esa-sceva/satcom-chunk-collection huggingface dataset:
# FOG De-Noising Algorithm Based on Augmented Nonlinear Differentiator and Singular Spectrum Analysis
## 2 Algorithm
### Singular Spectrum Analysis Algorithms  
Singular spectrum analysis (SSA) algorithm consists of two complementary stages, which are decomposition and reconstruction, and both two steps contain two distinct steps. In this section, a brief description of SSA algorithm is presented. Let \(X=(x_{1},\ldots,x_{N})\) of length \(N\) denote an observed finite realization of a stochastic process. We assume that \(X\) has been corrupted by noise. It is noted thatonly the decomposition process of SSA is used in our proposed method, therefore the description of reconstruction process is not given.  
The decomposition stage starts with the embedding step, which maps the original times series to a sequence of multidimensional lagged vectors of size \(L\) by forming \(K=N-L+1\) lagged vectors \(X_{i}=\left(x_{i},\cdots,x_{i+L-1}\right)^{T}\), which have dimension \(L\) and \(1\leq i\leq K\). The matrix formed by these vectors is the trajectory matrix presented in Equation (5):  
\[X=\left[X_{1}:\cdots:X_{K}\right]=\left(x_{ij}\right)_{i,j=1}^{L,K}=\left( \begin{array}{ccccc}x_{1}&x_{2}&x_{3}&\cdots&x_{K}\\ x_{2}&x_{3}&x_{4}&\cdots&x_{K+1}\\ x_{3}&x_{4}&x_{5}&\cdots&x_{K+2}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ x_{L}&x_{L+1}&x_{L+2}&\cdots&x_{N}\end{array}\right) \tag{5}\]  
The matrix \(X\) is a Hankel matrix, which means that the matrix has equal elements on the diagonals \(i+j=constant\).  
The next step is the singular value decomposition (SVD) of the trajectory matrix \(X\) into a sum of biorthogonal elementary matrices of rank one. Take \(S=XX^{T}\) and the eigenvalues of \(S\) in the decreasing order of magnitude (\(\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{L}\geq 0\)), also the orthonormal system of the eigenvectors denoted by \(U_{1},\ldots,U_{L}\) corresponding these eigenvalues. Let \(d=\max\{i,such\;\lambda_{i}>0\}=rank\;X\), so if we denote \(V_{i}=X^{T}U_{i}/\sqrt{\lambda_{i}}U_{i}V_{i}^{T}\) and \(X_{i}=\sqrt{\lambda_{i}}U_{i}V_{i}^{T}\), where \(i=1,\ldots,d\), then the SVD of the trajectory matrix \(X\) can be written as Equation (6):  
\[X=X_{1}+X_{2}+\cdots+X_{d} \tag{6}\]  
This work applies the useful property of Equation (6) to evaluate the contribution of each component \(X_{i}\) in the expansion of the original time series. It can be done as follows: Among all the matrices \(X^{(r)}\) of rank \(r<d\) (\(d\) id the number of \(\lambda_{i}>0\)), the matrix \(\sum_{i=1}^{r}X_{i}\) provides the best approximation to the trajectory matrix \(X\), which means that the Frobenius norm is minimum \(\left|X-X^{\prime}\right|\right|_{M}\). In practical terms, it means that the components contributing to form the original time series can be measured by Equation (7). Therefore, we can decide which components could be selected to perform the reconstruction stage by selecting the first values sorted in decreasing order of their contribution:  
\[\%contrib=\frac{\lambda_{i}}{\sum_{i=1}^{d}\lambda_{i}} \tag{7}\]
# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
## 5 Basin Amplification Characteristics Prediction
### Results of Each Prediction Model
#### 5.2.3 Joint Prediction Model for Basin Amplification Characteristics  
The prediction models in the previous two sections show that CNN can be used for either single or multiple outputs. In this section, the peak value of the amplification factor, which reflects the basin amplification characteristics, was combined with the hazard locations as the outputs of the prediction model. The eight parameters of the CNN-AF were used as the input of the CNN-AP-8, and the model output was based on the CNN-AF amplification factor peak output while considering the value of the hazard location. Figure 16 shows the relative error of the CNN-AP-8 model in predicting the amplification factor, which was below 10 percent for all 151 samples tested, and the evaluation indicators are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & _MAE_ (Train/Test) & _RMSE_ (Train/Test) & \(\mathbf{R^{2}}\) (Train/Test) \\ \hline CNN-AF & 0.027/0.0385 & 0.0344/0.0509 & 0.9998/0.9995 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The evaluation indicators of CNN-AF model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CNN-RL** & **Direction** & _MAE_ & _RMSE_ & \(\mathbf{R^{2}}\) \\ \hline
1 & 0.9338 & 1.2025 & 1.8211 & 0.9534 \\
2 & 0.9669 & 1.3593 & 2.1358 & 0.9534 \\
3 & 0.9404 & 1.3725 & 2.1660 & 0.9437 \\ Average value & 0.9470 & 1.3114 & 2.0410 & 0.9502 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The evaluation indicators of CNN-RL model.  
