esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Based on the reported results, coaches can know the PST in which their player substitutions can alter the FR of the match when their team is tying. Coaches usually made the first player substitution in the period from the 60:01 to the 70:00 minute, the second player substitution from minute 70:01 to 80:01 and the third from minute 80:01 to 90:00. When teams are losing, as the match progresses, the probability of losing becomes greater; therefore, the PST is important for coaches. [PERSON] [40] also reported that most of the player substitutions occurred between 60 and 85 minute. [PERSON] et al. [15] indicated that most first and second player substitutions occurred in the period from the 61 st to the 90 th minute, while third player substitution predominantly occurred in the period from the 76 th to the 90 th minute. These findings provide coaches with information on substitution patterns that they can integrate into their management approach. The decision tree served as a confirmatory element of the results presented above, and affirmed that the MS is an indicator to take into account when making player substitutions and that it acts as a predictor variable of the FR. In the UCL, [PERSON] et al. [5] reported that MS was one of the key factors to take into account when studying player substitutions and performance in soccer. On the other hand, positive impact predicted a win when teams were tying and winning, and to a lesser extent when they were losing. Along this line, it has been shown that player substitution creates a new factor in the match, in which the substituted player increases the probability of scoring more goals [41], regardless of the MS. A period of time is required after the player substitutions are made in which the substituted players are in a process of adaptation to the intensity of the game, and the team in a process of adaptation to the tactical changes produced by the player substitutions [27]. It was also observed that winning and facing a negative impact from a player substitution predicted a win to a greater extent than when teams were losing and got a positive impact. [PERSON] et al. [27] pointed out that the probability of scoring was substantially higher if the teams were losing at the time of player substitution. Finally, it should be noted that the differences shown according to the SC could be induced by the rules, i.e., in the knockout stage there must be a winning team that advances to the next round.
# Virtual Gymnasium: Personalized Weight Perception Interface in Lifting Virtual Objects
## 6 User Study
### Results of Experiment 2  
#### 6.4.1 Weight Perception  
Figure 15 shows the results of the weight perception questionnaire measurements for the three two-handed exercises. According to the results, the dumbbell curl process shows the best weight perception, and the barbell curl shows the worst weight perception. Because the participants could only lift one virtual dumbbell with their hand during the curl lifting process, they were generally satisfied with the dumbbell curl process. As the length of the movement trajectory was extended and the standard force increased in the shoulder curl process, the weight perception results of the shoulder curl process were slightly lower than those of the dumbbell curl process. Some participants had difficulty performing the barbell curl process owing to the challenge of perceiving the weights of the two hands simultaneously and recognition errors during the lifting process. Table 9 describes the results of the ANOVA test on the weight perception questionnaire for the dumbbell, shoulder, and barbell curls. The results were statistically significant (\(p=0.05\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **ANOVA Result (pa = 0.05)** & **Mean and SD Results, Stored as in Figure 14** \\ \hline \multirow{3}{*}{WP1} & F = 3.873, & Dumbbell (M = 5.933333, SD = 0.638) \\  & pa = 0.028594 & Shoulder (M = 5.533333, SD = 0.542857) \\  & MSw = 0.625397 & Barbell (M = 6, SD = 0.695238) \\  & F = 5.543147, & Dumbbell (M = 6.066667, SD = 0.749) \\  & pa = 0.0073 & Shoulder (M = 5.4, SD = 0.685714) \\  & MSw = 0.625397 & Barbell (M = 5.13333, SD = 0.552381) \\  & F = 4.7233, & Dumbbell (M = 5.6, SD = 0.4) \\  & pa = 0.0141117 & Shoulder (M = 4.933333, SD = 0.495238) \\  & MSw = 0.653968 & Barbell (M = 4.733333, SD = 1.066667) \\  & F = 7.09396, & Dumbbell (M = 5.8, SD = 0.457143) \\  & pa = 0.002217 & Shoulder (M = 5.466667, SD = 0.4) \\  & MSw = 0.473 & Barbell (M = 4.866667, SD = 0.552381) \\  & F = 3.37931, & Dumbbell (M = 5.4, SD = 0.685714) \\  & pa = 0.043567 & Shoulder (M = 5.133333, SD = 0.695238) \\  & MSw = 0.7365 & Barbell (M = 4.6, SD = 0.828571) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Order of exercising tasks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**New User Group** & **Order of Tasks** \\ \hline \multirow{3}{*}{WP2} & NU1 & Dumbbell \(\rightarrow\) Shoulder \(\rightarrow\) Barbell \\  & NU2 & Shoulder \(\rightarrow\) Barbell \(\rightarrow\) Dumbbell \\  & NU3 & Barbell \(\rightarrow\) Dumbbell \(\rightarrow\) Shoulder \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of the ANOVA test of the weight perception in Experiment 2.
