esa-sceva/satcom-chunk-collection huggingface dataset:
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 4 Test and Analysis  
### Time Complexity of the NSS Model  
As our aim was to reduce the computational complexity and guarantee similar positioning accuracy with original solution in GPS/BDS/GLONASS kinematic PPP, the performance of the NSS model and other satellite selection algorithms should be compared. The time complexity is an important indicator of measuring the complexity and the amount of computation in an algorithm. Although we can evaluate the time complexity of algorithms with the running time of the source program, it is not appropriate because of the huge workload and the programming effect. In this study, we used asymptotical algorithm analysis to calculate the time complexity of different satellite selection algorithms. Additionally, considering the final positioning accuracy of kinematic PPP after the application of the satellite selection algorithm, we chose the optimal DOP algorithm for comparison. The positioning accuracy will be compared in the next part. The number of basic operations in these two algorithms is both assumed to be same, which is represented as \(n\). The number of cycles is the deciding factor in the size of time complexity. As mentioned earlier, it is obvious that the number of cycles in the optimal DOP algorithm, maximum volume algorithm, fast-rotating partition satellite selection algorithm, and elevation partition satellite selection algorithm is \(C_{N}^{n_{1}}\), \(C_{N}^{4}\), \(360/N\), and \(90/N\), respectively. Thus, the time complexity of the optimal DOP algorithm and the NSS model can be described clearly in Equation (12).  
\[\begin{array}{l}T(n)_{Optimal\ DOP}=O(n^{C_{N}^{n}})\\ T(n)_{NSS}=O(n^{C_{G}^{4}}+\frac{360}{N}+\frac{90}{N})=O(n^{C_{G}^{4}})\end{array}, \tag{12}\]  
where \(O(n)\) is asymptotic time complexity; \(N\) denotes the total visible satellites and \(G\) denotes the number of GPS satellites.  
With the result in Equation (12), we can know that the NSS satellite selection model has a similar performance in time complexity when maximum volume algorithm is compared. Additionally, if the same number of satellites are selected, the NSS satellite selection model has a better time complexity than that of the optimal DOP algorithm.
## 3 The Cross Time Slot Partial Interference Alignment
### Nullifying Inter-Cell and Intra-Cell Interference  
To ensure the desired signal can be received without interference, we need to design the receive beamforming matrix at each user. According to (7), although the desired signals to the same CCU in different sub-time slots are aligned by relay, the aligned signal to UE[1,1] still overlaps with the interference caused by Relay 1. Furthermore, the interference in sub-time slot 1 should also be considered when designing the receive beamforming matrix.
## 3 Metabolic Responses to Different Antibiotic Classes
### \(\beta\)-Lactams and Peptidoglycan Metabolism  
\(\beta\)-lactam antibiotics act by inhibiting penicillin-binding proteins (PBPs), thereby blocking peptidoglycan synthesis and inducing osmotic lysis [35; 36; 37].  
However, the activation of compensatory metabolic pathways may mitigate their efficacy.  
In _Escherichia coli_, sublethal exposure to \(\beta\)-lactams is associated with increased expression of genes involved in the synthesis of peptidoglycan precursors, including UDP-N-acetylmuramyl. Concurrently, some evidence suggests a reorganization of carbohydrate metabolism, potentially aimed at supporting cell wall reconstruction under antibiotic stress [6; 38; 39].  
The co-activation of the glyoxylate cycle suggests a carbon-conserving strategy that facilitates the regeneration of damaged cell walls [40; 41].  
