esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 3 Existing Methods for Vbda
### Vary Beam Divergence by Adjusting the Focal Length F of the Collimating Lens
#### 3.1.3 Using Lens with Different \(F\)  
This method of VBDA can switch between small and large angles. There are two main ways to do this: switching collimators with different focal lengths or inserting different defocus lenses, as shown in Figure 15. Figure 15a demonstrates the use of an optical switch to select a collimator with a different focal length for transmitting the beam. [PERSON] et al. [55] used this method to change the beam divergence angle.  
Figure 15b shows lenses with different focal lengths that are moved to the optical axis using an electric drive. [PERSON] et al. [56] used this method. [PERSON] et al. [57] fixed multiple collimating lenses with different focal lengths on a disk and switched between them by driving the rotation of the disk, achieving a graded and adjustable beam divergence angle.  
This VBDA method has the advantage of a simple principle and easy operation. However, its disadvantage lies in the high requirements for repeated positioning accuracy of the electric displacement mechanism. Failure to meet these requirements may result in significant beam drift. This method can only be used in grades and cannot continuously change the divergence angle. Furthermore, the communication link will experience an outage when the divergence angle is switched.  
Figure 14: Experimental light path diagram of WDM based on liquid lens beam steering and divergence angle control system [53]. Reprinted/adapted with permission from Ref. [53], 2022, [PERSON].  
Figure 13: The schematic of the full 2D scan setup [50]. Reprinted/adapted with permission from Ref. [50]. 2016, The Optical Society.  
Vary Beam Divergence by Adjusting the Optical Path Length between the Beam Waist and the Collimating Lens
# A Lightweight Model for 3D Point Cloud Object Detection
## 3 Method
### Lightweight 3D Point Cloud Object Detection Algorithm Framework  
The overall framework of the algorithm in this paper is described in this section; the detailed network architecture is illustrated in Figure 2. Figure 2a shows the workflow of the algorithm. Firstly, the raw point cloud performs the voxelization to obtain voxels, which are used as the input of the network. The input voxels are encoded by voxel feature encoding (VFE) [28] to obtain voxel feature mapping. Then, the voxel features are sent to the 3D backbone. The extracted 3D features reshape to bird's eye view (BEV) along the \(z\)-axis direction and are fed into the 2D backbone for further feature extraction. Finally, they are sent to the detect head to obtain the output of the network. The most important feature is the architecture of the backbone, which is shown in Figure 2b. The 3D backbone in the algorithm is composed of 11 LW-Sconv modules and the 2D backbone is composed of 12 lightweight convolution layer modules (LW-conv module). For the 2D backbone, the design idea of the lightweight convolution layer module is the same as that of the 3D, but the filter is 2D. For the 3D backbone, an LW-Sconv module is not directly applied at the beginning because the number of input channels is relatively small. Starting from the second layer, the LW-Subsconv module and the LW-Regsconv module are stacked. The first convolution layer module in the second and third stage uses stride = 2. Other hyperparameters remain unchanged in the same stage and the number of output channels in the next stage is doubled.
## 2 Background
### Queries as Views  
In terms of the data integration of multiple data sources, three approaches have been proposed to model a set of _source descriptions_ that specify the semantic mapping between the source and global schema [15]. The first approach, called global-as-view (GAV), requires that each concept in the global schema be expressed as a query over the data sources. The GAV addresses the case in which a stable data source contains details not present in the global schema, so it is not used for dynamically adding or deleting data sources.  
The second approach, called local-as-view (LAV), requires the global schema to be specified independently from the sources, and the source descriptions between the stable global schema, such as the ontology and the dynamic data sources, are established by defining each concept in the data sources as a view over the global schema [4, 16]. LAV descriptions handle the case in which the global schema contains details that are not present in every data source.  
The third approach, called global-local-as-view (GLAV), is a source description that combines the expressive power of both GAV and LAV and allows flexible schema definitions to be independent of the particular details of the data sources [17].  
