esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Stage 2--Definition of Electric Power Demand  
The charging of the electricity demand taxes, according to Normative Resolution No. 414, of 9 September 2010 [16], is calculated by Equation (1).  
\[CDP=TDPC\times DPM \tag{1}\]  
where \(CDP\) is the monthly cost of power demand (R$), \(TDPC\) is the contracted power demand taxes (R$/kW), and \(DPM\) is the Measured Power Demand (kW).  
Overshoot occurs when an amount above the power demand contracted by the company is consumed. In this way, a different charge is made, obtained from Equation (2) [16].  
\[CUDP=(DPM-DPC)\times(2\times TDP) \tag{2}\]  
\(CUDP\) is the cost of exceeding power demand; \(DPC\) is the Contracted Power Demand; \(TDPC\) is the Contracted Power Demand Taxes.  
For example, during the contract period of one year, the company pays monthly for the previously contracted power demand, even if it is not used. Overshoot occurs when the electric power utility measures a value above the power demand contracted by the unit. This tax is stipulated by ANEEL and costs twice as much as contracted. In this condition, the company pays the contracted value plus the value of exceeding demand.  
As the company must contract only one demand value charged monthly during the contract period, the proposed model considers each value observed in recent years as a possible power demand to be contracted (\(DEC_{j}\)). This is compared with the other measured electrical demands (\(DEM_{i}\)) to check for overshoot.  
The power demand to be contracted is mandatory and continuously made available by the distributor, at the point of delivery, according to the value and period of validity previously established in the contract, and which must be paid in full, whether or not used during the billing period, expressed in kilowatts (kW). During the entire contract period, for example, one year, the company pays monthly for the previously contracted power demand, even if it is not used. Overshoot occurs when the electric power utility measures a value above the power demand contracted by the unit. The rate for exceeding demand stipulated by ANEEL is double the contracted rate. In this condition, the company pays the contracted value plus the value of exceeding demand. In practice, companies usually contract the highest power demand value measured between the months of the previous year to avoid exceeding demand during the next contract period.  
A contracted tax (\(TC_{i}\)) is applied to the contracted demand; if consumption is greater than the contracted demand, an overtaking tax (\(TUI_{i}\)) is also applied. \(TC_{i}\) and \(TUI_{i}\) are defined by ANEEL [17]. The cost during the period is the sum of the costs of the '\(n\)' months of the contract. The power demand to be contracted, then, is the one that results in the lowest cost of the period to be contracted (\(\min CDE_{j}\)), obtained by Equation (3).  
\[CDP_{j}=\sum_{i=1}^{n}\bigl{(}\bigl{(}DPC_{j}\times TDPC_{i}\bigr{)}+k\times \bigl{(}DPM_{i}-DPC_{j}\bigr{)}\cdot(2\times TDPC_{i})\bigr{)} \tag{3}\]  
where  
\[k=\left\{\begin{array}{ll}1,&\quad DPM_{i}>DPC_{j}\\ 0,&\quad\text{otherwise}\end{array}\right.\]
# Evaluating Pile-Supported Embankment Considering the Soil Anisotropy Effect
## 5 Soil Arching Considering Friction Angle Anisotropy
### Embarnent Settlement Is Affected by Friction Angle Anisotropy  
In investigating the impact of the anisotropy degree pertaining to the friction angle on embankment settlement, it is imperative to analyze the computational outcomes concerning the vertical displacement of embankments exhibiting distinct anisotropy coefficients allied with their respective friction angles (\(A_{r}\)). Figure 18 illustrates the vertical settlement curve of the embankment with varying anisotropy coefficients of the friction angle (\(A_{r}\)), in accordance with the aforementioned discussion. Position A represents the location above the pile top, while position B represents the central position of the four piles. It is evident that the embankment settlement increases significantly with an increasing anisotropy coefficient. The settlement curve tends to stabilize at the position of the iso-settling surface. The position of the iso-settling surface undergoes minimal change, approximately 2.3 m, with an increasing anisotropy coefficient of the friction angle.  
Figure 17: Effect of the friction angle anisotropy coefficient (\(A_{r}\)) on the stress distribution of geogrid.  
Figure 18: The influence of different (\(A_{r}\)) values on embankment settlement.  
