esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison with Other Rapid Detection Methods  
The best-performing ML pipeline, when deployed on a laptop with a 2.9 GHz Dual-Core Intel Core i5 processor and 8 GB of memory, demonstrated an average detection time of 1.7 seconds for classifying the NIR spectrum of a fish sample. This processing speed is notably faster compared to other rapid detection kits, such as those referenced in [7; 9], which require several minutes to complete the classification. Furthermore, a significant advantage of the proposed approach is the absence of sample preparation, a step that is often necessary in alternative methods.  
Despite these advantages, certain limitations must be acknowledged. The initial investment in a NIR scanner and computing infrastructure for real-time sample sensing and inference remains relatively high, and the classification accuracy is currently sub-optimal. However, ongoing advancements in the development of low-cost NIR sensors and miniaturized computing systems are expected to reduce the cost of implementing a rapid, on-site detection system based on NIR spectroscopy and ML [38]. Additionally, it is anticipated that the collection of larger NIR datasets will further enhance the performance of the ML model.
## 4 Numerical Simulations and Discussion
### Performance Comparison
#### 4.2.1 RMSE  
The RMSE of SAR imaging model is defined as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{L}\sum_{l=1}^{L}\frac{\|\mathbf{y}-\mathbf{A}\hat{\mathbf{y}}_{ l}\|_{2}^{2}}{\|\mathbf{y}\|_{2}^{2}}}, \tag{15}\]  
where \(\hat{\mathbf{y}}_{l}\) is the estimate of scatter coefficients in the \(l\)th Monte-Carlo independent trial.  
(1) Different SNRs  
The configuration of this experiment is the same as Section 4.1.1. The SNR range is 3\(\sim\)30 dB, and \(L\), the number of independent trials, is set to 500. The RMSE curves are shown in Figure 12.  
It is demonstrated from the RMSE curves that the performance of the proposed method is better than the others under the condition with low SNR. As the SNR increasing, the RMSE curves of the reference methods are approximately approaching to the one of the proposed method. Because of the sensitivity to noise, the reconstruction error of SVA algorithm is considerable in low SNR. In this respect, the sparse-reconstruction-based methods are robust to noise. The simulation results show that the performance of CoSaMP is slightly worse than that of OMP. The reason is that CoSaMP only chooses \(2k_{max}\) atoms at each iteration, which can help to improve the efficiency of the algorithm, but may reduce the success rate of reconstruction. In comparison, the proposed method performs much better, even if the noise interference is serious. This is because the Kronecker information of the echo signal is fully utilized.  
(2) Different number of scatterers  
The number of scattering points in the target area will directly affect the imaging quality. In the following experiment, the performance of the proposed method versus different number of scatterers will be investigated. The configuration of this simulation is the same as Section 4.1.1. The SNR is set to 5 dB, and the RMSE curves are shown in Figure 13.  
Figure 12: RMSE comparison versus different SNRs (\(\gamma=0.5\), \(K=200\), \(L=500\)).  
Figure 13: RMSE comparison versus different number of scatterers (\(\gamma=0.5\), SNR = 5 dB, \(L=500\)).  
The simulation result indicates that the performance of proposed method is superior to the reference methods with all the test scenarios. When the numbers of scatterers is less than 30, the performance of all reference methods seems to be close. With the number of scatterers increasing, the performance of SVA, limited by the resolution of itself, becomes worse. The sparse-reconstruction-based methods with super-resolution maintain better robustness even as the scatterers become dense. Exploiting the structured feature of target, the proposed method provides the advantage for imaging, and achieves effective sidelobe control. Of all the curves of RMSE versus scatterer quantity, the one of the proposed method grows most slowly and always has the lowest RMSE value, according to the curves shown in Figure 13. Even the number of scatterers is larger than 90, the RMSE of the proposed method is still lower 3 dB than that of the SVA method.
