esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
[12] applied the Taylor expansion to linearize the ESN in the context of an operating point and adopted an ESN to compensate for the truncation error in a partially observed dynamical system. [PERSON] et al. [13] implemented ESNs into the practical nonlinear MPC framework developed by [PERSON] et al. [14] by applying the analytically computed gradient from the ESN model to the predictive model. [PERSON] et al. [15] applied an ESN for the decentralized control problem of continuous-time nonlinear interconnected systems. They treated the interconnected terms between the subsystems as the disturbances added to the system dynamics. [PERSON] et al. [16] presented an improved algorithm to solve the optimal control problem of nonlinear systems with the Hamilton-Jacobi-Bellman (HJB) equation approximated by an ESN to solve the optimal control problem. In inference-based modeling, explicit knowledge regarding the structure of the mathematical model is not required. Moreover, highly nonlinear dynamics under uncertain disturbances can be modeled. Nevertheless, although inference-based modeling is useful to realize system identification and control, a clear relationship does not exist between the target system parameters and the associated effects, such as grey-box modeling. Furthermore, in real-time control, considerable time is required to realize MPC calculation within a control period. Considering the aforementioned aspects, to overcome the limitations of inference-based models and to enhance the model performance under uncertain disturbances, we propose an ESN-based MPC to achieve active vibration control of HEV powertrains. The key contributions of this study are as follows:  
1. A novel control scheme based on an ESN and MPC is developed to improve the control performance in cases involving highly irregular disturbances. 2. The proposed MPC control scheme can be applied even if the optimization calculation does not finish within the control period. 3. The predictive model for the HEV system is based on the state-space representation to understand the relationship between the model parameters and output. 4. The proposed method can be applied to several other control targets apart from the active vibration control of HEV powertrains.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 2 Related Work
MN handover process works by using Flush Request (Freq) and Flush Reply (FRep) messages. Freq message is communicated to the neighboring MAGs of nMAG when MN attachment is detected by target MAG. Using information of MN in the Freq message, the corresponding pMAG reply by using the FRep message. Furthermore, for solving packet loss, the buffered packets are transferred from pMAG to nMAG using the established tunnel. A more efficient location-based PMIPv6 extension is devised that effectively uses the location information for enhancing the signaling efficiency, and load on network entities [1; 13]. In such a scheme, the location of the MAG is shared with its corresponding MNs so that MN should only request for handover when its location is proper. Such a procedure eliminates unnecessary signaling and load on network resources. For providing better mobility solutions based on the requirement of current as well as future communication, a resource-friendly and performance-efficient mobility solution is provided that can handle many devices in a massive IoT environment.
## 2 Electrical Resistivity/Impedance Measurement: Is It Really that Simple?
### AC Measurement of Electrical Resistivity
#### 2.2.3 Other Methods Exploiting AC Excitation  
Also other methods are reported in the literature. [PERSON] et al. [128] invented and patented [129] a non-contact device for monitoring electrical resistivity during the hydration process of cement paste. This system is based on the transformer principle to avoid the issues related to the electrode-specimen interface. [PERSON] et al. [130,131] used the same technique with a 1-100 kHz sine wave in frequency domain, inducing the signal on a ring specimen of 1.4 L volume identified as the secondary coil of the transformer. The transformer-based method [132] for the measurement of electrical resistivity was used also by [PERSON] and [PERSON] [133] and by [PERSON] et al. [134,135] on ring-shaped specimens (1.67 L volume); the sampling interval was 1 min and the test was performed for 24 h. Similarly, [PERSON] et al. [14] performed measurements on cement paste ring-shaped specimens for 1 day every 5 s. [PERSON] et al. [136] adopted the same test setup, prolonging the measurement up to 72 h. The same method was used by [PERSON] et al. [137]. A specimen volume of 1.4 L was required and the test was carried out in a thermo-chamber with T = 20 \({}^{\circ}\)C, equal to the curing room. An electrical induced potential of 0.1 V at 1 kHz was used, and the measured data were recorded. The test lasted 3 days, starting when cement is mixed with water. Finally, [PERSON] and [PERSON] [138] used the non-contact method in specimens with a trapezoidal section (1.67 L in volume), carrying out the measurements for 48 h with 1-min sampling intervals. [PERSON] et al. [139] used a rolling probe, including a guard ring and a center electrode, for testing concrete bridge decks. They proved that this method is significantly faster (190 m\({}^{2}\)/h) than static probes (30 m\({}^{2}\)/h [140]), with a spatial resolution (0.1 m) 12 times higher than the latter. An excitation 3.3 V peak-to-peak sinusoidal signal at 190 Hz is used [141]. Pre-wetting of concrete surface (using a water and liquid soap solution) was necessary to guarantee a proper electric contact with the sensing electrodes [142]; the alterations caused by water were considered negligible. This method can provide an impedance map with valuable information on the reinforcement steel status related to protection against corrosion. Finally, [PERSON] et al. [143] used a three-electrode electrochemical cell, with an excitation signal of low-amplitude (25 mV) in the frequency range of 0.01 Hz-100 kHz, considering prismatic specimens (\(40\times 90\times 100\) mm). They used the reinforcement (carbon steel rebar, 6.3 mm diameter) with a copper wire at its end and a graphite bar as electrodes. They performed the measurement according to ASTM G106 [144].
