esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### MeSH-Based Search  
The vast majority of journal articles in PubMed as well as entries in ClinicalTrials.gov are annotated with several MeSH terms, which have already been used for searching for decades [37]. The MeSH-based method aims to leverage this knowledge for the search.  
Figure 1 visualizes the MeSH-based search method, which consists of four distinct steps. As a first step, the Medical Term Indexer (MTI) [38] extracts MeSH terms from the product descriptions and provides relevant documents.  
In the second step, a variation of the Term Frequency--Inverse Document Frequency [39] algorithm is used to compute the weights \(\omega_{MeSH}(t,\Delta)\) of all extracted MeSH terms. The weight of a MeSH term \(t\) for a set of seed documents \(\Delta\) consisting of relevant \(\delta_{pos}\) and irrelevant \(\delta_{neg}\) documents with \(\delta_{neg}\), \(\delta_{pos}\subseteq\Delta\) and \(\delta_{neg}\cap\delta_{pos}=\varnothing\) is defined as:  
\[\omega_{MeSH}(t,\Delta)=\Big{(}f_{t,\delta_{pos}}-f_{t,\delta_{neg}}\Big{)} \cdot idf(t), \tag{1}\]  
where \(f_{t,\delta}\) denotes the count of \(t\) in \(\delta\) with \(\delta\subseteq\Delta\), i.e., the number of times t occurs in \(\delta\). The Inverse Document Frequency \(idf(t)\) of the MeSH term \(t\) is defined by:  
\[idf(t)=log\bigg{(}\frac{1+N}{1+n_{t}}\bigg{)}, \tag{2}\]  
with \(n_{t}\) indicating the number of documents annotated with the MeSH term \(t\) and \(N\) representing all documents in the used database with \(n_{t}\subseteq N\).  
The resulting MeSH weighting reflects the importance of each MeSH term for a set of seed documents in relation to all documents of a database. Thus, frequently used MeSH terms are weighted lower than rarely used MeSH terms to focus the search more on the core topic. The computed MeSH weights for PubMed are used as well for the search in ClinicalTrial.gov. The provided product description is treated as a positive seed document for the calculation.  
In the third step, the search is conducted with the weighted MeSH terms in a logical disjunction, i.e., via Boolean OR operators. Thus, the search retrieves all documents containing at least one of the extracted MeSH terms. Then, the sum of the weights of the matched MeSH terms for each document establishes the ranking.  
In the fourth step, a feedback loop takes place by incorporating any identified relevant document back into the process. The annotated MeSH terms are added, and the MeSH weighting is recomputed according to step 2 with the now expanded set of relevant documents for the next search in step 3.  
Figure 1: Illustration of the iterative MeSH-based method via four steps: (1) MeSH terms are extracted via the Medical Term Indexer from the product description. (2) Extracted MeSH terms are weighted based on their frequency. (3) Documents with matching terms are retrieved from the document database and ordered by the totaled weights. (4) Relevant documents from search results identified through user feedback are loaded back into the process for iterative refinement.
## 2 Materials and Methods
### Example Data  
In this section, selected outputs of the HyPlant data processing are presented to give a visual impression of the quality of the final results of both processing chains. For this purpose, a dataset acquired during the 2018 ESA FLEXSense campaign was selected. The main aim of this FLEX preparatory campaign was to collect high-resolution airborne measurements of TOC reflectance and SIF across representative European monitoring sites, accompanied by ground-based measurements of important vegetation and atmospheric properties in parallel to Sentinel-3 satellite overpasses. A Cessna Grand Caravan (C-208 B) operated by CzechGlobe served as the airborne carrier for HyPlant during the entire campaign period from 10 June until 2 August.  
The presented dataset was recorded on 29 June 2018 at 12:30 MEST and covers a part of the agricultural research station Campus Klein-Altendorf (CKA), which is affiliated to the Agricultural Faculty of the University of Bonn (Germany). The area is located between the cities of Rheinbach and Meckenheim in the western part of Germany (50\({}^{\circ}\)37\({}^{\prime}\)N, 6\({}^{\circ}\)59\({}^{\prime}\)E), 40 km south of Cologne. CKA comprises 181 ha for field trials and approximately 4800 m\({}^{2}\) for greenhouse trials. Here, research can be conducted with all kinds of plants and crops, ranging from small plants or herbs to large crops like maize, and from annual crops like vegetables to perennial plants like miscanthus or fruit trees.  
