esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Comparison between Cartosat-1 DSMs and DSMs generated using other high resolution satellites  
More in general, Cartosat-1 stereo images have proven to be an excellent source of data for the production of DSMs with a ground resolution of about 10 m. Even if within the range of available high resolution optical remote sensing satellites there are several units with a higher geometric resolution than Cartosat-1, Cartosat-1 DSMs can nevertheless be compared to similar models produced from higher resolution input imagery. For example, using IKONOS data [PERSON] _et al._[44] reported for the Hobart (Australia) test field a RMSE of about 4 m for urban areas and a RMSE of about 5 m for forests when comparing results with high-resolution first-pulse LIDAR data (elevation accuracy of reference data of 0.25 m). [PERSON] and [PERSON] [45] found a RMSE of about 5 m for DSMs generated using IKONOS data collected over the test field of Thun (Switzerland), again using as reference data an accurate laser DSM (elevation accuracy of reference data of 0.5 m).  
[PERSON] _et al._[31] analyzed the generation of 10 m resolution DSMs using SPOT-HRV images collected over the test site of Granada (Spain) and found a RMSE between 7.7 m and 8.6 m when validating results using GCPs.  
Finally, [PERSON] [22] compared 10-meters DSMs generated using SPOT-5, EROS-A, IKONOS and QuickBird data with very high-resolution LIDAR data (elevation accuracy of reference data of 0.15 m) acquired over the test site of Quebec City (Canada). He found a LE90 of 9 m for QuickBird DSMs, a LE90 of 10 m for IKONOS DSMs and SPOT-5 DSMs and a LE90 of 31 m for EROS-A DSMs.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 5 Conclusions  
The simulations presented in this study show unequivocally that pitch error is the chief trigger of low-order excitation in helical gear pairs. Even a calibrated spacing deviation of only a few micrometers shifted the mean transmission error (TE) by almost one order of magnitude and produced a distinct shaft order tone at 16.66 Hz. When the measured tooth surface topology was added to the same model, the two error families did not cancel but reinforced one another; the negative TE offset deepened, and the peak-to-peak excursion nearly doubled. This non-linear coupling demonstrates that local flank shape can amplify the dynamic impact of indexing faults rather than masking it.  
In the frequency domain, pitch error generated the expected side-bands that flank the fundamental mesh frequency; these spectral skirts were absent from the ideal case but re-appeared when pitch error was re-introduced, confirming that the calibrated pitch table is correctly mapped into the multibody solver. Therefore, such side-bands offer a sensitive diagnostic marker for excessive indexing deviation during end-of-line testing.  
From a design standpoint, the results underline that precise indexing must accompany profile relief optimization. Digital twin models that ignore pitch error systematically under-predict TE and may lead to overly optimistic acoustic targets. Conversely, twins that combine a tooth-resolved surface scan with an accurately referenced pitch error map can evaluate corrective actions such as phase matching or intentional error phasing long before hardware is cut.  
Although the present work was limited to a single speed-load point with rigid wheels, the methodology provides a robust foundation. Extending the framework to flexible bodies, duty cycle sweeps and statistical pitch distributions will narrow the gap between simulation and test and support lifecycle NVH prediction in next-generation electric drivetrains.  
Conceptualization, K.H.; Methodology, K.H.; Software, K.H.; Formal analysis, K.H.; Resources, K.H.; Writing--original draft, K.H.; Supervision, D.F. All authors have read and agreed to the published version of the manuscript.  
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 on request from the corresponding author. The data are not publicly available due to industrial collaboration constraints and confidentiality agreements.  