## 3 Design
### Optical Design of MEPI  
As shown in Figure 5, the MEPI consists of a data processing unit with an image sensor, a front main system and a four-aperture subsystem. Each main system and the sub-aperture system constitute a polarization measurement channel. Among them, the front main system plays a key role. It can promote the efficient transmission of light with different polarization states through the same aperture so as to achieve the common field of view effect of the system. In addition, the existence of the pre-system makes the image plane more compact and the image will be more concentrated, which further improves the utilization rate of the detector pixel. The aperture stop is set on the first surface of the subsystem, where the off-axis operation is performed and cooperates with the subsystem. Finally, the entire optical system constructs four independent imaging channels. The aperture is located on the first surface of the subsystem. The MEPI can measure nine spectra, arranged in three rows and three columns. Four sub-aperture systems are used for the measurement of polarized light, three of which are polarized channels and the other is a non-polarized channel. The non-polarized channel provides an additional calibration parameter, which can reduce the error caused by the measurement system, so that the data can more truly reflect the actual physical quantities.  
The solar radiation reflected from the Earth enters the system, converges through the main system, and is then divided into four beams of light by the subsystems. The advantage of the system lies in the simultaneous polarization measurement of light in different spectral bands with high resolution and a significant reduction in overall weight and volume. The subsystem efficiency at different wavelengths will be predicted in the laboratory.  
Figure 5: MEPI model.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Specification** \\ \hline Number of channels & 4 \\ FOV & 2.5\({}^{\circ}\) \\ Effective Focal Length (EFL) & 313.67 mm \\ Entrance Pupil Diameter (EPD) & 30 mm \\ Polarizations & 0\({}^{\circ}\); 60\({}^{\circ}\); 120\({}^{\circ}\) \\  & 400–420; 433–453; 460–480 \\ Detected wavelength band (nm) & 480–500; 522–542; 545–565 \\  & 590–610; 660–680; 845–885 \\ Swath (to the Moon) & 1127.275 km \\ Ground Sampling Distance (GSD) & 10 km (Earth) \\  & 34.5 km (Moon) \\ \hline \end{tabular}
\end{table}
Table 3: Technical requirements.
# Dynamic Modeling and Validation of Peak Ability of Biomass Units
## 5 Conclusions  
Due to their advantages of environmental protection and short-term peak ability, biomass units can play a certain role in summer and winter. To analyze the peak ability of biomass units, this paper focuses on the dynamic modeling of their peak ability. Based on this, according to the characteristics of the biomass grate furnace unit, the process from the biomass unit feed to the unit power is divided into three modules. A biomass combustion unit model based on the principles of energy conservation and mass conservation is proposed, and remarkable results have been achieved. The specific results are as follows:  
1. According to the characteristics of a biomass grate furnace unit, a modular modeling method is proposed. It divides the process from biomass unit feed quantity to unit power into three modules based on the first principles: feed-heat module, heat-main steam pressure module and main steam pressure-power module.
