esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Physiological Processes Mediating Cadmium Accumulation in Plants
### Cd Uptake by Roots  
Plants take up Cd from the external environment mainly through their roots; the rate and amount of Cd uptake by roots are dependent on the Cd bioavailability or concentration in the soil [59] and controlled by plant genetic factors. The uptake of Cd by plant roots consists of two phases: apoplastic binding and symplastic uptake [60]. In the first phase, the positively charged Cd\({}^{2+}\) initiates an electrostatic interaction with the negatively charged carboxyl groups (-COOH) of polygalacturonic and hydroxycinnamic acids on cell walls, resulting in Cd accumulation in the root apoplast [61]. This phase is rapid and spontaneous, thereby indicating that it has no energy requirements (a passive system). In the second phase, Cd is symplastically taken up in an ATP-driven active process, which requires a great deal of energy and is highly dependent on the metabolic activity of plants [62]. The transport of Cd across the root cell plasma membrane is the initial step for its symplastic uptake. It is a concentration-dependent process, reflected by the saturation kinetics in the relationship between the uptake velocity and concentration of Cd in the external environment, thereby indicating that Cd is taken up via a carrier (transporter)-mediated system [63]. For instance, many transporters from the natural resistance-associated macrophage protein (NRAMPs, such as OsNRAMP1, OsNRAMP5, and AtNRAMP6) and zinc/iron-regulated transporter-like protein (ZIPs, such as AlRT1) families are responsible for Cd transport in the root cells of Arabidopsis plants and rice [64; 65; 66]. In addition, Cd may also enter root cells through the transport pathway for Ca [67; 68; 69], because of their similarities in charge (c.a. +2) and ionic radius (Ca\({}^{2+}\), 0.97 A; Cd\({}^{2+}\), 0.99 A). Adding La\({}^{3+}\) and Gd\({}^{3+}\) (the potent Ca channel inhibitors) or increasing the Ca concentration in the culture solution suppresses the metabolically dependent Cd uptake substantially in the Zn hyperaccumulator _Thlaspi caerullecens_[60], the halophyte _Suaeda salsa_[68], and rice [67]. Recently, one member of the major facilitator superfamily (MFS), OsCd1, was found to be associated with root Cd uptake in rice [70]. However, although the involvement of a great number of membrane proteins in Cd uptake has been demonstrated, limited knowledge exists regarding the transport of Cd across plant root plasma membranes at the molecular level.
## 3 Results
### Cell-Free DNA (cfDNA) Quantity and Library Yields in Clinical Specimens
#### 3.2.1 Read Coverage through Panel Amplicons  
The overall panel performance was assessed by average amplicon coverage across all the 92 samples studied. All 35 amplicons were covered on average to a minimum of 25,352 reads and a maximum of 85,866 reads.  
Figure 3 reported the performance of each single DNA region observed in our cohort of plasma samples, distributed across the 11 genes of the OlccfA. A high rate of variability in amplicon coverage was observed and it seems not to be related to cfDNA amounts used for libraries preparation (data not shown) but rather to the intrinsic technical characteristics of the panel. Indeed, the assay is based on a single tube multiplex PCR and primer pairs work under the same thermal conditions, and some differences in performance along the amplicons are expected.  
The variability was also identified at an intragenic level, as reported in Figure 3. For example, the _EGFR_ gene, represented by 5 amplicons covering exons 18-21 (orange histograms), contained both the best and the worst performing amplicons of the panel (i.e., 85,866 and 25,352 reads, respectively).  
Figure 2: Correlation between library yields and cfDNA input. Each dot in the scatterplot represents the single library prepared (n = 92) and the relative concentrations are expressed in pMol.  
Notably, the first corresponds to a target region covering hotspot positions in exon 21, instead the second one partly covers exon 20 of the _EGFR_ gene.
