esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate elevation data, which can be extracted from very high-resolution (VHR) satellite images, are vital for many engineering and land planning applications. In this way, the main goal of this work is to evaluate the capabilities of VHR Deimos-2 panchromatic stereo pairs to obtain digital surface models (DSM) over different land covers (bare soil, urban and agricultural greenhouse areas). As a step prior to extracting the DSM, different orientation models based on refined rational polynomial coefficients (RPC) and a variable number of very accurate ground control points (GCPs) were tested. The best sensor orientation model for Deimos-2 L1B satellite images was the RPC model refined by a first-order polynomial adjustment (RPC1) supported on 12 accurate and evenly spatially distributed GCPs. Regarding the Deimos-2 based DSM, its completeness and vertical accuracy were compared with those obtained from a WorldView-2 panchromatic stereo pair by using exactly the same methodology and semiglobal matching (SGM) algorithm. The Deimos-2 showed worse completeness values (about 6% worse) and vertical accuracy results (RMSE\({}_{Z}\) 42.4% worse) than those computed from WorldView-2 imagery over the three land covers tested, although only urban areas yielded statistically significant differences (\(p<0.05\)).  
Deimos-2; VHR satellite images; geometric accuracy; sensor orientation model; digital surface model +
Footnote †: journal: Sensors
## 4 Experiment and Analysis
### Experiment Result
#### 4.3.2 The Result of the Second Experiment  
The second experiment was conducted in the corridor of the office building, where 10 AP signals were covered. [PERSON] et al. [43] proposed an adaptive particle filter (APF) to enhance the PF for indoor positioning. AO-PSO-PF is the algorithm proposed in this paper. The results of the AO-PSO-PF algorithm and other algorithms for indoor positioning in the second experiment are displayed in Figures 18-23. These figures, respectively, illustrate the positioning path, positioning error, and the CDF values for PDR, PF, APF, and AO-PSO-PF. The AO-PSO-PF algorithm consistently outperforms the PDR, PF, and APF algorithms across all metrics (AE, RMSE, ME, CEP of 75%, and CEP of 95%) in the second experiment. The AO-PSO-PF algorithm shows significant improvements across several metrics in the general mode. The AE is reduced by 92.52% compared to PDR, 15.98% compared to PF, and 53.59% compared to APF. The RMSE improves by 91.66% over PDR, 16.52% over PF, and 53.84% over APF. The ME is reduced by 89.10% compared to PDR, 14.44% compared to PF, and 57.54% compared to APF. The CEP of 75% achieves improvements of 92.29% over PDR, 23.77% over PF, and 48.07% over APF. Lastly, the CEP of 95% shows enhancements of 90.34% over PDR, 15.63% over PF, and 53.88% over APF. These results demonstrate the effectiveness of the AO-PSO-PF algorithm in improving positioning accuracy and significantly reducing errors across various metrics. The detailed data for Experiment 2 are presented in Table 4. Table 5 below highlights the robustness and adaptability of the AO-PSO-PF algorithm across five users with four motions. It reflects the algorithm's capacity to maintain low AE values, ensuring reliable performance in real-world applications. Figure 18: Positioning trajectory for PDR in the 2 nd experiment: (**a**) calling gesture; (**b**) dangling gesture; (**c**) handheld gesture; (**d**) pocketed gesture. Figure 19: Positioning trajectory for PF in the 2 nd experiment: (**a**) calling gesture; (**b**) dangling gesture; (**c**) handheld gesture; (**d**) pocketed gesture. Figure 20: Positioning trajectory for APF in the 2 nd experiment: (**a**) calling gesture; (**b**) dangling gesture; (**c**) handheld gesture; (**d**) pocketed gesture. Figure 21: Positioning trajectory for AO-PSO-PF in the 2 nd experiment: (**a**) calling gesture; (**b**) dangling gesture; (**c**) handheld gesture; (**d**) pocketed gesture. Figure 22: Positioning errors for PDR, PF, APF, and AO-PSO-PF under different smartphone usage gestures in the 2 nd experiment: (**a**) positioning errors in the calling gesture; (**b**) positioning errors in the dangling gesture; (**c**) positioning errors in the handheld gesture; (**d**) positioning errors in the pocketed gesture.