Figure 15: Accuracy of CNN-AF model in predicting amplification factor.  
As shown in Table 9, the accuracy of the CNN-AP-8 model was lower than that of the CNN-AF model, but the errors were all around 1%. Compared with CNN-RL, the CNN-AP-8 joint model was only slightly lower than CNN-RL in direction prediction accuracy, by 2.2%, and was better than CNN-RL in other evaluation indicators.  
In summary, the joint prediction model was only about 1% lower than the dedicated CNN-AF in predicting the amplification factor. However, the overall prediction effect of the hazard location was better than the corresponding special model CNN-RL, and there was only a disadvantage in the direction prediction. This may be because the output of the joint prediction model has one more amplification factor peak, which increases the complexity of the model. Therefore, the CNN-AP-8 model was finally selected for the prediction of basin amplification characteristics in this paper.
# A Low-Cost Sensorized Vehicle for In-Field Crop Phenotyping
## 2 Materials and Methods
### Mechanical Components  
#### 2.1.1. _Chassis_  
The mechanical components are represented by a chassis made of standard grooved profiles and detachable joints in aluminum alloy. This has an easily variable setup and can be assembled/disassembled when the device is stationary, with a minimum crossbar height of 1.5 m from the ground.  
The chassis consists of three macro-sections, which can be disassembled and folded back on themselves for transporting the vehicle and, if necessary, for storing it. The first part consists of a central body of prismatic shape onto which two articulated quadrilateral structures are anchored, allowing both track variation and a macroscopic \"bellows\" closure (Figure 1a).  
Connected to the \"double articulated quadrilateral\" are the two side structures that contain the wheel motors and the front-view guide cameras (one on the left and one on the right pillar).  
While the right-hand side pillar houses only the wheel motor, the left-hand structure also consists of the \"longitudinal rocker\", which ends with the steering wheel and performs the function of directing the device's gait according to user needs (Figure 1b).  
The \"longitudinal rocker arm\" holds the following electronic components: battery pack--BMS; control electronics for wheel motors; electronic driver with microcontroller; guide monitor for frontal vision; display with relative power buttons; brakes.
## 4 Geometric Parameters Optimization and Performance Evaluation
### Geometric Parameters Optimization
Additionally, the ranges of the geometric parameters are given as 50 mm \(\leq L\leq\) 75 mm, 1 mm \(\leq t_{1}\leq\) 5 mm, 1 mm \(\leq t_{2}\leq\) 5 mm, 5 mm \(\leq d\leq\) 10 mm, and their initial values are given as \(L\) = 60 mm, \(t_{1}\) = 3 mm, \(t_{2}\) = 3 mm, and \(d\) = 7.5 mm. Finally, the optimized parameters are obtained as \(L\) = 50 mm, \(t_{1}\) = 3.56 mm, \(t_{2}\) = 2.57 mm, and \(d\) = 10 mm. The ICD of the optimized CPM is as low as 0.279%, which indicates that the 2-DOF CPM possesses an excellent input decoupling character. The analytical results of other performance indicators are also obtained. The results show that the CPM has a workspace of 47.54 \(\upmu\)m \(\times\) 47.54 \(\upmu\)m. The input and output stiffness are 3.68 N/\(\upmu\)m and 3.50 \(\times\) 10\({}^{4}\) Nm/rad, respectively. In addition, the natural frequency is 876 Hz. The analytical results show that the key performances of the optimized CPM all satisfy the design requirements.