The dataset was recorded 680 m above ground level (agl), which corresponds to a ground sampling distance (GSD) of 1 m for HyPlant. Figures 8-10 show selected processing results of both sensor modules. A true-color composite of HyPlant DUAL TOC reflectance and three derived reflectance indices (NDVI, cPRI and WBI) are depicted in Figure 8a-d. In addition, Figures 9 and 10 show the final SIF products and value distributions derived from HyPlant FLUO data, using the four different retrieval methods for the O\({}_{2}\)-A and O\({}_{2}\)-B absorption feature, respectively.  
The entire HyPlant data set presented in this study is available in form of a data publication in the electronic Data Archive Library (e!DAL) [60] and can be downloaded using the following link [61].  
Figure 8: Results of the HyPlant DUAL, showing a part of the agricultural research station Campus Klein-Altendorf, acquired on 29 June 2018: (**a**) true-color composite (RGB 640/550/460 nm) of the TOC reflectance of HyPlant DUAL; (**b**) NDVI map calculated from HyPlant DUAL TOC reflectance data; (**c**) cPRI map calculated from HyPlant DUAL TOC reflectance data; (**d**) WBI map calculated from HyPlant DUAL TOC reflectance data.  
Figure 9: SIF maps derived for the O\({}_{2}\)–A absorption feature, showing a part of the agricultural research station Campus Klein-Altendorf, acquired on 29 June 2018 and corresponding pixel value distributions for vegetation and soil pixels: (**a**) results of the SVD method; (**b**) results of the iFLD method; (**c**) results of the SFM method; (**d**) results of the NA method.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 5 Conclusions  
This paper studies the low-complexity detection for UWA OFDM-IM communication systems. Firstly, we formulate the index detection and the symbol equalization problem into the Bayesian estimation framework. Instead of a well-known AMP framework, the VAMP method with non-loopy FG is utilized to solve the problem. Secondly, considering the symbol sparsity inherently in the OFDM-IM data frame, the prior-aided shrinkage function is derived to achieve better denoising results. For the avoidance of non-existent index detection results, a trick is utilized, which involves the detected erroneous ones into the member of the look-up table. The experimental results show that the proposed detector has reduced complexity, and is robust and effective over different subcarrier allocations and modulation configurations. The proposed method approximates the ML detector, which proves that it can be a competitive alternative for UWA communication applications.  
Conceptualization, X.F. and F.T.; methodology, X.F. and M.Z.; software, X.F. and Z.A.H.Q.; validation, X.F. and H.S.; formal analysis, X.F.; investigation, F.T.; resources, X.F.; data curation, X.F.; writing--original draft preparation, X.F.; writing--review and editing, F.T.; visualization, X.F.; supervision, F.T.; project administration, F.T.; funding acquisition, F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications (Grant No.NY223137), the National Natural Science Foundation of China under Grants 61772287 and 62271426, and Key Program of Marine Economy Development Special Foundation of Department of Natural Resources of Guangdong Province (GDNRC[2023]24).  
The data presented in this study are available on request from the corresponding author.  
Figure 10: Real channel and BER comparisons of detectors. (**a**) Real channel from Wuyuan Bay; (**b**) BER over Wuyuan Bay channel.  
Figure 9: BER comparisons of detectors over different UWA channels. (**a**) BER over channel 1; (**b**) BER over channel 2.
## Materials and Methods
### Chemical Analysis  
The samples of OC, EC, and PAHs, including filter samples, field blanks, and trip blanks, were analyzed using GC/MS at Mokpo National University (MNU). OC and EC have been identified in previous studies as important indicators of PM sources, as their carbon content accounts for a large fraction of PM\({}_{2.5}\). The carbon component plays a major role in determining the characteristics of a PM source as well as the primary and secondary sources. The OC and EC analysis in this study used thermal-optical transmittance according to a protocol published by the National Institute of Occupational Safety and Health (NIOSH 5040). Briefly, the sample is first heated to 870 \({}^{\circ}\)C with He gas. In the second stage, it is heated to 870 \({}^{\circ}\)C in the presence of O\({}_{2}\). In the third and final step, OC and EC are quantified using an internal standard (5% CH\({}_{4}\) in He) for each sample. In the assay process, 2 ug-C/uL sucrose (monosaccharide, C\({}_{12}\)H\({}_{22}\)O\({}_{11}\)) was used as an external reference for testing and calibration of the equipment and quantification. The OC and EC classification of the NIOSH 5040 protocol is determined to be the point at which the transmittance of the laser returns to its initial transmittance after the gradual decrease that occurs when it passes through the filter. BC was monitored using a Multi-Angle Absorption Photometer (MAAP) (Model 5012, Thermo Fisher Scientific Inc., Waltham, MA, USA).  
