esa-sceva/satcom-chunk-collection huggingface dataset:
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Machine Learning Approaches
#### 4.2.2 Naive Bayes Classifier  
Naive Bayes classifiers are based on \"naive\" independence assumptions between the features used to construct classifiers, i.e., models that assign class labels to problem instances, where the class labels are drawn from some finite set [23]. Maximum-likelihood training can be performed by evaluating the number of occurrences of the sample [24], which requires linear time, rather than expensive iterative approximation (as used for many other types of classifiers).  
In our context, the goal of naive Bayes classification is to find the probability that data \(x\) belongs to indoor class I or outdoor class O based on [PERSON] theorem.  
\[P(A|B)\ =\ \frac{P(B|A)P(B)}{P(A)}. \tag{2}\]  
The probability of \(x\) belonging to the indoor or outdoor class is given as follows.  
\[P(y\ =\ I|x);\ P(y\ =\ O|x). \tag{3}\]  
We can find the class of data \(x\) by selecting the class with the highest probability.  
\[c\ =\ \underset{c\in[I,O]}{arc\text{max}}p(c|x). \tag{4}\]  
By applying naive Bayes to Equation (4), we can obtain \(c\) as follows.  
\[c\ =\ \underset{c\in[I,O]}{arc\text{max}}\frac{p(x|c)p(c)}{p(x)}, \tag{5}\]  
\[c\ =\ \underset{c\in[I,O]}{arc\text{max}}p(x|c)p(c). \tag{6}\]  
In addition, by applying the assumption that all features in \(x\) are independent, we can calculate \(c\) as follows.  
\[c\ =\ \underset{c\in[I,O]}{arc\text{max}}p(x_{1},x_{2}\ldots,x_{9}|c)p(c)c\ =\ \underset{c\in[I,O]}{arc\text{max}}\prod_{i\ =\ 1}^{9}p(x_{i}|c)\ p(c). \tag{7}\]  
In the test phase, the class of new data \(x\) is found as follows.  
\[c\ =\ \underset{c\in[I,O]}{arc\text{max}}\prod_{i\ =\ 1}^{9}p(x_{i}|c)\ p(c). \tag{8}\]  
Despite their naive design and apparently oversimplified assumptions, naive Bayes classifiers have worked quite well in many complex real-world situations. Due to the \"naive\" independent assumption between the features, the naive Bayes classifier is simple and provides fast coverage during the training phase. It can also solve pattern recognition problems with a small amount of training data to estimate the parameters required for classification. However, naive Bayes cannot work with data that never happened before, and the \"naive\" assumption is often impractical for many real-world problems.
# Difference in Stiffness between Biceps Brachii Muscle Bellies Using Shear Wave Elastography
## 2 Materials and Methods
### Shear Wave Elastography  
SWE was used to assess the stiffness of the two biceps brachii muscle bellies using a Philips EPIQ Elite (Amsterdam, The Netherlands) with a linear eL18-4 MHz transducer. The SWE and ultrasound assessments were performed by an experienced sonographer. The transducer was placed longitudinally on the arm at the level of the skin mark and adjusted in B-mode to view the fascicles of the muscle in the longest length, with the depth adjusted as required. Using the ElastQ SWE setting on the ultrasound machine, the elastogram box was placed in the mid-depth of the muscle belly.  
The participants were asked to relax and not move or contract their muscles during the scan. If the muscle was activated in this way, the SWE would display higher colour maps almost instantly. The participant would then be reminded to relax, and the measurement would be re-taken a few seconds later. Once the elastogram was stable, the image was frozen and a 5 mm calilper was placed in the middle of the elastogram, avoiding any fascia, nerves, or vessels (Figure 2). The ultrasound machine calculates the velocity of the Shear Wave within the calliper and returns a measurement in m/s. Five measurements were taken in each muscle belly, at each elbow position, in each arm.  
Figure 1: The three elbow positions: (**A**) full extension; (**B**) 30\({}^{\circ}\) elbow flexion; (**C**) 60\({}^{\circ}\) elbow flexion.
