esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Limitations  
This paper presented a novel framework to support autonomous machine tending in lights-out manufacturing, which minimizes the involvement of machine operators during the production process. Most machine tending robot systems adopt collaborative robots that connect to the machine via Ethernet to perform various tasks efficiently under the inspection and tending of onsite operators [40; 41; 42]. Autonomous mobile robots can use our proposed architecture to achieve fully autonomous machine tending through the integrated vision system and computer vision-based algorithms. The proposed intelligent manufacturing approach can truly achieve autonomous machines, HMI, and emergency detection without the operator's assistance to support various machine-tending tasks. The proposed methods are flexible, scalable, and adaptable; however, it has some limitations. Text detection and recognition are more sensitive to image quality than general object detection [43]. Sometimes, the mobile camera captures images and videos suffering from poor lighting conditions, such as shadow in the images or reflection of light due to inappropriate shooting distance or angle, making the feature extraction process challenging. Moreover, camera motion or shaking while capturing images can cause text to be blurred [35].  
Further research to address the problems mentioned above is necessary to implement the proposed algorithms in autonomous machine-tending applications in a real-world industrial environment. Since the performance of deep learning models largely relies on the size of training data, a bigger and better dataset will be collected in the future and used to improve the proposed approach. Image deblurring [44; 45; 46] and reflection removal [47; 48; 49]  
Figure 9: The accuracy of developed model for HMI detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline RNN & 78.2\% \\
**The Proposed Method (this paper)** & **85.7\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compared results for RNN and the proposed method (working status recognition).  
methods will also be applied to improve the quality of the captured images. Moreover, a reference dictionary containing common abnormal conditions of CNC machines will be designed to match and correct text recognition results.
## 2 The Proposed RDHEI Method
### Data Extraction and Image Recovery  
Depending on the key held by the receiver, either the embedded data or the original image can be obtained. There are three possible scenarios based on the receiver's possession of the different keys, as shown in Figure 13:  
1. If the receiver possesses the data hiding key \(K_{d}\), he can successfully retrieve the embedded data without any errors. The receiver begins by dividing the marked encrypted image into 8 bit planes. To identify the bit planes involved in the embedding process and extract the embedded data, the receiver follows the procedure explained earlier, starting from the top left and reading the first bits in each bit plane. These bits indicate whether the bit plane is utilized for data hiding and provide information about the available embedding space. The hidden data are then read from the encrypted image and decrypted using the key \(K_{d}\). However, since the receiver lacks access to the encryption key \(K_{e}\), they are unable to decrypt the image and restore it to its original form.
2. If the receiver has the encryption key \(K_{e}\), they can reconstruct the original image without any loss. The receiver starts by decrypting the image using its encryption key. Following that, they extract the auxiliary information from each bit plane that will be used in the image recovery process. Initially, the label map is extracted from the MSB plane of the decrypted image. Subsequently, the compressed binary strings and their corresponding lengths are extracted and decompressed to recover the positions and the values of all image reordered blocks. Finally, using the extracted decompressed label map, we inverse the mapping prediction error process to recover the original image.
3. When the receiver possesses both the decryption key \(K_{d}\) and the encryption key \(K_{e}\), they can successfully retrieve the data without any errors. By employing the same detailed steps as in the previous cases, they can decrypt the image and restore it to its original form without any loss or distortion.  
Figure 12: The data hiding process.
## 3 Results
### AsA Measurement  
To compare AsA content between wild type and _OsVTC1_ RNAi lines, we measured the amount of AsA in whole leaves. The AsA production was significantly reduced by ~34 and 40% in RI1-2 and RI1-3 lines compared to wild type (Figure 2a). The three different parts of fully expanded leaves referred to as top, middle and base areas were also measured for AsA content. The results revealed that the top part of the leaves contained the highest amount of AsA, followed by the middle and the base of the leaves. Moreover, the amount of AsA in the wild type was significantly higher than that in the mutant lines in the top part of leaves, but not different in the middle part and base of leaves, respectively (Figure 2b).  