**Acknowledgments:** The authors would like to thank the colleagues at Szechenyi Istvan University for their support during this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Analytical Method: Plane Wave Illumination  
The problem of a normally-incident plane wave (scenarios 1-4) at a planar interface between free space and a lossy region representing skin was solved analytically in [25]. Without loss, the electric field is given by:  
\[\mathbf{E_{inc}}=\mathrm{E_{0}e^{-jk_{0}d}\ \hat{x}}\, \tag{1}\]  
where \(\mathrm{E_{0}}\) is the amplitude of the electric field, \(\mathrm{k_{0}}\) is the free space wavenumber, and \(\mathrm{d}\) is the normal distance between the plane-wave source and skin model interface. Assuming that the plane wave is attenuated in free space, its E-field vector is given by:  
\[\mathbf{E_{inc}^{\prime}}=\mathrm{E_{0}e^{-jk_{0}d}f(d)\ \hat{x}}\, \tag{2}\]  
Figure 3: Radiation pattern of patch antenna arrays: (**a**) E-plane; (**b**) H-plane.  
Figure 2: Antenna topologies: (**a**) single patch antenna (SPA); (**b**) \(2\times 2\) patch antenna array (\(2\times 2\) PAA); (**c**) \(2\times 4\) patch antenna array (\(2\times 4\) PAA); (**d**) \(4\times 4\) patch antenna array (\(4\times 4\) PAA). Dimensions are in mm.  
where f(d) is an attenuation function. The transmitted E-field vector (\(\mathbf{E}_{\text{tr}}\)) at the phantom interface is therefore expressed as [25] (Appendix A):  
Scenario 1 :  
\[\mathbf{E}_{\text{tr}}=\text{T}_{1}\mathbf{E}_{\text{inc}}\, \tag{3}\]  
Scenario 2:  
\[\mathbf{E}_{\text{tr}}=\text{T}_{1}\ \frac{1}{1+\text{e}^{-|2\text{d}k_{0}} \text{R}_{1}}\mathbf{E}_{\text{inc}}\, \tag{4}\]  
Scenario 3:  
\[\mathbf{E}_{\text{tr}}=\text{T}_{1}\mathbf{E}_{\text{inc}}^{\prime}\, \tag{5}\]  
Scenario 4:  
\[\mathbf{E}_{\text{tr}}=\text{T}_{1}\mathbf{E}_{\text{inc}}^{\prime}+\text{T}_ {1}\mathbf{E}_{0}\text{e}^{-|\text{d}k_{0}}\sum_{n=1}^{\infty}\Bigl{(}-\text{e }^{-|2\text{d}k_{0}}\text{R}_{1}\Bigr{)}^{n}\text{f}((2n+1)\text{d})\hat{\kappa }\, \tag{6}\]  
where T\({}_{1}\) and R\({}_{1}\) are the transmission and reflection coefficients, respectively, calculated using Fresnel coefficients at the free space/skin model interface [25]. The TPD for a plane-wave can be computed as:  
\[\text{TPD}=\text{Re}\biggl{[}\frac{|\mathbf{E}_{\text{tr}}|^{2}}{\eta}\biggr{]}\, \tag{7}\]  
where \(\eta\) is the complex intrinsic impedance of the skin.
## 4 Mapless Localization  
In this section, we achieve localization that does not require true map information. Section 4.1 formalizes the position estimation based on a particle filter and Gaussian process. Section 4.2 introduces learning trajectories collected from a crowd for creating map information.
## 5 Challenges and Limitations
### Theoretical Limitations  
* Dependency Management: The framework assumes a lack of mutual dependency among requirements in some of its phases, which might not always be the case in complex IoT systems, where requirements are often interdependent. This simplification could lead to suboptimal prioritization outcomes if dependencies are critical.
* Subjectivity in Grouping: Even with an advanced grouping methodology, the subjective nature of categorizing requirements into essential, desirable, or inessential might still persist to some degree. This subjectivity can influence the prioritization process, especially in the initial classification stage.
* Adaptability to Changes: IoT systems are dynamic, with requirements that can evolve rapidly due to technological advancements or changing market conditions. The framework's ability to adapt to these changes efficiently without extensive reconfiguration remains a critical area for further development.
