esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Content of Other Esters and Aldehydes along Ripening in Italia and Bronx Seedless Table Grapes
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & **EthyI Hep-** & **EthyI Oc-** & **EthyI 3-Hy-** & **2-Me-** & **3-Me-** & **-Pentnal** & **Octual** & **Nonanal** & **Benzal-** & **Phenyla-** \\  & **tanoate** & **tanoate** & **droxbybutyrate** & **thybutanan** & **thybutanan** & **fruybutanan** & **Pentnal** & **Octual** & **Nonanal** & **dehyde** & **cetaldehyde** \\ \hline \multicolumn{1}{l}{**Variety (V)**} & & & & & & & & & & & \\ Italia & 119.97 a & 236.91 a & 375.09 a & 25.63 a & 23.45 a & 17.58 a & 30.61 a & 33.33 a & 37.56 a & 26.43 a \\ Bronx Seedless & 128.00 a & 239.57 a & 403.06 a & 30.00 a & 30.79 b & 25.98 b & 35.94 b & 42.67 b & 33.78 a & 24.30 a \\ \multicolumn{1}{l}{**Phenological stage (S)**} & & & & & & & & & & \\ \hline BCH-79 & 05.94 a & 170.89 a & 293.53 a & 20.99 a & 21.8 a & 16.27 a & 25.23 a & 27.88 a & 24.95 a & 19.14 a \\ BCH-79 & 104.02 ab & 193.44 a & 236.41 a & 23.34 ab & 23.96 a & 18.16 ab & 28.01 a & 31.31 a & 28.49 a & 21.28 a \\ BCH-81 & 115.67 abc & 218.98 b & 362.97 ab & 25.95 a & 25.88 a & 20.26 ab & 31.90 a & 35.16 a & 32.53 ab & 23.67 a \\ BCH-83 & 128.62 a & 247.88 c & 403.62 bc & 28.86 ac & 27.95 a & 22.61 abc & 34.51 abc & 39.49 abc & 37.15 a & 26.31 bc \\ BCH-85 & 143.20 bc & 280.60 d & 448.83 c & 32.09 bc & 30.18 a & 25.23 bc & 38.30 bc & 44.35 bc & 24.43 b & 29.26 c \\ BCH-89 & 159.05 c & 317.64 e & 499.10 d & 35.68 c & 32.95 b & 28.16 c & 42.52 c & 49.45 b & 48.45 b & 32.54 d \\ \multicolumn{1}{l}{**Significance \({}^{*}\)**} & & & & & & & & & & \\ \multicolumn{1}{l}{O} & 0.5008 & 0.6680 & 0.1586 & 0.0931 & 0.0016 & 0.0002 & 0.0431 & 0.0053 & 0.4163 & 0.1005 \\ \multicolumn{1}{l}{S} & & & & & & & & & & & \\ \multicolumn{1}{l}{S} & 0.0343 & 0.0000 & 0.0000 & 0.0227 & 0.0685 & 0.0142 & 0.0050 & 0.0033 & 0.0604 & 0.0000 \\ \multicolumn{1}{l}{V} \(\times\) S & 1.0000 & 1.0000 & 0.9999 & 0.9998 & 0.9862 & 0.9995 & 0.9957 & 1.0000 & 0.9988 \\ \hline \end{tabular} Data are expressed as mean of the data. \({}^{a}\) Significance (\(p\)-value) of variety (V), phenological stage (S), and V \(\times\) S interactions. For a given factor and significance (\(p<0.05\)), different letters within a column represent significant differences (Duncan test, \(p<0.05\)).  
\end{table} 
Table 7: Esters and aldehydes content (\(\mu\)g L\({}^{-1}\)) of Italia and Bronx Seedless table grapes harvested in six different phenological stages (BBCH-77, BBCH-79, BBCH-81, BBCH-83, BBCH-85 and BBCH-89). Figure 1: Total content of C6 compounds, alcohols, esters, fatty acids, aldehydes and terpenes in Italia (white) and Bronx Seedless (black) table grapes harvested in six different phenological stages (BBCH-77, BBCH-79, BBCH-81, BBCH-83, BBCH-85 and BBCH-89). Data are expressed as mean of the data with their corresponding standard deviation. For a given variable and phenological stage, different letters over the bars represent significant differences (Duncan test, \(p<0.05\)).
