the normal advancement zone as a result of the stronger soil in the reinforcement zone.  
In the normal advancement zone, the surface settlement above pipe jacking II is smaller than that of pipe jacking I, which is due to the fact that the construction of pipe jacking I puts the soil layer above pipe jacking II in an upward uplift and then a downward settlement due to the excavation of the soil body and the loss of the ground layer in the construction of pipe jacking II. The biggest longitudinal surface settlement occurs at the axis of the two tunnels, and the maximum settlement value--14.9 mm--is found at Y = 40 m, as illustrated in the graphs.
## 4 Results
### SAM vs. SAM 2
#### 4.1.6 Impact of SAM 2 on Clinical Workflows  
Manual segmentation of medical images is a time-intensive process that requires the intervention of highly experienced specialists. Recent studies have evaluated the time required to segment anatomical structures in various types of medical images. For instance, in the segmentation of 100 images from nine different modalities, three doctors with over 10 years of experience performed the annotation, requiring an average time of 4.27 min per image [27]. Similarly, in another study in which they used COVID-19 images obtained  
Figure 10: SAM vs SAM 2 time analysis.  
via CT, the manual segmentation of a CT scan consisting of 250 slices took a total time of \(400\pm 45\,\mathrm{min}\), which translates to an average of \(1.6\,\mathrm{min}\) per slice [28]. This process was conducted in three phases: preliminary annotations were performed by junior specialists, then refined by more experienced radiologists, and finally, verified by a senior radiologist.  
The integration of SAM in medical image segmentation has significantly reduced annotation time without compromising segmentation quality. In the annotation of medical images across various modalities, SAM decreased the average annotation time from 4.27 to \(2.96\,\mathrm{min}\) per image while also improving annotation quality, as measured by the Human Correction Effort (HCE) index [27], decreasing the HCE value from 5.07 to 4.80. Likewise, in the study of COVID-19 images in CT, where manual segmentation of a single slice took an average of \(1.6\,\mathrm{min}\)[28], SAM reduced this time to \(\sim\)1 s per slice, as illustrated in Figure 10, representing a reduction of over 96%. These findings highlight that incorporating SAM into medical image segmentation not only enhances process efficiency but also alleviates the annotation workload for specialists.  
Although the prediction time of SAM 2 is approximately ten times higher than that of SAM, the total segmentation time in both cases is \(\sim\)1 s. This implies that, within the segmentation process carried out by the medical specialist, which may take several minutes, the impact of SAM 2's segmentation time is negligible. It is important to emphasize that after SAM 2 automatically segments the image, the medical specialist must refine the segmentation outcome obtained by the model based on their expert knowledge and the specific clinical context. In this regard, SAM 2 cannot replace the experience of medical professionals in annotation or diagnostic processes; rather, it serves as an assistive tool that streamlines their workflow, reduces annotation time, and enhances the quality of segmented images through its segmentation capabilities.
## 3 The Proposed IAVMD Method
### The Proposed IAVMD Method  
As mentioned before, the advantages of waveform matching extension are utilized by this paper for signal pretreatment. The inside parameters (i.e., \(\hat{a}\) and \(K\)) of VMD are determined beforehand by GWO, which can make the algorithm adaptive and practicable. One advantage of TEO is that the edge errors of HT can be avoided in the signal demodulation process. Hence, by merging together several procedures, a novel TFA method called IAVMD is formulated in this paper. Figure 7 shows the flowchart of the proposed method. For a multi-component signal, the realization process of the proposed IAVMD method is as follows:  
_Step 1: Preparations of IAVMD._ Use the waveform matching extension illustrated in Figure 1 to stretch the left and right boundary points of the multi-component signal.  
Figure 6: Flowchart of the parameter optimization of VMD using the GWO algorithm.  
_Step 2: Parameter optimization automatically_. Introduce GWO to adaptively search the optimal inside parameters (\(\hat{\alpha}\), \(K\)) of VMD according to steps (1) to (5) of Section 3.2.  
_Step 3: Signal decomposition_. Apply the parameter optimization VMD method to decompose the extended signal acquired by step 1 into several IMF components shown in Equation (3), and truncate the extended data points of each IMF component to obtain the final decomposition results.  
