esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 3 TET-1 Data Processing Chain
### Co-Registration of MIR and TIR Channels  
TET-1 MIR and TIR channels are acquired with separate cameras, which causes uncertainties in inter-channel georeferencing, as the MIR and TIR pixels may not be optimally co-registered. The same problem arises for data acquired with a third camera in the visible (red and green channel) and near infrared spectrum (in the continuation abbreviated to VNIR; these three channels have 40 m spatial resolution).  
As clouds may cover a large fraction of the acquired images, they play a pivotal role in georeferencing new images. However, clouds have different spectral characteristics at different wavelengths. In the MIR spectrum, the clouds appear to be bright during the daytime, but dark during the night. Clouds always appear dark in the TIR spectrum. The gradient between neighboring pixels is used to overcome the wavelength dependent spectral characteristics. Gradients expose edges, for instance the edge between a cloud and the sea, between sea and land, _etc._ Generally, such edges are  
Figure 1: Basic scheme of the TET-1 processing chain.  
alike in all channels. We decided to use a simple gradient, but we could have used more sophisticated filters, such as the Sobel filter [41].  
The inter-channel co-registration was performed for a square area of data gradients centered over the volcano using a fast Fourier transform based algorithm for automatic image registration [42]. A MIR data gradient was used for a template, and we adjusted the data location from other channels to MIR. Once the data were appropriately co-registered, it was projected to the UTM projection in a 40 m spatial resolution using the nearest neighbor method. This resolution was chosen to:  
* combine VNIR data with MIR/TIR,
* preserve the radiometric values in MIR/TIR channels, and
* simultaneously preserve the geometry of MIR/TIR pixels.  
The final accuracy of this automatic co-registration is in most cases approximately 1 MIR/TIR pixel large, but it is worse under very cloudy conditions.
## 4 Discussion
### Objective Assessment
#### 4.2.4 Analogy of Our Proposal among Test Images A, B and C  
In addition to the performance comparison of our proposed approach and other shadow detection methods in [PERSON] [11], [PERSON] et al. [14], [PERSON] et al. [18], [PERSON] et al. [19] and [PERSON] et al. [20], we also evaluated the effectiveness of our method for detecting shadows in different scenes with accuracy data listed in the last row of Tables 1-3. As previously described, test image A is a simple scene of entirely vegetation, test image B includes mostly vegetation and a small fraction of buildings, and test image C consists of diverse land cover types including vegetation, buildings, roads, lawns, etc. Obviously, we noticed that similar OA values were achieved for test image A (95.33%), B (94.24%), and C (95.50%), which reveals that our method performed well for shadow detection in both simple vegetation scenes and complex scenes of diverse land cover. In terms of PA, SP, EO, and EC, on one hand, for the image mainly containing vegetation (i.e., test image A), higher SP (94.77%) and corresponding lower EC (5.23%) values were shown, which proves the efficacy of our method in accurately identifying vegetation pixels in the original images to the same class rather than as shadows, and reducing the probability of misclassifying vegetation pixels in the original images as shadows. Rather high PA (95.78%) and low EO (4.22%) values were shown, revealing that the problem of shadow omission was further resolved. On the other hand, for the complex image including diverse land cover types (i.e., test image C), higher PA (97.19%) and corresponding lower EO (2.81%) values were obtained, which shows that our method also performed well in correctly detecting shadow pixels in the original images as the same class rather than as nonsmadows, and avoided omitting shadow pixels in original images as nonsmadows as far as possible. Also, quite high SP (92.10%) and low EC (7.90%) values were achieved showing that the problem of vegetation misclassification was also further minimized. As for test image B, quite good shadow detection effectiveness was also obtained with our proposed approach.  
Consequently, our developed shadow detection approach is suitable for shadow detection in both simple scenes mainly containing vegetation and complex scenes including diverse land cover types. Also, excellent performance was demonstrated for both correctly identifying shadow pixels, and discriminating shadow from nonshadow pixels, especially for vegetation cover types. In other words, our method is effective for managing both problems of shadow omission and vegetation misclassification.
