esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 5 Security and Performance Analysis  
### Security Analysis  
This section discusses the security of the MSRLV. First, the purpose of the random value contained in the packet will be explained. Second, the security of the MSRLV will be shown by statistically analyzing theoretical attack situations. Third, the effect of D-filtering on the verification result will be discussed more carefully by considering the potential attackers' behavior. Fourth, problems with sniffing tokens will be discussed. Finally, MSRLV's responses to range-change, impersonation, wormhole and Sybil attacks will be discussed.
# Definition and Determination of Fin Substitution Factors Accelerating Thermal Simulations
## 2 Materials and Methods
### Analytical Formulas
#### 2.2.2 Parameters for Plates  
In general, heat transfer coefficients \(h\) can be calculated with the Nusselt number \(Nu\), the thermal conductivity of the fluid \(k_{fluid}\), and the characteristic length \(L_{spe}\) according to the following formula [14].  
\[h=\frac{Nu\;k_{fluid}}{L_{spe}} \tag{7}\]  
The case study shown in Section 3.3 requires the Nusselt numbers for vertical plates as well as for horizontal plates with both top and bottom heat dissipation. For the correlation of each Nusselt number, a specific Rayleigh number \(Ra\) has to be determined. This is calculated using the fluid-specific variables, density \(\rho\), specific heat capacity \(c_{p}\), thermal expansion coefficient \(\beta\), kinematic viscosity \(\
u\), as well as the acceleration of gravity \(g\) and the temperature difference between the surface \(T_{S,i}\) and the environment using the following formula [13].  
\[Ra_{i}=\frac{c_{p,i}\;g\;\beta_{i}\;\rho_{i}\;(T_{S,i}-T_{\infty})\;L_{spe,i}^{ 3}}{\upsilon_{t}\;k_{fluid,i}} \tag{8}\]  
Every part of the Formula (8) is case-specific. One of these is the characteristic length, which describes the dimension that is decisive for convective heat transfer. With a vertical plate, the characteristic length corresponds to the height of the plate [13].  
\[L_{spe,pp}=L \tag{9}\]  
In the case of a horizontal plate with an upward or downward heat flow, it is calculated according to the following formula [13].  
\[L_{spe,hp}=\frac{L\;W}{2\;(L+W)} \tag{10}\]  
According to [PERSON] and [PERSON] [15], Nusselt numbers for a vertical plate are determined using  
\[\begin{split} Nu_{pp}&=\left(0.825+0.387\left(Ra_{ pp}\left(1+\left(\frac{0.492}{Pr}\right)^{\frac{9}{16}}\right)^{-\frac{16}{9}} \right)^{\frac{1}{6}}\right)^{2}\\ &\text{for }10^{-1}<Ra_{pp}<10^{12}\text{ and }0.001<Pr<\infty\end{split} \tag{11}\]  
A distinction must be made between laminar and turbulent flow for the Nusselt number of a horizontal plate with upward heat flow [16].  
\[\begin{split} Nu_{hp,\mu\;lam}&=0.766\left(Ra_{ hp}\left(1-\left(\frac{0.322}{Pr}\right)^{\frac{11}{20}}\right)^{-\frac{20}{11}} \right)^{\frac{1}{6}}\\ &\text{for }7\times 10^{4}<Ra_{hp}\\ &\text{}Nu_{hp,\mu\;tur}=0.15\left(Ra_{hp}\left(1-\left(\frac{0.3 22}{Pr}\right)^{\frac{11}{20}}\right)^{-\frac{20}{11}}\right)^{\frac{1}{3}}\\ &\text{for }7\times 10^{4}<Ra_{hp}\end{split} \tag{13}\]  
Only a laminar boundary layer flow is expected for a horizontal plate with a downward directed heat flow, even with large Rayleigh numbers. So the Nusselt number is calculated using the following formula [16].  