In _Staphylococcus aureus_ strains, some evidence indicates that downregulation of the tricarboxylic acid (TCA) cycle, accompanied by increased fermentative metabolic activity, may contribute to reduced production of reactive oxygen species (ROS) and a deceleration of cellular growth rate.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Feature** & **Metabolic Rewiring** & **Genetic Resistance** & **References** \\ \hline Nature & Phenotypic and reversible & Genotypic and heritable & [11; 15] \\ \hline Genetic Modification & Absent & Present (mutations, gene acquisition) & [15; 16; 17] \\ \hline Duration & Transient & Persistent & [11; 15] \\ \hline MIC Change & Usually unchanged & Increased MIC & [18; 19] \\ \hline Mechanisms & Redox modulation, metabolic rerouting & Enzymatic inactivation, efflux, target alteration & [5; 6; 7; 16; 23] \\ \hline Evolutionary Role & Potential precursor to resistance & End-point of selection & [24; 31; 32] \\ \hline \end{tabular}  
MIC: minimum inhibitory concentration.  
\end{table}
Table 2: Distinguishing features of metabolic rewiring versus genetic resistance.  
These metabolic adaptations have been linked to increased tolerance to \(\beta\)-lactam antibiotics, likely due to the reduced activation of cell death mechanisms dependent on oxidative stress and active replication [42; 43].
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## 5 Results
Weak interference is depicted with a brown line, showing a negligible impact. By contrast, stronger interference (blue line) not only shifts the median location deviation across the onset of disturbance. Figure 11: Median (solid line) and standard deviation (shaded area) of 50 repeated measurements from the GP01 GNSS receiver under the different jamming conditions. The red dashed line indicates the onset of disturbance. Figure 10: Deviation of position vector amplitude from a reference point, illustrating the interference response of: **(a)** U-box MAX-M10 to \(-\)50 dBm three-frequency interference, and **(b)** Ai-Thinker GP-01 to \(-\)50 dBm CW interference. 50 repetitions but also introduces significant dispersion in the time series results of those repetitions. This highlights that a single measurement is insufficient for drawing conclusions about receiver response under jamming. As evidenced by the challenges in interpreting the cluttered charts of individual repetitions (like Figure 9b, indicating significant location determination problems reported by the receiver's software under high interference), a thorough analysis using repeated measurements and appropriate visualization is crucial for truly understanding GNSS receiver behavior under jamming.
# Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
## 2 System Architecture
### Visually Steered RIS Control Mechanism
Finally, the system loads the RIS reflection codebook (Patterns.txt), which stores the RIS reflection patterns in different directions. The system will select the appropriate beam direction according to the detected user position. 2. Object Detection and User Localization: The camera starts capturing environmental images, and the YOLOv7 model processes the input RGB image to output object detection results, including bounding box coordinates \((x_{1},y_{1}),(x_{2},y_{2})\), where \((x_{1},y_{1})\) represents the top-left corner of the detected object, and \((x_{2},y_{2})\) represents the bottom-right corner (as shown in Figure 4). YOLOv7 also classifies detected targets and provides a confidence score to ensure detection reliability. For RIS beam control, the system divides the image level into 8 grid cells (cells) and calculates the cell width:cell_width = frame_width/8.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 4 Secret Key Generation Algorithm  
In our proposed secure OTFS transmission scheme, the key generation algorithm plays the core role. In this section, we propose the key generation algorithm, named GOST, as well as provide an analysis of its characteristics.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 3 System Composition
### Sound and Light Alarm Module  
When the tunnel accumulative deformation and instantaneous deformation velocity exceeds the set threshold, the module is turned on and provides an early warning to the command center through a sound and light alarm; this gives a timely and effective early warning against tunnel collapse accidents, thereby reducing casualties and property losses.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 3 Results
### ChlorophyllI Correlation Analysis at Different Growth Stages  
According to the correlation between the vegetation index and chlorophyll content at the flag-picking stage, the two-dimensional matrix was analyzed to determine the optimal combination band. The optimal combination bands of NDVI, DVI, RVI, RDVI, and GCI are, respectively, \(R_{679}R_{683}=-0.581\), \(R_{984}R_{982}=-0.469\), \(R_{637}R_{431}=-0.368\), \(R_{681}R_{682}=-0.528\), and \(R_{683}R_{679}=-0.582\); It can be seen that the correlation coefficients of NDVI, RNDVI, and GCI are all greater than 0.5. Therefore, we chose the combination of NDVI and GCI with a high correlation coefficient with chlorophyll content during the flag-picking period, which can be used to establish the model in the later stage (Figure 4).  