As depicted in Figure 19, an increase in the anisotropy coefficient from 1 to 1.48 leads to a corresponding increase in the maximum embankment settlement from 151.6 mm to 191.2 mm, representing a 26.1% increase. Additionally, the settlement at the top of the embankment increases by 21.2% from 103.9 mm to 125.9 mm. These observations highlight the substantial influence of the anisotropy of the friction angle on the settlement of the embankment.
## 4 Results and Discussion
### Correlation Heatmap Analysis  
After analyzing the data, it was observed that certain environmental sensor data, such as air temperature and soil temperature, soil EC, and drainage EC, exhibited similar monthly changes. To examine these relationships, we computed the correlations among the data and constructed separate data heatmaps for both day and night periods, to provide a clear overview of the correlations. The Pearson correlation is given in Equation (13):  
\[r_{xy}=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{ \sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y} )^{2}}}, \tag{13}\]  
where \(n\) represents the size of the data, \(x\) and \(y\) represent the data \(x\) and data \(y\), respectively, and \(x_{i}\) and \(y_{i}\) represent the \(i^{th}\) value of \(x\) and \(y\), respectively [54; 55; 56].  
Table 1 illustrates the interpretation of correlation coefficient values. These values range from \(-1\) to \(1\), with values closer to \(1\) suggesting a strong correlation, and values closer to \(0\) indicating minimal to no correlation between the two datasets [57; 58].  
Figure 19: Drainage EC data.  
Figure 20: Drainage pH data.  
The analysis of the correlation heatmap in Figure 21 revealed the correlations among various environmental sensor data. Among these, we focused on the analysis of data related to air temperature for the evaluation of the stacking ensemble model's performance.  
From Figure 21, it can be observed that there exists a strong positive correlation between air temperature and soil temperature, as well as between soil EC and drainage EC. Moreover, a strong negative correlation was identified between air temperature and humidity. Moderate positive(negative) correlations were found between air temperature and CO\({}_{2}\), air temperature and insolation, humidity and CO\({}_{2}\), humidity and soil temperature, humidity and insolation, CO\({}_{2}\) and insolation, and soil temperature, and drainage pH. As for the remaining data, weak or very weak correlations, or no correlation at all, were observed. Based on these results, we proceeded to predict air temperature using a total of five sensor data: humidity, CO\({}_{2}\), soil temperature, insolation, and drainage pH.
## 3 Results  
### Comparison with Reference AGB Map  
Figure 3 shows the direct pixel-by-pixel comparison between the reference and estimated AGB in DOY 225, 2017, corresponding to the field campaign (Section 2.2.1). Whilst, slight over-estimation and under-estimation can be found when reference AGB is less than 200 g\(\cdot\)m\({}^{-2}\) and between 300 and 350 g\(\cdot\)m\({}^{-2}\), respectively, a satisfactory accuracy is obtained from the proposed physically-based retrieval method with RMSE and R-RMSE of 60.06 g\(\cdot\)m\({}^{-2}\) and 18.1%, respectively.  
The estimated AGB map on DOY 225, 2017 (Figure 4) exhibits nearly identical spatial pattern as that of reference map (Figure 1b). For example, areas near rivers (bottom-left part) are characterized by high AGB because of easy access to moisture.
## 3 Data Analysis  
Once all participant listeners had completed both listening sessions, the scales for each speaker sample were hand measured in mm and scored from the far left of each scale; this resulted in scores that ranged from 1 to 100 in all cases. For both OS and LC, lower scaled scores represented auditory-perceptual judgments of less OS or more favorable LC based on the definitions and anchors provided for each scale. These scaled scores for each listener were then submitted for further analyses as per the objectives of the study.  
Objective 1: Three different correlation analyses were used to quantify the association between OS and LC. In the first, the OS and LC scores from all listeners were pooled. The second analysis investigated the association between OS and LC when speaker scores were averaged across all listeners. The third investigated listener-to-listener variability in the association between OS and LC.  
Objective 2: Image representation was used to compute acoustic measures. This approach converted the spectrogram of a speaker's voice sample into an image and then computed two statistical models and variations in the (a) time, and (b) frequency domains [29]. The _temporal model_ reflects the probability of changes in the energy of a fixed sub-band between consecutive time segments (i.e., changes in the \(x\)-direction), whereas the _spectral model_ reflects the probability of changes in the energy of consecutive sub-bands of a spectrogram at a fixed time segment (i.e., changes in the \(y\)-direction).  
