esa-sceva/satcom-chunk-collection huggingface dataset:
# Moon Sensor Station to Improve the Performance of Lunar Satellite Navigation Systems
## 4 Pseudolite  
Pseudolites are ground-based transmitters that broadcast signals similar to satellites to improve navigation performance in areas where satellite signals are weak or unavailable. The pseudolite should be composed of a satellite navigation receiver and antenna to compute its own position and to be synchronized with the satellites, and an omnidirectional transmitter, in the same band, able to generate a ranging signal similar to the one produced by satellites. Like the previous one, this solution is simple and cost-effective and does not need any modifications on the user side: the user will track the pseudolite signal together with the satellite signals, exploiting the same receiver. As in the case of terrestrial pseudolite, the near-far problem must be taken into account for this configuration [39; 40], and also the coverage of the local sensor is reduced, due to visibility and link budget constraints.  
The additional measurement from the pseudolite has the same formulation as the pseudorange and can be directly added to the system of equations introduced in Equation (3):  
\[\mathbf{H}=\left[\begin{array}{c}\mathbf{H}_{PR}\\ \mathbf{H}_{PL}\end{array}\right]\text{; }\ \mathbf{n}(\boldsymbol{\theta})= \left[\begin{array}{c}\mathbf{n}_{PR}(\boldsymbol{\theta})\\ \mathbf{n}_{PL}(\boldsymbol{\theta})\end{array}\right] \tag{17}\]  
where \(\mathbf{H}_{PR}\) is the matrix corresponding to the satellite pseudorange measurements, \(\mathbf{H}_{PL}\) is the matrix (vector in case of one pseudolite) corresponding to the pseudolite pseudorange measurement, \(\mathbf{n}_{PR}(\boldsymbol{\theta})\) is the pseudorange measurement error vector, and \(\mathbf{n}_{PL}(\boldsymbol{\theta})\) is the pseudolite measurement error vector. The measurement errors to be taken into account for the pseudolite are similar to those described earlier for the satellite, but additional considerations arise due to the relatively close proximity of the receivers to the pseudolite. The following assumptions for the mentioned error sources were made: the pseudolite position error is modeled as Gaussian distributed, with zero mean and a standard deviation of 0.1 m (\(\sigma_{LE-pos}\)); the pseudolite is considered synchronized with the satellite constellation and its clock error is modeled as previously described in Section 2; for the Multipath error, the same model as before (open-sky Brenner model) is used, with a standard deviation of one meter.
# Towards Explainable Pedestrian Behavior Prediction:
## 2 Proposed Method
### Phase 1: Meta-Dataset Generation
#### 3.1.1 Pedestrian Orientation  
To calculate pedestrian body orientation, we used the pre-trained PedRecNet network [30], developed by Reutlingen University. This multi-task neural network supports various pedestrian detection functions based on 2D and 3D human pose estimation. By analyzing joint positions, the network estimates human body orientation, producing two key angles: the polar angle \(\theta\) and the azimuthal angle \(\varphi\).  
* The polar angle \(\theta\) ranges from 0\({}^{\circ}\)to 180\({}^{\circ}\).
* The azimuthal angle \(\varphi\) ranges from 0\({}^{\circ}\)to 360\({}^{\circ}\), determining whether the pedestrian is oriented left, right, toward the vehicle, or opposite to the vehicle.  
However, there is a discontinuity in the right-facing orientation, as angles in this range span 0-45\({}^{\circ}\) and 315-0\({}^{\circ}\). To standardize this representation, we applied a +45\({}^{\circ}\) shift to the final orientation (see Figure 4). This adjustment is particularly relevant for the neuro-symbolic approach, where the fuzzy set is characterized by a membership function \(f_{A}(x)\), represented by continuous truth values within an interval. Since the original discontinuous range cannot be effectively represented as a fuzzy set, this shift ensures a more coherent and interpretable classification.  
Figure 4: Shifted polar coordinates to represent pedestrian orientation.