In this paper, on the one hand, data integration uses LAV and GAV mappings within an SPD to reformulate a user query into a query over the source schemas. On the other hand, data exchange between super-peers uses GLAV mappings between different SPDs. More specifically, in a data exchange setting a tuple generating dependency (tgd) for a set of source-to-target dependencies or an equality-generating dependency (egd) for target dependencies is extended to Datalog shown as a GLAV mapping [18]. However, data integration and exchange are hampered by legitimate and widespread privacy concerns. We need a technique that enables data integration and exchange without losing a user's privacy [19; 20]. An acronym table for frequently used terms are shown as Table 1.
## 2 BDS-3 Satellites POD Method  
Satellite orbit determination techniques based on ground-based monitoring station observations are now the most important way to obtain precise orbits of navigation satellites. Unlike ground-based monitoring stations, the main purpose of loading GNSS receivers on LEO satellites is for LEO satellite orbit determination. When the precise orbit of the GNSS satellite is used as a known quantity, the orbit of the LEO satellite is obtained by using the satellite-based GNSS observation data. When the orbit of the GNSS satellite is unknown, the LEO satellite can be used as a space-based observation-data-receiving station, and their data can be jointly used with ground monitoring station data for precise GNSS satellite orbit calculation.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 2 Materials and Methods
### Experimental Variables and Setup
#### 2.5.1 Experimental Approach  
* Channel size variation: By systematically altering the channel sizes, the study aimed to determine how microchannel dimensions influence the fluidic behavior and sorting efficiency of particles. Smaller channels are expected to have more pronounced inertial effects, which may improve the focusing and sorting of particles.
* Concentration effects: The different particle concentration ratios were selected to simulate both physiological and pathological conditions. This variation allowed for the examination of sorting efficiency under different biological conditions, offering insights into the device's potential applications in clinical diagnostics.
* Flow rate impact: Adjusting the flow rates provided an opportunity to study the balance between the inertial lift forces and viscous drag. Lower flow rates are anticipated to produce less pronounced inertial effects, whereas higher flow rates may enhance particle migration but risk increased shear forces that could affect the sorting accuracy.
* Imaging and analysis: The use of both inverted and fluorescent microscopes enabled the capture of high-resolution images of particle movement. These data were crucial for quantifying the sorting efficiency and understanding the interaction between particles and the microchannel structure.
## 3 Results
### Hybrid Signal Processing  
Figure 6 shows the results comparing the performance of a complete hybrid optoelectronic structure. Figure 6a compares the optical and electronic equalization, individually. The black curve (right-side triangle) shows the result without optical or digital signal processing, namely our reference  
Figure 4: Optical pre-processing. (**a**) Complete optical module, (**b**) reduced optical module. Time delay and phase shift were trained to find the optimal point to mitigate CD.  
Figure 5: Digital processing algorithms used to process the signal. (**a**) NN equalizer. (**b**) MLSE.  
system. The blue (circle), purple (square), and dark blue (upside-down triangle) curves are the ones with only optical pre-processing. We note that increasing the number of stages in the n-stage SD/SC structure increases the maximum transmission reach. As we better compensate the CD, the narrow slices are required [21]. Using four-stage SD/SC, a transmission of more than 574 ps/nm of accumulated CD (35 km of equivalent fiber) is demonstrated with less than 1 dB of SNR penalty at KP4, compared to a back-to-back transmission. The dark green (star) dashed line curve and yellow (upper triangle) dashed line curve are the digital processing for MLSE and NN, respectively. It is worth to point out that the seven-memory MLSE and the NN had similar performance. Using only electronic processing with an NN equalizer or MLSE, we showed a transmission of 164 ps/nm of accumulated CD (10 km of equivalent SSMF), considering a 0 dB SNR penalty at KP4 FEC compared to a back-to-back transmission. A difference between optical and digital performance can be clearly seen. As the phase is not accessible in the digital domain after the PD, such a difference appears [8].  