For GC/MS analysis, 8 \(\times\) 10 inch\({}^{2}\) quartz filters were collected using high-volume samplers. In total, 13 PAH compounds (i.e., phenanthrene(Phen), anthracene(Anth), fluoranthene(Fluo), pyrene(Pyre), benzo[a]anthracane(BaA), chrysene(Chry), benzo[b]fluoranthene(BBF), benzo[k]fluoranthene(BkF), benzo[e]pyrene(BeP), benzo[a]pyrene(BaP), indenol23 cdpyrene(Indeno), dibenz[a,h]anthracane(DBA), and benzo[g,h,i]perylene(BghiP)) were speciated. Each filter was extracted individually by four rounds of 30 min sonication in dichloromethane (DCM). Samples were then spiked with 500 uL of 5 ppm internal standard (pyrene-d10 and benz(a)anthracene-d12) and were analyzed in batches, which included laboratory blanks spiked with matrix standards [33; 34]. The final volume for each sample was adjusted to 500 uL. Each extracted aliquot was analyzed using gas chromatography electron impact mass spectrometry (GC-EI-MS). The analytical recovery was 100 \(\pm\) 5%. Field blank tests were performed without collected aerosols to determine noise and contamination, and duplication analysis was performed using at least 10% of total samples for quality assurance and control.
## 5 Natural Gas Exploration Potential
### Quality of Reservoir  
Both volcanic reservoirs and clastic reservoirs are natural gas reservoirs in the Junggar Basin. The volcanic reservoirs are mainly distributed in the Carboniferous and Early Permian. The volcanic reservoirs lithology is mainly andesite and dactic, followed by tuff and volcanic breccia. The lithofacies are mainly consists of intermediate-acid effusive facies, followed by explosive facies. The volcanic reservoirs are highly heterogeneous, with primary pores, secondary dissolution pores and fractures [30; 31]. Usually, the physical properties of volcanic reservoirs are controlled by lithology and weathering process, not by burial depth [32]. Previous research showed that the equilibrium thickness of weathered is about 550 m, and the maximum porosity is as high as 23% [33]. The weathered crust of volcanic reservoirs are distributed in the Western Uplift, Eastern Uplift, Luliang Uplift, and the Msouwan Uplift, Mobei Uplift, Shixi Uplift in the Central Depression, nearly account for half of the Junggar Basin [34].  
The clastic reservoirs lithology is sandstones and sandy conglomerates. The sandstone is mainly distributed in the Jurassic. Due to shallow depths, the Jurassic sandstone usually has much better physical properties with porosity greater than 12%. The sandy conglomerate is mainly distributed in the Permian. Due to the second dissolution, the sandy conglomerate still had a porosity greater than 10% at a depth of 6000 m.  
In summary, both the wreathing volcanic rocks and secondary dissolution of sandy conglomerates are effective reservoirs for natural gas accumulation.
## 6 Simulation
### LoRa
#### 6.2.1 Time on Air (ToA)  
To compute the ToA in this study, we utilized the following parameters namely payload = 28 bytes, preamble = 8 symbols, CF = 433 MHz, and Tx power = 14 dBm. Here, we included three distinct BW: 125 kHz, 250 kHz, and 500 kHz, SF 7-SF 12, and CR from 1 to 4. ToA was calculated at SF7, SF12, and CR of 1,2,3,4. The graph illustrates that the ToA rose when the SF moved from 7 to 12 and ToA was decreased by half when the bandwidth rose from 125 kHz to 500 kHz.  
Figure 17 illustrates the ToA for the payload size of 28 bytes. The ToA graph in different CR indicates that after the SF 9 the ToA began to rise. The ToA was high for the 125 kHz BW of all three BW, while the 500 kHz BW was low. CR is shown to be exactly related to the growth of the ToA, e.g., at CR 1, BW 1 ToA was 56.58 ms, and the CR 4 ToA was 78.08 ms at the same BW1.
## 2 Materials and Methods
### Methods
#### 2.2.1 Missing Imputation  
We used single imputation (univariate method and multivariate method), multiple imputations, and KNN imputation as a missing imputation method.  
* Single imputation  
Single imputation is a basic method of imputing missing values, and it is divided into the univariate method, which considers only variables with missing data, and the multivariate method, which considers the relationship with other variables as well. The univariate method imputes missing data with statistical values such as the mean, median, and mode of the corresponding data.  