_Insects_**2014**, \(5\), 762-782; doi:10.3390/insects5040762  
_insects_  
**ISSN 2075-4450**  
www.mdpi.com/journal/insects/  
_Article_  
**Cloning, Expression, Sequence Analysis and Homology Modeling of the Prolyl Endoprotease from _Eurygaster_**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1,**  
**[PERSON] 3 and [PERSON] 1,* * 1 Department of Biology and Biotechnology, Stephen F. Austin State University, P.O. Box 13003, SFA Station, Nacogdoches, TX 75962, USA; E-Mails: [EMAIL_ADDRESS] (R.C.Y.); [EMAIL_ADDRESS] (R.G.); [EMAIL_ADDRESS] (V.D.) 2 University of Texas Health Science Center, School of Public Health, Center for Infectious Diseases, 1200 Hermann Pressler Dr., RAS E715, Houston, TX 77030, USA; E-Mail: [EMAIL_ADDRESS] 3 Biodiversity and Integrated Gene Management Program, International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat Office P.O. Box 6299 Rabat-Instituts, Rabat, Morocco; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-936-468-1017; Fax: +1-936-468-2056.  
External Editor: [PERSON]  
_Received: 13 May 2014; in revised form: 6 October 2014 / Accepted: 11 October 2014 / Published: 22 October 2014_  
**Abstract:**_Eurygaster integriceps_ Pution, commonly known as sunn pest, is a major pest of wheat in Northern Africa, the Middle East and Eastern Europe. This insect injects a prolyl endoprotease into the wheat, destroying the gluten. The purpose of this study was to clone the full length cDNA of the sunn pest prolyl endoprotease (spPEP) for expression in _E. coli_ and to compare the amino acid sequence of the enzyme to other known PEPs in both phylogeny and potential tertiary structure. Sequence analysis shows that the 5' UTR contains several putative transcription factor binding sites for transcription factors known to be expressed in _Drosophila_ that might be useful targets for inhibition of the enzyme. The spPEP was first identified as a prolyl endoprotease by [PERSON] _et al._, 2010. The enzyme is a unique serine protease of the S9A family by way of its substrate recognition of the gluten proteins, which are greater than 30 kD in size. At 51% maximum identity to known PEPs, homology modeling using SWISS-MODEL, the porcine brain PEP (PDB: 2 XWD) was selected in the database of known PEP structures, resulting in a predicted tertiary structure 99% identical to the porcine brain PEP structure. A Km for the recombinant spPEP was determined to be \(210\pm 53\)\(\upmu\)M for the zGly-Pro-pNA substrate in 0.025 M ethanolamine, pH 8.5, containing 0.1 M NaCl at 37 \({}^{\circ}\)C with a turnover rate of \(172\pm 47\)\(\upmu\)M Gly-Pro-pNA/s/\(\upmu\)M of enzyme.  
Hemiptera; Scutelleridae; prolyl endoprotease; gluten; serine protease; cDNA; homology modeling; insect; wheat
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 2 Architecture
### Relative Navigation Based on Vision and UWB
#### 2.2.1 Detection and Tracking of Adjacent UAVs  
YOLOv3-tiny [18] is used to detect the adjacent UAVs. YOLOv3-tiny is a lightweight state-of-the-art convolutional neural network (CNN) detector that can work on the onboard computer to provide detection. To utilize the detector to detect adjacent UAVs, an additional training set captured by the camera on the UAV, as well as containing the UAVs, is labeled and fed into the CNN. After training, the CNN is able to detect the UAVs efficiently.  
For the tracking of the drone, a KF filter is adopted to predict the trajectory. Moreover, we use the Hungarian algorithm, which is a matching algorithm, to match the detected and predicted result. Blinking LED markers are used to identify unique IDs, preventing all UAVs in the swarm from having a similar appearance.  