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 2 Material and Methods
### SimpleRTK2B Board and OpenLog Connection  
Two SimpleRTK2B boards together with u-blox GNSS antennas ANN-MB-00 were used in our case study (Figure 1a). In the initial phase of the experiment, simpleRTK2B boards were connected with the laptop, where the USB port was used for power supply as well as for data storage. However, it turned out that the laptop was not an adequate solution, since its battery ran out after three hours. The bad weather conditions were also not a positive factor for using laptop. Instead, we used Sparkfun's OpenLog DEV-13955 for data storage. It was connected to the simpleRTK2B with jumper wires (Figure 1b). The OpenLog can send all messages that come from RX1/TX1 pin to the mounted MicroSD. The TX1 pin of the board needs to be connected with the RX1 pin of the OpenLog to transmit messages, 5V_OUT pin, and the IOREF pin was connected with the VCC pin to define the voltage and the GND pin to GND pin [23; 24]. OpenLog can work with MicroSD that has a memory capacity from 512 MB up to 32 GB. Two lights are flashing during operation, one continuously for communication, and the other one when the OpenLog records data in MicroSD [24].  
The boards were configured with the u-center evaluation software to output messages through the UART1 port. The baud rate was set to 115,200--the recommended value for the 9 th generation of u-blox GNSS receivers. To get observations in RINEX format, RXM-RAWX and RXM-SFRBX messages were enabled in UART1. The data were stored in the UBX file that was converted to RINEX by using the modified version (demo5_b33b) of the RTKLIB. Since the version of RTKLIB developed by [PERSON] had difficulties converting the UBX file to the RINEX file, we decided to use the modified version developed by [PERSON] that is used for converting UBX files and data processing [25; 26]. To ensure power supply, two power banks Platinet PMPB20 TW with 20,000 mAh5V capacity were used. The energy consumption of the SimpleRTK2B, antenna, and OpenLog is pretty low (215 mAh in total); the survey can therefore last for approximately 90 h.
## 3 Discussion
We generated three different models of LAIR-1 interaction with one C1q collagen stem (Figure 8B and Figure S3). Based on our results and the constraint of a small enough residue in front of the LAIR-1 I102, the most relevant model is the one represented in Figure 8B. This model unveils two C1q residues potentially engaged in LAIR-1 docking: arginine 72 and lysine 65 on C1q A and B chains, respectively. These two CLR residues were mutated with a view to confirm their involvement in LAIR-1 binding. However, the generated C1 qCLR_nc2 fusion protein variants exhibited no or slight inhibitory effect on LAIR-1 interaction, which could be expected. Indeed, as mentioned above, the A_R72 and B_K65 CLR residues, in association with the respective E111 and E72 LAIR-1 residues, are not involved in generic collagen backbone recognition of C1q by LAIR-1 but are suggested to play a role in the recognition specificity and the correct positioning of both partners. Interestingly, the collagen sequence of C1 qC chain recognized in our model is exposed on the same side as the two most important lysine residues of C1q for the interaction with the C1r\({}_{2}\)s\({}_{2}\) tetramer (Figure 8B). This suggests that the LAIR-1 Ig-like domain may interact with one C1q collagen stem close to and with the same orientation as C1r\({}_{2}\)s\({}_{2}\), therefore explaining the strong competition effects observed in Figure 2B. However, we cannot exclude that LAIR-1 might interact with C1q through the two other putative binding sites (Figure S3). These two sites are located on the C1q B chain, far enough from the motif in the C chain to accommodate the binding of one LAIR-1 Ig-like domain on each site at the same time. Such simultaneous binding of two LAIR-1 copies on C1q collagen stem could be consistent with the required dimerization of LAIR-1 to trigger its inhibitory activity. Indeed, the dimerization of LAIR-1 upon ligand binding results in the phosphorylation of its ITIMs motifs, leading to a negative regulation of intracellular signaling associated with immune cell maturation, activation and differentiation [10; 37; 38; 39]. Therefore, the dimerization of LAIR-1 might be mediated by the binding of two copies on either the same site on two different C1q collagen stems or two different sites on the same C1q collagen stem.