# Wall-Climbing Mobile Robot for Inspecting DEMO Vacuum Vessel
## 2 Design of the WMR
### The Concept of the WMR  
The WMR shown in Figure 2 was designed based on the vortex principle. The main components of the WMR are the impeller, impeller drive system, robot drive system, and electric control system. The mass of the WMR structure itself is about 1.5 kg. The length of the robot is 290 mm, and the distance from the chassis to the horizontal floor is 4 mm, with a maximum that cannot exceed 10 mm. These dimensions include a minimum inner arc radius of 1750 mm and a minimum outer arc radius of 1626 mm for the maintenance area inside the vacuum chamber of the WMR. By calculating the dimensions of the vacuum chamber, the WMR meets more than 98% of the maintenance needs for the inner and outer surfaces of the fusion reactor vacuum chamber [25]. While working, the WMRs can be stored in the end toolbox of the MPD heavy-duty robot arm in clusters and brought into the VV via the DEMO from the intermediate maintenance window to complete the task. Alternatively, the WMRs can also enter the vacuum chamber in groups through the lower window of the VV to complete the task.  
The impeller is driven by a DC brushless motor with high rotational speed. When the impeller rotates, the air flows from the centre to the edge, generating a low pressure in the centre of the impeller and an attractive force between the WMR and the wall. The wheel with high friction rubber is driven by another DC motor and offers mobility. The size of the robot is 20 \(\times\) 250 \(\times\) 130 mm. In addition, the robot's radius can be optimized and changed according to the task. In the conceptual design stage, the force analysis is conducted with 0 acceleration. When the WMR moves on the DEMO wall in an upside-down position, the adhesion force of the robot must satisfy \(F_{a}>mg\), where \(m\) is the mass of robot, and \(F_{a}\) is the adhesion force. When the WMR moves on a vertical wall, the crucial role is the friction force \(F_{f}\). The friction force should satisfy \(F_{f}=\mu F_{m}\), where \(\mu\) is the friction coefficient and \(F_{n}\) the adhesion force to the surface. To hold the WMR and its components on the wall, \(F_{f}\) must be higher than the weight \(mg\) of the robot. The friction coefficient was assumed to be \(\mu=0.64\), and \(F_{f}>mg\) should be satisfied. In our case, the design payload capability was 2 kg, and together with the mass of the robot, the total mass was 3.5 kg. The driving force was designed to be 40 N. However, this was the preliminary calculation in conceptual design, and more aspects were considered in the simulation by assigning materials to components and changing the structure's geometry.
## 4 SMS-Based Telemetry Data Transmission Protocol
### User Data Segment  
The user data segment is 76 octets in the first SMS message, as this message always contains the header. However, if we move into the realm of concatenated SMS, our protocol supports a  
Figure 3: Field positions in the Protocol header.  
concatenated SMS consisting of up to 6 SMS messages. In this case, all subsequent SMS after the first message provide their full payload for user data. With six SMS concatenated into a protocol message, we can access up to 740 octets of user data. The available user data payload can contain any sort of information, ultimately translated into 8 bit binary form. An application using the protocol may use 7 bit ASCII data, 8 bit binary or 16 bit Unicode. The exact format of the payload is specific to the application using the protocol.  
In the case of critical data, where delivery failure is simply not an option, SMS is not the ideal choice of carrier medium due to the lack of delivery guarantees and standardized delivery receipts supported in the protocol. To address this problem, an appropriate message validity period, failover options (such as message receipting to validate delivery has occurred) and redundancies (such as application checks for receipts, fault and diagnostic messages) are proposed, with which SMS could be considered as a transmission medium for a wide variety of data. The architecture integrates medical sensors, a sensor network, electronic patient records and web portal technology to allow remote monitoring of patient physiological status in the homecare conditions and to alert medical personnel when life-threatening events occur. This way we are able to provide hassle-free environment for patients and other users, without excessive wires that limit movement and attract undesired attention during continuous monitoring. The proposed system organisation allows flexible design space for optimum trade-off between processing power, device power consumption and battery life, and storage capacity, sufficient for most medical applications. Based on the architecture, We proposes a generic communications protocol to work inside an SMS message and provide the necessary redundancy and robustness for the transmission of non-critical medical telemetry. Such telemetry could then be transmitted from a mobile telephone or other cellular mobile engine, and this capability could be used to increase the freedom of non-critical patients requiring monitoring.
## 6 Conclusions and Further Developments  
The presented work, starting from an analysis of the different methods in the literature, aimed to investigate PMM systems in the industrial environment considering their overall consistency in supporting companies to remain competitive in a continuously changing environment. In addition, a critical analysis of the main methodologies present in the literature is also provided it is important to emphasise that the choice of criteria for evaluating methods is affected by the subjectivity of the authors and thus by its very nature can be improved. Finally, the best method according to the chosen criteria was defined and validated in a real pharmaceutical company case.  
