esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparative Analysis of CBA Methodologies and Results
#### Calculation of Greenhouse Gas Emissions and Costs According to the CBA Methodology in Austria
Indirect emissions (WtT) of greenhouse gases, expressed in CO\({}_{2}\)es, represent a significant proportion of the total emissions from road transport vehicle operations. Indirect emissions amount to 15.8% for petrol, 17.69% for diesel, 12.7% for CNG, and 10.4% for LPG of the total GHG emissions according to EN 16258. Indirect emissions amount to 16.71% for petrol, 15.24% for diesel, 22.07% for CNG, and 17.79% for LPG of the total GHG emissions according to ISO 14083. The values are shown in Table 27. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**CO\({}_{2}\)**} & **CH\({}_{4}\)** & **N\({}_{2}\)O** \\ \hline CO\({}_{2}\)es & 1 & 25 & 298 \\ \hline \hline \end{tabular}
\end{table} 
Table 26: Conversion factors for calculating CO\({}_{2}\)es (t). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Standard** & **Unit** & **WtT** & **TtW** & **WtW (WtT + TtW)** \\ \hline \hline \multirow{4}{*}{EN 16258} & \multirow{4}{*}{kg CO\({}_{2}\)es/kg} & **Petrol** & & \\  & & 0.61 & 3.25 & 3.86 \\  & & 15.80 & 84.20 & 100 \\  & kg CO\({}_{2}\)es/kg & 0.64 & 3.19 & 3.83 \\  & & 16.71 & 83.29 & 100 \\  & & **Diesel** & & \\ EN 16258 & kg CO\({}_{2}\)es/kg & 0.69 & 3.21 & 3.9 \\  & & 17.69 & 82.31 & 100 \\ ISO 14083 & kg CO\({}_{2}\)es/kg & 0.57 & 3.17 & 3.74 \\  & & 15.24 & 84.76 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 27: CO\({}_{2}\)e pollutant emissions and their structure by WtT, TtW, and WtW for selected pollutants using EN 16258 and ISO 14083. If indirect GHG emissions from fuel consumption were also considered in the CBA methodology (similar to electricity), the change in total GHG emissions and the value of the associated external costs would be higher by the above indirect emissions percentages (%). Recommendation 6: Regular Updates of the CBA Methodology to Reflect Changes in EU Legislation and Changes in ISO and EN Standards Related to Pollutant Emissions, Greenhouse Gas Emissions, and Their Economic Valuation, and the Application of Additional Vehicle and Fuel Categories  
In the future, we recommend considering changing the structure of the in-service vehicles by additional categories regarding the type of fuel used and implementing emission factors for additional fuels into the methodology. Sources of information on parameter values can be ISO 14083, EN 16258, and the HBEFA methodology. When extending the categories of road vehicles, we recommend using the same categorization for pollutant emissions, GHG emissions, and associated external costs. Such an extension of vehicle categories is also possible regarding vehicle categories and fuel and energy sources, respectively, as listed in Chapters 4 and 5 of the _European Commission's Handbook on External Cost of Transport_, January 2019, on which the current _CBA Methodological Guide_, version 1.0, is based.
## 4 Experiments
### Ablation Study
#### 4.3.2 Regularization Strategy in GFSC  
Table 3 also shows the experimental results obtained with and without the regularization strategy. From the results, it can be seen that the regularization strategy can effectively  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Param (M)**} & \multicolumn{5}{c}{**Pretraining Details**} & \multicolumn{2}{c}{**NWPU-RESISC45**} \\ \cline{3-8}  & & **Total Images** & **Storage (GB)** & **Epochs** & **Pretraining GPU Hours** & **T.R. = 10\%** & **T.R. = 20\%** \\ \hline ResNeXt-50 & 23.07 & 71,900 & 4.59 & 300 & 5.92 & \(89.98\pm 0.13\) & \(92.96\pm 0.12\) \\ WRN-50 & 66.93 & 71,900 & 4.59 & 300 & 6.19 & \(90.70\pm 0.15\) & \(93.41\pm 0.19\) \\ \hline _SSL on NAP+_: & & & & & & \\ \hline ResNet-50 & 23.60 & 200,000 & 17.81 & 300 & 10.55 & \(91.55\pm 0.12\) & \(93.79\pm 0.16\) \\ ResNeXt-50 & 23.07 & 200,000 & 17.81 & 300 & 14.55 & \(92.70\pm 0.19\) & \(94.48\pm 0.12\) \\ WRN-50 & 66.93 & 200,000 & 17.81 & 300 & 14.61 & \(92.80\pm 0.17\) & \(94.55\pm 0.13\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **FAA** & **Mixup** & **CutMix** & **Accuracy** \\ \hline ResNet-50 (SSL) & — & — & — & \(93.79\pm 0.16\) \\ ResNet-50 (SSL) & — & — & \(\surd\) & \(93.60\pm 0.11\) \\ ResNet-50 (SSL) & \(\surd\) & — & — & \(93.98\pm 0.17\) \\ ResNet-50 (SSL) & — & \(\surd\) & — & \(94.01\pm 0.14\) \\ ResNet-50 (SSL) & \(\surd\) & \(\surd\) & — & \(\mathbf{94.16\pm 0.08}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study on NWPU-RESISC45 with a training ratio of 20%.  
improve the accuracy. By contrast, CutMix [53] does not work well on remote sensing images; we conjecture that this is because unlike in natural images, the objects in remote sensing images are often not in the middle, so this patch-based method of constructing samples is ineffective.  