# Preparation of Freeze-Dried Porous Chitosan Microspheres for the Removal of Hexavalent Chromium
## 2 Materials and Methods
### Characterization of Porous Chitosan Microspheres
#### 2.3.5 Adsorption Kinetics Experiments  
In this work, adsorption experiments were carried out to reveal the adsorption capacity of porous chitosan microspheres, as well as the effects of contact time on the adsorption capacity in a Cr(VI) solution at a certain pH value. Chitosan microspheres of 18 mg (m) were immersed in 250 mL of Cr(VI) solution with an initial concentration (\(C_{0}\)) of 235 mg/L by dissolving a certain amount of K\({}_{2}\)Cr\({}_{2}\)O\(\gamma\) in deionized water. The mixture was then shaken in a water-bath shaker at 120 rpm and 30 \({}^{\circ}\)C for different contact times. After certain time periods (0, 15, 30, 45, 60, 90, 120, 180, 240, 360, 480 min), a constant volume of mixture (V) was collected and the corresponding Cr(VI) concentrations were determined by analyzing at 540 nm using a UV spectrophotometer (Ruili Analytical Instrument Company, Beijing, China). The adsorption capacities of the porous chitosan microspheres were calculated as follows:  
\[q_{t}=\frac{C_{0}V_{0}-C_{t}[V_{0}-(t-1)V]-\sum_{1}^{i}C_{i}V}{m}\ \ \ \ \ (i=t-1,\ i>0) \tag{6}\]  
\[R_{a}=\frac{C_{0}-C_{t}}{C_{0}} \tag{7}\]  
where \(q_{t}\) (mg/g) is the amount of Cr(VI) on the mass of the microspheres at time t; \(C_{0}\) and \(C_{t}\) (mg/L) are the concentrations of Cr(VI) before adsorption and at contact time \(t\), respectively; \(t\) (min) is the contact time; \(V\) (mL) is the volume of the Cr(VI) solution taken out for UV spectrophotometer analysis; \(m\) (g) is the mass of the microspheres; and \(R_{a}\) is the ratio of the adsorption. The adsorption data were fitted by adsorption kinetic models to analyze the removal process and mechanisms.
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Alternative to Chlorine Disinfectants
#### 4.7.2 Ozonation
The toxicity information regarding these organic ODBPs is scarce, which raises concerns about the safety of ozonated water. During ozonation, ozone and hydroxyl radicals (generated from the decomposition of ozone) can oxidize NOM, resulting in the creation of readily biodegradable ODBPs. The amount and types of ODBPs generated are highly unpredictable. The formation of ODBPs is significantly influenced by various factors, particularly the composition and concentration of NOM, disinfection conditions (such as ozone dose and contact time), and other treatment processes at WWTPs. Over 60% of the organic compounds remain unidentified during the ozonation disinfection process. Among the identified organics (40%), 26-33% is classified as carbonaceous aromatic byproducts (CABPs). According to a study, 67% of CABPs were removed during water treatment processes, while other classes of ODBPs were eliminated at rates exceeding 80-85%. The higher formation levels and lower removal rates of CABPs in WWTPs have sparked increased research interest in CABPs [23]. During the ozonation of bromide (Br\(-\))-containing waters, bromate (BrO\({}_{3}\)\({}^{-}\)) can be produced through a complex mechanism involving reactions with both ozone and hydroxyl radicals (\(\bullet\)OH). Bromate is classified as a potential human carcinogen, and the World Health Organization (WHO) suggests a drinking water standard of 10 \(\mu\)g L\({}^{-1}\). Although no defined bromate standard for wastewater exists, Switzerland, for example, has put forward an environmental quality standard of 50 \(\mu\)g L\({}^{-1}\)[38].
