esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
## 2 System Model and Proposed Technique
### Optical Underwater Ranging  
Optical light passing through the aquatic medium suffers from widening and attenuation in angular, temporal, and spatial domains [28]. The widening and attenuation of the underwater optical signals are dependent on the wavelength. Combining the absorption coefficient \(a(\lambda)\) and scattering coefficient \(s(\lambda)\) results in the extinction coefficient [29] defined as  
\[e(\lambda)=a(\lambda)+s(\lambda). \tag{5}\]  
The propagation loss is the function of distance \(d_{ij}\) and extinction coefficient \(e(\lambda)\) and is given by  
\[\mathbf{\upmu}_{ij}=\exp^{-e(\lambda)d_{ij}}. \tag{6}\]  
In this paper, we consider line of sight communication, where the sensor node \(i\) directs the optical light to sensor node \(j\). Then, the received power at sensor node \(j\) is given as [30],  
\[P_{r_{j}}=P_{t_{i}}\eta_{i}\eta_{j}\mathbf{\upmu}_{ij}\frac{A_{j}\cos\theta}{ 2\pi d_{ij}^{2}(1-\cos\theta_{0})}, \tag{7}\]  
where \(P_{t_{i}}\) is the optical power transmitted by node \(i\), \(\eta_{i}\) and \(\eta_{j}\) are the optical efficiencies of node \(i\) and \(j\), respectively, \(A_{j}\) is the aperture area of node \(j\), \(\theta\) is the angle between trajectory of node \(i\) transmitter and node \(j\), and \(\theta_{0}\) is the divergence angle of the transmitted signal. The most common modulation method for optical wireless communications in the literature is the intensity modulation with direct detection (IM/DD). The bit error rate (BER) expression for IM/DD with on-off keying is developed for  
Figure 1: Acoustic transmission loss vs. distance  
the number of a photon arriving at the photon counter using Poisson model. The number of photons arriving at node \(j\) in time duration \(T\) is  
\[\varrho=\frac{P_{r_{j}}\eta_{j}\lambda}{TR_{d}h\epsilon^{\prime}}, \tag{8}\]  
where \(R_{d}\) is the data rate, \(h\) is the Planck's constant and \(\hat{c}\) is the free space speed of light. The BER for the photons arriving at node \(j\) is expressed as  
\[b_{j}=\frac{1}{2}\text{erfc}\left(\sqrt{\frac{T}{2}(\sqrt{r_{1}}-\sqrt{r_{0}}) }\right), \tag{9}\]  
where \(r_{1}=r_{d}+r_{j}+r_{b}\) and \(r_{0}=r_{d}+r_{b}\) represent the photons required for transmission of binary 1 and 0 respectively, while \(r_{d}\) is the dark count noise and \(r_{b}\) is the noise produced due to background illumination. Substituting the values of \(r_{1}\) and \(r_{0}\) in (9) and solving for \(r_{j}\) yields  
\[r_{j}=\left(\sqrt{r_{d}+r_{b}}+\sqrt{\frac{2}{T}}\text{erfc}^{-1}(2b_{j}) \right)^{2}-r_{d}-r_{b}. \tag{10}\]  
Substituting (7) and (8) in (10), then estimated optical distance \(\tilde{d}_{o_{ij}}\) between node \(i\) and \(j\) is obtained as [30],  
\[\tilde{d}_{o_{ij}}=\frac{2\cos\theta}{e(\lambda)}W_{0}\left(\frac{e(\lambda)} {2\cos\theta\sqrt{\frac{2\pi ThcR_{d}r_{j}(1-\cos\theta_{0})}{\eta_{j}\lambda r _{j}\eta_{j}\lambda\cos\theta}}}\right), \tag{11}\]  
where \(W_{0}(.)\) is the real part of Lambert \(W\) function.