At the flowering stage, the correlation coefficients of NDVI and RDVI can reach more than 0.6, while the correlation of RVI is lower than 0.5, which is not suitable for modeling. NDVI and RDVI were selected as input variables for modeling the chlorophyll content in wheat during the flowering stage.  
At the grain-filling stage, the correlation coefficients of NDVI, RVI, and GCI all reached more than 0.6, so we selected these three vegetation indices of NDVI, RVI, and GCI as the optimal vegetation indices of wheat chlorophyll content at the grain-filling stage, which were used to establish the model.  
At maturity, the correlation coefficients of NDVI (989, 996), DVI (403, 401), RVI (576, 709), RDVI (493, 401), and GCI (989, 996) were \(-0.588\), \(-0.532\), \(-0.344\), \(-0.550\), and \(-0.588\), respectively. Through the analysis, it can be seen that the correlation coefficients of NDVI, DVI, RDVI, and GCI all reached 0.5, so the two vegetation indices of NDVI and GCI were selected for the construction of the later model at the maturity stage. According to the correlation analysis, the vegetation index with a high correlation was selected to construct the model. Table 3 shows the selection of independent variables.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results
### Antenna Simulation in Anatomical Models
#### 2.3.1 Reflection Coefficient  
Male Phantom  
After inserting the antenna into the FDTD mesh, S11 was recalculated to verify antenna tuning to the selected frequencies (402.5 MHz, 2.45 GHz). For the male phantom, as we can see in Figure 22, the implanted antenna's reflection coefficient was \(-11.9\) dB at 402.5 MHz and \(-11.6\) dB at 2.45 GHz. This value was below the \(-10\) dB limit. As can be seen, the resonance has moved slightly. Still, because of the impedance bandwidth, 402.5 MHz and 2.45 GHz were satisfactorily covered. It should be noted though that not only changes in the environment but also different modeling techniques can affect the antenna's simulated performance.
## 6 Conclusions  
Cloud contamination in optical imagery is a critical obstacle to constructing a complete optical image time series for crop classification, particularly for early crop mapping. In this study, the benefits of using virtual cloud-free optical imagery using CGANs were investigated in the context of early crop mapping. This study presented a two-stage CGAN approach, including feature representation and extraction procedures tailored to SAR-to-optical image translation in croplands. The quality of the generated virtual optical imagery was assessed via early crop mapping using the virtual optical imagery as inputs for crop classification. The major findings from a crop classification experiment using Sentinel-1 and -2 images are summarized as follows:  
1. The use of various available inputs for feature extraction in CGANs, including the radar vegetation index and all of the correlated spectral bands, achieved a significant improvement in prediction performance compared to conventional CGAN models, which only used the backscattering coefficients from Sentinel-1 SAR imagery and the prediction target bands of Sentinel-2 imagery as inputs.
2. The use of the predicted virtual spectral bands taken at the optimal date as inputs for early crop mapping led to a classification accuracy comparable to the classification case using actual cloud-free optical imagery.  
Based on the above findings, with further development, virtual optical image generation from the presented CGAN model could be beneficial for reconstructing missing information in the cloud-contaminated optical imagery and for thematic mapping tasks using an optical image time series.  
Conceptualization, N.-W.P. and M.-G.P.; methodology, M.-G.P., N.-W.P. and G.-H.K.; formal analysis, M.-G.P.; data curation, M.-G.P., N.-W.P., G.-H.K. and S.H.; writing--original draft preparation, N.-W.P. and M.-G.P.; writing--review and editing, G.-H.K. and S.H.; supervision, N.-W.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2022R1F1A1069221).  
Not applicable.  
