esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A detailed study of the sensitivity of TDS-1 GNSS-R observables vs. SM has been conducted. First, different observables have been examined to understand their potential to infer geophysical data and their problems (e.g., non-geophysical noise, inconsistencies, etc.). Then, the data has been screened for quality and topography effects, compensated for antenna pattern, distance factor, vegetation opacity, and incidence angle. Finally, the sensitivity to SM has been evaluated by a linear robust fit of the scatter plots of the TDS-1 GNSS-R reflectivity with the collocated SMOS SM. While the obtained values are lower than the ones obtained in previous studies [1; 2], they are still significant \(\sim 9\) dB/100% for incidence angles smaller than 30\({}^{\circ}\). However, the large scattering that remains in the data prevents from performing an accurate SM retrieval. Part of this scattering is due to the variations in the estimated direct power, part to the different spatial resolution of the different variables used, part to the pixel inhomogeneity, and part to speckle and thermal noises. In this sense, further studies will be conducted to assess the sensitivity to SM using downscaled SMOS products [13; 14] and in situ data from CEMADEM (Brazil) [15].  
* Research idea, A.C., M.V.; software coding, A.C., G.P., and L.R.; formal analysis and investigation, A.C., M.V., G.P., and L.R.; data curation, M.V.; writing--original draft preparation, A.C.; writing--review and editing, M.V. and H.P., supervision, A.C.; project administration, A.C.; funding acquisition, A.C., M.V., and H.P.
* This research has been supported by ESA/ESTEC CONTRACT n. 4000120299/17/NL/AF/hh \"Potential of spaceborne GNSS-R for land applications\", by project AGORA (ESP2015-70014-C2-1-R) of the Spanish Ministry of Economy and Competitiveness, and by an ICREA Academia Award from the Catalan Government.
* The authors are thankful to [PERSON] from Deimos-Space for providing the TDS-1 data sets within the framework of the project. The authors are also thankful to [PERSON] and [PERSON] (U. Tor Vergata, Rome, Italy), [PERSON] (CESBIO, Toulouse, France), and [PERSON] (Deimos UK) for the helpful discussions during the execution of this project.
* The authors declare no conflict of interest.
## 3 Methodology
### Method for Identification of Main Impact Factors
The resulting data matrix is shown in Equation (5); the r\({}_{ij}\) is the indicator of x\({}_{ij}\) after the normalization. \[X= \begin{bmatrix}r_{11}&r_{12}&\cdots&r_{1j}\\ r_{21}&r_{22}&\cdots&r_{2j}\\ \cdots&\cdots&\cdots&\cdots\\ r_{i1}&r_{i2}&\cdots&r_{ij}\end{bmatrix}  \tag{5}\]  
For the positive impact indicators:  
\[r_{ij}=\frac{x_{ij}-x_{i}^{\text{min}}}{x_{i}^{\text{max}}-x_{i}^{\text{min}} }+1 \tag{6}\]  
For the negative impact indicators:  
\[r_{ij}=\frac{x_{i}^{\text{max}}-x_{ij}}{x_{i}^{\text{max}}-x_{i}^{\text{min}} }+1 \tag{7}\]  
Step 7: The weight of the \(i\)th sample under the \(j\)th indicator for that indicator is calculated as follows:  
\[f_{ij}=\frac{r_{ij}}{\sum\limits_{i=1}^{n}r_{ij}} \tag{8}\]  
where \(r_{ij}\) denotes the value of the \(j\)th indicator of the \(i\)th evaluation indicator object; \(n\) denotes the number of evaluated objects. Step 8: The entropy value of the \(j\)th indicator, \(H_{j}\), is calculated as follows:  
\[H_{j}=-k\sum\limits_{i=1}^{n}f_{ij}\ln f_{ij} \tag{9}\]  
where \(k=1/\ln n\), \(0\leq Hj\leq 1\). Step 9: Indicator weights are calculated according to Equation (10):  
\[w_{j}=\frac{1-H_{j}}{\sum\limits_{j=1}^{m}\big{(}1-H_{j}\big{)}}(j=1,2\ldots m) \tag{10}\]  