Figure 6b compares the complete hybrid optoelectronic system with optical equalization only. The red (asterisk) and grey (diamonds) show the result for the hybrid processing. We note that it is possible to further mitigate the CD and increase the transmission reach by adding post-processing in the digital signal part. This post-equalization is mitigating the residual CD of the system. In other words, instead of adding more stages in the SD/SC structure, which increases the optical complexity, it is possible to transfer part of this complexity to the digital domain. Figure 6b shows an equivalent SSMF fiber transmission of 12 km (\(\approx\)200 ps/nm) for a hybrid one-stage SD/SC module and 25 km (\(\approx\)400 ps/nm) for a hybrid two-stage SD/SC module, considering a 0 dB penalty. Another observation is that the minimum SNR penalty is 0 dB for optical pre-processing, but less than 0 dB for electronic processing. This indicates that the optical module is trying to compensate only for the CD, while the electronic one is also trying to mitigate some of the effects of the square-law reception.
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 2 Types and Characteristics of the Powertrains for HEVs
### Parallel Hybrid Electric Vehicle  
The engine and electric motor can both operate independently or cooperatively to provide traction in a parallel configuration. In this powertrain, the main motor is connected to the drive shaft. Meanwhile, the engine is also connected to the main motor and drive shaft via a mechanical coupling device [33]. While the vehicle is in motion, the power demand is distributed between the engine and the main motor in a particular ratio, allowing the engine to contribute directly to the overall vehicle propulsion. As the vehicle brakes, the main motor facilitates energy recovery [34]. Figure 2 shows a detailed schematic of the HEV in this setup.  
Typically, a parallel HEV has the option of being powered solely by the engine, the electric motor, or a combination of both. Compared to the series configuration, the engine used in the parallel HEV system is often larger, while the electric motor is typically smaller and less potent [35]. In comparison to series HEVs, parallel ones are more efficient in terms of energy utilization, allowing for a reasonable distribution of power output between the motor and engine for different operating conditions. At the same time, their control strategy is more complex and difficult to control. Despite the parallel setup, the engine and drive wheels remain mechanically linked, resulting in the engine's operating point being partially dependent on the power demands [36].
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
## 3 Results and Discussion  
### Density Measurements and Porosity  
Table 1 shows the results of the theoretical and experimental density and porosity of as-cast pure Mg, Mg-20 GMB (core), and as-cast Mg/Mg-20 GMB hybrid composite. It can be seen that the experimental densities of Mg-20 GMB and Mg/Mg-20 GMB hybrid composite are less than pure Mg due to the presence of lower density GMB (\(\sim\)1.05 g/cc) particles. It can be observed that porosity in the as-cast Mg/Mg-20 GMB hybrid composite was found to be 1.73%. Mg-20 GMB core material and Mg/Mg-20 GMB hybrid composite have higher porosity than pure Mg. The presence of core Mg-20 GMB in as-cast Mg/Mg-20 GMB causes a reduction in experimental density when compared to as-cast pure magnesium, with a slight increase in the porosity value. Note that porosity and inclusions have a huge collapsing effect on the mechanical properties of metal matrix composites [28]. The porosity of as-cast composite is less than 2% (near net shape) and can be attributed to the presence of reinforcement and the entrapped gas during solidification [26; 27]. The entrapment of gases predominantly relies upon the preparation strategy, for example, blending and pouring, holding time, and mixing speed. The position and size of the impeller can also have a significant effect on the porosity. Note that the density of the hybrid composite nicely comes between the density values of as-cast magnesium and core Mg-GMB core.
## 5 New Taxonomy for Constructability Constraints and Features
### Validation of the New Taxonomy Through External Project Examples
#### 5.2.1 The Eight Spruce Street Project in New York [35; 66; 67; 68]  
Also known as New York by [PERSON], this project is a 76-story residential skyscraper designed by [PERSON] and completed in 2011. Renowned for its undulating stainless steel facade and complex structural design, the project utilized advanced computational design approaches to achieve precise geometric control. This ensured seamless integration of the over 10,000 unique stainless steel panels, reducing fabrication and installation errors.  
