esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Sensitivity Analysis of the Meta-Ensembles
#### 4.2.3 Distribution of Residuals  
Normal frequency distribution of the residuals does provide another ground for which a ML model effectiveness and authenticity can be assessed. Figure 11 indicates the normal distribution and histogram of the meta-heuristic models under three validation methods. Notice how both the models trained and validated by TVS and kFCV tend to be biased towards predicting values that are higher and lower, respectively, than the actual values of intrinsic compression index thus confirming their behaviour as demonstrated previously by their residual plots. On the other hand, ML prediction carried out with the models trained and validated using MCCV shows a much-balanced distribution as shown by the curve and indicating much better prediction. The corresponding histograms are also used to validate the accuracy of ML prediction. In this case, a very good model will tend to have its residuals peaking at zero but with few of the stochastic errors at its extremes while a low performing model will have its residual distribution spreading out but with fewer errors around zero. Using this theory, it could be observed that the best performing meta-ensemble models do have their training and validation done by adopting the MCCV method. It should also be mentioned that the voting technique across the three cross validations tend to slightly perform better than the technique of voting in general.
## 4 Results and Discussion
### Third Step Results and Discussion  
As the third step permitted the evaluation of the robustness of the selected optimal model and the identification of the optimal subset of the best wavelengths predicting the required property, the results are related to the measure of the reliability of the selected optimal model varying S (from 1 to 10, so selecting a decreasing number of wavelengths) and changing EXTCV from 20% to 70% with increments of 5%.  
Figure 7 shows the FNR and FPR SCORE for the PLS-0-0 model. The optimal value for S is 5, where the higher SCORE is reached for both FNR and FPR. The S value selects the optimal wavelengths to be considered for the model, the subset being composed of 261 wavelengths.  
Figure 7: FNR and FPR SCORE for PLS-0-0 model. S value measures number of replicates of the second step (from 1 to 10) in which the specific wavelength was selected.  
Figures 8 and 9 show the values of FNR and FPR, respectively, versus EXTCV for the optimal \(\mathrm{S}\geq 5\). An exponential fit (\(\mathrm{y}=\mathrm{a}*\mathrm{exp}(\mathrm{b}*\mathrm{x})\)) has been considered on these data and the threshold of 0.01% and 0.1% have been used to express a SCORE (with regard to FNR and FPR respectively) for each subset of wavelengths. A higher SCORE measures a better statistical performance. The SCORE for FNR represents the EXTCV value at which the FNR reaches 0.01% (using the exponential fit of the data), while the SCORE for FPR represents the EXTCV value at which the FPR reaches 0.1%.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
###### Abstract  
The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is proposed in which the evaluation function of A* algorithm is improved by taking the safety cost into account. This results in a safer path which can stay farther away from obstacles. Then a new path smoothing method is proposed, which introduces a path evaluation mechanism into the smoothing process. This method is then applied to smoothing the path without safety reduction. Secondly, with respect to path planning problems in complex terrains, a complex terrain environment model is established in which the distance and safety cost of the evaluation function of the A* algorithm are converted into time cost. This results in a unification of units as well as a clarity in their physical meanings. The simulation results show that the improved A* algorithm can greatly improve the safety and smoothness of the planned path and the movement time of the robot in complex terrain is greatly reduced.  
