esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discretization and Inversion Algorithm  
The data are collected at \(N_{k}\) wavenumbers, equally spaced at the interval \([k_{min},k_{max}]\). In the following numerical examples, \(\Sigma\) belongs to the straight line \(y=Y_{o}\), and the data are collected at \(N_{o}\) equally spaced observation points of the abscissa \(x_{op},p=1,\ldots,N_{o}\). Thus, (15) yields the following equation:  
\[\left[\begin{array}{c}E_{11}\\ \ldots\\ E_{1N_{k}}\\ \ldots\\ E_{N_{b}1}\\ \ldots\\ E_{N_{o}N_{k}}\end{array}\right]=\left[\begin{array}{ccc}G_{111}&\ldots&G_{ 11N_{b}}\\ \ldots&\ldots&\ldots\\ G_{N_{b}N_{k}1}&\ldots&G_{N_{b}N_{b}N_{b}}\end{array}\right]\times\left[ \begin{array}{c}\gamma(1)\\ \ldots\\ \gamma(N_{b})\end{array}\right] \tag{16}\]  
where  
* \(E_{lp}=\hat{E}(k_{l},x_{op}),l=1,\ldots,N_{k};p=1,\ldots,N_{o}\) are the data with respect to wavenumber and observation point,
* \(G_{lpn}=C_{n}(k_{l})H_{o}^{(2)}\Big{(}\sqrt{(x_{op}-x_{n})^{2}+(Y_{o}-y_{n})^ {2}}\Big{)}\).  
In conclusion, the problem has been reformulated as the inversion of the following matrix equation:  
\[\underline{E}=\mathbf{G}\underline{\gamma} \tag{17}\]  
where the meaning of the symbols is immediately understandable by comparison with (16). Once the geometry of the investigation domain and measurement configuration is fixed, matrix \(\mathbf{G}\) can be computed. Subsequently, its inversion is performed using truncated singular value decomposition (TSVD). To this end, it must be considered that the analysis of the behavior of the singular values allows us to determine if enough data have been collected to retrieve the unknown. That is, the so-called \"information content\" of the data can be analyzed a priori, and it depends on (a) the measurement configuration, the frequency bandwidth, and the measurement line's extent and position with respect to the investigation domain, and (b) the number and positions of the \(N_{b}\) \"candidate\" bars.  
Figure 2: Reference system used to compute the integral in (14).  
Once the data bandwidth and measurement line have been fixed, refinement of data sampling above certain values does not provide an improvement in the singular value curve, nor finally on the possibility of recovering the unknown [32; 33]. As a final step in the inversion procedure, a threshold is applied to the recovered function \(\gamma(n)\), where the indices corresponding to values above the threshold indicate the positions of the detected bars.
## 2 Materials and Methods
### Selection of Parameters to Be Used in the Data-Mining Hub
Heat-stressed dairy cows also increased their drinking behavior [35] while dairy cows affected with subclinical mastitistis reduced their water intake as evidenced by a reduction in drinking behavior [32]. These subtle modifications in the behavior of the dairy cows materialize and can be observed before the clinical signs of the disease and, therefore, can be helpful in the accurate prediction of the heat stress, subclinical mastitistis, or normal condition of the animals. _Milk performance indexes_ selected for the use in the data-mining hub include the milk yield and the composition, such as somatic cell count (SCC), milk solids-not-fat (SNF), milk fat (MF), milk protein (MP), milk urea nitrogen (MUN), and milk lactose. Heat stress tends to increase the SCC in milk, especially during hot months [36; 37] leading to the reduction of the quality of the milk produced by the dairy cows. Heat stress has also been reported to reduce the SNF [38], MF, MP, and milk lactose [36; 39; 40; 41] and tends to increase MUN [42] in dairy cows. Milk yield decreased two days after the initiation of heat stress [43] and is also reduced in dairy cows with subclinical mastitistis [44]. Milk composition of dairy cows with subclinical mastitistis such as SNF, MF, MP [45] is reduced, while the MUN has been observed to tend to decrease [46]. Most importantly, the SCC of milk with subclinical mastitistis expresses a sharp increase in count [24; 47]. Specifically, it has been reported that subclinically infected dairy cows with mastitistis have shown a somatic cell number of above 2.0 \(\times\) 10\({}^{5}\) per mL of milk [48]. This increase in somatic cell count has shown a negative relationship with milk lactose [48; 49; 50; 51] thus, subclinical mastitistis-infected dairy cows could reduce milk lactose. The changes in the milk composition are associated with the metabolic status and health of dairy cows, and therefore, the combined effect of these milk performance parameters could help predict the conditions of the animals more accurately. Feed intake_ is the most important index in measuring the health and wellness of dairy cows as the changes in intake could be measured as early as two weeks before calving or any clinical problems [52] and thus, this parameter could help in identifying cows that are at risk. Feed intake of cows that are heat-stressed is reduced as early as one day before the initiation of the heat stress [43] and is normally reduced in dairy cows with subclinical mastitistits [53]. The 21 parameters selected and used in developing the data-mining hub that will detect the conditions of the dairy cows should reflect the heat stress, subclinical mastitisti, or normal status of the cows more accurately than a single or few parameters alone.