where the coefficient of variation for indicator \(x_{j}\), denoted as 1-\(H_{j}\), increases as the difference of \(x_{ij}\) becomes larger; this indicates a greater contribution of \(x_{j}\) to the comprehensive evaluation. Conversely, a lack of difference in \(x_{j}\) among the evaluated objects results in \(H_{j}\) equating to 1 and the coefficient of variation becoming 0. If the difference of \(x_{ij}\) is smaller, \(H_{j}\) increases, leading to a decrease in the coefficient of variation. The term \(m\) represents the number of evaluation indexes. Step 10: The weights of the evaluation indicators calculated by the entropy method are normalized to regain the weight set \(A=[a_{1}\), \(a_{2}\), \(\ldots\), \(a_{m}]\) and used as the final weight ranking of the risk factors.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 3 Proposal Architecture
### Road-Side Unit  
The RSU device is responsible for communicating vehicles with the fixed infrastructure and forwards the information to a Gateway before resending the data compiled from the vehicular network to the big data Cluster. The RSU can function as a gateway when it connects to another infrastructure. It is comprised of a System on Module (SoM) that contains a Quad Core Cortex A9 processor at 1 GHz and a 1 GB DDR3 64 bit RAM memory. Additionally, the RSU has an 802.11 b radio that supports ad-hoc communication with 12 dBm transmission power and an omnidirectional antenna with 9 dBi gain for V2I and I2I communication. When the RSU functions as a Gateway, it will connect either via Ethernet to the same network segment as the big data Cluster, or directly to the internet to communicate to the big data Cluster.
## 2 Methodology
### Data Extraction and Validity Control  
The complete summary of the data extraction strategy is presented by a flowchart shown in Figure 1. The 1136 papers are retrieved from the various online academic databases. The reviews presented are from independent reviewers, which are later compared and the results indicate no discrepancies between the comparative data. Out of the 1136 papers, 342 are rejected based on the title and abstract criteria. 9 papers are also rejected for being either a survey or a review and 667 proceedings are rejected due to few citations. Finally, 118 papers qualify for the final review. To retrieve the maximum number of publications, a further review of the references of the 118 qualified papers is undertaken, these are compared with local databases in order to find any missing papers that may satisfy the inclusion requirements, which previously are not included. Fortunately, none are missing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S/N** & **Academic Database** & **Papers Returned** \\ \hline
1 & ScienceDirect Library & 257 \\
2 & Association for Computing Machinery (ACM) Digital Library & 228 \\
3 & SpringerLink Library & 212 \\
4 & IEEE Xplore Library & 152 \\
5 & Web of Science & 77 \\
6 & Dblp computer science bibliography & 37 \\
7 & Google Scholar & 58 \\
8 & Scopus & 115 \\  & Total & 1136 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Papers retrieved from academic databases.  
Figure 1: Data extraction strategy.  
Each of the 118 qualified papers is searched for in Google scholar ([[https://scholar.google.com/](https://scholar.google.com/)]([https://scholar.google.com/](https://scholar.google.com/))) these present a brief overview of the title and an abstract of the papers with citations, related articles, and versions. Four (4) papers are then found to satisfy the inclusion criteria making a total of 122 qualified papers. Each of the 122 papers is then prudently reviewed and classified into various application domains, exploiting their incorporated contexts, identifying the approach used to model user profiles, the data extraction strategies, the filtering techniques, and the methods of evaluation.