## 2 Materials and Methods
### Module 1: Noise Removal  
Due to various factors, such as the different atmospheric conditions, radar parameters, transmitted power of the radar transmitter, sensitivity of the radar receiver, and accuracy of the signal processing and non-meteorological targets, the radar measurements were inevitably contaminated by random noise in addition to ground clutter gates, second trip gates, and side lobe gates, etc. There is a need to eliminate most of the noisy data before radial velocity dealiasing. HE2012 presented a scheme that utilized the threshold of reflectivity and spectrum width to identify noise in the CIDA algorithm. The same scheme was used in ADDA in the current study, but different thresholds were applied for different radar networks and radar wavelengths to effectively remove the noise. For radar data observed from the S-band radar station, the radial velocities were removed when the corresponding reflectivity was less than 20 dBZ and the spectrum width was larger than 8 m/s. For C-band radar data, the velocity data was removed if the corresponding spectrum width was higher than 3 m/s and the reflectivity was less than 16 dBZ.
## 3 Methods
### Description of the Case Study: Monitoring Cobot Arm Joints  
This section describes a case study where a cobot (UR3) performs pick and place tasks with a human operator, considering different loads--maximum, medium and minimum. The components of the experiment consist of a human operator, a UR3 robot and a shared workspace as shown in Figure 3. Both the robot and the human worker can access all the components necessary for the assembly in the workspace, such as the base, flanges, bolts and nuts. The HRC assembly application and integrated assembly method is described in [25]. During the case study, Physical and hardware data, such as temperature, load, speed, power, and programmed stops, protective stops and so forth, were acquired from the developed data acquisition framework during the case study. The robot was used without any workpiece to indicate the minimum load. A medium load corresponds to a 1.5 kg workpiece and a maximum load to a 3.0 kg workpiece. The monitoring system monitored all the physical parameters and predicted the parameters of influence of the cobots using different ML models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Description of the Metrics** \\ \hline AUC PR & The area under the precision-recall (PR) curve. This value ranges from zero to one, and a higher value indicates a higher-quality model. \\ AUC ROC & The area under the receiver operating characteristic (ROC) curve. This ranges from zero to one, and a higher value indicates a higher-quality model. \\ Accuracy & The fraction of classification predictions produced by the model that were correct. \\ Log loss & The cross-entropy between the model predictions and the target values. This ranges from zero to infinity, and a lower value indicates a higher-quality model. \\ RMSE & The root-mean-square error metric is a frequently used measure of the differences between the values predicted by a model, or an estimator, and the observed values. This metric ranges from zero to infinity; a lower value indicates a higher quality model. \\ MSE & This is an estimator that measures the average of the squares of the errors, that is, the average squared difference between the estimated values and the actual values \\ Feature importance & AutoML generates tables that indicate how much each feature impacts a specific model. The values are provided as a percentage of each feature: the higher the percentage is, the more that feature impacts model training. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics of the classification model.
# ZnO/Ag Nanocomposites with Enhanced Antimicrobial Activity
## 2 Materials and Methods
### Bactericidal Test of ZnO/Ag Nanocomposites  
The antimicrobial effect of the ZnO/Ag nanocomposites was tested against the _S. aureus_ ATCC 29213 and the _E. coli_ NCIMB 9484 strains, both from the Spanish Type Culture Collection (CECT).  
The strains were pre-cultivated on Luria Bertani (LB) agar plates at 37 \({}^{\circ}\)C to obtain the inoculum. After 24 h, the bacteria were harvested in sterilized MgSO\({}_{4}\) (10 mM). LB supplemented with ZnO or ZnO/Ag nanocomposites was used in the bacterial growth assays.  
The growth assays were carried out in a Multiskan FCMicroplate Photometer (Thermo Scientific, Waltham, MA, USA) with a total volume of 200 \(\mu\)L in microlitre wells using an initial bacterial density of 10\({}^{6}\) cfu mL\({}^{-1}\) of _S. aureus_ and 10\({}^{5}\) cfu mL\({}^{-1}\) of _E. coli_. Bacterial growth was incubated at 37 \({}^{\circ}\)C with continuous agitation and monitored by measuring the optical density every 10 min with periodic shaking for 24 h. The results were printed out as growth curves.  
To test whether the applied nanocomposites had an antimicrobial or bacteriostatic effect, live and dead cell quantification was used. The proportion of living vs. dead cells was quantified using the fluorescent LIVE/DEAD BacLight Bacterial Viability Kit, L13152 (Molecular Probes, Invitrogen, Paisley, UK). For live and dead cell quantification, 50 microL of bacterial suspension was mixed with 25 microL of each of the two components of the LIVE/ DEAD BacLight kit and incubated in the dark for 20 min. Then, the ratio of live/dead cells was determined by flow cytometric analysis. Samples were analyzed on a Becton Dickinson FACScan flow cytometer using the CellQuest software, which was also used to determine the percentage of live/dead cells. SYTO9 (Live) was excited at 480 nm and fluorescence was analyzed at 500 nm, whereas Propidium Iodide (Dead) was excited at 490 nm and fluorescence analyzed at 635 nm.  