\[Nu_{hp,n~{}lam} =0.766\bigg{(}Ra_{hp}\bigg{(}1-\big{(}\frac{0.322}{Pr}\big{)}^{\frac{11} {20}}\bigg{)}^{-\frac{20}{11}}\bigg{)}^{\frac{1}{5}} \tag{14}\] \[\text{for}~{}7\times 10^{4}<Ra_{hp}\]
## 4 Learning Scenarios Using Sensors
### Sound  
Sound can easily be observed using the microphone found on every phone. It could be used to create a worldwide sound sensor network for observing the kinds of sound environments we live in. For example, students in classrooms could compare their sound environments against other schools in other countries. The ambient noise may also be an important issue when we think about neighborhoods we would like to live in. Sound pollution is an environmental issue, and it could be visualized using data from mobile phone sound systems. For example, student teams could go around a selected area or city and measure sound levels with their phones. The data could then be uploaded to a server, and a colored layer could be overlaid on a map based on the measurements. One of the existing systems is the WideNoise mobile application and webpage [38]. Again, this is an example of a collective sensor data augmented experience.  
Another application could be the automatic recognition of different sounds. [PERSON], [PERSON] and [PERSON] [39] have studied how inharmonic and transient bird sounds can be recognized efficiently. The results indicate that it was possible to recognize bird sounds of the test species using neural networks with only four features calculated from the wavelet packet decomposition coefficients. A modified version of this kind of automatic sound recognition system could be also used in learning environments. In this case, technology would be used to augment the learning experience--without the technology, it would be difficult to learn the names of the birds singing.  
Furthermore, voice can be used even to control gaming applications. For example, Uplause Ltd. [40] has developed social games for big crowds. Games are designed for large events, where the audience can collectively participate in playing the interactive mini-games, in real-time, on location. Game controllers for the game are the audience's voice--clapping and shouts. Although these games are not yet used in learning, they represent collective sensor data augmented experiences.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 2 Sensor Technologies
### Flow Rate Measurement
#### 2.2.8 Compression Load Cells  
Compression load cells use strain gauge sensors to determine the flow rate and enthalpy in geothermal pipelines and wells. The sensor is based on the piezoelectric effect, which is based on converting the mechanical power applied by the object (pipeline, well) to an electrostatic signal. The load sensor is positioned above a platform (compression type, Figure 4a) or above the object (tension type, Figure 4b) [35; 47].  
The advantages of this method are simple construction, measurement in real time and the lack of direct contact with the fluid medium [35; 47].
## 3 Results
### Three-Satellite Constellation
#### 3.1.1 Representation of Precipitation at RO Observation Times
Therefore, the shortest colored rays (in this case, the ones closer to the N-W corner of Figure 8) represent the highest rays (shorter portions of them are below 6 km), while the longest represent the rays closer to the surface (Figure 9). Figure 8: A simulated three-satellite RO orbit scenario, superimposed upon nearest 30 min IMERG precipitation pattern. (**a**) Representation of an event where two of the RO observations (RO-1 and RO-2; green and orange, respectively) provide profiles that intersect precipitation, while a third (RO-3; blue) profiles the environment outside of the precipitating structure. The colored rays represent a realistic representation of RO rays generated with a ray-tracer and are positioned here for illustration purposes. Only the portion of the rays below 6 km is shown. For each RO, the associated “X” symbol depicts the location of the tangent point. The black dashed line shows a single simplified 300 km ray used to characterize each RO. (**b**) The same RO event as (**a**), depicting the difference that the relative angle (\(\alpha\)) between the azimuth on the surface and the along-track occultations has from the geometry of the observations. In this event, the angle is almost 90\({}^{\circ}\), with an across-RO separation (denoted by d) of about 100 km. (**c**) The same as (**b**), but a different RO event where the relative angle (\(\alpha\)) is around 10\({}^{\circ}\). The effective distance d\({}_{\text{eff}}\) between the rays is reduced to near 20 km, even though the separation between the tangent points d remain nearly the same as event (**b**). Another quantity to take into consideration is the angle between the projection on the surface of the along-ray direction and the line joining the different satellite tangent points (\(\alpha\)). In the right panels of Figure 8, this angle \(\alpha\) is defined as the angle between any of the dashed lines and the gray line. The top and bottom right panels in Figure 8 (Figure 8a,b) highlight the importance of such an angle: in the top panel, \(\alpha\) is around \(90^{\circ}\) and the distance between the rays remains similar to the distance between tangent points, while in the bottom panel, where \(\alpha<10^{\circ}\), the effective distance between rays is significantly reduced. Such effective distance is defined as the distance between rays following a perpendicular line:  
\[d_{eff}=d\cos(90-\alpha) \tag{2}\]  
where \(d\) is the distance between tangent points.