## 4 Conclusions  
In the present investigation, original fracture tests were carried out on U-notched rectangular specimens made of PMMA and GPPS polymers under compressive loading. As the main outcomes of the experimental campaign, the failure evidence and the load-carrying capacity (LCC) of the different notched specimens were obtained and analyzed. Experimental calibration methods based on the theory of critical distances (TCD) were implemented to obtain the critical stress and the critical distances of the MTS and the MS criteria. The compressive critical stress of the two analyzed polymers were also estimated through the equivalent material concept (EMC), which was extended for the first time from tensile loading to compressive loading. The results indicated that the EMC was quite successful in estimating the critical stress derived from the experimental calibration. It was also revealed that the MTS, MS, EMC-MTS, and EMC-MS criteria were all efficient in predicting the experimentally achieved fracture loads of the U-notched PMMA and GPPS samples under negative mode I loading conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Fracture Criterion** & **Notch Radius, mm** & **Fracture Load (pred.), N & **Fracture Load (exp.), N & **Discrepancy, \%** & **Average Discrepancy, \%** \\ \hline \multirow{8}{*}{PMMA} & & 0.5 & 6726 & 6678 & +0.7 & \multirow{8}{*}{+1.0} \\  & EMC–MTS & 1.0 & 7369 & 7298 & +0.9 & \\  & 2.0 & 8794 & 8681 & +1.3 & \\  & 4.0 & 9403 & 9266 & +1.4 & \\  & 0.5 & 6888 & 6678 & +3.1 & \\  & EMC–MS & 1.0 & 7616 & 7298 & +4.3 & \\  & 2.0 & 9135 & 8681 & +5.2 & \\  & 4.0 & 9835 & 9266 & +6.1 & \\ \hline \multirow{8}{*}{GPPS} & & 0.5 & 4123 & 3996 & +3.2 & \multirow{8}{*}{+4.6} \\  & EMC–MTS & 1.0 & 4530 & 4365 & +3.8 & \\ \cline{1-1}  & 2.0 & 5404 & 5173 & +4.4 & \\ \cline{1-1}  & 4.0 & 6558 & 6125 & +7.0 & \\ \cline{1-1}  & 0.5 & 4131 & 3996 & +3.4 & \\ \cline{1-1}  & 1.0 & 4572 & 4365 & +4.7 & \\ \cline{1-1}  & 2.0 & 5483 & 5173 & +6.0 & \\ \cline{1-1}  & 4.0 & 6591 & 6125 & +7.6 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between experimental (exp.) fracture loads and fracture prediction (pred.) derived from MTS and MS criteria (experimental calibration of critical parameters).  
**Author Contributions:** Conceptualization, A.R.T.; methodology, A.R.T.; software, K.H.; validation, A.R.T., K.H., A.S.R., and S.C.; investigation, A.R.T., K.H.; writing--original draft preparation, A.R.T., S.C; writing--review and editing, A.R.T., K.H., A.S.R., and S.C.; project administration, A.R.T.; 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.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the main text of this document.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
## 4 Pre-Evaluation and Health Monitoring System of FAST
### Health Monitoring System  
In the daily observation process of FAST, through analysis of real-time collected data, the cable force, stress, and other parameters of each component in the structure can be accurately obtained. The safe operating status of the cable network structure can be monitored in real time, and early warning is also provided when a dangerous situation occurs, which makes the real-time health monitoring of FAST possible.  
Because each round of the finite element analysis takes approximately 7-8 s (the time will be lower with the update of the hardware), the obtained stress of the cable-net structure reflects the state of the construction 8 s ago. The piston of the FAST actuator has a maximum operating speed of about 1.6 mm/s; therefore, in a calculation cycle, the maximum actuator stroke is \(\pm\)12.8 mm [21]. According to our calculation, the maximum variation in cable force resulting from this stroke is around \(\pm\)16 MPa, far from the tensile strength (1860 MPa), which means that the stress level of the FAST cable net will not change significantly during this period of time. Therefore, real-time finite element analysis can accurately reflect the internal force distribution of the structure, and it is entirely feasible to establish an effective health monitoring system.  
Currently, the arrangement work of the PHM system has been completed on the FAST field to serve this significant project. The system has played a significant role in ensuring the stable observation of FAST. The schematic diagram of the PHM system is described in Figure 8.
