esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 3 Smart Building Monitoring Applications
### Water Consumption End-Use Monitoring  
Water end-use pertains to the specific locations within a building where water is utilized, such as sinks, showers, toilets, etc. Water consumption monitoring helps to detect water leaks and get rid of water waste. Monitoring occupant end-use habits can be useful for the preservation of water resources in a building as well as predicting water consumption in the future [36]. Furthermore, water quality sensors allow for monitoring the quality of water [37].  
Typically, water consumption measurements for multiple end-use sites involve the use of multiple smart water meters that measure water flow rate within an IoT sensor network [38]. Water flow rate can be calculated by measuring liquid flow velocity and the cross-sectional area through which it passes, as demonstrated by [PERSON] et al. [39]. Smart meters can be classified into various categories according to the principles they use to measure liquid flow [40]:  
1. Mechanical or positive displacement flow meters: have mechanical parts that are displaced or rotated when water flows through them;
2. Vortex flow meters: utilize the vortex shedding principle, where a bluff object is placed in the way of liquid flow, which creates alternating vortices downstream;
3. Electromagnetic flow meters: generate an electromagnetic field, which fluctuates when conductive fluid flows through them;
4. Ultrasonic flow sensors: these sensors operate similarly to radar, sending sound waves in both downstream and upstream directions to measure the time difference, which is then used to calculate the volumetric flow rate.
5. Turbine flow meter: employs a turbine whose rotation speed is directly proportional to the flow rate.
6. Single- and multi-jet flow meters: employ single or multiple impellers, respectively.  
When setting up an IoT network with the intention of measuring water consumption, it is advisable to install a separate water flow meter for each end-use site. Flow meter characteristics vary for the different types of sensors but the primary criteria to consider while choosing an optimal flow meter type are cost and required accuracy (Table 2).  
Figure 2: Stages of NILM implementation  
Significant contributions to the development of a smart hot water control system are detailed by [PERSON] and [PERSON] [42]. The system analyzes actual data from water heaters to understand human behavior concerning domestic hot water usage. Optimized heating schedules are calculated utilizing Gaussian process-based models and neural networks. Accordingly, the control of the hot water heating system adjusts heating times based on data collected about user behavior, resulting in energy savings, as demonstrated by [PERSON] et al. [43].
# Modeling and Control of Satellite Formations: A Survey
## 4 Leader-Follower Formation Architecture
Although any design criterion can be used, a fuel consumption approach is used, since the fuel cost (loss) function is extremely high for a spacecraft (SC). It is shown how certain elements of the theory of optimal periodic control provide a very natural formulation of this problem. Using versatile dynamic optimization softwareit is shown how one can easily design satellite formations without using any analytical results. If a natural zero fuel solution does not exist, a byproduct of the proposed approach automatically determines the minimum amount of fuel and the associated controls required to maintain the formation.
## 4 Analysis Results
### Comparisons with the Nonlinear Time-History Analysis
#### 4.2.1 Artificial Ground Motion  
Figures 20 and 21 show the peak relative displacement at X\({}_{3}\)A\({}_{3}\)Y\({}_{3}\), which is the closest point to the center of the floor mass at each level. In these figures, the nonlinear time-history analysis results are compared with the results predicted by MABPA (for complex damping ratios (\(\beta\)) of 0.10, 0.20, and 0.30). As shown here, the predicted peaks can approximate the envelope of the time-history analysis results, except in the case of Model-Tf34 subjected to  
Figure 19: Prediction of the peak equivalent displacements (model: Model-Tf44, ground motion: TCU): (**a**) Prediction of the peak of the first modal response and (**b**) prediction of the peak of the second modal response.  
the Art-2 series shown in Figure 20b. In addition, the predicted peak with \(\beta\) = 0.10 is larger than that with \(\beta\) = 0.30. When the models were subjected to the Art-1 series, the predicted peak closest to the envelope of the time-history analysis results was found with \(\beta\) = 0.10 for Model-Tf34 (Figure 20a) and with \(\beta\) = 0.30 for Model-Tf44 (Figure 21b). Differences in the predicted peaks resulting from the value of the complex damping were small in the case of Model-Tf34 but relatively noticeable in the case of Model-Tf44.  
