esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Prediction of the Omnidirectional and Translation Capabilities
In cases 1B-2C and 1A-2D, the mobile platforms can only move in the direction of two specific angular orientations: \(\alpha_{i}=0^{\circ}\) or \(180^{\circ}\) for the case 1B-2C, and \(\alpha_{i}=90^{\circ}\) or \(270^{\circ}\) for the case 1A-2D. In both cases, they can rotate with any angular speed \(\omega_{j}\) while performing these displacements, but their applicability is also limited because only two angular orientations are possible. Finally, Table 6 summarizes the analysis of the motion capabilities of all the set of mobile platform configurations described in Table 1. Table 6 shows that the omnidirectional capability results obtained with the procedure proposed in this paper agree with the state-of-the-art methods while a new differentiated estimation of the translational capabilities is also provided. Specifically, Table 6 presents the configuration name, a schematic representation of each platform configuration, the analysis of its omnidirectional capabilities based on two state-of-the-art methods [49], and the estimation of the omnidirectional and translation capabilities obtained with the procedure proposed in this paper. The first state-of-the-art method is based on computing the rank of the Jacobian matrix of the kinematic model of the mobile platform: if the matrix is full rank, the system is omnidirectional. The second state-of-the-art method is based on the bottom-roller axle intersections approach proposed by [PERSON] et al. [17] applied to omni wheels with \(\gamma_{k}=0^{\circ}\). This method analyzes the intersections between the axes of the passive rollers in contact with the ground to determine geometrically if a system is omnidirectional: when the axes intersect at two or three points,the platform is omnidirectional, whereas when the axes do not intersect or intersect only at one point, it is non-omnidirectional. As stated before, the results of Table 6 shows that the estimation of the omnidirectional capabilities provided by these state-of-the-art methods agree with the procedure proposed in this paper.
## 5 Comparison of Experimental and Theoretical Results  
_Verification of the Accurate Prediction of Experimental Results_  
Firstly, the experimental values of the horizontal displacements were compared with their analytical predictions. The experimental results took into account the effect of friction between the backfill and the side walls of the test box. It should be also noted that the horizontal displacement caused only by external loading was measured during the experiment.  
The results obtained are listed in Table 5 and presented in Figure 14. It can be seen that the experimentally recorded horizontal displacements are lower than the predicted horizontal displacements. The largest differences between the recorded and calculated values of deformation were observed at the top of the model. This was due to friction between the sand and the lower surface of the footing, which significantly reduced the horizontal deformation in this zone. However, compatibility between the experimental results and their theoretical predictions was considered satisfactory (i.e., acceptable for engineering practice). The average difference between the measured and calculated values of horizontal deformation was not greater than twenty five percent. Differences between the model results and the realistic behavior of the RS model structure may result from errors and inaccuracies that arose when the key material parameters used for the construction of the model wall were determined, or over the course of the experiments.  
Figure 14: Comparison of experimental results with their analytical predictions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Dist. from** \\ **the Top [m]** \\ \end{tabular} } & \multicolumn{8}{c}{**Displacement of the Facing at Subsequent Load Levels [mm]**} \\ \cline{2-13}  & \multicolumn{2}{c}{**184 N**} & \multicolumn{2}{c}{**512 N**} & \multicolumn{2}{c}{**841 N**} & \multicolumn{2}{c}{**1170 N**} & \multicolumn{2}{c}{**1499 N**} & \multicolumn{2}{c}{**1830 N**} & \multicolumn{2}{c}{**2123 N**} \\ \cline{2-13}  & \(\mathbf{u_{T}}\) & \(\bm  
The predicted horizontal displacements were larger than those that were experimentally recorded. From the point of view of design standards, there is a tendency for this to occur. Our results confirm that the proposed analytical method upholds the principles of safe design, which includes safety factors related to the potential occurrence of unexpected events.