The multivariate method models the imputation variable as y and the other variable as x in order to impute missing data. There are several available methods such as Bayesian ridge [22] and extreme randomized tree [23]. Despite being easy and fast to calculate, there is a chance of inaccuracies, arising from bias or underestimation of the standard error of the estimator affecting the results.  
* Multiple imputations  
Multiple imputations [24] is a method proposed to compensate for the shortcomings of the single imputation method. This method utilizes multiple single imputations and consists of three steps: imputation, analysis, and pooling.  
In the imputation step, \(m\) imputation datasets are generated using the single imputation method, and in the analysis step, statistical values (estimator, variance) are calculated from the \(m\) imputation datasets created in the previous step. Finally, in the pooling step, the statistical information from the previous step is combined and the missing value is imputed to the optimal value. Figure 3 is an example of the multiple imputation method.  
* K-nearest neighbor (KNN) imputation  
KNN imputation finds the nearest \(n\) neighbor data through the Euclidean distance metric from the missing data to be imputed. It then imputes the missing data using the weighted or unweighted average of the neighbor data. Figure 4 shows KNN imputation with \(k=3\)[25].
## 5 Numerical Results and Analysis
### The BER Performance Comparison in Multipath Environment  
In order to verify the performances of CA-DFC and VL-DFC based MUD algorithms in the indoor environment, which is the most common situation where UWB technology can be employed, we carry out this experiment. Without loss of generality, the recommended IEEE 802.15.3a CM2 multipath channel [50, 51] is used here, and the path with the strongest energy is gathered at the receiver.  
The BER performance curves of these MUD algorithms are compared in Figure 9. It is clear that the performances of these two proposed fusion-based algorithms are much better than those of other sub-optimal MUD algorithms, and even close to that of OMD. Taking account of the lower computational complexity they have, we can see that the good tradeoff between performance and complexity is realized by these novel fusion based algorithms.  
Figure 8: The BER _versus K_ curves in AWGN channel when \(E_{b}/N_{0}=5\) dB.  
Figure 9: The BER _versus E\({}_{b}/N_{0}\)_ curves in multipath channel when \(K=10\).
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 4 Simulation Systems and Results
### Simulation Results
#### 4.2.5 The MSE Performance of Algorithm 1 in a Multi-User Scenario  
In practical communication scenarios, the 5G NR base station communicates with multiple users by assigning different RBs to different users. In the multi-user scenario simulated in Figure 9, two users UE0 and UE1 are adopted, and the RB index sets of UE0 and UE1 are configured as \(\{3,4,\ldots,22\}\) and \(\{29,30,\ldots 48\}\), respectively. The normalized Doppler shifts of UE0 and UE1 are set to \(\epsilon=-0.3\) and \(\epsilon=0.1\), respectively. In the single-user scenario simulated in Figure 9, the \(N_{rb}\) is configured as 20, the RB index set is \(\{3,4,\ldots,22\}\), and \(\epsilon\) is set to \(-0.3\) and \(0.1\). As shown in Figure 9, Algorithm 1 efficiently estimated the different frequency offsets of different users in the multi-user scenario, and the MSE performance of Algorithm 1 in the multi-user scenario is close to that in the single-user scenario.
# Control Design and Assessment for a Reversing Tractor-Trailer System Using a Cascade Controller
## 3 Control Design
### Cascade Controller
#### 3.2.1 Trailer's Heading Rate Controller  
The trailer's heading rate feedback controller is the first controller in the cascade design with an objective to drive the heading rate error between the trailers heading angle rate and the commanded heading rate in Equation (18) to zero. The trailer's heading rate controller is a simple proportional gain feedback controller shown in Equation (21).  
\[\psi_{c}=\ K_{THC}(\dot{\theta}_{c}-\dot{\theta}_{2}) \tag{21}\]  
where \(K_{THC}\) is a tunable gain, \(\dot{\theta}_{2}\) is the trailer's heading rate, and it is a feedback signal. The output from this controller is the required articulated angle \(\psi_{c}\).
