esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Data Preprocessing
#### 2.1.2 Half-Spaces' Definition  
A half-space is a core element for shaping the roof in this work. It is obtained from the LiDAR point cloud, which is first segmented into sets of LiDAR points that describe each individual planar roof face. For the segmentation of planar faces various segmentation methods are applicable. Considering the type of input LiDAR point cloud, segmentation methods that take the LiDAR point cloud density into account [40; 41; 42; 43] are more suitable for this purpose. The reason for this is variable density of the input point cloud, which occurs due to different laser scanner technology and height of the aerial vehicle that performs point cloud acquisition. Segmentation that takes variable point cloud density into account should, therefore, be used for the best results. The overview of segmentation methods is out of the scope of this paper, for more information see [44]. An example segmentation of a LiDAR point cloud that belongs to the same building as the base model from Figure 2 is shown in Figure 3.  
The roof of each building will be shaped by a set of half-spaces \(H=\{H_{i}\}\). Half-space \(H_{i}\) is defined by the corresponding set of LiDAR points \(S_{i}\) and a plane \(P_{i}\) that is calculated from \(S_{i}\). \(H_{i}\) is given as \(xP_{i}a+yP_{i}b+zP_{i}c+d>0\). The definition of each half-space is obtained from the segmented LiDAR point cloud, where sets of segmented LiDAR points \(S_{i}\) that describe individual roof faces are taken. The plane \(P_{i}\) that is best-fitted to the corresponding set of LiDAR points is determined as \(P_{i}=[a,b,c,d]=\text{LSqFit}(S_{i})\), where \(P_{i}c>0\) and LSqFit is a function that fits a plane to a set of LiDAR points by least-square fitting [45].  
Figure 3: _Cont._  
Figure 2: For a building outline (**a**) a base model is generated (**b**), where visible faces and points are marked.
# Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
## 2 Materials and Methods
### Water Quality, Soil, and Plant Analyses  
Water samples were collected twice during each lettuce-growing season and analyzed according to the standard methods of APHA [17] for pH, T-N, T-P, Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), K\({}^{+}\), chloride (Cl\({}^{-}\)), and sulfate (SO\({}_{4}\)\({}^{2-}\)). The sodium adsorption ratio (SAR) was calculated from the analyzed concentrations of Ca\({}^{2+}\), Mg\({}^{2+}\), and Na\({}^{+}\). EC\({}_{\rm w}\) was measured weekly using an EC meter (Hanna Instruments Inc., HI98192) and determined twice in the laboratory during each lettuce-growing season.  
At the end of each experiment, five soil samples from each treatment were collected and then mixed to produce one representative soil sample. Chemical analyses were conducted according to the soil analysis methods of the American Society of Agronomy (ASA) and the Soil Science Society of America (SSSA) [18]. The soil properties determined included pH, electrical conductivity of the saturated-soil extract (EC\({}_{\rm e}\)), CEC, T-N, T-P, P\({}_{2}\)O\({}_{5}\), OM, and exchangeable cations including Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), and K\({}^{+}\).  
At the end of each experiment, the number of leaves, leaf length, maximum leaf width, and fresh weight were measured for twenty different plants that were randomly sampled from each treatment (10 samples from each replicate). To determine the accumulation of Na\({}^{+}\) in the leaves, the sampled lettuce were first washed with distilled water in the laboratory to remove soil particles and then oven-dried at 60 \({}^{\circ}\)C. Afterward, 0.5 g of the lettuce tissue was dissolved in 10 mL HNO\({}_{3}\). The digested solution was filtered into a 25.0 mL volumetric flask through a Whatman No. 40, 125 mm filter paper. In the solution, the Na\({}^{+}\) content was measured using inductively coupled plasma optical emission spectrometry (ICP-OES; iCAP 7000, Thermo Fisher Scientific Inc., Waltham, MA, USA).