## 4 Results and Discussion
### Spectral Characteristics
#### 4.1.1 Oil-Adulterated Red Chili  
With the addition of both types of oil in red chili, a decreasing pattern in the spectral reflectance between 650 and 680 nm has been detected, which is similar to the grounded chili purchased from the local market. Although the difference looks minimal in the mean spectrum, our previously designed algorithm [32] can distinguish and accuracy decreased from 99% to 56% as shown in Figure 11b.  
Figure 10: Reflectance Spectra of Different Kinds of Red Chili.  
The difference in spectral reflectance becomes more apparent by removing the baseline effect from the spectra using first-order Savitzky-Golay derivative as shown in Figure 12.  
This change in spectral signature did not follow a uniform pattern with the change in the oil adulteration level (Figure 12). Although the spectral signature of red chili adulterated with small quantities of oil is almost similar to the pure red chili spectra, the one-class SVM algorithm still detected a difference in pixel level. This was expected as small quantities of oil though mixed with electric mixer yet adulteration at particle level cannot be reached. Moreover, the penetration depth of visible wavelengths is almost negligible. Thus, only surface particles are included in the mean spectrum.  
Figure 11: Classification of (**a**) pure red chili, (**b**) oil-adulterated red chili, and (**c**) Sudan oil-adulterated red chili using one-class Support Vector Machine (SVM), where “1” and “0” represent red chili and adulterants pixels, respectively.
# Space-Constrained Scheduling Optimization Method for Minimizing the Effects of Stacking of Trades
## 3 Space-Constrained Scheduling Optimization Method  
### Method Overview  
This study presents an area-oriented method for establishing schedules that minimize workspace interference while achieving minimal stacking of trades. It must be noted that this method uses different meanings for \"workspace\" and \"work area,\" which could be mistaken for synonyms. Workspace implies the space that is occupied by the physical volume of resources (e.g., workers, equipment, and materials) that are allocated during the execution of activities. Therefore, workspaces differ from each activity. Work areas are spaces that divide construction sites into specific areas with certain dimensions. A work area may have been occupied by an activity's workspace. In addition, this study introduces an occupation density function to find area-oriented solutions that minimize workspace interference. This function is presented in detail later in the section describing the processes of computation. Further, to resolve workspace interference, a meta-heuristics-based optimization technique must be introduced. The optimal solution search range is focused on (1) adjustments start time of noncritical activities within the TF (total float) (2) changes in activity execution patterns that do not have penalties such as increased construction time or cost. This study adopts GA, and a GA-based optimization method for establishing schedules with minimal workspace interference is presented. The computational processes of the method are shown in Figure 2. MATLAB (ver. 2015b) was used to implement this method in the form of the software called SSO. SSO consists of five modes: (1) inputting schedule data, (2) defining occupation density function, (3) executing CPM and GA chromosome encoding, (4) defining fitness function and executing GA, (5) and outputting the near-global optimal schedules report. A detailed description of each mode is provided below.
## 3 Proposed Method
### Optimization Algorithm  
Optimization was performed using a genetic algorithm (GA), using Matlab's R2023b built-in gamultiobj function to solve multi-objective optimization problems. Gamultiobj uses a variant of the NSGA-II (Non-dominated Sorting Genetic Algorithm II) as its core algorithm. It is computationally efficient and ensures diversity among solutions. Gamultiobj optimizes multiple conflicting objectives simultaneously by evolving a population of solutions over generations. The algorithm finds a Pareto front for optimization problems involving two or more conflicting objectives. Key features of the NSGA-II include non-dominated sorting, which ranks solutions into hierarchical fronts based on Pareto dominance, and crowding distance, thus ensuring diversity along the Pareto front by favoring less crowded solutions. Elitism preserves the best solutions across generations, ensuring convergence toward the optimal front, while crossover and mutation explore the solution space by generating new and diverse solutions [22]. The population size is set to 500 for all cases.
## 3 Results  
### Geometric Accuracy  
The geometric accuracy of the DL auto-contouring approach was higher than that of the atlas-based method overall. Figure 2 shows that the DL auto-contouring performed more consistently with smaller DSC, HD and HD95 variations for nearly all OARs when compared with the atlas-based auto-contouring. Additionally, its geometric accuracy (in terms of higher DSC and lower HD and HD95 values) was better for 10 out of 16 OARs. These 10 OARs were brain stem, left and right eyes, left and right lens, left and right optic nerves, left and right parotid glands, and pituitary. Except for the DSC values of left eye, the DL auto-contouring had statistically significantly higher mean DSC and lower HD and HD95 values for these 10 OARs as well (Table 2).  