## 4 Case Study
### Parameter Attack Scenario I  
In scenario I, an unbalanced parameter FDI attack is injected to the series and shunt parameter of the line 23-32 (\(-18\%\) on parameter \(g\), \(12\%\) on parameter \(b\), \(-6\%\) on parameter \(b^{sh}\)) on the IEEE 118-bus system.  
In this case, the parameters of line 23-32 are attacked. The first process of the framework is FDI detection. Results are presented in Table 1. One can see that the objective function \(J(x)\) is 1246.587, which is higher than threshold value (C = \(\chi^{2}\) = 775.1861), thus a cyber attack is detected. For identification, a descending list of \(CME^{N}\) is built. From the resultant list, the largest absolute value of \(CME^{N}\) which is 8.4156 is related to reactive power injection for bus 23. In addition, one can see that the \(CME^{N}\) value of corresponding reactive power flow \(Q_{32-23}\), real power flow \(P_{23-32}\) and reactive power injection \(Q_{32}\) are also above the threshold value (\(\beta=3\)). This situation is characterized as a parameter attack on line 23-32 [19]. For parameter correction, the process described in Section 2.1 is implemented. The results are shown in Table 2. In the state of the art parameter correction model [31] (also presented in (7)), one can clearly see that the model requires at least three different real-life measurements: two real power flow \(P_{km}\), \(P_{mk}\) and one reactive power flow measurement \(Q_{km}\). However, there is no guarantee that the required measurements will exist. For example, in the current testing measurement configuration, only 1 real power flow \(P_{23-32}\) measurement and 1 reactive power flow \(Q_{32-23}\) measurement are assumed to exist. Therefore, the correction model in (7) is unable to process the expecting correction due to the unavailability of the power flow measurement \(P_{32-23}\). To resolve this issue, in the presented framework, synthetic measurements are considered only for unavailable measurements. These synthetic measurements are generated by running WLS SE using weights obtained from machine learning linear regression prediction. For this task, no gross error detection analytics is performed. In this case, 358 SMs are generated (adding 358 SM yields a GRL increase from 3.029 to 4.55). Then, two synthetic measurements \(P_{32-23}\) and \(Q_{23-32}\) are obtained and augmented from SM dataset. Parameter correction is processed by using 2 existing measurements and two synthetic measurements in the presented parameter FDI correction model (18). Results of such correction are presented in Figure 3. As one can see, the parameter correction process converges at 26 th iteration while approximation errors for \(g_{23-32}\), \(p_{23-32}\), \(b_{23-32}^{sh}\) are (0.525%), (0.183%), (0.340%) respectively, which are all lower than state of the art model in [31]. After parameter correction is obtained, a new state estimation process is performed in which objective function \(J(x)\) is found to be 684.5437, which is lower than threshold value C. Hence, no FDI attack is detected.
## 3 Finite Element Analysis of the Process
### Material Characterization
#### 3.1.2 Thermal Conductivity of Core-Rubber Composite Material  
The orthotropic core-rubber composite belt layer, as well as ply, overlay, and chipper, are shown in Figure 5a, where e is the distance between two cords; 1-direction, along the cords; 2-direction, transverse to the cords; and 3-direction, transverse to the layer. The thermal conductivity of composite material depends on the directions above. The blackened area is shown in Figure 5a is a representative volume element as shown in Figure 5b, where h denotes ply thickness and a is the effective length of cord circle. Thermal conduction in the material is closely correlated with the current flow conduction in the circuitry, so the thermal resistance is represented by electric resistance. In addition, thermal conduction in rubber and steel/fiber cord are completely independent, so in the 1-direction, the effective composite conductivity is represented by the rule of mixtures as [19]  
\[\mathrm{K_{1}}=\mathrm{K_{c}}\times\mathrm{V_{c}}+\mathrm{K_{r}}\times\mathrm{ V_{r}}=\frac{\mathrm{a}^{2}\times\mathrm{k_{c}}}{\mathrm{a}^{2}+\mathrm{h}/4 \mathrm{e}}+\frac{(\mathrm{h}/4\mathrm{e})\times\mathrm{k_{r}}}{\mathrm{a}^{2} +\mathrm{h}/4\mathrm{e}}, \tag{5}\]  
Figure 4: Relations of (**a**) thermal diffusivity versus temperature and (**b**) specific heat capacity versus temperature.  