Statistical analysis was carried out using a one-way analysis of variance in the Statgraphics-plus software of Windows V.5 (Statistical Graphics Corp., Rockville, MD, USA). The means were expressed with standard errors and compared using a [PERSON]'s least-significant difference test at the 95% confidence interval. Each experiment consisted of eight technical repetitions which were repeated three times.
# An Experimental Strain-Based Study on the Working State of Husk Mortar Wallboards with Openings
## 3 The Experimental Results
### Test Results of HMES Wallboods  
Typically, experimental data are analyzed to have a reliable and accurate formula for calculating the ultimate/failure load and the structural parameters corresponding to some working behavior  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cube Number**} & **Compressive Strength** & **Density** & **Water Content** & **Thermal Conductivity** & **Elastic Modulus** \\ \cline{2-6}  & **MPa** & **kg/m\({}^{3}\)** & **\%** & **W/(fm \(\times\) k)** & **MPa** \\ \hline A & 21.20 & 1733 & 3.1 & 0.394 & \\ B & 14.13 & 1467 & 3.5 & 0.233 & 4976 \\ C & 20.60 & 1633 & 3.4 & 0.380 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of husk mortar.  
Figure 6: The layout of the strain gauges.  
Figure 7: Cube for material test.  
characteristics. For the experimental data related to this study, such an analysis was also performed [20]. However, it seems that the experimental records of cracking phenomena and strain values have not been fully studied thus far. A great amount of unseen knowledge is certainly implied in the experimental data, and only some innovative method is necessary to reveal this knowledge. Here, the analysis of the experimental data focuses on the cracking mechanism and the stressing state of the wallboard from a structural perspective based on the concept of the structural stress state.  
Figure 8 draws the failure patterns of the eight wallboards based on the experimental observations. It should be noted that the concrete bonding failure occurs in the steel groove of specimen 2-1. Through upgrading the strength of the concrete, which was poured in the steel groove, similar failure patterns do not reappear in the following tests.  
The experimental records of the HMES wallboards corresponding to Table 1 are summarized in Table 3.  
From the experimental phenomena of the HMES wallboards, the following comment regarding their future design and engineering application can be made: the first crack occurs at the weakest zone in the wallboards where the maximum stress exists. Clearly, the weakest zone is dependent on the configuration of the wallboard. Importantly, the cracking pattern of the wallboard provides a reference for its configurational improvement. For instance, for the wallboard with a door opening, the cracking zone between the top edges of the wallboard and the door indicates that some strengthening measures may be taken there; additionally, some strengthening measures may be taken in the cracking zone between the door and window openings. Therefore, efforts should be made to ensure a shearing working state for the engineering application of this wallboard.  
Figure 8: The experimental cracking patterns of the HMES wallboards.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wallboard No.** & **Lateral Loading Condition** & **CL-O (kN)** & **FL-O (kN)** & **UL (kN)** \\ \hline
1-1 & cyclic & 54.29 & 88.48 & 93.20 \\
1-2 & cyclic & 39.73 & 69.14 & 78.54 \\
1-3 & monotonic & 36.00 & 42.00 & 62.31
## 3 IoV Enpowered IVI in 3G and 4G
### Limitations of IVI Solutions in 3G and 4G  
As aforementioned, IVI functions and applications are continuously enriched with higher performance gains of IoV. The dilemma of information island is gradually solved and meanwhile the demands of IVI users become more diverse. However, we think that the media potential of vehicles has not yet been fully released. There still exists some issues impeding the further development of IVI. The cons of IoV-based IVI solutions are listed as follows:* Firstly, IVI systems have numerous types corresponding to various automobile factories and models as listed in Tables 3 and 4. Except for universal IVI systems (e.g., Carplay), the proprietary IVI systems are only suitable for the target models, which degenerate the interoperability between either different brand vehicles or other intelligent systems (e.g., smart home system, governmental dedicated network, and traffic infrastructures).
* Secondly, IVI functionalities are mainly concentrated in the interior space. As a natural extension, the vehicle surface which is relatively flat and quite large, is completely ignored, except for video-undeliverable bus advertising. It has the congenital condition to serve as carriers to display useful information for the surrounding people.
* Thirdly, vehicles' software and hardware resources are not fully utilized. The collaboration among the swarm of vehicles and other intelligent systems is quite necessary to accelerate the resource sharing in the era of 5G/B5G.  