2. A \"two-input and two-output\" (TITO) dynamic model framework based on biomass combustion characteristics was constructed, using the feed rate and the steam turbine valve opening as inputs, and the main steam pressure and steam turbine output power as outputs.
3. The actual operation data of a 30 MW biomass unit were used to verify the model and the open-loop step response of the model. Experiments show that the model has good fitting effect. The RMSE values of the two output parameters turbine output power and main steam pressure were 0.2201 MW and 0.4655 MPa, respectively. The MAE values were 0.1520 MW and 0.4017 MPa, respectively. The model demonstrates high accuracy, with the steam turbine output power and main steam pressure output exhibiting minor deviations from actual operation data.  
Building on this research, future work can further enhance the accuracy and reliability of power prediction by optimizing the model to adapt to data from diverse biomass-fired units and dynamically adjusting parameters with actual plant operational conditions, thereby providing more robust theoretical support for optimized operation of biomass power generation units. In addition, suitable control strategies can be designed for this model and may be applied to the field units in the future work.  
Investigation and writing--original draft, D.X.; methodology, G.C. and J.P.; formal analysis, X.W.; data curation, K.M.; resources, Y.S.; project administration, supervision and writing--review and editing, Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the State Grid Henan Electric Power Company Technology Project (Grant No. 521702240003).  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** Author [PERSON] was employed by the State Grid Henan Electric Power Company. [PERSON] was employed by the State Grid Shangqiu Electric Power Supply Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
# Self-Powered Fast Brazing of Ti-6 Al-4V Using Ni/Al Reactive Multilayer Films
## 3 Results and Discussion
### Mechanical Properties  
The shear strength of the brazed joint was evaluated as a function of pressure and the RMF thickness. Applied pressure serves an important role in RMF brazing because it ensures intimate contact between the RMF and the brazing material [27]. However, excessive pressure will cause the molten brazing material to be squeezed out of the joint, which is why the bonding strength decreases above 20 MPa applied pressure, as shown in Figure 10a. The RMF thickness was kept to 66 \(\upmu\)m for the pressure investigation. The maximum bonding strength (10.6 MPa) was achieved using 20 MPa applied pressure and RMF thickness = 165 \(\upmu\)m. The effect of increasing the RMF thickness is, primarily, increasing the overall heat release, which increases the amount of molten liquid during brazing. The low bonding strength, despite the sufficient interface temperature, is attributed to the limited metallurgical bonding and low reaction time. These two causes are related by diffusion. Low diffusion is a result of a short reaction time and limited metallurgical bonding is partially caused by low interdiffusion. The fracture surfaces, shown in Figure 11, display some small cleavage fracture features. Several microcracks also appear in the surface as the RMF thickness increases. The appearance of microcracks and cleavage fracture features is partially due to the brittle nature of the AlNi and Ni\({}_{5}\)Al\({}_{3}\) intermetallic compounds that comprise the RMF layer after brazing. Also, as previously stated, the low interdiffusion between the brazing and RMF layers ensures that the brazed joint fails primarily at the RMF-BAI interface due to a lack of strong metallurgical bonding. Additionally, the hardness map, shown in Figure 12, shows that there is almost no hardness gradient across the RMF-BAI interface, further indicating a lack of a reaction zone along that interface. Furthermore, the hardness is uniform within the RMF (\(\sim\)524 HV) and BAI (\(\sim\)75 HV) layers of the brazed joint. The Ti64-BAI interface, however, has a hardness gradient across the interface due to the presence of the Al\({}_{3}\)Ti at the interface.  
Figure 10: (**a**) Bonding strength as a function of applied pressure (RMF thickness kept constant at 66 \(\upmu\)m). (**b**) Bonding strength as a function of total RMF thickness (Applied pressure = 20 MPa).