## 1 Introduction  
Permeable sandstone carrier is the most common oil and gas migration channel in elastic rocks [1, 2, 3]. Traditional research on oil and gas migration and accumulation regards the oil and gas carrier layer on a basin scale as homogeneous and does not affect the direction of oil and gas migration [4, 5]. In recent years, studies have shown that differences in tectonic background, and sedimentary and diagenetic environments lead to changes in sandstone lithofacies and heterogeneity. The transportability of sandstone shows spatial heterogeneity, which in turn affects the migration and accumulation of oil and gas [6, 7, 8, 9], making the migration paths and accumulation of oil and gas inside the carrier unevenly distributed [10, 11, 12, 13]. The sedimentary stratigraphic interface, rock structure and sedimentary structure of elastic rock sedimentary strata control the heterogeneity of sandstone reservoirs. The differences in diagenesis controlled by sedimentary structure and structure further strengthen the heterogeneity characteristics of reservoirs [14, 15, 16, 17]. Multiple single sand bodies deposited in similar environments are superimposed to form a composite sand body with a large thickness. The argillaceous sediments developed at the sand body interface form argillaceous interlayers between the upper and lower sand bodies. Epigenetic diagenesis may form densely cemented calcareous interlayers that are discontinuously distributed along the interface [3, 18-20]. The discontinuity of the development and distribution of interlayers inside the sand body leads to incomplete sealing effect [21]. Parameters such as the sand-land ratio and sandstone physical properties are often used in the evaluation of sandstone transportability to quantitatively characterize the geometric connectivity and fluid dynamic connectivity of sandstone and comprehensively evaluate the transportability of sandstone aggregates [22-26]. The Donghe sandstone of the Carboniferous system is an important exploration series in the Tarim Basin. The discovered and developed Hudson Oilfield takes the large-scale developed shore sedimentary sand body as the main reservoir. The physical property differences of the sand body and the superimposed distribution of interlayers form a structural-lithologic composite oil reservoir with complex oil-water distribution characteristics. The oil-water interfaces of multiple fluid systems gradually decrease from south to north and are distributed in a stepped manner [27,28]. The author uses the physical properties, interlayers and oil and gas test data of the Donghe sandstone in the Hudson Oilfield to statistically analyze, in detail, the longitudinal and transverse transportability and main controlling factors of the sand body, and discuss the influence of sand body transportability on oil and gas migration and accumulation, providing a geological basis for the oil and gas development and rolling exploration of the Donghe sandstone in the Tarim Basin.
## 1 Introduction
Our results show that the Adaboost algorithm outperforms the reference system in complexity, achieving information rate of 48 Gb/s below the adopted 3.8 \(\times\) 10\({}^{-3}\) HD-FEC threshold at the fiber range of 45 km SSMF. The Adaboost algorithm obviously has stronger robustness in the long distance than ANN. Instead, in contrast to ANN, it has less training and running time.
### Metric for Evaluation  
The SSD model for object detection was evaluated using mean average accuracy (mAP). The mAP calculates a score by comparing the ground truth bounding box to the detected box. The mAP score is determined by precision (P), recall (R), and intersection over union (IoU). Precision is calculated using Equation (1) and is the proportion of true detection by the model (TP) to total positive detection (true positives and false positives (FP).  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{1}\]  
Recall measures the proportion of accurate positive predictions among all possible positive and false negative (FN) predictions, is calculated by Equation (2).  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{2}\]  
The IoU calculates the overlap between the ground truth bounding box and the boxes predicted by the model. It is a number in the range between zero and one. The number 1 means that the ground truth box completely matches the predicted box and zero means that there is no overlap between the two boxes, and is calculated according to Equation (3).  
\[\text{IoU}=\frac{\text{area of overlap}}{\text{area of union}}. \tag{3}\]  
The precision-recall curve is calculated by the trade-off between precision and recall values for different IoU thresholds. The average precision is defined by the area under the precision-recall curve, and the mAP is the average over the average precision for each predicted box.
## 4 The Dynamic Effects
### Frontogenesis Dynamic Effect
#### 4.2.2 The Frontogenesis Effect  
For frontal systems, frontogenesis can trigger ageostrophic circulation and updrafts, lifting vapor to produce clouds and precipitation. At the initial stage of cloud formation, the role of frontal circulation is significant. However, the convective system develops rapidly if it is located in both super-saturated and unstable environments. At this stage, the thermal circulation driving by latent heat dominates and overwhelms frontal circulation [42; 81]. Actual circulation is the superimposition of the two (Figure 15).  