## 3 An Automated Framework for DSM
### Data-Driven Model  
The LSTM is a recurrent neural network model whose cells are structured as presented in Figure 4[27]. The update of the cell state is controlled by forgetting gates, input gates, and output gates. The data in the cell state are decided to be dropped or added at the forgetting gate and input gate, respectively, while the output gate decides the output of the cell state. The LSTM is more suitable for predicting the deformation of dams because of its advantage of being able to effectively use time series data over long distances [27; 28; 29]. The historically  
Figure 3: The flowchart of dynamic inversion.  
monitored data are assumed to be \(D^{S}=\left\{x_{1}^{S},x_{2}^{S},\cdots x_{t}^{S}\right\}\). From Equations (6)-(8), it can be seen that the parameters are calculated during the forward propagation of the model.  
The LSTM model is computed in three main stages:  
The forgetting stage mainly involves the forgetting gate \(F_{t}\), which determines the information that should be discarded or retained from the previous state. \(F_{t}\) activates the \(\sigma\) function based on the output \(h_{t-1}\) of the previous moment and the current memory cell input \(C_{t}\), and outputs the result, which is multiplied by the memory cell state \(C_{t-1}\) of the previous moment to produce a number between 0 and 1, where 0 indicates the complete discarding of the previous state information and 1 indicates the complete retention of the previous state information, calculated using the following formula:  
\[F_{t}=\sigma\big{(}W_{fh}\cdot h_{t-1}+W_{fx}\cdot x_{t}+b_{f}\big{)} \tag{6}\]  
In the selection-memory phase, the input gate \(I_{t}\) decides whether to update the current information into \(C_{t}\), and \(\widetilde{C}_{t}\) generates the output \(h_{t}\) of the next state's implicit layer based on \(h_{t-1}\), \(C_{t}\), and \(tanh\), which is calculated as follows:  
\[\begin{split}& I_{t}=\sigma(W_{ih}\cdot h_{t-1}+W_{ix}\cdot x_{t}+b_{ i})\\ &\widetilde{C}_{t}=tanh(W_{ch}\cdot h_{t-1}+W_{cx}\cdot x_{t}+b_ {c})\\ &\quad\quad C_{t}=F_{t}\bigodot C_{t-1}+\widetilde{C}_{t}\bigodot I _{t}\end{split} \tag{7}\]  
In the output stage, the result of the input gate is multiplied by the \(\sigma\) function to identify what information needs to be output from the memory cell. The result of the output gate \(O_{t}\) is multiplied with the activation function tanh to obtain the final output \(h_{t}\), which is calculated as follows:  
\[\begin{split} O_{t}=\sigma(W_{oh}\cdot h_{t-1}+W_{ox}\cdot x_{t }+b_{o})\\ &\quad\quad h_{t}=O_{t}\bigodot tanh(C_{t})\end{split} \tag{8}\]  
where \(x_{t}\) is the input series at time \(t\); \(W_{fx}\), \(W_{ix}\), \(W_{cx}\), and \(W_{ox}\) are the weight matrices acting on the current input sequence; \(W_{fh}\), \(W_{ih}\), \(W_{ch}\), and \(W_{oh}\) are the weight matrices acting on the last output sequence; \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) are the corresponding bias vectors; \(\widetilde{C}_{t}\) is the cell candidate vector of time \(t\); and \(\bigodot\) is the Hadamard product.