The Sentinel-1 and -2 images are made publicly available by European Space Agency via the Copernicus Open Access Hub at [[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu)) (accessed on 10 January 2022). The Cropland Data Layer data are also publicly available by the United States Department of Agriculture via National Agricultural Statistics Service at [[https://nassgeodata.gmu.edu/CropScape](https://nassgeodata.gmu.edu/CropScape)]([https://nassgeodata.gmu.edu/CropScape](https://nassgeodata.gmu.edu/CropScape)) (accessed on 10 January 2022).  
The authors thank the three anonymous reviewers for providing constructive comments that greatly improved the presentation of the manuscript.  
The authors declare no conflict of interest.
## 2 Priority-based Coverage-aware Congestion Control (PCC)
### Protocol and Algorithm Design of PCC
Additionally, since the dropping probability also needs to monotonically increase with \(N\) to relieve congestion, we can conclude that when there are \(N_{E}>Q_{max}\) packets with a new incoming packet, we have \(N\leq F_{E}(N)\leq(N+1)\). Consequently, we have \(F_{E}(N)=N+1-d_{E}(N)\), where \(d_{E}(N)\) is a non-decreasing function with the value bounded between [0,1]. While the above scheme looks similar to a tail-dropping scheme, it is important to point out that our mechanism is different from tail-droppingsince we may drop a packet from any position in the queue. The only similarity is that the average number of remaining packets \(F_{E}(N)\) is similar to that in the tail-dropping scheme. The curve \(d_{E}(N)\) is shown in Figure 5, and it can also be a convex, linear or concave function. The convex function is conservative, the concave function is aggressive, and the linear function is in between since \(d_{E}(N)\) is the minus term.
# Online Monitoring of Power Converter Degradation Using Deep Neural Network
###### Abstract  
Power semiconductor devices in the power converters used for motor drives are susceptible to wear-out and failure, especially when operated in harsh environments. Therefore, detection of degradation of power devices is crucial for ensuring the reliable performance of power converters. In this paper, a deep learning approach for online classification of the health states of the snubber resistors in the Insulated Gate Bipolar Transistors (IGBTs) in a three-phase Brushless DC (BLDC) motor drive is proposed. The method can locate one out of the six IGBTs experiencing a snubber resistor degradation problem by measuring the voltage waveforms of the three shunt resistors using voltage sensors. The range of the degradation of the snubber resistors for successful classification is also investigated. The off-the-shelf deep Convolutional Neural Network (CNN) architecture ResNet50 is used for transfer learning to determine which snubber resistor has degraded. The dataset for evaluating the above classification scheme of IGBT degradation is obtained by measuring the shunt voltage waveforms with varying snubber resistance and reference current. Then, the three-phase voltage waveforms are converted into greyscale images and RGB spectrogram images, which are later fed into the deep CNN. Experiments are carried out on the greyscale image dataset and the spectrogram image dataset using four-fold cross-validation. The results show that the proposed scheme can classify seven classes (one class for normal condition and six classes for abnormal condition in one of the six IGBTs in a three-phase BLDC drive) with over 95% average accuracy within a specific range of snubber resistance. Using grayscale images and using spectrogram-based RGB images yields similar accuracy.  
deep neural network; degradation monitoring; power converter +
Footnote †: journal: Applied  
1
Footnote 1: [[https://doi.org/10.3390/appl12411796](https://doi.org/10.3390/appl12411796)]([https://doi.org/10.3390/appl12411796](https://doi.org/10.3390/appl12411796))
## 2 Related Works
### Low-Light Image Enhancement Methods  
Current infrared and visible image fusion techniques often neglect illumination attenuation in nighttime visible imagery, leading to weak details being lost in noise or poor lighting. A practical solution is to apply advanced low-light enhancement algorithms as a preprocessing step, thereby improving image clarity and informativeness [29]. Common methods include global curve transformation [37], histogram equalization [38], retinex [39; 40], and deep learning-based methods [41; 42], each with its own limitations. Global curve transformation lacks local adaptivity, reducing contrast; histogram equalization enhances contrast but risks detail loss; retinex methods are computationally expensive and prone to color distortion; while deep learning requires large datasets, is time-intensive, and lacks interpretability. An improved approach is needed to effectively integrate low-light enhancement with image fusion.
