esa-sceva/satcom-chunk-collection huggingface dataset:
# Scaling Equipment Effect on Technical-Tactical Actions in U-13 Basketball Players: A Maturity Study
## 3 Results
### Ball Obtained
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Early (n = 25)**} & \multicolumn{2}{c}{**Late (n = 12)**} & \multicolumn{2}{c}{**Total (n = 37)**} \\ \cline{2-7}  & **FEB** & **Modified** & **FEB** & **Modified** & **FEB** & **Modified** \\ \hline Ball Obtained & 99.16 \(\pm\) 48.28 & 95.64 \(\pm\) 40.3 & 79.08 \(\pm\) 32.46 & 97.58 \(\pm\) 30.98 & 92.65 \(\pm\) 44.35 & 96.27 \(\pm\) 37.11 \({}^{*}\) \\ Static Ball Obtained & 9.40 \(\pm\) 4.58 & 13.32 \(\pm\) 8.54 & 13.83 \(\pm\) 4.15 & 8.58 \(\pm\) 4.42 & 10.84 \(\pm\) 4.87 & 11.78 \(\pm\) 7.72 \\ Dynamic Ball Obtained & 89.76 \(\pm\) 47.04 & 82.32 \(\pm\) 35.26 & 65.25 \(\pm\) 34.38 & 89.00 \(\pm\) 30.31 & 81.81 \(\pm\) 44.4 & 84.49 \(\pm\) 33.46 \\ Pass Reaches & 8.16 \(\pm\) 6.3 & 7.80 \(\pm\) 4.99 & 4.75 \(\pm\) 2.99 & 6.75 \(\pm\) 3.65 & 7.05 \(\pm\) 5.64 & 7.46 \(\pm\) 4.57 \\ Pass Not Reaches & 66.76 \(\pm\) 33.51 & 68.24 \(\pm\) 32.9 & 54.83 \(\pm\) 25.18 & 56.58 \(\pm\) 35.36 & 62.89 \(\pm\) 31.22 & 64.46 \(\pm\) 33.76 \\ Pass Out-Out & 59.32 \(\pm\) 31.79 & 62.84 \(\pm\) 32.48 & 45.67 \(\pm\) 22.21 & 58.00 \(\pm\) 30.63 & 54.89 \(\pm\) 29.43 & 61.27 \(\pm\) 31.55 \({}^{*}\) \\ Pass Out-In & 5.64 \(\pm\) 3.55 & 4.92 \(\pm\) 3.28 & 4.75 \(\pm\) 4.05 & 6.17 \(\pm\) 4.15 & 5.35 \(\pm\) 3.68 & 5.32 \(\pm\) 3.58 \\ Pass In-In & 2.56 \(\pm\) 2.8 & 2.08 \(\pm\) 1.93 & 2.83 \(\pm\) 2.55 & 3.17 \(\pm\) 4.22 & 2.65 \(\pm\) 2.69 & 2.43 \(\pm\) 2.86 \\ Pass In-Out & 5.64 \(\pm\) 3.72 & 4.76 \(\pm\) 3.95 & 4.42 \(\pm\) 3.12 & 4.17 \(\pm\) 2.92 & 5.24 \(\pm\) 3.54 & 4.57 \(\pm\) 3.62 \\ Out Court Pass & 0.68 \(\pm\) 1.18 & 0.52 \(\pm\) 0.71 & 0.83 \(\pm\) 0.94 & 0.75 \(\pm\) 0.97 & 0.73 \(\pm\) 1.1 & 0.59 \(\pm\) 0.8 \\ \hline \hline \end{tabular}  
* Statistical significance \(p<\) 0.05.  
\end{table} 
Table 1: Mean values and standard deviation of Ball Obtained variables, according to maturating timing and tournament. Figure 2: Ball Obtained evolution, according to maturating timing and tournament. In the Static Ball Obtained variable (Figure 3), a significant increase was observed for the Modified Tournament and Early Maturity players (\(p\) = 0.008), and a significant decreased tendency was observed for the Late Maturity players (\(p\) = 0.081). On the other hand, in the Dynamic Ball Obtained variable (Figure 4), there was a significant increase in the Modified Tournament in the Late Maturity players (\(p\) = 0.018), and a slight decrease that was not significant in the Early Maturity players. Finally, there was a significant increase in the number of Out-In Passes by Late Maturity players (\(p\) = 0.093), and a slight non-significant decrease in the Early Maturity players (see Figure 5). Figure 4: Dynamic Ball Obtained evolution, according to maturating timing and tournament. Figure 3: Static Ball Obtained evolution, according to maturating timing and tournament.
