esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 2 Materials and Methods
### Road Network Matching  
Road network matching is crucial for analyzing and understanding travel behaviors. By aligning trajectory data with the road network, we can accurately determine the start and end points of residents' travels, along with their paths, providing insights into travel characteristics, hotspot paths, and pick-up/drop-off locations. The trajectory data collected by the taxi terminal ideally correspond to the associated road network. However, due to positioning inaccuracies and signal interferences, some trajectory points may deviate from the actual roads. To counter this, the hidden Markov model is employed for road network matching [27]. The process involves the following:  
1. Calculating transition probabilities: This step involves computing the transition probabilities between taxi trajectory points and multiple candidate paths on the map: \[Pr\left(rss_{tj}\big{|}path_{t-1j}\right)=\mathrm{e}^{-|Dist(prj_{t},prj_{j})- Dist(P_{bat},\,P_{first})|},\] (1)  
Figure 4: OD point visualization: (**a**) visualization of picking-up points; (**b**) visualization of drop-off points.  
Figure 3: Taxi operation diagram.  
where \(P_{first}\) represents the first track point record, \(P_{last}\) represents the last track point record, \(prj_{t}\) represents the projection point of trajectory data on the map path, \(Dist(prj_{t},prj_{j})\) represents the shortest distance between projection points \(prj_{t}\) and \(prj_{j}\) on the road network, and \(Dist(P_{last},P_{first})\) represents the Euclidean distance between the starting trajectory point and the ending trajectory point of a certain path.
2. Observation probability calculation: Here, the observation probability between the taxi trajectory points and multiple corresponding paths is determined: \[Pr\big{(}traj_{t}\big{|}rss_{t,j}\big{)}=\frac{1}{\sqrt{2\pi}\sigma_{z}}e^{- \frac{1}{2}}\big{(}\frac{Dist\big{(}traj_{t},rss_{t,j}\big{)}}{\sigma_{z}})^{2},\] (2) where \(Dist\big{(}traj_{t},rss_{t,j}\big{)}\) is the distance between the trajectory point and the corresponding path, and \(\sigma_{z}\) is the standard deviation of the distance between the trajectory point and the corresponding path.
3. Maximum probability matching: This step calculates the maximum probability for aligning the taxi trajectory points with the map paths: \[\prod\
olimits_{t=1}^{m}Pr\big{(}traj_{t}\big{|}path_{t,j}\big{)}*\prod \
olimits_{t=2}^{m}Pr\big{(}path_{t,j}\big{|}path_{t-1,j}\big{)},\] (3) where \(traj_{t}\) represents various trajectory segments, \(path_{t}\) represents the candidate path, \(Pr\big{(}traj_{t}\big{|}path_{t,j}\big{)}\) represents the observation probability between the taxi trajectory and the map path, and \(Pr\big{(}path_{t,j}\big{|}path_{t-1,j}\big{)}\) represents the transition probability between adjacent taxi trajectory points and map paths. The concept underlying this formula is illustrated in Figure 5.
## 6 Results and Discussion
For the signal parameters estimation in the Mars EDL case, the Doppler frequency and Doppler rate could be compensated by the designed nominal EDL trajectory if a good model of the Mars atmosphere and the thrusting forces onboard the spacecraft are available, or by the previous estimations, and the performance has been analyzed by [PERSON], et al. [8]. A similar operation could also be made on the IFR tracking approach, which may lead to a lower tracking threshold SNR. These works still need more investigation. The formal analyses in this paper try to evaluate the parameter estimation performance for the two approaches with no reduction of the parameter search space. Another point that needs to be emphasized is that the a priori Doppler phase model is supposed to be generated from the carrier reconstruction by a third-order phase-locked loop when the spacecraft is approaching Mars. Further simulations have been made for the influence of the a priori phase model accuracy on the estimation performance of an IFR tracking approach. The results show that an IFR tracking approach is more tolerant with the uncertainty of the initial phase and frequency rate, while sensitive with the uncertainty of the frequency in the phase model. A rule of thumb for the initial frequency in the a priori phase model could also be selected as the deviation is smaller than 1 Hz, which is not hard to satisfy when the downlink carrier is tracked by a third-order PLL at a moderate or high SNR.