# A Comparison of Electrical Breakdown Models for Polyethylene Nanocomposites
## 3 Results
### Simulation Results of EBEF Model  
A voltage having a ramping rate of \(k_{ramp}\) was applied to the electrodes on both sides of the nanocomposite. As the time \(t\) increases, the voltage \(V_{appl}\) applied to the electrodes on both sides of the nanocomposite increases gradually, that is, \(V_{appl}=k_{ramp}t\). The elec  
Figure 3: Trap density (**a**), trap energy (**b**), and attempt-to-escape frequency (**c**) versus filler content of LDPE/Al\({}_{2}\)O\({}_{3}\) nanocomposites.  
tric field inside the nanocomposite increases gradually, and the charges injected into the material from the electrodes gradually increase. Due to the slow charge transport inside the nanocomposite, the space charges of the same polarity gradually accumulate inside the material. Homogeneous space charges can distort the electric potential and electric field in the nanocomposite. Figure 4 demonstrates the time-dependent changes in space charge distribution and electric field distribution in the pure LDPE and LDPE/Al\({}_{2}\)O\({}_{3}\) nanocomposites. As the voltage increases gradually, the accumulated space charges in all samples increase greatly, and the distortions of the electric fields become stronger and stronger. When the maximum electric field in the material reaches a certain threshold, that is, \(E_{max}\geq E_{C}\), the sample will be broken down. By comparing the experimental results, \(E_{C}\) was set to 290 kV/mm.  
With a small amount of doping, the trap density and energy levels of LDPE/Al\({}_{2}\)O\({}_{3}\) nanocomposites increase. The ability of the traps to capture charges is enhanced, and more mobile charges are captured by the traps in the insulating material near the electrodes. The trapped charges of the same polarity near the electrodes increase, so that the electric field near the electrodes is greatly reduced. It can be seen from the Schottky emission equation that the charge carriers injected by the electrodes into the nanocomposite are greatly reduced at low electric fields. This can decrease the degree of electric field distortion inside the nanocomposite. Under the same voltage, the maximum electric field \(E_{max}\) in the nanocomposite becomes smaller. This increases the breakdown electric field of the nanocomposite. When a large amount of doping is used, the trap density and energy level of the LDPE/Al\({}_{2}\)O\({}_{3}\) nanocomposite gradually decrease, the space charge distribution becomes longer, and the electric field concentration becomes larger, leading to a larger maximum electric field \(E_{max}\) in the nanocomposite. This can lead to a reduction in the breakdown strength of nanocomposites having larger filler contents.
## 3 The Proposed New Method MDA-LETCN
### Domain Adaptation  
In the same wind farm, the bearing models of the WT generators are the same, and their degradation process is the same theoretically. However, the location of each WT and the degradation reasons for bearings are different from the others due to factors such as assembly and lubrication. So, the degradation state of each WT is different. After learning the degradation features from one WT generator bearing, the model is directly applied to the generator bearing of another, and the prediction effect will be worse.  