## 4 Results
Peak signal fluctuations of 0.193 mV and 0.277 mV were retrieved for SP1 and SP2, respectively. These fluctuations are higher than the noise level for both ADMs. However, the certified gas mixture of 1% C\({}_{2}\)H\({}_{6}\):N\({}_{2}\) flowing into the ADM using the gas mixture has an uncertainty of 1% on the concentration value, corresponding to a fluctuation of 100 ppm in ethane concentration. The signal fluctuations expected from a 100-ppm gas concentration uncertainty are \(\Delta\)C1 = 0.129 mV for SP1 and \(\Delta\)C2 = 0.630 mV for SP2. Thereby, the standard deviation expected considering the 1-\(\sigma\) noise level and gas concentration fluctuations results in \(\Delta\)S1 = 0.160 mV for SP1 and \(\Delta\)S2 = 0.645 mV for SP2. Comparing \(\Delta\)S1 and \(\Delta\)S2 with the measured peak signal fluctuations, it can be observed that the noise fluctuations measured using the T-Shaped QTF spectrophone are lower than expected, denoting a better mechanical stability of S08-T based sensor (SP2) with respect to the standard QTF based one (SP1). Once determined the most performing spectrophone in terms of SNR, the linearity of SP2 photoacoustic response over a wide range of ethane concentrations in pure N\({}_{2}\) was verified. The gas line depicted in Figure 3 was modified in order to connect a pure N\({}_{2}\) gas tank to a separate flow controller of the gas blender to obtain different dilutions starting from the certified concentration of 1% C\({}_{2}\)H\({}_{6}\):N\({}_{2}\) down to 100 ppm C\({}_{2}\)H\({}_{6}\):N\({}_{2}\). The measurements reported in Figure 7 were performed at the same experimental conditions adopted to acquire the 2f-signal scan of the ethane absorption shown in Figure 5 for the SP2 spectrophone. As can be seen from Figure 7, the QEPAS signal acquired with SP2 spectrophone shows a linear dependence on ethane concentration over two orders of magnitude, from 100 ppm up to 1%, with a slope of 0.00627 mV/ppm. Figure 6: QEPAS peaks trend as a function of the acquisition time, obtained by peak extraction in a continuous spectral scan acquisition.
## 2 Dynamic Centrifuge Tests
### Instrumentation Layout  
The study mainly focuses on the shaking acceleration of the sand, excess pore pressure, and ground settlement. Therefore, the instrumentations used include accelerometers, pore pressure transducers, and laser displacement transducers. As shown in Figure 4, the instrumentations are arranged by the following principles: (1) monitoring of the shaking table movement: accelerometers are placed on the shaking table surface to monitor the output motion of the table; (2) monitoring of the acceleration response of the sand: A row of single-axis accelerometers (A1-A5) are equally spaced on the left side of the model's central axis, and placed as close to the center as possible. (3) monitoring of pore pressure response in the sand: a row of pore pressure transducers (P1-P4) is equally spaced on the right side of model's central axis, and P1\({}^{\prime}\), P3\({}^{\prime}\) are placed at the same depth as P1, P3, respectively, for verification; (4) monitoring of ground settlement: a laser displacement transducer D1 is arranged in the center of the model to monitor the settlement of the model.
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 4 Erase Algorithm
### Cell Variation Improvement  
As shown in the previous description of program operation, several similar techniques in the erase operation have been proposed to improve cell variation caused by 3D NAND process. An on-chip erase speed detection method has been proposed to improve the problem that the erase speed increases toward the lower WL due to the electric field concentration effect [57]. This is because when the coaxial capacitor is biased, the electric field on the inner surface is greater than the electric field on the outer surface according to [PERSON]'s law. Before actual erase operation is performed, a pre-erase pulse is applied to check the erase speed for each WL, and an offset bias is automatically applied to each WL to erase all WLs toward the desired target Vt level. However, this method also adds erase time overhead, so a method to reduce the time overhead, such as correcting the erase speed at the wafer-level test stage, not during chip operation, is needed.  
Meanwhile, [PERSON] et al. proposed a method to maintain the entire PE window by lowering the Program verify level toward the lower WL without correcting the erase speed variation for each WL [50]. This method has the advantage of not requiring additional time, such as the pre-erase pulse above, but retention loss is likely to become more severe as it descends to the lower WL, because the retention characteristic is dominated by number of holes in the CTL (i.e., erased Vt) [85].