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 3 DIR-Net Method
### Overview of the Framework  
The whole framework of the proposed DIR-Net is shown in Figure 1. The input is the encoded signal transmitted through the channel, while the output is the decoded signal by DIR-Net.  
The information sequence sent by the source transmitter is encoded into polar codes. Subsequently, the codeword is modulated to suit transmission in the communication channel. Usually, there is noise existing in the channel. Thus, the signal model for received symbols can be formulated as follows:  
\[\mathbf{z_{n}}=\left(\mathbf{x_{n}}\right)^{*}+\mathbf{\mu_{n}}, \tag{9}\]  
where \(\mathbf{\mu_{n}}\sim\left(0,\frac{\zeta^{2}}{\gamma}\right)\) represents Gaussian white noise. The goal of our work is to predict of estimation \(\hat{\mathbf{x}}_{n}\) of \(\mathbf{x_{n}}\) from the received signal \(\mathbf{z_{n}}\).  
As shown in Figure 1, there are three processing stages in our decoding method, the preprocessing stage, the denoising stage, and the decoding stage. The preprocessing stage is to obtain the log-likelihood ratio (LLR) values by assuming the distribution of Gaussian noise. The LLR of the transmitted codeword is  
\[\mathrm{LLR}(\mathbf{z_{n}}){=}\ln\frac{P(x=0|\mathbf{z_{n}})}{P(x=1|\mathbf{z_{n}})}= \frac{2\mathbf{z_{n}}}{\sigma^{2}}. \tag{10}\]  
Figure 1: Overview of the proposed framework.  
The denoising stage and decoding stage are performed by the two subnetworks in DIR-Net, which refine and decode the signal via cascaded attention modules. Assuming that the output is \(O_{\text{LLR}}=\{l1,l2\ldots IN\}\) through the LLR process, where \(O_{\text{LLR}}\) contains the noise superimposed in the process of channel transmission, the obtained LLR value is fed into the denoising subnetwork to perform refinement and obtain the denoising codewords \(M=\{m1,m2\ldots mN\}\). After the denoising subnetwork, a step-wish decoding subnetwork with attention mechanism is utilized to decode the polar codes.  
The reasons for building a two-stage decoding network are as follows: The signal will introduce much noise during transmission in the channel. This noise will bring a negative impact on decoding accuracy. So, we construct a denoising subnetwork to reduce the noise in the signal. The decoding accuracy of the decoder will be greatly improved after that.  
In addition, we introduce an attention mechanism to the denoiser and decoder. The attention mechanism in the denoiser separates the valid information from the noise, which further refines the information and prepares it for decoding. The attention mechanism in the decoder allows further adaptive selects useful information in decoding process. The important information is given higher weights compared with non-important information. The accuracy can be improved by the two-stage information refinement. On basis of the above considerations, we design a more advanced network structure than existing NND methods, as shown in Figure 1.  
The structure of decoder and denoiser whole work is explained in detail below.
###### Abstract  
New configurations of 2-D phased arrays are proposed in this paper for reducing the number of phase shifters. This design methodology is based on the use of a novel coherently radiating periodic structures (CORPSs) block for 2-D phased arrays. Two new antenna systems for 2-D phased arrays are studied and analyzed utilizing the CORPSs blocks of four inputs and nine outputs. These CORPSs feeding blocks are applied in a smart way to feed the planar antenna arrays by generating the required phase plane and reducing the number of control ports. Interesting results are provided based on the experimental measurements and full-wave simulations. These results illustrate a great reduction of the active devices (phase shifters), providing a good design compromise in terms of the scanning range and side lobe level performance. Furthermore, the provided results illustrate a maximum reduction capability in the number of phase shifters of 81%, considering a scanning range of \(\pm 30^{\circ}\) in azimuth and \(\pm 30^{\circ}\) in elevation. A raised cosine distribution is applied to reach side lobe levels of \(-19\) dB for \(\pm 18^{\circ}\) and \(-17\) dB for \(\pm 30^{\circ}\) in elevation. These benefits could be of interest to designers of phased antenna systems.  