# Flow Duration Curves from Surface Reflectance in the Near Infrared Band
## 3 Results
### Comparison in Terms of River Discharge: Validation Phase  
In the validation phase, the same polynomial laws found in the calibration are applied to the reflectance ratio observed in the period 2016-2019. Validation errors are summarized in Table 3, while the discharge time series are shown in Figure 7. The performance indices are globally deteriorated at all sites, with some exceptions. The two upstream stations of St. Cloud and St. Paul also maintain bad performances in the validation period. The same considerations apply for Keokuk, Grafton, and Chester. Unexpectedly, at St. Louis, the simulated discharges that well match the calibration period deviate from the observed discharges in the validation period, especially in 2016 and in 2019, with consequences in the performances: _NSE_ becomes almost null and _rRMSE_ increases up to 52%. For Winona and Clinton, even if in the validation period the performances are worsened, the simulated discharges maintain acceptable results with _NSE_ around 0.29 and _rRMSE_ of about 47 and 41%. Good performances are obtained for Prescott, Thebes, Memphis, and Vicksburg, with _NSE_ in the range 0.42-0.76 and _rRMSE_ between 27% and 40%.
# Thai Rice Vinegars: Production and Biological Properties
## Materials and Methods
### Biological Properties of Rice Vinegar Determination
#### 2.8.4 Antioxidant Activity Determination  
ABTS and DPPH methods were used to determine the antioxidant activity of Thai rice vinegars. The reaction times at 50% ABTS and DPPH remaining in each type of rice vinegar were determined every 10 s for 300 s (5 min). The commercial fermented rice vinegar (5%) and commercial distilled vinegar (4.2%) were purchased from a local market in Chiang Mai province, Thailand, to compare the antioxidant activity with four types of rice vinegar sample.
* DPPH method The assay mixture contained 1 mL of 0.2 mM DPPH radical solution in 95% ethanol and 0.5 mL of the diluted rice vinegar sample. The absorbance was measured at 515 nm every 10 s for 5 min and reported as the remaining percentage of DPPH radical scavenging according to the following formula. \[\%\text{remaining}= \left(\frac{\text{A}_{\text{515}}\text{ at that time}}{\text{A}_{\text{515}}\text{ at 0 s}}\right)\times 100\] (1)
* ABTS method The assay mixture contained 1.96 mL of ABTS and 0.04 mL of the rice vinegar sample. The absorbance was measured at 734 nm every 10 s for 5 min and reported as the remaining percentage of ABTS radical scavenging according to the following formula. \[\%\text{remaining}= \left(\frac{\text{A}_{\text{734}}\text{ at that time}}{\text{A}_{\text{734}}\text{ at 0 s}}\right)\times 100\] (2)
# An Assessment of Environmental RF Noise Due to IoT Deployment
## 5 Data Collection Campaign  
The data collection campaign was conducted outdoors at five candidate sites in the Kwazulu-Natal province, South Africa, as shown in Table 2. Figure 5 illustrates the measurement campaign's standard operating procedure (SOP). The USRP hardware involved arranging power, ethernet cables, and the front-end components for the frequency under test as part of the hardware preparation. Inserting the external batteries was the only task required to power up the boards. By default, the ING units started in radio silence mode, Tx0. A dedicated button press incremented the transmit rate from Tx0 to Tx10. The operation required all fifteen ING units to emit noise under the same transmit rate. The recording lasted 5 min. Figure 6 shows the outdoor setup of equipment at the candidate sites as measurements were underway. A Google(r) Earth map, in Figure 7, illustrates the locations of interest. Easy accessibility to the venues was one of the considerations dictating the selection of these locations. As an outdoor campaign, prudence cautioned us to avoid areas where the experiment would have attracted unpleasant curiosity. As an RF operation, each ING unit emits radio signals randomly. It was necessary to provide an uninterruptible power supply (UPS) as a power source for the whole system. The GRC manipulation was to set the frequency, edit the filename, start/stop the recording, and save the raw data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Site** & **Description** & **Date** \\ \hline Berea Steve Biko Campus (DUT) & Urban & 6 July 2022–8 July 2022 \\ Glenmore (GLM) & Suburban & 10 July 2022 \\ Montclair (MNC) & Suburban & 16 July 2022 \\ Morningside (MNS) & Urban & 19 August 2022 \\ New Germany (NGM) & Industrial & 30 June 2022–4 July 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Candidate sites for IoT noise surveying campaign.  
Figure 5: Measurement campaign SOP.  