# Crystal Structure of Human Lysozyme Complexed with \(N\)-Acetyl-\(\alpha\)-D-glucosamine
## 3 Results and Discussion
### NAG Binding
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Hydrophobic Interactions**} \\ \hline \begin{tabular}{c} **hLZM** \\ **Residue [Atom]** \\ \end{tabular} & **Distance (Å)** & \begin{tabular}{c} \(\alpha\)**-D-NAG** \\ **Atom** \\ \end{tabular} \\ \hline Val117 [CG1] & 3.60 & C8 \\ \hline Ala126 [CB] & 3.97 & C8 \\ \hline Trp127 [CD2] & 3.70 & C8 \\ \hline \multicolumn{3}{c}{**Hydrogen Bonds**} \\ \hline \begin{tabular}{c} **hLZM** \\ **Residue [Atom]** \\ \end{tabular} & **Distance (Å)** & \begin{tabular}{c} **Distance (Å)** \\ **H-A 1** \\ \end{tabular} & \begin{tabular}{c} **D-A 2** \\ \end{tabular} & \
\begin{tabular}{c} **Donor** \\ **Angle (°)** \\ \end{tabular} &
\begin{tabular}{c} \(\alpha\)**-D-NAG** \\ **Atom** \\ \end{tabular} \\ \hline Asn64 [ND2] & 2.06 & 2.92 & 145.86 & O6 \\ \hline Asn64 [OD1] & 2.17 & 3.16 & 177.97 & O6 \\ \hline Asn78 [ND2] & 2.31 & 3.27 & 164.54 & O6 \\ \hline Asn78 [N] & 2.90 & 3.57 & 127.25 & O7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interaction distances between hLZM and \(\alpha\)-D-NAG. Figure 2: **NAG binding to the active site of lysozyme. ** (**a**) Active-site cleft of hLZM and schematic representation of peptidoglycan. In peptidoglycan, \(N\)-acetylmuramic acid (NAM) is covalently linked to a peptide chain (2–5 amino acid), including a dibasic amino acid [typically meso-diaminopimelic acid (mDAP) or \(L\)-lysine]. (**b**) 2 mFo-DFc (1.2 \(\sigma\), grey mesh) and mFo-DFc (+3 \(\sigma\): green mesh, \(-3\)\(\sigma\): red mesh) electron-density maps of \(\alpha\)-D-NAG in hLZM-NAG. (**c**) Interaction between \(\alpha\)-D-NAG and hLZM. Hydrophobic interactions, hydrogen bonds, and water bridges are indicated by grey dotted-lines, blue lines, and light blue lines, respectively. (**d**) Superimposition of native hLZM (magenta) and hLZM-NAG (cyan). The \(N\)-acetyl group of \(\alpha\)-D-NAG (yellow) and acetate ion (orange) are indicated by the red dots. The electron-density map corresponding to the acetate ion of native hLZM was observed at subsite C (Supplementary Figure S4). The oxygen atom interacted with the amino group of Asn78 at a distance of 2.90 A (Supplementary Figure S5). The methyl group of acetate was stabilized by hydrophobic interactions with Val117 and Ala126 (Supplementary Figure S5), showing similarities with the binding mode of the \(N\)-acetyl group of \(\alpha\)-D-NAG in hLZM-NAG. Superimposition of the native hLZM and hLZM-NAG structures showed that the position of the acetate ion in native hLZM was similar to that of the \(N\)-acetyl group of \(\alpha\)-D-NAG in hLZM-NAG (Figure 2d).