Computational tools aligned the facade's complex geometry with the structural frame, minimizing conflicts between architectural intent and engineering feasibility. Leveraging BIM, the design team oversaw the off-site prefabrication of facade panels, streamlining on-site assembly and reducing construction time. BIM also facilitated rapid exploration of design iterations, balancing aesthetics with constructability and cost constraints.  
The constructability challenges addressed in this project highlight the innovative approaches required to realize its extraordinary architectural vision. According to [35; 37; 66; 67; 68], the Eight Spruce Street project faced several geometric constructability challenges, including the requirement for the window sill in the facade panels to maintain a specific distance from the finished floor. Additionally, the size of the aluminum frame panels was limited, and both fabrication and transportation faced constraints due to size limitations. The project also dealt with the complexity of curvy and sharply angular curtain walls, which necessitated the use of embedded aluminum brackets to attach the curtain wall to the main structure. Furthermore, the facade components required connections through rivets, and a significant effort was made to reduce most of the curtain wall panels from free-form shapes to single-curved geometries. Finally, the design ensured that the facade gave the impression of the building being wrapped in a silky fabric.  
Beyond the geometric aspects, other constructability challenges included the use of reinforced concrete and the integration of outrigger walls to ensure lateral stability, both of which fall under structural considerations. Aligning the undulating facade with the structural frame posed another structural challenge. From a project management perspective, the development, design, and construction processes required careful coordination, alongside addressing significant financial challenges associated with the project [69; 70; 71].
## 2 Materials and Methods
### Experimental Settings  
The CPC solar collector systems (Figure 1c) were located in the city of Merida, in the Yucatan Peninsula, Mexico (21.02\({}^{\circ}\) N, 89.63\({}^{\circ}\) W). The CPC was instrumented with K-type thermocouples (TT-K-20, accuracy of \(\pm\)0.75%, Omega Engineering Inc., Norwalk, CT, USA) to measure the inlet, outlet and absorber temperatures. A calibration procedure was implemented for each thermocouple to reduce measurement uncertainty. The temperature of the air inside the CPC collector and the ambient temperature were also measured. A submersible pump WP350 (LAWN Industry, Atizapan de Zaragoza, Mexico) was used to produce a forced flow of \(-\)0.36 L/min (0.0076 m/s). The flow was measured using a flow sensor Omega FLR1000 with an accuracy of \(\pm\)1% (Omega Engineering Inc., Norwalk, CT, USA). The temperatures and flow data were wirelessly recorded with an acquisition frequency of 0.2 Hz using NI-9213 and NI-9207 modules, respectively, and a data acquisition system NI cDAQ-9191 (National Instruments Corp., Austin, TX, USA). Incident solar irradiance and ambient temperature were measured with a Davis Vantage Pro2 Plus weather station (Davis Instruments Corporation, Hayward, CA, USA) with an acquisition frequency of 0.017 Hz and accuracies of \(\pm\)5% and \(\pm\)0.3 \({}^{\circ}\)C, respectively. For this study, measurements were obtained on 1 March 2020 from 6 a.m. to 6 p.m. using two separate CPC collector systems operating simultaneously (Figure 1c), one with the absorber without fins (Figure 1d) and the other one with the absorber with segmented fins (Figures 1e and 2).  
Figure 1: (**a**) Cross-section of CPC solar collector; (**b**) side view of CPC solar collector; (**c**) image of the CPC solar collector systems; (**d**) image of CPC with an absorber without fins; (**e**) image of the CPC with an absorber with segmented fins.  
Figure 2: Tubular absorber with segmented fins.