Although it is difficult to separate the two types of circulation, ageostrophic circulation generated by frontogenesis is objectively present; and stronger frontogenesis generates stronger ageostrophic circulation. Note that frontal circulation is not the same as typical thermal convective circulation. The interaction of wind shear and the cool pool at the frontal boundary separates updraft and downdraft corridors, forming tilted frontal circulation. This is similar to the slantwise convective circulation generated by wind shear and cool pools that make precipitation systems more \"well-organized\", strengthening intensity and prolonging its lifecycle [82]. High pollution aerosol has obvious impacts on both the thermal and dynamic fields of Mei-Yu frontal systems. As clouds and precipitation develop along the Mei-Yu front in the polluted conditions, frontogenesis is markedly intensified (Figure 15). Stronger, continuing frontogenesis benefits and maintains cross-front ageostrophic circulation (Figure 16). It also strengthens and prolongs slantwise updraft, resulting in a more \"well-organized\" Mei-Yu frontal system. This leads to the more sufficient interactions of the microphysical and dynamic processes influenced by aerosol and enhances precipitation. Hereinafter, \"the frontogenesis effect\" refers collectively to forced cross-frontal ageostrophic circulation, strengthened  
Figure 15: Vertical cross sections of zonal mean vertical velocity (\(w\), shading) and cross-front ageostrophic circulation (\(v\)-\(V_{g}\), \(w\)) at 21 h for (**a**) C-case and (**b**) P-case. Only a 500 km \(\times\) 15 km of the domain is presented.  
and maintained slantwise updraft, and organized precipitation system. The frontogenesis effect is an indirect dynamic effect resulted from microphysical processes, latent heat release, and its DDE. In order to distinguish it from the DDE, we suggest that the indirect dynamic effect may be referred to as the second type of dynamic effect--frontogenesis dynamic effect (FDE).
## 3 Results and Discussion
### Atmospheric Deposition
#### 3.2.4 Metals
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metals** & **Roccaccia** & **Rocca Resp.** & **Allumiere** & **S. Agostino** \\ \hline As & 0.073 & 0.084 & 0.441 & 0.409 \\ Ba & 1.470 & 2.947 & 4.583 & 1.541 \\ Be & 0.009 & 0.037 & 0.016 & 0.012 \\ Cd & 0.013 & 0.009 & 0.020 & 0.072 \\ Co & 0.083 & 0.092 & 0.105 & 0.082 \\ Cr & 0.452 & 0.435 & 0.758 & 0.588 \\ Cu & 0.858 & 0.550 & 0.751 & 0.869 \\ Mn 1 & 4.773 & 3.266 & 3.581 & 3.034 \\ Ni & 0.394 & 0.300 & 0.470 & 0.479 \\ Pb & 1.105 & 1.452 & 0.921 & 2.553 \\ Sb & 0.024 & 0.042 & 0.059 & 0.048 \\ Se & 0.025 & 0.025 & 0.025 & 0.027 \\ Sn & 12.881 & 14.402 & 17.955 & 113.356 \\ Sr 1 & 1.519 & 2.998 & 3.534 & 1.592 \\ T1 & 0.006 & 0.016 & 0.015 & 0.006 \\ Te 1 & 0.001 & 0.001 & 0.001 & 0.001 \\ Ti 1 & 1.227 & 2.181 & 1.327 & 1.292 \\ V & 0.375 & 0.487 & 0.592 & 0.531 \\ Zn & 6.174 & 3.846 & 6.115 & 5.449 \\
**Total** & **31.460** & **33.168** & **41.265** & **131.941** \\ \hline \hline \end{tabular} 1 detected only in winter and spring seasons.  
\end{table} 
Table 8: Mean levels of metals (\(\mu\)g m\({}^{-2}\) d\({}^{-1}\)) determined in the atmospheric depositions at the four investigated locations. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metals**} & \multicolumn{3}{c}{**Type of Area**} \\ \cline{2-4}  & **Rural** & **Urban** & **Industrial** \\ \hline As & 0.082–0.43 & 0.22–3.4 & 2.0–4.3 \\ Cd & 0.011–0.14 & 0.16–0.90 & 0.12–4.6 \\ Ni & 0.03–4.3 & 5–11 & 2.3–22 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Limit value concentrations (\(\mu\)g m\({}^{-2}\)d\({}^{-1}\)) reported in European legislation for different areas.
## 4 Materials and Methods
### Volatiles Analysis  
In each of three experimental replicates, 2.0 g of petals of each fresh flower was sampled at random. A manual SPME device with 40 mL vials and PDMS-DVB fibers (65 \(\upmu\)m) was used for extraction of flower volatiles (Supelco Inc., Bellefonte, PA, USA). The samples were placed in 40 mL glass vials with flowers occupying approximately two thirds of each vial. To maximize the release of volatile compounds, the SPME fiber was exposed in the upper space of the sealed vial for 50 min at 45 \({}^{\circ}\)C to adsorb the analytes. After that, the fiber was directly introduced into the injector of the GC-MS for 3 min at 250 \({}^{\circ}\)C to desorb the volatiles.  