2-D phased array; scanning; CORPS; phase shifters +
Footnote †: journal: Applied sciences
###### Abstract  
Electrocardiographic (ECG) R-peak detection is essential for every sensor-based cardiovascular health monitoring system. To validate R-peak detectors, comparing the predicted results with reference annotations is crucial. This comparison is typically performed using tools provided by the waveform database (WFDB) or custom methods. However, many studies fail to provide detailed information on the validation process. The literature also highlights inconsistencies in reporting window size, a crucial parameter used to compare predictions with expert annotations to distinguish false peaks from the true R-peak. Additionally, there is also a need for uniformity in reporting the total number of beats for individual or collective records of the widely used MIT-BIH arrhythmia database. Thus, we aim to review validation methods of various R-peak detection methodologies before their implementation in real time. This review discusses the impact of non-beat annotations when using a custom validation method, allowable window tolerance, the effects of window size deviations, and implications of varying numbers of beats and skipping segments on ECG testing, providing a comprehensive guide for researchers. Addressing these validation gaps is critical as they can significantly affect validatory outcomes. Finally, the conclusion section proposes a structured concept as a future approach, a guide to integrate WFDB R-peak validation tools for testing any QRS annotated ECG database. Overall, this review underscores the importance of complete transparency in reporting testing procedures, which prevents misleading assessments of R-peak detection algorithms and enables fair methodological comparison.  
arnythmia; MIT-BIH dataset; R-peak detection; WFDB; R-peak validation
###### Abstract  
In order to study the effects and mechanism of Monascus on the quality of hairtail surimi, high-throughput sequencing technology, headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS), and electronic nose techniques were used to investigate the changes in the quality, microbial diversity, and volatile flavor compounds of Monascus-fermented hairtail surimi (MFHS) during fermentation. The results showed that the total volatile basic nitrogen (TVB-N) index of hairtail surimi fermented by Monascus for 0-5 h met the requirements of the national standard. Among them, the 1 h group showed the best gel quality, which detected a total of 138 volatile substances, including 20 alcohols, 7 aldehydes, 12 olefins, 4 phenols, 12 alkanes, 8 ketones, 15 esters, 6 acids, 16 benzenes, 4 ethers, and 8 amines, as well as 26 other compounds. In addition, the dominant fungal microorganisms in the fermentation process of MFHS were identified, and a Spearman correlation analysis showed that 16 fungal microorganisms were significantly correlated with the decrease in fishy odor substances in the fermented fish and that 8 fungal microorganisms were significantly correlated with the increase in aromatic substances after fermentation. In short, Monascus fermentation can eliminate and reduce the fishy odor substances in hairtail fish, increase and improve the aromatic flavor, and improve the quality of hairtail surimi gel. These findings are helpful for revealing the mechanism of the quality formation of fermented surimi and provide guidance for the screening of starter culture in the future.  
Monascus-fermented hairtail surimi (MFHS); microbial diversity; fermentation; volatile compounds; correlation +
Footnote †: journal:  
Acticle
# Temperature Prediction Model in the Main Ventilation System of an Underground Mine
## 4 Results and Discussion
If the analysis was carried out with the intermediate or final nodes, the correlation between observed and estimated values increased considerably (see Figure 9). Additionally, there was a substantial reduction in terms of range of deviation (~50%) compared to the analysis of the whole system detailed in Figure 7. This means that the model can be used to predict the temperature in a possible extension of the ventilation circuit, even when only using data from a part of the intake drift, by determining the effective temperature at the entrance of each extracting panel of the mine. Figure 8: Final temperature comparison for different node lag steps as a function of node-to-node (red) and multi-node lag steps (blue represents two steps and gray represents three steps). Figure 7: Comparison between values observed and estimated. Further research should be done to analyze different ventilation circuits and additional variables related to the underground mining environment, such as pollutants or humidity. The prediction of temperatures in other sectors was studied using machine learning techniques [40]. Other models and approaches should also be explored in the future.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 2 Related Works
### Dictionary Learning
#### 2.2.1 Unsupervised Dictionary Learning Methods  
Unsupervised dictionary learning methods do not use any label information in the learning process. The most well-known unsupervised dictionary learning method is K-singular value decomposition (K-SVD) [19], which combines the K-means clustering algorithm and sparse representation to learn the most accurate representation. However, because label information is not used, the discriminative abilities of these methods are limited.