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 1 Introduction
[16] selected transverse and vertical vibration modes to detect oblique cracks on the rail bottom and analyzed the relationship between the angle of the oblique cracks and the scattering characteristics of the guided waves. [PERSON] et al. [17] utilized finite element software to simulate the rail wear and applied different excitation frequencies. Location analysis shows that the location error is affected by the excitation frequency. However, as a result of the dispersion characteristics of ultrasonic guided waves and the frequency aliasing phenomenon, it is impossible to accurately locate the defect from the group velocity. For this reason, there are relatively few studies on the application of ultrasonic guided waves in rail defects location. Nevertheless, because of the results related with the prediction of defect sizes and the identification of defect types, it seems practical to study this method in terms of defect location. To locate rail cracks accurately by using ultrasonic guided waves, it is generally necessary to obtain a single mode propagating in the rail. The research ideas can be roughly divided into two kinds: one is to directly excite a single mode in the rail, and the other is to extract a single mode from the signal propagating in the rail. In previous studies, the research group proposed a single mode excitation method, which can excite a relatively pure single mode in rails [18]. At the same time, the group also carried out research on the optimal mode selection of rail crack detection in which a selection model of crack detection mode is created. For a specific crack, the guided wave frequency and mode suitable for crack detection are quickly selected. In this paper, a single mode extraction algorithm (SMEA) is proposed to ascertain the precise location of defects. The method is as follows: Firstly, the mode and frequency of defect detection are selected and a three-dimensional model of the defect present in the railroad is established. The simulation analysis is carried out using the three-dimensional finite element analysis software ANSYS. The defect models of the railhead are stimulated with low-frequency (200 Hz) and high-frequency (60 kHz) signals. The content of each mode of guided wave is quantitatively analyzed, and the position of the railhead defect is calculated according to the single mode extraction method. The semi-analytical finite element method and accurate modal identification method are described in Section 2. In Section 3, the SMEA and verification process are discussed. The defect location method and simulation results are described in Section 4. Conclusions are given in Section 5.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 5 Discussion
### Limitations  
While deep learning models such as Long Short-Term Memory (LSTM) networks have shown promise in financial time-series forecasting due to their ability to capture long-range dependencies [19], they were not utilized in this study for several reasons. First, LSTM models are data-hungry and typically require large volumes of high-frequency or granular data to generalize effectively [20]. Given our dataset's monthly frequency and modest size, implementing LSTM would likely have resulted in overfitting and reduced robustness. Second, this study prioritized model transparency and interpretability, particularly for stakeholders such as policymakers and analysts. Deep neural architectures like LSTM are often criticized for their \"black box\" nature, which can be a barrier in policy-driven or regulated environments [21]. Finally, LSTM implementation involves significant computational overhead and hyperparameter tuning, which was beyond the scope of this study's focus on accessible and widely used forecasting methods. Future work will explore the application of deep learning methods including LSTM and Transformer-based architectures to address these challenges and potentially enhance forecasting performance in longer time horizons or higher-frequency datasets.
## 1 Actinobacteria and Their Primary Metabolism as Basis for Secondary Metabolism Precursor Supply
### Primary Metabolism in Actinobacteria: Phosphate Metabolism  
#### 1.3.1 Biochemistry of Phosphate Metabolism  
Another essential nutrient element required for metabolism in Actinobacteria is phosphate. Its deficiency causes the down-regulation of primary metabolism. On the other hand, it can trigger the production of secondary metabolites [47]. Phosphorous is present in nature in the form of phosphate salts, organophosphates and phosphonates. Actinobacteria are able to transport inorganic phosphate using the phosphate transport system PstSCAB as well as the PitH transporters. The PstSCAB system has a higher affinity and consists of four components [48].  
Figure 3: Scheme of the post-translational regulation of the GS enzyme in _S. coelicolor_. The activity of GlnA (GSI) is regulated through reversible adenylation and deadenylation by an adenylyltransferase (GlnE). In contrast to _E. coli_, in _S. coelicolor_, the PII protein as well as the adenylyltransferase GlnD are not essential for the GlnE-dependent GSI regulation. Straight arrows represent confirmed interactions, dashed lines—unstudied. AMP, adenosine monophosphate; GSI, glutamine synthetase I; UMP, uridine monophosphate; question mark, unknown regulatory protein (modified from [8]).  
In _Streptomyces_ spp., PstS (phosphate-binding protein) is attached to the outer side of the cell membrane, glycosylated and released. The transport of phosphate is strictly regulated by the PstSCAB system through the concentration of inorganic phosphate and mediated by the binding of phosphorylated PhoP to the promoter region of the PstSCAB operon [48]. Some Actinobacteria, e.g., _Mycobacterium smegmatis_, have an additional transport system with high-affinity PhnCDE, which is also regulated by PhoP. Furthermore, _Streptomyces_ spp. have a phosphate transport system with a low affinity, encoded by the _pitH1-pitH2_ genes. In _S. coelicolor_, PhoP regulates the expression of _pitH2_, but not that of _pitH1_. In _Streptomyces_, an increase in the concentration of hexoses can favor the uptake of inorganic phosphate to control carbon and phosphate metabolisms. This occurs through the pentose phosphate pathway, glycolysis and TCA cycle [49; 50].  