###### Abstract
Furthermore, the method can significantly reduce the impact of bad radial wind estimates and can provide effective results in low carrier-to-noise ratio (CNR) regions. The effectiveness of the proposed CWML is validated via calibration experiments, comparison experiments, and continuous observation experiments. Furthermore, the effect of atmospheric wind fields on the variation in VOCs is reported. The remainder of this paper is organized as follows: In Section 2, the materials and methods are described. In Section 3, the results and discussion are listed. Finally, the conclusions are presented in Section 4.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Data Imputation
#### 2.3.2 NULL Values Insertion  
After identifying the missing samples, the next step is to insert the NULL values to fill the space of the missing samples. Before inserting the NULL values, it was verified whether the missing samples count is greater than the sample frequency rate, i.e., the number of samples recorded in one second; then, the NULL values are not inserted, and the excerpt is ignored. Thus, if the missing samples count in each excerpt is more than 100 missing samples in case of the accelerometer and gyroscope or more than 10 missing samples in case of the magnetometer, then the excerpt is ignored. It is done to be closer to the originality of the data than filling all synthetic samples to fill the space of missing samples. On the other hand, if the missing samples count is less than or equal to the sample frequency rate, then NULL values are inserted after every constant time interval, i.e., after 1/100 s in case of an accelerometer and gyroscope and 1/10 s in case of a magnetometer, to fill the space of missing samples.  
Figure 2: Workflow of the data imputation scheme.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 4 Applications Related to the Water Cycle
### Hydrogeology  
Storms with high-intensity, long-duration rainfall have a clear impact on the water cycle, and they often trigger rapidly-moving landslides, resulting in casualties and property damage. The monitoring of landslide-prone areas has normally been conducted through in situ geological mapping. However, satellite precipitation data makes it possible to conduct global-scale landslide hazard assessments. The potential of the TMPA product to advance our understanding of rainfall-triggered landslides, and also to investigate our predictive ability, was first explored by Hong et al. [292; 293]. A stochastic algorithm was then developed to conduct near real-time estimates and forecasts of landslide-prone areas by combining a calculation of landslide susceptibility (derived from slope and soil characteristics) with satellite derived rainfall estimates [294]. A global landslide catalog (GLC) quantifies the relationship between landslide occurrence and climate variations [295; 296] (see Figure 10). Moreover, satellite approaches are particularly effective in understanding and quantifying cascading hazard processes which are not necessarily induced by heavy rainfall only, but also from seismic activity or snow melt on vulnerable complex terrain [297].  
Rainfall contributes also to soil erosion through its kinetic energy, which determines erosivity and is, in turn, greatly dependent on rainfall intensity. Satellite rainfall data contribute to erosivity mapping, especially in data sparse areas such as the African continent [298].
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 3 Proposed MAC Protocol: AdHoc-FDMAC
### Control Frame  
In this proposed AdHoc-FDMAC protocol, we use a number of control frames, where some are the same as the frames that exist in the IEEE 802.11 standard, and these are RTS, CTS and ACK. However, some control frames are modified to support the IBFD communications. The modified control frames are CTS with Acknowledgement Indicator (CTS-AI), CTS with Secondary Receiver Address (CTS-SRA), which are depicted in Figure 3. A one-bit field named the Acknowledgement Indicator (AI) is appended with CTS frame to obtain CTS-AI, where the AI value '1' means the PR has data to the PT as well as '0' means the PR does not have data to the PT, and this new frame is used for the BFD and source-based TNFD communication. Similarly, to obtain CTS-SRA, a 6 bytes Secondary Receiver Address (SRA) field is comprised with the CTS frame, and it is utilized for destination-based TNFD communications. As the destination is out of the range of the transmitter, this paper applies the Ad hoc On-demand Distance Vector (AODV) routing algorithm to select the route in ad hoc networks. The Route Request (RREQ) and Route Reply (RREP) are the two frames of the AODV algorithm, which are also shown in Figure 3[20].
