esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering
## 2 Fusion Algorithm
### Visual Odometry Algorithm
\begin{bmatrix}\frac{\partial u}{ \partial X^{\prime}}&\frac{\partial u}{\partial Y^{\prime}}&\frac{\partial u }{\partial Z^{\prime}}\\ \frac{\partial v}{\partial X}&\frac{\partial v}{\partial Y^{\prime}}&\frac{ \partial v}{\partial Z^{\prime}}\end{bmatrix}  \tag{5}\]  
where \(\frac{\partial P^{\prime}}{\partial\delta\xi}\) is the derivative of the transformed point with respect to Lie algebra. \[\frac{\partial P^{\prime}}{\partial\delta\xi}= \begin{bmatrix}I,-P^{\prime \Lambda}\end{bmatrix}  \tag{6}\]  
Multiply the above two equations to obtain the Jacobian matrix \(J\) of the pose. After finding the Jacobian matrix, the direct method is used to solve the linear equation system so that \(A=J^{T}J\) with \(b=-J^{T}e\), and the update quantity \(\delta\xi\) of the optimal pose is obtained by solving \(A\delta\xi=b\). The second scenario occurs when the translational and rotational velocities of the previous frame are unknown, or if the pose estimation from the previous frame fails, in which case nearby keyframes are used to estimate the current camera pose. After obtaining the current pose, the system enters local map tracking. It projects the local map points generated from the previous frame onto the current frame for tracking to further optimize the pose and creates keyframes based on predefined conditions. The local mapping thread receives keyframes from the tracking thread and generates new map points using the co-visibility relationships of the keyframes. It searches for and merges map points from adjacent keyframes, then performs local map optimization, removes redundant keyframes, and establishes more connections between existing keyframes. This process generates more reliable map points, optimizes the poses of co-visible keyframes and their map points, stabilizes tracking, and ensures more accurate poses for keyframes involved in loop closure. The loop closing thread is used to determine whether the camera has returned to a previously visited location, thereby adjusting the map and pose to prevent map drift caused by accumulated errors. The Bag of Words (BoW) model is used to search the keyframe database for loop closure candidate keyframes corresponding to the current keyframe. Continuity checks are performed on the selected loop closure candidate keyframes to ensure the reliability of the loop closure. If a loop closure is detected, the Sim3 transformation [22] between the current frame and the loop closure candidate frame is used to correct the current keyframe. Moreover, global optimization is performed on the poses of all keyframes and map points to improve the accuracy of the map and pose estimation.
# A Low-Profile Ferrite Dipole VHF Antenna for Integrated Mast Applications
###### Abstract  
In this paper, an extremely low-profile ferrite dipole antenna operating on the VHF band (30-300 MHz) is proposed. The design antenna consists of 44 plate-type ferrite cells arranged into two stacked ferrite layers: a bottom ferrite layer with \(2\times 12\) grid cells and a top ferrite layer with \(2\times 10\) grid cells. The antenna is excited by an electric loop feeding structure and maximum gain performance is achieved when the loop feeding structure has five loops. To validate the performance of the proposed antenna, an antenna prototype is fabricated and tested at an outdoor range. The antenna weighs 1.45 kg and has electrical dimensions of approximately \(0.0636\times 0.0112\times 0.0008\lambda_{L}^{3}\) at the lowest operating frequency of 30 MHz. The measured realized gain varies from \(-31.48\) to \(-2.44\) dBi within the VHF band. Reasonable agreement is also obtained between the measurement and simulation results. To assess the performance of the proposed antenna, it was mounted on the integrated mast of an offshore patrol vessel (OPV) model. The antenna on the OPV was also simulated and the results discussed.  
dipole antenna; ferrite; integrated mast; loop feed; offshore patrol vessel; VHF +
Footnote †: journal: Applied Sciences
# Experimental Study on Microstructure of Unsaturated Expansive Soil Improved by MICP Method
## 4 Microbial Improvement of the Microstructure of Expansive Soils
### Analysis of Test Results
At the same time, the microbially induced formation of calcium carbonate adheres to the surface of the expansive soil particles, reducing the interaction area between hydrophilic soil particles and water, reducing the water film thickness and interparticle spacing, tightening the soil structure and improving the strength of the soil effectively.
## 5 Experimental Results
### Compared to Other Algorithms  
We compare our approach with monocular state-of-the-art methods on the KITTI benchmark, including ROI-10D [32], MonoPair [44], M3D-RPN [30], UR3D [45], SMOKE [34], Lite-FPN [31], RTM3D [8], Pseudo-Lidar [19], D4 LCN [18], AM3D [17], and PatchNet [12]. The judging criterion is to compare the average precision of the 3D detection and the location. \(AP_{3d}\) reflects the accuracy of the position estimation of the object in 3D space. \(AP_{hev}\) reflects the accuracy of the location of the object on the bird's-eye view.
