esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.7 Performance Evaluation  
Before modelling and prediction, the dataset was divided into training (80%) and testing (20%) datasets. Subsequently, XGBoost and four hybrid systems (XGBoost-HHo, XGBoost-JSO, XGBoost-DA, and XGBoost-FA) were then developed to predict the axial load bearing capacity of fully grouted rock bolts. After developing hybrid models, the five following statistics criteria, R-squared coefficient (R\({}^{2}\)), mean absolute error (MAE), root mean square error (RMSE), variance accounted for (VAF), and Bias factor (B\({}_{\text{f}}\)), were used to evaluate performance in both the training and testing stages of the models [57; 58].  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(m_{PL_{i}}-\overline{m}_{PL_{i}}\right)^{2}- \sum_{i=1}^{n}\left(m_{PL_{i}}-\overline{m}_{PL_{i}}\right)^{2}}{\sum_{i=1}^{n }\left(m_{PL_{i}}-\overline{m}_{PL_{i}}\right)^{2}} \tag{2}\]\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(m_{PL_{i}}-P_{PL_{i}}\right)^{2}} \tag{3}\]  
\[\mathit{VAF}=100\cdot\left(1-\frac{\mathit{var}\left(m_{PL_{i}}-P_{PL_{i}} \right)}{\mathit{var}\left(m_{PL_{i}}\right)}\right) \tag{4}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|P_{PL_{i}}-m_{PL}\right| \tag{5}\]  
\[B_{f}=\frac{1}{n}\sum_{i=1}^{n}\left(P_{PL_{i}}-m_{PL_{i}}\right) \tag{6}\]  
where \(m\) presents the number of data points, and \(m_{PL_{i}}\), \(\overline{m}_{PL_{i}}\), and \(P_{PL_{i}}\) are measured, predicted, and average values of the real peak loads, respectively.  
Considering the interplay between these metrics when evaluating model performance is essential. For example, differences in MAE and RMSE can reveal the distribution of prediction errors. A significant discrepancy suggests a wider range of errors, indicating potential outliers despite good performance. Indeed, by comprehensively analysing these indicators, we can gain a deeper understanding of the model's strengths, weaknesses, and suitability for real-world engineering applications.  
Next, we conducted a final rating of the model (FRM) for all five XGBoost models. The FRM procedure evaluated R\({}^{2}\), RMSE, MAE, VAF, and Bias values. A model with the highest R\({}^{2}\) and VAF, combined with the lowest RMSE, MAE, and Bias, was considered the best. It is important to note that the highest possible rating depends on the number of models and statistical parameters. For example, with 10 models, including training and testing datasets and five statistical parameters, the best model would receive a rating of 100. Equation (7) formulated the FRM rating system [58]  
\[FRM=\sum_{i=1}^{2}(r_{i}^{R2}+r_{i}^{MAE}+r_{i}^{RMSE}+r_{i}^{B_{f}}+r_{i}^{ VAF}) \tag{7}\]  
where \(i\) would be (1) for training and (2) for testing datasets.