For this research, the routine of data collection produced the following:  
* Eleven transmit rates (Tx0-Tx10);
* Thirty-three raw datasets (three frequency bands per site);
* A total of 165 raw datasets (5 candidate sites);
* One raw dataset weighted 30 GB in 5 min recording;
* Required storage: 4.95 TB;  
Figure 6: Equipment setup during the surveying campaign.  
Figure 7: Google Earth map of candidate sites.  
* Raw data type: linear numerical;
* Estimated minimum operation time: 3 h per site.
## 4 Codeword-Independent Localization
### Location Estimator  
The fingerprint-graph transformer and the feature extractor cooperate to generate fingerprint representations. To further obtain the location estimation, we need to apply the location estimator to read out the fingerprint representations. In our system, we simply adopt a three-layer perceptron as the location estimator activated by the leaky ReLU [86].
## 4 Illustrative Examples
### MOMI PID with Reference Filters  
Besides using the reference weighting factor \(b\), the 2-DOF controller can also be realised via a PID with an additional reference filter (see Section 3).  
Here, the proposed PID\({}_{\text{b}}\) controller will be compared to the proposed PID\({}_{\text{I2}}\) and PID\({}_{\text{fh}}\) controllers. For this purpose, the same process models used in Section 4.1 (45) are used here.  
The chosen controller filter time constant was the same as before (\(T_{F}\) = 0.01 s). The characteristic areas \(A_{0}\) to \(A_{4}\) are given in Table 2. The PID\({}_{\text{b}}\) controller parameters calculated using various values of \(b\) are given in Table 3. Note that, for the PID\({}_{\text{I2}}\) and PID\({}_{\text{fh}}\) controllers, the proportional, integral, and derivative gains for PID\({}_{\text{b}}\) with \(b\) = 0 were used.  
The reference filter parameters for the PID\({}_{\text{I2}}\) and PID\({}_{\text{fh}}\) controllers were calculated from Equation (31) (PID\({}_{\text{I2}}\) controller) and Equation (43) (PID\({}_{\text{fh}}\) controller) using the calculated characteristic areas from Table 2 and the calculated PID\({}_{\text{b}}\) controller parameters (\(b\) = 0) from Table 3. The values of the PD controller parameters, the estimated second-order process model parameters, and the chosen speed-up coefficient of the closed-loop time constant are shown in Table 8. The calculated reference filter parameters for the PID\({}_{\text{I2}}\) and PID\({}_{\text{fh}}\) controllers are given in Tables 9 and 10, respectively.  
Figure 16: The comparison of process \(G_{P1}\) and the controller output responses using the PID\({}_{b}\), PID\({}_{\Omega 2}\), and PID\({}_{\Omega}\) controllers.  
Figure 17: The comparison of process \(G_{P2}\) and the controller output responses using the PID\({}_{b}\), PID\({}_{\Omega 2}\), and PID\({}_{\Omega}\) controllers.  
Figure 19: The comparison of process \(G_{P4}\) and the controller output responses using the PID\({}_{b}\), PID\({}_{\text{f2}}\), and PID\({}_{\text{fh}}\) controllers.  
Figure 18: The comparison of process \(G_{P3}\) and the controller output responses using the PID\({}_{b}\), PID\({}_{\text{f2}}\), and PID\({}_{\text{fh}}\) controllers.
# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 5 Conclusions  
In this paper, we analyzed the problems of FPNs and DETR-based detectors and found the problems of missing textural information in feature maps, severe confounding effects caused by cross-scale fusion in FPNs, problems of DETR detectors with Shifted Window-based Self-Attention due to the interaction between windows, and the problem of the weakened perception of larger objects by the model due to the limited interaction between windows. Based on these findings, we proposed a new target detection model, TIG-DETR, to further improve the performance of the model. By integrating three simple and effective components, namely enhancing textural information with a bottom-up architecture, Feature-wise Attention, and Instance-Based Advanced Guidance Module, TIG-DETR can significantly improve the baseline approach.  