## 3 Experimental Design and Implementation
### Experimental Procedure
#### 3.3.3 Feature Engineering Implementation  
Feature engineering transforms raw data into features suitable for analysis, primarily including:  
1. Temporal feature extraction: Using the addTime() function to convert Unix timestamps to a readable time format, identifying time periods contained in the data through the timeSegment() function, and adding time period identifiers to each record using the addTimesegNo() function.
2. Vehicle feature construction: Generating vehicle basic feature data tables through the getVid() function, adding derived features such as time periods and start-end row numbers using the vinfoAdd() function, and constructing structured vehicle information tables to support rapid querying and analysis.
3. Spatial coordinate calibration: Importing processed data into the MATLAB environment, visualizing the relationship between intersection positions and trajectories using the plotRoad() function, manually adjusting intersection coordinates to trajectory convergence centers based on visualization results, and verifying calibration effects to ensure accurate positioning of all intersections.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 1 Introduction and Motivation
### Paper Goals, Research Questions, and Main Contributions  
In this paper, we focus on improving the modelling of random ionospheric delays for PNT applications. Therefore, we develop a new model called the Interpolated and Averaged Memory Model (IAMM), which is derived from averaging ionospheric data spanning 11 years, relying on the fact that ionospheric variations have a typical periodicity of 11 years [6; 21]. Our model gives us a 2D representation of the electron content inside the ionosphere worldwide based on the solar year, month, and time. In further sections, we give special attention to the LEO-PNT scenario and show the difference in modelling compared to GNSS signals.  
The primary focus of our research is to address the following questions. Firstly, how can we develop an ionospheric model that can function offline without requiring correction coefficients to reduce the amount of correction data sent from satellites to ground receivers? Secondly, what is the accuracy gain (or loss) provided by such a model compared to the established ionospheric models that rely on online corrections, such as Klobuchar and IRI models? Finally, how can we expand the GNSS ionospheric models to LEO-PNT systems? Our paper makes the following key contributions:  
* We propose a novel IAMM for ionospheric delay compensation at the receiver;
* We validate the IAMM model by comparing it with current state-of-the-art models used today using real observation data from a variety of GNSS reference stations, as well as from Android mobile receivers;
* We discuss the proposed model's challenges and possible improvements to the LEO-PNT context.  
The rest of the paper is structured as follows: Section 2 gives a brief overview of the main ionospheric delay models in the current literature, with a main focus on GPS signals, which were chosen for our validation campaign. Section 3 describes our methodology and introduces the IAMM model. Section 4 validates the IAMM model with three types of measurement data: data collected by a professional receiver with a fixed roof antenna at our university campus, data available by open access from Finland GNSS reference stations of the FGI network, and data collected via the GNSSlogger app from various mobile devices in dynamic conditions across Europe. A total of 24 scenarios are used (8 per each of the three types mentioned above) for validation purposes. Section 5 discusses the results and how they could be expanded in the context of LEO-PNT, and Section 6 summarizes our findings.
## 3 Spatial-Spectral-Emissivity Land-Cover Classification Based on Conditional Random Fields (SSECRF)  
### Background  
#### 3.1.1 Emissivity Retrieval from the Thermal Infrared Hyperspectral Imagery  
Land surface emissivity (LSE), which can be retrieved from the thermal infrared hyperspectral imagery, is an intrinsic property of natural materials, and is sensitive to the material composition, especially to the silicate minerals [20]. The physical background is the fundamental spectral absorption features of silicate minerals and carbonates, which are the major components of the terrestrial surface and the primary constituent of man-made construction objects. The emissivity is critical data for studying soil development and erosion, bedrock mapping, etc. [21], and it is also used to study the relationship between LSE and surface energy budgets [22]. Emissivity data are widely used in mineral mapping [23], gas detection [24], vegetation monitoring [25], etc. With the continuous improvement of sensor manufacturing technology, the acquisition of airborne thermal infrared hyperspectral data has been made possible by the Hyper-Cam and Thermal Airborne Spectrographic Imager (TASI) instruments, which can help us to perform target detection and material classification, regardless of illumination conditions. In the urban land-cover classification task, the thermal infrared hyperspectral imagery is a potential data source for the identification of man-made objects.