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 4 Results and Discussion  
Data from clinical studies carried out on 19 patients were used for system modeling and validating the proposed approach. The clinical study data was divided into two subsets, the first to model the system (data for modeling) and the second to validate the proposed approach (data for validation). The proposed approach was implemented in MATLAB(r) for building the model, calculating the compensated uncertainty, and evaluating the reliability.  
Figure 7: Map showing the location of the first four patients from Table 1.
# Retention of Antioxidants from Dried Carrot Pomace in Wheat Bread
## 3 Results and Discussion
### The Influence of Carrot Pomace on Bread Properties
#### 3.3.2 Antioxidants in Bread
In conclusion, it should be stated that the baking process affects the loss of some phenolic compounds [57; 58]. According to the authors, losses of these compounds can reach up to 60%. These losses are influenced by a great number of factors, such as thermal, enzymatic and oxidative degradation, as well as the isomerization/epimerization and decarboxylation processes of phenolic acids mentioned earlier [58; 59]. Despite the fact that thermal processes such as baking contribute to the loss of polyphenols, the carrot pomace introduced in finely ground form proved to be a valuable source of bioactive compounds, providing a significant content of phenolic compounds in the final product. Another reason for the elevated health-promoting properties of bread with carrot pomace could be the formation of new antioxidant compounds, i.e., products of the Maillard reaction and transformation of polyphenic compounds at various stages of bread making. On the other hand, the increase in carotenoids was not as big as the addition of carrot pomace; it could be observed that only half of carotenoids added in the formulation was extracted from the bread. The loss could be ascribed mainly to the baking step. In earlier studies on carrot pomace, the amounts of total carotenoids varied largely from 5 to more than 100 mg/100 g d.m. [15]. Some of this variation is probably due to different juicing and drying methods, which are known to affect the level of carotenoids. The obtained value (56 mg/100 g d.m.) is in the middle of this range. Thus, the use of carrot pomace in breads has significantly increased the proportion of health-promoting substances (polyphenols and carotenoids) which exhibit, among other things, anti-inflammatory, anti-allergic, anticoagulant, antiviral and anti-cancer effects [60; 61; 62]. There is a lack of use of carrot pomace in baked goods in the literature, the exception being the following publication. Powdered carrot pomace dried in the microwave or on heat was included in the cookies (replacing 30% of wheat flour) to increase their phytochemical content by [PERSON] et al. [63]. Cookies with the addition of microwave-dried carrot pomace revealed the highest levels of \(\beta\)-carotene, epicatechin, gallic acid and ferulic acid compared to the control.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 5 Conclusions  
Three methods for integration of navigation signal and communication signal with the least influence on the communication performance are analyzed comprehensively. The pseudorange accuracy of method 1 is high, but it causes interference to the communication signal. Our analysis shows that the balance of navigation performance and communication performance is achieved when \(CNSR\) is 15 dB. Under this condition, the navigation signal is of high effective CN0 and the increment of \(CN0_{total}\) to maintain the BER performance is less than 1 dB. The accuracy of method 2 is far lower than the others. The Doppler accuracy of the three methods is identical, but the integration time length of method 2 is much higher than methods 1 and 3.  
Comparing method 1 with method 3, it can be got that method 1 is of higher pseudorange accuracy. However, the CN0 margin of method 3 can be up to 13.04 dB higher than method 1. The accuracy and communication capacity loss are analyzed for methods 2 and 3. The capacity loss of method 3 is 7.27 times of method 2, but method 3 holds the advantage of high pseudorange accuracy. The performance can be improved further with the usage of carrier phase for method 3 compared with method 2. To conclude, method 3 can reach the balance of accuracy and CN0 margin to realize the LEO-augmented GNSS system. Methods 1 and 3 can be combined if possible.  