s 2018; 11, 44; doi:10.3390/a11040044
# Research on Key Technologies of Quantum-Safe Metro-Optimized Optical Transport Networks
## 2 Methods
### OSU Service Mapping/Multiplexing
Figure 1: OSU-based M-OTN hierarchy. The mapping and multiplexing of OSU services to OPUs are implemented based on transmission cycles. Each transmission cycle sequentially indexes \(p\) payload blocks (PBs) as \(PB_{1}\) to \(PB_{p}\). During the mapping of OSU services to OPUs, a maximum of \(C\) PBs is allocated per service per cycle. For \(m\) multiplexed OSU services, their respective PB allocations \(C_{1},C_{2},\ldots,C_{m}\) must satisfy \(\sum_{i=1}^{m}C_{i}\leq p\), ensuring that the OPU provides sufficient bandwidth to accommodate all services. To guarantee bandwidth, a maximum of \(C\) PBs should be allocated to each OSU in each transmission cycle during the mapping and multiplexing of OSU traffic to OPUs. The mapping and multiplexing mechanism employs a method analogous to signal-delta modulation: \(C\) out of \(p\) PBs are evenly distributed to a given OSU service in each transmission cycle, as illustrated in Figure 3. The actual number of PBs used by each OSU service depends on its traffic volume, and any vacant PBs can be assigned to the OSU IDLE frame. Consequently, OSUs implement hard-pipeline isolation, a stringent resource isolation mechanism that ensures the exclusive transmission of service flows through fixed resource reservation (PBs) within the OPU. This approach eliminates the need for services to compete for resources, thereby reducing transmission jitter. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**OPU Type** & **\(\boldsymbol{R_{\text{OPU\_PLD}}}\) (Kbps)** & \(\boldsymbol{p}\) **Values** & **PB Actual Bit Rate (Mbps)** \\ \hline OPU0 & 1,238,954.310 & 476 & 2.602845189 \\ OPU1 & 2,488,320.000 & 956 & 2.602845188 \\ OPU2 & 9,995,276.962 & 3840 & 2.602936709 \\ OPU3 & 40,150,519.322 & 15,426 & 2.602782272 \\ OPU4 & 104,355,975.330 & 40,096 & 2.602653016 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Corresponding \(p\)-values and PB values for different OPUs. Figure 2: OPU frame structure based on payload blocks.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 3 Proposed Statistical Dimensioning Model
### Distribution of the Sensor Power Consumption  
We assume that the transmitted power is fixed to \(P_{\mathrm{RR}}\) per RR and the total power over \(N_{\mathrm{max}}\) RRs does not exceed \(P_{\mathrm{max}}=N_{\mathrm{max}}P_{\mathrm{RR}}\). The power is then a discrete random variable and its Probability Mass Function (PMF) is given in Proposition 4.  
**Proposition 4** (Total sensor power PMF).: _For the general case, the sensor power PMF is,_  
\[\mathrm{Prob}\{P_{t}=kP_{\mathrm{RR}}\}=\frac{N_{s}}{N_{g}}\ \mathbb{E}_{r}\ \mathbb{E}_{t,P_{t}}\ \mathbb{E}_{A_{f}}\left[\mathds{1}_{\{A_{fj}\leq A_{f}<A_{fj-1}\}}\sum_{i\in \mathbb{N}^{*}}\mathrm{Prob}\left\{\left|\mathcal{B}(r)\right|=in_{u}-1\right\} \right], \tag{28}\]  
_with \(1\leq k\leq N_{\mathrm{max}}\), \(N_{g}\) the number of the groups of \(n_{u}\) sensors in the typical cell such that,_  
\[N_{g}=\int_{0}^{\infty}2\pi\lambda_{a}r\exp(-\lambda_{b}\pi r^{2})\exp(- \lambda_{a}\pi r^{2})\sum_{i\in\mathbb{N}^{*}}\frac{(\pi\lambda_{a}r^{2})^{( in_{u}-1)}}{(in_{u}-1)!}dr \tag{29}\]  
_and \(N_{s}=\lambda_{a}/\lambda_{b}\)._  
Proof.: The proof is similar to Proposition 3 and the PMF can be obtained by averaging over the whole power in the typical cell normalized by the size of this latter. In each ring delimited by the \((i-1)\times n_{u}^{\mathrm{th}}\) and \(i\times n_{u}^{\mathrm{th}}\) neighbor, the \(n_{u}\) sensors adjust their power to the the \(i\times n_{u}^{\mathrm{th}}\) furthest neighbor.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 3 Hardware Structures for Nonlinear Operation  
The linear operation of a neural network can solve relatively simple problems. The nonlinear activation function is the root of the artificial neural network's powerful expression ability. This affects the speed of network convergence and the accuracy of recognition. As shown in Figure 7, the position of nonlinearity in the system is behind the linear neural network.  
There are a lack of practical photonic devices that express nonlinear functions. Currently, photon calculation is an optoelectronic hybrid computing architecture. The nonlinear  
Figure 6: Optical reservoir plan (**a**) MRR structure [36]. The data matrix is input into the reservoir network composed of MRR, and after network circulation, the data matrix is output. (**b**) Silicon-based delay line [37]. The data are input into a network composed of delay lines, which make the front and back input signals interact with each other. PD is used to detect and convert optical signal into electrical signal, which is processed by a micro-processor.  