###### Abstract  
This paper investigates the potential of performing orbit determination directly in the Earth-fixed frame based on Inter-Satellite Ranging (ISR) measurements as primary observables, combined with Ground-to-Satellite Ranging (GSR) measurements from a small regional ground network. Current Global Navigation Satellite Systems (GNSSs) use L-band pseudo-range and carrier phase measurements from global or regional ground station networks to perform dynamic Orbit Determination and Time Synchronization (ODTS), whereas sparse Satellite Laser Ranging measurements are mainly used for validation. Future GNSSs may be equipped with inter-satellite links (ISLs) to enable inter-satellite clock offset estimation, ranging and data relay. These capabilities carry the potential to significantly improve ODTS procedures. In this work, we assume a fully connected constellation via pair-wise ISLs, with measurement topology assigned by appropriate link schedulers. The satellite orbits are parametrized with the standard 15 Galileo broadcast perturbed Keplerian elements, estimated by using ISR and GSR measurements. This processing strategy eliminates the complex modeling of gravitational and non-gravitational forces, making it particularly suitable for on-board applications and offering an alternative to classical GNSS orbit determination processing architectures. The proposed orbit determination scheme can be used in case of a ground segment failure as a back-up procedure to estimate the orbits of the GNSS satellites onboard of each satellite and guaranteeing a continuous navigation message generation for the system users. The performance of the proposed method depends on a number of factors, such as the length of the data fitting interval, the measurement quality (precision and accuracy), the scheduling and geometry of ISR and GSR measurements, the number and distribution of ground stations, and the accuracy of the ground station coordinates. Preliminary results show that an orbit-only Signal-in-Space Range Error (SiSRE) in the order of 7-9 cm can be obtained by processing 2 to 3 h data with a limited set of supporting ground stations. In this study, the orbit determination scheme proposed is tested on different scenarios, providing a first assessment of attainable performance.
## 4 Experimental Evaluation  
The proposed system for cooperative driving environment perception was evaluated experimentally in test-track environments. Two test vehicles were utilized for the experiments where both vehicles were equipped with V2X communication devices such that the exchange of the BSM between the two test vehicles was enabled. In order to evaluate the benefits of introducing V2X communications to the currently available environment perception systems, the host vehicle in this work was additionally equipped with radar and camera systems that have already been adopted in production vehicles.
## 5 Analysis of the Steric Parameters  
### Number of Molecules Adsorbed per Captured Site (n)  
The parameter n provides significant details regarding the adsorption of CO\({}_{2}\) on ZSM-5. In particular, this parameter is employed to determine the adsorption position of CO\({}_{2}\) molecules on the surface of ZSM-5 and their degree of aggregation. Previous research has revealed that three possibilities can take place to analyze the adsorption position according to the 'n' values [27; 28]:  
Possibility 1: if n < 0.5, for this condition, the receptor site adsorb a portion of a molecule per site defining a parallel adsorption orientation and consequently a multi-linking adsorption mechanism.  
Possibility 2: if 0.5 < n < 1, this case assumes that the CO\({}_{2}\) molecules are accepted through a non-parallel and parallel orientation with two various percentages at the same time.  
Possibility 3: if n \(\geq\) 1, the active site accepts one or more CO\({}_{2}\) molecules showing that the position of adsorption is non-parallel, which reveals a multi-molecular adsorption phenomenon.  
According to the values of this parameter, as depicted in Table 2, the values of n for the La-ZSM-5 system are 0.85, 0.81 and 0.79, respectively, at 0, 30, and 60 \({}^{\circ}\)C. These values indicate that the CO\({}_{2}\) molecules are adsorbed via a mixed adsorption orientation (i.e., non-parallel and parallel at the same time). For the Na-ZSM-5, Mg-ZSM-5 and Zn-ZSM-5 systems, all values of n are higher than one (multi-molecular adsorption process), showing that the adsorption of CO\({}_{2}\) molecules takes place through a non-parallel orientation. For instance, the value of n is 2.39 during the CO\({}_{2}\) adsorption on Na-ZSM-5 at 60 \({}^{\circ}\)C. This value is analyzed with the following equation: 2.39 = 2.x + (1-x) \(\times\) 3, where x depicts the sites occupied by two molecules and 1-x represents the sites occupied by three CO\({}_{2}\) molecules. This calculation reveals that 61% of the sites are occupied by two molecules of CO\({}_{2}\), while 39% are occupied by three molecules. Figure 5 illustrates the relationship between n and the tested temperature. We are observed the decrease in the number of docked molecules per site with the temperature since it varies from 2.39 to 3.23, from 0.99 to 1.44, from 0.79 to 0.85, and from 1.01 to 1.51, respectively, for CO\({}_{2}\)-Na-ZSM-5, CO\({}_{2}\)-Mg-ZSM-5, CO\({}_{2}\)-La-ZSM-5, and CO\({}_{2}\)-Zn-ZSM-5. Thus, we can conclude that the adsorption systems are thermally deactivated and the temperature presents a negative impact (i.e., decrease inn) on the aggregation phenomenon.  