## 3 Experiments and Results
### Comparison of Hybrid Application Results and Similarity Matching Results
The optimal recommendation given by the model is \"shut down sensitive resources\", such as affected economic facilities and fish farms. The other recommendations include \"spontaneous recovery\" and \"biological recovery\". Emergency response actions can be also achieved by scenario instance similarity matching from historical cases in CBR systems. As a comparison, the matrix of typical scenarios is used for similarity calculation, and when the Euclidean distance is less than \(\tau\) (defined in Section 2.1), it matches a historical scenario instance. The results are compared in Table 5:  
Figure 4: Policy application results for marine oil spill decision support. (**a**) Q value of the emergency response action for tanker collision scenario instance, (**b**) Q value of the emergency response action for tanker caught fire scenario instance, (**c**) Q value of the emergency response action for oil spill scenario instances with different sea condition, (**d**) Q value of the emergency response action for marine organism death scenario instance. From the results, it is obvious that the proposed method provides richer emergency response action suggestions for the decision maker. Because we changed the sea condition parameters in the oil spill scenario instances \"Oil spill scenario--BREAR\" and \"Marine organism death scenario\", they do not match appropriate scenario instances in the existing CBR system, which need to be revised according to expert knowledge. Moreover, the proposed method suggestions have clear decision intentions: to reduce the severity of oil spills. In general, the application results show that the optimal emergency response model trained to reduce the severity of oil spills can provide a variety of reasonable response actions for decision makers and aid in making decisions during marine oil spill emergencies.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 5 Design Guidelines
### Radiation Hardening Strategy  
The hardening strategy against radiation effects mainly depends on the target application (Step 1) and the selected manufacturing technology (Step 2). Nevertheless, an optimum outcome is only possible when the strategy is tailored for the whole design flow. Below, we have broken down our proposal into a sequence of activities linked with the design steps (specified in parentheses):  
1. For mature applications, reference data are extracted from previous radenv-ADC solutions. (Step 1)
2. Electrical and radiation requirements are specified based on the high-level requirements of the target application. (Step 1)
3. Technologies and architectures in line with the specified requirements are noted down. (Step 1)
4. Hardening-by-process options and hardening-by-design techniques are identified and analyzed for each considered technology. (Step 2)
5. Technologies offering libraries of hardened cells and/or basic IP-cores are prioritized. (Step 2)
6. As a result of the two previous activities, the manufacturing technology is selected from the preliminary options. (Step 2)
7. Devices to be used in the design are chosen from the technology libraries. (Step 2)
8. Needed rad-hard cells (but unavailable in the selected technology) are identified. (Step 2)Relying on the previous technological choices, the basic ADC architecture is selected from the preliminary options. Hardening blocks against SEFI (current alarms, watch-dogs, etc.), and test structures (test buses, built-in self tests, etc.) are included as part of the radenv-ADC architecture. (Step 3)
* Unhardened ADC designs with compatible requirements, implemented in the same technology, and with the same architecture, are compiled and analyzed. If plausible, one of them could be used as the starting point of the circuit design. (Step 4)
* Unavailable rad-hard cells are designed with a full-custom approach. (Step 4)
* Hardening-by-design techniques are applied at block-level. (Step 4)
* Electrical and radiation requirements are verified by simulation for the whole design. (Step 4)
* Electrical and radiation requirements are validated with test measurements. (Step 5)  
Note that this hardening strategy can be adapted with minor changes to the design flow of other mixed-signal devices.
## Materials and Methods  
### Development of the Dyskinesia Impairment Mobility Scale (DIMS)  
In the first step towards developing the adapted DIMS protocol, the DIS was examined thoroughly for dystonia and choreoathetosis characteristics which would be relevant to be included in the DIMS [9; 14; 15; 16]. The DIS measures the presence and severity of dystonia and choreoathetosis over 12 body regions; i.e., central body (eyes, mouth, neck and trunk) and limbs (upper and lower, proximal and distal limbs) [14]. Dystonia and choreoathetosis are assessed at rest and during two requested voluntary activities, both for duration (i.e., the amount of time that dyskinesia is present) and amplitude (i.e., the maximum range of motion (ROM) achieved due to dyskinesia) [14]. Duration and amplitude are scored on a five-point ordinal scale ranging from 0 to 4. Summation of region scores gives a total rest score, ranging 0-96, and a total action score, ranging 0-192. A total dystonia subscale score and total choreoathetosis score is obtained by the sum of the total rest and action score with a range from 0-288. A diagram of the DIS protocol is shown in Appendix A, Figure 11.  