## 2 Materials and Methods
### Sampling and Monitoring Parameters
Composite samples were collected in pre-cleaned 2L polyethylene plastic bottles (sterilized glass bottles were used for biochemical oxygen demand (BOD) and chemical oxygen demand (COD) analyses) for different parameters. The bottles were washed with concentrated nitric acid and distilled water before sample collection and thoroughly rinsed with sample water during collection to avoid possible contamination. The water samples were aseptically handled, labelled, preserved in sterile glass bottles, stored in the cooler (Mobicool v30 AC/DC, Germany) and ice box, and transported to the laboratory of Hawassa University Environmental Engineering, Addis Ababa City Government Environmental Protection, and Green Development Commission and Engineering Corporation of Oromiya for analysis. \begin{table}
\begin{tabular}{c c c c} \hline
**No** & **Monitoring Stations** & **Site Code** & **Location** \\ \hline
1 & Wesha River & MS1 & LHW upstream \\
2 & Hallo River & MS2 & LHW upstream \\
3 & Wedessa River & MS3 & LHW upstream \\
4 & BGI effluent discharge site & MS4 & LHW middle \\
5 & Pepsi factory oxidation pond & MS5 & LHW middle \\
6 & Tikur-Wuha River & MS6 & LHW middle \\
7 & Amora-Gedel (fish market) & MS7 & Eastern side of LH \\
8 & Amora-Gedel (Gudumale) & MS8 & Eastern side of LH \\
9 & Nearby Lewi resort & MS9 & Eastern side of LH \\
10 & Fikerhaky center (FH) & MS10 & Center of LH \\
11 & Fikerhaky (mezengna) & MS11 & Eastern side of LH \\
12 & Center of LH (Towards HR) & MS12 & Center of LH \\
13 & Nearby Haile resort & MS13 & Eastern side of LH \\
14 & Tikur-Wuha site & MS14 & Eastern side of LH \\
15 & Referral Hospital & MS15 & Eastern side of LH \\
16 & Ali-Girma site (opposite to HR) & MS16 & Western side of LH \\
17 & Sima Site (opposite to Mount Tabor) & MS17 & Western side of LH \\
18 & Dore-Bafana Betemengist & MS18 & Southern part of LH \\
19 & Hawassa Industrial Park & MS19 & LHW middle \\ \hline \end{tabular} The site codes are indicated in Figure 1. FH designates Fikerhaky, HR labels Haile Resort, LHW designates Lake Hawassa Watershed, LH designates Lake Hawassa.  
\end{table} 
Table 1: Monitoring stations in Lake Hawassa Watershed. The collection, handling, preservation, and treatment of the water samples followed the standard methods outlined for the examination of water and wastewater by the American Public Health Association guidelines [20] and all the parameters were presented with their respective analytical methods and instruments used for analysis in Table 2 below. Un-Ionized Ammonia Determination from Total Ammonium Nitrogen (TAN)  
The un-ionized free ammonia was calculated by the mass action law in its logarithmic form (1). The pKa as function of temperature was taken from [PERSON] et al. [21]:  
\[\%\ \text{Un}-\text{ionized NH3}-\text{N}=\frac{1}{\left(1+10^{(\text{ pK}_{\text{a}}-\text{pH})}\right)} \tag{1}\]  
\[\text{pK}_{\text{a}}=\frac{0.09108{+}2729.92}{(\text{T}_{\text{k}})} \tag{2}\]  
where \(\text{T}_{\text{k}}\) is temperature in Kelvins (273 + \({}^{\circ}\)C).