The comparison of the numbers of adsorbed molecules per site for all tested samples at all operating temperatures indicates that n(La\({}^{3+}\)) < n(Mg\({}^{2+}\)) < n(Zn\({}^{2+}\)) < n(Na\({}^{+}\)). We can explain this behavior by the affinity of each adsorbent and the difference of porosity.  
Figure 5: Effect of temperature on the number of CO\({}_{2}\) molecules captured on the Na-ZSM-5, Mg-ZSM-5, La-ZSM-5 and Zn-ZSM-5 adsorbents.
## 3 Results and Discussion
### Synthesis Development and Optimization
#### 3.1.1 ATR-IR Analysis  
At first, the raw materials were analyzed on ATR-IR for correct assignment of the main peaks and structure identification. The characteristic ester peak from the stretching of C=O group is observed at 1735 cm\({}^{-1}\), while the stretching of C=O from the carboxylic acid occurs at 1705 cm\({}^{-1}\), which allows for identification of residual acid in the ester product. At 1150 cm\({}^{-1}\), a sharp peak for C-O-C stretching from esters is visible, while it is completely absent in the acid spectra. The peaks at 2850 and 2920 cm\({}^{-1}\) belong to the alkane chain, and the ratio between them and the ester peak at 1735 cm\({}^{-1}\) can be seen increasing for increasing carbon chain length [23]. A characteristic broad peak from the stretching of O-H in alcohols is observed between 3100-3600 cm\({}^{-1}\). A shouldering broad peak from 2300 to 3000 cm\({}^{-1}\) from the stretching of O-H in carboxylic acids is observed in the spectra of PA, but as expected is completely absent in MEPA spectra, which allows for further observation of impurities in produced esters (Figure 2).  
Following structural characterization of the raw materials, the synthesis was performed and the product compared with commercial MEPA. In order to investigate the effect of heating time and COOH:OH ratio on the product formation, synthesis with increasing heating times and with two different ratios (COOH:OH 1:4 and 1:5) have been performed. As Figure 3 shows, slight improvements in the ester yield were observed when raising the COOH:OH ratio from 1:4 to 1:5 excess. In fact, when comparing the absorbance of the ester peak (1735 cm\({}^{-1}\)) against the unreacted PA peak (1705 cm\({}^{-1}\)), a growth in the ester:acid ratio from 4.7 to 4.85 was observed for higher ratios COOH:OH of 1:5. As a result, such excess of alcohol was considered optimal for ester formation and used for all further synthesis (Figure 3).  
Figure 3: Investigation on the influence of COOH:OH ratio on the ester formation. A gain in ester yield is observed for ratio of 1:5 COOH:OH compared to 1:4.  
Figure 2: ATR-IR of commercial MEPA from Sigma Aldrich compared to raw materials PA and MeOH. The main peaks of interest for correct characterization of the esters produced and structure control are highlighted; in particular, the stretch of C=O at 1705 and 1735 cm\({}^{-1}\) for carboxylic acids and esters respectively, the broad peaks between 3600–3100 cm\({}^{-1}\) and 3000–2300 cm\({}^{-1}\) from O–H stretch of alcohols and carboxylic acids, and finally the peak at 1150 cm\({}^{-1}\) from C–O–C stretch in esters.  
Concerning the influence of heating time on the ester formation, as shown in Figure 4, the unreacted acid can be seen decreasing over increasing heating times, while conversely the ester peak is increased. No further change in the ester formation is observed following 5 h heating time, since the curve for 9 h and 45 m heating time equals the one from 5 h heating. Therefore, 5 h was considered as the end point of the reaction, and followed for all subsequent syntheses.