## 4 Experiment Setup
### Dataset
#### 4.2.2 MFPT Dataset  
The MFPT dataset [30] is another well-established benchmark for bearing fault diagnosis. It includes vibration signals recorded under a variety of load and fault conditions. The dataset is divided into the following categories:  
* Three baseline conditions (270 lbs of load, input shaft rate of 25 Hz, sample rate of 97,656 sps, for 6 s);
* Three outer race fault conditions (270 lbs of load, input shaft rate of 25 Hz, sample rate of 97,656 sps, for 6 s);
* Seven outer race fault conditions (25, 50, 100, 150, 200, 250, and 300 lbs of load, input shaft rate 25 Hz, sample rate 48,828 sps, for 3 s);
* Seven inner race fault conditions (0, 50, 100, 150, 200, 250, and 300 lbs of load, input shaft rate 25 Hz, sample rate 48,828 sps, for 3 s).  
For our experiments, we selected Categories 1, 3, and 4 to construct a 15-class classification task: 1 normal class and 14 distinct fault classes (7 outer race and 7 inner race fault conditions). The normal class consists of 572 samples, while each of the fault classes contains 143 samples. All vibration signals were uniformly segmented into samples of length 1024.
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 6 Pricing and Cost Considerations  
Cost is a significant factor in choosing a CI/CD platform, as organizations must balance functionality, scalability, and security with the financial impact of adopting and maintaining a DevOps solution. The pricing models of Azure DevOps and GitHub differ significantly, reflecting their distinct approaches to software development workflows, as summarized in Table 4. While Azure DevOps follows a pay-per-user and per-service model, offering flexibility in selecting individual components, GitHub adopts a tiered pricing structure that differentiates between open-source and private repositories, team collaboration features, and enterprise-grade security offerings. A detailed cost analysis is necessary to understand the total cost of ownership, hidden expenses, and cost-effectiveness of each platform across different organizational sizes and use cases. Azure DevOps and GitHub offer distinct pricing models for users, ranging from individual developers to large-scale enterprises. Azure DevOps is structured around modular services, allowing organizations to pay only for the features they need. GitHub's pricing tiers bundle features together, requiring organizations to upgrade plans to access specific functionalities. Azure DevOps offers a free tier, which includes basic features for up to five users, providing limited access to Azure Repos, Azure Pipelines, and Azure Boards. Additionally, organizations must purchase separate Azure DevOps Services, each billed individually. Azure Repos, which provides version control, is free for unlimited public and private repositories. Azure Pipelines, which powers CI/CD, includes 1800 free pipeline minutes per month for public repositories, but private repositories require payment for
## 3 Experimental Validation  
The microstrip circular patch antenna is manufactured using a conventional etching process on the Rogers RT/duroid 5880 substrate (\(\varepsilon_{r}\) = 2.2), with a thickness of 0.79 mm, as shown in Figure 9a. The proposed lens is printed using Form 3 from Formlabs Inc., with the resin material RL-F2-GPCL-04 and the printing resolution of 100 \(\upmu\)m. Its photo is shown in Figure 9b. The reflection coefficient is measured with the Anritsu MS46522B, and its impedance is matched from 5.75 GHz to 5.92 GHz with the -10 dB criterion, as shown in Figure 9c. The measured value is slightly broader, meaning that the built prototype is slightly lossy compared to the computed expectation. A little higher loss could occur from the printed lens and the fabrication tolerance that the lens and the patch are not perfectly attached. Nevertheless, the measurement agrees sufficiently with the simulation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Operating Frequency**} & \multirow{2}{*}{**Largest Dimension [\(\lambda\)]**} & \multirow{2}{*}{**Gain [dBi]**} & \multirow{2}{*}{**FOM**} \\ \cline{1-1} \cline{5-5} \cline{7-7} \multicolumn{1}{c}{[19]} & 5.4–8.2 & 1.8 & 12.1 & 6.72 \\ \hline
[25] & 8–12 & 2 & 12.58 & 6.29 \\ \hline
[27] & 14–20 & 2.71 & 11.2 & 4.13 \\ \hline
[30] & 26.6–29.4 & 3 & 15.6 & 5.2 \\
27.6–29 & 1.5 & 12.1 & 8.07 \\ \hline This work & 5.8 & 1.4 & 10.12 & 7.23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table of the lens antenna. All the values are from the measurement. The wavelength for the dimension is calculated at the center frequency.  
Figure 8: Poynting’s vector plot at 5.8 GHz. (**a**) Model 1. (**b**) Model 2. (**c**) Model 3.  
Figure 7: E-field distribution at 5.8 GHz. (**a**) Model 1. (**b**) Model 2. (**c**) Model 3.  