Figures 22 and 23 show comparisons of the horizontal distributions of the peak displacement at level 0, which is just above the isolation layer. In Figure 22, the results of Model-Tf34 subjected to the Art-1 series are shown. The predicted peak agrees with the envelope of the time-history analysis results, even though the predicted peak was not conservative at Y\({}_{1}\) and Y\({}_{2}\) (the stiff side in the X direction). A similar observation can be made in the case of Model-Tf44 subjected to the Art-1 series, as shown in Figure 23, where the predicted peak with \(\beta\) = 0.30 agrees very well with the envelope of the time-history analysis results, except at Y\({}_{1}\) and Y\({}_{2}\).  
Based on the above results, the updated MABPA can predict the peak relative displacement at X\({}_{3\Lambda}\)Y\({}_{3}\) and the horizontal distribution of the peak displacement at level 0 for both models according to the artificial ground motion datasets.
# Pre-Flight SAOCOM-1A SAR Performance Assessment by Outdoor Campaign
## 6 Conclusions  
The SAOCOM-1A outdoor test took place in Bariloche, Patagonia, Argentina, during June 2016.  
The main objective of the outdoor test (ODT) was to provide an end-to-end validation of the SAOCOM-SAR functionality, in a realistic condition, where the SAR pulses are radiated by the antenna, reflected by a target, and then received by the antenna and recorded as SAR data. The test setup was made up of two main elements: the SAOCOM SAR engineering model, including one of the seven antenna tiles, and the moving target. It was proven to work as expected throughout the full duration of the tests. Several datasets were successfully acquired through the setup and processed to L0B data.  
Overall, the ODT objectives were met and the SAOCOM-1A proved to show an excellent signal quality, both from radiometric and interferometric points of view. The ODT provided the unique occasion to obtain a set of pre-flight far-field measurements. The results can be considered representative for SAOCOM-1A and for SAOCOM-1B as well, so no additional experiments are foreseen. The conclusive and formal verification of the SAOCOM performance is left to the laboratory pre-flight tests and to the commissioning phase.  
The authors would like to thank CONAE and INVAP for the fruitful cooperation before, during, and after the execution of the outdoor test campaign.  
[PERSON] originally conceived the experiment. [PERSON] carried out the feasibility study, coordinated the moving target design and development and processed the acquired SAR data. [PERSON] provided a thorough review of the feasibility study and coordinated the actual setup and execution of the experiment.  
The authors declare no conflict of interest.  
Figure 8: Far-field elevation antenna pattern validation against the theoretical model.
## 1 Introduction  
There are different design approaches for WPDs, such as tapered transmission lines [1], three-section [2], dual-band structures [3], dual-lines [4], and triple lines [5]. In addition to these, open-/short-circuited stubs and defected ground structures are used to achieve improved isolation [6; 7; 8]. Single- or multi-band WPDs operating in a wide frequency band have also been studied. Among these, semi-lumped-element power dividers with dual-band characteristics in the UHF/SHF bands [9], flexible design schemes for single\(-\) and dual\(-\)band power-dividing operations [10], dual-band unequal WPDs [11], multi-T-section characterization of high-impedance transmission lines [12], multi-band reconfigurable differential power dividers [13], multiband WPDs based on multisection LC ladder circuits [14], and single/multi-band WPDs with transversal filtering sections [15] stand out. Such WPDs may find application areas especially in multifunctional communication systems. Moreover, the placement of power dividers in antenna arrays is one of the main focuses for researchers. Therefore, size reduction is another important parameter for WPDs, since it is required for feeding the elements of the antenna array within a relatively reduced area. To date, slow-wave structures have been especially used for size reduction. For this purpose, capacitively loaded stubs [16; 17] and narrow-slit-loaded and meandered transmission lines [18; 19] are largely employed for WPD designs. Periodically, loaded slow-wave structures are modified for different targets, including dual-band applications [20; 21], SiGeBiCMOS processes [22], filtering power dividers having resonant LC branches [23], etc. In some structures, coupled lines are used to suppress undesirable harmonics so that the signal can be transmitted within the desired frequency region [24]. On the other hand, in order to adjust the directivity, antenna array elements may need different power ratios at different elements. Therefore, unequal power dividers may also find applications in antenna arrays to meet the asymmetric power division requirement [25; 26; 27]. There are two types of equal WPDs: N-way and cascaded models. N-way WPDs have an input port and N output ports with the impedance of \(Z_{0}\sqrt{2}\), whereas cascaded model WPDs divide the input power by 2 at each cascaded path [28].  