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 2 System Model
### Signal Model  
Consider a MS using the aforementioned analog beamformer, and which communicates with a BS in data-plane at \(d\) (meters) separation distance. The MS uses its primary beam \(b_{i}^{\text{MS}}\) for initial access procedure (e.g., iterative search). The DL signal, \(y_{\text{MS}}\), at the \(l\)-th path after the RF stage at the MS is as  
\[y_{\text{MS}}=\sqrt{pr_{\text{MS}}}v_{\text{MS}}^{\text{H}}\Big{(}\Theta_{i}^ {\text{MS}}\Big{)}\text{H}p_{\text{BS}}\Big{(}\Theta_{i}^{\text{BS}}\Big{)} \text{Z}+v_{\text{MS}}^{\text{H}}\Big{(}\Theta_{i}^{\text{MS}}\Big{)}w, \tag{4}\]  
where \(pr_{\text{MS}}\) and \((.)^{\text{H}}\) denote the average received power and the Hermitian matrix, respectively. Here the combining vector \(v_{\text{MS}}\) is at \(\Theta_{i}^{\text{MS}}\) pointing direction, and the beamforming vector \(p_{\text{BS}}\) is at \(\Theta_{i}^{\text{BS}}\) pointing direction. Furthermore, \(Z\) is the control signal that carries the synchronization information. Finally, \(w\) denotes the additive white Gaussian noise (AWGN), i.e., \(w\)\(\sim\)\(N(0,\,\,\sigma_{w}^{2})\), with \(\sigma_{w}^{2}\) variance. Finally, H is the channel between MS and BS, specified by the geometric model. This is attributed to the small wavelength at mmWave bands, which results in high dependence on the geometry of the objects in the propagation channel, i.e.,  
\[\text{H}=\sqrt{\frac{N_{\text{BS}}N_{\text{MS}}}{\Gamma_{pl}}}\sum_{l=1}^{L}h _{l}V_{\text{MS}}P_{\text{BS}}^{\text{H}}, \tag{5}\]  
where \(\Gamma_{bl}\) and \(h_{l}\) in order denote the blockage loss and the gain of the \(l\)-th path, for \(L\) paths received in \(K\) clusters.  
Figure 1: Beam indices at the mobile station (MS).
# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
## 3 Results
### Experimental Results of the Improved YOLOv5 Model
#### 3.2.3 Test Results
structural loose distresses, and in Figure 12b, the model trained on the dataset after data augmentation can recognize the distress, but with lower accuracy, and cannot recognize the loose structure distress in Figure 12d,e. Whereas, after combining the SAHI framework in both the training and inference stages, it can recognize all the loose structure distresses that were missed, and all of them have higher accuracies of more than 70%, which greatly reduces the missed detection rate. As can be seen in Figure 12c,d, after using SAHI in the training and inference stages, some false detections occur, mainly because the feature expression of non-targets that are similar to the target features after image slicing is also enhanced, but from the point of view of the improvement of detection accuracy and the reduction of the leakage detections, the use of the image-expanded dataset generated by DCGAN and the use of SAHI in the training and inference stages can significantly improve the detection effect. Especially, the missed detection is greatly reduced after using SAHI. Figure 11: ROC curves. Figure 12: _Cont._  
Figure 12: Cont. Figure 12: Visualization of the detection results on the test set. (In each sub-figure, **top**: detection results on the test set of the model trained using dataset I, **middle**: detection results on the test set of the model trained using dataset II, **bottom**: detection results on the test set of the model trained using dataset IV and after SAHI was used in the inference stage, where the green part represents the real label box). To estimate and discuss the variability of the classification results, this paper reclassified the training, validation, and test sets to obtain ten different dataset divisions, which were then fed into the model for training, respectively. Table 6 shows the mAP50 of the models obtained after training on each dataset applied to each test set respectively, YOLOv5 indicates that the dataset was trained and tested directly using the base YOLOv5, and Ours represents the image-expanded dataset generated using DCGAN and SAHI was used in the training and inference stages. It can be concluded from the table that, for the individual experiments, using the DCGAN-generated, image-expanded dataset and using SAHI in the training and inference stages, the mAP50 is higher than the mAP50 of the base YOLOv5. The average mAP50 of the 10 experiments reaches 0.669, which is higher than the average value of the mAP50 of the base YOLOv5, which demonstrates the validity of the method used in this paper.