where \(k_{c}\) and \(k_{r}\) are the thermal conductivity of cord and rubber, respectively.  
The thermal resistance model in the 3-direction is shown in Figure 5c, and the thermal resistance in general is  
\[R_{th}=L/(A\times k), \tag{6}\]  
where \(L\) is the length of the heat flow, \(A\) is the cross-sectional area, and \(k\) is the thermal conductivity. Thus, the effective conductivity in the 3-direction is  
\[K_{3}=\frac{L}{A}\times\frac{1}{R}=\frac{1}{eh}\times\left(\frac{1}{R_{r2}}+ \frac{1}{R_{r1}+R_{c}}\right)=\frac{\left(\frac{1}{2e}-a\right)\times K_{r}}{ eh^{2}/2}+1/\left(\frac{eh}{K_{c}}+\frac{h/2-a}{aehk_{r}}\right), \tag{7}\]  
where \(R_{c}\), \(R_{r1}\), \(R_{r2}\) are thermal resistances of three parts shown in Figure 5b. \(R_{c}\) is the thermal resistance of the square shadow cord region, \(R_{r1}\) is the thermal resistance of the up rectangle rubber region, and \(R_{r2}\) is the thermal resistance of the right rectangle rubber region. \(R_{r1}\) and \(R_{r2}\) are different because of the width and length differences of the two rubber regions. The effective conductivity in the 2-direction can be calculated as well. After determining the effective conductivities, the heating rate can be calculated based on Equation (2) in the tire-curing process. The discrete local CS of every element in belt 1 is shown in Figure 5d. Effective conductivities \(k_{1}\), \(k_{2}\), and \(k_{3}\) are assigned to each element in the three directions of the discrete local CS.
# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 3 Results
During the whole training process, modified models tend to have a better performance with an obvious increase in accuracy for every epoch as compared to the raw models. Table 5 shows that the best accuracy of 93.94% by a CNN pre-trained on AlexNet, and the modified version improves the accuracy of the CNN to 94.49% for a training/validation ratio of 8:2. U-Net with GOA outperforms all models with an accuracy of 95.31% for a training/validation ratio of 8:2. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**Train/Valid = 7:3**} \\  & \multicolumn{8}{c}{**Accuracy of Validation Set for Each Epoch during the Training Process (\%)**} \\ \cline{2-11}  & **E 1** & **E 2** & **E 3** & **E 4** & **E 5** & **E 6** & **E 7** & **E 8** & **E 9** & **E 10** \\ \hline D-Net [33] & 66.22 & 72.31 & 68.01 & 73.45 & 75.37 & 83.8 & 80.23 & 83.8 & 82.43 & 81.88 \\ \hline CNN-HPF [43] & 77.1 & 75.22 & 77.94 & 75.22 & 77.8 & 88.76 & 85.6 & 86.07 & 89.22 & 89.22 \\ \hline RRU-Net [45] & 62.71 & 67.8 & 73.41 & 75.4 & 73.4 & 76.13 & 73.4 & 76.01 & 75.5 & 75.5 \\ \hline Mask R-CNN + Sobel filter [43] & 65.07 & 77.91 & 76.33 & 80.53 & 76.49 & 77.97 & 78.62 & 75.58 & 77.78 & 80.53 \\ \hline CNN based on pre-trained Alex Net model [46] & 76.91 & 85.97 & 88.84 & 90.05 & 90.05 & 88.29 & 91.63 & 90.29 & 88.66 & 91.63 \\ \hline Deep Neural  Architecture-Buster Net [47] & 63.21 & 66.8 & 73.97 & 70.92 & 73.97 & 70.92 & 70.38 & 71.34 & 73.97 & 71.73 \\ \hline \multirow{10}{*}{**CNN-HPF**} & D-Net & 77.5 & 85.3 & 88.91 & 87.16 & 86.04 & 88.91 & 88.74 & 87.1 & 87.81 & 87.98 \\ \cline{2-11}  & CNN-HPF & 80.11 & 91.24 & 89.58 & 90.28 & 90.92 & 92.43 & 90.82 & 90.32 & 90.03 & 91.71 \\ \cline{1-1} \cline{2-11}  & RRU-Net & 66.77 & 78.19 & 75.89 & 75.14 & 75.47 & 77.13 & 74.91 & 78.19 & 76.13 & 77.13 \\ \cline{1-1} \cline{2-11}  & Mask R-CNN + Sobel filter & 70.7 & 81.47 & 82.12 & 79.69 & 82.12 & 80.01 & 82.03 & 80.92 & 81.83 & 80.86 \\ \cline{1-1} \cline{2-11}  & CNN based on pre-trained Alex Net model & 81.46 & 85.71 & 91.28 & 92.75 & 92.8 & 91.28 & 92.75 & 92.09 & 92.64 & 92.75 \\ \cline{1-1} \cline{2-11}  & Deep Neural  Architecture-Buster Net. & 67.86 & 75.77 & 82.46 & 80.69 & 80.78 & 82.28 & 78.17 & 79.81 & 79.56 & 82.83 \\ \cline{1-1} \cline{2-11}  & U-NET network optimized  with Grasshopper & 78.78 & 85.75 & 90.57 & 92.3 & 92.34 & 94.63 & 93.57 & 93.47 & 93.67 & 94.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation accuracy of each epoch during training with train/validation ratio = 7:3. Table 6 shows that a CNN pre-trained on AlexNet provides the best accuracy of 93.94%, and the modified version improves the accuracy to 95.38% for a training/validation ratio of 9:1. U-Net with GOA outperforms all models with an accuracy of 97.98%.
# Robust Control for a Slung-Mass Quadcopter Under Abrupt Velocity Changes
## 6 Conclusions and Discussion  
A control algorithm with robustness with respect to abrupt velocity changes in a quadrotor with slung load is proposed to compensate and reduce oscillations. The full dynamics considering the slung mass is assumed as a subactuated system with two inputs (\(\tau\),\(\mu\)) and four states (\(x\),\(y\),\(\alpha\),\(\theta\)). The slung mass has a critical stable point and a small perturbation induces an oscillatory behavior. When velocity quadrotor changes in fast navigation maneuvers, particularly in abrupt acceleration and deceleration, the oscillations generated by these behavior could produce catastrophic scenarios. The proposed strategy used a sliding mode approximation on the attitude dynamics. A control input based on a tangent function is used in a translational model in order to compensate the nonlinearities and disturbances.  
Angular position and velocity measurements of the load provided by an additional IMU are used as feedback to synchronize the acceleration of the aerial vehicle to damped oscillations. A Lyapunov-based stability analysis is provided for the altitude and the translational dynamics to verify the convergence of the error dynamics in a finite time. Numerical simulations and real-time experiments demonstrated the validity of the proposed robust control algorithm. The stability of the slung-mass dynamics was verified using a Lyapunov function.  
In order to validate the control strategy a sawtooth velocity reference was induced to the horizontal displacement, this behavior was performed in real-time experiments, the maximal speed was almost 29 km/h. In the case of \(\alpha\) angle feedback the oscillations were reduced from 0.4 rads to almost 0 in 1 s.  