## 4 Results
### Signal Processing Results
#### 4.2.4 Mean Absolute Percentage Error (MAPE)  
MAPE is a metric used to evaluate the accuracy of a forecasting or prediction model. It measures the average absolute percentage difference between the predicted and actual values. MAPE is computed using Equation (21):  
\[\text{MAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\abs{\frac{\text{BPM}_{\text{ reference}}[i]-\text{BPM}_{\text{method}}[i]}{\text{BPM}_{\text{reference}}[i]}}, \tag{21}\]  
Figure 13: Relative BPM estimation errors, as quantified by the MAPE metric. Shows results across all electrode configurations for three test subjects. Higher errors are shown in red and lower differences in green, using a color gradient for values in-between.  
where \(n\) is the number of comparisons, \(\text{BPM}_{\text{reference}}\) is the BPM estimated from a reference signal (PPG), and \(\text{BPM}_{\text{method}}\) is the BPM estimate from one of the evaluated methods. In the context of this research, MAPE is used to quantify the accuracy of the BPM estimates in relation to the reference signal.  
Similar to the previous colormap graphs with a consistent view, Figure 13 shows the calculated MAPE values according to Equation (21). The graph separates the results of the 'findpeaks' and 'BPM DL' algorithms, allowing a detailed comparison of the two methods.  
Generally, the 'BPM DL' method consistently produces lower MAPE values, indicating better accuracy in estimating HR. For example, in configurations 1-3 and 1-4, MAPE values for 'BPM DL' are often significantly lower. In contrast, 'findpeaks' can exhibit much larger errors, with MAPE values exceeding 50% (e.g., configurations 1-3, 1-4, and especially in 2-3)--highlighting its weaker performance.  
The results suggest that the 'BPM DL' method is particularly effective across both used electrode systems (bipolar and tetrapolar). However, it also demonstrates an advantage of bipolar setups, where signal quality is typically lower. In bipolar configurations, which are generally more challenging due to higher noise and less optimal signal capture, 'BPM DL' still achieves lower MAPE values, indicating more accurate HR estimation compared to 'findpeaks'. The fact that 'BPM DL' performs well even in these difficult conditions highlights its robustness.
# Causality in Control Systems Based on Data-Driven Oscillation Identification
## 3 Research Case Studies
### Industrial Data Overview  
Industrial data used in this part of the research originates from the ammonia production plant, specifically from the ammonia synthesis loop. The process is using the catalytic reaction of the hydrogen, which is produced in earlier stages of production, with nitrogen being obtained from ambient air to produce anhydrous liquid ammonia. The process runs in the so-called synthesis loop, where the synthesis gas circulates and mixes with the fresh gas input. The reaction takes place on the ferric catalyst. The heat, which is obtained, heats up the steam and preheats the gas entering the installation. Included time series of the control errors describe five PID loops controlling the final stage of the ammonia productions, i.e., the synthesis loop. Loops denoted as \(u,w,x,y\) represent level control in the separators while the loop \(z\) is the temperature control.  
Determining causality between variables of any large-scale industrial process requires proper preparation of data for analysis. As processes are mostly complex and non-linear, the data series obtained from such plants are characterized by a non-Gaussian distribution. The data is affected by noise as well as by the outliers appearing during measurements or resulting from external actions, for example, human activity. Basic preprocessing of such data consists primarily of removing the trend that appears in them, as well as the separation of other components important for the analysis of given signals.  
Causality detection is performed on oscillations that are obtained through the decomposition process from the initially pre-processed dataset. It contains five separate variables designed as \(u,w,x,y\), and \(z\). Initial data pre-processing consists of trends removal based on the most common polynomial interpolation (shifting the signals by a constant value--0 order polynomial) for \(u\) to \(y\) and 2 nd order spline interpolation for \(z\). The detrended data for the analysis is presented in Figure 8. Moreover, signals are characterized by quite high stationarity with a clear presence of outliers. Their impact on the quality of results using the Transfer Entropy approach has been tested [27] and may be ignored for the purposes of this paper. Each given variable is a sequence of 59,900 samples, which is crucial information in further calculations.  
Figure 8: Control error time series collected from real large-scale ammonia synthesis plant.  
Figure 7: The actual causality diagram of the simulated benchmark.  
Similar to the simulation case, Figure 9 presents control error histograms, which are not Gaussian as well, justifying the use of robust estimators.