esa-sceva/satcom-chunk-collection huggingface dataset:
# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 6 Discussion  
In this study, based on RGB-D vision technology, the dimensions of tread products after cutting at a fixed length were detected. This method achieved non-human participation and high accuracy, which is significant for improving the intelligence of production lines and the efficiency of product quality detection in the industry. It also promotes the development of traditional rubber extrusion production lines towards higher quality. However, there are still some areas that need further optimization and improvement:  
1. In the tread dimension inspection process, equipment hardware constraints lead to lengthy data processing times, making it difficult to detect tread images and point cloud data collected by the RGB-D camera's RGB and IR lens modules in real-time. Consequently, all measurements are processed offline, preventing online debugging. The next step involves upgrading the acquisition equipment, further improving algorithm efficiency, reducing detection complexity, and developing stable preview software to provide real-time feedback for monitoring tread quality detection results.
2. This study initially verifies the quality inspection of rigid rubber treads. The next step is to study the point cloud alignment and 3D reconstruction methods for non-rigid treads, designing an algorithm with improved robustness to inspect the quality of irregular and curved surface rubber products and archive the 3D data of retained products.
3. This study only conducts experimental verification, demonstrating the feasibility, reliability, and accuracy of detecting tread quality based on point cloud data. The next step involves converting the inspection program written in Python and Matlab into an industrial control program and integrating this inspection method into the production line after the treads are cut to fixed lengths. Subsequently, extensive on-site experiments and verifications will be conducted.  
Conceptualization, Z.P.; methodology, L.H.; software, L.H.; validation, L.H.; formal analysis, L.H.; investigation, L.H. and Z.P.; resources, M.C.; data curation, M.C.; writing--original draft preparation, L.H.; writing--review and editing, L.H.; visualization, L.H.; supervision, M.C.; project administration, M.C.; funding acquisition, M.C. and L.H. All authors have read and agreed to the published version of the manuscript.  
This study is funded by the National Natural Science Foundation of China [Grant No. 61863009] and the Guangxi Key R&D Program [Grant No. Guike-AB22080093].  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article and further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Guilin GLESI Scientific Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 2 Proposed Methodology
### Data Description
#### 2.1.2 Breast Ultrasound Images Dataset Description  
One of the biggest causes of death for women worldwide is still breast cancer, and patient outcomes are greatly enhanced by early detection. A useful resource for developing computer-aided diagnosis techniques is the Breast Ultrasound Images Dataset, which is accessible on Kaggle [58]. Three different kinds of ultrasound scans, normal, benign, and malignant, are included in this collection. This kind of classification makes it easier to create and assess ML models for tasks including segmentation, detection, and classification in the diagnosis of breast cancer. In this study, only two classes, benign and malignant, are considered, excluding the normal category to focus on differentiating pathological cases. The dataset, which was gathered in 2018, consists of 780 high-resolution photos that are saved in PNG format and have an average size of 500 \(\times\) 500 pixels. These pictures were taken from 600 female patients who ranged in age from 25 to 75 years. For each case, corresponding ground truth notations are provided, allowing for supervised learning and precise model evaluation. The inclusion of both pathological and non-pathological samples makes this dataset particularly suitable for training robust DL models aimed at enhancing diagnostic accuracy in breast ultrasound analysis.  
Figure 4 displays sample images from the mammography dataset, divided into benign and malignant categories.  
* Top Row (Benign): Three benign images show uniform textures with minimal irregularities, indicating non-cancerous conditions;
* Bottom Row (Malignant): Three malignant images exhibit distinct anomalies, suggesting potential malignancies.  
The 'normal' class in the BUSI dataset was excluded from training due to its limited representation and lack of pathological markers, which rendered it insufficient for robust learning and generalization. Moreover, preliminary experiments indicated that inclusion of the normal class introduced class imbalance without contributing significantly to model  
Figure 3: MIAS Dataset Description.  
discriminability, justifying its omission to preserve diagnostic focus on benign versus malignant classification.