Conceptualization, K.W.; methodology, Z.L. and K.W.; software, Z.L.; validation, Y.W.; formal analysis, C.L.; data curation, K.W.; writing--original draft preparation, Z.L. and K.W.; writing--review and editing, Z.L. and K.W.; visualization, Z.L. and K.W.; supervision, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fund of Jilin Provincial Science and Technology Department: Research and development of junior high school physical intelligence experimental platform in mobile environment (20200401087 GX).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **AP [val]** & **AP** & **AP\({}_{\text{50}}\)** & **Person** & **Rider** & **Car** & **Truck** & **Bus** & **Train** & **Motorcycle** & **Bicycle** \\ \hline Mask R-CNN [fine-only] & 31.5 & 26.2 & 49.9 & 30.5 & 23.7 & 46.9 & 22.8 & 32.2 & 18.6 & 19.1 & 16.0 \\ Mask R-CNN [COCO] & 36.4 & 32.0 & 58.1 & 34.8 & 27.0 & 49.1 & 30.1 & 40.9 & 30.9 & 24.1 & 18.7 \\ \hline TE-FPN [fine-only] & 34.2 & 29.5 & 54.8 & 34.0 & 27.8 & 52.7 & 25.6 & 35.2 & 23.0 & 21.1 & 19.1 \\ TE-FPN [COCO] & **39.6** & **34.9** & **61.2** & **39.1** & **31.1** & **54.3** & **31.5** & **43.9** & **31.1** & **26.2** & **22.4** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effectiveness of TE-FPN on Cityscapes.
## 4 Experiment and Results
### Parameter Setting  
When utilizing the semantic similarity method to extract sentiment words, the base positive/negative words should be carefully selected to calculate the polarity of a word. Therefore, we extract the most frequent words in the lexicon from the experimental data. Recall that in Section 3.1, we mentioned that the number of base words and a threshold T should be determined. This is a trial and error process. We selected 10, 20, and 30 pairs of positive/negative words and set the threshold values as 0.3, 0.4, and 0.5. Thus, we obtained nine sets of classification results. Table 3 shows the accuracy of classification without considering the emoticons, from which we can clearly see the number of words should be set to 30 and the value T should be set to 0.4.  
In addition, \(\propto\) refers to the weight of emoticon, \(\beta\) refers to the weight of text, and \(\propto+\beta=1\) (see Section 3.1). In order to extract the feature of Chinese microblogs, we conducted several experiments on our training datasets to get the optimal results, i.e., \(\propto=0.7\) and \(\beta=0.3\).
# Wavelength Selection for Satellite Quantum Key Distribution
## 2 Transmittance and Spectral Radiance
### Key Assumptions and Limitations of MODTRAN Simulations
#### 2.1.1 Scenario 1: Tucson, Arizona  
At noon on the summer solstice (21 June), the desert climate of Tucson is replicated using a 70 km visibility to represent clear skies while wind speeds of 3.33 m/s, desert aerosols and desert albedos imitate the atmospheric conditions. The transmittance (blue) and spectral radiance (black) for a ground receiver pointed at the zenith is plotted across the electromagnetic spectrum and shown in Figure 1.
## 5 Simulation Results
### Energy Consumption  
In relation to energy consumption, the computation takes into account the sum of both the average energy used in transmission and the average energy used in reception for all nodes when all the paths have been created, i.e., the instant when all the nodes belong to a route that ends in the gateway node.  
Figure 12a,b contain the average energy consumption in the network. The value is a little worse for FAHP than for SPRINT-Fuzzy in this section, but it is justified by longer routes in FAHP, which take more energy for packets to reach the gateway.  
Figure 11: Average delay in reaching the gateway node. Number of nodes: (**a**) 100, (**b**) 400.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No. Nodes**} & \multirow{2}{*}{**Tx Range (km)**} & \multicolumn{2}{c}{**E2E Delay (ms)**} \\  & & **Sprint** & **FAHP (\%)** \\ \hline
100 & 4 & 3.98 & 4.96 (+24.6\%) \\
100 & 5 & 3.64 & 4.35 (+19.5\%) \\
100 & 6 & 3.8 & 3.89 (+2.4\%) \\
100 & 7 & 4.04 & 3.84 (\(-\)5\%) \\ \hline
400 & 2 & 4.76 & 5.64 (+18.5\%) \\
400 & 3 & 4.46 & 5.43 (+21.7\%) \\
400 & 4 & 4.39 & 5.38 (+22.6\%) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Statistical comparison for E2E packet delay.  
Figure 12: Average energy consumption. Number of nodes: (**a**) 100, (**b**) 400.  
Table 13 shows the values obtained in the bar graph of Figure 12.