## 5 Low-Power Cellular Technologies for IoT  
Wireless technologies play different important roles in the development of IoT. The comparison of key technologies enabling IoT deployments was presented in Table 2. The goal is to develop IoT technologies that maximise ROI in communication infrastructure and still maintain reliable connection and secure data transmission. While wired technology is a unique component of IoT networks in special use cases--mostly for on-premises applications and for backhaul connection to 5G or 4G infrastructure--wireless technologies are increasingly being used for cost reduction, mobility, deployment flexibility, and power consumption reasons. For deployment flexibility, a smart manufacturing environment for instance can be re-configured within a day without having to re-do the infrastructure, while on the other hand, the re-laying of fibre might take days or weeks to complete the same task.  
For the seamless convergence of wired and wireless networks, it is necessary to advance the throughput, latency, and capacity of wireless technologies to meet the requirements of critical IoT applications in the 5G/IoT era [55]. The communication requirements, QoS, and use cases for WiFi (802.11) standards and wireless IoT technologies are different [56]. Factors such as hardware design, area of application, and the technology of implementation impact the technology of choice. In the use cases of IoT that require the transmission of a small amount of latency-insensitive data over a long distance at lower costs, such as water quality and environmental monitoring, licensed technologies should be considered if QoS is needed. Unlicensed technologies should be considered (in addition to licensed options) if QoS/reliability is not needed. Other wireless technologies such as ZigBee, Wifi, and Z-wave are alternatively used to support short-range applications but are categorised as power-hungry technologies [57].  
3 GPP initially standardised licensed technologies such as NB-IoT and LTE-M in release 13, and some improvements were introduced in subsequent 3 GPP releases to provide a more efficient, secure and trusted IoT environment [30]. Currently, NB-IoT can achieve coverage enhancement of between 18 km and 25 km in urban and rural locations [9], operates through GSM and LTE infrastructure in a 200 kHz [58] and 180 KHz [59] wide carriers respectively, and supports low-power consumption. LoRaWAN and Sigfox are examples of unlicensed LPWAN technologies with LoRaWAN as a popular option in this category operating in the sub-GHz ISM band. However, deploying LoRa and NB-IoT together is a potential opportunity to scale the coverage and capacity of IoT networks [9], however, the backhaul of each network should be provisioned separately since their QoS/reliability requirements are different.
## 3 Results and Discussion
### Antibacterial Activity  
The SiO\({}_{2}\), SiO\({}_{2}\)-HNO\({}_{3}\), SiO5, and SiO5-HNO\({}_{3}\) systems were tested against four bacterial strains (Figure 11). All systems exhibited an amount-dependent antimicrobial effect, but it was evident that the SiO5-HNO\({}_{3}\) inhibition halos, compared with the other systems, were larger against all assayed bacteria (see orange curves in Figure 11). This enhanced antimicrobial activity is due to a combination of the presence of NO\({}_{3}\)- ions (whose residue's presence was confirmed by the characteristic nitrate stretching band in the relative FTIR spectrum) and the hydroxybenzofuranone structure derived from quercetin oxidation. Indeed, the former could be released in the form of nitrogen oxides during incubation with different bacterial strains, influencing the environment and affecting bacterial growth and vitality [63]. Indeed, studies have demonstrated that derivatives of nitric oxide (NO) can hinder bacterial growth and exert inhibitory effects on a wide range of bacteria, including Gram-negative, Gram-positive, and acid-resistant ones. This is due to NO's ability to easily diffuse through biological membranes. Once inside the bacterial cell, NO can interfere with vital processes such as DNA and protein synthesis [64,65]. Meanwhile, the latter acts as an antibacterial agent due to its benzofuran moiety, as the benzofuran structure with hydroxyl groups as substituents can interact with bacterial cellular structures, disrupting their vital processes and leading to cell death [66]. Moreover, the evidence of nitrates' antimicrobial effect can also be observed in the case of the SiO\({}_{2}\)-HNO\({}_{3}\) system. Indeed, it  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **pH Value after 24 h Release** \\ \hline SiO5 in SBF pH 7.4 & 7.47 \(\pm\) 0.06 \\ \hline SiO5 in SBF pH 5.0 & 5.20 \(\pm\) 0.12 \\ \hline SiO5-HNO\({}_{3}\) in SBF pH 7.4 & 7.43 \(\pm\) 0.11 \\ \hline SiO5-HNO\({}_{3}\) in SBF pH 5.0 & 4.80 \(\pm\) 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: pH value of samples after 24 h of kinetic release in SBF.  