## 3 Results
### Trends of Vegetation GPP, NPP, and VT over the Rajmahal Hills
The oldest mining patches (in 1990) over the Rajmahal coal mines (_Z3_) were observed to have some vegetation recovery due to the mine's reclamation activity in subsequent years. Undoubtedly, vegetation recovery over reclaimed mining sites has increased GPP, NPP, and VT trends, as highlighted in _Z3_ using a maroon color circle in Figure 6. Apart from the mining regions, some vegetation patches showed decreasing GPP, NPP, and VT trend pixels across the Rajmahal Hills. However, these patches were not affected by the mining operations, which could be affected by other factors such as anthropogenic (e.g., logging, deforestation, settlement encroachments), natural (forest fires, droughs, etc. ), or environmental (e.g., meteorological, soil, topography). Especially, the contributions of climatic, environmental, and other anthropogenic factors behind the large area under the decreasing trend needs to be investigated in future studies. Figure 7: Long-term (2001-2021) GPP, NPP, and VT trend over respective mining clusters (zoomed regions) in Rajmahal Hills. A total of eight mining zones (zoomed region) are highlighted in Figure 4. All zones have four images showing mining extent, NPP, GPP, and VT, respectively, as shown for Z1. The linear trend of vegetation productivity and transpiration in two mining sites over time and space is also shown in Figure 8. These sites comprise both vegetation degradation as well as vegetation regrowth site. The results exhibited both gradual decreasing and increasing trends of vegetation GPP, NPP, and VT over a vegetation degradation site and vegetation regrowth site, respectively, in the mining locations. Eventually, it can be conferred that increasing mining activity has negatively impacted vegetation productivity and transpiration over the Rajmahal Hills. In contrast, vegetation regrowth due to the mine's reclamation activity has offered increasing trends in vegetation productivity and transpiration.
## 3 Case Study
### Analysis of the Effect  
Figure 12 is the velocity vector diagram of the BSCN with the best drag reduction effect and a velocity increase rate of 13.45%. In Figure 12, each circular annular groove contained a swirl opposite the direction of the internal flow.  
As shown in Figure 13, the BSCN and the conventional straight cone nozzle were divided perpendicular to the axis. The first dividing line was at the left end of the first circular groove, the second dividing line was located at the middle line of the first circular groove slot pitch in the inlet section, the third dividing line was located at the right end of L1, the fourth dividing line was located at the left end of L3, the fifth dividing line was located at the middle line of the first circular groove slot pitch in the outlet section, and the sixth dividing line was located at the right end of L3.  
Figure 11: Velocity increase rate of the BSCN.  
Figure 12: The velocity vector of a BSCN.  
Figure 13: Line plan:(**a**) BSCN; (**b**) conventional straight cone nozzle.  
As shown in Figure 14, based on the results of the CFD numerical simulation, the value of the velocity at each dividing line was extracted, and the curve of the change in the velocity at each dividing line with the distance of the dividing line was plotted. Line01/line1 to line06/line6 represent the positions shown in Figure 13.  
It can be concluded from Figure 14a that in the inlet section, the velocity of the BSCN at different positions perpendicular to the axis was greater than that of the conventional straight cone nozzle at a distance of 2 mm from the wall. It can be seen from Figure 14b that in the outlet section, at the position of 0.75 mm away from the wall, the velocity of the BSCN at different positions perpendicular to the axis was greater than that of the conventional  
Figure 14: Velocity distribution along lines in the line plan: (**a**) Line01/line1 to line03/line3 plan; (**b**) Line04/line4 to line06/line6 plan.  
straight cone nozzle. In the outlet section, compared with the conventional straight cone nozzle, the BSCN improved the fluid velocity and had a more vital constraint on the fluid.
# A Wideband Hybrid Envelope Tracking Supply Modulator with Slew-Rate-Enhanced Linear Amplifier
## 2 Circuit Design
### LA Design with an SRE Structure
#### 2.2.2 AC Analysis of the LA with the Proposed SRE Circuit
In the recommended LA, the slew rate is determined by Equation (17):  
\[SR= \frac{I_{B1}+I_{SRP}}{C_{1}} \tag{17}\]  
In the transition state, is much greater than the static current parameter \(I_{B2}\) related to the SRE circuit. The recommended LA in our work can achieve a high slew rate with low quiescent power consumption. Figure 11: The simplified model of the entire LA. The UGB of the proposed LA can be expressed as:  
\[UGB\cong\frac{g_{ml}}{C_{c}}=(4k\frac{g_{m3P}g_{m3N}}{g_{m3P}+g_{m3N}}+2\frac{g_{ m1a}g_{m1b}}{g_{m1a}+g_{m1b}})\frac{1}{C_{1}+C_{2}} \tag{18}\]  
Observing Figure 5, we can determine the expression of the quiescent current of the LA:  
\[I_{Q}=(2I_{B1}+I_{Q\_3a})+(5I_{B2}+2\mathrm{k}I_{B2}) \tag{19}\]  
where \(I_{Q\_3a}\) is the quiescent current of the output stage. Based on the design targets for UGB and the slew rate, through multiple iterations, we are seeking a design solution that minimizes quiescent power consumption.
