esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification
### Accuracy Validation for the Proposed Strain Transfer Model
The effective refractive index of the optical fiber \(n_{\mathit{eff}}\) is 1.456; the Poisson's ratio \(v\) is 0.170; the elasto-optical coefficients \(P_{11}\) and \(P_{12}\) are 0.121 and 0.270, respectively. The optical wavelength is 1550 nm. When tensile load was applied to specimens, the strain of specimens was transferred to the FBG sensors. The interrogator recorded the wavelength shift, and the measured strain can be calculated. Moreover, in the experiment, the hypotheses (1) and (2) should be satisfied by fully curing the adhesive layer and preventing the damage of the measurement structure. In fact, when the hypotheses are not satisfied, there can be cracks in the measurement structure, and the FBG sensors may have been damaged. In that case, the measured strain is significantly different from the original strain, and the measurement results are inauthentic. In the experiment, the applied strain \(\epsilon_{\mathrm{m}}\) was set to be 500 \(\upmu\)e, 1000 \(\upmu\)e, 1500 \(\upmu\)e, 2000 \(\upmu\)e, 2500 \(\upmu\)e, and 3000 \(\upmu\)e by changing moving distances of the fixture. The experiment was conducted with the specimens 1, 2, and 3. Three samples of each kind of specimen were used to repeat the experiment. The strain of the three samples was sensed by the FBG sensors, and the influence of temperature changes was compensated as described in Section 3.4. The average value of the strain recorded by the sensors under the same moving distance was regarded as corresponding measured strain \(\epsilon_{\mathrm{s}}\) to reduce random errors. The relationship between the measured strain \(\epsilon_{\mathrm{s}}\) and the applied strain \(\epsilon_{\mathrm{m}}\) is shown in Figure 11. The experimental data of the three stacking sequences were fitted by the linear regression functions. All the regression correlation coefficients \(R^{2}\) are more than 0.99, which indicates a good linear relationship between the measured strain \(\epsilon_{\mathrm{s}}\) and the applied strain \(\epsilon_{\mathrm{m}}\). The slopes of the linear regression functions indicate average strain transfer ratios. They are 79.6% for specimen 1, 82.6% for specimen 2, and 85.3% for specimen 3. The experimental results illustrate that average strain transfer ratios are inconsistent for CFRP laminates with different stacking sequences. The theoretical average strain transfer ratios for specimen 1, specimen 2, and specimen 3 are 80.0%, 82.3%, and 84.9%, respectively. The maximum error between the theoretical and experimental results is less than 0.5%. This demonstrates that the theoretical results are coincident with experimental results for all the three stacking sequences. Therefore, the accuracy of the proposed strain transfer model can be verified. Figure 10: Specimens and the measurement structure. (**a**) Three kinds of CFRP laminate specimens; (**b**) FBG sensor bonded to a specimen.
## 3 Results and Discussion
### Comparison of Porosity Investigation Methods
The uncalendered electrodes had a porosity of 44%. After calendering, porosities of 39%, 30%, 26%, and 18% were produced. To verify the reproducibility of the results, an electrode sheet, which was identically processed as the one with 30% porosity, was additionally considered. The total porosity of electrodes from this sheet was 28% (gauge measurement). The corresponding electrode densities, \(\rho_{bulk}\), ranged from 2.47 g cm\({}^{-3}\) (uncalendered) to 3.65 g cm\({}^{-3}\) (18% porosity). Measuring 60 electrodes each, the standard deviation (=statistic error) was the largest for the electrode with the lowest porosity (18 \(\pm\) 1.6%) and the smallest for the uncalendered electrode (44 \(\pm\) 0.7%). This finding was attributed to the larger absolute influence of the inaccuracy of the gauge (\(\pm\)1 \(\upmu\)m) for the thinner electrodes. Therefore, the systematic error of the measured porosity originating from the gauge was only \(\pm\)0.6% for the electrode with the highest porosity and with an electrode thickness of 94 \(\pm\) 1 \(\upmu\)m but \(\pm\)1.3% for the electrode with the lowest porosity (64 \(\pm\) 1 \(\upmu\)m). Considering the natural thickness inhomogeneity of the electrodes and the systematic error of the thickness measurement, the according overall standard deviation amounted to 0.7-1.6% porosity. Consequently, the porosity values are specified as integer values, as accuracy to the decimal place cannot be reasonably accomplished for composite electrodes. This is important when comparing the results and errors of the different methods in this study and to values in the literature. A difference of 1-2% in porosity cannot be considered significant for most analytical methods. Therefore, the difference between the two identically processed electrodes with 30% and 28% porosity can be considered negligible as the deviations were within the margin of error.