_Step 4: Signal demodulation_. Perform TEO to estimate the IF and IA of each IMF component, and plot the IF of all IMF components together to get the ultimate time-frequency representations of the original signal. Observe and identify the frequency contents displayed in TFR, and output the fault diagnosis results.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology
### Rock-Fall Prediction Model
#### 4.3.3 Model Performance Validation  
In this section, the overall model performance (recall, specificity, precision, F1-score, and accuracy), as well as the mean squared error (MSE) and area under a receiver operating characteristic (ROC) curve (AUC), were used to validate the model's ability to distinguish between the occurrence of a rock-fall and a non-rock-fall event. The system's performance was calculated using the confusion matrix [33]. The first metric is recall, which is referred to as sensitivity or the true-positive rate. The following calculation measures how well the model predicted the rock-fall event:  
\[\text{Recall}=\frac{TP}{TP+FN}\times 100\% \tag{6}\]  
The second metric, specificity, is used to assess a system's capacity to verify the absence of a rock-fall event, which is described as:  
\[\text{Specificity}=\frac{TN}{TN+FP}\times 100\% \tag{7}\]  
The third metric is precision, it is used to determine how many samples really fall into the positive class out of all those that the model projected would.  
\[\text{Precision}=\frac{TP}{TP+FP}\times 100\% \tag{8}\]  
The fourth metric is F1-score, is used as a model's predictive power assessment. The F1-score is derived by combining the accuracy score and the recall score of a model, and its definition is as follows:  
\[\text{F1 score}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{9}\]  
The fifth metric is accuracy, which is a reflection of how accurately the system can identify the rock-fall event, its defined as follows:  
\[\text{Accuracy}=\frac{(\text{TN}+\text{TP})}{(\text{FP}+\text{FN}+\text{ TN}+\text{TP})}\times 100\% \tag{10}\]  
where true positive (TP) indicates that all events were indeed discovered, false negative (FN) indicates that some events took place but went undetected, and true negative (TN) indicates that no events took place. A false positive (FP) event is absent, yet the system records it as present. The system reports that the event is absent.  
Equation (11) uses the mean squared error (MSE) to calculate the average squared difference between the values of real and predicted data points in order to quantify the degree of error in the learning model.  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}-\hat{Y}_{i}\right)^{2} \tag{11}\]  
where n is the total number of data points in the dataset, Yi is the actual data point values, and i is the projected data point values. The overall performance of a prediction is measured using the area under a receiver operating characteristic (ROC) curve (AUC), which is based on a confusion matrix.
## 4 Genetic and Ontogenetic Factors  
### Chemodiversity between Thymus Taxa  
The essential oil composition within the _Thymus_ genus displays significant variability. In the study conducted by [PERSON] et al. [79], _Thymus moesicaus_, _T. jankae_, _T. vandasii_, _T. longicanulis_, and _T. sibthorpii_ share linalool as the primary component, but show variations in the concentrations of other prominent components such as geranyl acetate, geraniol, and linalyl acetate. [PERSON] et al. [80] identified thymol, p-cymene, eucalypt, camphor, \(\alpha\)-pinene, and carvacrol as the main components in the essential oils of _Thymus pectinatus_, _T. convolutus_, and _T. vulgaris_. Furthermore, _Thymus atticus_, _T. leucotrichus_, and _T. striatus_ are characterized by high amounts of sesquiterpenoids, primarily \(\beta\)-caryophyllene, while _T. thracicus_ exhibited a mixed mono-/sesquiterpene chemotype with \(\beta\)-myrce, cis-sabinene hydrate, \(\tau\)-cadinol, and elemol as its major components [9; 79]. _Thymus kotschyanus_ var. _kotschyanus_ and var. _glabrescens_ reveal distinct chemotypes, characterized by major compounds such as thymol, carvacrol, p-cymene, and \(\gamma\)-terpinene [81]. Similar findings regarding these components were reported by [PERSON] et al. [82] in the case of _Thymus capitatus_.  
Moreover, different chemotypes were identified in _Thymus pannonicus_, each characterized by specific dominant compounds. Some chemotypes showed main monoterpenes like thymol, p-cymene, and \(\gamma\)-terpinene, while others contained compounds such as linalool, ggeraniol, and linalyl acetate. Sesquiterpenes, including germacrene-D, \(\beta\)-caryophyllene, and \(\beta\)-bisabolol, are also present in certain chemotypes [83]. Lemon thyme, scientifically identified as _Thymus citridorus_, exhibits distinct chemical constituents, as evaluated by  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **ISO 19817:2017** & **Ph. Eur. 11.0, 10,000 (01/2023)** \\ \hline
**Component** & **Minimum (\%)** & **Maximum (\%)** & **Minimum (\%)** & **Maximum (\%)** \\ \hline \(\alpha\)-Thujene & 0.50 & 1.50 & 0.20 & 1.50 \\ \hline \(\alpha\)-Pinene & 0.50 & 2.50 & ns & ns \\ \hline \(\beta\)-Myrcene & 1.00 & 2.80 & 1.00 & 3.00 \\ \hline \(\alpha\)-Terpinene & 0.90 & 2.60 & 0.90 & 2.60 \\ \hline \(\gamma\)-Terpinene & 4.00 & 13.00 & 4.00 & 12.00 \\ \hline p-Cymene & 14.00 & 28.00 & 14.00 & 28.00 \\ \hline Linalool & 0.50 & 6.50 & 1.50 & 6.50 \\ \hline Terpinen-4-ol & 0.10 & 2.50 & 0.10 & 2.50 \\ \hline Thymol & 35.00 & 55.00 & 37.00 & 55.00 \\ \hline Carvacrol & 0.50 & 5.50 & 0.50 & 5.50 \\ \hline \(\beta\)-Caryophyllene & 0.50 & 4.00 & ns & ns \\ \hline trans-Sabinene hydrate & \textless{}0.01 & 0.50 & ns & ns \\ \hline Carvacrol methyl ether & 0.10 & 1.50 & 0.05 & 1.50 \\ \hline \end{tabular}
\end{table}
Table 1: Chromatographic profile standards of the essential oil of _Thymus vulgaris_ and _Thymus zygis_ (thymol type) as mentioned in ISO 19817:2017 [77] and in Ph. Eur. 11.0, 10,000 (01/2023) [78].  