showed larger higher inhibition halo diameters (see blue curves in Figure 11), especially with respect to _P. aeruginosa_ and _S. aureus_. The antimicrobial effect of this system was also better than that of the SiQ5 system (see yellow curves in Figure 11), which, in turn, showed an increase in the inhibition halo diameters compared the SiO\({}_{2}\) (Si) control. In this case, the higher antimicrobial activity can be related to the incorporated drug. Despite the loss of the C3-C2 double bond in the C ring (see Figure 7), which is well known for its involvement in antibacterial activity, the presence of polyhydroxybenzoic groups in the A and B rings remains essential for the antibacterial activity [67].
## 2 Antennas with a Single Reconfigure Feature
### Bandwidth-Reconfigurable Antennas  
The operational bandwidth can also be changed from narrow-band to wide-band, which is usually achieved by employing a matching-network or reconfigurable-filter structure in the feed-line of the antenna [21; 22; 23]. The antenna must have good performance over the whole band of interest to cover both the narrow-band and wideband operating spectrums. Another method is to use a modified ground plane, especially for patch or monopole antennas. In [24], a bandwidth reconfigurable antenna was introduced for wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications.  
The proposed antenna configuration is illustrated in Figure 2a. It has a rectangular radiation patch with a rotated F-shaped slot connected to a microstrip feed-line and a truncated ground plane in the back-side of an FR-4 substrate. A pair of PIN diodes in the middle of the slot on the radiation patch controls the current flow. By switching the operating states of the diodes, the frequency-bandwidth characteristic of the antenna can be reconfigured from narrow-band to wide-band, as illustrated in Figure 2b. The impedance bandwidth ranges from 22% to 78% which makes the antenna suitable for use in various wireless communication systems. In addition, the antenna can also have a reconfigurable characteristic to switch between a multi-resonance and wideband/UWB characteristic. This could be used to avoid interference between the UWB systems and other wireless systems such as fourth generation (4G), WLAN, WiMAX, and satellite communication systems [25]. This characteristic increases the flexibility of the antenna, combining different services in a single unit, suitable for multi-mode wireless communications.  
Figure 1: (**a**) Fabricated prototype of the reconfigurable antenna and (**b**) its frequency response for different voltage values [20]. Reproduced with permission from [20], Copyright IEEE, 2014.  
Figure 2: (**a**) Bandwidth-reconfigurable antenna configuration and (**b**) its performance for different states of the PIN diodes [24]. Reproduced with permission from [24], Copyright IEEE, 2016.