## 3 Results  
All twenty-nine provisional restorations fabricated with a healing cap remained at their original sites and there was no dislodge of any component during the healing phase. In evaluation of the pull-out strength of the healing caps, the values exhibited an incremental trend from one-unit to three-unit restorations; one-unit restorations had a pull-out strength of 7.8-14.8 N, two-unit restorations, 10.4-13.9 N and three-unit restorations, 16.7-20.6 N. There were significant differences between the unit groups (\(p<0.05\)). The average and standard deviation are shown in Table 1.  
Pull-out strength values of the new healing caps were shown to be 12.0-15.0 N for one-unit, 26.4-31.5 N for two-unit and 22.9-38.3 N for three-unit restorations. The incremental trend as the unit increased was also shown with the new healing caps. There was a significant difference between the one-unit and two- and three-unit groups (\(p<0.05\)), whereas no statistical differences were seen between the two-unit and three-unit groups. The average and standard deviation are shown in Table 1.  
The results of compressive fracture strength of the used provisional restorations are shown in Table 2. The two-unit group showed the lowest values with a range of 882.0-1267.1 N, the one-unit group ranged from 1017.2-1477.8 N and the three-unit group showed the highest values, ranging 1278.9-1759.1 N. There were statistically significant differences between the groups (\(p<0.05\)), and the mean and standard deviations are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**Std**} \\  & & **(N)** & \\ \hline \multirow{3}{*}{New} & 1 unit & 14.2 1  & 0.9 \\  & 2 unit & 27.8 2  & 1.6 \\  & 3 unit & 29.8 2  & 5.2 \\ \hline \multirow{3}{*}{Used} & 1 unit & 10.2 1  & 2.5 \\  & 2 unit & 12.0 2  & 1.1 \\  & 3 unit & 18.5 2  & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pull-out strength of the healing caps.  
The sinking was recorded as the maximum force at the point of restoration fracture during compressive strength evaluation. The trends in this test were similar to those of the compressive strength test, with values increasing in order of two-unit, one-unit and then three-unit groups. The two-unit group showed the lowest values of 0.49 mm to 0.78 mm, the one-unit group had values of 0.50 mm to 0.90 mm, and the three-unit group showed values of 0.66 mm to 0.93 mm. The mean and standard deviation are shown in Table 2.  
Figure 4 shows the representative load-displacement graph of one-unit, two-unit and three-unit provisional restorations.
# Corpsia--A Feasible Second Best Solution?
## 2 Institutional and Technical Background
### Challenges for CORSIA's Climate and Regulatory Integrity
#### 2.3.5 Other Unsolved Issues  
Outside the scope of verification standards, several additional unsolved issues remain. One example is double counting, where the same emissions reduction is counted by both the host country and the offset purchaser. Prior to the Paris Agreement this was less of a concern, because only developed countries had absolute emission reduction targets. As countries with a reduction target were able to purchase offsets from countries without such targets, the risk of double counting was less relevant. Under the Paris Agreement, all parties have absolute emissions reduction targets. Therefore, it must be ensured that emission reductions counted for offsets are not also counted by the host country's Paris target as well. It remains unclear how such double counting can be avoided [46].  
Taken together, there are several trade-offs between additionally and adverse effects on the one hand and permanency and leakage on the other. Carbon reduction projects suffer more from additionality and adverse effects, while carbon storage schemes such as reforestation are more prone to permanency problems and carbon leakage. Furthermore, there is a trade-off between transaction costs and additionality with regard to project size. Transaction costs are low for large-scale projects; however, large-scale projects have a lower probability of being additional.