For the KF filter of the trajectory predicted, the state of the system is  
\[x=\left[u,v,\gamma,h,\dot{u},\ddot{v},\dot{\gamma},\dot{h}\right] \tag{16}\]  
where \(u\) and \(v\) are the center of the object in the image, \(\gamma\) is the aspect ratio, \(h\) is the altitude and the observation equation can be expressed as  
\[z=(u,v,\gamma,h) \tag{17}\]
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 5 Selected HPC Approaches in InSAR Fundamental Functional Stages
### InSAR Filtering  
The non-local InSAR algorithms (NL-InSAR) have become popular, because of their ability to jointly estimate interferometric image, amplitude, coherence, and phase. However, they are quite expensive algorithms from the computational point of view (Section 2.4). Different parallel versions of the non-local InSAR filters have been addressed, and they are discussed in the following.  
In [193], an improved parallel scheme for iterative non-local InSAR filtering was developed using an MPI-based approach. The efficiency of the parallel algorithm was investigated on up to 1024 processors.  
An efficient parallel algorithm for interferometric phase denoising with an NL-InSAR algorithm, targeted to NVIDIA GPUs, was developed using the CUDA framework and is presented in [194]. Experiments using TerraSAR-X data led to execution times of 2 min per iteration for 1 GB images, thus enabling deployment in InSAR processing chains for operational applications.  
Finally, in [195], a convolutional neural network (CNN)-based generative model for joint phase filtering and coherence estimation that directly learns the InSAR data distribution has been implemented on GPU. Conducted performance analysis shows an almost linear speedup with the increasing number of GPUs. However, experiments were performed by using only small size interferograms.
# Unsupervised Offline Changepoint Detection Ensembles
###### Abstract  
Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal's changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach's novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures--Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.  
anomaly detection; changepoint detection; ensemble; time series; signal processing; signal segmentation; aggregation function; unsupervised learning; Tennessee Eastman Process (TEP); Skoltech Anomaly Benchmark (SKAB)
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 5 Results and Discussion
### Effectiveness of C-MAB on Connection Time
The C-MAB framework is computationally lightweight, making it amenable to real-time implementation at base stations or edge servers. At each decision epoch, the algorithm performs a lookup in the reward table indexed by the current context and selects the beam-IRS pair with the highest historical average. Assuming a bounded context space (e.g., 36 combinations from 3 distance bins \(\times\) 3 directions \(\times\) 2 traffic states), the table remains compact. No gradient updates or offline training are required. We empirically observe rapid convergence: Figure 3 shows that the proposed C-MAB outperforms the classical MAB by a factor of two only after 5000 s of exploration time. To further improve efficiency,  
Figure 4: Average beam sojourn time of twelve beams with C-MAB. future extensions may apply context pruning strategies that remove low-utility or rare contexts from the table, thus focusing learning on high-value decision regions.
## 3 Results
### Distance-Based Analysis Results of EMFs from 5G Bs  
Figure 5 presents scatterplots of the RMS values of the electric field (log10(E)) recorded in the 5G 3.5 GHz band versus the average distance (log10(distance)) to the five closest BS, covering the urban regions of Attica and Central Macedonia, as well as the suburban region of Attica. The closest horizontal distance of a spot measurement to the nearest 5G BS was 31 m, considering all three regions. The corresponding farthest distance was 777 m, considering the fifth closest BS from the spots. It is worth noting that in the 3.5 GHz band, over 95% of the measurements fell below 0.4 V/m across all the environments.  
Figure 4: (**a**) Percentage contribution of different wireless applications to the total electric field, (**b**) percentage contribution of 2G, 4G, and 5G to the total field from cellular networks only.  
The above graphs also include linear regression lines to highlight the negative slope, suggesting that increased distance correlates with lower electric field strength. Additionally, Table 3 presents [PERSON]'s correlation coefficients for the measurements and the nth closest BS (n = 1, 2, 3, 4, 5). The negative coefficients further confirm that as the distance to the nearest BS increases, the recorded electric field measurements decrease. It is worth noting that the highest negative correlation coefficient was found in the urban area of Central Macedonia. In this region, we cross-checked and adjusted the publicly available data on BS locations by using up-to-date imagery from Google Street View. For instances where BS were not visible due to outdated images or other factors, we performed on-site visits to accurately document their positions. In the other two regions, discrepancies in the registered BS locations led to lower correlation coefficients.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 3 Materials and Methods  
While satellite imagery is readily available (in the case of Sentinel-2 since 2015), open datasets containing SOC information are scarce. In this study, we focused on two such datasets with the largest amount of SOC information within the time frame of Sentinel-2's operation: The Land Use/Cover Area Frame Statistical Survey (LUCAS) dataset and the Chilean Soil Organic Carbon Database (CHLSOC).  