When both the Mixup regularization strategy and the data augmentation strategy search method are used simultaneously, the accuracy is consistently improved, and better results are achieved.
## 2 Materials and Methods
### Handheld SSOCT System  
An SSOCT system (Figure 1) was used for the pre-acquired dataset, which was described in the previous study [40]. This system is a handheld, non-invasive, high-resolution, and high-speed acquisition interferometric imaging device. The light source of this system is a 200-kHz vertical-cavity surface-emitting (VCSEL) swept-source laser (SL132120, Thorlabs Inc., Newton, MA, USA), which can generate light with a central wavelength of 1310 nm and a bandwidth of 100 nm. Theoretically, this light source can provide an axial resolution of around 8 \(\upmu\)m in air and around 7.4 \(\upmu\)m in tissue. At the reference arm, there was an electronic optical delay line and a polarization controller which ensured the system had the best performance. At the sample arm, the components were housed in a handheld probe. Inside the probe, there was a pair of XY galvo scanners (6210H, Cambridge Technology, Bedford, MA, USA) that would operate BM scans. Together with the other configurations, the objective lens (LSM03, Thorlabs Inc., Newton, MA, USA) can provide a lateral resolution of around 20 \(\upmu\)m and a scanning range of 10 \(\times\) 10 mm\({}^{2}\). The scanning ranges in this study were set as 5.1 \(\times\) 5.1 mm\({}^{2}\) in both width and length. The interferogram signal, containing the depth information of the tissue, would be received by a balanced photodetector (PDB470C-AC, Thorlabs Inc., Newton, MA, USA).  
Each pre-acquired dataset contains 340 \(\times\) 600 \(\times\) 600 \(\times\) 4 voxels (z-x-y-N, where z is the axial axis, x and y are the transverse axis, and N is the repeated times). The software operation of this system was performed through a LabVIEW program, while the post-processing was performed on MATLAB, which was implemented on a Windows 10 computer using an i9-10980 XE (3.00 GHz) processor with random access memory (RAM) of 128 GB.
## 3 Method
### Detecting Change Dates
#### 3.2.2 Step 2: Detecting Change within Years  
Step 2 is implemented to detect change points if a year was flagged with change in Step 1; otherwise, this step is skipped. The goal is to find the time point where the NDVI values deviate from the previous part. The procedure compares the time series of a flagged year with the year before and seeks a shift point in deviation. We refer to a point in a time series as a date hereafter, represented by a temporal index in a year. Consider a time series having \(n\) observation records each year for \(m\) years. Let \(X=\left\{X_{,i}\right\}\) denote the observation value (e.g., NDVI) on date \(t\) in year \(i\) with \(i\in M=\left\{1,2,...,m\right\}\) and \(t\in T=\left\{1,2,...,n\right\}\), where \(n\) is the number of records in each year (\(n=23\) for the 16-day NDVI timeseries). The absolute difference \(\textbf{\emph{D}}=\left\{D_{i-1,i,j}\left|i\in M,t\in T\right\}\right.\) is calculated between the two years on each date \(t\) as  
\[D_{i-1,i,t}=\left|X_{i-1,t}-X_{i,t}\right| \tag{1}\]  
The change from one land-cover type to another will cause an abrupt shift toward greater value in \(D\) on the change date. This jump ought not to occur only on a single date but also on the following days with similar greater deviation. The reason is that a change in land cover is likely low frequency in a certain period of time and distinct from recurrent intra-annual variations. Specifically, suppose year \(i\) be flagged in Step 1, the date \(t^{*}\) in year \(i\) will be determined as the change date if it satisfies two criteria at the same time:  
\[\begin{split}\text{i)}\ D_{i-1,i,t-p}<\kappa,\ \forall p=1,2,3,...,t*-1\\ \text{ii)}\ D_{i-1,i,t+p}>\kappa,\ \forall p=0,1,2,...,p* \end{split} \tag{2}\]  
where \(\kappa\) is a threshold, and \(p*\) is the least number of records that show deviation after the change date. We determined \(p*\) to be 3, based on some trial and error experiments. The threshold \(\kappa\) is calculated by multiplying a scale factor (\(\beta\)) and the maximum of the absolute difference (\(D_{\text{max}}\)):  
\[\kappa=\beta\cdot D_{\text{max}} \tag{3}\]  
where the value of the scale factor \(\beta\) is determined during the training process.  