## 3 Results and Discussion
### Antioxidant Phenolic Compounds in Blackcurrants
The quantity of each compound detected by LC-MS is shown in the tile maps. There are four groups of phenolics based on the amounts, and they are more than 1000 ng/g (red tiles), 100–1000 ng/g (orange tiles), 10–100 ng/g (green tiles), and less than 10 ng/g (purple tiles) on average. The intensity of the color indicates the amount of each compound in each category. including anti-inflammatory, antiviral, and anti-obesity effects [68]. Chlorogenic acid in tomato mediated the protective effect under UVC stress. However, the protective capacity was also determined by the total levels of phenolics and the specific composition of the phenolic profile [69]. For the low amounts (0-10 ng/g) of phenolic compounds, luteolin, apigenin, and phloretin were detected. The quantity of phloretin was slightly increased after storage. As one of the best-known and abundant dihydrochalcones, phloretin exhibits versatility with multiple biological functions, including anti-inflammatory, antibacterial, and anticancer activities [70; 71]. Luteolin and apigenin have been used to treat various disorders, including hypertension, inflammatory disorders, and cancers [71; 72]. In summary, pretreatment with UVC degraded some phenolic compounds, including cyanidin, catechin, epicatechin, and chlorogenic acids, while most of the compounds remained unchanged. Few compounds were even enhanced during storage, such as phloretin.
# Diagnosing Phosphorus Limitation in Subtropical Forests in China under Climate Warming
## 3 Results
### Prediction of Future Phosphorus Limitation in Forest Sites  
To reveal if the past increasing trend in additional P demand will continue in subtropical China, we used the outputs of BCC-CSM 1.1 to estimate the future AAPD in the four forest sites. The temperature will be increased by 4 \({}^{\circ}\)C, 3.4 \({}^{\circ}\)C, 2.9 \({}^{\circ}\)C and 2.3 \({}^{\circ}\)C, while the NPP will be increased by 327 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\), 368 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\), 371 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) and 381 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) in Shennongjia, Qian Yanzhou, Mt. Gongga and Dinghushan are 1.24 mg\(\cdot\)m\({}^{-2}\), 1.24 mg\(\cdot\)m\({}^{-2}\) and 1.24 mg\(\cdot\)m\({}^{-2}\), 1.24 mg\(\cdot\)m\({}^{-2}\) and 1.24 mg\(\cdot\)m\({}^{-2}\), respectively. The AAPD and AAPD/ASP for S\({}_{1}\) are 1.24 mg\(\cdot\)m\({}^{-2}\), 1.24 mg\(\cdot\)m\({}^{-2}\), 1.24 mg\(\cdot\)m\({}^{-2}\), and 1.24 mg\(\cdot\)m\({}^{-2  
Dinghushan, respectively, by 2099 under RCP8.5, compared with that in 2018 (Table 3). Future climate warming will stimulate the increase of NPP at an average rate of 4.2 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-2}\) in the four sites, which are more than double that during 1951-2010. For the four sites, future AAPD are estimated in the range of 568-668 mg\(\cdot\)m\({}^{-2}\) and 57-67 mg\(\cdot\)m\({}^{-2}\).  
The AAPD/ASP indicates the proportions of P consumption to P supply. The higher the values, the higher the degree of potential P limitation. By the end of the 21 st century, for RCP8.5, the AAPD/ASP values for the four forest sites are predicted to be in the range of 32.2% to 68.4% under S\({}_{1}\), and in the range of 3.3% to 6.3% under S\({}_{2}\). For RCP4.5, the AAPD/ASP values for the four forest sites are predicted in the range of 12.8% to 32.1% under S\({}_{1}\), and in the range of 1.3% to 3.2% under S\({}_{2}\) (Table 4).  