GC-MS analysis was carried out using a Trace 1300 gas chromatograph coupled with an ISQ-LT mass spectrometer (Thermo Fisher Scientific, Sunnyvale, CA, USA). A DB-5 MS capillary column (5% phenylmethylsiloxane, 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Thermo Fisher Scientific, Sunnyvale, CA, USA) was employed to conduct the analyses. Helium was used as a carrier gas with a flow rate of 1.0 mL/min. The front inlet was kept at 250 \({}^{\circ}\)C in splitless mode. The initial column temperature was maintained at 40 \({}^{\circ}\)C for 2 min, programmed up to 110 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C per min and held for 2 min; it was then raised to 150 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min. This temperature was maintained for 2 min and then increased to 200 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and held constant for 4 min. The flow rate for split injection was 1.0 mL/min. The mass selective detector was used in EI mode with an ionization voltage of 70 eV, and the scanning range was m/z 33-450. The ion source, transfer line, and quadrupole temperatures were 250 \({}^{\circ}\)C.  
Retention indices (RIs) were calculated by using the retention times of C7-C30 n-alkanes (BNCC, Beijing, China) that were injected under the same chromatographic conditions [60]. The volatile constituents were identified by comparison of their RI and their mass spectra with the NIST, PubChem, and Pherobase databases [2; 61]. The relative content of each component in the total volatiles was calculated according to the ion current peak area normalization method [62]. The spectrum of each compound was analyzed by Xcalibur and NIST 2014 (NIST Database, ChemSW Inc., Fairfield, CA, USA), and compounds were identified according to their retention times and the NIST Database. The spectra of the main peaks were compared with those in the relevant literature [29; 33; 52; 53; 63] to determine the chemical composition of the floral substances of _N. hybrid_.
## 3 Proposed Solutions
### Follow-Up Clock Synchronization and Optimization of Listening Window's Size  
The second problem we consider is how to select the time window for a consecutive transmission of packets, assuming we have received at least one packet from the transmitter. Assuming the interval between the last successful reception of the packet is known and the next packet is transmitted, the algorithm should define the starting and ending point of the listening window.  
We analyze the following transmission scenario: the transmission takes place twice during each simulated day. Every single transmission takes place in one of two windows: between 0:00-3:00 and 3:00-6:00, and the exact moment of transmission in a given window is randomized according to a uniform distribution within the adopted limits. The receiver knows the expected time of arrival of each subsequent packet, while the actual time of its transmission by the transmitter is burdened with transmitter crystal skew.  
The two reception windows solutions are presented as an emergency mechanism to assure the largest probability of packet reception. The transmissions are duplicated in each window to minimize the possibility of packet loss.  
The solution was divided into two separate elements--the selection of the correct length of the listening window and the selection of a listening moment in time.  
This time window management is based on a smart metering scenario, where the readouts of the meters need to be transmitted by a specific hour in the morning (in our example 6 am) and cover the reading of the water/energy/gas utilization at midnight. The proposed mechanism is generic and may be used for any time windows. The specific times are set as defaults in devices produced by AIUT company (www.aiut.com accessed on 29 December 2020), according to the real working system that was implemented in a few Polish cities for water and gas metering.
# Design of a Rotary Transformer for Installations on Large Shafts
## 3 Finite-Elements Analysis
### Three-Dimensional Analyses  
Electromagnetic 3D simulations were carried out in ANSYS Maxwell, as well, to check the results obtained with 2D analysis. Since the structure was highly symmetrical, it was possible to cut the 3D model of the rotary transformer into a small slice to obtain simpler and faster simulations. Indeed, it was possible to define symmetry planes to mirror the structure of the device on the plane normal to the axis of rotation. In addition, master and slave planes were used to repeat the considered slice periodically around the rotational axis. An example on the exploitation of symmetry planes is reported in Figure 7.  
This strategy worked only to the limit of a section of a plane (2D) repeated over all 360 deg, likewise an axisymmetric FEMM analysis. In light of this consideration, the 3D simulations may appear completely useless for this kind of device, since they may be seen as a re-run of a simpler simulation which is already available. However, 3D simulations allow us to perform virtual tests with rotating parts as well as to include fringing effects in an accurate way, which effects play a key role during the optimization of the magnetic core aimed at weight reduction.
