esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples
### Experimental Runs Results
#### 5.2.2 Experiment 2  
Although the initial state of the pipeline was set to be the same, the total cost after multiple maintenance services varied due to the stochastic nature of the succeeding state selection and actions taken based on uncertain observations. A medium size maintenance service campaign (32 steps) replicated 100 times was run for this second test to assess this feature of the model. The two solvers were also implemented for the CS and DS problem models. Figure 4 shows the distribution of the total cost (reward) of the CSPOMDP model using the POMCP & ARDESPOT solver. Since the two plots overlap, the two model run results did not differ significantly (\(-7717\) and \(-4567\) for POMCP and \(-11\),591 and \(-5809\) for ARDESPOT 25 and 75 percentile values). Overall, the POMCP results looked better with cheaper cost distribution. It can be observed that there were some outliers in both solvers' run results. For the first DSPOMDP model, the POMCP and ARDESPOT attained similar 25 and 75-percentile values, i.e., \(-13\),819 and \(-9725\) and \(-12\),370 and \(-8607\) (Figure 4(a)). The ARDESPOT results overlapped with the result obtained for CS, unlike the result for POMCP. The 25 and 75 percentile values for the second DSPOMDP model were \(-8315\) and \(-4488\) and \(-39\),774 and \(-37\),096 for POMCP and ARDESPOT solvers, respectively \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**First DSPOMDP Model**} & \multicolumn{3}{c}{**Second DSPOMDP Model**} \\ \hline \multicolumn{3}{c}{**POMCP**} & \multicolumn{3}{c}{**POMCP**} \\ \hline \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\(s^{\prime}\)) & \(r\) & \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\(s^{\prime}\)) & \(r\) \\ \hline
1 & Inhibitor & 4 & 4 & \(-2000\) & 2 & Do-nothing & 2 & 2 & \(-500\) \\
4 & Replace & 1 & 1 & \(-2250\) & 2 & Inhibitor & 2 & 2 & \(-350\) \\
1 & Do-nothing & 5 & 5 & \(-200\) & 2 & Do-nothing & 2 & 3 & \(-500\) \\
5 & Replace & 1 & 2 & \(-250\) & 2 & Pigging & 5 & 5 & \(-3500\) \\
1 & Do-nothing & 1 & 2 & \(-200\) & 5 & Replace & 1 & 1 & \(-250\) \\ \hline \multicolumn{3}{c}{Discounted (0.9) total cost =} & \multicolumn{3}{c}{\(-4500.47\)} & \multicolumn{3}{c}{Discounted (0.9) total cost =} & \multicolumn{3}{c}{\(-4488.19\)} \\ \hline \multicolumn{3}{c}{**ARDESPOT**} & \multicolumn{3}{c}{**ARDESPOT**} \\ \hline \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\(s^{\prime}\)) & \(r\) & \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\(s^{\prime}\)) & \(r\) \\ \hline
1 & Do-nothing & 3 & 3 & \(-200\) & 2 & Do-nothing & 2 & 2 & \(-500\) \\
3 & Inhibitor & 4 & 3 & \(-300\) & 2 & Inhibitor & 2 & 2 & \(-350\) \\
4 & Do-nothing & 4 & 5 & \(-2000\) & 2 & Inhibitor & 2 & 3 & \(-350\) \\
4 & Replace & 1 & 2 & \(-2250\) & 2 & Inhibitor & 3 & 3 & \(-350\) \\
1 & Do-nothing & 3 & 4 & \(-200\) & 3 & Inhibitor & 5 & 5 & \(-300\) \\ \hline \multicolumn{3}{c}{Discounted (0.9) total cost =} & \multicolumn{3}{c}{\(-3861.47\)} & \multicolumn{3}{c}{Discounted (0.9) total cost =} & \multicolumn{3}{c}{\(-1692.81\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Example of DSPOMDP model run results.
## 7 Results and Performance Evaluation
### Evaluation of Base Station-Centric (Centralized) Versus Node-Centric (Decentralized) Approaches
#### 7.6.2 Computational Energy Efficiency  
The energy consumption for computation of jamming detection for both approaches, centralized or decentralized, is the same for the same algorithm. However, the centralized system has the advantage of spending that energy from the base station and saving the same at the energy-starved nodes. Therefore, the centralized approach is decidedly better than the decentralized approach in this aspect.
# Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System
## 2 Overall Structure  
### System Design Objectives  
The real-time monitoring system of citrus soil moisture and nutrient is designed to monitor the soil moisture and nutrient status in the citrus orchard, so that fruit farmers can grasp the condition of orchard in time, and under the guidance of decision-making support system adjust fertilization irrigation strategy. This determines the design objectives of the system.  
1. Put forward the scientific distribution technology of the soil nutrient and moisture real-time monitoring system in mountain citrus orchard. It makes the layout more reasonable and the monitoring area is effectively covered.
2. Research and develop scientific detection methods to obtain more accurate citrus growth environment conditions.
3. Integrate wireless sensor network of citrus soil nutrient and moisture and remote information management system and intelligent decision support system based on ZigBee technology [13; 14]. Finally, carry on scientific experimental demonstration.
## 3 Results and Discussion
### Validation of the TSS Concentration Model  
To predict the TSS concentration using the Landsat 8 OLI data, the atmospherically corrected band ratio of the validation points coordinates obtained from the second field trip on 16 April 2019 were used. Only seven points from the second campaign were used for validation. This is because 27 sampling points were covered by clouds and failed the cloud masking process. From the previous researcher, the number of points for validation is considered acceptable if it is comparable to the test point data [59; 60; 61]. The reflectance ratios of the validation points were given as the input to the prediction model Equation (4) and the model output was considered as the predicted TSS concentration values of the validation points (satellite-derived). The regression model statistics between laboratory-derived in situ TSS concentration value and model-fitted TSS concentration value of the validation points revealed a strong correlation R\({}^{2}=0.8406\) with an RMSE of 1.50 mg/L and MRE = 9.14%. The testing of the accuracy level of TSS concentrations from satellite data within situ data is presented in Figure 3. The root means square error (RMSE) has been used as a standard statistical metric to measure model performance in meteorology, air quality, and climate research studies [62; 63; 64]. The mean relative error (MRE) is another useful measure widely used in model evaluations. While they have both been used to assess model performance for many years, there is no consensus on the most appropriate metric for model errors. In the field of remote sensing, many present the RMSE as a standard metric for model errors [62; 63; 64], while a few others choose to avoid the RMSE and present only the MRE, citing the ambiguity of the RMSE claimed by [65; 66; 67; 68; 69]. While the MRE gives the same weight to all errors, the RMSE penalizes variance as it gives errors with larger absolute values more weight than errors with smaller absolute values. The calculation results of the accuracy test between the measurement of in situ data with the prediction of the distribution of TSS are distinguished by RMSE and MRE. Based on Figure 3, the root mean square error in the statistical test was 1.50 mg/L. The size of the error is shown based on the difference between the value of the estimated data and in situ data. The greater the value of RMSE means that the results of the estimation model produced were increasingly inappropriate when compared to in situ. In this study, the RMSE value was smaller than the MRE value shown in Figure 4, which indicated that this model was accurate. There is no size or limit for determining how big the RMSE value is, but the condition is that the RMSE value should not be greater than the MRE value. The mean relative error (MRE)  
Figure 3: Regression model for TSS Concentration Over Study Region.  
value of 9.14% indicated the error rate expressed in percent (%). Error values (MRE) below 30% could be used as proof of the validity of the model [70].