Ultimately, the work also extended what the method had previously defined at the start by introducing the dimension of Key Activity Indicators.  
Once the best method had been defined according to the defined criteria and applied to an application case, the method of [PERSON] et al. (2021) was validated, the PMM system identified and quantified changes in the external environment and also assessed their impact on the strategic and operational organisation. This approach, therefore, allows the identification of possible problems in the different organisational levels and also guides the design of the industrial PMM system. This work presents further insights that would be interesting to explore. Indeed, the choices of evaluated methods and criteria inevitably result from the subjectivity of those who conducted the analysis. Therefore, further work could investigate and consider different ways according to standards broadened from those used here. Secondly, it would be interesting to introduce new dimensions to be considered for the methods examined. For example, being able to introduce aspects concerning the human dimension within the indicator tree could lead to interesting results. The methodology identified in this paper could be expanded and fortified in its elements through different applications in different industrial sectors. Being able to be dropped and tested in different organisations operating in different markets would fortify the method and also lead to identifying the main elements that can guide companies in defining their strategic and operational objectives.  
**Author Contributions:** Conceptualization, I.C.F. and S.D.L.; data curation, I.C.F.; formal analysis, I.C.F. and S.D.L.; investigation, I.C.F.; methodology, I.C.F. and S.D.L.; project administration, S.D.L.; supervision, M.M.S.; validation, I.C.F.; writing--original draft, I.C.F. and S.D.L.; writing--review & editing, I.C.F. and S.D.L. and M.M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Results and Discussion
### Droplet Length Correlation  
As mentioned earlier, the dimensionless length \(L_{\text{d}}/W\) of the sub-droplets is positively correlated with \(q\) and negatively correlated with \(Ca_{\text{c}}\). Therefore, \(L_{\text{d}}/W\) can be written as a function of \(q\) and \(Ca_{\text{c}}\), where only the coefficients change. It can be seen from the correlation that the effect of the two-phase flow rate ratio \(q\) on the droplet length is positively correlated, while the effect of the continuous phase capillary number \(Ca_{\text{c}}\) on the droplet length is negatively correlated. The law of this correlation is consistent with the conclusion in Section 3.3. According to the experimental data, the length correlation of the sub-droplets generated by the breakup behavior in the symmetric Y-junction microchannel is fit as shown in Formula (3):  
\[L_{\text{d}}/W=0.593q^{0.385}Ca_{\text{c}}{}^{-0.11} \tag{3}\]  
Figure 8 shows the error comparison between the experimental value of the sub-droplet length in the microchannel and the predicted value of the correlation. Obviously, the error range between the predicted value of the correlation and the experimental value is within \(\pm 20\%\). The predictive performance of length correlations was quantitatively described using mean absolute deviation (MAD) and mean relative deviation (MRD). MAD and MRD are shown in Formulas (4) and (5), respectively. The MAD was \(6.5\%\) and theMRD was \(-1\%\). In summary, it shows that the droplet length correlation proposed in this paper can better predict the experimental results.  
\[\text{MAD}=\frac{1}{N}\sum_{i=1}^{N}\frac{\left|U_{\text{pre}}-U_{\text{exp}} \right|}{U_{\text{exp}}}\times 100\% \tag{4}\]  
\[\text{MRD}=\frac{1}{N}\sum_{i=1}^{N}\frac{U_{\text{pre}}-U_{\text{exp}}}{U_{ \text{exp}}}\times 100\% \tag{5}\]  
Figure 8: Comparison of the experimental value of the sub-droplet length and the predicted value of the correlation formula.  
Figure 7: Influence of two-phase fluid parameters on the length of sub-droplets: (**a**) \(q_{\text{d}}\), (**b**) \(q_{\text{c}}\), (**c**) \(q\), and (**d**) \(Ca_{\text{c}}\).