Before the prediction of a new WT, we can easily obtain the vibration data of the health state of the WT generator bearings. In this paper, the lifetime data used in training are added as the source domain, the predicted bearing is taken as the target domain, and part of the normal data of the target is taken as the training set. The model is transferred to the model available in the target domain through DA between the source domain and the health state of the target domain. Therefore, the model adds DA losses:  
\[L_{DA}\big{(}D_{1}^{\text{S}},D_{1}^{\text{T}}\big{)}=\text{MMD }\big{(}D_{1}^{\text{S}},D_{1}^{\text{T}}\big{)} \tag{18}\] \[=\left|\left|\frac{1}{\left|D_{1}^{\text{S}}\right|}\sum\limits_{ i=1}^{\left|D_{1}^{\text{S}}\right|}\sum\limits_{j=1}^{\left|D_{1}^{\text{S}} \right|}k\Big{(}x_{i}^{s},x_{j}^{s}\Big{)}+\frac{1}{\left|D_{1}^{\text{T}} \right|}\sum\limits_{i=1}^{\left|D_{1}^{\text{T}}\right|}\left|\sum\limits_{j =1}^{\left|D_{1}^{\text{T}}\right|}k\Big{(}x_{i}^{t},x_{j}^{t}\Big{)}-\frac{1} {\left|D_{1}^{\text{S}}\right|}\frac{1}{\left|D_{1}^{\text{T}}\right|}\sum \limits_{i=1}^{\left|D_{1}^{\text{S}}\right|}\sum\limits_{j=1}^{\left|D_{1}^{ \text{T}}\right|}k\Big{(}x_{i}^{s},x_{j}^{t}\Big{)}\right|\right|\]  
where \(x_{i}^{s}\in D_{1}^{\text{S}}\) and \(x_{i}^{t}\in D_{1}^{\text{T}}\) represent the health status data in the source and target domain, respectively, MMD(.) represents MMD function, and \(k\) (, ) represents the kernel function.  
In this paper, the degradation state of WT generator bearings is divided into three stages and the distribution between each two stages is aligned. Therefore, it is necessary to match \((3\times 2)/2=3\) times; then, the final loss function is:  
\[L=L_{r}+\alpha L_{\text{C}}+\frac{1}{3}\beta\sum\limits_{i,j}^{i\
eq j}L_{ MDSDM}(D_{i},D_{i},\lambda)+\gamma L_{DA}\Big{(}D_{1}^{\text{S}},D_{1}^{ \text{T}}\Big{)} \tag{19}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are hyperparameters and \(L_{r}^{t}\) is the regression loss of the training set.
## 4 Results and Discussions
We argue that the curated non-occluded test set is representative of the original test, as it consisted of 300 test samples. The experimental results after hand-picking the problematic samples are tabulated in Table 2. The results show that there was an interesting type of image-ground truth pair inconsistency case, where the human annotator annotated the occluded and thus unseen roads on the map. The DNN model performed as expected, since it output the prediction map solely from the input map. However, this scenario led to a vast deformation in the quantitative assessment, since the evaluation metrics calculated the difference between the ground truth and the output. The results tabulated in Table 2 point out that the DNN model's performance heavily relied on the quality of the ground truth masks that the DNN model was trained on. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Metrics (Overall)** & **Score** & **Non-Occluded Dataset** \\ \hline Accuracy & 98.73 & 49.61 \\ IoU & 41.99 & 40.76 \\ F1 Score & 46.61 & 65.19 \\ Precision Score & 37.94 & 46.42 \\ Recall Score & 58.78 & 49.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of occlusion in the ground truth masks. Figure 11: Normalized confusion matrix. Automatizing the task of road extraction from historical maps still poses several challenges, with occlusion-caused challenges being one of them, which was also examined in this study. According to the results tabulated in Tables 1 and 2, it is possible to conclude that curating a dataset for the supervised classification task requires extra attention. Since there is no common benchmark dataset for the task of road classification from historical maps, comparing the evaluation results of similar studies, especially from the quantitative analysis perspective, would yield incoherent results. By open-sourcing the dataset curated in this study, we aim to propose a benchmark for researchers to further investigate. This, we believe, is the most effective way of pushing the boundaries of the task at hand, as happened with the ImageNet challenges, which have been acting as a testbed for deep learning approaches.