Figure 7: Ideal PAIC system implementation diagram. The dotted line is nonlinear structure.  
calculation part is all performed in the electrical domain. In this way, multiple photoelectric and electro-optical conversions are involved in the network. This not only limits the speed of the photonic neural network, but also brings additional energy consumption. Ideally, some optical nonlinear devices can realize the nonlinear calculation, and they have the features of low threshold, reconfigurability, easy integration, and fast response. The current ideas are divided into two types: special materials (saturated absorbers and two-dimensional graphene materials) and the combined structure of the optical modulator (MZI, MRR and SOA).
## 3 Results and Discussion
### Method Validation
#### 3.2.6 Limit of Detection (LOD) and Limit of Quantification (LOQ)  
The detection and quantification limits were determined (Table 2) based on Equations (1) and (2):  
\[\text{LOD}=3.3\times\frac{S.D.}{Slope} \tag{2}\]  
\[\text{LOQ}=10\times\frac{S.D.}{Slope} \tag{3}\]  
where S.D. is the standard deviation of the intercept and slope is the slope variable of the calibration curve.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**APIs**} & \multicolumn{4}{c}{**Intraday Precision**} & \multicolumn{4}{c}{**Inter-Day Precision**} \\ \cline{2-7}  & **Concentrations** & \multirow{2}{*}{**\%RSD (n = 3) 1 st Day**} & \multirow{2}{*}{**\% Recovery**} & \multirow{2}{*}{**\%RSD (n = 6)**} & \multicolumn{4}{c}{**\%RSD (n = 3)**} \\ \cline{3-3}  & **(\(\mu\)g:mL\({}^{-1}\))** & & & & **2 nd Day** & **3 rd Day** & **Total** \\ \hline \multirow{3}{*}{FERA} & 0.1 & 2.0 & 102.0 & & 2.0 & 1.2 & 2.0 \\  & 0.3 & 1.4 & 98.7 & 0.1 & 1.2 & 0.1 & 1.0 \\  & 2.1 & 0.1 & 99.6 & & 0.3 & 1.5 & 0.2 \\ \hline \multirow{3}{*}{RSV} & 0.2 & 0.1 & 101.0 & & 0.8 & 1.4 & 2.0 \\  & 0.47 & 0.1 & 100.1 & & 2.0 & 1.7 & 1.5 \\  & 3.1 & 1.6 & 100.5 & & 0.3 & 0.4 & 1.0 \\ \hline \multirow{3}{*}{QR} & 0.6 & 0.1 & 100.2 & & 1.6 & 0.1 & 1.2 \\  & 1.4 & 1.3 & 97.1 & 0.1 & 1.4 & 1.6 & 2.0 \\  & 9.4 & 1.3 & 100.8 & & 0.2 & 0.2 & 1.9 \\ \hline \multirow{3}{*}{RTN} & 0.3 & 1.0 & 100.8 & & 1.4 & 0.2 & 1.6 \\  & 0.6 & 0.5 & 100.7 & 0.2 & 0.3 & 0.5 & 1.2 \\  & 4.2 & 1.5 & 97.6 & & 0.2 & 0.4 & 0.4 \\ \hline \multirow{3}{*}{\(\alpha\)-TOC} & 3.2 & 0.7 & 99.7 & & 2.0 & 0.3 & 1.7 \\  & 8.1 & 1.6 & 100.0 & 0.6 & 0.0 & 0.4 & 1.3 \\ \cline{1-1}  & 27 & 1.7 & 100.2 & & 0.4 & 0.2 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the reliability tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analytes**} & \multicolumn{2}{c}{**Concentration**} & \multirow{2}{*}{**Calibration Curves**} & \multirow{2}{*}{**\%y-Intercept**} & \multirow{2}{*}{**Correlation**} & \multirow{2}{*}{**LOD**} & \multirow{2}{*}{**LOQ**} \\  & **Range (\(\mu\)g:mL\({}^{-1}\))** & & & & **Coefficient (\(\mu\)g:mL\({}^{-1}\))** & & **(\(\mu\)g mL\({}^{-1}\))** \\ \hline RSV & 0.4–3.2 & y = 186,124x \(-\) 1035.5 & 0.75 & 0.998 & 0.13 & 0.39 \\ FERA & 0.2–2.1 & y = 128,958x \(-\) 199.1 & 0.3 & 0.999 & 0.04 & 0.12 \\ QR & 1.2–9.4 & y = 98,150x \(-\) 1391.0 & 0.6 & 0.998 & 0.48 & 1.45 \\ RTN & 0.6–4.2 & y = 87,131x \(-\) 705.8 & 0.8 & 0.999 & 0.22 & 0.66 \\ ATOC & 6.4–27.0 & y = 4171.8x \(-\) 1260.8 & 2.0 & 0.998 & 2.28 & 6.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical results for linearity.
