esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## 3 Results and Discussion
### Extraction of Feature Wavelengths
The selected wavelengths between 1100 nm and 1300 nm were ascribed to the second overtone of C-H stretching [31; 32]. The spectral bands between 1300 nm and 1400 nm corresponded to the combinations of C-H vibration [33; 34; 35]. The selected bands between 1400 nm and 1600 nm corresponded to the first overtone of O-H and N-H telescopic auto-correlation [39]. The bands around 1480 nm were attributed to the second overtone of O-H stretching. The bands between 1600 nm and 1650 nm were attributed to the first overtone of C-H vibration, especially around 1630 nm band corresponding to aromatic C-H bond [40]. Reflecting the chemical bonds' vibrations, these wavebands with fingerprint characteristics were the symbol of chemical component difference, such as starch, protein, and fat, among different wheat varieties. These laid the foundation for building accurate discrimination models based on reduced feature wavelengths effectively and reliably. Figure 4: The loading curves of the first six principal components (PCs) and corresponding distribution of feature wavelengths.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 4 Speech Enhancement Experiment
### Specifications of the Competitive SEAs  
The performance of the proposed SEA was carried out by comparing it with the following benchmark SEAs (\(p:\) order of \(\{a_{i}\}\), \(\sigma_{w}^{2}\); the excitation variance of AR model, \(w:\) analysis frame duration (ms), and \(s:\) analysis frame shift (ms)).  
1. Noisy: No enhancement (speech corrupted with noise);
2. KF-oracle: KF, where (\(\{a_{i}\}\), \(\sigma_{w}^{2}\)) and \(\sigma_{v}^{2}\) are computed from the clean speech and the noise signal, \(p=10\), \(w=32\) ms, \(s=16\) ms, and a rectangular window is used for framing;
3. KF-Non-oracle: KF, where (\(\{a_{i}\}\), \(\sigma_{w}^{2}\)) and \(\sigma_{v}^{2}\) are computed from the noisy speech, \(p=10\), \(w=32\) ms, \(s=16\) ms, and rectangular window is used for framing;
4. MMSE-STSA [9]: It used \(w=25\) ms, \(s=10\) ms, and Hamming window for framing;
5. AKF-IT [13]: AKF operates with two iterations, where initial (\(\{a_{i}\}\), \(\sigma_{w}^{2}\)) and (\(\{b_{j}\}\), \(\sigma_{u}^{2}\)) are computed from the noisy speech followed by re-estimation of them from the processed speech after first iteration, \(p=10\), noise LPC order \(q=10\), \(w=20\) ms, \(s=0\) ms, and rectangular window is used for framing;
6. AKF-RMBT [22]: Robustness metric-based tuning of the AKF, where (\(\{a_{i}\}\), \(\sigma_{w}^{2}\)) and (\(\{b_{j}\}\), \(\sigma_{u}^{2}\)) are computed from the pre-whitened speech and initial silent frames, \(p=10\), \(q=40\), \(w=20\) ms, \(s=0\) ms, and rectangular window is used for framing;
7. MDKF-MMSE [17]: Modulation-domain KF, where (\(\{a_{i}\}\), \(\sigma_{w}^{2}\)) is computed from the pre-filtered speech using the MMSE-STSA method [9], \(p=2\), \(q=4\), \(w=20\) ms, \(s=0\) ms in modulation domain;
8. Proposed: Robustness and sensitivity tuning of the KF, where (\(\{\hat{a}_{i}\}\), \(\sigma_{w}^{2}\)) and \(\sigma_{v}^{2}\) are computed from the pre-whitened speech and estimated noise, \(p=20\), \(q=20\), \(w=32\) ms, \(s=16\) ms, rectangular window is used for time-domain frames, and Hamming window is used for acoustic frames.
## 4 Conclusions  
The main conclusions that can be obtained in view of the results already described and discussed can be summed up as follows:  
* The addition of red mud produced a higher microstructure refinement of the mortars compared to those made with cements type I (ordinary Portland cement) and type II (Portland cement with fly ash), as shown by the higher number of pores with smaller diameters noted for red mud binders.
* The progressive pore refinement with hardening time observed for red mud mortars, as well as the reduction of the heat per gram corresponding to the peak of the decomposition of the portlandite in samples that were not carbonated, suggest that red mud can have pozzolanic activity.
* The impedance spectroscopy results agree with mercury intrusion porosimetry and differential scanning calorimetry results about the possible pozzolanic activity of red mud. The increase with time of resistance R\({}_{1}\) and capacitance C\({}_{2}\) for red mud mortars could indicate the creation of new solid phases as products of the pozzolanic reactions between the red mud particles and the clinker hydration products.