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 3 Results
### Validation of SAM Segmentation Accuracy
#### 3.3.2 Results for Gordal  
The Gordal set shows very high pixel-wise concordance between SAM and the manual ground truth. The mean IoU is 0.963 \(\pm\) 0.003, and Dice is 0.981 \(\pm\) 0.002, with recall = 1.00 (no false negatives) and precision mirroring IoU (\(\approx\)3% over-segmentation). Area-level metrics confirm the good agreement (MAPE = 3.9% and RMSE \(\approx\) 912 px\({}^{2}\)), while the Pearson correlation of areas is virtually perfect (\(r\) > 0.98). The coefficient of determination against the identity line, \(R^{2}_{y=x}\) = 0.88, indicates a small but systematic positive bias (\(\approx\)4%), much lower than for Hoijblanca (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Mean \(\pm\) SD** & **Median (IQR)** & **Interpretation** \\ \hline IoU & 0.963 \(\pm\) 0.003 & 0.963 (0.0034) & Excellent spatial overlap \\ Dice & 0.981 \(\pm\) 0.002 & 0.981 (0.0017) & Confirms IoU result \\ Precision & 0.963 \(\pm\) 0.003 & 0.963 (0.0034) & \(\approx\)3–4\% over-segmentation \\ Recall & 1.000 \(\pm\) 0.000 & 1.000 (0.0000) & No false negatives \\ \hline MAE (px\({}^{2}\)) & 910 & – & Small absolute size error \\ MAPE (\%) & 3.87 & – & Size bias \textless{} 5\% \\ RMSE (px\({}^{2}\)) & 911 & – & Consistent with MAE \\ Pearson \(r\) & \textgreater{}0.98 & – & Rank/order preserved \\ \(R^{2}_{y=x}\) & 0.881 & – & Uniform \(\
eq\) 4\% bias \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation metrics for the Gordal set.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 2 Related Works
### Learning-Based Feature Extractor  
Mainstream handcrafted extractors, including ORB [17], SIFT [18], and Shi-Thomas [19], are employed in visual SLAMs like ORB-SLAM2 [5] and VINS-Mono [20]. However, these methods rely on gradient information face challenges in dynamic lighting and low-texture environments. To address these issues, researchers explored deep feature extractors. SuperPoint [14] employs a shared encoder for high-level information and distinct decoders for detectors and descriptors, using synthesized labels for self-supervised training. D2-Net [21] eliminates the need for a separate keypoint decoder, ensuring robustness. To enhance repeatability detection, Key.Net [22] combines multi-scale gradients with depth feature maps. It achieves state-of-the-art performance on the HPatches [23] with 53.4% repeatability under viewpoint changes. Additionally, HF-net2 [24] is a multi-task teacher-student model for keypoint detection and description in unstructured environments. It reduces visual SLAM localization RMSE by 52% in low-light sandy terrains. Despite their success, the mentioned deep extractors exhibit inherent flaws in the face of diverse scenes, leading to potential performance degradation.
# Geovisualization Techniques of Spatial Environmental Data Using Different Visualization Tools
## Materials and Methods
### Tools of Geovisualization  
#### 2.2.1 KML and Google API  
The Keyhole Markup Language (KML) is an XML notation developed for describing data about features with a geospatial location, with the objective of geovisualization [51]. Most GIS software and Google Earth are capable of importing and exporting KML files, but for non-professional users it is the graphical user interface that is preferred to generate KML files. The language determining the format belongs to the family of markup languages and organizes its elements into a hierarchical class tree structure where elements can be inherited from the ancestors. The KML Version 2.2, under the name  
Figure 1: Location of study area and investigated wells.  
OpenGIS KML 2.2 Encoding Standard, has been adopted as an OGC (Open Geospatial Consortium) implementation standard for presenting geographic information visually. The Google application programming interfaces (APIs) are fundamentally native JavaScript interfaces which can be used to control the functioning of the map (e.g., Map type, layer management, size, KML management, etc.). Additionally, this helps with managing the KML files so that all of its functions can be used [52].