## 2 Materials and Methods
### Other Analytical Measurements  
The physicochemical characterization of RW and TRW, including analyses of color, total suspended solids (TSS), BOD\({}_{5}\), COD, oil and grease (OG), organic nitrogen (TKN), ammoniacal nitrogen (N-NH\({}_{3}\)), and potassium (K\({}^{+}\)), were carried out in a laboratory accredited by the National Quality Institute, Lima, Peru ([[http://www.cerper.com](http://www.cerper.com)]([http://www.cerper.com](http://www.cerper.com))) (accessed on 24 August 2024) and certified by international standards ISO 9001:2015 [23], ISO 14001:2015 [24], ISO 45001:2018 [25], and ISO 37001:2016 [26], which legitimizes the control of equipment, calibrations, validations, measurements, and authenticity of the analyses. These methods followed the standard methodology as prescribed in SMEWW-APHA-AWWA-WEF 2017 [27]. The method of each analysis is described in detail in a previous study [13]: color (Part 2120 C); TSS (Part 2540 D); BOD\({}_{5}\) (Part 5210 B); COD (Part 5220 D); OG (Part 5520 B); TKN (Part 4500-N\({}_{\text{org}}\) B); N-NH\({}_{3}\) (Part 4500-NH\({}_{3}\) D); and K\({}^{+}\) (Part 3111 B).  
The quantification of inorganic anions--NO\({}_{3}\)--, NO\({}_{2}\)--, PO\({}_{4}\)\({}^{3-}\), and SO\({}_{4}\)\({}^{2-}\)--was performed using ion chromatography (Sykam, S-150 IC Systems, Eresing, Germany). The detailed procedure for analyzing each analyte using ion exchange chromatography is described in a previous study [13]. Nutrient removal efficiencies (RE) were assessed as follows:  
\[RE=(C_{i0}-C_{\vec{j}})/C_{i0}\times 100\qquad(\%) \tag{4}\]  
where \(C_{i0}\) and \(C_{\vec{j}}\) represent the initial and final nutrient (i) concentrations in the supernatants of the cultures.  
The analysis of the biochemical biomass composition followed established protocols as previously described [13]. Briefly, total carbohydrate content was determined by the spectrophotometric method [28]. Total protein content was calculated by multiplying the Kjeldahl organic nitrogen value by 4.78 [29]. Lipid content was determined using EPA Method 1664--Review B [30]. The contents of total chlorophyll (\(Chl_{total}\)) and carotenoids (\(Car\)) (expressed in mg L\({}^{-1}\)) were quantified using the following equations described elsewhere [20]:  
\[Chl_{total}=5.24\times\text{OD}_{664}-22.24\times\text{OD}_{649} \tag{5}\]  
\[Car=(1000\times\text{OD}_{470}-2.13\times Chl_{a}-97.64\times Chl_{b})/209 \tag{6}\]  
where OD\({}_{664}\), OD\({}_{649}\), and OD\({}_{470}\) are optical densities at wavelengths of 649, 664, and 470 nm, respectively, corresponding to extracts from microalgal pellets obtained as indicated by [PERSON] [20].
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 4 Validation
### Single Shooting Pattern
#### 4.1.6 Pareto Optimization of Single Shooting Patterns
Considering specific requirements or constraints unique to each project (e.g., limited total working time, required minimum accuracy level, required model detail), this study provides quantitative grounds for reasonably selecting the most efficient shooting strategy (parameter combination) from among multiple alternatives on the Pareto frontier that best fits the given conditions. For example, in a project with a very tight deadline, the solution located farthest to the left on the time-related axis among the analyzed frontiers (the fastest time) can be prioritized. Conversely, if the final model's detail is the top priority, the optimal balance point can be sought around the solutions with the highest number of points on the 'Time_vs_Points' or 'Error_vs_Points' frontier.
## 7 Experiment Design
### Feedback Capturing, Annotations and Data For Analysis
#### 7.6.3 Answers and Durations  
For the answer search task following items were recorded for each user.  
* The number of questions attempted.
* The number of questions correctly answered.
* Time taken to complete the task.
* Duration per correct answer (by normalization).  
**Normalizing duration per correct answer**  
To assess the efficiency of answer searching, measuring the average time to find an answer would not be appropriate since the videos are of different lengths. To normalize that, the percentage of video length is used. For example, if a user took 5 min to search for answers for a 20 min video, it is considered as 25% of the length of the video was required by the user to find all the answers. Dividing it by the number of correct answers gives the length of the video required per correct answer.