# Assessment of GNSS Galileo Contribution to the Modernization of CROPOS's Services
###### Abstract  
CROPOS, as the Croatian GNSS network, was modernized and upgraded to support the Galileo system in 2019. Two of CROPOS's services--VPPS (Network RTK service) and GPPS (post-processing service)--were assessed for the contribution of the Galileo system to their performance. A station used for field testing was previously examined and surveyed to determine the local horizon and to carry out a detailed mission planning. The whole day of observation was divided into several sessions, each with a different visibility of Galileo satellites. A special observation sequence was designed: VPPS (GPS-GLO-GAL), VPPS (GAL-only), and GPPS (GPS-GLO-GAL-BDS). All observations were taken on the same station with the same GNSS receiver, Trimble R12. Each static observation session was post-processed in Trimble Business Center (TBC) in two different ways: considering all available systems (GGGB) and considering GAL-only observations. A daily static solution based on all systems (GGGB) was considered as the reference for the accuracy assessment of all obtained solutions. The results obtained with VPPS (GPS-GLO-GAL) and VPPS (GAL-only) were analyzed and assessed; the results obtained with GAL-only have shown a slightly higher scatter. It was concluded that the inclusion of the Galileo system in CROPOS has contributed to the availability and reliability of solutions but not to their accuracy. By complying with the observation rules and taking redundant measurements, the accuracy of GAL-only results can be improved.  
CROPOS; GNSS; Galileo; modernization; VPPS; GPPS 20232324661 Faculty of Geodesy, University of Zagreb, Kaleeva 26, 10000 Zagreb, Croatia 1  
22 German Aerospace Center (DLR), Institute of Communication and Navigation, Muenchener Str. 20, Oberpfaffenhofen, 82234 Wessling, Germany 2
## 3 Multicarrier CDMA Based Modulation Schemes
### SS-Mc-CDMA  
[PERSON] _et al_. [51] proposed the Selective Subcarrier MC CDMA (SS-MC-CDMA) scheme which identifies the spectral holes and selects subcarriers in order to utilize them without interfering with the transmission of the primary user. In NC-MC-CDMA systems, adaptive subcarrier deactivation degrades the BER performance of the system, because the data in subcarriers are not independent as a result of the spreading operation. In the SS-MC-CDMA system, the data allocation to bins takes place only after the subcarriers are selected based on the channel state information and spectrum occupancy.
## 4 Asset Digitalization Use Case: IoT and AHI App for a Bridge Maintenance
### The Bridge Health Index Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Bridge 1** & **Bridge 2** & **Bridge 4** & **Bridge 4** \\ \hline IHi & 2.573 & 1.912 & 2.458 & 1.919 \\ IH & 3.015 & 2.668 & 3.485 & 2.478 \\ Relative deterioration & 0.442 & 0.455 & 1.027 & 0.560 \\ Months of operation since last OH & 300 & 320 & 410 & 480 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample results obtained for different bridges in the use case. Figure 11: AHI versus age projection. Representation of the current health index versus the initially estimated bridge age. Comparison among assets in the same asset class. A summary of the valuable insights offered by the results presented in Figure 11 is as follows:  
* Objective Measurement of Degradation: The framework enables the precise identification of the asset's degradation percentage relative to expected deterioration over a given period. This is achieved using real-time monitoring data from IoT platforms. * Strategic Maintenance Planning: By analyzing trends in asset health, the framework helps optimize the timing of major maintenance activities, preventing excessive corrective interventions and minimizing operational risks. * Risk Mitigation: Early identification of critical conditions allows managers to proactively address potential failures, enhancing asset reliability and safety. * TOTEX Optimization: The approach facilitates a balance between operational expense (OPEX) and capital expenditure (CAPEX), providing a comprehensive view of total ownership costs (TOTEX). This balance is achieved by assessing and adjusting preventive maintenance policies based on their impact on degradation.
# Assessment of Appropriate Geometry for Thermally Efficient CO\({}_{2}\) Adsorption Beds
###### Abstract  
Carbon capture is one of the recently raised technologies to mitigate greenhouse gas emissions. Adsorption was introduced as an energy-efficient carbon capture process, and the literature primarily shows the utilization of circular cross-sectional adsorption beds for this purpose. In this regard, this paper investigates different shapes of adsorbent beds to determine the thermal and adsorption uptake enhancements. Three geometries are considered: circular, square, and triangular cross-sectional beds. Mg-MOF-74 is used as an adsorbent, and numerical simulation is developed using a user-defined function coupled with ANSYS-Fluent. The results show that the triangular cross-sectional bed exhibits better adsorption capacity and thermal management compared to other beds. For example, the triangular cross-sectional bed shows 6 K less than the circular one during the adsorption process. It is recommended that the triangular cross-sectional bed be used for temperature swing adsorption when pumping power is not important. The square bed comes second after the triangular one with a lower pressure drop, suggesting such beds as good candidates for pressure swing adsorption. The square bed could be an excellent choice for compact beds when CO\({}_{2}\) uptake and pumping power are both important.  