## 2 Materials and Methods
### Surface and Subsurface Vehicles
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Manufacturer** & **Sensor** & **Variables** \\ \hline \multicolumn{3}{c}{Wave Glider} \\ \hline \multirow{3}{*}{Kongsberg Maritime} & WBT Mini echo sounder (ES70-18 CD, ES333-7 CDK & Acoustic backscatter \\  & split & \\ \hline \multirow{2}{*}{LiCor} & Light Sensor Li1500 & Photosynthetically active \\  & radiation in air \\ \hline SAIV & SAIV-SD204 & Salinity, temperature, density \\ \hline Seapoint (on SAIV CTD) & Seapoint Turbidity Meter & Turbidity \\ \hline Seapoint (on SAIV CTD) & Seapoint Chlorophyll & CHI-\(a\) \\  & Fluorometer & \\ \hline Aanderaa Xylem & Optode 4831 & Oxygen \\ \hline Aanderaa Xylem & Submersible CO\({}_{2}\) 4797 & \(p\)CO\({}_{2}\) \\ \hline Airmar & Airmar 200 WX & Wind speed and direction, air \\  & Airmar & temperature, barometric \\  & & pressure \\ \hline Ocean Instruments & Soundtrap HF hydrophone (parts of the deployment) & Underwater sound \\ \hline \multirow{3}{*}{Kongsberg Maritime} & WBT Mini echo sounder (ES333-7 CDK split) & Acoustic backscatter \\ \cline{2-2}   Neil Brown Ocean Sensors & NBOSI Cabled CT sensor & Salinity, temperature, density \\ \hline Aanderaa Xylem & Optode 4831 & Oxygen, temperature \\ \hline \multirow{3}{*}{Sea-Bird Scientific} & SeaBird CT-Sail & Temperature, conductivity \\ \cline{1-1}   Discontinued (formerly & CONTROLs HydroFlash®O\({}_{2}\) & Oxygen \\ \cline{1-1}   Kongsberg Maritime Contros) & optode & \\ \hline Sea-Bird Scientific & SeaOWL UV-A & Total particulate concentration \\ \hline Sea-Bird Scientific & SeaOWL UV-A & CHI-\(a\) \\ \hline Sea-Bird Scientific & SeaOWL UV-A & FDOM fluorescence \\ \hline JASCO & Observer/AMARG4 & Underwater sound \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensors integrated on the gliders for the 2018 LoVe deployment.
# Joint Power and Time Allocation in Hybrid NoMA/OMA IoT Networks for Two-Way Communications
## 5 Conclusions  
A two-way IoT transmission scheme combined with scalable TDD and hybrid NoMA/OMA was proposed to address the asymmetric QoS requirement and the unbalanced receivers processing capability on the UL and DL. By means of joint optimization of the transmit power and the transmit time between the UL and DL, the optimal allocation of these transmission resources was reached, thereby achieving the adaptation of transmission resources to the asymmetric QoS requirement. By hybrid NoMA/OMA, the benefits of performance improvement and complexity reduction are achieved on the UL and DL, respectively. The proposed hybrid multi-access scheme and joint resource allocation method performed better than the conventional OMA scheme with joint resource allocation.  
**Author Contributions:** Methodology, D.-H.C.; Software, D.-H.C.; Validation, D.-H.C.; Investigation, D.-H.C.; Resources, E.-H.J.; Data curation, D.-H.C.; Writing--original draft, D.-H.C.; Visualization, E.-H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Performance comparison between the proposed hybrid NoMA/TDMA scheme and the OMA scheme.
## 1 Introduction
### Objectives  
As it is clear from the preview literature review, the most accurate possible navigation function is essential for the further development of modern agricultural technology (robotic structures, UAVs and others). Nevertheless, the existing research provides only minimal information on the influence of motion on the loss of accuracy, and the study of specific values of accuracy depending on motion is largely under-researched, although it is clear that the vast majority of field operations take place when the machine is moving, not when it is standing. The knowledge of navigation errors in the dynamic mode of operations is therefore essential for the further development of this technique.  
Therefore, the main aim of this research was to investigate the effects of speed and acceleration on the properties of RTK receivers and compare the horizontal errors in the static and dynamic modes of operation. For these purposes, a new method was developed, based on a robotic MRA platform (Measurement Robotic Arm). The evaluation of four RTK receivers in this work was divided into the following subunits: the ability of the system to solve the problem of carrier phase ambiguities (\(SSR\)) (1), positioning the deviations of and their error indicators for the whole measurement period (2), positioning the deviations of and their error indicators for the period \"FIX\" (3), error indicators of the static measurements (4), recovery time of the \"FIX\" state after its loss (5), the linear dependence between the obtained series (6), the comparison of the positioning deviations according to the current state of the system (7) and the comparison of the positioning deviations according to the scenario phase (8).