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 4 Cervical Cancer
### Primary Tumour  
With regard to diagnosis, radiomics analysis of cervical mucosa combining contrast-enhanced T1 WI images and T2 WI seems promising for predicting MRI invisible early stage CCs, and machine learning MRI-based radiomics models may allow for the detection of carcinogenic HPV status in CC [72]. Whole-tumour volumetric 3D radiomics analysis had a good performance in stratifying the histological grade of CC and performed better than the radiomics evaluation of the centre slice of the tumour alone [73]. [PERSON] et al. [74], instead investigated the role of radiomics for predicting different histological subtypes of CC, discovered that multiparametric MRI-based radiomics models may be a promising method to differentiate adenocarcinoma and squamous cell carcinoma, achieving a specificity of 94% by using five combined MRI sequences. More recently, [PERSON] et al. [75] investigated the evaluation of pathological types and the FIGO stage. These authors found that MRIradiomics features, obtained using T2 WI and contrast-enhanced T1 WI, achieved an AUC of 0.777 and 0.750, respectively, for differentiating between adenocarcinoma and squamous cell carcinoma. From these two sequences, the AUC that distinguished low from high FIGO stages was 0.716 and 0.676, respectively, while the AUC for node status was 0.730 and 0.618 on T2 WI and contrast-enhanced T1 WI, respectively.
# Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins
## 5 Conclusions  
Decomposing the amide I band into its separate components is key to a successful FTIR analysis of the protein secondary structure. This has been achieved by different mathematical methods developed during the recent years. In this study, a data analysis technique to explore protein secondary structure through infrared and Raman spectroscopy has been optimized. Both FT-Raman and FTIR data revealed well-defined spectral features with similar secondary structural estimation for the proteins studied. The relative amounts of different secondary structures were determined from band curve fitting and agreement was found between the results derived from FTIR and FT-Raman spectroscopy and literature reports. The present results show the strength of FTIR and FT-Raman as non-invasive techniques to study the conformation of the proteins and provide support for using the amide I infrared and Raman spectra to study protein secondary structures in complex systems. The data obtained by these non-invasive techniques will in the near future provide new insights into the structure of various proteins in complex food systems.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/17/5918/s1](http://www.mdpi.com/2076-3417/10/17/5918/s1)]([http://www.mdpi.com/2076-3417/10/17/5918/s1](http://www.mdpi.com/2076-3417/10/17/5918/s1)), Figures S1-1-S1-16 Curve fitted, second derivative, residual plot, and peak analysis result of FTIR and FT-Raman spectra of all replicate measurements of standard proteins; Tables S1-1-S1-16 Statistical analysis of curve fitting performed on FTIR and FT-Raman spectra of all replicate measurements of standard proteins; Figures S2-1-S2-10 Curve fitted, second derivative, residual plot, and peak analysis result of FTIR and FT-Raman spectra of all replicate measurements of gluten and zein proteins; Tables S2-1-S2-10 Statistical analysis of curve fitting performed on FTIR and FT-Raman spectra of all replicate measurements of gluten and zein proteins.  
**Author Contributions:** Conceptualization, A.S. and I.J.J.; methodology, A.S. and I.J.J.; formal analysis, A.S.; investigation, A.S.; resources, I.J.J.; data curation, A.S. and I.J.J.; writing--original draft preparation, A.S.; writing--review and editing, I.J.J.; visualization, A.S.; supervision, I.J.J.; project administration, I.J.J.; funding acquisition, I.J.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** [PERSON] wishes to acknowledge the Ontario Trillium Scholarship for funding her project. This research was funded by the NSERC Discovery Grant program (Canada, RGPIN-2017-05213).  
**Acknowledgments:** The authors would like to thank [PERSON] for providing the FT-Raman facility and technical assistance in the FT-Raman measurements and [PERSON] for providing technical and scientific assistance in the FTIR measurement and data analyzation.  
**Conflicts of Interest:** The authors declare no conflict of interest.