In addition, we measured the AsA contents in leaves, stems and roots of three different Thai rice cultivars (KDML105, JHN and RD6) and measured the AsA contents of KDML105 rice cultivar at different rice growth stages (seed, seedling, 3 weeks-old, 12 weeks-old and 20 weeks-old plants) in leaves, stems and roots. The results showed that the AsA content was not different in three Thai rice cultivars, but the AsA contents were significantly higher in leaves than in stems and roots (Figure 2c). The AsA was not detected in the seed and seedling stage. On the other hand, the AsA was detected at a 3-week-old plant at a relatively high level and stayed at this high level at 12- and 20-week-old plants, in which the highest amount was detected in leaves (Figure 2d).  
Figure 1: qPCR analysis of genes related in AsA biosynthesis pathway and antioxidant enzyme are shown as follows: (**a**) _OsVTC1-1_; (**b**) _OsVTC1-3_; (**c**) _OsVTC1-8_; (**d**) _OsGME1_; (**e**) _OsGME2_; (**f**) _OsVTC2_; (**g**) _OsVTC4_; (**h**) _OsGDH_; (**i**) _OsGPX1_ genes. Bars represent the means and standard errors of two biological replicates (three technical replicates per biological replicate). Asterisk indicates statistically significant differences from Zhonghua17 (wild type) analyzed using Student’s _t_-test (**_**
## 3 Experimental Validation and Analysis
### Overall Deformation Reconstruction
#### 3.6.2 Reconstruction of Overall Deformation Using Predicted Values  
Figure 18 shows the overall deformation reconstruction based on GNSS displacements and accelerations predicted by the CEEMDAN-SC-LSTM model. Table 3 provides detailed error statistics for the overall deformation reconstruction. As shown in the table, the correlation coefficient between the displacements reconstructed by the CEEMDAN-SC-LSTM model and the reference values is 96%, with an RMSE of 0.005 m and a mean peak error of 0.004. Compared to the CEEMDAN-LSTM and LSTM prediction models, the CEEMDAN-SC-LSTM model performs best in overall dynamic displacement reconstruction, closely matching the true values and accurately capturing the deformation trends and details. In summary, the CEEMDAN-SC-LSTM model excels in the overall deformation reconstruction after prediction, offering higher accuracy and reliability.
## 2 Materials and Methods  
### Data  
Experiments were approved by the Institutional Review Board (IRB) of the University of Pennsylvania (UPenn). Quantitative evaluation was based on public and private clinical data from four retrospective cohorts (spleen (3D-CT, \(n=20/41\), Medical Segmentation Decathlon [18]); breast tumor (2D-DCE-MRI, \(n=50\), multimodality trial at UPenn; NIH P01 CA85484); lung nodules (2D-CT, \(n=50/89\), The Cancer Imaging Archive [19; 20; 21]); brain glioblastoma (3D-MRI, \(n=20/335\), BraTS'19 [3; 4; 22])). Cohort subsets were created, following random selection, to facilitate the exhaustive manual annotations described hereafter. The brain (11 males, 9 females: mean age = 62.84/64.36, age range = 44.82-77.48/39.64-77.09) and breast (female: mean age = 50.41, age range = 32.68-71.97) datasets were acquired from 2006-2014 and 2002-2006, respectively. The spleen (13 males, 7 females: mean age = 63.85/58, age range = 40-81, 48-68) and lung (34 males, 16 females) were acquired from 2000-2013 and 2004-2011, respectively. Age information was not available for the lung dataset. Ground truth segmentations were available for all datasets, except for lung which were created by a fellowship-trained, board-certified thoracic radiologist (S.K., 21 years of experience).
## 2 Materials and Methods
### Experimental Setup  
Considering that this report focuses on the FDTD NEAM model development, materials absorption coefficients were selected from others scientific reports. Nevertheless, as it was shown in previous sections, the model simplicity only requires the absorption coefficient measured in an impedance tube, which can be calculated with ISO 10534 standard. Thus, the present work presents just two measures of the absorption coefficient, one for a porous absorber [37], and the other for PET fibers [38], later described in Sections 3.1 and 3.2.