For the DIMS, a similar protocol as for the DIS was adopted (Figure 1). The final DIMS protocol is a video-based protocol which consists of the dystonia subscale (DIMS-D) and choreoathetosis subscale (DIMS-CA). Both subscales measure duration (i.e., the amount of time that dystonia and choreoathetosis were present during the powered mobility task) and amplitude (i.e., the maximum ROM achieved due to dystonia and choreoathetosis) during five different powered mobility tasks. Scoring of duration and amplitude ranges from 0 to 4 (Table 1). For the duration factor, score 0 means the movement disorder is absent during powered mobility, score 1 means occasionally present (i.e., less than 10%), score 2 means frequently present (i.e., between 10 and 50% of the time), score 3 means mostly present (i.e., between 50 and 90% of the time) and score 4 means always present (more than 90% of the time). For the amplitude factor, the range of percentages is the same as for the duration factor with score 0 assigned when the movement disorder is absent during powered mobility, score 1 when the movement disorder is present in a small ROM, score 2 when present in moderate ROM, score 3 when present in submaximal ROM and score 4 when present in maximal ROM. The DIMS is comprised of the neck region, representing the voluntary movements used to steer the wheelchair to the right and to the left, and the arm region, representing the dystonia and choreoathetosis overflow movements during mobility tasks. For the arm region, a distinction is made between left and right and proximal and distal parts. As such, the DIMS is comprised of five body regions: neck, right arm proximal (i.e., arm RP), left arm proximal (i.e., arm LP), right arm distal (i.e., arm RD), and left arm distal (i.e., arm LD). Even though the feet are used to accelerate and brake with the wheelchair, they are strapped during powered mobility manifesting minimal movement, and were therefore excluded from the final DIMS protocol.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 4 Algorithm Design  
Due to its discrete nature, constraint diversity, and multi-objective optimization, the site selection problem is difficult to solve in polynomial time, and thus, it is essentially an NP problem. This is especially true for the co-matching model proposed in this paper, where the complexity of the functions in the model, the large size of the problem, and the diversity of constraints determine that it is not suitable to be solved by an exact algorithm [15]. Using heuristic algorithms for solving is a proven method.  
As an innovative bio-heuristic optimization algorithm, the gray wolf optimization algorithm solves complex optimization problems by imitating the social hierarchy and hunting behavior of gray wolves; shows excellent global optimization-seeking ability, strong exploratory power, and adaptability in dealing with multidimensional NP problems; and can adapt well to the various constraints in the site selection problem in terms of population initialization and updating strategies. Compared with other heuristic algorithms, the gray wolf algorithm has a fast convergence speed, a high solution rate, a strong global exploration ability, and high stability, with simple implementation, which makes it an ideal choice for solving combinatorial optimization problems, such as site selection and layout [16]. In recent years, the gray wolf algorithm has been widely used to solve unconstrained, constrained, and multi-objective NP problems in engineering, healthcare, and environment [17; 18; 19; 20; 21; 22].  
Therefore, this paper proposes a two-stage site selection solution algorithm. First, based on K-means clustering, alternative point sets for co-location center sitting are calculated by demand points; next, the optimal solution for sitting that satisfies the constraints is selected from the co-location center selection set by using the gray wolf optimization algorithm.
# Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins
## 2 Materials and Methods
### Peak Fitting  
The spectra were analyzed using OriginPro 2019 (OriginLab Corporation, Northampton, MA, USA). Peak analyzer (using the Levenberg-Marquardt algorithm) was used to perform non-linear fitting of the peaks in the spectral data. Baseline corrections were performed using a second derivative (zeroes) method for finding anchor points and detecting the baseline. Hidden peaks were also detected using a second derivative method followed by smoothing with the 7-9 point Savitsky-Golay function with polynomial order of 2.  
The peak fitting was then performed using the Voigt function which is the convolution of a Gaussian function and a Lorentzian function [36] (Equation (1)); where \(y_{0}=\) offset, \(x_{c}=\) center, \(A=\) area,\(W_{G}\) = Gaussian full width at half maxima (FWHM), \(W_{L}\) = Lorentzian FWHM, \(t\) = wavenumber, \(n\) = number of peaks.  
\[y\ =\ y_{0}\ +\ \sum_{1}^{n}A\frac{2 ln2}{\pi^{3/2}}\ \frac{W_{L}}{W_{G}^{2}} \int\limits_{\infty}^{\infty}\frac{e^{-t^{2}}}{\left(\sqrt{ln2}\frac{W_{L}}{W_ {G}}\right)^{2}\ +\ \left(\sqrt{4 ln2}\frac{x-x_{c}}{W_{G}}\right)^{2}}dt \tag{1}\]  
The baseline, peak center, peak width parameters were fixed and released during fitting to help with initializing the parameters. The iteration procedure was stopped when the best fit was achieved (reduced chi-square \(<1\times 10^{-6}\)). The residual data plots have been obtained for all the curve fittings and are shown in supplementary data files. All the secondary structural content was estimated by dividing the areas under the bands assigned to specific secondary structures by the total secondary structure area under the amide I band and reported as a percentage.