Figure 2: _Cont_.  
Figure 2: Boxplots of three geometric accuracy evaluation parameters, Dice similarity coefficient (**A**), Hausdorff distance (**B**), and 95 th-percentile of Hausdorff distance (**C**) for organs at risk contoured by atlas-based and deep learning (DL) auto-contouring approaches. L-left; R-right.  
Nonetheless, Table 2 also illustrates that both atlas-based and DL auto-contouring approaches had very poor/poor accuracy (DSC < 0.50/HD95 \(\geq\) 6 mm) in contouring larynx, optic chiasm, oral cavity and spinal cord. In addition, the DL auto-contouring only had mean DSC of 0.31-0.35 for contouring left and right cochleae. Figures 3 and 4 show two examples of OARs segmentations by the three approaches, namely manual contouring (gold standard), DL and atlas-based auto-contouring.  
Figure 3: Organs at risk segmentation for a 75-year-old man with T3N2M0 nasopharyngeal cancer by manual contouring (**A**), DL (**B**) and atlas-based auto-contouring (**C**) with the following Dice similarity coefficient, Hausdorff distance, and 95 th-percentile of Hausdorff distance values for brainstem (0.81, 7.20 mm and 4.39 mm vs. 0.83, 7.13 mm and 4.11 mm), left (L) cochlea (0.45, 5.24 mm and 2.92 mm vs. 0.81, 1.17 mm and 1.17 mm), right (R) cochlea (0.42, 6.83 mm and 3.53 mm vs. 0.79, 2.32 mm, 1.17 mm), L eye (0.85, 4.00 mm and 3.30 mm vs. 0.89, 3.30 mm, 2.33 mm), R eye (0.87, 3.52 mm and 2.33 mm vs. 0.88, 6.00 mm and 3.54 mm), larynx (0.47, 24.82 mm and 13.87 mm vs. 0.28, 18.89 mm and 12.51 mm), L lens (0.77, 2.32 mm and 1.41 mm vs. 0.53, 3.30 mm and 2.46 mm), R lens (0.67, 2.32 mm and 1.41 mm vs. 0.56, 3.30 mm and 2.46 mm), optic chiasm (0.47, 7.86 mm and 5.25 mm vs. 0.44, 9.61 mm and 4.42 mm), L optic nerve (0.66, 7.77 mm and 3.91 mm vs. 0.62, 4.33 mm and 3.31 mm), R optic nerve (0.70, 4.64 mm and 2.70 mm vs. 0.46, 8.45 mm and 3.93 mm), oral cavity (0.84, 19.31 mm and 10.24 mm vs. 0.68, 16.57 mm and 11.12 mm), L parotid gland (0.83, 11.71 mm and 4.78 mm vs. 0.57, 19.18 mm and 9.34 mm), R parotid gland (0.81, 10.73 mm and 4.74 mm vs. 0.69, 17.25 mm and 7.38 mm), pituitary (0.51, 3.71 mm and 2.54 mm vs. 0.46, 5.32 mm and 3.49 mm) and spinal cord (0.52, 220.13 mm and 104.18 mm vs. 0.79, 34.90 mm and 12.26 mm), respectively.
# New Results for the Error Rate Performance of LoRa Systems over Fading Channels
## 5 Conclusions  
In this work, we elaborated the LoRa system model to include an extensive performance analysis in the presence of various types of fading channels, using exact single integral expressions as well as accurate approximations. The results presented herein are valid for most of the well-known fading models available in the open technical literature. Moreover, they are computationally efficient and thus they may serve as a useful tool for system engineers for performance evaluation purposes.  
**Author Contributions:** Conceptualization, K.P., S.K.C. and D.L.; methodology, K.P., S.K.C. and D.L.; software, K.P.; validation, K.P., S.K.C. and D.L.; formal analysis, K.P., S.K.C. and D.L.; investigation, K.P., S.K.C. and D.L.; resources, K.P.; writing--original draft preparation, K.P.; writing--review and editing, S.K.C. and D.L.; visualization, K.P.; supervision, K.A.; project administration, K.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data can be available upon mail request.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: SER of LoRa systems operating in the presence of \(\kappa\)-\(\mu\) fading as a function of the SNR, \(\gamma\), for \(\kappa=10\), \(\mu=2.1\) and various values of SF.