# Band-Pass Sampling in High-Order BOC Signal Acquisition
## 4 Performance Analysis
### An Experiment  
In this subsection, we tried to acquire BOC(15,2.5). The signal parameters are: \(C/N_{0}=45\,\mathrm{dBHz}\), \(f_{d}=2\,\mathrm{kHz}\), \(f_{dsc}=19.5\,\mathrm{Hz}\). The sampling frequency and the intermediate frequency are \(f_{s}=12.7875\,\mathrm{MHz}\) and \(f_{1F}=25.575\,\mathrm{MHz}\), respectively. The integration period is 1 ms. Figure 8 shows the 2-dimensional search results obtained by the proposed method. On account of the strong signal strength, the correct peak is easy to identify, which demonstrates the feasibility of the proposed algorithm.
## 3 Experiments and Discussion
### Thickness Measurement of Polyimide Films  
Polyimide films with different thicknesses were used to verify the feasibility of the system for industrial production measurement. Widely used in electrical and electronic fields, polyimide is a polymer material with a low dielectric constant, good heat resistance, and mechanical properties [38; 39; 40]. Polyimide film products are made from the gel film salivated by the polyamide acid resin solution. Subsequent preparation processes include directional stretching, imidization, and some post-treatment processes. Online thickness monitoring of polyimide films is related to the quality of subsequent products.  
PI films can be divided into electrical-grade and electronic-grade films. We measured three electronic-grade polyimide films with various thicknesses, as depicted in Figure 6. The integration time of the spectrometer was set to \(3\) ms, and other parameters remained unchanged. The nominal thicknesses of these three films are \(3\)\(\upmu\)m, \(8\)\(\upmu\)m, and \(12.5\)\(\upmu\)m, respectively. However, the average thicknesses of these films measured \(20\) times at the sampling point were \(3.3144\)\(\upmu\)m, \(8.2791\)\(\upmu\)m, and \(12.3290\)\(\upmu\)m, while the standard deviations were \(1.74\) nm, \(7.89\) nm, and \(24.17\) nm, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Mean Value (nm)** & **Deviation (nm)** & **Std (nm)** & **Time (ms)** \\ \hline Extremum & \(668.65\) & \(-20.48\) & \(1.65\) & \(0.02\) \\ Fast Fourier transform & \(607.91\) & \(-81.22\) & \(0.26\) & \(0.06\) \\ Proposed method & \(686.52\) & \(-2.61\) & \(0.27\) & \(1.17\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation results of the SiO\({}_{2}\) standard film sample by three methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multicolumn{3}{c}{**SiO\({}_{2}\)**} & \multicolumn{3}{c}{**Parylene**} \\ \cline{2-5}  & **Thickness (nm)** & **Deviation (nm)** & **Std (nm)** & **Thickness (nm)** & **Deviation (nm)** & **Std (nm)** \\ \hline \(1\) & \(686.52\) & \(\mathbf{-2.61}\) & \(0.27\) & \(7150.9\) & \(\mathbf{-15.1}\) & \(1.0\) \\ \(2\) & \(688.43\) & \(-0.70\) & \(0.29\) & \(7152.9\) & \(-13.1\) & \(0.9\) \\ \(3\) & \(687.81\) & \(-1.32\) & \(\mathbf{0.30}\) & \(7156.0\) & \(-10.0\) & \(0.7\) \\ \(4\) & \(686.54\) & \(-2.59\) & \(0.29\) & \(7163.5\) & \(-2.5\) & \(\mathbf{1.1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement results of SiO\({}_{2}\) standard film and parylene standard film.
# Responses of Forest Carbon Cycle to Drought and Elevated CO2
## 3 Elevated CO2 Effect on Plants and Ecosystem
### How Plants Respond to Elevated CO2
#### 3.1.2 Photosynthesis  
The mechanism of plant photosynthesis stimulation by elevated CO\({}_{2}\) is mainly driven by the activity of Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco). Rubisco catalyzes the carboxylation reaction to fix CO\({}_{2}\) into 3-phosphoglyceric acid (3 PGA) [47]. Since Rubisco has a low affinity for CO\({}_{2}\) carboxylation and is not saturated at current CO\({}_{2}\) concentration, elevated CO\({}_{2}\) can stimulate and promote the carboxylation reaction [4]. However, Rubisco can also catalyze the oxygenation reaction of Ribulose-1,5-bisphosphate (RuBP) and O\({}_{2}\), that is, the first step of photorespiration that inhibits photosynthesis by consuming light energy and releasing assimilated carbon [45]. The balance of carboxylation and oxygenation reactions depends on the ratio of O\({}_{2}\) to CO\({}_{2}\) concentration of the site of carboxylation. Elevated CO\({}_{2}\) stimulates carboxylation reactions and inhibits oxygenation reaction, promoting the process of photosynthesis. However, the affinity of Rubisco for CO\({}_{2}\) and the solubility of CO\({}_{2}\) (relative to O\({}_{2}\)) decrease with increasing temperature, thus the increase in temperature will promote photorespiration [48]. The stimulating effect of elevated CO\({}_{2}\) on photosynthesis increases with the rising temperature, and CO\({}_{2}\) fertilization has a stronger effect with high temperature.  