## 2 Materials and Methods
### Overall Structure and Working Principle of the Machine
#### 2.1.2 Working Principle and Main Technical Parameters  
The picking and selecting integrated remote-operation-type dragon-fruit-picking device adopts the design scheme of remote operation, branch swinging and clamping, picking and removing, and sorting. During the orchard operation, the operator controls the dragon-fruit-picking device to reach the designated working position through remote control, which recognizes the dragon fruit image through the visual recognition system and completes the location of the dragon fruit to ensure the accuracy of the picking work. According to the image information and location information of the dragon fruit, the picking device uses the end of the flexible claw and picking scissors to complete single-dragon-fruit and multiple-dragon-fruit picking. In the picking process, the fruits are covered with a blade-shifting device to clip out the branches to complete the picking of the covered fruit, so that the picking work is carried out normally. When there are damaged and over-ripe fruits in the picking process, the bad fruit removal system allows the suction cup to reach the designated sucking position and complete the elimination of bad fruits through the mutual cooperation of the cylinder and the slipway. The fruit picked through the funnel into the sorting device according to the weight of the fruit will be divided into large fruit and small fruit. The two kinds of fruit will go onto the tracks for transporting fruit into different collection boxes and complete the dragon fruit picking.  
The key technical specifications of the entire machine are presented in Table 1.  
The machine design schematic is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technical Parameters** & **Numerical Value** \\ \hline Sizes (L \(\times\) W \(\times\) H)/(mm \(\times\) mm \(\times\) mm) & 1480 \(\times\) 820 \(\times\) 1720 \\ Quality of the machine/(kg) & 74.8 \\ Stepper motor for lifting device torque/(N m) & 2.8 \\ End Flex jaw motor torque/(N m) & 1.6 \\ Twin rod cylinder actuator speed/(m/s) & 0.15 \\ Sorting device conveyor belt line speed/(m/s) & 0–0.2 \\ Work efficiency/(pieces/hour) & 499 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main technical parameters of the dragon-fruit-picking device.  
Figure 1: A picking and selecting integrated remote-operation-type dragon-fruit-picking device: (a) picking and sorting system; (b) bad fruit removal system; (c) visual identity system; (d) aluminum profile frames; (e) control system; and (f) inverted trapezoidal caterpillar track travel system.
## 2 WSN-Based ITS Applications
### Smart Parking Applications  
The lack of parking spaces in cities is a concern which leads to illegal parking, congestion due to low speed driving and long searching times suffered by drivers. In order to minimize inconvenience to drivers, numerous smart parking systems have been developed which guide drivers to vacant parking spots (PGIS, Parking Guidance Information System) and enable smart payment and reservation options. WSNs are useful for the deployment of smart parking systems as a substitute of more expensive wired sensors. Simpler applications using WSNs may involve detecting the distribution of vacant parking slots throughout several floors by emplacing sensor nodes at the entrance of each floor [27]. However the power of sensor networks comes from the accuracy they provide, allowing to find out about the state of each parking space. There is a considerable number of applications that take benefit from this characteristic [28-30]. In them the WSN is deployed in a grid layout over the parking area, being responsible of the detection of vehicles and leaving other functions such as reservation of parking spaces or guidance to other external subsystems.  
Another advantage of WSNs is that they facilitate the development of on-street parking applications. In these applications, unlike off-street parking lots, it is not cost-effective to install additional VMS (Variable Message Signs) or other informative panels in the streets merely for parking purposes. Therefore, on-street parking systems must rely on smart vehicles equipped with On Board Units (OBU), which receive parking information, and with devices for visualization. In order to obtain the location of nearby vacant spaces, vehicles must poll the WSN which will answer with the appropriate information. However, polling from vehicles introduces the problem of mobility, making new routing protocols necessary, which consider that the answers may be delivered to locations different than those where the polling was originated [31].
# Applications of Cold Atmospheric Pressure Plasma in Density
## 8 Conclusions  
Cold atmospheric pressure plasma (CAPP) has antimicrobial and anti-inflammatory effects that are useful in several areas of Dentistry, such as in Cardiology, Periodontology and Endodontics. Additionally, CAPP has been showing potential to be used in the treatment of oral fungal diseases and to control oral cancer. However, additional in vivo studies to standardize clinical protocols are still needed.