In order to break through the existing limitations of IVI solutions, considering the restrictions of the vehicle-mounted media scenarios stated in Section 2.1, it is urgent to propose novel interesting IVI solutions for the MaaS.  
**4. 5G-V2X Media Scenario: IVI Solutions for the MaaS**  
Nowadays, vehicles have become an important mediation between home and the workplace, which can achieve seamless connection in a fast paced lifestyle. With the commercial launch of 5G as well as high-level autonomous driving, the IVI will thoroughly release its potentials and a lot of imaginings about the MaaS will become true. On one hand, 5G-V2X empowered vehicles will create new interesting functionalities. On the other hand, the usage scopes and user engagements could be enlarged, i.e., IVI users consist of not only the occupants in the cabin, but also the observers in exterior space, whose IVI requirements need to be satisfied in vehicle-mounted scenarios.  
For our proposed IVI solutions in the MaaS, we intend to enlarge the scope of vehicle-mounted media scenarios to the urban environments and achieve ubiquitous connectivity for improving user engagements, i.e., the deep interaction and interoperation with existing smart ecosystems can be realized. Therefore, we present the Linkage Display and IVI Collaborative modes for MaaS, which aim to break limitations of the current IVI solutions.
## 3 Results and Discussion
### XPS Analysis  
To further verify the successful co-doping of Eu and Na elements in ZnO nanocrystals, XPS data were investigated to evidence the different chemical composition, transition states and functionality of both doping elements.  
Figure 6a shows the full-survey spectrum of the Eu/Na co-doped sample and the presence of expected Zn, O, Eu, and Na elements, which demonstrates the purity of the ZnO phase. Figure 6b) depicts highly symmetric peaks centered at 1021.5 eV and 1044.5 eV, attributed to Zn 2P\({}_{3/2}\) and Zn 2P\({}_{1/2}\) levels, respectively, which is consistent with the expected material [30]. Furthermore, the dominance of the Zn 2p\({}_{3/2}\) peak over the Zn 2p\({}_{1/2}\) one indicates that most of Zn at the surface exist in the ionic form Zn\({}^{2+}\)[18]. The Eu scan (Figure 6b) shows two intense peaks centered at 1134.1 eV and 1163.6 eV are assigned to Eu\({}^{3+}\)(Eu\({}^{3}\)d\({}_{5/2}\)) and Eu\({}^{3+}\)(Eu\({}^{3}\)d\({}_{3/2}\)) spin-orbit splitting core levels, which evidence the successful doping of the europium ions in the ZnO lattice [31]. However, the peaks centered at 1136.7 eV and 1166.3 eV can be attributed to Eu\({}^{2+}\)(Eu\({}^{3}\)d\({}_{5/2}\)) and Eu\({}^{2+}\)(Eu\({}^{3}\)d\({}_{3/2}\)) spin-orbit splitting core levels. Their presence provides a partial reduction of Eu\({}^{3+}\) to Eu\({}^{2+}\) in the ZnO host to keep charge valence when Eu\({}^{3+}\) substitutes Zn\({}^{2+}\)[32]. In addition, the high resolution of O1s was deconvoluted into principal peaks (see Figure 6c). The intense one located at 530.28 eV could be assigned to the lattice oxygen bound with zinc (Zn-O). While the other peak at 531.7 eV is more probably relative to the oxygen defects on the nanocrystal surface. The peak at 1071.7 eV is assigned to the Na1s level, for Na doping [33].
## 4 Experimental Validation
### Results and Discussion
#### 4.2.2 Network Training
Based on the observation of Figures 14 and 15, the following conclusions can be drawn. Figure 12: The streaming accuracy of the probed methods. Figure 13: The average accuracy of the probed methods. Figure 14: Comparison analysis of the ensembled and single convolutional GAN. Figure 15: Quantitative analysis of increased accuracy after application of HSE. 1. The HSE effectively reduces the accuracy fluctuation of a single GAN under different training epochs. 2. One can observe that the accuracy of the ensembled model remains at the upper bound of the single GAN, indicating that the proposed method can improve the performance of convolutional GAN in a general sense. 3. With a smaller number of training epochs ranging from 50 to 300, the HSE method can effectively improve the model accuracy by an average of 10% or more, as shown in Figure 15. It also indicates that the proposed ensemble method can improve the model efficiency with a smaller training budget
4. The model performance is directly related to the number of base models, i.e., the robustness of the ensembled model increases with the number of base models. In summary, we have demonstrated that HSE improves the overall performance of the single GAN in terms of model stability and accuracy.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 4 Example Analysis and Solution Validation  
### Theoretical Solution  
In order to compare the theoretical results with the subsequent numerical simulation results, the three cases shown in Table 1 were considered herein. It should be emphasized here that the inclination angle of the anchor cable represents the angle of the cable axis relative to the horizontal ground surface (i.e., \(\alpha\) shown in Figure 2). The material properties of the soil used for this study were as follows: the density was 1770 kg/m\({}^{3}\), the elastic modulus was 13.5 MPa, the shear modulus was 5 MPa, the friction angle was 10.6\({}^{\circ}\), and the cohesion was 8 kPa.  