In this paper, two-, four-, and eight-way cascaded WPDs are designed by using slow-wave structures. For this purpose, the two-, four-, and eight-way WPDs constructed by using narrow-slit-loaded transmission lines in [28] were first taken into account. Next, they were replaced with meandered transmission lines to improve the compactness. Two-, four-, and eight-way WPDs having meandered transmission lines were also fabricated and successfully measured for demonstration. Based on the meandered transmission lines, three- and five-way WPDs with new input/output port arrangements were designed, fabricated, and successfully measured for demonstration. The proposed three- and five-way circuits allow halving the input power at the next output port.
## 1 Introduction  
Depth data have become a key source of information for a wide variety of applications, such as robotics and augmented reality. Notably, a depth map is an image whose pixels represent the distance from the object's surface to a fixed point in the scene (typically a sensor capturing the data). There are many sensors to acquire depth data, including structured light and RealSense sensors, ToF (Time-of-Flight) cameras, LiDAR (Light Detection and Ranging), and stereo vision. Regardless of the sensor type, depth data are characterized by low quality in terms of missing data, noise, and errors. Missing data are particularly dramatic, since in some cases the depth maps present large areas with no information or \"holes\". Completing depth information in those cases becomes an extremely challenging interpolation problem.  
Hence, we can define the depth completion problem as the problem of interpolating or filling in the missing gaps in a depth image. It was originally tackled using classical interpolation techniques in image processing and later using more sophisticated inpainting methods. These last methods performed well in simple scenarios at high computational costs. Unfortunately, they presented limitations in more complex real-world situations. For that reason, researchers explored other approaches, such as bilateral filtering and non-local means filtering, which leverage local image features and image statistics to propagate depth information across empty regions. These models achieved significant improvement but still performed poorly in some real-world situations, for instance, in the presence of large holes corresponding to portions of the reference (color) image without significant texture. It was not until the arrival of deep learning that depth completion experienced a breakthrough, leading to high-quality maps. Nevertheless, despite the success of deep learning algorithms, classical approaches remain relevant in some applications, mainly due to their explainability, computational efficiency, and ability to operate on resource-constrained platforms.  
In this work, we explore a non-deep-learning approach that provides good-quality depth maps in constrained environments. We propose a simple and flexible model that requires few images for training and can, consequently, run on a laptop with only one GPU; thus, it represents a low-cost implementation alternative. This paper extends the work presented at the 14 th International Workshop on Parallel and Distributed Algorithms and Applications, PDAA [1]. The contributions of this manuscript are as follows:  
1. We employ a balanced metric, which considers a balance term between spatial and photo-chromatic distance.
2. We evaluate the impact of an additional Texture+Structure decomposition stage.
3. We propose two variations of the previous interpolation model.
4. We apply an ablation test to our model in the NYU_V2 data set.
## 2 Material and Methods
### Environmental Covariates
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt}} \hline \hline