## Low Threshold Plasmonic Nanolaser Based on Graphene  
**[PERSON], [PERSON] \({}^{\ast}\), [PERSON], [PERSON] and [PERSON]**  
Optical Information Technology Lab, School of Optoelectronics and Energy, Wuhan Institute of Technology, Wuhan 430205, China; [EMAIL_ADDRESS] (L.X.); [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (F.Y.); [EMAIL_ADDRESS] (Y.L.)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +86-027-8799-2024  
Received: 13 October 2018; Accepted: 2 November 2018; Published: 8 November 2018  
**Featured Application: The paper describes the theoretical modelling of a surface plasmon laser, based on Ag and CdS nanowires spaced with either SiO\({}_{2}\) and graphene. A quite extensive parametric study is carried out, exploring the effects of the variation of the geometrical parameters. Results of the study are compared with the references. The most relevant result of the investigation is that the use of graphene as a space layer has apparently a very positive impact on the losses of the structure, and to lower the threshold for plasmon oscillation.**  
**Abstract: A hybrid plasmonic nanolaser based on nanowire/air slot/semicircular graphene and metal wire structure was designed. In this structure, the waveguides in the nanowires and the graphene-metal interface are coupled to form a hybrid plasma mode, which effectively reduces the metal loss. The mode and strong coupling of the laser are analyzed by using the finite-element method. Its electric field distribution, propagation loss, normalized mode area, quality factor, and lasing threshold are studied with the different geometric model. Simulation results reveal that the performance of the laser using this structure can be optimized by adjusting the model parameters. Under the optimal parameters, the effective propagation loss is only 0.0096, and the lasing threshold can be as low as 0.14 \(\upmu\)m\({}^{-1}\). This structure can achieve deep sub-wavelength confinement and low-loss transmission, and provides technical support for the miniaturization and integration of nano-devices.**  
**Keywords: hybrid plasma; nanolaser; finite element; threshold; mode properties**
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 5 Conclusions and Future Work
The Abel inversion can be further improved to include the podTEC data from both positive and negative elevation angles, making it less dependent on the orbital altitude of the RO receiver. It is also feasible to develop an algorithm for the joint retrievals of \(TEC\) and \(N_{e}\) using the \(\Delta hTEC\) profile, which should work in the cases where the absolute \(hTEC\) calibration is challenging. Finally, we will conduct a retrieval exercise for the tomographic inversion of \(TEC\) and \(N_{e}\) once the GNSS-RO constellations begin to provide a sufficient sampling density. The following are available online at [[https://zenodo.org/record/6057](https://zenodo.org/record/6057)]([https://zenodo.org/record/6057](https://zenodo.org/record/6057)) 919#YJOldhNBzyl, Figure S1: As in Figure 10 but for COMSIC-1 monthly \(N_{e}\) in the /E-region as a function of local solar time (LST) and latitude from 2006-2020. Figure S2: As in Figure 12 but for COSMIC-1 \(N_{e}\) maps at 70 and 110 km from all months in 2006-2020. Figure S3: As in Figure 13 but for the 2-hourly zonal mean from Spire data in Sep, Oct, and Nov 2021, and in Jan 2022. Figure S4: Spire \(N_{e}\) monthly climatology at the magnetic equator (\(6^{\circ}\)S-\(6^{\circ}\)N) as a function of local time and height from September, October, November, December of 2021, January of 2022. The NOAA CWDP NRT data delivered to CDAAC for September 2021 have 17 days, and the other months are complete. Figure S5: As in Figure 14 but for the solar-cycle variations at 60 and 80 km. Figure S6: As in Figure 20 but for \(TEC\) maps from November and December 2021 and January 2022. Conceptualization, D.L.W. ; methodology, D.L.W. ; software, D.L.W. ; validation, D.J.E. and N.S. ; formal analysis, D.L.W. ; investigation, D.L.W. ; resources, D.L.W. ; writing, D.L.W. and D.J.E. ; funding acquisition, D.L.W. All authors have read and agreed to the published version of the manuscript. The work is supported by NASA's Living with Star (WBS 936723.02.01.12.48) and Sun-Climate research (WBS 509496.02.03.01.17.04) funds to Goddard Space Flight Center (GSFC). The RO data used in this study were obtained from UCAR COSMIC Data Analysis and Archive Center (CDAAC) except for Fengyun-3 (FY3). The FY3 RO Level-1 profiles were obtained from the published NRT data at Fengyun Satellite Data Center (FSDC). The F10.7 data were obtained from the GSFC/SPDF OMNIIWeb interface at [[https://omniweb.gsfc.nasa.gov](https://omniweb.gsfc.nasa.gov)]([https://omniweb.gsfc.nasa.gov](https://omniweb.gsfc.nasa.gov)) (accessed 1 March 2022). UCAR CDAAC services for data processing and distribution are acknowledged. The authors declare no conflict of interest.