# Highly Sensitive and Selective H\({}_{2}\)S Chemical Sensor Based on ZnO Nanomaterial
## 3 Results and Discussions  
### Structural and Morphological Characterization  
Figure 1a reports the XRD spectrum of the nanomaterial obtained on a silicon substrate. As can be seen, the as-prepared structure transformed to crystalline ZnO after the thermal treatment procedure at 500 \({}^{\circ}\)C. All the strong diffraction peaks in the spectrum were indexed to hexagonal wurtzite ZnO with lattice constants of a = 0.325 nm and c = 0.520 nm (JCPDS files no. 36-1451). The relative strength of the observed diffraction peaks perfectly matches with the hexagonal phase (JCPDS files no. 36-1451) [58; 59]. Figure 1b shows the SEM images of the obtained ZnO nanomaterial with different resolutions. The morphological analyses indicate that the prepared materials have a nanosized structure, which consists of nanoparticles connected to each other and forming chains with lengths of a few microns. The average thickness of nanoparticles is 30 nm. Furthermore, the nanostructuration of metal oxide materials is an important issue, which determines their chemical sensing properties [16; 46]. The synthesized ZnO material fully covered the surface of alumina substrates. Energy-dispersive X-ray (EDX) analyses were performed on different areas of the structure. The obtained EDX results confirmed the presence of Zn and O elements in the material with the Zn:O atomic ratio of 1:1 (Table 1).  
Figure 1: (**a**) X-ray diffraction (XRD) pattern of the ZnO nanomaterial obtained on a silicon substrate, (**b**) SEM images of the ZnO nanomaterial with different magnifications.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 4 Data Analysis Result
### Hybrid Model  
Since the adjusted COST231 model produced a more realistic and accurate estimation, its parameters are used as the inputs of the neural network. The total number of the LoS-blocking walls is extracted automatically during the execution of the COST231 model. However, the total attenuation of these walls are identified after the optimization of the COST231 model, which serves as the second input to the ANN. The third input is also calculated after the optimization of the path loss exponent \(n_{A231}=2.4\), which is a fixed parameter, while \(L_{FS}(d)\) is changing from one location to another. To consider the spatial influence of the building on propagation, such as fixtures and other clutter that is not included in the model, the fourth and fifth inputs are the position of the measurement locations inside the building. This \((x_{i},y_{i})\) pair is also extracted from the blueprint image of the COST231 model automatically. The schematics of the hybrid model during and after the training are depicted in Figure 5a,b, respectively.  
The network is then trained using all of the collected data on the eighth floor. Implementing the ANN with the adjusted COST231 assistance drastically facilitated the training process and yet increased the accuracy of the prediction. The desired output of the network was the difference between the optimized adjusted COST231 prediction (\(F_{A231}\)) and the RSSI measurement on the eighth floor; in other words, \(E_{A231}=RSSI-F_{A231}\). Therefore, the only responsibility of the network was to discern the deficiencies of the adjusted COST231 model, rather than learn the whole propagation mechanism. For the seventh floor, the ANN made an estimation (\(T_{E_{NN}}\)) of the adjusted COST231 error (\(E_{A231}\)),  
Figure 4: Comparison of wall attenuations for both the COST231 model and its adjusted variation.  
Figure 5: Flowchart of the hybrid model, (**a**) depicting the model input/output during the training; (**b**) demonstrating the input/output after the artificial neural network (ANN) is trained.  
which is depicted in Figure 6a. Figure 6b compares both of the optimized estimations of the adjusted COST231 (\(F_{A231}\)) and assisted ANN models (\(F_{A231}+T_{E_{NN}}\)) with the practical measurements (RSSI). This is further explained in the discussion.
## 6 Simulations and Results
### Evaluation of the Multiple Terminals
This is due to the fact that when the channels are relatively good, the probability of channels with mild fading is large. The transmission without path selection also exhibits a large probability to experience mild fading, so the gaps of the energy consumption among the direct path, relay paths and the adaptive path selection are reduced. However, there is still superiority of the adaptive path selection since the optimal transmission power \(P_{r}^{opt.,i,j}(t)\) and an optimal transmission path \(v_{i}^{opt. }\) are performed during the whole transmission. Figure 6: Energy consumption for the direct path, relay paths and adaptive path selection _versus_ the number of terminals at the system level. In the simulations above, the number of HAPs is maintained at four. A more practical IHS scenario is that the number of HAPs is nondeterministic. Therefore, we also investigate the energy consumption _versus_ the number of HAPs. The number of HAPs used is 0, 1, 2, 4, 8 and 16. We assume that the  