Two separate stepwise multiple linear regression analyses were used to explain the perceptual score of LC in terms of acoustic measures of TE speech. The outcome variable in both analyses was the average value of speaker judgments; the predictor variables were the inertia and the sum of rows of the temporal model for the first analysis, and the inertia and the sum of rows of the spectral model for the second. The inertia of a row quantifies the distribution of the probability of changes in the spectrogram (e.g., a low-energy region in a spectrogram followed by a high-energy region). The sum of a row quantifies the probability of different energy levels in the spectrogram (e.g., whether the spectrogram contains concentrated energy bands or the energy is dispersed).
## 3 Results and Analysis  
### Scatterplot of Spectral Indices  
In order to analyze the correlations between SI1 and GRNDVI, GNDVI, NDVI, and DVI, this study takes SI1 as the horizontal axis of coordinates and GRNDVI, GNDVI, NDVI,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Spectral Index** & **Expression** \\ \hline SI1 & SI1 = \(\sqrt{(}\)G \(\times\) R) \\ GRNDVI & GRNDVI = \((\)N \(-\) R \(-\) G)/(N \(+\) R \(+\) G) \\ GNDVI & GNDVI = \((\)N \(-\) G)/(N \(+\) G) \\ NDVI & NDVI = \((\)N \(-\) R)/(N \(+\) R) \\ DVI & DVI = N \(-\) R \\ S1 DI1 Model (SI1-GRNDVI) & S1 DI1 = \(\sqrt{(}\)GRNDVI \(-\) 1)\({}^{2}\) + SI1\({}^{2}\) \\ S1 DI2 Model (SI1-GNDVI) & S1 DI2 = \(\sqrt{(}\)GNDVI \(-\) 1)\({}^{2}\) + SI1\({}^{2}\) \\ S1 DI3 Model (SI1-NDVI) & S1 DI3 = \(\sqrt{(}\)NDVI \(-\) 1)\({}^{2}\) + SI1\({}^{2}\) \\ S1 DI4 Model (SI1-DVI) & S1 DI4 = \(\sqrt{(}\)DVI \(-\) 1)\({}^{2}\) + SI1\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The expression of spectral indices.  
Figure 2: The demonstration masks of buildings, water, fruit trees, and desert.  
and DVI as the vertical axes of coordinates, respectively, to establish an SI1-GRNDVI, SI1-GNDVI, SI1- NDVI, and SI1-DVI two-dimensional scatter plot, as shown in Figure 3. The fitness among different indices is also provided in Table 2.  
In Figure 3 and Table 2, the salinity index 1 (SI1), green-red band normalized difference vegetation index (GRNDVI), green normalized vegetation index (GNDVI), and normalized difference vegetation index (NDVI) can be seen. The SI1-GRNDVI, SI1-GNDVI, SI1-NDVI,  
and SI1-DVI two-dimensional scatter plots constructed by the difference environmental vegetation index (DVI) have the best fitting degrees: R\({}^{2}\) = 0.9332, R\({}^{2}\) = 0.9317, R\({}^{2}\) = 0.9211, and R\({}^{2}\) = 0.8236, respectively. The above analysis shows that the salinity index 1 and the four vegetation indexes have a very large correlation. Consequently, the RS-based salinity 1 monitoring index model can be built. Based on the four types of remote sensing salinity monitoring 1 index models for the cotton field soil salinity in the Alar reclamation area, for determining whether the accuracy of chemical inversion is good or bad, further verification is needed.
# Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks
## 4 Experiments
### Experimental Results and Discussion
#### 4.3.3 Multi-Modality Sentiment Analysis  
From Tables 1 and 2 we can see that sentiment prediction from images is not as effective as sentiment prediction from text. Therefore, based on a grid search, we choose the best fusion result by assigning a greater weight to the textual information when fusing the predicted class probabilities for text and images ((0.62, 0.38) in the Two-Class and (0.66, 0.34) in the Three-Class evaluations). From the results shown in Figure 3 we find that the visual information is helpful for sentiment prediction (it improved the performance from 0.811 to 0.826 in Two-Class evaluation and from 0.748 to 0.765 in the Three-Class evaluation). The experiment demonstrates that visual information functions as an effective supplement to textual sentiment prediction, and that sentiment analysis via multi-modality information achieves better performance than considering any modality alone.  