Investigation, S.S.; investigation, J.F.; conceptualization, Y.R.-L.; data curation, I.G.-H.; project administration, R.L. All authors have read and agreed to the published version of the manuscript.  
This project was funded by Department of Research and Multidisciplinary Studies of Research and Advanced Studies Center of the National Polytechnic Institute (CINVESTAV-IPN).  
Not applicable.  
Dataset available on request from the authors.  
The authors are grateful to National Council of Science, Humanities, and Technology (CONAHCYT) for its support.  
The authors declare no conflicts of interest.  
Figure 10: Slung-mass quadrotor under abrupt velocity changes in outdoor experimentation.
# Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector
## 2 Methodology: Platform Design and Implementation and AD Approach
### AD Approach Applied for Large-Scale Retail Sector  
In order to further improve the performance of the model, the augmented data technique was used. This approach was already validated in the case of LSTM neural networks [43; 44; 45], where data were artificially created to feed a data-poor training dataset. In [44], the training dataset was increased from 768 records to 10,000 records. In the present case, the dataset consisted of 7,212,348 records (instances or rows) relating to 897 sampling days. Each of this record refers to the quantity, which is expressed in pieces or weight according to the type of article, sold out of a single item in a store branch. For each article, the data are not sufficient to reach the desired accuracy, and for this reason increased data have been used. The following 14 parameters were the dataset attributes (variables or columns):  
1. receipt identification code
2. receipt date
3. customer cluster
4. plu code
5. day of the week
6. week
7. quantity sold (number of items)
8. eventual promotion
9. store branch identifier
10. unit amount
11. measured quantity (kg)
12. product description
13. number of receipts
14. shop department identifier  
Each of these observations refers to transactions relating to a specific product, sold on a specific day in a specific branch, by customers belonging to a specific cluster. Table 1 summarizes the statistics of the data.  
Figure 6 shows the structure of the records that make up the original dataset and the methodology used to create artificial data. The technique consists of generating a certain number of receipts for each product in a sequential manner for each day. The identification number of the first artificial record is equal to the number of the last record of the original dataset increased by 1. For this reason, a reordering of the data was carried out based on the receipt date and not on the identification number.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Datum** & **Number** \\ \hline Number of records & 7,212,348 \\ number of daily customers & \(\sim\)1000 \\ Sampling days & 897 \\ Number of products & 30,159 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the data.  
The number of receipts generated for a certain product in a given day is chosen randomly by setting an upper and lower threshold. In a similar way to the technique used in [23], we used the random method _random(a,b)_ to generate real numbers in the range (a,b). Furthermore, in generating the quantity sold and the number of receipts relating to a product, the average quantity sold for each item for that product was taken into account.  
Figure 7 shows the statistic distribution of the number of receipts. The Log Scale option was used to make the histogram of this dataset attribute.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 2 Satellite Missions, Projects and Data
### Gpm
The dataset is available from 1 January 1998, to the present. Single image or animation can be made with this dataset to track weather events in the data domain [33].
## 3 Experimental Process and Results
### Experimental Results Based on Office Seismic Profile Signals
Since SRUnet employs grouped processing, the GPU usage is significantly reduced, and the parameter count remains within a relatively small range. To address gradient \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **PSNR** & **SNR** & **RMSE** & **SSIM** \\ \hline WT & 19.112 & 8.953 & 5.732 & 0.787 \\ Unet & 21.332 & 12.035 & 3.571 & 0.805 \\ UNet++ & 20.972 & 12.357 & 3.452 & 0.811 \\ TUnet & 24.837 & 14.033 & 1.952 & 0.842 \\ SUnet & 25.029 & 13.783 & 1.801 & 0.837 \\
**SRUnet** & **26.993**\(\pm\) 0.140 & **14.326**\(\pm\) 0.151 & **1.135**\(\pm\) 0.038 & **0.883**\(\pm\) 0.007 \\
**[26.853, 27.133]** & **[14.175, 14.477]** & **[1.097, 1.173]** & [0.876, 0.890] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation indicators of each model when the decimation ratio is 0.5. Figure 24: Changes in evaluation indicators for each model when the decimation ratio is 0.5.  