Figure 9d shows the simulated and measured radiation patterns in the elevation plane. The radiation pattern is measured in an anechoic chamber, and it agrees well with the simulation. The measured gain of the proposed lens antenna is 10.12 dBi, which is very close to the simulated value of 9.82 dBi. Therefore, we prove that resin can be used for a 3D printed lens design, and furthermore, it can be used even more efficiently with one-take 3D printing when a proper modification is implemented.  
Lastly, in Table 1, the performance of the proposed lens is compared with previously reported lens antenna designs in terms of the dimension and measured gain. We set a figure-of-merit (FOM) to be a gain value over the largest electrical dimension of the lens. The proposed design occupies the smallest dimension with an acceptable level of the FOM. Note that the 3 DP lenses in the table are all made via a one-take 3D printing process.
###### Abstract  
The ultrasonic nonlinearity parameter is used to evaluate the nonlinear elasticity of a material, which is determined from the displacement amplitude of the fundamental and second-order frequencies components in an ultrasonic wave propagating through a material. However, the displacement amplitude of the second-order harmonic component generated during propagation through a material is very weak because it is easily affected by measurement conditions such as surface roughness. In this study, we analyzed the influence of surface roughness on the measurement of the ultrasonic nonlinearity parameter. For this purpose, Al6061-T6 and SUS304 specimens were prepared with different surface roughness ranging from 0.5 to 2.9 \(\upmu\)m. Then, the absolute and relative ultrasonic nonlinearity parameter measurements were conducted using a through-transmission technique involving two cases: both surfaces being rough, and one being a rough surface and the other being a smooth surface. The experimental results showed that the surface roughness had a lesser influence on the absolute measurement than on the relative measurement and that the transmission surface was less affected by the reception surface. These results were similar regardless of the types of specimens. Therefore, to perform accurate measurements, it is desirable to measure the nonlinearity parameter after polishing the material surface.  
Keywords:surface roughness; ultrasonic nonlinearity parameter; absolute nonlinear measurement; relative nonlinear measurement +
Footnote †: journal: Applied Physics Letters
## 6 Simulation Results and Discussions
### Robustness  
The SER performance versus SNR per bit with different imperfect estimation errors of the desired directions for three transmit antenna arrays is displayed in Figure 11. For clarity, the signal power allocation factor is set as \(\beta_{1}=0.9\), and only the curves for the first antenna array are analyzed, from which the same conclusions can also hold for other two antenna arrays. It is easy to observe that there is only approximately \(0.5\ \mathrm{dB}\) loss of SNR per bit when the estimated direction error is \(\Delta\theta=1^{\circ}\) for a fixed SER performance (e.g., \(SER=10^{-4}\)). As long as the estimated direction errors for one desired direction are within \(\Delta\theta=1^{\circ}\), at most \(0.5\ \mathrm{dB}\) extra SNR per bit is needed to realize the same SER as the ideal case. Therefore, to achieve secure and precise transmission for the LU, the estimated direction errors for all antenna arrays should be less than \(\Delta\theta=1^{\circ}\).  
Figure 9: SER performance versus azimuth angle for the proposed DM schemes, where \(\beta_{1}\)= 0.9 and \(10\mathrm{log}_{10}(E_{b/}N_{0})=10\ \mathrm{dB}\).  
Figure 11: Robustness of the proposed DM schemes with various estimation errors of the desired directions.  
Figure 10: Achievable rate of the proposed DM schemes: (**a**) achievable rate of the LU versus SNR; (**b**) achievable rate of the Eve in Location 1 versus SNR; (**c**) achievable rate of the Eve in Location 2 versus SNR; and (**d**) secrecy rate performance of the proposed DM schemes.
## 4 Materials and Methodology
### Algorithm Tuning  
#### 4.1.1 Training Process  
Figure 2 illustrates the overall framework of the training methodology, from reading database records to the outcomes generated by the classifier.  
The training process consists of the following key phases:  
* Data Augmentation: Before entering the masking phase, data augmentation enriches the training set by generating new image samples using techniques such as rotation, translation, scaling, and other transformations. This aims to enhance the model's ability to generalize patterns and reduce the risk of overfitting.