## 2 Materials and Methods
### Characterization of La-EPRC Particles for Static Adsorption of Phosphorus Removal
#### 2.3.5 Coexisting Anion Experiments  
Common water anions Cl\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), HCO\({}_{3}^{-}\), and CO\({}_{3}\)\({}^{2-}\) (added as sodium salts) were chosen for study in terms of their impact on La-EPRC particle adsorption. Nine 5 mL portions of a phosphorus standard stock solution (1 gP/L) were individually diluted to 250 mL with deionized water, resulting in a solution with a concentration of 20 mg P/L. Coexisting anions Cl\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), HCO\({}_{3}^{-}\), and CO\({}_{3}\)\({}^{2-}\) were added at molar concentration ratios of elemental phosphorus (0.5:1, 1:1, 2:1). The corresponding masses of NaCl, Na\({}_{2}\)SO\({}_{4}\), NaHCO\({}_{3}\), and Na\({}_{2}\)CO\({}_{3}\) were weighed and added to the phosphorus solution. A total of 2 g of La-EPRC particles was introduced into each solution and placed on a shaker (25 \(\pm\) 1 \({}^{\circ}\)C, 150 \(\pm\) 5 r/min) for continuous shaking. After the adsorption reaction reached equilibrium at 48 h, the reaction was stopped, supernatant samples were taken, and after filtering through a 0.45 \(\upmu\)m filter, the absorbance was measured. The remaining phosphorus concentration and adsorbent adsorption were calculated to analyze the impact of coexisting anions on adsorption efficiency.
## 4 Evaluation and Experiments
### Dataset Creation and Annotation  
We assembled a dataset of 100 label images, carefully curated to reflect the diversity of real-world label data encountered in practical applications, specifically focusing on drug labels. The dataset comprised labels predominantly in the Thai language, reflecting our focus on PDPA compliance in Thailand, with a subset in English for comparison. The photographs were taken under varying lighting conditions and angles to simulate real-world capture scenarios.  
To establish a robust ground truth for evaluation, each label image within the dataset underwent a manual annotation process. Trained human annotators, fluent in both Thai and English and possessing a thorough understanding of PDPA guidelines, performed three key annotation tasks:  
1. Generating accurate ground truth text transcriptions for each label image, serving as the benchmark for OCR accuracy assessment.
2. Delineating precise bounding boxes around all text segments unequivocally classified as personal data according to PDPA criteria (names, addresses, hospital numbers, etc.), providing the ground truth for redaction effectiveness evaluation.
3. Categorizing each identified personal data segment into specific PDPA-relevant categories (e.g., names, addresses, phone numbers), enabling granular analysis of redaction performance across different PII types. This categorization also included a \"non-PII\" class for text that should not be redacted.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 4 Data Fusion by Platforms  
Varying the scale of platforms allows for precise monitoring and options for fusion methods in field trials. These platforms include ground-based [139], space-borne [140] and aerial [3] platforms, which are outlined in the following subsections.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Optical** & **Thermal Infrared** & **Multispectral** & **Hypenspectral** & **LiDAR** & **Radar** & **References** \\ \hline \multicolumn{2}{c}{Crop Parameters} & & & & & & \\ \hline \multirow{3}{*}{N} & Fusion of Multiplex and & infrared with the & Fusion of & Fusion of & & & \\  & Unitedx with the & unified & multispectral & & multispectral & & & \\  & ALS-2 N & infrared & hyperspectral data & LiDARAR & & & \\ \hline \multirow{3}{*}{Chleophyll} & Fusion of & Fusion of near & Fusion of & Fusion of & & & \\  & ClorchlOC and & infrared (NIR) & multispectral & & & & \\  & Groupee with & with the & spectrometer with & & & & \\  & the SFAD & visible-near & the hyperspectral & & & & \\  & infrared (vis-NIR) & imagery & & & & \\ \hline \multirow{3}{*}{LII} & Fusion of Crop & Fusion of vis-NIR & Fusion of & & & & \\  & Circle with & with spectral & multispectral with & imagery & data with & & \\  & the OptRx & camera & vegetation indices & & & & \\  & & & vegetation indices & & & & \\  & & & imagery & & & \\ \hline \multirow{3}{*}{AGB} & Fusion of OptRx & Fusion of NIR & Fusion of & & & & \\  & with the Multiplex & with the vis-NIR & multispectral & & & & \\  & and Crop Circle & and spectral & photogrammetry & data & & & \\  & & & imagery & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different sensors fusion methods for crop parameter estimation.
# Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China
## 2 Data and Methods  
### Study Area  
Southwest China covers a region bounded by 20\({}^{\circ}\)54\({}^{\prime}\)-34\({}^{\circ}\)19\({}^{\prime}\) N and 97\({}^{\circ}\)21\({}^{\prime}\)-112\({}^{\circ}\)04\({}^{\prime}\) E (Figure 1). It includes five provinces (municipalities, autonomous regions): Chongqing, Sichuan, Guizhou, Yunnan, and Guangxi. The area has a total coverage of 1.38 \(\times\) 106 km\({}^{2}\), which is about 14.34% of China. The study area is dominated by the subtropical monsoon climate, affected by the southeast and the southwest monsoon, with a mean annual temperature of 14.6 \({}^{\circ}\)C and an average annual precipitation of 1195 mm, of which 600 mm occurs in the growing season. The terrain is high in the west and low in the east, with a stepped distribution, diverse landform types, and high landscape heterogeneity [48]. Southwest China is the most concentrated area of karst landforms, accounting for 23.14% of China's karst distribution area. The forest has a large area and is widely distributed in the study area. Grassland is mainly distributed in the western Sichuan Plateau, and cropland is mainly distributed in the Sichuan Basin, the middle of the Guangxi hills, and the eastern part of the Yunnan-Guizhou Plateau. The three main LULC types are grassland, forest, and cropland, accounting for more than 98% of the total area of southwest China.
# Pressure Transient Model of Water-Hydraulic Pipelines with Cavitation
## 4 Simulation Results
### Case 3: Pressure Transients with Cavitation on the Upstream Side of the Valve
The corresponding cavitation volumes in the element close to the valve are shown in Figure 9. The maximum size of the vaporous cavity is \(3.955\times 10^{-6}\) m\({}^{3}\) (the duration is about 0.59 s) using the FDM and \(1.305\times 10^{-5}\) m\({}^{3}\) (the duration is about 0.61 s) using the MOC. When the pressure reduces to vapor pressure again, using the FDM, the second cavity has a volume of \(6.785\times 10^{-7}\) m\({}^{3}\) and a duration of 0.25 s; however, using the MOC, the cavity has a volume of \(1.281\times 10^{-6}\) m\({}^{3}\) and a duration of 0.12 s.  
Figure 8: Simulation results and experimental data of pressure pulsations on the upstream side of the valve. Figure 9: Simulation results of the cavitation volume in the first element between the MOC and the FDM. As listed in Table 5, based on a comparison between Case 2 and Case 3, the durations of cavitation are much longer and the maximum cavitation volumes are larger on the downstream side of the valve. It is clear that cavitation is more likely to occur on the downstream side of the valve.
## 2 Materials and Methods
### Eudiometer Test and Hydrogen Evolution  
A euidometric system (Figure 2) was purchased from Weinkauf Medizin & Umwelttechnik (Hallerndorf, Germany), which was set up by a Cimarec(tm) i Telesystem Multipoint Stirrers (Thermo scientific, Stuttgart, Germany), sealed audiometers, level bottle and reaction bottles (Selutec, Walkenmuhleweg, Hechingen, Germany), a LAUDA ECO Silver immersion thermostat bath system (Lauda-Koenigshofen, Stuttgart, Baden-Wurttemberg, Germany), and a RMP controller (Thermo Scientific, Germany). The tested alloys were installed into a plastic net-basket and immersed into Hank's Balanced Salt Solution (HBSS, Sigma-Aldrich, St. Louis, MO, USA) in eudiometer bottles to measure the hydrogen gas evolution (Figure 1). The hydrogen volume was recorded from the eudiometer every 48 h. Thus the volume of hydrogen gas generated every 2 days was used to calculate the corrosion rate of magnesium and its alloys based on the chemical equation Mg + 2H\({}_{2}\)O \(\rightleftharpoons\) Mg\({}^{2+}\) + 2 OH\({}^{-}\) + H\({}_{2}\). The volume of hydrogen gas generated by pure zinc was used to calculate corrosion rate based on the chemical equation Zn + 2H\({}_{2}\)O \(\rightleftharpoons\) Zn\({}^{2+}\) + 2 OH\({}^{-}\) + H\({}_{2}\).