# Interval-Based LDA Algorithm for Electrocardiograms for Individual Verification
## 2 Materials and Methods  
The ECG signal reflects the electrical activity of the heart. A basic ECG signal cycle is shown in Figure 1 with a single lead ECG sensor. The signal cycle consists of the P wave, followed by the QRS complex, and the T wave [1; 33].  
Figure 2 shows a simplified block diagram of the proposed interval-based LDA algorithm. This algorithm mainly consists of signal preprocessing, enrollment, and verification.  
In the preprocessing stage, the raw ECG signal's noise is removed or suppressed. For biometric authentication, every user can use his/her ECG sensor in one of the two operation modes, that is, enrollment or verification mode.  
In the enrollment mode/stage, a distinctive feature set is extracted from the ACP (autocorrelation profile) of the ECG on an interval basis to create an association between the user and his/her biometric characteristics. Subsequently, the extracted feature set is stored in a template. This process is called training in the enrollment mode. The template is updated over time, mainly to handle the intra-subject variation of cardiac signals. Longer training of the ECG signal leads to lower chances of false rejection (FR). In this study, the enrollment is performed for 2 min to provide a stable signature.  
Similarly, during the verification mode/stage, the newly acquired unknown input is compared with the template stored in the database to verify a claimed identity. The verification decision is made through a matching process based on a feature vector threshold. The obtained matching score is finally compared with the threshold to either accept or reject the target user's identity claim.
# The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey
###### Abstract
Based on a descriptive survey completed by 670 respondents, the current paper aims to understand the degree of penetration of the phenomenon, covering many different open points of the paradigm and addressing these in multiple dimensions (distinctive solutions knowledge and utilization rate, benefits, and challenges). The current paper concentrates on the Italian context. This choice was driven by the fact that, given the composition of the research group, the number of companies that could be involved was larger and because the Italian agriculture system is first in agriculture in Europe based on added value and third based on gross saleable production. Italy is also the world's leading producer of wine by volume and leading European producer of vegetables by value [13]. The present study also provides a systematization of the technological solutions adopted within the Agri 4.0 paradigm. Finally, the current paper provides a rationalization of the benefits and obstacles related to the implementation of the aforementioned digital technological solutions in the primary sector. Figure 1: Reference scheme. The current article is structured as follows: Section 2 gives an overview of the paradigm and presents the Agri 4.0 studied solutions. Section 3 describes the research methodology used, which is followed by Section 4, in which the four main thematic analyses are discussed. Next, Section 5 discusses the results, providing the research implications of the study and proposals for future research agendas in Agri 4.0.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 3 System Design and Scheduling Problem Definition
### Integration with Fault Prediction  
Fault prediction insights are seamlessly integrated into the scheduling process, creating a feedback loop between the deep learning model and the SMT solver. Figure 1 illustrates the system's feedback loop, where fault prediction and scheduling optimization interact seamlessly. This interplay ensures proactive and fault-aware decision-making, as detailed below:  
* Fault Prediction: Using both historical and real-time machine states as input, a deep learning model (e.g., 1D-CNN) estimates fault probabilities \(P(M_{i},J_{j},t)\). These probabilities highlight high-risk periods for each machine, enabling the system to anticipate potential issues and adjust schedules accordingly.
* Scheduling Optimization: The predicted fault probabilities are dynamically incorporated into an SMT-based scheduler. This allows the system to assign tasks in a manner that avoids high-risk machine states while respecting deadlines, task dependencies, and machine constraints.
* Synergy: Integrating predictive insights with optimization techniques not only improves scheduling reliability but also proactively addresses fault risks. For instance, tasks are rescheduled dynamically to minimize interruptions caused by unexpected machine failures, thereby reducing downtime and enhancing overall operational efficiency.