Compared with the predicted AAPD/ASP in RCP4.5, the predicted AAPD/ASP in RCP8.5 is greater in each forest site. For both S\({}_{1}\) and S\({}_{2}\), from RCP8.5 to RCP4.5, the AAPD/ASP decreased by about 48.3%, 63.8%, 60.2% and 42.6% in Shennongjia, Qian Yanzhou, Mt. Gongga and Dinghushan, respectively.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 4 Discussions and Conclusions
### Impact of Coupling Effects on Thrust Optimization  
The system's design includes shared combustion chambers among multiple valves, causing fluctuations in the total exhaust area. This configuration poses challenges in optimizing thrust distribution across multiple axes, considering constraints imposed by thrust equations for various directions. The coupling effects lead to acceptable pressure fluctuations, demonstrating the system's robustness in handling complex interactions within the propulsion system.
# Switched Observer Design for a Class of Non-Linear Systems
## 2 System and Observer Description
### Switched Luenberger-Like Observer  
The following extended Luenberger-like observer is proposed to estimate the state vector \(x(t)\):  
\[\begin{split}\dot{x}(t)&=A\dot{x}(t)+B_{f}f(\dot{x} (t))+g(y(t),u(t))+L_{\sigma(t)}(\dot{y}(t)-y(t))\\ \dot{y}(t)&=Cx(t)\end{split} \tag{3}\]  
where \(\dot{x}(t)\in\mathbb{R}^{n_{x}}\) is the estimated state and \(L_{\sigma(t)}\in\mathbb{R}^{n_{x}\times n_{y}}\) is a switched output injection gain with \(\sigma:\mathbb{R}^{n_{y}}\to\mathbb{N}\) as a switching rule to be designed. Now, let  
\[\dot{x}(t)=x(t)-\dot{x}(t) \tag{4}\]  
be the state estimation error and \(\tilde{\mathcal{X}}\) be the set induced by  
\[\tilde{\mathcal{X}}=\{\tilde{x}\in\mathbb{R}^{n_{x}}:\tilde{x}=x-\hat{x},\;\;x,\;\hat{x}\in\mathcal{X}\}.\]  
Then, the estimation error dynamics is given by  
\[\dot{\tilde{x}}(t)=A\tilde{x}(t)+B_{f}\big{(}f(x(t))-f(\hat{x}(t))\big{)}+L_{ \sigma(t)}(\dot{y}(t)-y(t))+B_{d}d(t) \tag{5}\]  
where \(\tilde{x}\in\tilde{\mathcal{X}}\). The switched observation problem consists in finding a gain \(L_{\sigma(t)}\) and the corresponding switching rule \(\sigma(y(t))\) to ensure the stability of the estimation error dynamics (5).
## 3 Results and Discussion
### Relationship between Freeze-Thaw Life of HSC and the RH of Curing Environment  
As mentioned earlier, the freeze-thaw resistance of the reference HSC is highly sensitive to the curing environment's relative humidity (RH). In Figure 8, the relationship between HSC's freeze-thaw life and curing environment RH is illustrated. The graph indicates a nearly linear correlation between the freeze-thaw life of reference HSC-1 and the RH of the curing environment. In drier conditions, the freeze-thaw life is shorter. Comparing the freeze-thaw life of HSC-1 under standard conditions (>95% RH) as a baseline, the relative freeze-thaw life at 30% RH and 50% RH is only 60% and 78%, respectively. For HSC-2, with the addition of an AEA expansive agent, the curing environment RH significantly influences its freeze-thaw life, with relative freeze-thaw life being only 51% and 62% at 30% RH and 50% RH, respectively. The freeze-thaw life of HSC-3, with the addition of steel fibers, remains unaffected by the curing environment RH. The sensitivity of the freeze-thaw life of HSC-4, with the addition of HEMPF fibers, to the curing environment RH has improved, especially at 50% RH, where its relative freeze-thaw life reaches 95%.  