**Topographic Indices Derived by Evaluating the \(3\times 3\) Altitude Submatrix or Elevations Within Search Radii** \\ \hline \(\beta\) & slope [50] \\ north, east & northness and eastness derived from cosine and sine transformation [51] of aspect [50] \\ \(\beta_{\rm d}\) & downslope index [52] \\ \(C\), \(C_{\rm plan}\), \(C_{\rm prof}\) & isotropic, planform, and profile curvature [50] \\ \(\Psi_{L\ldots}\) & negative openness (radii 100, 250, 500 m in [53]) \\ \(\Psi_{\rm S}\) & sky-view factor [54] \\ \(\Psi_{\rm T}\) & terrain configuration factor [55] \\ \(\Psi_{\rm wte}\) & wind exposition [54] \\ \(\hat{\beta}\) & direct solar beam irradiance [56] \\ \(TPI_{\ldots}\) & topographic position index (radii 25, 125, 500 m in [57]) \\ \(TRI_{\ldots}\) & terrain ruggedness index (radii 50, 300 m in [58]) \\ \hline \hline \multicolumn{3}{p{128.0 pt}}{**Topographic Indices Calculated at the Catchment Scale**} \\ \hline \(A_{\rm C}\) & catchment area [59] \\ \(\beta_{\rm C}\) & average slope [50] within catchment \\ \(L\), \(S\), \(LS\) & slope length, slope steepness, and topographic factors from the Revised Universal Soil Loss Equation (RUSLE in [60]) \\ \(TWI\) & topographic wetness index [61] \\ \(SPI\) & stream power index [62] \\ \hline \multicolumn{3}{p{128.0 pt}}{**Terrain Attributes Derived from Drainage (DN) and Ridge Networks (RL) [63; 64] at Different Spatial Scales (Large Scale (1), Small Scale (3))**} \\ \hline \(\beta_{\rm RL}(\ldots)\), \(\beta_{\rm DN}(\ldots)\
## 2 Materials and Methods
### Schottky Diodes
\begin{table}
\begin{tabular}{l l c} \hline \multicolumn{1}{c}{**Physical Parameters**} & \multicolumn{1}{c}{**Electrical Parameters**} \\ \hline Epilayer doping = 2 \(\times\) 10\({}^{17}\) cm\({}^{-3}\) & Junction potential (\(V_{j}\)) & 0.9 V \\ Epilayer thickness = 0.1 \(\mu\)m & Zero-bias junction capacitance (C\({}_{\mathrm{j}0}\)) & 2 fF \\ \hline N+ layer doping = 5 \(\times\) 10\({}^{18}\) cm\({}^{-3}\) & Series resistance per anode (\(R_{S}\)) & 7 \(\Omega\) \\ N+ layer thickness = 3 \(\mu\)m & Reverse saturation current (\(I_{sat}\)) & 30 fA \\ \hline Anode radius = 0.55 \(\mu\)m & Reverse breakdown voltage (\(V_{br}\)) & \(-\)9.6 V \\ SiO\({}_{2}\) layer thickness = 0.5 \(\mu\)m & Reverse breakdown current (\(I_{br}\)) & \(-\)10 uA \\ \hline Substrate thickness = 12 \(\mu\)m & Ideality factor per anode (\(\eta\)) & 1.2 \\ \hline Chip length = 220 \(\mu\)m & Grading coefficient (M) & 0.5 \\ Chip width = 70 \(\mu\)m & Typical cut-off frequency (\(f_{T}\)) & 10 THz \\ \hline \end{tabular}
\end{table} 
Table 2: Physical and electrical diode parameters of the 0.34 THz mixer. and 6.3 \(\mu\)m in height, not counting the thickness of the substrate. It was essential to use those physical parameters to build a three-dimensional modeling of the diode. In terms of lumped equivalent circuits, a large amount of literature is available on the modeling of Schottky diodes in millimeter and submillimeter waves operating at room temperature [30; 31; 32; 33]. For this design, a simplified equivalent circuit model was used, which is marked on the cross-sectional view in Figure 1. The Schottky junction is represented by a diode symbol, modeled as voltage-dependent junction capacitance (\(C_{j}\)) and resistance (\(R_{j}\)). The diode's series resistance (\(R_{S}\)) consists of airbridge finger resistance (\(R_{f}\)), buffer-layer spreading resistance (\(R_{spread}\)), ohmic-contact resistance (\(R_{dmic}\)), and top junction epi-layer resistance. Several parasitic elements were used to characterize parasitic coupling effects dependent of the diode geometry, where the magnetic field coupling within the airbridge finger was modeled as \(L_{f}\) and electric field coupling near the surface channel was modeled as \(C_{pp1}\), \(C_{pp2}\), and \(C_{fp}\). Based on these equivalent circuit components, the electrical parameters obtained through measurements are shown in Tables 1 and 2. The equivalent model of the diode had to be slightly different, depending on the specific application. For multipliers, nonlinear junction capacitance \(C_{j0}\) played a leading role, which could be improved by properly increasing the anode junction area and reducing the doping concentration of the epitaxial layer. However, mixers were more dependent on the nonlinear resistance characteristics of the diode junction because broadband impedance matching was easier to achieve when nonlinear resistance dominated. Additionally, the HFSS 3D model of the diode is presented at the bottom of Figure 1, including anti-series 4-die SBDs for 0.34 THz triplers and anti-parallel SBDs for 0.34 THz SHMs.