The particle swarm optimization algorithm was used to obtain the optimal solution, which produced the optimal upper-bound solution for the end resistance (i.e., Equation (9)). Table 2 presents the calculation results. In this table, the lateral resistance was obtained by Equation (8), and the ultimate pullout capacity was computed by summing the end resistance and lateral resistance.  
Only case M3 is presented here as an example, to limit the length of the manuscript. In this case, the values of the calculation parameters in the particle swarm optimization algorithm were as follows: \(w=0.8\), \(c_{1}=c_{2}\)\(=0.5\), and \(r_{1}\) and \(r_{2}\) were random variables between 0 and 1. Figure 11 presents the relationship between the optimal solutions (i.e., the ultimate end resistance) and the number of iterations calculated by the particle swarm optimization algorithm in the case M3. It can be seen that when the population number was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Anchorage** & \multirow{2}{*}{**Free Segment**} & **Anchorage** & **Inclination** & & **Statum Friction** & **Buried Depth** \\  & **Segment** & & **Length (m)** & **Diameter (m)** & **Cable (\({}^{\circ}\))** & **Angle (\({}^{\circ}\))** & **(m)** \\ \hline M1 & 10 & 9 & 0.6 & 30 & 10.6 & 7 \\ M2 & 12 & 9 & 0.6 & 30 & 10.6 & 7 \\ M3 & 10 & 9 & 0.6 & 45 & 10.6 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Control parameters of expanded anchor cable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Lateral Resistance \(P_{1}\) (kN)** & **End Resistance \(P_{2}\) (kN)** & **Ultimate Pullout Force \(P\) (kN)** \\ \hline M1 & 610.1 & 217.4 & 861.7 \\ M2 & 761.5 & 217.4 & 1019.3 \\ M3 & 580.8 & 144.9 & 782.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Theoretical solutions.  
\(N=50\) and the number of iterations was \(G=80\), the calculation approached a convergence state. Thus, the best solution value for the ultimate end resistance was about 144.9 kN.  
The critical failure model for the logarithmic spiral region obtained from the spatial discretization technique is presented in Figure 12 for the case M3, with the help of MATLAB software. The 3D velocity discontinuity surfaces all intersected with the potential slip surface.
# Band-Pass Sampling in High-Order BOC Signal Acquisition
## 4 Performance Analysis
### Detection Probability  
With the presence of a signal, the decision statistic \(X\) in (18) is a non-central \(\chi^{2}\) random variable with \(\
u=4\) degrees of freedom and the non-central parameter \(\lambda=\left|\widetilde{R}(\varepsilon,\Delta f_{dsc},\Delta f_{d})\right|^{2}\). Its probability density function (PDF) is given by \(p(x)=1/2\sqrt{x/\lambda}e^{-1/2(x+\lambda)\,I_{1}(\sqrt{\lambda x})}\). Given the detection threshold \(\eta\), the detection probability can be expressed in terms of the generalized Marcum Q-function \(Q_{M}(\alpha,\beta)\)[21] as \(P_{d}=Q_{1}\Bigl{(}\sqrt{\lambda},\sqrt{\eta}\Bigr{)}\).  
Figure 6 depicts the detection probabilities of the proposed method, dual-side band method, and the match filter method. For the proposed method, the sampling frequency and the intermediate frequency are \(f_{s}=12.7875\,\mathrm{MHz}\) and \(f_{IF}=25.575\,\mathrm{MHz}\), respectively. The integration period is \(1\) ms.  
Figure 5: Dual-loop based acquisition structure.  
Figure 6 shows that match filter method outperforms the other two methods, and the performance of the proposed method and dual-side band method is nearly the same.  
Figure 7 illustrates the detection probability under different sampling frequencies and intermediate frequencies, which are listed in Table 1.  
Figure 7 shows that the detection performance suffers from a slight degradation with a lower sampling frequency. The reason is that more signal component is filtered out.