Figure 8: Energy consumption for the direct path, relay paths and adaptive path selection _versus_ the Rician factor at the system level (\(N_{T}=100,\)\(\kappa_{1}=12\) dB). Figure 7: Energy consumption for the direct path, relay paths and adaptive path selection _versus_ the Rician factor at the source level (\(N_{T}=100,\)\(\kappa_{1}=12\) dB). locations of the added HAPs obey a uniform distribution in the disaster area. Figure 9 depicts the energy consumption _versus_ the number of HAPs with \(\upkappa_{1}=12\:\mathrm{dB}\), \(\upkappa_{2}=7\:\mathrm{dB}\) and \(N_{T}=500\) at the source level. The energy consumption of the relay path is calculated as the average energy consumption of all possible relays. We can observe from Figure 9 that the energy consumption of the relay path and proposed adaptive algorithm decreases as the number of HAPs is increased. The adaptive algorithm is more energy-efficient than the direct path and the relay path. When the value of \(N_{H}\) is small (e.g., zero or one), the added HAPs can significantly reduce the energy consumption. However, when the value of \(N_{H}\) is large (e.g., eight or 16), the added HAPs can also reduce the energy consumption, but indistinctively. Therefore, permanently increasing the number of HAPs is not necessary. On the contrary, the small number of HAPs will lead to a cost-effective system. Additionally, Table 3 summarizes the saving of energy _versus_ the number of HAPs with \(\upkappa_{1}=12\:\mathrm{dB}\), \(\upkappa_{2}=7\:\mathrm{dB}\) and \(N_{T}=500\) at the system level. The unit of the
## 1 Introduction  
Zirconia (ZrO\({}_{2}\)) exhibits excellent mechanical, thermal, and chemical properties even in harsh environments, making it a widely studied material. Despite being a ceramic material, it possesses high toughness comparable to metals and is therefore applied in various material development fields, including structural and functional applications [1, 2, 3, 4]. Zirconia is frequently utilized under ultra-high-temperature conditions because of its high melting point and chemical durability; however, its crystal structure and mechanical properties change significantly with temperature. Zirconia is stable below 1170 \({}^{\circ}\)C in the monoclinic phase, between 1170 \({}^{\circ}\)C and 2370 \({}^{\circ}\)C in the tetragonal phase, and above 2370 \({}^{\circ}\)C in the cubic phase. The phase transition from tetragonal to monoclinic is accompanied by volume expansion and results in the formation of cracks and grain boundary fractures, which rapidly decrease the strength [5; 6; 7]. The dopant species and its concentration determine whether zirconia is classified as partially stabilized zirconia (PSZ) or fully stabilized zirconia (FSZ). The phase transformation to the monoclinic phase induces crack formation, but PSZ and FSZ enhance their mechanical properties by absorbing the stress generated during this process. Thus, the stabilization of zirconia phases is governed by the type and amount of the dopant [8; 9; 10]. Various doping strategies have been studied to improve the high-temperature mechanical properties of zirconia through the use of stabilizers such as Y\({}_{2}\)O\({}_{3}\), MgO, Al\({}_{2}\)O\({}_{3}\), Bi\({}_{2}\)O\({}_{3}\), and CaO [11; 12]. Among the various stabilizers, CaO-stabilized zirconia demonstrates enhanced hardness and oxidation resistance at high temperatures compared with zirconia stabilized with other oxides [13; 14; 15]. Although CaO-PSZ exhibits excellent mechanical and thermal behavior, extended exposure to temperatures above 1500 \({}^{\circ}\)C can result in phase destabilization due to the inherent instability of Ca as a dopant. Therefore, ongoing research is necessary to address the destabilization of CaO-PSZ and to further enhance its high-temperature mechanical and thermal performance.  
Previous studies have examined the trend in the mechanical properties of CaO-PSZ as the doping amount of CaO increased from 5 to 10 mol\(\%\), and it was found that 9 mol\(\%\) CaO-stabilized zirconia (CSZ) showed superior mechanical properties [16]. To further enhance the phase stability and mechanical properties of CSZ under harsh thermal conditions, MnO\({}_{2}\), which can exhibit various valence states and substitute the cations in the lattice, was doped into 9 mol\(\%\) CSZ. This trend is expected to be different from that observed when ZrO\({}_{2}\) is doped solely with CaO or MnO\({}_{2}\). The effects of MnO\({}_{2}\) doping on the structural and mechanical properties of CSZ were then investigated before and after thermal shock to assess its durability under thermal stress.
# Coastal Wind Measurements Using a Single Scanning LiDAR
## 3 Results
### Data Availability and Accuracy Indicators  
Overall, the wind measurements using the 100s and the VVP method agreed with the V1 values in the current study. Our validation results suggest that using offshore wind measurements via a single scanning LiDAR would be a promising way to assess the nearshore wind conditions. However, the comparisons above were made with references located only 400 m from the scanning LiDAR position. In reality, the wind turbines would be situated a few kilometers off the coast even for nearshore wind farms. Accordingly, the data availability further offshore and the indicators for assessing the data quality were investigated by analyzing the CNR value and the goodness of fit obtained from the curve fitting in the VVP method.