* After 90 hardening days, the non-steady state chloride migration coefficients obtained for all the red mud mortars showed practically no differences in comparison with those noted for CEM I. Therefore, the use of up to 20% red mud as clinker replacement is as efficient as ordinary Portland cement without additions regarding chloride ingress resistance, with the advantage of the contribution to sustainability.
* The addition of red mud produced lower compressive strengths after 90 hardening days in the mortars compared to those made with CEM I and CEM II. Nevertheless, the analysed mortars with the addition of red mud would achieve the requirements for strength cement class 32.5 according to Spanish and European standard UNE-EN 197-1 from 28 hardening days.  
**Author Contributions:** Some results included in this paper were obtained during the PhD thesis carried out by [PERSON] at the University of Alicante (Spain), under the supervision of [PERSON] and [PERSON]. [PERSON] wrote the paper. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] supervised the research work and revised the paper. All the authors contributed to conceive and design the experiments, and to analyse and discuss the results.  
**Funding:** Part of this research was funded by the Spanish Agencia Estatal de Investigacion (grant code BIA2016-80982-R) and by the European Regional Development Fund (grant code BIA2016-80982-R).  
**Acknowledgments:** Authors would like to thank Cementos Portland Valderrivas, S.A. and Lafarge Espana, S.A. for providing the cements studied in this work. We also thank the company Alcoa (factory at San Cibrao, Spain) for providing the red mud.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Semi-Analytical Planetary Landing Guidance with Constraint Equations Using Model Predictive Control
## 5 Simulation Results
### Optimality and Efficiency Evaluation  
The starting point for the powered descent is set to \([x_{0};y_{0};z_{0}]=[1000;0;3000]\) m, and the initial velocity is \(v_{0}=[-50;-10;-75]\) m/s. The other parameters, which are chosen from [10], are shown in Table 1. After initialization, the judgment of reachability and motion mode is conducted. The target landing point is reachable, and the thrust in the X direction is positive first, then negative. The fuel consumption is 236.5185 kg, and the detailed calculation results are shown in Table 2. In addition, we run the algorithm 100 times on Intel i7-7700 CPU (3.60 Hz, 24 GB RAM), using MATLAB R2018a (fsolve), and the average CPU time is only 19.6880 ms.  
For comparison, the same optimization problem is solved by the ECOS solver in MATLAB, using the multi-phase pseudospectral convex optimization. Three phases (on-off-on) are considered, and the number of discrete points in each phase is set to 20. The detailed convexification, relaxation, and initialization methods can be referred to in Ref. [25]. The trajectory and control profile comparisons are shown in Figures 9 and 10, respectively. The red dashed lines represent the control curves obtained by our semi-analytical powered descent guidance algorithm (denoted as SAPDG), and the blue lines represent the results calculated by the multi-phase pseudospectral convex optimization (denoted as MPPSCvx). For MPPSCvx, the solved time intervals of three phases are 8.4622 \(\times\) 10\({}^{-10}\) s, 10.1025 s, and 33.1810 s, respectively. This result shows that the optimal control mode in this situation is off-on control mode. The ignition times calculated by the two methods are in good agreement. The fuel consumption solved by the MPPSCvx is 227.8372 kg, with only  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline \(m_{0}\) & 1905 kg & \(T_{\text{max}}\) & 13,258 N \\ \(m_{\text{dry}}\) & 1505 kg & \(g\) & 3.7114 m/s\({}^{2}\) \\ \(\eta\) & 6.8665 kg/s & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Example 1 (SI units).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \([\mathbf{t_{1}},\mathbf{t_{\text{f}}}]\) & \([\mathbf{\mu_{\text{x}}},\mathbf{\mu_{y}}]\) & \([\mathbf{t_{\text{x}}},\mathbf{t_{y}}]\) & **Fuel Consumption** \\ \hline \([10.2375,44.6828]\) s & \([0.30924,0.13819]\) & \([38.2801,32.8509]\) s & 236.5185 kg \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation results of Example 1.  