## 3 Reinforcement Corrosion Monitoring Techniques
However, it requires the intervention of an operator and, in some areas, the installation of means of access. These constraints turn the use of external sensors extremely expensive if an assessment over time is necessary. Furthermore, in painted structures, submerged or buried concrete areas, the use of external sensors requires the removal of the coating, to expose the concrete, which may affect the results. The use of sensors embedded in the concrete allows the use of automatic data acquisition systems, which enable the collection of information over large periods of time without accessing the structure. Embedded sensors can also collect valuable information in areas that are difficult to reach. Nevertheless, they have some disadvantages, such as providing more limited sampling information and the fact that the sensors have to be installed during construction or after repair works. The first applications of sensors embedded in RCS occurred in the late 1970s with the introduction of reference electrodes to control corrosion potential in cathodic protection [65; 66]. In the early 1990s, a sensor based on the measurement of the galvanic current in macro cells was proposed by [PERSON] and [PERSON] [67]. In the last two decades' sensors were developed for measuring the resistivity of the concrete [2; 68; 69], \(i_{corr}\)[57; 70], reduction limit current of oxygen [71; 72] and chloride content [73; 74; 75; 76; 47]. There are several commercial systems that allow the simultaneous measurements of all or some of the abovementioned parameters [78]. These measurements can be performed from inside or outside of the RCS. Nevertheless, these systems are usually high in cost and are used mainly in developed countries [78]. In 2009, an integrated and cost-effective embeddable sensor to monitor the corrosion state of RCS was proposed [78]. The sensor measures the OCP and \(i_{corr}\) of reinforcement steel, the resistivity, the availability of O\({}_{2}\), the Cl\({}^{-}\) concentration in concrete, and the temperature inside the structure. The integrated system consists of different electrodes embedded in concrete together with a software system to acquire and analyze the data [78]. The efficiency of the embedded sensors depends on the selection of suitable areas and the type of parameters that are monitored. The selected areas must be representative of the environmental exposure that the structures are subjected to and, should be the ones with the highest probability of corrosion (e.g., areas of higher condensation or water stagnation; tidal and splashes zones in maritime structures). Moreover, the specific aspects of structural design or construction practices (e.g., high-density areas of reinforcement; corners; concreting and expansion joints) must also be taken into account.
# Oxygen Isotopic Compositions of San Carlos Olivine Standard NMNH 111312-42
## 1 Introduction  
Natural forsterite-rich olivine grains have been used as elemental and isotopic geos standards during in situ analyses of olivine samples [1, 2]. Among them, the San Carlos olivine standard developed by [PERSON] and co-workers [3] at the Smithsonian Institution, named NMNH 111312-44, is the most widespread olivine geostandard.  
The NMNH 111312-44 (mineral size: 0.250 mm-0.177 mm) was mined in the San Carlos Apache Reservation, which is one of Earth's richest resources for olivine-rich rocks. It is located in San Mateo County, California, United States. [PERSON] [4] suggested that the composition of the NMNH 111312-44 shows only slight major element variability (Fo89.6 to Fo90.5). [PERSON] et al. [3] calculated the 'Boyd homogeneity indices' for NMNH standards by measuring 100 spots on 10 grains of each standard. The range of heterogeneity in Si, Fe, and Mg was 0.81, 0.90, and 1.00, respectively, which were considered to be homogeneous (values \(<\) 3). The \(\xi^{18}\)O of NMNH 111312-44 was also considered to be homogeneous with the majority of values falling within the range of 5.2% to 5.3% (relative to VSMOW) [5, 6, 7].  
Due to its homogeneity, a large number of labs have taken it as standard in analyses of geological samples. In particular, it was often used as reference material in olivine oxygen isotope determination. However, the NMNH 111312-44 olivine grains (referred to here as -44) have been completely consumed. Another San Carlos olivine, whose grains are larger (mineral size: 0.500 mm-0.250 mm), named NMNH 111312-42 (referred to as -42), wasdistributed by the Smithsonian Institution. Recent studies have regarded -44 and -42 as the same geostandard (e.g., [PERSON]. et al. [5]). Although they are chemically indistinguishable in terms of major and trace elements (Figure 1), whether they have the same oxygen isotopic composition is still unclear. In this study, the oxygen isotopic compositions of -42 and -44 were measured simultaneously and compared to determine whether -42 could replace -44 as the same geostandard for in situ oxygen isotope analysis.