## 2 Circuit Implementation  
Figure 1 shows the proposed resistive sensor readout analog-front-end (AFE) IC architecture. The architecture consists of a multipath CFIA, low-pass filter (LPF), buffer, 12-bit successive approximation register analog-to-digital converter (SAR ADC), R-2R DAC, and feedback resistor. To drive a variety of resistive sensors, the feedback resistor R\({}_{2}\) is designed by implementing a 5-bit programmable resistor. Depending on the external 5-bit digital input, the proposed resistive sensor readout AFE has an adjustable gain of 16-44 dB. Figure 2 shows the operation of the R-2R DAC calibration. The R-2R DAC is designed to compensate for the external offset generated by sensor element mismatches. The R-2R DAC has the ability to adjust the amplified external offset voltage between 3.3 V and -3.3V, with 1.61 mV intervals through the 12-bit digital input. The output voltage of the proposed CFIA without the external offset and R-2R DAC is expressed as (1).  
\[OUTP-OUTN=\left(1+2\ \times\frac{R_{1}}{R_{2}}\right)\times\left(INP-INN\right) \tag{1}\]  
Each unit of the R-2R DAC, consisting of \(R_{D}\) and \(2R_{D}\) resistors, has an output resistance of \(R_{D}\). The output resistance between the DACP and DACN node can be interpreted as 2R\({}_{D}\). Therefore, the output voltage of the proposed CFIA with R-2R DAC is expressed as (2).  
\[\begin{array}{ll}OUTP-OUTN=&\left(1+2\ \times\frac{R_{1}}{R_{2}}\right) \times\left(INP-INN\right)\\ &+\ \frac{R_{1}}{R_{D}}\times\left(\left(INP-INN\right)-\left(DACP-DACN\right) \right)\end{array} \tag{2}\]Through the second term of (2), the amplified external offset can be calibrated. In this design, R1 can influence offset calibration. Therefore, R2, which is independent from offset calibration, is configured as a programmable resistor.  
Figure 3 shows the block diagram of the proposed CFIA. The proposed fully differential CFIA circuit consists of two main signal paths: a low-frequency and a high-frequency path. The low-frequency path employs the chopper stabilization technique to reduce flicker noise and DC offset in the baseband. To reduce the ripple caused by chopping, the RRL is implemented. To compensate for the notch in the transfer function of the chopping frequency band, the high-frequency path is used.  
Figure 1: Proposed architecture of resistive sensor readout analog-front-end integrated circuit (IC).  
Figure 2: Operation of R-2R digital-to-analog converter (DAC) calibration.
# Dose Estimation by Geant4-Based Simulations for Cone-Beam CT Applications: A Systematic Review
## 3 Results and Discussion
Let us remark that it is necessary to consider the tradeoffs between image quality and radiation dose. The ALARA principle does not necessarily mean the lowest radiation dose, an accurate diagnosis must be achieved. Conventional methods for improving image quality (scatter and artifacts reduction) based on physical equipment or measurements may lead to a dose increase. [PERSON] et al. proposed a method that fulfills the necessary conditions for data sufficiency employing a circle-plus-line trajectory instead of a single circular trajectory with less than 0.18% increase in average glandular dose to the breast per projection [120]. In addition, a precomputed scatter library-based software approach to suppress scatter without a dose or scan time increase has been recently proposed by [PERSON] et al. [110]. However, a very promising solution based on deep learning-based methods which uses MC simulations results as real data to train a CNN for image restoration and scatter correction has demonstrated high efficiency, accuracy, and reliability [132].
## 4 Experimental Results  
In this section, we explain the experimental setup and the metrics used to evaluate the proposed system and provide an analysis of the results obtained during experiments. We tested our system in the main building of our university (Norwegian University of Science and Technology (NTNU)) with a total of 15 participants divided into four different groups. All groups were given demonstrations of the test scenarios, which are discussed in Sections 4.1 and 4.2 Three cameras were mounted in different locations of the building to monitor three different areas. Overall, 28 experiments were conducted to test the proposed system. The number of violations detected and the successful enforcements performed under different configurations are shown in Table 4.  