# EEG Pattern Classification of Picking and Coordination Using Anonymous Random Walks
## 3 Data Processing and Analysis
### Preprocessing Pipeline and Graph Construction  
Before performing the coherence calculation, the EEG data underwent the following preprocessing pipeline as was previously used in [11; 12; 13; 34]. The preprocessing pipeline consisted of finite impulse response (FIR) band-pass filtering (BPF) [1; 32] Hz and artifact removal following iCA. The data were re-referenced to the average reference and downsampled from 512 to 64 Hz following baseline correction. Data were analyzed on a 1 s epoch window from the onset of each task (for stages 2 and 3). In the resting-state condition, a 30 to 90 s epoch was extracted from a 120 s interval from trial onset, resulting in 60 s epochs per participant. However, in the picking and coordination conditions, there were in total 12 decision points per participant.  
Next, we calculated the coherence (ranged [0, 1]) for each pair of electrodes included in the array. The coherence calculation was performed for each subject on all 1 sec epochs (60 resting-state epochs, 12 picking epochs and 12 coordination epochs). In order to convert the results to a graph, we performed discretization, where, for an absolute coherence value \(\geq 0.5\), we determined that synchronization existed between each electrode pair; otherwise, there was no synchronization.  
The constructed graph is undirected. The adjacency matrix (\(16*16\)) is symmetric and the maximal number of total edges in each graph (i.e., in each epoch) is therefore \(120\) (\(\frac{n*(n-1)}{2}\), where n is the number of nodes). Each node in the graph (e.g., each electrode) can have a maximum of 15 edges excluding self-loops. Self-loops were excluded since the coherence of a signal with itself is always 1. Figure 3 presents the graph obtained for an exemplar player in a specific coordination task. For example, let us consider the Fp1 electrode in a single coordination epoch. Fp1 had an absolute coherence value \(\geq 0.5\) with Fp2, F3 and F7. Hence, the nodes representing these electrodes are connected to the Fp1 node.
## 4 Limitations and Future Perspectives  
**Limitations:**  
1. The sample size and number of field sites may be somewhat limited; however, the study employs a robust bifactorial design with replications to ensure reliability.
2. The focus of the study does not include nutritional markers, such as vitamins (B and E), essential minerals (iron and zinc), and amino acid profiles; however, it prioritized key bioactive compounds, such as total phenolic compounds and gamma-oryzanol.
3. The analyses focused on plots located in one province of Ecuador, providing a valuable local perspective.  
**Future Perspectives:**  
1. The sample and field sites should be expanded, incorporating different growing areas and environmental conditions. This would allow analysis of variability associated with external factors and improve the representativeness of the findings.  
2. Analysis of vitamins, minerals, and amino acid profiles should be included in future studies to provide a more complete assessment of the impact of pest control methods on the functional and nutritional quality of rice. III. The geographical scope of the study should be expanded to rice cropping systems in different regions of the country, evaluating variations in the biochemical and nutritional composition of rice according to specific edaphic and climatic characteristics.
###### Abstract  
The rising level of CO\({}_{2}\) in the atmosphere has attracted attention in recent years. The technique of capturing CO\({}_{2}\) from higher CO\({}_{2}\) concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO\({}_{2}\) directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO\({}_{2}\) adsorption and diffusion properties in the air with very low concentrations of CO\({}_{2}\). First, the law influencing CO\({}_{2}\) adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO\({}_{2}\) adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO\({}_{2}\) selectivity, with an \(R\) value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO\({}_{2}\) from the air was the diffusion performance of CO\({}_{2}\) in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO\({}_{2}\) dynamic diameter, this MOF could possess higher CO\({}_{2}\) diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO\({}_{2}\) from the air.  
CO\({}_{2}\) capture; Monte Carlo; machine learning; metal-organic framework; adsorption; diffusion +
Footnote †: journal: Applied Sciences
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 4 Results and Discussion
### EEGLAB Study Results  
The band power data provided by the EEG device software were supported through the processing and visual analysis of the raw EEG data. By utilizing the EEGLAB STUDY tool, changes in EEG signals under different task conditions were examined graphically. Figure 6 presents the frequency ranges that exhibited significant differences in band power based on task conditions on a channel basis. The power spectra graphs illustrate the frequency range of 4-45 Hz. Notably, there was a substantial power change, particularly in the low-frequency theta band, in channels AF3, AF4, F7, F8, FC5, and FC6, which corresponded to the prefrontal, frontal, and central regions. Additionally, a significant power change was observed in channels O1, O2, P7, P8, T7, and T8, representing the temporal, parietal, and occipital regions, particularly in the alpha band.
## 1 Introduction  
The huge amount of information stored in audio and audiovisual repositories makes it necessary to develop efficient methods for search on speech (SoS). Significant research has been carried out for years in this area, and, in particular, in the tasks of spoken document retrieval (SDR) [1, 2, 3, 4, 5, 6], keyword spotting (KWS) [7, 8, 9, 10, 11, 12, 13], spoken term detection (STD) [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] and query-by-example spoken term detection (QBe STD) [26, 27, 28, 29, 30, 31].