## 2 Experiment Setup and Data Analysis
### Data Analysis  
The time series of the pressure coefficients C\({}_{\rm{pl}}\) (\(t\)) are defined as follows [21]:  
\[{\rm{C}_{\rm{pl}}}(t)=\frac{{p_{i}(t)-p_{0}}}{{0.5\rho u_{h}^{2}}} \tag{1}\]  
Figure 3: Measuring tap location for scale model (unit: cm).  
Figure 2: Experimental model.  
where \(p_{i}(t)\) is the time series of the wind pressure measured by the pressure scanning valve in the wind tunnel test; \(p_{0}\) is the mean value of static pressure at the wind tunnel test section, which is measured using a pitot tube; \(\rho\) is the air density, taken \(\rho=1.225\)kg/m\({}^{3}\); and \(u_{h}\) is the mean wind speed at the reference height. The reference height is the height of building. The extreme pressure coefficients of taps are defined as follows:  
\[\text{C}_{\text{pmx}}=\frac{p_{\text{max}}-p_{0}}{0.5\rho u_{h}^{2}} \tag{2}\]  
\[\text{C}_{\text{pmn}}=\frac{p_{\text{min}}-p_{0}}{0.5\rho u_{h}^{2}} \tag{3}\]  
The time series of the area average wind pressure coefficient are defined as follows:  
\[C_{f}(t)=\sum_{i=1}^{n}\left[\text{C}_{P,i}(t)\times A_{i}\right]/\sum_{i=1}^{ n}\left(A_{i}\right) \tag{4}\]  
where \(A_{i}\) is the tributary area of the tap number \(i\), and \(i=1,2,3,\dots\)\(n\) (\(n\) represents total number of taps).  
The extreme pressure coefficients of taps or area-average are calculated by the extreme value probability distribution fitting method using 20 independent sampling peaks, where the distribution parameter is determined by the maximum likelihood method. The result is taken as 78% of the extreme quantile.
## 5 Conclusions  
In this paper, a novel active disturbance rejection control framework was proposed to achieve a high-accuracy prescribed tracking performance for EHSs without velocity measurement mechanism in the presence of both lumped mismatched and matched disturbances caused by parameter deviations, modeling errors, and unknown external loads. To precisely approximate the angular velocity and these disturbances, dual observers, i.e., an ESMO and an ESO, were established. In addition, a trajectory planner considering the kinematics constraints of the EHSs inherited from [19] with minor alterations was constructed to customize the original step reference trajectory, which ensures that the modified reference trajectory always commences at the origin, tracks the original in finite-time, and is sufficiently smooth. Moreover, the integration of the PPC into the CF-BSC based on the designed dual observers and trajectory planner guaranteed that the \"explosion of complexity\" of the traditional backstepping was effectively avoided and the predefined tracking performances were attained. The stability of the observers and closed-loop control system was rigorously proven by Lyapunov theory. The numerical simulation results showed that the recommended control framework ensures the smallest tracking errors in comparison with other CF-BSC approaches based on well-known ESO design in both cases of the smooth and non-smooth reference trajectories. Parametric uncertainties and time-varying constraints on kinematics and dynamics of the EHS will be deliberately considered in future works.  
[PERSON] was the supervisor, providing funding and administrating the project, and he reviewed and edited the manuscript. [PERSON] performed the investigation, proposed the methodology, analysis, and validations by using the MATLAB/Simulink software for simulations, and wrote the original manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT, South Korea (NRF 2020R1A2B5B03001480) and \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (MOE) (2021 RIS-003).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this paper:  
ADCC Active Disturbance Compensation Control  
ADRC Active Disturbance Rejection Control  
BSC Backstepping Control
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
###### Abstract  
The alarm data contain abundant fault information related to almost all components of the wind turbine. Reasonable analysis and utilization of alarm data can assist wind farm maintenance personnel in quickly identifying the types of turbine faults, reducing operation and maintenance costs. This paper proposes a clustering analysis method that groups similar alarm sequences with the same fault type. Firstly, the alarm data are preprocessed, where alarm sequences are segmented, and redundant alarms are removed. Then, a domain knowledge-fused Word2 vec (DK-Word2 vec) method is introduced to transform non-numeric alarm codes into numeric vector representations. Finally, new distance metrics are incorporated into the K-means clustering algorithm to improve clustering performance. The performance of the proposed clustering method is assessed by applying it to labeled alarm sequences. The results demonstrate that the clustering performance is the best when using DK-Word2 vec and the word rotator's distance compared with other methods. Additionally, with the optimal parameter combination, the fault types of unlabeled alarm sequences are also analyzed.  