[PERSON] et al. [84]. Noteworthy compounds include geraniol (36.6%), geraniol (17.5%), nerol (11.5%), and neral (10.2%).
## 3 Methodology
### Numerical Discretisation  
The computational domain in Figure 2 was discretized using a tetrahedral mesh. For the fluid domain, a relatively coarse tetrahedral mesh 3 mm in size was applied. This was conducted since the main area of focus in this research study is the heat transfer on the drill bit and workpiece. Hence, it was important to refine the mesh and the tip of the drill bit and workpiece. Accordingly, the drill bit and workpiece tip meshed with an element size of 0.32 mm and 0.4 mm, respectively. Similarly, the remaining body of the drill bit and the workpiece meshed with an element size of 0.65 mm and 1 mm, respectively.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Cutting Fluid** & **Density (kg/m\({}^{3}\))** & **Dynamic Viscosity (kg/m-s)** & **Thermal Conductivity (W/m-K)** & **Specific Heat (J/kg-K)** \\ \hline BASE FLUID-Mineral Oil & 856.5 & 0.06380925 & 0.1335 & 1918 \\ Al\({}_{2}\)O\({}_{3}\) (1%)-MWCNT (1%) & 904.75865 & 0.07078726 & 0.141672463 & 1891.6553 \\ Al\({}_{2}\)O\({}_{3}\) (2%)-SWCNT (2%) & 952.3946 & 0.07849515 & 0.150185403 & 1865.5412 \\ Al\({}_{2}\)O\({}_{3}\) (3%)-SWCNT (3%) & 999.40785 & 0.08670424 & 0.159060549 & 1839.6577 \\ Al\({}_{2}\)O\({}_{3}\) (4%)-SWCNT (4%) & 1045.7984 & 0.0955383 & 0.168321521 & 1814.0048 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermophysical and mechanical properties of mineral oil dispersed with Al\({}_{2}\)O\({}_{3}\)-SWCNT.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Cutting Fluid** & **Density (kg/m\({}^{3}\))** & **Dynamic Viscosity (kg/m-s)** & **Thermal Conductivity (W/m-K)** & **Specific Heat (J/kg-K)** \\ \hline BASE FLUID-Mineral Oil & 856.5 & 0.06380925 & 0.1335 & 1918 \\ Al\({}_{2}\)O\({}_{3}\) (1%)-Cu (1%) & 968.08865 & 0.07583949 & 0.14165834 & 1891.2553 \\ Al\({}_{2}\)O\({}_{3}\) (2%)-Cu (2%) & 1079.0546 & 0.0889343 & 0.150155968 & 1864.7412 \\ Al\({}_{2}\)O\({}_{3}\) (3%)-Cu (3%) & 1189.39785 & 0.10318694 & 0.159014499 & 1838.4577 \\ Al\({}_{2}\)O\({}_{3}\) (4%)-Cu (4%) & 1299.1184 & 0.11869949 & 0.168257425 & 1812.4048 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermophysical and mechanical properties of mineral oil dispersed with Al\({}_{2}\)O\({}_{3}\)-Cu.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Cutting Fluid** & **Density (kg/m\({}^{3}\))** & **Dynamic Viscosity (kg/m-s)** & **Thermal Conductivity (W/m-K)** & **Specific Heat (J/kg-K)** \\ \hline BASE FLUID-Mineral Oil & 856.5 & 0.06380925 & 0.1335 & 1918 \\ Al\({}_{2}\)O\({}_{3}\) (1%)-MWCNT (1%) & 894.75865 & 0.0709487 & 0.141671272 & 1895.3653 \\ Al\({}_{2}\)O\({}_{3}\) (2%)-MWCNT (2%) & 932.3946 & 0.07684677 & 0.150182921 & 1872.9612 \\ Al\({}_{2}\)O\({}_{3}\) (3%)-MWCNT (3%) & 969.40785 & 0.08410158 & 0.159056666 & 1850.7877 \\ Al\({}_{2}\)O\({}_{3}\) (4%)-MWCNT (4%) & 1005.7984 & 0.09189906 & 0.168316115 & 1828.8448 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermophysical and mechanical properties of mineral oil dispersed with Al\({}_{2}\)O\({}_{3}\)-MWCNT.