## 4 Discussion
### Absorption and Residual Stress  
There are several sources of attenuation that are independent of grain boundary scattering such as thermoelastic attenuation, lattice defects like dislocations, grain boundaries and inclusions like precipitates. Literature suggests that thermoelastic attenuation is typically orders of magnitude smaller [63] than grain boundary scattering, and therefore we can discount its effects. Attenuation due to dislocation damping scales quadratically with the frequency. This effect is higher at lower frequencies, and minimal above 10 MHz [64]. Based on the Granato-Lucke model [65], attenuation scales to the fourth power of the dislocation loop length (\(\propto L^{4}\)) and linear to the dislocation density. Based on the micrographs, we can hypothesize that the relatively small cell size might result in smaller dislocation loop lengths and therefore a smaller dislocation damping contribution. However, the dislocation density within the cells can be higher for the DED samples, which needs to be experimentally validated.  
The rapid solidification can result in long-range and short-range residual stresses and texture in the structure [66]. This is expected to manifest in the ultrasonic velocity due to acoustoelasticity, i.e., stress-induced velocity changes [67]. In Figure 2a,b, we can observe that the longitudinal velocity and shear velocities along the build directions are different compared to the transverse direction. The presence of texture and residual stress can contribute directly to the anisotropy [51]. This suggests that more studies are needed to fully understand the contributions to ultrasonic velocity in DED structures.
# Nanoplasmonic Sensor Based on Surface Plasmon-Coupled Emission: Review
## 6 Physical Interaction of Surface Plasmon and Fluorophore  
The cross-section of plasmonic-enhanced fluorescence from metal nanoparticle is \(\sigma_{PEF}(\lambda_{L},\lambda,d_{av})\). It depends on the emission of wavelengths (\(\lambda\)), the excitation wavelengths (\(\lambda_{L}\)) and the average distance of molecular fluorophores from the gold thin layer (\(d_{av}\)). The cross-section of plasmonic-enhanced fluorescence is as follows [36; 37; 38]:  
\[\sigma_{PEF}=|M_{EM}|^{2}\times\frac{\sigma_{FL}}{|M_{d}|^{2}}=|M_{1}|^{2} \times|M_{2}|^{2}\times\frac{\sigma_{FL}}{|M_{d}|^{2}} \tag{19}\]  
where \(M_{EF}\), \(M_{1}\) and \(M_{2}\) are the total enhanced fluorescence factor, enhanced fluorescence corresponds to evanescent wave and molecular fluorescence emission, respectively. \(\sigma_{FL}\) is cross section related to fluorophore in air. In SPCE, it was observed that there was a rapid energy transfer between fluorophore and gold thin layer that is defined with \(M_{d}\). Equation (19) expresses that a balance in the increase of excitation rate and radiative decay rate during the procedure of SPCE was achieved. Moreover, the quenching effect of nonradiative energy from fluorophore to gold layer is in equilibrium with transfer  
Figure 5: The surface plasmon-coupled emission set up is presented in two configurations: (**a**) Kretschmann configuration with direct excitation of surface plasmon, the light beam generates the surface plasmon wave and surface plasmon wave will excite the fluorescence; (**b**) revers Kretschmann configuration, the surface plasmon is exited using the fluorescence emitted from molecules and it excites the surface plasmon wave.  
Figure 6: Scheme of surface plasmon wave and penetration depth of evanescent wave.  
energy to gold layer. These parameters depend on penetration of evanescent wave or the distance between the gold thin layer and the fluorophore [38; 39].
## 3 Results and Discussion
### Summary Results of Optimization Algorithms
The MATLAB software platform was used on a PC (RAM: 4 GB, Processor: Intel Core i3 @ 1.2 GHz CPU) to perform a computation of biodiesel yield. The computation time corresponding to the maximum fitness values were recorded and are found to be equal to 0.8 s for BB-BC, 15.06 s for FA, and 1.66 s for GWO, respectively. BB-BC showed lesser computation time than GWO and FA, which might be due to tuning only one algorithm-specific parameter and better exploration capability and search mechanisms; however, GWO is recommended to obtain the maximum biodiesel yield with a better convergence rate of 1.66 s.