## 3 Results
### The Landcover and Water Storage Changes  
Figure 7 shows the land cover changes between 2010 and 2019 over BSh, BSk, BWh and BWk regions. Table 1 shows the pixel count of the areas that changed and the percentages for each type: BSh has the highest change of 10.57%, while BWh has the lowest change of 3.92%. It is noted that such changes are most plausible in four regions: (1) Region A: north of Syria and Iraq in the Mesopotamia area; (2) Region B: east of Ethiopia and Somalia in Eastern Africa; (3) middle and east of Australia; and (4) Region D: south of Argentina. The time series plots in Figure 8 illustrate the GRACE Water Equivalent Height values in each region with the pink areas covering the periods from 2010 to 2019, which indicate different types of water storage changes: (1) Region A had a steady decrease from 2010 to 2019 until recent recovery in 2020; (2) Region B has been increasing since 2010; (3) Region C decreased from the historical peak in 2011 and returned to the average level; (4) Region D has experienced a continuous decrease since 2002.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Climate Type**} & **Not Changed** & **Changed Landcover** & **Changed Landcover** \\  & **Landcover (Pixel Count)** & **(Pixel Count)** & **(Percentage)** \\ \hline BSh & 1,972,365 & 233,177 & 10.57\% \\ BSk & 2,043,856 & 154,848 & 7.04\% \\ BWh & 4,997,033 & 203,959 & 3.92\% \\ BWk & 1,392,325 & 89,749 & 6.06\% \\ \hline Total & 10,405,579 & 681,733 & 6.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The areas of changed landcover between 2010 and 2019 presented by pixel counts.  
Figure 7: The land cover changes between 2010 and 2019, red color refers to the areas that has changed in terms of land cover types.
## 2 Materials and Methods
### Preparative Biotransformation  
To 10 Erlenmeyer flasks (300 mL) containing 3-day cultures was added substrate (100 mg) in acetone (10 mL). The biotransformation was conducted in the same conditions as the screening ones. The entire content of these flasks was extracted with dichloromethane (3 \(\times\) 40 mL) after seven days. The combined organic fractions were dried (MgSO\({}_{4}\)) and concentrated. Pure product was separated on silica gel (hexane: acetone 3:1). The physical and spectral data of hydroxylactone **5** (2-hydroxy-4-methyl-9-oxabicyclo [4.3.0] nonan-8-one) are presented below:  
Colourless oil, 1H NMR (300 MHz, CDCl\({}_{3}\)): 0.77-0.83 (m, 1H one of CH\({}_{2}\)-5), 0.99 (d, \(J\) = 6.5 Hz, 3H, CH\({}_{3}\)-9), 1.17-1.27 (m, 1H one of CH\({}_{2}\)-3), 1.43-1.50 (m, 1H, H-4), 1.64-1.72 (m, 1H, one of CH\({}_{2}\)-5), 1.82-1.89 (m, 1H, one of CH\({}_{2}\)-3), 2.27 (d, \(J\) = 16.7 Hz, 1H, one of CH\({}_{2}\)-7), 2.44-2.55 (m, 1H, H-6), 2.73 (dd, \(J\) = 16.7 and 6.4 Hz, 1H, one of CH\({}_{2}\)-7), 3.75 (ddd, \(J\) = 12.1, 4.4, 3.8 Hz, 1H, H-2), 4.58 (t, \(J\) = 3.8 Hz, 1H, H-1), 1\({}^{3}\)C NMR (151 MHz, CDCl\({}_{3}\)): 21.7 (C-9), 29.1 (C-4), 35.4 (C-5), 35.4 (C-6), 37.1 (C-3), 38.4 (C-7), 70.0 (C-2), 81.7 (C-1), 176.3 (C-8), IR (film, cm\({}^{-1}\)): 3378, 2927, 1765, 1170, 952, calcd for C\({}_{9}\)H\({}_{14}\)O\({}_{3}\)Na, _m/z_ 193.0841 (M + H)\({}^{+}\), found 193.0851. (SI, Figures S21-S25)
## 2 Biorefinery Concept
### Lignocellulosic Biomass Treatments  
The composition of lignocellulosic biomass varies with the type of biomass, with cellulose generally being its main component [27]. Cellulose is a highly crystalline, water-insoluble structure, consisting of linear polymeric chains of \(\beta\)(1\(\rightarrow\)4) linked \(\beta\)-D-glucopyranoside units [28; 29]. Concerning hemicelluloses, they are amorphous polymers, mainly arabinoxylans composed of D-xylose and L-arabinose, D-glucose, or other sugars that are embedded in the plant cell walls to form a complex network of bonds. They provide structural strength by linking cellulose fibers into microfibrils and by cross-linking with lignin [30]. Thanks to the high resistance to enzymatic and chemical degradation