# Augmented Reality: A Systematic Review of Its Benefits and Challenges in E-learning Contexts
## Augmented Reality
### Application of AR in E-Learning  
Vast transformations are currently taking place in learning practices and paradigms. Presently, the continued rise of the internet and related technologies has led to the introduction of new pedagogical approaches based on the sensation of time and space in the use of knowledge. In the realm of e-learning, shrewd instructors are looking for an appropriate mix of innovative teaching technologies and methodologies to enhance the efficacy of instruction. AR was developed for this reason. This innovative technology has allowed e-learning learners to receive instruction using a mixture of the virtual and real world in varying proportions to achieve a degree of immersion that no virtual equipment can provide [16]. It is a novel medium that combines various elements from social computing to tangible and ubiquitous computing. Given its capabilities, [4] argues that AR (especially game-based AR) brings to the e-learning segment the unique affordances and benefits of adaptive learning, including enhancement of student motivation, engagement, involvement, and comprehension.  
Given the evolution of AR, other related technologies, and technological concepts, [17] have identified the need for e-learning platforms to shift from simplistic and monolithic frameworks dealing with the fundamentals of course management, design, and delivery, to more \"flexible\" paradigms involving active learners and the use of external stimuli that augment their motivation. With innovative technologies, such as virtual reality (VR) and AR, e-learning institutions now have a robust and flexible learning environment that serves learners with improved dynamicity and personalization [17]. This convenience is underscored by the ability of innovations, such as VR and AR to present information by orchestrating a number of functionalities that are grounded on service-oriented architectures. For example, [18] emphasize that AR is vital to e-learning because it encourages kinesthetic and 3D perspectives. As demonstrated subsequently, such advantages allow students who are e-learners that struggle with the visualization of phenomena to envisage such phenomena in the real world, even in extreme complex scenarios. As pointed out by [19], and importantly for this review, AR in e-learning contexts entails combining digital information within an existing environment (physical, real-world settings), where the elements are augmented or supplemented through computer-generated sensory inputs that may include video, sound, GPS data, graphics, and visualizations.
# Water Modification by Cold Plasma Jet with Respect to Physical and Chemical Properties
## 2 Experimental Setup and Materials
### Basic Characterization of the Liquids  
The probed liquids in this work are lab-prepared, demineralized, double distilled water (DDW) and phosphate-buffered saline (PBS). Four different physicochemical parameters are studied. (i) Temperature: it is measured in-situ (during treatment) or ex-situ (before/after treatment) by employing a non-invasive technique, i.e., a GaAs-crystal based, optical thermometer (LumaSense Technologies Inc.; Santa Clara, CA, USA) [31]. The data are collected by a datalogger, with recommended standard 232 communication, and time resolution of 0.5 s. (ii) Conductivity: it is measured using a bench meter (Mettler-Toledo; FP30) equipped with a probe with an integrated temperature sensor (LE703); (iii) pH: it is measured using a bench meter (Mettler-Toledo; FP20) equipped with a pH micro-probe (LE422). (iv) Redox potential: it is measured by employing the same bench meter as for pH, but with an appropriate redox electrode (Mettler-Toledo; LE501). Quantities (ii)-(iv) are reduced to the actual temperature of the liquid, with the latter to be measured within the first 10 s before/after plasma treatment. Conductivity and pH meters are both calibrated before any series of experiments, using standard liquids provided by Mettler-Toledo (solution of \(\sigma\) = 1413 \(\upmu\)S cm\({}^{-1}\); buffers of pH = 4.0, 7.0, and 9.2).  
Evaporation tests are realized with micro-balance measurements (Highland; HCB 123; readability 0.001 g; repeatability 0.002 g). The initial, i.e., the before plasma treatment, quantity of the water is fixed at 4.5 \(\pm\) 0.007 g. As will be seen in the results below, liquid evaporation indeed takes place during plasma treatment; however, even for a prolonged time (30 min), it does not exceed a percentage of about 15%. Accordingly, the present treatment protocol makes a compromise and does not include any refill which could disturb the experiment.  