vanishing issues arising from these structural changes, a novel residual channel attention mechanism is integrated into skip connections, adopting dual-path residual links with global max and average pooling and replacing the traditional ReLU with SiLU activation function, which effectively mitigates gradient degradation. Experimental results across both datasets show that all networks exhibit satisfactory reconstruction performance at a decimation ratio of 0.1. However, as the decimation ratio increases--particularly at the extreme ratio of 0.5--the traditional wavelet transform demonstrates poor performance, while Unet and Unet++ show moderate improvements. TransUnet and SwinUnet achieve better results but still struggle with incomplete reconstruction of certain seismic traces. In contrast, the proposed Swin-ReshoUnet not only delivers the best visual reconstruction quality in seismic profiles but also outperforms other models in quantitative metrics including PSNR, SNR, RMSE, and SSIM. Residual signal analysis and F-K spectral comparisons further confirm that its reconstructed signals exhibit the highest similarity to original seismic profiles, demonstrating superior preservation of stratigraphic structures and frequency-domain features.
## 4 Application and Results
### CSE Corpus Annotation  
Following the methodology presented in Section 3.2, we initially made a plan and set the goal that we wanted to achieve by annotating the CSE corpus. Afterwards, a simple model was developed to represent the annotation task in abstract terms and a CSE ontology was created to represent the in-context entities and their interconnection. Next, a specifications file was created where the entities and interconnections were described in a formal way and become tags. This file, along with the corpus guidelines, was given to the annotators to perform the annotation task. After the annotators finish their job, an inter-annotator agreement assessment took place using [PERSON]'s Kappa metric, and the gold standard version of the CSE corpus is finally produced.
## 2 Materials and Methods
### Research Methods
#### 2.3.2 Calculation Formula and Grade Division of Drought Index  
The calculation method for monthly water deficit \(D_{i}\), defined as the difference between precipitation and potential evapotranspiration, is as follows:  
\[D_{i}=P_{i}-PET_{i} \tag{1}\]  
where \(D_{i}\) represents the water deficit in month \(i\), \(P_{i}\) denotes the precipitation in month \(i\), and \(PET_{i}\) denotes the potential evapotranspiration in month \(i\). As the \(D_{i}\) data series may contain negative values, it was standardized using a logarithmic normalization function to obtain SPEI values at different timescales. Lower SPEI values indicate more severe drought conditions. Drought intensity is categorized into five levels, ranging from extreme drought to no drought, as follows: no drought (SPEI \(\geq-0.5\)), mild drought (\(-1.0\leq\) SPEI \(<-0.5\)), moderate drought (\(-1.5\leq\) SPEI \(<-1.0\)), severe drought (\(-2.0\leq\) SPEI \(<-1.5\)), and extreme drought (SPEI \(<-2.0\)).
## 4 Results
### Mean Annual VI and Variability
#### 4.2.2 Mean Annual VI Variability  
The mean annual VI variability for the 2001-2012 period is displayed in Figure 7. The figure illustrates spatial patterns showing how the mean annual VI varies from year to year.  
The statistical results of the variability are listed in Table 3. The results indicate that 70%-80% of the area of the XUAR has low annual VI variability (\(<\)10%) and 20%-30% of the area has variability higher than 10%. In particular, 10%-15% of the area shows variability between 17% and 24%, and only 3%-6% of the area varies by more than 30%. Comparing the three variability maps quantitatively, the EVI image shows the highest variability (Table 3). Therefore, the EVI time-series captures larger inter-annual variability from 2001 to 2012 for the entire XUAR comparing with NDVI and SAVI. Considering the scarcity of vegetation in the Taklamakan desert, the high variability observed only from EVI data in this area might be caused by the variation in soil background property.  
Figure 7: Mean annual VI variability for 2001–2012 for the XUAR. Mean percentage variability was calculated as the mean of the absolute percentage deviations from the mean annual VI for 2001–2012.