adsorbent bed configuration; carbon capture; separation; adsorption; desorption +
Footnote †: journal: Applied Sciences
# Progress in THz Rectifier Technology: Research and Perspectives
### Rectification Efficiency (\(\eta\))  
Currently, the efficiency obtained for this technology is less than 1%, well below the efficiency of PV solar cells (about 22%). The energy conversion efficiency of a nanorectenna essentially depends on two elements, the antenna and the rectifier element. The efficiency of the antenna reflects its ability to concentrate the radiation and guide it in the desired direction. The efficiency of the rectifier is related to its ability to rectify the generated alternate current into direct current. However, there are numerous loss mechanisms for each stage of the energy conversion process. Therefore, the efficiency of the antenna depends on the combined efficiency of several factors such as the ability to couple the incident electromagnetic radiation and the dissipative losses within the antenna structure. Other factors such as the matching of the impedance to the nanorectifier and the coupling efficiency of the nanorectifier to the load also affect the efficiency. The overall efficiency \(\eta\) for a nanorectenna can be written as:  
\[\eta=\eta_{a}\eta_{S}\eta_{c}\eta_{q} \tag{1}\]  
where  
* \(\eta_{a}\) is the efficiency of the coupling between the incident EM radiation and the antenna.
* \(\eta_{S}\) is the efficiency given by the ratio between the energy transmitted to the nanodiode and the energy collected by the nanoantenna.
* \(\eta_{c}\) is the coupling efficiency between the nanoantenna and the rectifier.
* \(\eta_{q}\) is the efficiency given by the ratio between the rectified power and the power received by the nanodiode.  
The radiation pattern and the bandwidth of the nanoantenna, the material conduction property, a poor AC-to-DC rectification at THz frequencies, and the impedance mismatch between the antenna element (hundreds of ohm) and the diode used for rectification (several kilo ohms) affect the overall efficiency of the nanorectenna-based energy harvester.  
THz Diode Technology: General Information, FOMs, Open Questions, State of the Art, and Perspectives  
This section introduces a detailed discussion of THz diode technology, in particular, general information, figures of merit (FOMs), open questions, state of the art, and perspectives on metal-insulator-metal (MIM), metal-multi-insulator-metal diodes (MI\({}^{\text{p}}\)M), and geometric diode (GD). The aforementioned diodes are governed by two different mechanisms: quantum-tunneling effect; ballistic and quasi-ballistic transport. Although there have been continuous improvements for these diodes, they are not yet optimized for THz frequencies. Therefore, the greatest efforts of the scientific community are aimed at design and manufacturing.
# Preliminary Field Results of a Dedicated 5G Positioning Network for Enhanced Hybrid Positioning +
###### Abstract  
Dedicated fifth generation (5G) positioning networks are envisaged as a key technology to complement Global Navigation Satellite Systems (GNSS) in challenging situations, such as safety-critical automotive and aerial applications. These 5G networks are explicitly designed to fulfil the positioning needs in a specific environment, by using dedicated pilot signals and locating the base stations (BSs) in favorable conditions for user reception. To prove this concept, the Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed demonstrates the enhanced positioning capabilities resulting from the deployment of ground and aerial BSs. This paper presents a preliminary assessment of the first real-time ranging measurements obtained with a flying BS in a dedicated 5G positioning network. Under hovering and optimal conditions, field results obtained over an 80 MHz downlink bandwidth indicate a ranging precision below 1 m in 95% of cases.  