Once a change date \(t^{*}\) is detected in year \(i\), the algorithm returns to the yearly scale and implements Step 1 for the next two consecutive years. In the cases that a change date \(t^{*}\) is detected in the year \(i-1\) of two consecutive years, only the records acquired on dates from \(t^{*}+1\) to \(n\) in years \(i-1\) and \(i\) will be utilized to calculate \(D\) and tested against the two criteria in Equation (2).
## 3 Hardware Implementation
### ECG R-Peak Detection Engine
#### 3.2.1 R-Peak Detection State Machine  
The R-peak detection engine uses hardware time-division multiplexing (TDM) to improve resource efficiency. This is followed by an increase in the control complexity of multiple microcontroller units sharing the PE and memory, and a decrease in signal processing speed. Therefore, the state machine design concentrates on controlling the input of the arithmetic unit and the data write-back of the data buffer. In addition, the sampling frequency of ECG signals is often relatively low, for example, 250 Hz or 360 Hz. Therefore, the delay in signal processing is not a major issue.  
Figure 8a depicts the state diagram of the R-peak detection state machine. This FSM (finite state machine) controls every intermediate FSM at every stage. It primarily comprises four components: raw data write, bandpass filter, triangle filter, and Hilbert filter. In each intermediate filtering stage, data reading, calculation, and data return are necessary. The difference and square operation are optional.  
After receiving the data from the SPI interface (data_en = 1), the R-peak detection state machine will be activated, and the computation will start by storing the data in the buffer. Figure 8b depicts each independent filtering operation, primarily involving data read, computation, difference squaring (optional), and result return.  
Each stage of the process shares the same PE to improve resource utilization. The state machine design focuses on controlling the input of the arithmetic unit and managing the data write-back address. For the bandpass filter and triangle filter, the calculated results are stored as intermediate data in SRAM. As for the calculation result of the Hilbert filter, it is transmitted to the PZC detection unit.  
In addition, the state machine also needs to manage the truncation and saturation strategies for PE results at different stages and determine whether the PE should perform a difference square. Specifically, in the case of triangle filtering, the filter coefficients are not stored in parameter SRAM but instead generated by the FSM.
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
## 4 Comparison with Existing Models  
Table 2 compares the performance of the proposed structure with the performance of various previously published UWB-MIMO antenna configurations. In contrast with the other reported designs, the proposed UWB-MIMO antenna structure provides obvious benefits. As can be observed from the table, the proposed UWB-MIMO antenna structure outperforms all the other designs. The proposed candidate is ideally suited for numerous UWB wireless applications owing to its small size, moderate impedance bandwidth, strong isolation capability, low ECC, high gain, and relatively steady radiation efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref** & \begin{tabular}{c} **Size** \\ **(mm2)** \\ \end{tabular} & \begin{tabular}{c} **Ports** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Bandwidth** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Isolation** \\ **(dB)** \\ \end{tabular} & \begin{tabular}{c} **Decoupling** \\ **Technique** \\ \end{tabular} & \
\begin{tabular}{c} **ECC** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Radiation** \\ **(dBi)** \\ \end{tabular} \\ \hline
[3] & \(34\times 34\) & 4 & 2.5–12 & 15 &
\begin{tabular}{c} Perpendicular Placement and \\ a Parasitic Strip \\ \end{tabular} & \(<\)0.05 & \(>\)75 & 2.5–5.5 \\ \hline
[8] & \(26\times 31\) & 2 & 3.1–11 & 25 & Ground Stub and EBG & \(<\)0.01 & \(>\)70 & 0–5.5 \\ \hline
[9] & \(42\times 24\) & 2 & 3.1–10.9 & 15 & Vertical Placement & \(<\)0.2 & \(>\)75 & 0–3.5 \\ \hline
[12] & \(40\times 40\) & 4 & 3.1–14 & 18 & Swastika-shaped Stub & \(<\)0.012 & \(>\)89 & 5.5 \\ \hline
[20] & \(42\times 27\) & 2 & 3.1–11.5 & 15 &
\begin{tabular}{c} Defected Ground Structure \\ (DGS) \\ \end{tabular} & \(<\)0.005 & \(>\)75 & 0–2 \\ \hline
[21] & \(29\times 23\) & 2 & 3.0–12.0 & 15 &
\begin{tabular}{c} Inverted L-shaped Stub and \\ CSRR \\ \end{tabular} & \(<\)0.15 & \(>\)82 & 4.7 \\ \hline
[22] & \(26\times 28\) & 2 & 2.9–10.8 & 15 & T-shape Stub & \(<\)0.08 & Not Given & 1.6–4 \\ \hline
[23] & \(32\times 32\) & 2 & 2.9–12 & 15 & Placed Perpendicularly & \(<\)0.04 & \(>\)60 & 1.7–4.2 \\ \hline
[24] & \(35\times 35\) & 4 & 3.8–15 & 15 & Ground Stubs & \(<\)0.07 & \(>\)70 & 3–5 \\ \hline
[25] & \(28\times 22\) & 2 & 2.9–11.8 & 20 & H-shape Slot & \(<\)0.03 & Not Given & 1.4–3.7 \\ \hline This work & \(30\times 18\) & 2 & 4.3–15.63 & 20 & Ground Branch & \(<\)0.0075 & 85–93 & 2.5–5.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of several reported UWB-MIMO antennas.  