## 3 Segmentation of Nanostructured Materials for Gas Sensing
### Zinc Oxide (ZnO)
Thin-film sensing platforms based on T-ZnO exhibited the highest response at 200 \({}^{\circ}\)C (RM = 2.98; R = 66.4%) compared to ZnO-NR thin films at 230 \({}^{\circ}\)C (RM = 1.34; R = 25.5%), attributed to the interconnected network and effective bandgap and barrier height reduction in T-ZnO [45]. Operating temperature is another critical parameter that affects the gas sensing properties of ZnO. Higher temperatures can activate additional adsorption sites and accelerate the reactionkinetics on the ZnO surface, enhancing sensitivity and selectivity to certain gases. However, excessive temperatures may also lead to the desorption of target gas molecules, impacting sensor stability and limiting the temperature range suitable for specific applications. For instance, ZnO-based sensors typically perform optimally at temperatures between 200 and 300 \({}^{\circ}\)C, where gas molecule adsorption and desorption are balanced to maintain steady signal output [45]. A synergetic effect of decreasing grain size and increasing operating temperature was observed towards the improvement of sensitivity, reaching a value of 54 and a limit of detection as low as 0.61 ppm. The decrease in grain size resulted in a prolonged response time but faster recovery. In any case, both response time and recovery time were <400 s. The results demonstrate that room-temperature magnetron sputtering is a viable approach to enhance the performance of ZnO films in sensors for ethanol vapor [48]. Research into ZnO composite materials, such as ZnO loaded with noble metal catalysts (e.g., Pt, Pd, or Au), has also yielded promising results. These catalytic metals can create localized hot spots on the ZnO surface, increasing the sensor's sensitivity and reducing the response time. For example, Pd-loaded ZnO sensors exhibit an enhanced response to hydrogen due to the spillover effect, where hydrogen dissociates on Pd sites and migrates to the ZnO surface, allowing for more effective charge transfer [49]. These composite materials extend the capabilities of ZnO-based sensors by introducing additional mechanisms for gas interaction and detection, facilitating the selective and rapid detection of target gases, even at low concentrations. ZnO's unique properties, combined with advances in doping, nanostructuring, and composite formation, make it a versatile platform for developing high-performance gas sensors. The continued exploration of these modifications will likely lead to ZnO-based sensors with even greater sensitivity, stability, and selectivity, suitable for a broad range of applications (Figure 5).
# CHARMIE: A Collaborative Healthcare and Home Service and Assistant Robot for Elderly Care
## 3 Results
### Follow Me\" and \"Lay the Table  
Another beneficial task of a service robot is to follow a user who needs help performing a task. At times, CHARMIE might be in a different compartment than where the user needs it to complete the next job. Therefore, to ease the whole process, the user might request CHARMIE to follow him/her to the new room. This chore is particularly useful in large indoor environments where following a user is significantly more efficient than looking for the worker. In the following process, the robot can avoid static and dynamic obstacles or even calculate a new trajectory when it realises it cannot pass.  
In Figure 21, extracted from the video in Appendix A, all of the steps regarding the \"Follow Me\" task can be seen. The user starts by asking the robot to follow him/her to navigate to the compartment where help is needed, as can be seen in Figure 21a. The robot uses both the 2D LiDAR and the RGB-D camera to lock the user it is following, as can be seen in Figure 21b. During the process, the robot can navigate narrow spaces and even avoid collisions with humans that pass between the followed user and CHARMIE, as can be seen in Figure 21c,d. Almost at the end of the route, a new obstacle comes up, the robot detects that the followed user has an insuperable barrier similar to a wall (represented by the black cardboard demonstrated in the video in Appendix A that the robot cannot surpass, as can be seen in Figure 21e. Thus the robot, using the environment's map, calculates an alternative route to get back to its user, as can be seen in Figure 21f. In the return path, CHARMIE finds novel obstacles that it can successfully overcome.  
Figure 21: A step-by-step demonstration of CHARMIE’s “Follow Me” task. From receiving the task to navigating behind the human user while avoiding static and dynamic obstacles that cross the path between the robot and the user. When the robot cannot continue following the user, it recalculates a new trajectory.  
As an example of a collaborative duty, the robot can lay the table with a human user. The methodology used is similar to the storing/cleaning tasks but with the collaboration twist. When this task is selected, CHARMIE knows that it needs five items to lay on the table: a plate, a fork, a spoon, a knife and a cup. The robot analyses the objects already laid on the table by a worker or a human user. Furthermore, it complements the user's work by signalling which items it will distribute while analysing whether the human user forgot one item on a previously laid set.