Compared with C3 plants, elevated CO\({}_{2}\) has no direct effect on the photosynthesis of C4 plants, because C4 plants have a mechanism which can avoid photorespiration. In FACE experiments, the response of C3 plants to elevated CO\({}_{2}\) is three times that of C4 plants [44]. Recent studies [49; 50] argue that C4 plants benefit from elevated CO\({}_{2}\) indirectly by the interaction of decreased \(g_{s}\) and water stress, that is, the drought stress is mitigated by rising CO\({}_{2}\)-induced higher water use efficiency.
## 4 Improved Cumulative Damage Model  
According to the Formula (11) mentioned above, the load ratio effect parameter n is introduced, so the improved cumulative damage model is expressed  
\[D_{2}^{\prime}=\bigg{(}\frac{\sigma_{2}}{\sigma_{1}}\bigg{)}^{b}D_{2} \tag{12}\]  
Formula (12) reflects the effect of load loading sequence on damage, and the damage relationship between the improved model and the original model as:  
\[D_{2}^{\prime}\bigg{\{}\begin{array}{ll}\geq D_{2}&\sigma_{2}\geq\sigma_{1} (Low-High)\\ \leq D_{2}&\sigma_{2}\leq\sigma_{1}(High-Low)\end{array}\]  
According to Formula (11), the number of cycles \(n_{1}\) under the first-stage load amplitude \(\sigma_{1}\) is equivalent to the equivalent number of cycles \(n_{2}^{\prime}\) under the second-stage load amplitude \(\sigma_{2}\), expressed as:  
\[D_{1}=-\frac{\ln\Big{(}1-\frac{n_{1}}{N_{f1}}\Big{)}}{\ln N_{f1}}=-\bigg{(} \frac{\sigma_{2}}{\sigma_{1}}\bigg{)}^{b}\frac{\ln\Big{(}1-\frac{n_{2}^{\prime }}{N_{f2}}\Big{)}}{\ln N_{f2}}=D_{2}^{\prime} \tag{13}\]  
\[\frac{n_{2}^{\prime}}{N_{f2}}=1-\bigg{(}1-\frac{n_{1}}{N_{f1}}\bigg{)}^{\frac {\ln N_{f2}}{\ln N_{f1}}(\frac{\sigma_{1}}{\sigma_{2}})^{b}} \tag{14}\]  
\[D=-\bigg{(}\frac{\sigma_{2}}{\sigma_{1}}\bigg{)}^{b}\frac{\ln\Big{(}1-\frac{n _{2}^{\prime}+n_{2}}{N_{f2}}\Big{)}}{\ln N_{f2}} \tag{15}\]  
When the total damage degree is 1, the specimen is damaged. Formula (15) \(D=1\), represents the material under the action of two levels of load, after the number of cycles \(n_{1}\) of the first level load amplitude \(\sigma_{1}\), and the remaining life fraction of the second level load amplitude \(\sigma_{2}\), expressed  
\[\frac{n_{2}}{N_{f2}}=\bigg{(}1-\frac{n_{1}}{N_{f1}}\bigg{)}^{\frac{\ln N_{f2} }{\ln N_{f1}}(\frac{\sigma_{1}}{\sigma_{2}})^{b}}-\bigg{(}\frac{1}{N_{f2}} \bigg{)}^{(\frac{\sigma_{1}}{\sigma_{2}})^{b}} \tag{16}\]By analogy, the total damage under the multi-stage load and the remaining life fraction \(n_{i}/N_{fi}\) of the last stage load \(\sigma_{i}\) can be derived as:  
\[D=\sum_{i=1}^{n}{(-\left(\frac{\sigma_{i}}{\sigma_{i-1}}\right)^{n}\frac{\ln \left(1-\frac{n_{i}^{\prime}+n_{i}}{N_{fi}}\right)}{\ln N_{fi}})} \tag{17}\]  
\[\frac{n_{i}}{N_{fi}}=\left(1-\frac{n_{i-1}}{N_{fi-1}}\right)^{\frac{\ln N_{fi}} {\ln N_{fi-1}}\bullet\left(\frac{\sigma_{i-1}}{\sigma_{i}}\right)^{b}}-\left( \frac{1}{N_{fi}}\right)^{(\frac{\sigma_{i-1}}{\sigma_{i}})^{b}} \tag{18}\]  
According to the data obtained from the test, for 18 CrNiMo7-6 material, \(b\) = 2.8. One can substitute \(b\) = 2.8 into Formula (16) as:  