* Masking Phase: This phase occurs after data augmentation and before localization. It involves isolating the relevant image area from non-essential regions, ensuring that only masked images proceed for further analysis.
* Localization, Color Filtering, and Parameter Extraction: After locating objects in the images, specific color filters are applied to group the original classes (C1, C2, C3, and C4). This results in the creation of two new classes: one combining C1 and C3 based on a specific color range, and another combining C2 and C4. This approach visually identifies and segregates defects or objects of interest based on common color characteristics, without detailed classification at this stage. Following color filtering, parameter extraction algorithms are employed to capture relevant features from the masked and filtered images. This step effectively represents the characteristics of the chicken pieces for subsequent classification.
* CNN Model Generation: Based on the masked, color-filtered, and feature-extracted data, two convolutional neural network (CNN) models are developed. These models  
Figure 2: Schematic diagram of the training process for classification of C1, C2, C3, and C4.  
are pivotal in discriminating between different classes of chicken pieces during the testing phase.
###### Abstract  
This study aims at the qualitative and quantitative determination of porosity, mineralogical and textural changes in carbonate rock samples after injection of (i) supercritical CO\({}_{2}\)-rich brine and (ii) dry supercritical CO\({}_{2}\), under similar experimental conditions (\(P\approx 75\) bar, \(T\approx 35\)\({}^{\circ}\)C, 970 h exposure time and no CO\({}_{2}\) flow). The studied rocks were sampled in the western Basque-Cantabrian Basin, North Spain, and consist of vuggy carbonates (\"Carnionas\") of the Puerto de la Palombera formation (Hettangian). Mineralogical and pore space characterization is completed using optical microscopy, scanning electron microscopy and optical image analysis. In addition, X-ray fluorescence analyses are performed to refine the mineralogical information and to obtain whole rock geochemical data and the brine composition is analysed before and after the experiment. Mineralogical and chemical results indicate that the carbonate rocks exposed to supercritical CO\({}_{2}\) in dry conditions do not suffer significant changes. However, the injection of supercritical CO\({}_{2}\)-rich brine induces chemical and physical changes in the rock due to the high reactivity of calcite at the low pH conditions produced by the acidified brine. Numerical modelling validates the experimental observations. These results can be used to characterize the behaviour of carbonate rocks under conditions similar to the vicinity of a CO\({}_{2}\) injection well. The results should be considered only at the scale of the studied samples and not at reservoir scale.  
carbonate rocks; SC CO\({}_{2}\); SC CO\({}_{2}\)-rich brine; Quantitative petrography; numerical modelling
# Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
## 6 Summary
With respect to the pseudorange error model in the vicinity of jammer, we will extend the lab experiment in order to calibrate an error model based on both the elevation and carrier-to-noise density ratio. Finally, an appropriate error model for the GNSS Doppler measurements needs to be developed. Conceptualization, R.Z. and M.R. ; Methodology, R.Z., M.R. ; Investigation, R.Z., D.M., M.R. and C.L. ; Resources, R.Z., C.L. and S.G.; Writing--Original Draft, R.Z., D.M. and M.R. ; Writing--Review & Editing, R.Z., D.M., M.R., C.L. and S.G.; Supervision, R.Z. This research received no external funding. The authors would like to thank Baltic Taucher GmbH and especially the crews of the vessels BALTIC DIVER II and AARON for their support during the measurement activities. The authors are also grateful for the assistance of their colleagues [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. The authors declare no conflict of interest.
## 2 Materials and Methods
### Phenological Variables  
In order to find out the variations of DBF growth and development over the recent decade, two vegetation variables--seasonal amplitude (SA) and integrated EVI (SI)--were extracted from the MODIS-EVI time series data. SA was calculated as the difference between the maximum and base EVI value over the season. SI was defined as area under the vegetation curve and above the base level during the growing season. Several studies have suggested SA and SI are closely related to Net Primary Production (NPP) during the growing season [22]. An analysis of grasslands in the United States indicated a positive and statistically significant relationship between integrated NDVI and field aboveground NPP [23]. Thus, SA and SI provide good indicators for capturing the activity of vegetation growth over the season. Other two phenological variables were also estimated: the start-of-season (SOS) were estimated with the threshold of 20% SA [24] and length-of-season (LOS) corresponded to the date difference between SOS and end-of-season.  