offered by lignin, LB pre-treatmentscan involve aggressive treatments and expensive procedures, which greatly increases the cost of these raw materials and, consequently, the price of the final products. Different types of processes, of physical, chemical, physicochemical, and biological nature, are currently employed. The idea is to improve the digestibility of LB carbohydrates, trying to avoid the generation of inhibitory compounds that may result from further degradation (e.g., aldehydes, phenolic acids, and furfural) when harsh chemical conditions are used [31]. These pre-treatments include, among others, mechanical size reduction, steam, ammonia or supercritical CO\({}_{2}\) explosion, acid, alkali or biological processes, ionic liquid extraction, or various combinations of these [28; 29; 30; 31; 32; 33; 34; 35; 36]. Through research and development, the optimization of an LB pretreatment has great potential to improve the efficiency and to reduce of the production cost [33]. A recent review highlights the nanobiotechnology contribution to this preliminary step [31]. Further hydrolysis to obtain monomeric sugars is usually performed using enzymatic catalysis, although other methods (such as chemical hydrolysis) can be employed. The sugar monomers obtained from hemicelluloses are mainly D-xylose, D-mannose, D-glucose, D-arabinose, D-galactose, and D-galacturonic acid, and their amounts depend on the LB source [37; 38]. The integration of various biomass treatment methods with other processes, such as enzymatic saccharification, detoxification, fermentation of hydrolysates, and product recovery greatly reduce the overall cost of using lignocellulosic for practical purposes [39; 40].
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 7 Conclusions and Future Work  
In this paper, the research work is preceded by a dataset preparation block. The datasets consist of observation vectors \(Vt\), which are composed of two humidity, i.e., \(H1\), \(H2\), and two temperature measurements, i.e., \(T1\), \(T2\), at three successive instances. After that, the datasets are injected with two faults, i.e., spike and data loss faults, at different fault rates (10%, 20%, 30%, 40%, and 50%). Following that, six classifiers, i.e., SVM, CNN, MLP, SGD, RF, and PNN, are applied on outdoor data collected from multi-hop WSNs. The classifiers are then evaluated on the basis of four different performance metrics: DA, TPR, MCC, and F1-score. The extensive simulation results show that RF outperformed in terms of DA and TPR. It also emerged as victorious in terms of MCC and F1-score as compared to the rest of the classifiers.  
For future work, we will use the same classifiers to predict an upcoming fault in data so that a mechanism can be developed to prevent faults. Furthermore, we will work on fault identification in WSNs, which will accurately identify and then detect faults on the sensor (node) level. The robustness of the RF algorithm can also be checked by increasing the number of sensors. This will help to understand how resilient a WSN is against attacks.  
[PERSON] and [PERSON] proposed and implemented the scenario. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote rest of the manuscript. All authors together organized and refined the manuscript and responded to the comments by respected editor and reviewers.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Theoretical Analysis Model and Calculation Method
### Method for Calculating Vibration Power Flow
To facilitate a more intuitive comparative analysis, the evaluation employs the relative power flow of the structure, which is expressed as follows. \[P(k)=20\log\left[\frac{P_{w}(k)}{P_{0}}\right] \tag{3}\]  
where \(P_{w}(k)\) is the total power flow of the structure corresponding to frequency \(k\), and the unit is N\(\cdot\)m/s; \(P_{0}\) is the reference power flow, and the reference power flow on the bridge under the action of track irregularity is \(P_{0}=1\times 10^{-10}\), and the unit is N\(\cdot\)m/s.