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 5 Discussion
### Need of High-Quality Real-World Datasets  
The answer to RQ3 reveals that the categories of ML algorithms mostly investigated in the domain of smart LSs are, in decreasing order of relevance, supervised ML methods,DL methods, Ensemble Learning methods and Reinforcement Learning methods. It is worth repeating that the number of studies that have explored the adoption of ML methods in the LS domain is low. An objective cause behind this delay emerges from the very recent study conducted by [PERSON] et al. [103]. In the study, the authors explore strategies for prototyping an IoT- and BIM-based efficient LS of a library, as an alternative to that based on the adoption of ML algorithms. The motivation behind this choice lies in the lack of high-quality real-world data and the lack of procedural understanding which prevent ML models from producing trustworthy findings. Concerns about the quality and size of datasets used to train ML methods are reported in most studies that adopt the AI technology to make predictions [81; 104].
# Methods of Analyzing Microsized Plastics in the Environment
## 4 Thermal Analyses
### Differential Scanning Calorimetry  
Differential scanning calorimetry (DSC) is a useful method to study thermal properties. It shows the changes in dissolution, crystallization, transition temperatures, and corresponding enthalpy and entropy to confirm the physical properties of polymers [62]. Since each plastic product has different properties in DSC, it can be used to identify polymer types [28]. However, there are limitations due to overlapping peaks when DSC identifies microplastics with similar melting points [63]. It can only be used to identify certain primary microplastics, such as PE and PP [64].
## 3 Materials and Methods
### Segmentation Approach  
#### 3.2.1 DeeplabV3+  
DeepLabv3+ is an extension of DeepLabv3 architecture that includes an encoder and decoder structure that help the model work more efficiently. Dilated convolution is used by the encoder module to deal with multiscale contextual information, whereas the decoder structure improves the segmentation performance by focusing on object boundaries. Thus, the encoder-decoder architecture is adopted by Deeplabv3+ [9] in the present study. The model is divided into three blocks: encoder, effective decoder, and a spatial pyramid pooling block, as shown in Figure 3. To create a rich feature map, the encoder uses dilated or atrous convolution at different rates [9]. The present work applies an atrous convolution to each location on the output and filter, where the rate of convolution corresponds to how quickly the inputs are sampled. We can maintain a constant stride while increasing the field of view using atrous convolution without increasing the number of parameters or the amount of computation. Finally, a larger feature map is obtained through this process as an output, that enhances the segmentation process.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Corsican Dataset** & **FLAME** & **Firefront\_Gestosa** \\ \hline Fire pixels count & \(2.95\times 10^{8}\) & \(9.71\times 10^{7}\) & \(1.77\times 10^{5}\) \\ \hline Background pixels count & \(1.72\times 10^{9}\) & \(1.66\times 10^{10}\) & \(6.58\times 10^{8}\) \\ \hline Contribution in final dataset & & & \\ in terms of percentage (\%) & 24.74 & 75.22 & \textless{}1 \\ \hline \end{tabular}
\end{table}
Table 4: The pixel distribution of our final dataset and the fire pixel contribution in terms of percentage.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Corsican Dataset** & **FLAME** & **Firefront\_Gestosa** & **Total** \\ \hline Fire pictures & \(1775\) & 2003 & 238 & 4016 \\ \hline \end{tabular}
\end{table}
Table 3: Statistics about the data used to train and test the detection module in terms of the number of samples.  