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 3 Results
### 50 kW Case Study
#### 3.2.1 LCOE--Calculation Method Analysis for 50 kW and 1100 kWh/kWp Case  
Introducing all the previous parameters and fixing the regulated tariff at 17.4 c\(\upxi\)/kWh including taxes and with a fixed interest rate of 10% produces the following results (Table 8), considering an energy production of 60,600 kWh/yr, OM annual costs of 1149 \(\upxi\) and a lifetime for the installation of 25 years. The recovery factor value results as 0.1102.  
With the results obtained for the LCOE, it can be concluded that Spain would reach grid parity under conditions where the PV modules cost between 0.8 \(\upxi\)/Wp and 0.6 \(\upxi\)/Wp. The IRR calculated at 25 years with a standard loan of 10% reaches positive values at between 0.8 \(\upxi\)/Wp and 0.6 \(\upxi\)/Wp. It is concluded that there is a floor of 1.39% of IRR for the current panel cost scenario and the worst-case scenario of yield and interest rate.  
Figure 9 illustrates a clear presentation of the point at which the retail costs and generation costs reach an equilibrium. As can be seen, there is a considerable deviation which impacts the grid parity forecast.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**PV Module Cost (\(\upxi\)/Wp)**} \\ \cline{2-6}  & **1** & **0.8** & **0.6 \({}^{*}\)** & **0.4** & **0.2** \\ \hline PV module (60 kWp) & 60,000 & 48,000 & 36,000 & 24,000 & 12,000 \\ Fixed installation costs & 38,000 & 38,000 & 38,000 & 38,000 & 38,000 \\ TOTAL PV installed cost (\(\upxi\)) & 98,000 & 86,000 & 74,000 & 62,000 & 50,000 \\ VAT (21\%) & 20,580 & 18,060 & 15,540 & 13,020 & 10,500 \\ \hline TOTAL PV installed cost, & & & & & \\ including taxes (\(\upxi\)) & 118,580 & 104,060 & 89,540 & 75,020 & 60,500 \\ Total specific cost (\(\upxi\)/Wp) & 1.98 & 1.73 & 1.49 & 1.25 & 1.01 \\ Total specific cost (\(\upxi\)/W) & 2.37 & 2.08 & 1.79 & 1.50 & 1.21 \\ \hline \hline \end{tabular} \({}^{*}\)Current case.  
\end{table}
Table 7: Cost of a PV system at 60,000 Wp (50,000 W inverter).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PV Module Cost (\(\upxi\)/Wp)** & **System Cost (\(\upxi\))** & **LCOE (\(\upxi\)/kWh)** & **IRR (\%)** \\ \hline
1 & 118,580 & 0.2154 & \(-\)2.049 \\
0.8 & 104,060 & 0.1911 & \(-\)0.53 \\
0.6 & 89,540 & 0.1669 & 1.39 \\
0.4 & 75,020 & 0.1427 & 3.97 \\
0.2 & 60,500 & 0.11841 & 7.46 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of LCOE and IRR based on a recovery factor at 10% interest for the 50 kW case.  
This case is analysed by introducing a sensibility analysis based on different interest rates, determining which loan terms and conditions allow the system to achieve the regulated tariff target for grid parity for a different module cost (\(\xi\)/Wp), as shown in Table 9. The positions where the LCOE is lower than the retail cost of energy have been highlighted
## 4 Discussion
### Artificial Macropores Enhances Desalinization and Plant Yield  
Soil column experiments showed a significant improvement for \(K\)s by more than 260% when cohesive particles were removed. This result confirmed the key role of macropore in water movement, especially under saturated condition. Meanwhile, this result also provided a basis for the promotion of water infiltration and solute migration in flood irrigation events.  