In overall experiments, the system was unable to properly detect the violation on three occasions. In two of them, the reason was that one individual was fully occluded by the other person standing in front them in-line with the camera's angled view in a two-person group. The third system failure was due to the failure in group re-identification, which occurred due to a large variation in lighting conditions. However, the violation was detected, and enforcement was performed precisely. Details of these experiments and metrics used to evaluate the proposed system and their results are discussed as follows:
# Influence of Ball-End Milling Strategy on the Accuracy and Roughness of Free Form Surfaces
## 5 Conclusions  
In this study, the milling of freeform surfaces using a ball-end mill, programmed using a CAM system, was experimentally investigated. Three milling strategies (linear, offset, and spiral) and two milling methods (3- and 5-axis) were compared. The size and distribution of the deviations simulated using a CAM system were inaccurate. A comparison of textures showed that 5-axis milling is advantageous in terms of the regularity of tool marks on the finished surface. This is important in the final finishing if the surface is located on a forming tool (mold, die, or pressing tool). A comparison of the surface roughness also shows the advantages of 5-axis milling, which achieved identical or better results than 3-axis milling.  
The results obtained complement the knowledge about milling freeform surfaces. The quality of the data acquired by a 3D laser scanner proved the validity of its use. The laser scanner allows to quickly capture the desired surface with high accuracy and save the data (point cloud) as a 3D model. Due to this option, evaluation software for 3D models allows the results to be presented in several ways. The advantages of the 3D surface mapping compared to the 2D techniques provided by other evaluation devices are obvious. Based on the reviewed literature, it can be stated that the proposed method of verifying the accuracy of the production of freeform surfaces brings a new perspective on this issue. The improving accessibility of these devices, high level of operability and rapid data evaluation, contribute for their rapid expansion in both experimental and practical activities. The availability of the conversion function in the programming system greatly simplifies the preparation of 5-axis operations.  
The aim of future work is to design and verify other methods for detecting shape and surface integrity deviations on finished parts. The results obtained can then be compared with the results presented in this article.  
Conceptualization, P.I. and Z.G.; methodology, P.I.; software, L.K., J.B. and M.D.; validation, M.V., P.I. and Z.G.; formal analysis, Z.G.; investigation, L.K. and J.B.; resources, P.I.; data curation, Z.G.; writing--original draft preparation, P.I. and Z.G.; writing--review and editing, M.V.; visualization, P.I. and M.D.; supervision, M.V.; project administration, P.I.; funding acquisition, M.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic (projects VEGA 1/0457/21 and KEGA 048 TUKE-4/2020).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
## 3 Contextual Detection Model  
In this section, we design a new contextual model to better exploit contextual information for our remote sensing dataset. After generating the object proposals in the RPN stage, an ROI-Pooling layer is used to project each proposal onto the shared feature maps based on the strides of the network in the Fast R-CNN stage. The feature maps corresponding to each proposal are then encoded into a fixed-dimensional representation with a predefined spatial resolution. Following this, several fully connected layers are fed with these presentations for classification and class-specific bounding box regression.  
Since context contributes to the object detection, we expect that it will help to effectively detect small remote sensing objects. Moreover, the feature maps corresponding to the small candidate proposals, whose spatial resolution may be less than \(1\times 1\) after enduring multiple down-sampling process, are less discriminable because small objects usually only occupy a small image area. Therefore, we focus on leveraging the context information to boost the performance of small remote sensing object detection.  
As is demonstrated in the Figure 3, we incorporate the corresponding context region enclosing the proposal region with the candidate proposal after the ROI-Pooling layer in which the spatial resolution follows the default setting, namely \(7\times 7\). This is a simple and intuitive manner to merge the context information, which is proved to be effective in our experiments. Furthermore, the concatenated feature maps are fed into a \(1\times 1\) convolutional layer to reduce channel dimensions considering for the computation cost. Besides, we construct the shared feature maps with all the convolutional layers from the pre-trained ResNet-50 model which has no fully connected layers. So we attach two hidden 1024-d fully connected layers (each followed by Dropout [27] and ReLU layer), which are randomly initialized by the Xavier method, before the final classification and bounding box regression.  
Figure 2: Illustration of the proposed modified Faster R-CNN for small remote sensing objects.