GNSS; 5G; positioning; flying base stations +
Footnote †: Academic Editors: Editors: Editors: Editors: Editors: Editors: [PERSON] and [PERSON]  
Footnote †: Academic Editors: Editors: Editors: Editors: Editors: Editors: Editors: Editors: Editors: Editors: Editors: Editors: Editors: Ed: Editors: Editors: Editors: Editors: Ed: Editors: Editors: Editors: Ed: Editors: Ed: Editors: Ed: Editors: Ed: Editors: Ed: Editors: Ed: Editors: Ed: Ed: Editors: Ed: Ed: Editors: Edaerial vehicle (UAV)-mounted BSs, or flying BSs have been mainly considered for communication purposes [3; 4] but not for enabling enhanced-positioning services, which can be especially beneficial thanks to the increased diversity in the vertical dimension with respect to ground-fixed BSs. Thus, to prove this new opportunity, the Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed has been proposed as a first-of-a-kind demonstrator of dedicated aerial 5G positioning networks.  
The design considerations of these dedicated 5G positioning networks have been studied in [5] and their expected positioning capabilities in a representative scenario in [6]. Experimental 5G positioning results have been presented in predecessor testbeds, such as in [7; 8], as well as with signals of opportunity approaches in [9; 10]. To the best of the authors' knowledge, the use of flying 5G BSs for positioning has not been demonstrated yet. Thus, this paper presents the first ranging results of a flying 5G BS deployed specifically for positioning purposes.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 5 Comparative Analysis and Discussion  
Equation (67) defines RMSE, used in a quantitative analysis to compare the three algorithms' estimation performance under two conditions. The results are as shown in Table 2.  
Table 2 shows that compared to the MCCKF and CKF algorithms the IMM-MCCKF algorithm has significantly lower RMSE values in the double-lane-change and sinusoidal steering scenarios. The IMM-MCCKF algorithm integrates IMM and MCCKF, allowing it to adapt to different noise environments dynamically and more effectively suppress non-Gaussian noise.  
In the double-lane-change scenario, the IMM-MCCKF algorithm reduces the RMSE in longitudinal speed by approximately 15.3% compared to MCCKF and 68.6% compared to CKF. The reduction in RMSE for lateral speed is even more significant, with decreases of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Simulation** & **Parameters** & \begin{tabular}{c} **IMM** \\ **MCCKF** \\ \end{tabular} & \begin{tabular}{c} **MCCKF** \\ \end{tabular} & \begin{tabular}{c} **CKF** \\ \end{tabular} \\ \hline \begin{tabular}{c} Double-lane- \\ change scenario \\ \end{tabular} & \begin{tabular}{c} Centroid sideslip angle \\ Longitudinal velocity \\ Lateral velocity \\ \end{tabular} & 0.0007 & 0.0020 & 0.0127 \\ \hline \
\begin{tabular}{c} Sinusoidal \\ steering scenario \\ \end{tabular} &
\begin{tabular}{c} Centroid sideslip angle \\ Longitudinal velocity \\ Lateral velocity \\ \end{tabular} & 0.0073 & 0.0221 & 0.1406 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analyzing RMSE values in two different simulation scenarios.  
Figure 7: Simulation results for the sinusoidal steering scenario: (**a**) longitudinal velocity, (**b**) lateral velocity, and (**c**) centroid sideslip angle.  
67.0% and 94.8%, respectively. The IMM-MCCKF algorithm makes real-time adjustments to the model's probabilities, aligning the output with the model with the fewest errors. In contrast, the CKF algorithm exhibits significant fluctuations and divergence.  
The IMM-MCCKF algorithm also demonstrates excellent estimation performance in the sinusoidal steering scenario. It reduces the RMSE in longitudinal speed by 16.5% compared to MCCKF and by 89.6% compared to CKF. The RMSE is reduced by 32.1% and 82.5% for lateral speed, respectively.  
In summary, CKF performs well in Gaussian noise environments but poorly under non-Gaussian noise conditions, resulting in significant estimation errors. MCCKF, which combines the MCC with CKF, better handles non-Gaussian noise; however, a single MCCKF filter has limitations in dynamically adapting to noise variations, leading to decreased estimation accuracy when noise changes suddenly. By incorporating IMM theory into MCCKF, the IMM-MCCKF algorithm overcomes the limitations of CKF and MCCKF. IMM-MCCKF can dynamically adapt to noise variations by integrating multiple submodels with different characteristics, maintaining estimation accuracy and stability under non-Gaussian noise conditions.