esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Reliability Analysis
After repeated tests and the resistivities are arranged, the 60% of the resistance value is taken as a boundary, the colour of resistivity value lower than 60% is hidden, and the colour of resistivity value higher than 60% is displayed. The result, after adjustment, is shown in Figure 9g; the tunnel location and path have been shown clearly. From the existing literature, we already know the approximate location and direction of the underground tunnel in reality. The results of the MLP study show that a few (three layers) 2D-ERT data to predict the 3D ERT data can be seen and that the location of the tunnel is consistent with the actual location. \begin{table}
\begin{tabular}{c c} \hline
**Structure** & **Parameter** \\ \hline Number of layers & 3 \\ \hline Number of Neurons in the layers & Input = 2, \\  & Hidden1 = 32, Hidden2 = 32 \\  & Output = 1 \\ \hline Initial weights and biases & Random \\ \hline Activation function & Sigmoid \\ \hline Optimization & Adam \\ \hline \end{tabular}
\end{table} 
Table 1: MLP network structure parameter list. Figure 9: _Cont._  
Figure 9: Cont. Figure 9: _Cont._  
Figure 9: Cont. Figure 9: Deep learning prediction results (**a**–**g**). (**a**) Original data of L1, L2, and L3; (**b**) Error rate of MLP network training process; (**c**) Result of the original data of L1, L2, and L3 after MLP data prediction; (**d**) Result of unknown regions of L1, L2, and L3 after MLP data prediction; (**e**) Result of successive profiles after MLP data prediction; (**f**) Result of 3D resistivity model; (**g**) MLP predicted tunnel location and path.
## Materials and Methods
### Modelling of the Rat LHCGR Structure and Thytrotropin-Stimulating Hormone (TSH) Receptor (TSHR)  
The structures of the rat LHCGR and TSHR modeled with the use of homology modeling methods and molecular dynamics (Figure S5). The template structures for receptor modeling were taken from RCSB Protein Data bank and GPCR db. The initial three-dimensional structure of transmembrane domain of the rat LHCGR and TSHR was built on the basis of the 3D models of bovine rhodopsin (Protein Data Bank ID: 1 GZM, 4 WW3, 1L9H) [96]. Several receptor-specific corrections were made based on sequence alignments using Schrodinger Suite 2020-2. The transmembrane domain design was based on the Ballesteros-Weinstein nomenclature. The secondary structure of the transmembrane domains was built in accordance with the models of the human LHCGR and TSHR, from the GPCR database [97]. Amino acid sequence mismatches between proteins were fixed by the homology structure modeling with use of multiple sequence alignment. Missing loops were added by best fit and homology to fragments of other proteins (with Prime module from Schrodinger Suite 2020-2) [98]. The stability of the protein structure was studied by molecular dynamics runs in a water (TIP3P, simulated by 0.15 M NaCl solution) box (20 A buffer zone from protein) system without any restraints (15 ns, periodic boundary box, charges neutralized by adding chlorine ions) using Desmond software [99]. The stability and quality of the model were controlled by checking the geometry with Ramachandran plot and during the MD simulation (backbone RMSD less than 2 A). All MD manipulations processed in OPLS3e force field [100].
###### Abstract  
In this paper, results from the fabrication and study of a piezoelectric microgenerator using nanobranched zinc oxide (ZnO) film grown on poly(3,4-ethylenedioxythiophene) doped with a sulfonate (PEDOT:PSS)-coated flexible substrate are presented. The aim of the study is to extract information about the electrical behavior of the harvester at different frequencies, temperatures, and positions, as related to the ZnO nanostructure, as well as to examine its piezoelectric response. Radiofrequency (RF) sputtering with oxygen deficit during growth on an amorphous sublayer was used to obtain the nanobranched structure. The microdevice was studied at frequencies ranging from 1 Hz to 1 MHz for temperatures in the range of \(-10\)\({}^{\circ}\)C to 40 \({}^{\circ}\)C, in both a non-bonded position, and a radius of curvature position bended to 12 mm. It was found that non-ordered ZnO nanoformations facilitate the dipoles' motion, thus leading to low dielectric losses of \(10^{-3}\), and a higher relative permittivity of \(\epsilon_{r}\) -15, compared with typically known values. The losses increase with one order of magnitude at bending, but still remain low. Dielectric characteristics indicate that the favorable working range of the microgenerator is within the lower frequency region, from 10 Hz to 10 kHz. The results were confirmed by the measured open circuit voltage, which reaches approximately 1 V within this range, versus 300 mV out of the range.  