It is noted that all experimental points in this work are obtained cumulatively, i.e., for each series of experiments, a water specimen of 4.5 \(\pm\) 0.007 g is used and subjected to the process for successive time intervals. At the end of each interval, the liquid is probed ex-situ and then brought back to the process immediately. In this way, the study of the cumulative action of the plasma on a specific specimen is secured for each series of experiments. The liquid is inserted in a standard 10 mL beaker, while the distance between the orifice of the plasma reactor and the surface of the liquid is set at about 25 mm. The argon physical flow and the electro-hydrodynamic effects [32] induced by the plasma jet itself provide adequate agitation of the liquid specimen (see inset in Figure 1).  
Figure 2: Representative discharge waveforms in absence of liquid target. (**a**) Voltage (black) and DBD current (red). (**b**) Voltage (black) and Jet current (red). Operating conditions: 11 kV\({}_{\text{pp}}\), 17.5 kHz, and 1.5 slm.
## 4 Construction of Octree-Based Shape Information Model
### Construction and Visualization of an Octree-Based Shape Information Model  
Figure 10 shows the target image obtained from the 3D laser scan data with 1,367,274 points extracted from the target spillway wall. The octree-based shape information model was constructed according to the flow chart shown in Figure 11. The first step was to convert the scan data from pixels to voxels (in 3D pixels). The second step was to convert the voxel model to the octree data structure, and the last step was the creation of an octree-based shape information model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scan Parameter** & **Setting Value** & **Scanning View** \\ \hline Left & \(-11^{\circ}\) \\ \hline Right & \(11.5^{\circ}\) \\ \hline Top & \(90^{\circ}\) \\ \hline Bottom & \(-45^{\circ}\) \\ \hline Resolution Mode & Highest Resolution \\ \hline \hline \end{tabular}
\end{table}
Table 4: Setting values of terrestrial laser scanner.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Width (mm)** & **Length (mm)** & **No.** & **Width (mm)** & **Length (mm)** \\ \hline
1 & 0.2 & 1070 & 11 & 0.1 & 770 \\
2 & 0.2 & 1810 & 12 & 0.1 & 1560 \\
3 & 0.2 & 1840 & 13 & 0.1 & 670 \\
4 & 0.2 & 650 & 14 & 0.1 & 810 \\
5 & 0.2 & 960 & 15 & 0.1 & 1330 \\
6 & 0.2 & 1350 & 16 & 0.1 & 890 \\
7 &The first step (i.e., the creation of a voxelization model) involved converting the point-cloud data to the voxel type. In this step, the point cloud was read on the basis of each X, Y, and Z direction. The read data was reconstructed as a voxel model and was used to generate the root node in the octree data structure. The voxel model stores the x, y, and z coordinate values and depth, height, and width values of the 3D object model. These were used as input parameters in building the octree information model. The algorithm consists of a numerical code for generating an octree data structure via a voxel model, and it determines the octree resolution by setting a res value. After creating the octree, the inner nodes of the octree were iteratively divided until they reached the res value. After the segmentation was completed, the center point (x, y, z) of the 3D object and the depth, height, and width values were sequentially read and stored in the inner nodes of the octree. If all the point values were stored, the empty areas were removed and the octree file was finally created.  
Figure 11: Process of building the octree information model.  
Figure 10: The scan image extracted from the TLS data.  
Octovis [50], a viewer program with a 3D tool kit, was used to identify the shape information of the generated octree. The number of points on the target wall was originally 1,367,274, and the file size was 19.56 MB and it was reduced to about 30%, 50%, and 80% as presented in the Table 5 to confirm that cracks could be detected even at a small number of points. Also, the extracted scan images according to the compression ratio is shown in Figure 12.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 3 Analysis of Stability Factors for GER with Paste Backfill  
Using paste backfill, GER technology forms an effective load-bearing structure in gob by filling it with paste materials. This structure supports the roof, alters the movement characteristics of overlying strata, mitigates mining-induced stress manifestations, and enhances the stability of the surrounding rock [41; 42]. Therefore, the stability of GER is influenced by this load-bearing structure. Based on the filling process and the physical and mechanical properties of paste materials, factors such as the backfill ratio and the load-bearing characteristics of the paste material significantly affect the stability of GER.