## 5 Results and Discussion
However, Figure 9 shows that the patterns of the potential distribution across different channels for the AP gradients were significantly different for the spherical phantom in comparison to the human head. This clearly revealed that any characterization study of GA to reduce these artefacts in EEG data in concurrent EEG-fMRI experiments might not be accurate when the spherical phantom is used. In the study 2, the induced voltages for different trials (subjects and phantoms) were not significantly different. Moreover, the average RMS of the EEG data over time over the channels were 455 \(\pm\) 21 \(\mu\)V, 368 \(\pm\) 13 \(\mu\)V, and 383 \(\pm\) 16 \(\mu\)V for subjects, head-shaped phantom, and spherical phantom trials, respectively. It is clear from Figure 10 that the highest gradient peaks occurred for the same gradients in the human subjects' and head-shaped phantoms' data, whereas some variations were observed in the beginning of the R period (fat suppression and slice select). However, the mean RMS value and standard deviation for the human subjects were much higher than those of the phantoms, which also agrees with the findings, as shown in Figures 7 and 9. It can be noted that a higher variation of the standard deviation for the human subjects can be expected, due to the variations of the size and shape of the human head. Moreover, it is evident from the average potential map of the FH gradient for the human subjects (Figure 7) that it is quite different from that of the phantoms, possibly in relation to the shape of the human subjects and of their head. The paired _t_-test to compare the different trials with the phantoms with the sessions with the subjects showed that the head-shaped phantom and spherical phantom data were significantly different (_p_ < 0.005) from the subjects' data, whereas the difference between the head-shaped phantom's and spherical phantom's data was not significant (_p_ = 0.07). Furthermore, the movement parameters were identified in the different experimental sessions for all subjects, and no significant deviation among the parameters was observed. The maximum average displacements were smaller than a 0.5 mm translation (z-translation) and a 0.005 rotation (pitch) for all  
Figure 9: The average RMS of the induced GA recording for repeats of the AP orthogonal gradient for each channel on the subjects’ head, head-shaped phantom, and spherical phantom. Error bars show the corresponding standard deviation of the mean RMS over trials. subjects. These movements were small, which implies that the related MA could not be the reason of the similarity of the subjects' data with the head-shaped phantom's data. Moreover, the consistency of the RMS amplitude of the induced voltage in time over the channels (small standard deviation) also indicates that this might not be due to MA but, rather, could be the result of the induced voltage.
## 2 Materials and Methods
### Methodology  
For comparing the datasets, an inter-method accuracy was calculated by the root mean square error (_RMSE_) and mean absolute error (_MAE_) for each method. Recognizing the susceptibility of certain methodologies to the disruptive effects of outliers, a _modified Z-score_ technique was implemented [30]. This approach leveraged the mean absolute error (_MAD_) as a robust measure of dispersion, thereby mitigating the impact of extreme values. By calculating the Z-scores of individual data points relative to the mean and employing the _MAD_ as a reference, the method effectively identified and excluded outliers from subsequent analyses. Such meticulous attention to statistical robustness not only enhancesthe reliability of the findings but also underscores the commitment to methodological precision [31].  
\[RMSE=\sqrt{\sum_{t=1}^{n}\frac{\left(\mathit{Optimize\ Synchronization\ dataset-Manual\ Notational\ dataset}\right)^{2}}{n}}\]  
\[MAE=\frac{1}{n}\sum_{t=1}^{n}\lvert Optimize\ Synchronization\ dataset-Manual\ Notational\ dataset\rvert\]  
\[MAD=\frac{\sum\lvert differences\ between\ datasets-mean\ difference\rvert}{n}\]  
\[Modified\ Z-score=\frac{0.6745\times\left(differences\ between\ datasets-mean\ difference\right)}{MAD}\]  
The _RMSE_ was selected as the metric to quantify the inter-method linear error due to its capability to provide a comprehensive assessment of the deviation between predicted and observed values, considering both the magnitude and direction of errors. On the other hand, the _MAE_ was utilized to estimate the average absolute errors between methods, offering a straightforward measure of the magnitude of discrepancies without considering their directionality.  
Finally, the spatial differences (i.e., distance of identified event) of the determined location of events of OSD were also compared to the RSD data and to the original data provided by the tracking system provider.  
To evaluate the accuracy of the novel technique, both _RMSE_ and _MAE_ were computed under two conditions: with and without outliers. Outliers were excluded by applying a _modified Z-score_. The comparisons were made between MND and both RSD and OSD, as these results will allow an understanding of the differences when compared to the \"gold standard\". Finally, a comparison between both synchronized datasets will provide the real differences between procedures for non-manual application.