8.6813 kg (3.8104%) fuel less consumed than our algorithm. It is indicated that the proposed semi-analytical powered descent guidance algorithm has the ability to guarantee the real-time trajectory generation and meanwhile maintain considerable optimality.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
###### Abstract  
Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.  
indoor localization and positioning; pedestrian dead reckoning; MEMS IMU; smartphone sensors; step detection; attitude correction; heading estimation +
Footnote †: journal: Robotics and Automation Letters  
1
###### Abstract  
To investigate the influence of surface tension and viscosity on the atomization performance of solid cone nozzles and improve their dust reduction efficiency in industrial and mining enterprises, this study employed a self-built PDPA dust-fog coupling experimental system to explore the effects of different surface tension and viscosity solutions on atomization performance from three aspects: axial, radial, and fog field distribution. The experimental results indicate that compared with surface tension, surface tension has a greater influence on droplet size and velocity in the axial direction. In the radial direction, increasing surface tension and reducing viscosity within a certain range can make the droplet size and velocity distribution more uniform. Additionally, surface tension and viscosity significantly affect the fog field distribution. It was found that a decrease in surface tension can result in a closer proximity of the droplet velocity and size expansion area to the nozzle, while an increase in viscosity can lead to a more prolonged stable area. Furthermore, optimizing the surface tension and viscosity can significantly enhance the efficacy of dust reduction for respirable dust. Consequently, the application of the aforementioned atomization principles to regulate the fog field characteristics of solid cone nozzles can effectively mitigate dust in the production process and augment the dust reduction rate of industrial and mining enterprises.  
atomization effect; PDPA; droplet size and velocity; surface tension; viscosity Article
## 5 Key 6G Technologies
### Improved Wireless Interface
#### 5.2.6 In-Band Full-Duplex (IBFD) Technology: Unlocking Enhanced Spectrum Efficiency in 6G  
IBFD is a new wireless communication technology that differs from traditional frequency-division duplex (FDD) and time-division duplex (TDD) methods. With IBFD, radio can transmit and receive data simultaneously within the same frequency band. This groundbreaking approach can potentially double spectrum efficiency, expand wireless transmission capacity, and offer more adaptable network access. This innovative technology stands as a key focus in the exploration of future wireless communication systems [58].  
While IBFD has historical roots in continuous wave radar systems [148], practical applications were limited due to technical constraints until recent years. Research on IBFD has been reignited, exploring various applications such as relaying [149], full-duplex techniques for simultaneous communication among multiple nodes [150], and radar communication systems. The potential benefits of IBFD include improved throughput, wireless sensing capabilities, and joint communication opportunities.  
However, the practical implementation of IBFD faces challenges, primarily in developing in-band self-interference cancellation (SIC) techniques with moderate complexity and cost. Electronic and optical SIC techniques are investigated, with a focus on sub-6 GHz applications [151]. Addressing the increasing difficulty with higher bandwidths, researchers have explored shared antenna structures [152], iterative estimation and cancellation techniques [153], and theoretical analyses for IBFD systems [154]. Challenges persist, particularly in extending IBFD to the THz and optical wireless communication (OWC) bands in 6G systems. Optical SIC (OSIC) has emerged as an area of interest, leveraging its large bandwidth and high accuracy to tackle the challenges associated with IBFD in these advanced frequency bands.  
Efforts in the development of IBFD technology involve a comprehensive exploration of electronic and optical SIC techniques, aiming to make IBFD a practical reality in the evolving landscape of wireless communication [155; 156].
# Improved Road Segment-Based Geographical Routing Protocol for Vehicular Ad-hoc Networks
## 3 Improved Road Segment-Based Geographical Routing Protocol
### Head Node Selection Process, Flowchart and Algorithm  
An HN performs the following operations:  
* The HN multicasts its unique ID to its segment members after election and to its neighboring HN.
* The HN collects the segment information (SI), which contains other vehicle information and disseminates it to the other available HNs in neighboring segments.
* The HN chooses the node-to-node segment transmitter that is based on the collected information, as explained above.
* After moving out of the segment, the protocol reinitiates the process for selecting a new HN.  
For the HN selection, the proposed protocol multicasts the unique ID message to its neighbor nodes and also collects the SI that includes the other vehicle node's information before forwarding it to otheravailable HNs in neighboring road segments. Figure 2 shows the complete process of the steps of HN selection, where the ISR processes the routing metrics and selects the best candidate node as the HN.  
Explanation of Algorithm 1: The HN selection starts with the segment formation. Once the number of segments is identified, the center point of segment and the nearest node to center point are identified in steps 3-5. In steps 6-12, for each candidate HN, in cases of more than one possibility, the distance of nodes is calculated between communicating nodes. Furthermore, in steps 13-17, the score of each node is calculated using Equations (5) and (6) by considering the direction- and speed-based weighting score of nodes. Moreover, the overall score is calculated by adding the direction-based score and the speed-centric link quality score for each node for the final HN selection.  