From Table 3 and Figure 3, we find our framework achieves better performance than CBM [23] in this benchmark not only in the Two-Class evaluation (0.826 _vs._ 0.80) but also in the Three-Class evaluation (0.765 _vs._ 0.66).  
Our textual model utilizes large-scale unlabeled texts by employing unsupervised pre-training of word vectors to transfer ample semantic information for a simple CNN. This approach learns more discriminative features than traditional n-gram features. At the same time, our visual model extracts more abstract features via a deep and large neural network with billions of parameters and effectively relieves the overfitting problem by leveraging a regularized version of Dropout. Finally, we combine these representative features to analyze the sentiment expressed in samples. This combination inevitably leads too great improvement in sentiment prediction.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**CBM\_Image**[23]} & \multicolumn{5}{c}{**DNN\_Image**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Two-Class & \textless{}0.725 & 0.763 & 0.955 & 0.747 & 0.838 \\ Three-Class & \textless{}0.66 & 0.688 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of visual methods.  
Figure 3: Sentiment prediction based on textual features, visual features and multi-modality features.
# Feasibility of Osseous Landmarks for ACL Reconstruction--A Macroscopic Anatomical Study
## 4 Discussion
### Limitations  
The hypothesis for this study was that the correct identification correlates with the experience of the investigator. To answer this question, the number of specimens used in this study was deemed sufficient, especially as the prevalence of the two ridges has already been reported adequately in a large number of specimens [24] and the number of specimens was limited by their availability and the inclusion criteria. As the age in anatomical specimens is on average higher, a large number of specimens could not be included due to degenerative changes to the knee joint.  
Additionally, the rather high mean age of the specimens could be questioned, particularly when following the argumentation of [PERSON] et al. [26], who showed a high number of LBRs in fetal lower extremities and hypothesized the possibility that the LBR is lost during aging. If, however, this argumentation were true, we would expect an equally diminished number of LBRs and LIRs in the specimen. As in this study the LIR was present in 80.6% of sample, we argue that this line of argumentation [9; 26] is highly questionable. Based on the reported existence of LIR and LBR, aging seems not to be the most influential factor.  
In contrast to the study of [PERSON] et al. [24], where the low detectability of the LBR was shown on the basis of macroscopic photos from the intercondylar notch, in this study, the intercondylar notch was photographed from an arthroscopic perspective, with similar results.  
The photographic setting in this study could also be questioned. Using a reflex camera with a custom-made adaptor to attach the arthroscope in place of an arthroscopic camera does not reflect circumstances in a clinical setting, and therefore could also challenge the applicability of the results presented in this study. However, to achieve standardized photographs which guaranteed the same position in each specimen, the authors decided in favor of the combination of a reflex camera and tripod to minimize this bias in the photographic setting.  
Regarding the comparison of the different experience levels, the low number of investigators could be seen as a limiting factor. In addition, one important group, orthopedic residents, was missing. However, this group would be very difficult to rate, as the experience level varies widely regarding the different levels of qualification and experience in arthroscopy.  
Furthermore, the knowledge of the existence of the LIR and LBR, as we were able to show in this study, may bias the results. Therefore, the bony ridges were located in a dual control step with the help of the anatomical specimens, which reduced falsification.