Later, these features are bilinearly upsampled by a factor of four and combined with the low-level output of the backbone. To ensure information integrity and minimal computational cost, a 1 \(\times\) 1 convolution is applied to the obtained features to limit the number of channels. The present work utilized multiple backbone types as encoders for experimental simulations, namely ResNet-18 [39], ResNet-50 [40], MobileNetV2 [41], Xception [42], and InceptionResNetV2 [43]. In the final section of the decoder, 3 \(\times\) 3 convolutions and bilinear upsampling are further applied for obtaining an affine feature map.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
### Contributions  
In this work, we review the latest advancement and research work published since 2018 to complement studies such as [11; 12; 13]. Our survey also covers some missing analysis of more recent surveys on the Ethereum smart-contract security vulnerabilities, attacks, prevention, and tools [14; 15; 16; 17; 18; 19]. We studied 13 vulnerabilities in Ethereum smart contract and their countermeasures, and investigate nine security-analysis tools. Our findings indicate that a uniform set of smart-contract vulnerability definitions does not exist in research work and bugs pertaining to the same mechanisms sometimes appear with different names. This inconsistency makes it difficult to identify, categorize, and analyze vulnerabilities. We explain some safeguarding approaches and best practices. However, as technology improves new vulnerabilities may emerge. With regards to tool support, SmartCheck, DefectChecker, contractWard, and sFuzz tools are better choices in terms of more coverage of vulnerabilities; however, tools such as NPChecker, MadMax, Osiris, and Sereum target some specific categories of vulnerabilities if required. While contractWard has good accuracy and fast process time, it can only detect specifically pre-defined vulnerabilities. The NPChecker has a slower processing time, but it may find new patterns for each vulnerability.
###### Abstract  
Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in the context of detecting incipient faults in satellites. Under the assumption that the variables obey a multidimensional Gaussian distribution and using KL divergence to detect incipient faults, this paper models the optimum PV for detecting incipient faults as an optimization problem. It proves that the PVs obtained by principal component analysis (PCA) are not necessarily the optimum PV for detecting incipient faults. It then compares the on-line probability density function (PDF) with the reference PDF for detecting incipient faults on the local optimum PV. A numerical example and a real satellite fault case were used to assess the validity and superiority of the method proposed in this paper over conventional methods. Since the method takes into account the characteristics of the actual incipient faults, it is more adaptable to various possible incipient faults. Fault detection rates of three simulated faults and the real satellite fault are 98%, 84%, 93% and 92%, respectively.  
Kullback-Leibler (KL) divergence; principal component analysis (PCA); optimum projection vector (PV); incipient fault; satellite +
Applied Sciences
## 3 Results and Discussion
### Maintained Freezing Phase  
Considering that the thickness of frozen soil is increasing in the synergy freezing scheme of the solid pipe and hollow pipe, it is necessary to adopt the control freezing scheme to realize the effective control of the frost heave. The test compared two freezing control schemes, heat control mode and cold control mode.  
Heat control mode is the process of taking cold away from the freezing zone by circulating a relatively high brine temperature (\(2~{}^{\circ}\)C) in the limiting freezing tube. Because of the heat source concentration, this mode has the most obvious control effect near the limiting freezing tube. Figure 13a shows the temperature cloud chart when the limiting freezing tube was opened for 20 days. Compared with that without being opened in Figure 12a, the soil temperature near the limiting freezing tube is significantly controlled, while there is no obvious difference in the distant region. This shows that the heat control mode had a local characteristic and had little effect on the temperature field of other regions. This heat control mode would have a better effect when the frozen soil near the solid pipe developed too fast and the thickness was too large.  
The cold control mode is achieved by adjusting the freezing brine temperature from \(-24~{}^{\circ}\)C to \(-20~{}^{\circ}\)C. This mode will affect the freezing effect throughout the whole frozen regions due to the adjustment of the freezing cycle of the cold supply, which can be confirmed by the comparison of cloud chart in Figure 13b after 17 days of cold control and that in Figure 13a with heat control. Besides, the temperature gradient of temperature field is very different to that in the earlier situation, shown in Figure 13b. In a word, the cold control mode can not only control the thickness of frozen soil curtain, but also adjust the effect of temperature gradient across the whole temperature field.  
If the frozen soil curtain was too thick or the boundary of the frozen soil developed rapidly during the freezing construction of the FSPR method, the cold control mode is suggested to achieve an overall effect of controlling frost heave.