Salt toxicity is a major restrictive element in soil structural degradation characterized by high exchangeable sodium percentage (ESP), which will worsen soil fertility and water permeability extremely [28; 29]. AMSF method in irrigation experiments showed some benefits for improving soil leaching in the upper 30 cm soil profile, as well as environmental improvement for rooting zone. These positive results benefited from more water infiltrating into the deeper layer. Artificial macropores provided continuous pores, which introduced flow channels in the soil. Another possible reason for this would come from the eliminated surface flooding [30], which reduced the tendency of generating secondary salinization resulting from soil evapotranspiration. Secondary salinization or anthropogenic salinization is the result of improper water input from human activities such as excessive irrigation and longstanding flooding [31; 32]. More alfalfa biomass was observed, which may result from the enhanced soil leaching to deeper layer, as a lower salinized environment can alleviate the salt stress on crops.  
Furthermore, salt crust, i.e., crystals or efflorescence was reported to formed at soil surface when salt content exceeded its solubility. This hydrological phenomenon could maintain the underlying soil wetter and allow more salt accumulation below the crust. However, it was also a complicated process and required long-term continuous monitoring. In present study, no salt crust was detected throughout the whole testing duration, probably because the shorter monitoring time.
# An Empirical Study on Ensemble of Segmentation Approaches
## 3 Results
### Experiments  
#### 3.3.1 Baseline Ensembles  
As the aim of this paper is to study approaches to increase the diversity of ensembles, we report in Table 2 the performance of some baseline classifiers and ensembles based on different network architectures (all combined with the data augmentation DA1, see Section 2.3). The tests reported in Section 3.3.1 are all based only on the dice loss; moreover, for the sake of space, for the polyp and skin datasets we report only the average performance value among the set of datasets. Each ensemble is made up of \(N\) models (\(N=1\) denotes a stand-alone model) which differ only for the randomization in the training process:  
* _RN18_ is a stand-alone DeepLabV3+ segmentator with backbone Resnet18 (pretrained in ImageNet);
* _ERN18(\(N\))_ is an ensemble of \(N\)_RN18_ networks (pretrained in ImageNet);
* _RN50_ is a stand-alone DeepLabV3+ segmentator with backbone Resnet50 (pretrained in ImageNet);
* _ERN50(\(N\))_ is an ensemble of \(N\) RN50 networks;
* _RN101_ is a stand-alone DeepLabV3+ segmentator with backbone Resnet101 (pretrained as detailed in the above Section 2.1);
* _ERN101(\(N\))_ is an ensemble of \(N\)_RN101_ networks.  
The results in Table 2 show that, although the overall performance increases when switching from the stand-alone version to an ensemble, the improvement is not as high as one might expect, indicating that the individual approaches are quite stable. Maybe this result is related to the architecture of the DeepLabV3+ network: its internal modules apply atrous convolutions in cascade or in parallel to capture a multi-scale context by adopting several atrous rates. This solution, which has been designed to solve the problem of segmenting objects at multiple scales, also mimics an ensemble approach thanks to the fusion of activations taken at different levels of the encoder, making the resulting segmentation quite stable. The best method is to use ResNet101 as a backbone.
## 5 Radiometric Calibration Processing Results
### Backscattering Coefficient Calculation and Calibration Accuracy Analysis  
According to Equation (8), the calibration constants obtained by the peak method and the integral method shown in Tables 6 and 7 were used to radiometrically calibrate the two modes of SAR data, and the backscattering coefficient images of the SAR data were obtained. The RCS of each CR were extracted from the images, and the calibration accuracy was evaluated via the relative calibration accuracy and absolute calibration accuracy indexes [24].  
The relative calibration accuracy is defined as the standard deviation of a set of reference point targets with the same theoretical RCS in the calibration site [9; 22; 24], and it is calculated as follows:  
\[\Delta RCA=\sqrt{\sum_{i=0}^{N-1}\frac{(\tilde{\sigma}  
absolute calibration accuracies obtained by the integral and peak methods are 0.333 and 0.466 dB, respectively.
# Indoor Microclimate Analysis of the San Panfilo Church in Tornimparte, Italy
## 3 Results
### Continuous Monitoring
#### 3.2.2 Spikes Analysis  
As mentioned in the previous section, some spikes in temperature and relative humidity were monitored at the showcase position. An inspection of data collected in the main wave reveals the presence of spikes in some positions during several periods in the year. In particular, abrupt variations in temperature and relative humidity were measured at the left balustrade and on the choir surface both on the left side and right side.  