\[\frac{n_{2}}{N_{f2}}=\left(1-\frac{n_{1}}{N_{f1}}\right)^{\frac{\ln N_{f2}}{ \ln N_{f1}}\times(\frac{\sigma_{1}}{\sigma_{2}})^{2}}-\left(\frac{1}{N_{f2}} \right)^{(\frac{\sigma_{1}}{\sigma_{2}})^{28}} \tag{19}\]
## 4 Experiment and Result Analysis  
### Evaluation Criteria  
For the performance evaluation of the proposed approach, the mean absolute error (MAE), maximum error (MAX), root mean square error (RMSE) and mean absolute percentage error (MAPE) are selected. The equations are as follows:  
\[\text{MAE}=\frac{1}{n_{c}}\sum_{i=1}^{n_{c}}|y_{i}-\widetilde{y}_{i}| \tag{21}\]  
\[\text{MAX}=\text{max}(|y_{i}-\widetilde{y}_{i}|) \tag{22}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n_{c}}\sum_{i=1}^{n_{c}}\left(y_{i}-\widetilde{y }_{i}\right)^{2}} \tag{23}\]  
\[\text{MAPE}=\frac{1}{n_{c}}\sum_{i=1}^{n_{c}}\left|\frac{y_{i}-\widetilde{y} _{i}}{y_{i}}\right| \tag{24}\]  
where the \(\widetilde{y}_{i}\) and \(y_{i}\) are the predicted capacity and actual capacity for the lithium-ion battery, respectively. \(n_{c}\) represents the number of cycles when the battery reaches the EOL and \(i\) denotes the index of \(n_{c}\). The lower these values, the more accurate the predicted results. In this study, computational time is also a pivotal evaluation criterion.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 7 Conclusions  
In this paper, we consider energy management of data collecting WSNs that apply asynchronous duty-cycling. The fundamental challenge of such networks is the energy consumption due to overhearing that drastically reduces the network lifetime. Special consideration is given to rechargeable WSNs that suffer from the additional problem of spatial and temporal variations of energy availability in the network. We propose a scheme for building a multi-channel tree in data gathering wireless sensor networks to alleviate this issue. The proposed scheme DRCS enables nodes to adapt their  
Figure 11: Comparison of (**a**) network-wide packets overheard; (**b**) worst case network lifetime and (**c**) packet delivery ratio with different data rates.  
Figure 12: Comparison of (**a**) network-wide packets overheard; (**b**) worst case network lifetime and (**c**) packet delivery ratio with different number of channels.  
energy consumption based on availability. It involves distributed channel selection to enable nodes to reduce overhearing, and dynamic parent selection for minimizing the load of nodes that have the worst expected lifetime. Through simulations and experiments, we demonstrate that DRCS significantly improves the network lifetime without sacrificing the packet delivery ratio. The proposed scheme has no additional overhead other than periodic beacon updates, which makes it suitable for implementations in real-life applications to prolong the network lifetime.  
This work was supported in part by NSF grant CNS-1117790.  
The authors worked jointly in the development of the proposed DRCS protocol. [PERSON] was involved in the experimental work and data analysis. The paper was written collaboratively by both authors.  
The authors declare no conflict of interest.