## 6 Discussion and Verification  
We used ESPI to measure the out-of-plane and in-plane vibration characteristics of piezoelectric materials. The white regions in the images were the nodal lines, and the first lines next to them show the sub-micron vibration displacements [25; 26]. The resonant frequencies measured by the impedance analyzer were also listed. Next, we applied the material constants obtained using the mathematical calculations in RM to finite element analysis software (ABAQUS) to derive the out-of-plane and in-plane resonant frequencies and mode shapes. The bold black lines in the mode shape results were the nodal lines, and we compared these numerical calculation results with experimental measurements to verify the reliability of the material constants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(d\) Form & RM & Manufacturer Provided & \(e\) Form & RM & Ref [27] \\ \hline \hline \multicolumn{6}{c}{Elastic compliance in constant electric field (\(\times 10^{-12}\) m\({}^{2}\)/N)} & \multicolumn{4}{c}{Elastic stiffness in constant electric field (\(\times 10^{10}\) N/m\({}^{2}\))} \\ \(s_{11}^{E}\) & 14.261 & 16.295 & \(C_{11}^{E}\) & 8.089 & 12.18 \\ \(s_{12}^{E}\) & \(-\)3.475 & – & \(C_{12}^{E}\) & 2.451 & 8.021 \\ \(s_{13}^{E}\) & \(-\)3.645 & & \(C_{13}^{E}\) & 1.876 & 8.527 \\ \(s_{33}^{E}\) & 20.475 & 23.518 & \(C_{33}^{E}\) & 5.552 & 11.644 \\ \(s_{44}^{E}\) & 43.096 & – & \(C_{44}^{E}\) & 2.320 & 2.299 \\ \(s_{66}^{E}\) & 35.473 & – & \(C_{66}^{E}\) & 2.819 & 2.080 \\ Piezoelectric strain constant (\(\times 10^{-12}\) C/N) & \multicolumn{4}{c}{Piezoelectric stress constant (C/m\({}^{2}\))} \\ \(d_{15}\) & 661 & 720 & \(e_{15}\) & 15.338 & 14.26 \\ \(d_{31}\) & \(-\)253 & \(-\)276 & \(e_{31}\) & \(-\)16.984 & \(-\)10.48 \\ \(d_{33}\) & 516 & 630 & \(e_{33}\) & 19.156 & 16.60 \\ Dielectric constant in constant stress & \multicolumn{4}{c}{Dielectric constant in constant strain (\(\times 10^{-9}\) F/m)} \\ \(\varepsilon_{11}^{T}\) & 29.674 & – & \(\varepsilon_{11}^{S}\) & 19.536 & 24.082 \\ \(\varepsilon_{33}^{T}\) & 29.414 & 29.218 & \(\varepsilon_{33}^{S}\) & 10.936 & 23.020 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Material constant of APC-85 by resonance method, manufacturer, and Ref [27].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(d\) Form & RM & Manufacturer Provided & \(e\) Form & RM & Manufacturer Provided \\ \hline \hline \multicolumn{6}{c}{Elastic compliance in constant electric field} & \multicolumn{4}{c}{Elastic stiffness in constant electric field (\(\times 10^{-12}\) m\({}^{2}\)/N)} \\ \(s_{11}^{E}\) & 12.881 & 14.106 & \(C_{11}^{E}\) & 12.085 & 7.3 \\ \(s_{12}^{E}\) & \(-\)3.834 & \(-\)4.232 & \(C_{12}^{E}\) & 6.102 & 1.848 \\ \(s_{13}^{E}\) & \(-\)5.355 & \(
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 4 Examples and Application
### Discussion  
Two laboratory-scaled cases and two field-scaled cases validated and demonstrated the developed sediment-transport model. For all cases, the fractional sediment transport played important roles, which was one of the model limitations identified by [PERSON] et al. [34]. Multisized sediment particles may have interactions among each other in the transport or movement, such as collisions, flocculation, and disaggregation, which would affect the sediment-transport rates, concentrations, and settling behavior. Such interactions are better considered in two-phase flow models [21]. In this study, both the suspended-load and the bed-load transport-capacity formulas [41] considered the hiding-and-exposure effects in sediment mixtures, which partially and indirectly address the particle-particle interactions.  
According to the comparisons with the measured data, the developed model obviously had better performances in laboratory cases than in field cases. One reason lies in the relatively simple and easy-to-control flow and sediment initial and boundary conditions in laboratory cases. In field cases, there are few well-monitored sediment data, and data scarcity has been a concern in sediment transport modeling. Specifically, in the case of the Jiji Reservoir, only limited bed samples were available, there was no inflow fractional sediment data, the measured cross section topography data were insufficient, and the dredging activities were not well recorded.  
As stated previously, the flow model is the backbone of the sediment-transport model. Accurate flow calculations would provide solid conditions for qualitatively correct sediment-transport modeling. To ensure stable and accurate flow calculations, this study adopted second-order edge-gradient evaluation to consider corrections from mesh irregularities [36], and multipoint momentum-interpolation corrections for nonphysical oscillations in the wetting-and-drying process [51].