ZnO nanostructures; nanobranches; piezoelectric energy harvesting; polymer coatings; flexible devices; thin films growth; PEDOT:PSS; dielectric losses; microgenerator +
Footnote †: journal: Applied  
Article
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 3 Numerical Analysis
In particular, the global mean orientation errors are \(\epsilon_{\theta_{UWB}}=0.31\) rad, \(\epsilon_{\theta_{IMU+OFS}}=-0.26\) rad, and \(\epsilon_{\theta_{UKF}}=-0.01\) rad, whereas the standard deviations are \(\sigma_{\theta_{UWB}}=2.11\) rad, \(\sigma_{\theta_{IMU+OFS}}=0.11\) rad, and \(\sigma_{\theta_{UKF}}=0.05\) rad. This means that the accuracy of UWB system alone with a single tag is not enough to correctly retrieve the forklift orientation. Figure 3: Top view of the simulated trajectory along with the UWB anchors (gray squared markers). Ground truth trajectory (continuous gray line), UWB (continuous blue line), IMU + OFS (dashed red line), and UKF (pointed green line) estimated trajectories of the forklift are also represented. Figure 4: Localization errors for the different considered localization methods for the simulated trajectory of Figure 3: (**a**) \(x\)-coordinate, (**b**) \(y\)-coordinate, (**c**) distance error. Figure 5: Cumulative distribution function of the distance error \(\epsilon_{d}\) for \(M=100\) test cases with similar trajectories as the one represented in Figure 3.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## Appendix C Proof of Lemma 3  
When \(q\) is a large number, i.e., \(q\in[q_{1},\infty)\), the SINR constraint can be ignored, and thus the optimal received power of the IoT user is \(p=\bar{P}\).  
Substituting \(q_{1}\), \(q_{2}\), \(\gamma_{1,s}\), \(\gamma_{2,s}\) into (10) and subtracting the two equations yields  
\[\frac{\alpha_{2}}{1+\gamma_{2,s}}-\frac{\alpha_{2}}{1+\gamma_{1,s}}+\frac{ \alpha_{1}}{1+\frac{q_{2}}{p}\gamma_{2,s}}-\frac{\alpha_{1}}{1+\frac{q_{1}}{p }\gamma_{1,s}}=0. \tag{14}\]  
For \(q_{1}>q_{2}\), if we assume \(\gamma_{1,s}\geq\gamma_{2,s}\), then, we have  
\[\frac{\alpha_{2}}{1+\gamma_{2,s}}-\frac{\alpha_{2}}{1+\gamma_{1,s}}\geq 0, \tag{15}\]  
and  
\[\frac{\alpha_{1}}{1+\frac{q_{2}}{p}\gamma_{2,s}}-\frac{\alpha_{1}}{1+\frac{q_ {1}}{p}\gamma_{1,s}}\geq 0. \tag{16}\]  
Based on (15) and (16), for \(q_{1}>q_{2}\), if \(\gamma_{1,s}\geq\gamma_{2,s}\), then (14) is impossible to be satisfied. Thus, for \(q_{1}>q_{2}\), we \(\gamma_{1,s}<\gamma_{2,s}\), which proves Lemma 3.
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 2 Methods
### Key-Frame Pool Strategy Based on Dual-Layer Mask
#### 2.2.1 Dual-Layer Mask Generation  
To generate the first layer of the background mask, we utilize a video segmentation network to segment the object region in the current frame. For online segmentation of the target mask, we employ XMem [24], as it does not require prior knowledge about the target object or interactive objects (such as hands) and is adaptable to various scenes and objects. XMem effectively segments the object without the need for explicit annotations or scene-specific tuning.  
The second year, the foreground occlusion mask, is computed based on depth information. In the current camera view, the occluded area must satisfy the condition that the depth value of the occluding surface is smaller than that of the target object's surface. To achieve this, we first filter the depth values of the target object using a threshold set at three times the standard deviation to eliminate outliers caused by noise from depth sensors. We then compute the mean depth value of the target object's surface and use this as a threshold to separate pixels with smaller depth values, distinguishing them from the background. Next, we apply an erosion operation to the intermediate results to disconnect weakly connected components in the environment, retaining only the most probable foreground occlusion mask. Finally, starting from the edge of the target object mask, we search for the connected component adjacent to the target object mask, which represents the potential occlusion area. The results of this process are illustrated in Figure 2.  