On the showcase, the spikes occurred during winter (from September to April) at the same day time (12:00-13:00 UTC+1) with a variation of temperature of about 3 \({}^{\circ}\)C and of relative humidity of about 12% and a time rising of more than 1 h (Figure 9a,b). As mentioned before, the specific humidity did not vary.  
Figure 9: (**a**) Temperature on the case and at crucifix top on 18 and 19 January 2022; (**b**) relative humidity on the case and at crucifix top on 18 and 19 January 2022; (**c**) temperature at the choir from 12 to 14 September 2021; (**d**) relative humidity at the choir from 12 to 14 September 2021.  
Near the left balustrade, spikes in temperature and relative humidity are recorded during two periods: from 19 August to 10 September and from 3 to 15 April at about the same day time (from 07:30 to 07:50 UTC+1). The intensities of peaks were, respectively, in the range of 2.1 \({}^{\circ}\)C to 3.0 \({}^{\circ}\)C and 8% to 10% with a rising time of 20 min.  
On the right and left side of the choir, the peaks in temperature and relative humidity were detected in four periods during the year that are not always overlapping. As shown in Figure 9c,d, in the period 12-14 September, some peaks occurred in temperature and relative humidity on the right choir side but not on the left side. The daytime peak was always in the afternoon (from 14:00 to 15:10 UTC+1) on the choir's left side and in the morning (from 09:00 to 09:50 UTC+1) on the choir's right side with a rising time of 20 min. The intensity of these temperature peaks ranged from 6 \({}^{\circ}\)C to 12 \({}^{\circ}\)C, and the intensity of relative humidity peaks was from 15% to 30%. At the same time, the specific humidity values were subject to small positive peaks.  
The timing in temperature and relative humidity peaks and the church window positions suggest that the peaks can be due to the presence of direct radiation on the surfaces. In particular, the glass showcase is exposed to solar radiation through the central apse window in the central day hours in winter when the sun is low on the horizon. The right size of balustrade and the choir are exposed to solar radiation in the morning or in the afternoon on different months of the year as a consequence of the different positions of windows on the lateral side of the apse and the main nave.
## 4 Suitability Evaluation and Effect Analysis of Non-Conventional Lanes
### Comprehensive Evaluation System  
A comprehensive evaluation system is an important tool for scientifically assessing the overall benefits of non-conventional lanes, requiring the establishment of a multi-dimensional evaluation system that includes spatial, temporal, safety, and efficiency dimensions.  
[PERSON] et al. [71] established a lane-based intersection lane function and signal phase optimization model that can simultaneously optimize entrance and exit lanes, effectively reducing critical flow ratios, cycle lengths, and average vehicle delays. [PERSON] et al. [72] studied the synergistic problem of dynamic lane functions and signal control at intersections, establishing a dynamic lane function and signal phase combination model based on time-space relationship analysis of signal-controlled intersections, comprehensively applying traffic flow theory and signal control technology. [PERSON] et al. [73] proposed a dynamic lane function optimization model based on entrance groups, significantly reducing the number of variables that need to be optimized simultaneously while ensuring the accuracy of optimization results, thereby improving solution efficiency.  
Research on comprehensive evaluation systems provides a methodological foundation for the holistic assessment of non-conventional lanes. Existing research has made progress in multi-objective optimization and collaborative control but still needs improvement in standardizing evaluation indicators, scientifically determining weights, handling uncertainties, and other aspects. Future research should establish standardized evaluation indicator systems and develop intelligent evaluation decision support systems capable of providing customized evaluation solutions according to the characteristics of different cities and regions. Simultaneously, research on the interpretability of evaluation results needs to be strengthened to enable decision-makers to better understand and apply evaluation results. Furthermore, feedback mechanisms for evaluation results should be established, incorporating actual operational data into the continuous improvement of the evaluation system.