## 4 Data-Level Sampling and Resampling Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Oversampling** & **Hybrid** & **Generative** & **Ensemble** & **References** \\ \hline Autoencoder (AE) & ✗ & ✗ & ✓ & ✗ & [46, 58] \\ BalanceCascade & ✗ & ✗ & ✗ & ✓ & [50, 51, 59, 60] \\ EasyEnsemble & ✗ & ✗ & ✗ & [50, 51, 60] \\ KMeanSMOTE & ✓ & ✗ & ✗ & ✗ & [52, 61] \\ LoRAS & ✓ & ✗ & ✗ & [53, 62] \\ Random Oversampler (ROS) & ✓ & ✗ & ✗ & [2, 54, 61] \\ SMOTE & ✓ & ✗ & ✗ & [2, 55, 61, 63, 64, 65, 65] \\ SMOTEEN & ✗ & ✓ & ✗ & [2, 56, 61, 65, 66, 67] \\ SMOTETomek & ✗ & ✓ & ✗ & ✗ & [2, 56, 61, 65, 66, 65, 66, 67] \\ SVMSMOTE & ✓ & ✗ & ✗ & [2, 61, 65, 66, 67] \\ Variational Autoencoder (VAE) & ✗ & ✗ & ✓ & ✗ & [34, 35, 68] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of data-level methods for imbalanced learning.
# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 2 Materials and Methods
### Modeling Methodology and Quality Indicators  
The set of variables used included several energy metrics: Electricity prices by type of user, Energy productivity, Final energy consumption, Gross domestic product at market prices, Recycling rate of municipal waste, Domestic material consumption per capita, Environmental tax revenues, and Share of energy from renewable sources.  
The initial phase of the study was dedicated to examining the interrelationships between variables through correlation analysis. This step is crucial as interdependencies among input variables can introduce ambiguity in the model's learning phase. Typically, [PERSON]'s correlation analysis is employed to detect and mitigate multicollinearity by discarding one or more interdependent variables [38].  
After analyzing the correlation and determining the input data for modeling energy recovery from MSW, the modeling phase began using three machine learning methods such as ElasticNet, Decision Trees, and Neural Networks. The methodology for the development of this research is shown in the flowchart below (Figure 1).  
Figure 1: Flowchart of the research methodology.  
The quality of the models was evaluated based on specific metrics listed in Table 3. The selection of quality indicators such as Regression Value (R), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Relative Importance of Errors (RIE), and Mean Absolute Error (MAE) is grounded in their robustness for evaluating predictive models. The Regression Value (R) assesses the strength and direction of the linear relationship between observed and predicted values. MSE and RMSE are crucial for determining the average power of prediction errors, with RMSE giving more weight to larger errors due to the squaring process. RIE offers a normalized comparison of errors, providing insight into the relative magnitude of predictive inaccuracies. Lastly, MAE provides an average of the absolute errors, which is especially useful for understanding the practical significance of the prediction errors in the context of energy recovery. Together, these indicators deliver a comprehensive understanding of model performance, balancing both the magnitude of errors and the consistency of the model's predictive ability.
## 5 Simulation Results
Finally, due to the fact that the information data are usually assumed to be binary, the bit error rate (BER) is generally used for the error performance comparison between two schemes, and the BERs of the two scheme over the AWGN channel and the non-linear satellite channel are also given in Figure 9. The conversion of binary symbol set to septinary symbol set should introduce some amount of redundancy as 2 and 7 are both prime numbers. Nevertheless, because of \(\frac{14\log_{2}2}{5\log_{2}7}\approx 0.997\), we convert every 14 binary symbols to five septinary symbols to make the redundancy associated with this conversion be negligible. The simulation results shown in Figure 10 present the 7-QAM as having better performance at low SNR and has a 0.5 dB gain at BER \(=10^{-3}\) over the non-linear channel. However, because of the conversion redundancy and error propagation, the BER curves of the two schemes are getting closer under both the AWGN channel and the non-linear channel as the SNR increases. Despite this, the FER is the primary indicator to evaluate the performance of a scheme,  
Figure 8: FER and SER of different modulations combined with NB-LDPC codes over the non-linear satellite channel. so that we still consider that the proposed scheme has a better performance than the NB-LDPC coded 8-PSK modulation scheme, especially under the non-linear channel.