## 5 Discussion
### Performance Issues  
Here, we should claim that the adopted partitioning method is simple. Hence, to improve the performance of the suggested privacy preserving \(CkNNs\), we could employ the approach proposed in [34] and extend Algorithm 1 by applying the smart partitioning technique. Especially, we could consider to compute \(AkNN\) queries based on \(kdANN\) or \(kdANN+\) (where \(d\) stands for the dimensionality), instead of simple \(k\)-NN queries and study the effect of \(d\) on the performance of such queries and thus, in \(k\)-anonymity set computation performance, which is the main issue in the context of this research work. At this point, it should be noted that that approach exploits a space decomposition technique suitable for Big Data and processes the classification in a parallel and distributed manner considering multidimensional objects, as well. Through an extensive experimental evaluation it has been proved that the solution is efficient, robust and scalable in processing the \(k\) nearest neighbor queries.  
Although the trajectory (spatio-temporal) data utilized in this article are 2-dimensional (2D), the methodology and techniques are general and applicable to higher dimensions. Hence, observing Figures 5 and 6 in Section 4, the efficiency of the proposed approach, which now is investigated utilizing 2D trajectory (spatio-temporal) points datasets, is expected to scale up exponentially with respect to both data size, as shown in Figure 7, and dimensions, when \(d>2\), in the same trend as in [34]. This results from the curse of dimensionality. As the number of dimensions increases, the number of distance computations as well as the number of searches. For further experimentation with the dimensionality parameter \(d>2\) in the context of GeoSpark, functions for data preprocessing to prepare Points RDD structure need to be redefined. Let us recall that GeoSpark RDD operations and \(k\)-NN queries processing work with 2D points.  
Finally, as the previous finding shows in [34], it is expected that the increment of computing nodes (VMs) will improve scalability and time performance in support of both snapshot and continuous spatial queries over moving objects, having a great effect when the data follows a uniform distribution due to better load balancing. Indeed, the increment of number of computing nodes decreases the amount of distance calculations and update steps on \(k\)-NN lists that each computing node undertakes. A similar behavior we expect to see in our case if the processing is distributed in multiple nodes in the GeoSpark environment.  
Figure 12: Vulnerability Performance Comparison in Euclidean and Hough-\(X\) Space.
# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 3 Simulation Verifications
### Results and Discussion
#### 3.2.1 Comparison of Curvature and Strain Reconstruction Errors between LI-CPM and NCV-CPM  
Taking the example of a leading-edge deflection angle of 10\({}^{\circ}\), simulation data corresponding to 26 strain-sensing points along Path1 were extracted according to the method described in Section 2.2. The reconstructed surface curvature field and strain field information of the variable wing leading edge is depicted in Figure 9.  
According to Figure 9a, along the circumferential direction of the wing leading edge, the curvature angles decreased and then increased, with the minimum curvature angle being observed in the central region. Therefore, after the deformation of the variable wing leading edge, the reconstructed curvature extremum was located in the middle region of the leading edge. Additionally, as indicated in Figure 9b, due to the predominant control of the wing leading edge by the main spar, the reconstructed strain field exhibited a sudden change at the main spar location.  
The curvature and strain reconstruction results for various validation points along Path1 are illustrated in Figure 9. For the conventional curvature propagation algorithm based on linear interpolation (LI-CPM), the relative error average of the curvature reconstruction was 20.24%, and the root mean square error was 2.61 \(\upmu\)m\({}^{-1}\). In contrast, the curvature propagation algorithm based on segment curvature nodal vectors (NCV-CPM) achieved a reduced relative error average of 5.98% and a decreased root mean square error of 0.42 \(\upmu\)m\({}^{-1}\), while the relative error average for the strain reconstruction was 4.66%.  
Figure 8: Arc segment division scheme.  
Figure 9: Comparison of reconstruction results in Path1: (**a**) curvature result; (**b**) strain result.  
There are two main reasons for this phenomenon: Firstly, the curvature calculation accuracy of the sensing points is improved due to the strain-curvature function model proposed in Section 2.1. Secondly, the adoption of higher-order curvature functions to solve the curvature vector at element nodes enhances the accuracy of curvature calculation at locations where actual sensors are not deployed.  
Therefore, this method, utilizing a small number of discrete strain-sensing points, not only significantly improved the curvature reconstruction accuracy at the positions of unattached sensors (virtual sensing points) but also allowed for the reverse reconstruction of the leading-edge strain distribution information.