## 2 Materials and Methods
### Finite Element Model  
A finite element (FE) model of the implant under study is needed in step 3 of the methodology (see Figure 3) in order to achieve the axial load \(F_{a}\) and the transverse load \(F_{e}\) acting on the screw head. The authors used Ansys Workbench(r) 19 R1 software (Ansys Iberia S.L, Madrid, Spain) to run the analyses, which consist of two load steps: in the first one, the preload calculated in step 2 of the methodology was applied to the screw via pretension section. As explained, Equation (3) was used to calculate the preload \(F_{p}\) for each of the three tightening torques applied, namely 232 N the corresponding preload for 10 Ncm, 349 N for 15 Ncm and 465 N for 20 Ncm. In the second load step, the external load \(F\) was applied to the abutment through the ring shown in Figure 7 in order to reproduce the experimental conditions described in the previous section. Implant and abutment are made of grade 4 commercially pure titanium (Ti CP4) and the prosthetic screw is made of Ti6 Al4V ELI (Ti Gr5). Chemical composition of both materials is described in Table 1. Both materials were modeled as linear elastic, with \(E=103\) GPa, \(v=0.35\) for CP4 and \(v=0.31\) for GR5.  
The sole purpose of the FE analysis is to obtain the values of the loads transmitted to the screw, and not to reproduce the exact condition of screw self-loosening itself. Consequently, a relatively simplified FE model is used to minimize the complexity and cost of the analysis. Along this line, only half dental implant assembly was modeled, and accordingly half the preload and the external load were applied to the model. Cylindrical threads where modelled in the screw since the error introduced is negligible as demonstrated in authors' previous work [36]. Since stress results are not needed at all in the screw-loosening prediction methodology presented in this paper, a very refined mesh is not necessary. In this sense, a mesh sensitivity study was performed and the mesh shown in Figure 8 was finally chosen in order to have a good compromise between the accuracy of the results(this is, loads acting on the screw) and the analysis cost. Quadratic elements were used with a total of 185.320 Degrees of Freedom (DoF). Target and contact elements were used to model the interface of the mating surfaces with a friction coefficient obtained experimentally as already pointed out. The external surface of the implant part attached to the specimen holder was held by a fixed support in the analyses. Finally, the forces transmitted to the screw head were directly obtained by using the force reaction tool of Ansys Workbench(r) to be used to test the self-loosening state using Equation (8).
# A Deep Learning Estimation for Probing Depth of Transient Electromagnetic Observation
## 2 The Calculation of the TEM Probing Depth  
In this chapter, we present the conventional methods for probing depth estimation, aiming to facilitate a comparison with deep-learning-based prediction algorithms. Additionally, we will utilize these methods to obtain labels for the dataset, which will guide the training of the network mode.  
The traditional methods for calculating probing depth mainly involve two approaches. The first approach directly calculates the probing depth using the following theoretical formula [18]:  
\[z_{d}=\sqrt{\frac{2t}{\mu\sigma}} \tag{1}\]  
where t represents the cut-off time, \(\mu\) denotes magnetic permeability, and \(\sigma\) represents the electrical conductivity of the half-space model. This approach has significant uncertainty because it fails to fully consider the non-uniform conductivity structure and the influence of the number of sampling points. Moreover, it relies on a reasonable estimation of the half-space model.  
Another approach is the Jacobian-matrix-based calculation method proposed by [PERSON] and [PERSON] (2012) [19]. It is generally obtained through the following steps: (1) Per form one-dimensional inversion on the measured data. (2) Interpolate the one-dimensional inversion model to obtain a multi-layer model with thin layer thickness. (3) Calculate \(G_{ij}\) for the interpolated model using \(G_{ij}=\frac{\partial\log(d_{i})}{\partial\log(m_{j})}\), where \(d_{i}\) represents the value of the i-th data points and the \(m_{j}\) represents the value of the j-th layer in the inversion model. (4) Calculate \(S_{j}\) using \(S_{j}=\sum_{i=1}^{N}\frac{G_{ij}}{\Delta d_{i}}\) and consider the depth where \(S_{j}\) equals 0.8 as the probing depth; here the \(\Delta d_{i}\) is noise on the data points. However, this method is highly dependent on the inversion results, which themselves have uncertainty due to the non-uniqueness of the inversion solution. Additionally, the computational efficiency of this method is low when dealing with large amounts of data.