## 4 Hybrid Trust-Based Congestion-Aware Cluster Routing (HTCCR)
### CH Selection Phase
#### 4.1.1 Hybrid K-IGS Algorithm  
The hybrid K-IGS algorithm considers QoS parameters (fitness) like the packet delivery ratio (PDR), energy, distance, and traffic load as multiple objectives for selecting the best route with improved security. The hybrid K-IGS algorithm is a widely used optimization clustering method that is extensively applied in various fields.
## 5 Conclusions  
This study assessed the effectiveness of support vector machines (SVM), long short-term memory (LSTM) networks, and boosting ensemble decision trees in tool condition monitoring during the milling processes. Each method demonstrated unique strengths and limitations when applied to the high-dimensional and sequential data typical for industrial applications. The findings illustrate that SVM provided the highest accuracy, while LSTM lagged in performance due to its sensitivity to data size and feature selection challenges.  
Several implications arise for future research. The potential for hybrid models integrating the temporal learning capabilities of LSTM with the robust classification abilities of SVM and decision trees should be explored. Such models may better capture both the temporal dynamics and complex feature interactions of the tool wear data.  
The development of domain-specific models tailored for industrial applications could address the requirement for extensive labeled training data for models such as LSTM. Investigating techniques for effective transfer learning and unsupervised pre-training in this domain might reduce this dependency and enable the implementation of advanced machine learning models in industrial settings.  
Moreover, future work should consider expanding the dataset and including diverse machining operations to validate and generalize the current findings. Extending the feature set through advanced signal processing techniques and exploring the integration of multimodal data sources, such as temperature and material properties, could also provide deeper insights into the condition monitoring processes.  
Lastly, addressing the interpretability of machine learning models in manufacturing contexts remains a crucial research direction. Developing methods that provide clearer insights into the decision-making processes of complex models will enhance their practical applications and trustworthiness in real-world industrial solutions.  
Conceptualization, A.P.-M. and J.K.; data acquisition, K.S.; formal analysis, investigation, A.P.-M. and J.K.; methodology, J.K., A.P.-M., A.S., A.D., P.S. and M.K.; project administration J.K.; supervision, J.K.; visualization, A.P.-M., A.S., A.D., P.S. and J.K.; writing--original draft, I.A. and J.K.; writing--review and editing, all the authors (A.P.-M., I.A., K.S., M.K., A.S., A.D., P.S. and J.K.). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Proposed Shadow Detection Approach  
Based on the study of the properties of the shadow regions with respect to the hue and the intensity components in the HSV invariant color space, as well as in terms the reflectivity of multispectral bands, we proposed a mixed property-based shadow index (MPSI) shown in Equation (16):  
\[MPSI=(H-I)\times(R-NIR) \tag{16}\]  
where \(H\) and \(I\) are the normalized values of hue and intensity components, respectively, and \(R\) and \(NIR\) are normalized reflectivity values of the red band and the near infrared band, respectively.  
Due to the properties of the shadow regions in the HSV invariant color space (i.e., higher difference value between the hue component and the intensity component), in this proposed shadow index, we highlighted shadow regions by using the difference of the normalized hue and normalized intensity component, i.e., \(H-I\), to further solve the shadow omission problem. Concurrently, based on the higher reflectivity difference of the red band and the near infrared band for shadow regions in comparison with vegetation cover in nonshadow regions, we used the reflectivity difference of the red band and the near infrared band, i.e., \(R-NIR\), to effectively distinguish shadow regions from vegetation cover in nonshadow regions. From the property analysis mentioned previously, in which both the difference terms \(H-I\) and \(R-NIR\) in shadows are higher than in nonshadows, we developed a mixed property-based shadow index, as shown in Equation (16), with the product of the difference of the normalized hue and the normalized intensity values, \(H-I\), and the reflectivity difference of the normalized values of the red band and the near infrared band, \(R-NIR\). In principle, the proposed shadow index achieves higher values for shadow pixels when compared with general nonshadows and vegetation cover in nonshadows.  
In this paper, we first separated shadow pixels from general nonshadows, as well as vegetation pixels within nonshadow regions, with the developed mixed property-based shadow index (MPSI), from which we draw a shadow index image. Then, we applied a certain threshold technique over the achieved shadow index image to generate the final shadow image automatically which is described in the following section.