Figure 2: Visualization of the dual-layer mask results (yellow: target object mask; pink: detected foreground occlusions).
## 1 Satellite Constellations with Zenith Propagation Paths at Any Site
To determine the slowly time-varying passband and baseband equivalent transfer functions distorted by rain, we must know the rain attenuation \(A(t)\) (dB) and the phase delay \(\Phi(\mathrm{t})\) (degrees) time series. Both are calculated with the SST as shown in [4; 18]. After this introduction, Section 2 shows examples of the results obtained with the SST; Section 3 recalls how to calculate passband and baseband transfer functions; Section 4 reports histograms of interference; Section 5 models probability distributions of interference; Section 6 calculates the theoretical channel capacity loss; Section 7 reports a general conclusion and indicates future work.
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 3 Establishment of the NSS Model in Kinematic PPP  
### The Combination of Three Different Satellite Selection Algorithms  
To reduce the excessive redundant information and guarantee similar positioning accuracy with an original solution in GPS/BDS/GLONASS kinematic PPP, an appropriate satellite selection model which can adapt PPP technique should be proposed. However, although GNSS constellations have similar capabilities, the satellite type, geometric spatial structure, and communication mode of each constellation are not necessarily the same. For instance, GPS and GLONASS are composed of MEO satellites while BDS includes GEO, MEO, and IGSO satellites. Due to different characteristics of GNSS, a single satellite selection algorithm sometimes seems to not be suitable when it comes to GPS/BDS/GLONASS satellites. Thus, to reduce the computational load of the GNSS equipment and obtain appropriate accuracy and reliability in GPS/BDS/GLONASS kinematic PPP, we propose the NSS model that includes three different satellite selection algorithms: the maximum volume algorithm, the fast-rotating partition satellite selection algorithm, and the elevation partition satellite selection algorithm.  
In the process of establishing the NSS model, the accuracy and characteristics of different constellations and satellite selection algorithms were all considered. As the first and well-known GNSS system that has been fully operational, GPS has better positioning accuracy and reliability when BDS and GLONASS are compared. Thus, to ensure the positioning accuracy of the NSS model, the maximum volume algorithm is applied in the GPS satellites because GDOP is inversely proportional to the volume and better GDOP can partly reflect the theoretical impact of the observational geometry. For the BDS and GLONASS satellites, they both have rather good coverage almost worldwide. Thus, we used two satellite selection algorithms (based on azimuth and elevation, respectively) in BDS and GLONASS to avoid the situation that excessive satellites are selected at some epochs if one algorithm is used. In this study, the fast-rotating partition satellite selection algorithm based on equal distribution of sky was adopted for the GLONASS satellites, which can greatly reduce the computation time when compared to the optimal DOP algorithm. Like the fast-rotating partition satellite selection algorithm, the elevation partition satellite selection algorithm is based on equal distribution of elevation. Additionally, we applied the elevation partition satellite selection algorithm in BDS. These three different selection algorithms formed the NSS model. It is succinct and fast because the huge amount of computation is reduced in the process of satellite selection. In addition, the considered characteristics of multi-GNSS can provide some assurance for the positioning accuracy. The flowchart of satellite selection is shown in Figure 3.  
Figure 2: The elevation partition satellite selection algorithm.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 4 Management Lessons
### Procurement of COTS  
Procurement is always critical, although the NewSpace vendors make it appear more accessible. Adding geopolitical events, like the Covid-19 pandemic, will turn this task into a high-risk activity in the project that must be dealt with accordingly.  
Acquiring COTS early in the project is vital as the lead time and the tests may last much longer than expected. The delivery dates and delays are different for every company, and that is essential to be followed up rigorously by the project manager. Although the vendors may be known by word-of-mouth reputation, managers should not trust them to provide an adequate system and must be strict with the manufacturer in case of subpar system delivery.  
Payment often needs to be made upfront, which reduces the customer's leverage. Usually, it is complicated to negotiate any penalties, but our team was negotiating discounts in case the manufacturer failed to deliver or created considerable delays. It is essential to consider that all deliveries will not occur on time, and schedule margins shall always be planned.  