wind turbine; fault diagnosis; alarm; clustering; Word2 vec; domain knowledge +
Footnote †: journal: Applied sciences
# Control of Porosity in Parts Produced by a Direct Laser Melting Process
###### Abstract  
Recent advances in direct laser melting (DLM) have demonstrated its great potential for manufacturing three-dimensional porous metal parts. Various combinations of powder layering and processing parameters can be set to adjust the porous properties of the final parts. This study presents the effects of powder morphologies and process parameters on porosity formation during DLM. Four types of Fe-powders composed of spherical or non-spherical particles with different sizes were experimentally investigated. Furthermore, the laser processing parameters, such as laser energy density, laser focus, and line spacing, which have a significant effect on the results, were characterized. In the case of a mixed powder composed of spherical and non-spherical powders, the packing density decreases as the non-spherical powder size increases. The porosity of the laser-melted layer increases with the degree of size misfit between the non-spherical and spherical powders. Decreased laser absorption and enlargement of the powder-depleted zone as a result of decreased packing density increases the porosity during DLM. The overall results show that the porosity of DLM parts could be actively controlled by adjusting the process parameters and powder morphologies.  
porosity; direct laser melting; mixed powder; densification; additive manufacturing Article
## 6 Empirical Research on New Heterogeneous Traffic Control Mechanisms
### Testing and Validation of Control Mechanisms in Real Road Environments
#### 6.2.2 Validation of System-Level Collaborative Control  
From a system-level perspective, vehicle-infrastructure collaboration is one of the core technologies for heterogeneous traffic control. The large-scale field test conducted by [PERSON] et al. in the Netherlands provided important empirical data. This test involved seven CACC vehicles traversing a provincial road corridor with five intelligent intersections, exchanging cooperative awareness messages and signal phase information through vehicle-infrastructure communication [71]. The study not only validated the technical feasibility of CACC platooning and vehicle-infrastructure communication but also predicted system benefits under different penetration rates through calibrated simulation models. The results showed that full CACC penetration can reduce average travel time by 5%, further improving to 11% when combined with vehicle-infrastructure communication technology, while at the provincial network scale, it can reduce total user delay by 12%.  
Research by [PERSON] et al. [68] validated the effectiveness of trajectory data-based adaptive control strategies in the field from another perspective. Through actual deployment testing, their two simple adaptive strategies significantly reduced delay, oversaturation, and spillover ratios, proving the feasibility of low-cost, high-efficiency heterogeneous traffic control solutions under existing infrastructure conditions.  
These system-level test results reveal important conclusions: (1) vehicle-infrastructure collaboration technology can indeed significantly improve traffic performance in practical environments; (2) benefits typically increase with CAV penetration rates, with limited gains at low penetration rates below 10%; (3) even under limited conditions, simple but targeted control strategies can bring substantial improvements; (4) factors such as communication delays and signal interference in real environments have significant impacts on control effectiveness, which need to be fully considered in actual deployment.  
Combining vehicle-level and system-level empirical research, we can see that heterogeneous traffic control mechanisms have shown good application prospects in real environments. Despite still facing challenges such as low penetration rates and unstable communication, a certain degree of traffic improvement has been achieved by combining intelligent vehicle control with infrastructure collaboration, accumulating valuable experience for larger-scale deployment.