Complexity of Algorithm 1: The complexity analysis of an algorithm majorly depends on the execution of the computing-centric steps. Here, the complexity of the algorithm depends on two criterion parameters: number of segments \(S_{N}\) and the average number of vehicles in each segment \(V_{N}\). There are two computing-centric loops in the algorithm. The first loop is executed \(S_{N}\) times in steps 1-18, and the second loop is executed \(V_{N}\) times in steps 6-12. Considering the operation in steps 3-5 as unit or linear operations, the overall complexity of the algorithm can be expressed in the order of \(O(S_{N}\times V_{N})\).  
It is clarified that head node selection is performed on the same road segment, so Euclidean distance is considered in Equation (4). Furthermore, the selection is carried out by not only considering distance rather over the other parameters discussed in Sections 3.2-3.4.
## 3 Results  
### R. venosa Catch Evolution and Fishing Pressure in the Danube Delta--Marine Zone  
In the past decade, rapa whelk catches increased sharply all over the Romanian coast, but especially in the northern part, off the Danube Delta, an area targeted by vessels using the beam trawl [50]. If before 2013, when this gear was legalized in Romania [30], _R. venosa_ catches barely reached 1 ton, after this turning point they increased to almost 6000 tons in 2016-2018. In 2020, catches started to drop constantly [41] (Supplementary Material Table S2, Figure 2).  
As rapa whelk shifted from invasive species to commercial fisheries targets, more and more Romanian fishermen fitted their vessels with specialized gears (beam trawls). As such, a remarkable increment in fishing effort (expressed by the number of vessels and beam trawls used) was noted after 2013: from only 7 vessels and 14 beam trawls (in 2013) to 38 vessels and 76 beam trawls (in 2019) [41].  
The analysis of VMS data (available starting with 2015, when all vessels >12 m were bound to be equipped with satellite communication tools in order to signal their position) revealed that 2017 was the year with the longest distance recorded, reaching up to almost 42,000 km, and the largest swept area during the investigated period (more than 87 km\({}^{2}\)). The years 2018, 2019, and 2021, respectively, also recorded intense fishing activity in the Danube Delta--Marine Zone, with significant distances covered and areas swept, respectively, while the last year analyzed (2022) showed a decrease (Supplementary Material Table S2, Figure 3).  
Figure 2: Dynamics of _R. venosa_ catches using the beam trawl (TBB) during 2015-2022 in ROSCI0066 (extended zone 20–40 m).  
As such, fishing pressure by beam trawling in the Danube Delta--Marine Zone was highest in 2017, when a SAR value of 0.041 was recorded. The lowest SAR values were calculated at the beginning (2015) and the end (2022) of the study period (SAR = 0.009). Moreover, a low SAR was registered in 2020 (SAR = 0.012), most probably due to COVID-19 restrictions (Supplementary Material Table S2, Figure 4).  
According to VMS data, most of the fishing operations were concentrated between the 20 and 30 m isobaths in a soft bottom area covered by circalittoral mud (Figures 1 and 3), thus with a significant probability of affecting the unique mussel bed habitats, which start clustering at these water depths.  
Figure 4: Fishing pressure by beam trawling (SAR) evolution in ROSCI0066 (extended zone 20–40 m) (2015–2022).  
Figure 3: VMS lines for rapa whelk fishing in the Danube Delta—Marine Zone for the peak beam trawling year (2017).
## 4 Implementation and Deployment
### Input Data  
To perform the above operations, EGC needs to store and manipulate information on the considered geographical zone where vehicles operate, i.e., on roads, traffic, and vehicle positions. In the current prototype, EGC exploits map data provided by the OpenStreetMap Foundation [[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) (accessed on 25 March 2024). Roads are represented by means of the so-called _ways_, which are ordered lists of _nodes_. Each node consists of a single point in space defined by its latitude, longitude, and an identifier. When two ways intersect at the same altitude (for example, at a road junction), the two ways must share a node. Hence, the result is a directed graph consisting of points connected by directed edges, representing the road network in an area. Additional sets of data can be added to the network built as above. In particular, such data include (i) vehicle positions (of both vehicles that belong to the fleet and those that do not), which can be added on the fly and, when added, are associated with the closest node in the network; (ii) the lengths of road segments between two nodes (representing, e.g., road junctions); (iii) the estimated travel times of road segments; (iv) the positions of users' locations; (v) distances between users' locations.