If the hardware manufacturer provides insufficient documentation of the system or its functionality, it is hard to detect this until tests have started. Therefore, in addition to the schedule margins, we recommend assigning people to test the procured parts as soon as they are delivered.  
We experienced various issues in communication with the COTS providers: for instance, the delivery of the reaction wheels was delayed by several months, and their mechanical quality (vibration, rotation speed) was not as specified, the requested nozzle orientation of the propulsion was not understood, a precision milled part was improperly manufactured. There are always communication issues: minutes of meetings at the order and pre-delivery milestones are required and must be signed by both parties to avoid excuses of miscommunication.
## 3 Preliminaries
### DIRECT Algorithm
#### 3.1.1 Selection Scheme  
The DIRECT algorithm evaluates potentially optimal hyper-rectangles by assessing the lower-bound estimates for the performance function over each of them [28]. Definition 1 formally specifies the requirement for the potentially optimal hyper-rectangle.  
Definition 1: (Potentially optimal hyper-rectangle) Consider a partition of the unit hypercube into \(n\) hyper-rectangles. Let \(\mathbf{c}_{j}\) represent the center point of the \(j\)th hyper-rectangle, \(d_{j}\) be the distance from the center point to the vertices, and \(\epsilon>0\) denote a positive constant. Let \(f_{\min,i}\) denote the current best function value, specifically, \(f_{\min,i}=\min_{j=1,\ldots,n}f_{i}(A^{-1}\mathbf{c}_{j}+\mathbf{x}^{\text{low}})\). If there exists \(\widetilde{K}>0\) such that  
\[f_{i}(A^{-1}\mathbf{c}_{t}+\mathbf{x}^{\text{low}})-\widetilde{K}d_{t} \leq f_{i}(A^{-1}\mathbf{c}_{j}+\mathbf{x}^{\text{low}})-\widetilde{K}d_{ j},\quad\text{for all }j=1,\ldots,n, \tag{4a}\] \[f_{i}(A^{-1}\mathbf{c}_{t}+\mathbf{x}^{\text{low}})-\widetilde{K}d_{j} \leq f_{\min,i}-\epsilon|f_{\min,i}|, \tag{4b}\]  
a hyper-rectangle \(t\) is considered potentially optimal.  
The hyper-rectangle \(t\) is considered potentially optimal when its value, as evaluated by the lower Lipschitz bound on the left-hand side of (4a), is the smallest among all hyper-rectangles, with a positive constant \(\widetilde{K}\). Parameter \(\varepsilon\) in (4b) can prevent the excessive fine-tuning of the local minimum [24; 28].  
Potentially optimal hyper-rectangles can be identified through at least two different methods: the modified [PERSON]'s scan algorithm [29] or the rule described in Lemma 2.3 of [30]. Typically, these methods yield similar results. Therefore, the modified [PERSON]'s scan algorithm is employed in this paper.
## 4 A Case Study Using PEL
### Study with Stroke Patients
#### 4.1.2 Results  
At the beginning of the survey, the patients answered two questions that were related to the use of systems similar to the one evaluated. 47% of the respondents answered that they had used more than one system in their rehabilitation process, when considering that our proposal is more intuitive and simple to use (see Table 1).  
Apart from the analysis that is shown in Table 1, the existence of correlations between different items has been studied (see Table 2). For this purpose, the Shapiro-Wilk test was applied to contrast the normality of the data set, given that the sample size is less than 50. The result revealed, with a 95% confidence level, that the variables do not fit a normal distribution, which resulted in the use of the Spearman correlation coefficient.  