The Timesat (v. 3.02) software was used to smooth EVI time series and extract vegetation and phenological variables with a double logistic function [25,26]. The program was designed to derive phenological information from time series of satellite data by fitting their upper envelopes with multiple processing methods. The MOD13Q1 pixel reliability band was used to weight each value at same pixels in the time series ensuring that clouds and other low quality data had minimum effects to the fittings. The weight of good data was set to 1.0, marginal data to 0.5 and poor quality data to 0.1.  
The DBF pixels with no significant seasons due to data noises failed to extract vegetation and phenological variables, and were removed from the analysis. Additionally, the resultant grids with abnormal phenology values (e.g., SOS \(\leq\) 0 or LOS \(>\) 360) were also removed from the trends analysis.
## 4 Use Case
### Application to Water Supply Networks Management
In this context, Listing 3 shows a SPARQL sentence that retrieves, as a result, the water supplied and the population from Wikidata (using the SERVICE instruction) in the particular zone 4.1. This sentence can be used in order to identify a possible relation between the water supplied and the population. ```
SELECT?waterSupplied?population WHERE{ water:Zone4.1 water:hasWaterSupplied?waterSupplied. water:Zone4.1 owl:sameas?wikidataLink.?waterSuppliedwater:inYear?year. BIND(concat(\"\",?year)as?yearTr)?waterSuppliedwater:value?value-. FILTER(regex(str(?wikidataLink),\"wikidata\")) SERVICE<[[https://query.wikidata.org/sparql](https://query.wikidata.org/sparql)]([https://query.wikidata.org/sparql](https://query.wikidata.org/sparql))>{?wikidataLink:P:1082?populationStatement. ?populationStatementps:P1082?population; pq:P585?date. FILTER(xsd:integer(YEAR(?date))=xsd:integer(?yearTr)) }
```  
Listing 3: SPARQL federated query to retrieve the water supplied by year in a specific zone and the population from Wikidata. In Figure 6, the results obtained with the SPARQL federated query can be shown. We studied the correlation between the water supplied and the population although few data were available in our case study. We used the [PERSON]'s correlation [48] to measure the degree of variation between both variables. The correlation coefficient \(R(5)\) was \(-0.6262\), which means a moderate negative correlation. We analyzed if the correlation was significant, the value being \(p=0.1326\) (greater than the statistical significance levels of \(0.05\) and \(0.10\)), and we concluded that the result was not statistically significant. Furthermore, we calculated the [PERSON]'s correlation [49] obtaining a correlation coefficient \(R_{s}=-0.5371\) and \(p=0.2152\) concluding that the relation between the two variables would not be considered statistically significant [50].
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 4 Results and Discussion
### On-Ground Temperature Profile Measurements  
Four surface temperatures were collected to monitor the rise in temperature of each cell throughout all 42 cycles.  
A rapid surface temperature increase was observed during the cycling of the battery for each cell. Similar observations were made by [PERSON] et al. (2014) and [PERSON] et al. (2016). In Figure 6a, during the first cycle, each cell was at room temperature (24 \(\pm\) 0.3 \({}^{\circ}\)C (TA)) before the on-ground experiments began. After the on-ground experiments commenced, there was a gradual rise in temperature due to the constant current charging of the battery pack, as shown in Figure 6b. Starting from room temperature, the surface temperature reached just above 32 \(\pm\) 0.3 \({}^{\circ}\)C for each cell in the battery pack by the end of the constant current charge. A decrease in the surface temperature was then observed due to the constant voltage charge, followed by a 0.167 h rest. Subsequently, constant current discharge was applied to the battery by the BAn, causing the temperature to rise to 37 \(\pm\) 0.3 \({}^{\circ}\)C. The constant voltage charging phase caused the surface temperature to gradually decrease, which was similar to the observations made by [PERSON] et al. (2016). After that, 1.0 h of rest was provided; at this time, the surface temperature of the battery dropped to room temperature. The highest temperatures were observed at the end of each constant current phase of the batteries. These observations were consistent with all the cycles performed on the ground, as shown in Figure 6a.  
The surface temperature of cell 4 was observed to be comparatively higher than the other three cells in the battery pack, as shown in Figure 6a. This could be due to various factors such as battery aging, changes in the SOC, and other influences (K