Conceptualization, L.W. and X.T.; methodology, L.W. and Z.L.; validation, Z.L., K.Z. and F.W.; writing--original draft preparation, L.W.; writing--review and editing, X.T. and F.W.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Conclusions  
This research introduces a dynagraph card diagnosis approach using the Swin Transformer, demonstrating its superior performance compared to traditional convolutional neural network methods and ViT methods in dynagraph card condition diagnosis. The experimental results and analysis have led to the following conclusions:  
1. To address the classification problem of dynagraph cards in the rod pumping system, we have developed a neural network model based on attention mechanisms to achieve the effective identification and classification of Normal Operation, Fluid Pound, Gas Interference, Gas Locked Pump, Delayed Closing of Traveling Valve, Pump Barrel Split, and Fluid Pound and Delayed Closing of Traveling Valve. Compared to previous methods, this approach enables the more efficient and accurate automatic identification of dynagraph cards.
2. By utilizing the Swin Transformer model and transfer learning, we introduce a hierarchical window mechanism through the Swin Transformer, which captures local information and details in images more effectively through local window-level attention mechanisms, thus improving the accuracy of condition diagnosis. Transfer learning allows our model to benefit from the pre-trained Swin Transformer model parameters, improving training efficiency and saving time. However, the model to some extent relies on large-scale datasets to achieve better performance.
3. Our method demonstrates high accuracy in pumping unit condition diagnosis and holds significant research value for intelligent oilfield development.  
Although the Swin Transformer model has achieved remarkable results in dynagraph card diagnostics through transfer learning, its success relies on the support of manually labeled dynagraph card data. This process involves investments in both human resources and finances. In future investigations, we will persist in exploring alternative methodologies to further improve the accuracy of condition diagnosis. Moreover, our future endeavors involve integrating multimodal techniques with pumping system analysis, aiming to accomplish real-time condition diagnosis and the intelligent analysis of oilfield well sites.  
Conceptualization, G.D., W.L. and Z.D.; methodology, G.D., W.L. and C.W.; software, W.L., Z.D. and S.Q.; validation, S.Q. and T.Z.; formal analysis, X.M.; investigation, L.Z.; writing--review and editing, G.D., W.L. and Z.D.; visualization, Z.L.; supervision, K.L., G.D., W.L. and C.W.; revised and review, G.D., W.L. and Z.D. All authors have read and agreed to the published version of the manuscript.  
We would like to thank the Project \"Productivity Evaluation of Shale Gas Wells based on Machine Learning\" and the Project \"Influencing Factors of Development Effect in Tight Oil Reservoirs with Big Data Analytics, Changqing Oilfield\" for their support and valuable discussions.  
Not applicable.  
Restrictions apply to the datasets: The datasets presented in this article are not readily available because of confidentiality restrictions on work-related data.  
The authors declare no conflicts of interest.
# Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control +
###### Abstract  
The performance of global navigation satellite system (GNSS) receivers is significantly affected by interference signals. For this reason, several research groups have proposed methods to mitigate the effect of different kinds of jammers. One effective method for wide-band interference mitigation (IM) is the high-rate DFT-based data manipulator (HDDM) pulse blanker (PB). It provides good performance to pulsed and frequency sparse interference. However, it and many other methods have poor performance against wide-band noise signals, which are not frequency-sparse. This article proposes to include automatic gain control (AGC) in the HDDM structure to attenuate the signal instead of removing it: the HDDM-AGC. It overcomes the wide-band noise limitation for IM at the cost of limiting mitigation capability to other signals. Previous studies with this approach were limited to only measuring the carrier-to-noise density ratio (\(C/N_{0}\)) performance of tracking, but this article extends the analysis to include the impact of the HDDM-AGC algorithm on the position, velocity, and time (PVT) solution. It allows an end-to-end evaluation and impact assessment of mitigation to a GNSS receiver. This study compares two commercial receivers: one high-end and one low-cost, with and without HDDM IM against laboratory-generated interference signals. The results show that the HDDM-AGC provides a PVT availability and precision comparable to high-end commercial receivers with integrated mitigation for most interference types. For pulse interferences, its performance is superior. Further, it is shown that degradation is minimized against wide-band noise interferences. Regarding low-cost receivers, the PVT availability can be increased up to 40% by applying an external HDDM-AGC.  
Global navigation satellite system (GNSS); automatic gain control (AGC); interference mitigation (IM); high-rate DFT-based data manipulator (HDDM); sub-band processing +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines
### WSN in Wind Turbines
#### 4.3.6 Small and Micro Wind Turbine Market
It should be said that it operates twelve generators and delivers the permanent monitoring of wind and video signal from four cameras. From a scientific point of view, it is necessary to collect data quite frequently, and this is every 2 s. The information transmitted over the month or a year is very large. Two way communication is necessary to control the wind turbine. At present, our remote control systems are being implemented and the experience on power plant operation will be available in the near future.