The obtained results show the correlation between INT2 and PU, ITU1, ITU2, CONS, and PSA. This reflects that, as the patients rated the activity as more interesting, they considered that the system would help them perform rehabilitation exercises; they would use it at home; they would recommend it to family members; it would increase their consistency; and, it would motivate them to perform more exercise routines. In addition, the correlation between the variable PREF and variables CONS/PSA indicates that the patients would prefer to use the system at home, because it motivates them and increases their determination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimension** & **Item** & **Mean** & **Standard Deviation** & **Mode** \\ \hline \multirow{4}{*}{**Activity Perception**} & 1. INT1 & 4.41 & 0.87 & 5.00 \\  & 2. INT2 & 4.53 & 0.62 & 5.00 \\  & 3. EFF & 4.35 & 1.06 & 5.00 \\  & 4. LEA & **4.65** & 0.49 & 5.00 \\ \hline \multirow{4}{*}{**Cognitive Load**} & 5. TD & 3.71 & 0.99 & 4.00 \\  & 6. E1 & 3.82 & 0.88 & 4.00 \\  & 7. E2 & 3.65 & 1.00 & 4.00 \\  & 8. DD & **2.88** & 1.17 & 2.00 \\ \hline \multirow{4}{*}{**Utility**} & 9. PREF & **4.00** & 1.22 & 5.00 \\  & 10. CONS & 3.88 & 1.17 & 4.00 \\  & 11. PSA & 4.06 & 1.20 & 5.00 \\  & 12. GAM & **4.71** & 0.59 & 5.00 \\ \hline \multirow{4}{*}{**Game elements**} & 13. GE1 & **4.59** & 0.71 & 5.00 \\  & 14. GE2 & 4.41 & 0.80 & 5.00 \\ \cline{1-1}  & 15. GE3 & 4.24 & 0.97 & 5.00 \\ \cline{1-1}  & 16. GE4 & **4.59** & 0.71 & 5.00 \\ \cline{1-1}  & 17. GE5 & **4.65** & 0.79 & 5.00 \\ \cline{1-1}  & 18. GE6 & 3.24 & 0.75 & 3.00 \\ \hline \multirow{4}{*}{**TAM**} & 19. PEU & **4.35** & 0.79 & 3.00 \\ \cline{1-1}  & 20. PU & 3.88 & 1.11 & 4.00 \\ \cline{1-1}  & 21. ITU1 & 4.06 & 1.20 & 5.00 \\ \cline{1-1}  & 22. ITU2 & **4.35** & 0.79 & 5.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the dimensions evaluated by patients.
# Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review
## 3 Results
### Performance Measure Techniques  
The performance measure is important for all types of models, tools, techniques, and algorithms used in industrial projects and data-fetching mechanisms. Decision-making, visualization, and prediction are the key roles played by BD analysts and their role in making the system efficient and effective. In this paper, different types of performance are measured in different studies, which are shown in Table 12. Among all, precision, recall, and F-score are dominant in all types of BD-, ML-, DL-, and NLP-based models and frameworks. Accuracy is used by many researchers, where business intelligence tools are used only by business firms which was highlighted by [31].  
\begin{table}
\begin{tabular}{c c} \hline
**Techniques** & **Studies’ References** \\ \hline URI & [43] \\ RDF & [43,47] \\ Sensor & [40,44,54] \\ TXT & [5,35,44,75,79,93] \\ CSV & [35,44,48,75] \\ XML & [44,47,62,66] \\ SSU & [3,31,33,34,36,37,41,45,46,54,58,60,61,63,68,69,70,71,73,74,76,77] \\ GIS & [46] \\ JSON & [47,48,55] \\ SQL & [48] \\ OCR & [49] \\ INDUSTRIAL & [2,53,67] \\ VIDEO & [51,92] \\ AUDIO & [51,92] \\ IMAGE & [51,92] \\ WEB & [11,35,37,39,52,56,59,64,72,91] \\ TEXTUAL & [52] \\ XLS & [5,65,75] \\ GPS & [35] \\ STRING & [55] \\ \hline \end{tabular}
\end{table}
Table 11: Data formats.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Study Reference** & **Precision** & **Recall** & **F-1** & **Accuracy** & **BI** & **Score** & **Correlation** & **Time** & **Ratio** \\ \hline
[43] & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ \\
[66] & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ \\
[49] & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\
[51] & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\  
Ratio comparison and similarity score are calculated in studies where there is a comparison of pairs or similar sentences. Correlation of terms used for linking different health records of similar codes can be found in Reference [76]. Time is used for conversion or fetching records from larger datasets [5; 51]. Based on the performance measurement techniques, it is found that it will help BD analysts to use a data harmonization model, relevant tools, effective techniques, and efficient algorithms that are used for disparate nature of data domains and real-time applications.
# Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
## 4 Results and Discussion  
The three pre-processing techniques including digital pre-equalization (Preq), WHT-based FDSP and fast WHT-based PDSP are compared for PS-DMT at three IRs regarding electrical spectra, SNR equalization, BER, and GMI performance.