In many _Streptomyces_ spp., the co-localized gene clusters _pitH1-pstSCAB-ppk_ (polphosphate kinase) are linked in a supercluster formed by nine genes related to phosphate metabolism. The _ppk_ gene encodes an enzyme that catalyzes the reversible polymerization of the gamma phosphate of ATP into polyphosphate. It was shown to play a negative role in the control of antibiotic biosynthesis in _Streptomyces_. Nucleotides and sugar phosphates are used as phosphate sources by _Streptomyces_ spp. too. They are dephosphorylated by extracellular phosphatases and nucleotidases. Furthermore, strains of _Arthobacter_ and _Streptomyces_ were reported to be able to degrade simple phosphonates [48; 51; 52].
## 4 Simulations Results
### Sensitivity and Robustness Testing
#### 4.4.1 Models with Inaccuracy  
This subsection contains the results of the tests aimed at checking how sensitive the designed control system is to such parameter changes. If the model and plant parameters \(K\) and \(\theta\) differ, their influence on the PI and PID controller settings only applies to the proportional gain \(K_{p}\) (45). If their product \(K\cdot\theta\) decreases by no more than 20%, the overshoot will remain as less than 2% error bands, which result from (47). However, as this product increases, the system will become overdamped, and the settling time will also increase. The inaccuracy due to the determination of the time constant \(T\) affects all the PID controller settings proportionally, which with an ideal structure results in a change in the relationship between the integrating part on the one hand and the proportional and derivative part on the other. Moreover, the zeros of the controller will no longer compensate the poles of the closed system. The time series of the output variable for both cases are shown in Figure 5.
# Responses of Forest Carbon Cycle to Drought and Elevated CO2
## 3 Elevated CO2 Effect on Plants and Ecosystem
### How Plants Respond to Elevated CO2  
Enriched atmospheric CO2 concentration interferes with plant physiological processes by reducing plant stomatal conductance, increasing water use efficiency and simultaneously enhancing photosynthesis rate and light use efficiency [4]. However, the CO2 fertilization effect is usually provisional since plants often acclimate to long-term elevated atmospheric CO2 with lower photosynthetic capacity. Furthermore, CO2 fertilization effects on plants may also be limited by other nutrients such as nitrogen (N) and phosphorus (P) [44].
## 4 Data Processing and Analysis
### Heart Rate (HR)  
Heart rate is another bio-signal measurement used to evaluate physical and psychological activity changes. Compared to EDA activity, literature shows a less significant correlation between stress level and heart rate. These correlation constraints occur because changes in heart rate measurements are due to both sympathetic and parasympathetic nervous systems, which are part of the autonomous nervous system. However, unlike the sympathetic system, which activates the flight or fight responses under stress, the parasympathetic system depicts the human body's natural activity after exposure to stress. Although EDA is widely used as a physiological indicator to measure stress as the parasympathetic system less influences it, heart rate can also be a vital indicator of stress. Heart rate data for this study were collected using a Polar H10 heart rate sensor. Polar H10 is considered a reliable device by researchers as it is specifically designed to measure the cardiac output activity of the human body. Heart rate data were collected for each participant at a 1 Hz frequency using a chest strap device installed at the base of the sternum. Data were transferred to Polar Beat software version 3.2.0 in real time for further evaluation. Like EDA data, segmentation was conducted for heart rate data. Individual heart rate data were divided into stressor activity and eye-tracking activity for each experiment trial. About 210 individual data sets were provided for three experimental conditions, with two activities for each condition.