## Nomenclature  
List of symbols and acronyms used in this Paper:  
**Symbol/Acronym Definition**  
**IoU**  
intersection over union  
**Re-ID**  
re-identification  
**TEDDY**  
proposed tracking framework (full name here)  
**MCT**  
multi-camera tracking  
**SCT**  
single-camera tracking  
**FPS**  
frames per second  
**CNN**  
convolutional neural network  
**LSTM**  
long short-term memory  
**GRU**  
gated recurrent unit  
**OSNet**  
omni-scale network  
**YOLOv8**  
You Only Look Once version8
# Code Similarity and Location-Awareness Automatic Program Repair
## 4 Empirical Study
### Datasets of Bugs  
An effective evaluation of the effectiveness of an automatic program repair approach relies on high-quality benchmark datasets. To evaluate proposed techniques, researchers need to create datasets or rely on publicly available bug datasets. The latter is critical to advancing research in the field of automatic program repair because the publicly available datasets can better reproduce the experimental process and facilitate comparison between different methods. Therefore, Defects4J [9] was used as the experimental object to evaluate our approach. Defects4J is a database and extensible framework provided by [PERSON] et al. at the University of Washington, USA. It provides real bugs to enable reproducible studies in software testing research. This benchmark has been widely used in software-engineering related fields, including automatic program repair [20].  
This study selects Defects4J 1.5.0 and involves 4 projects: JFreeChart, Apache Commons Lang, Apache Commons Math, and Joda-Time. We exclude the Closure Compiler project because the test cases in the Closure Compiler are organized in an unconventional way, using a custom test format rather than the standard JUnit test format. Table 1 shows the statistical data for 224 real bugs in 4 projects.
# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
## 3 Results
### Post-Fire Water Quality Assessment  
To determine whether a change in average concentration of Chl-a and turbidity happened after a fire in the catchment of our studied lakes for the period 2017-2020, we calculated the mean Chl-a and turbidity concentrations over a six-month period after a maximum fire occurrence and compared with mean concentrations over the same six months either without or with smaller fires. From the initial number of 106 lakes, 7 lakes have been excluded from the analysis because the occurrence of their major fires was close to the end of the studied period (end of year 2020). Thus, the period following the major fire was shorter than six months, leaving a final number of 99 lakes analyzed for this assessment.  
Figure 10 shows the global distribution of lakes in different classes according to positive or negative changes (in percentage) of Chl-a and turbidity. For Chl-a, 28 lakes showed an increase (>10%), 33 lakes had no relevant change (range \(-\)10 to +10%), and 38 lakes showed a decrease (>\(-\)10%) in Chl-a concentration six months after maximum fire occurrence. For turbidity, 37 lakes showed an increase, 38 lakes had no relevant change, and 24 lakes showed a decrease in turbidity concentration six months after maximum fire occurrence.  
Figure 9: Boxplot of burned land cover clusters (4 = evergreen boreal forest; 5 = crops to natural shrubbery; 6 = deciduous broad leaves), and timing (days) of max Chl-a peak development.  
There are no evident geographical patterns of increases or decreases in the concentration of parameters, except for a prevalence of a percentage increase in turbidity in African lakes (Figure 10b). Regarding lake characteristics, hypereutrophic lakes showed the most relevant positive changes (mean percentage difference in Chl-a of 29%).  
In order to see which explanatory variables might influence the trends in Chl-a and turbidity, we carried out a multinomial logistic regression using the lakes grouped as stable (=no change), increasing, and decreasing. The pool of predictors was increased sequentially starting with just latitude and altitude, then landcover types were added, and finally also total annual rainfall and the maximum fire peak were included. However, none of the included parameters were found to be significant in determining the trend in either Chl-a or turbidity (Table S3 in the Supplementary Materials).  
Figure 10: Global map showing geographical distribution of lakes with (**a**) chlorophyll-a (Chl-a) and (**b**) turbidity % change, calculated as the difference between mean concentration over a six-month period after maximum fire occurrence and after no or weaker/smaller fire occurrence.