HSC-5, with the combined addition of expansive agent and steel fibers, exhibits a remarkably high relative freeze-thaw life at 30% RH and 50%, ranging from 98% to 104%. Similarly, HSC-6, with the combined addition of expansive agent and HEMPF, shows a relative freeze-thaw life of 81% and 98% at the same RH levels. This indicates that the combined addition of AEA expansive agent and fibers with high elastic modulus effectively addresses the sensitivity of HSC's freeze-thaw resistance to the curing environment RH. This provides substantial experimental evidence for the application of HSC technology in key engineering projects in arid regions, particularly in northwest China.  
Figure 8: Relationship Between Freeze-Thaw Life of HSC and Curing Environment RH.
## 2 Study Area and Datasets
### Model-Driving Datasets  
The input forcing data of LPJ-DGVM was obtained from continuous series of monthly climate data covering the period 1901-2012. This data, consisting of cloud cover, temperature, precipitation, and wet day frequency on a 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) global grid [38], was provided by the Climatic Research Unit (CRU), University of East Anglia [39]. Monthly data were linearly interpolated to obtain \"quasi-daily\" values and were used to simulate processes within LPJ-DGVM at daily time steps. An annual dataset of historical atmospheric CO\({}_{2}\) concentrations during 1901-2012 was obtained by combining ice-core measurements and atmospheric observations at the Mauna Loa Observatory in Hawaii [40,41]. Soil texture data was obtained from the Food and Agriculture Organization of the United Nations (FAO) soil dataset [42].
## 4 Results  
Using the previous methodology, and with a frequency of 10 min, about 80 samples/day during the unoccupied period over more than 1 week were needed to obtain a minimum training data of 300 samples per office and per season. For NN validation, when the percentage of error is reduced during the training process, it can be modified from the standard 60% of data for training and 40% for validation [29] to a more interesting 75% of data for training the network and 25% to validate it, by comparing the NN results with real sampled data and their derived indexes of thermal comfort and energy saving, obtained from this sampled data, inside each office building.  
The stopping criteria was the minimum absolute number of errors obtained in most of the indoor vapor pressure predictions. In particular, the maximum absolute error allowed was fixed to 6 during the training period and 9 during the testing period, with a standard deviation of this error in both cases of 8%, which represented an nearly null percentage of incorrect predictions, providing a clear example of the power of NNs to model this process. Next, as an example of the accuracy obtained to define indoor air variables, such as partial vapor pressure (p\({}_{\text{v}}\)), the sampled and predicted values were compared during the validation process, as shown in Figure 4.  
For the indoor partial vapor pressure prediction, more interesting thermodynamic variables and thermal comfort indexes were predicted. The moist air enthalpy, PD\({}_{\text{IAQ}}\) and ACC\({}_{\text{IAQ}}\), was calculated for each season and for each internal covering during the unoccupied period from 19:00 to 09:00. Thus, partial vapor pressure, ACC\({}_{\text{IAQ}}\), PD\({}_{\text{IAQ}}\), and enthalphy were represented as the three most used materials for internal covering (paper, paint, and plastic) during the winter season in Figures 5-8 and during the summer season in Figures 9-12. Taking into account the permeability of these materials (Table 2) and the results obtained in previous works [1; 2; 3; 4; 5], based on a statistical study of indoor ambiences with these wall internal coverings, the following assumption is considered in the present study: Paper represents the permeable materials family, paint represents the semi-permeable materials family, and plastic represents the impermeable materials family.  
Figure 4: Example of sampled and predicted partial vapor pressure in the winter season.  
Figure 5: Indoor partial vapor pressure predicted for each internal covering during the winter season.  
Figure 6: Indoor air acceptability (ACCIAQ) predicted for each internal covering during the winter season.  
Figure 7: Indoor air percentage of dissatisfied persons (PDIAQ) predicted for each internal covering during the winter season.  
Figure 8: Indoor air enthalpy predicted for each internal covering during the winter season.  
Figure 10: Indoor air acceptability (ACC\({}_{\text{IAQ}}\)) predicted for each internal covering material during the summer season.  
Figure 9: Indoor air partial vapor pressure predicted for each internal covering during the summer season.