# MAG Welding Tests of Modern High Strength Steels with Minimum Yield Strength of 700 MPa
## 1 Introduction
Especially in quenched and tempered (Q&T), HSS brittle and hard domains can occur in the coarse grained HAZ (CGHAZ) [11; 12]. The steels subjected to the thermomechanical control process (TMCP) and precipitation hardening and characterised by a high yield point as the S700 MC are obtained through precipitation, solution, and strain hardening as well as by using grain-refinement-related processes. [PERSON] [13] studied the effect of micro-constituents responsible for the hardening of the S700 MC steel, namely the Ti, Nb, N, and C. The hardening is primarily affected by (Ti,Nb)(C,N)-type dispersive precipitates sized from several nanometres to between ten and twenty nanometres. The fusion welding process such as MAG, considerably differs from the original TMCP conditions, leading to the reduction of plastic properties both in the HAZ and in the weld zone. In comparison with the application of a single pass advanced welding solutions to modern TMCP HSS with a minimum yield strength of 700 MPa, as demonstrated by [PERSON] et al. [14; 15] in the application of friction stir welding with faster welding speed conditions, or [PERSON] et al. [16] in the application of hybrid laser arc welding, the multi-pass fusion welding can induce excessive decrease of impact-toughness, eventually due to the intercritically reheated domains that are formed in the HAZ [17; 18]. On this scope, [PERSON] et al. [19] observed that V-N micro-alloying improved the toughness of the ICCCHAZ (inter-critically reheated coarse grained heat affected zone) by decreasing the size of M/A (martensitic/ausenitic) constituents and refining the ferrite forming on the prior austenite grain boundaries [19]. Concerning the improvement of the impact toughness of the HAZ, [PERSON] et al. [20]reported that an increased amount of acicular ferrite in HAZ is beneficial for the impact toughness. Oxide inclusions in sufficient sizes acted as nucleation sites for acicular ferrite and thereby promoted the formation of it [20]. It has also been proposed by [PERSON] et al. [21] that boron in sufficient contents (less than 0.0025 wt.%) improves the impact toughness by promoting the formation of acicular ferrite and inhibiting the grain boundary ferrite formation [21]. This work investigates the influence of four different heat inputs of MAG welds on the mechanical properties and inherent microstructure of three different modern HSS with minimum yield strength of 700 MPa.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 2 Materials and Methods  
### Data Description  
Data on the number of vessels transiting the Panama Canal were obtained from the Annual Reports published by the Panama Canal Commission [27], Panama Canal Authority [8], and National Institute of Statistics and Census of the Office of the Comptroller General of the Republic of Panama [28].  
The dataset used has a monthly frequency, corresponding to vessel transit data through the Panama Canal over a 23-year period, from January 2000 to December 2022, totaling 276 observations.  
Ships navigating the Panama Canal follow a maritime route connecting the Caribbean Sea with the Pacific Ocean, crossing the narrowest section of the Isthmus of Panama (Figure 1).
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 2 Materials and Methods
### PV Cell Models
#### 2.1.4 Statistical Tests  
To evaluate the performance of the PSA, an objective function that minimizes the differences between the measured current collected from the dataset and the estimated current obtained from the algorithm must be provided. This study uses the root mean square error, known as RMSE, see Equation (4), as the primary objective function due to its ability to penalize larger errors more significantly than other metrics, making it particularly effective for precise parameter estimation. Moreover, RMSE provides a comprehensive measure of accuracy and reliability, as it considers both the magnitude and distribution of errors.  
Additionally, this study uses the mean bias error, known as MBE, shown in Equation (5), and absolute error, known as AE, shown in Equation (6), as another measure of performance to further assess the algorithm's accuracy. These metrics are used to compare the PSA's performance to those of other algorithms in the literature [37].  
\[RMSE(X)=\sqrt{\frac{\sum_{i=1}^{n}\ \left(I_{ic}-I_{im}\ \right)^{2}}{n}}\] \[X(SDM)=I_{ph}\ I_{sd1}\ R_{s}\ R_{sh}\ \text{, }and\ n_{1} \tag{4}\] \[X(DDM)=I_{ph}\ I_{sd1}\ \text{, }I_{sd2}\ R_{s}\ \text{, }R_{sh}\ \text{, }n_{1}and\ n_{2}\text{,}\]  
\[MBE=\frac{\sum_{i=1}^{n}\ \left(I_{ic}-I_{im}\ \right)}{n} \tag{5}\]  
\[AE=\sum_{i=1}^{n}\left|I_{ic}-I_{im}\right| \tag{6}\]  
where \(X\) represents the five parameters from the SDM and the seven parameters from the DDM. \(n\) is the total number of measured points, \(I_{ic}\) is the algorithm's calculated current, and \(I_{im}\) is the actual measured current.
## 5 Results and Discussion  
Simulations of our suggested approach are carried out with the help of the NS2 (v2.34) running on a system with Ubuntu 20.04 with Intel core i7 having 16 GB of RAM. AODV algorithms such as B-AODV, Ids-AODV, and EAODV were compared to our method. EAODV has been made accessible for MANETs. Furthermore, in [26], the researchers additionally stated that these concepts could be useful to the enormous majority of ad hoc networks, which they believe to be accurate. Their reasoning, as stated in the preceding paragraph, have been taken advantage of, and the findings have been compared to [26]. EAODV would have failed if the attacker had scanned a network, but that was not the case, if OEAODV (our technique) had been evaluated in the same situation.