## 2 Materials and Methods
### ATR-FTIR Spectroscopy  
FTIR spectra of honey samples were obtained without pretreatment using a Thermo Nicolet 6700 FTIR, (Thermo Electron Corporation, Madison, WI, USA) equipped with a deuterated triglycine sulfate (DTGS) detector. The spectra were in ATR mode with a Horizontal ATR accessory (Spectra-Tech Inc., Stamford, CT, USA) employed with a trapezoid shape ZnSe crystal (800 \(\times\) 10 \(\times\) 4 mm). The crystal provided an angle of incidence of 45\({}^{\circ}\). The speed of the interferometer moving mirror was 0.6329 mm/s. Spectra were recorded with a resolution of 4 cm\({}^{-1}\) and 100 scans. A background spectrum was collected using only ATR crystal, prior to spectrum recording of each sample. Triple FTIR spectra of each sample were obtained, using a different sub-sample each time.  
Spectrum processing was performed using the software (OMNIC ver.9.1, Thermo Fisher Scientific Inc., Waltham, MA, USA). ATR-FTIR spectra were recorded in triplicates, smoothed using Savitsky-Golay algorithm, and their baselines were corrected. These pre-treatments were performed with \"automatic smoothing\" (5-point moving second-degree polynomial) and \"baseline correction\" (second-degree polynomial, twenty iterations) functions. Finally, using the \"Statistical Spectra\" function, the average of the three spectra for each sample was calculated, and each average spectrum was normalized (absorbance maximum value of 1) and saved as a CSV file for further use in the statistical analysis.
## 5 Performance Evaluation
### Experiment 5  
In this experiment, the traffic load of flow 1 is twice the load of flow 2. We vary the rate of flow 2 from 270 kbps to 410 kbps with step of 10 kbps to test the jitter performance of flow 1, network throughput, average end-to-end delay and packet loss ratio of the two mechanisms. The results are shown in Figure 11.  
Figure 11: _Cont._  
Figure 10: (**a**) Average packet jitter _versus_ offered load; (**b**) Network throughput _versus_ offered load; (**c**) Average end-to-end delay _versus_ offered load; (**d**) Packet loss ratio _versus_ offered load.  
From Figure 11, it is observed that BLJCAR performs better than the ONC policy in jitter, network throughput, average end-to-end delay and packet loss ratio by considering the queue state of coding node in packet coding algorithm and the effective queue management mechanism. The effective queue management mechanism of BLJCAR can result in improved jitter benefits, alleviates congestion, reduces the queuing delay of packets and allows more data transmissions in each time unit.  
Figure 11 shows that the capability of BLJCAR to cope with the longer communication paths is also fine for the multiple rate case.
## 2 Light Propagation in Biological Tissue and Time Domain Diffuse Optical Spectroscopy  
In TD DOS, also termed time-resolved spectroscopy (TRS), tissue is irradiated by ultrashort (picosecond order) laser pulses, and the intensity of the emerging light at the tissue surface is recorded over time to show a temporal point spread function (TPSF) with picosecond resolution. The mean total length of the light path is determined by multiplying the light speed in the media by the mean transit time of the scattered photons, which is calculated with the TPSF [5]. The TPSF reflects the propagation of light in biological tissue, which is characterized by the optical properties of absorption, scattering, scattering anisotropy, and refractive indexes. It is widely accepted that the radiative transfer equation (RTE) correctly describes the light propagation in biological tissue [11; 12]. Since, however, the computational cost is extremely high in numerically solving the RTE, the photon diffusion equation (PDE), a diffusion approximation to the RTE, is often used. Based on the PDE, it is possible to estimate the absorption (\(\mu_{a}\)) and reduced scattering (\(\mu_{s}\)) coefficients with the TPSF, and to calculate concentrations of biological chromophores, including Hb. The TPSF also carries information about depth-dependent attenuation based on the correlation of the detection time with the penetration depth of photons. Accurate numerical modelling of light propagation is critical for the quantification of TD measurements and the image reconstruction of diffuse optical tomography (DOT), as will be described below.