These datasets contain tens or thousands of samples, but they are still of limited use when it comes to training DNNs end-to-end. To circumvent the problem, we propose to use a DNN trained on a visual task for which ample data are available (land cover classification) as a rich feature extractor for our SOC estimator, similar to the approach [PERSON] et al. used in their seminal paper [PERSON] et al. (2018). Our feature extractor, however, was trained on multispectral Sentinel-2 imagery and is based on an image segmentation architecture.  
To train the feature extractor, we require a dataset providing maps of land cover classes. We opted for a publicly available dataset of land cover (LC) data for the central region of Slovenia. The LC data are available for the year 2019, and we paired them with corresponding data from Sentinel-2 for the same year. The data cover a comparatively small area of 15 km \(\times\) 15 km, but since the resolution is 10 m per pixel, that corresponds to 1500 \(\times\) 1500 (2.25 million) values for training and validating our feature extractor. This is two orders of magnitude more than the number of data samples we have for SOC in our datasets, and the LC data are in the native resolution of the Sentinel-2 sensor, while the SOC data available are scattered over vast areas.  
Since determining SOC does not make sense for some types of land cover (e.g., water or artificial surfaces), to make the the process of generating SOC maps more efficient and accurate, we also need a land cover dataset that that has much larger coverage than the one used to train the feature extractor, even if the resolution is not as high.  
Fortunately, Europe is the continent with the widest range of supra-national LC maps. Plenty of detailed, high-quality datasets are now available, providing LC information for the European continent, such as: HILDA, CORINE, PELCOM, Urban Atlas, etc. Of all the European LC datasets, Coordination of Information on the Environment (CORINE) Land Cover (CLC) is one of the best-known, oldest, and most-used [PERSON] et al. (2018). The CLC project can be considered the most-relevant European LUC database for several reasons, but primarily because of its history, comprehensive coverage, method of production, and degree of detail [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). We, therefore, used CLC to post-process the maps generated by our pipeline, making sure that the estimates are available only for land cover classes the model is trained to handle.  
Since the training data have a profound effect on the quality of our models, we provide a brief overview of salient points for each of them.
## 6 Conclusions and Future Works  
### Conclusions  
In this paper, we propose a novel framework based on luminance spectrums for the identification of the visual images based on the presence of brightness/luminance for classification of images as dark, semi-dark, and bright. Later, we propose a well-defined and consistent protocol to classify the visual data based on the luminance presence. The luminance is computed using the known HSL color model recommended by ITU-BT.709 primaries. On top of the individual manipulation of RGB color space, we introduce an off-the-shelf method for identification and calculation of luminance for entire visual image rather than just a singular pixel. The proposed Relative Perceived Luminance Classification (RPLC) algorithm along with the luminance spectrums are extensively tested against the state-of-the-art approaches of convolutional neural network and support vector machine. The benchmarking results verified that the performance of proposed algorithm RPLLC is better than CNN. Additionally, it has higher reliability and consistency in rates of accuracy for the classification of images in terms of Dark, Semi-dark and Bright images.  
The proposed algorithm RPLLC achieved accuracy up to 78% and it can be further tuned to get better classification accuracy. The results show that RPLLC outperforms CNN based and SVM-based Classification. The gain margin of RPLLC over CNN is 89.25% for the training set and 8.14% for the validation set. Whereas, for SVM it is up to 7.5% with bare minimum prior requirements of implementation. Finally, the computational complexity of RPLLC is substantially less than the CNN-based and SVM classification algorithm.