## 3 Results and Discussion
### Bioprecipitation in Liquid Batches  
A bioprecipitation experiment in liquid batches was conducted to attest calcite formation using the synthesised CA-Cu nanoflower with a calcium source. The bioprecipitation results showed the distinct difference in morphology between precipitates from chemical  
Figure 6: (**a**) Stability of CA and CA-Cu hybrid under different pH; (**b**) Stability of CA and CA-Cu hybrid under different temperatures.  
reactions without enzyme and enzyme-promoted bioprecipitation, as shown in Figure 7a-c. The chemical reaction between CaCl\({}_{2}\) and NaHCO\({}_{3}\) without enzymes showed a combination of spherical and cubic morphology of calcite at different equimolar concentrations. However, different morphologies were seen in both free CA and CA-Cu nanoflower bioprecipitation. Generally, the reaction proceeds slowly at low equimolar concentrations forming mainly amorphous and irregular unidentifiable crystals seen in either the CA or the CA-Cu nanoflower case. The difference is, however, that the CA-Cu nanoflower had nanoflower co-precipitate together with the calcite, as seen in Figure 7c. Calcite is the most stable form of calcium carbonate compared to vaterite and aragonite. The findings are consistent with the study by [PERSON] et al. on the calcium carbonate induced by carbonic anhydrase, which also revealed calcite formation after 24 h of bioprecipitation [54].  
The results from [PERSON]'s spectroscopy identified the precipitates as calcite (Figure 8) (see the matching signals of the three superimposed spectra in Figure 8, which include those of calcite mineral). The peaks corresponded to the lattice vibration of calcite and the in-plane bending vibration peak of the carbonate group of calcites at approximately 716 cm\({}^{-1}\) and were therefore identified as calcite. For Raman spectroscopy, the peak at 288 cm\({}^{-1}\) arises from the vibrations of the CO\({}_{3}\)\({}^{2-}\) groups that involve rotatory oscillations of those groups. The results thus indicate that both the free CA and the Cu-CA carrier enzymes can be used to biocenter soil due to calcite production, as previously reported [55]. This result suggests that Cu-CA hybrid carriers can be a good alternative to the free-enzyme catalysing calcite precipitation for soil biocementation.
## 2 Ultrasound Generation
Despite these advantages, it is important to note that ultrasonic probe systems also have limitations, such as potential sample heating with prolonged use and the need for careful handling to prevent probe damage. Choosing the right system depends on the specific application requirements and the desired outcomes. **Advances in ultrasonic probe technology**  
The probe system is constantly being developed and improved, as exemplified by the following trends. **New materials from which tips are made. ** Currently, tips made of silica glass appear on the market. These probes do not contaminate samples with metal particles, which makes them particularly useful during trace analysis in food and pharmaceutical analysis. Silica glass has a high melting point and is resistant to thermal shock. Silica glass has a relatively low coefficient of thermal expansion, which means that it undergoes minimal dimensional changes under the influence of temperature fluctuations. Silica glass is chemically inert and does not react with most substances. This property makes it valuable for use in corrosive or reactive environments. On the other hand, these types of probes have limited strength and therefore can be used at much lower ultrasonic intensities compared to metal probes [12]. **New probe shape. ** The latest probes are spiral-shaped, and, unlike traditional probes, the ultrasonic power is distributed across the entire surface of a spiral probe (lateral irradiation). They are used in slim reaction vessels [12]. **Multiples probes. ** Multi-element probes increase productivity and minimize repetitive tasks by processing numerous samples simultaneously. Units are available with 4, 8, 16 and 24 tips [13]. **Cup horns. ** Cup horns offer indirect sonication, which means the ultrasonic waves pass through the wall of the sample container. A cup horn can process multiple sealed tubes or vessels at one time without contacting an ultrasonic probe. This method eliminates cross-contamination, sample foaming, overheating and aerosolization, which can all occur when using a probe. Most importantly, a cup horn enables samples under 200\(\mu\)L to be effectively processed [13]. **Flow cells. ** They enable continuous processing of liquid. The process fluid enters through the inlet at the bottom of the unit. As it passes through the cavitation field, it is ultrasonically processed. The fluid exits the unit through an outlet port. The intensity and extent of processing are controlled by adjusting both the flow rate and amplitude setting [13].