Figure 14: The comparison of simulated and measured TARC.
# DisA Limits RecG Activities at Stalled or Reversed Replication Forks
## 3 Results
### DisA Inhibits the DNA-Dependent ATPase Activity of RecG
Alternatively, DisA forms large complexes on the cssDNA that unspecifically inhibit the ATPase activity of \begin{table}
\begin{tabular}{c c} \hline \hline
**DNA Substrate and Proteins** & **K\({}_{\text{cat}}\) (min\({}^{-1}\)) a** \\ \hline RecG (15 nM), no ssDNA & \(<\)0.1 \\ RecG + HJ DNA & 2777 \(\pm\) 47 \\ RecG + HJ DNA + 24 nM DisAd & 2770 \(\pm\) 45 \\ RecG + HJ DNA + 36 nM DisAe & 370 \(\pm\) 10 \\ HJS + 36 nM DisA & \(<\)0.1 \\ RecG + HJ DNA + 24 nM DisA D77 Ne & 2772 \(\pm\) 51 \\ RecG + HJ DNA + 36 nM DisA D77 Ne & 403 \(\pm\) 17 \\ HJS + 36 nM DisA D77N & \(<\)0.1 \\ RecG + HJ DNA + 24 nM DisA D77 Ne & 2781 \(\pm\) 42 \\ RecG + HJ DNA + 36 nM DisA D77 Ne & 2745 \(\pm\) 37 \\ HJS + 36 nM DisA D77N & \(<\)0.1 \\ \hline RecG + ssDNA & 4000 \(\pm\) 51 \\ RecG + ssDNA + 6 nM DisAb & 3333 \(\pm\) 43 \\ RecG + ssDNA + 12 nM DisAc & 952 \(\pm\) 14 \\ RecG + ssDNA + 24 nM DisAd & 435 \(\pm\) 9 \\ RecG + ssDNA + 36 nM DisAe & 256 \(\pm\) 4 \\ ssDNA + 36 nM DisA & \(<\)0.1 \\ RecG + ssDNA \(\rightarrow\) 24 nM DisAd & 3234 \(\pm\) 39 \\
24 nM DisAd + ssDNA \(\rightarrow\) RecG & 425 \(\pm\) 11 \\ RecG + ssDNA + 6 nM DisA AC290b & 3991 \(\pm\) 37 \\ RecG + ssDNA + 12 nM DisA \(\propto\)2290c & 3993 \(\pm\) 39 \\ RecG + ssDNA + 24 nM DisA \(\propto\)2290d & 3981 \(\pm\) 45 \\ RecG + ssDNA + 36 nM DisA AC290d & 3970 \(\pm\) 36 \\ ssDNA + 36 nM DisA AC290d & \(<\)0.1 \\ RecG + ssDNA + 6 nM DisA DC77 Nb & 3547 \(\pm\) 35 \\ RecG + ssDNA + 12 nM DisA D77 Nc & 1066 \(\pm\) 29 \\ RecG + ssDNA + 24 nM DisA D77 Nd & 467 \(\pm\) 13 \\ RecG + ssDNA + 36 nM DisA D77 Nd & 467 \(\pm\) 13 \\
*ATP hydrolysis was measured as indicated in Materials and Methods. The protein(s) were pre-incubated with ssDNA or HJ DNA (10 \(\mu\)M in nt). The stoichiometry of DisA with ssDNA is indicated (DisA/200\({}^{\circ}\), 100\({}^{\circ}\), 50\({}^{\circ}\), or 33\({}^{\circ}\)-nt). The steady-state kinetic parameters for RecG (1 protein/ 667-nt) were derived from the data presented in Figure 4. The average rate of ATP hydrolysis was obtained from more than three independent experiments and it is shown as the mean \(\pm\) SEM.  
\end{table} 
Table 1: RecG rates of DNA-dependent ATP hydrolysis. RecG. To test whether DisA-mediated inhibition of RecG is specific, we analyzed the ATPase activity of PrcA (Supplementary Figure S3B). In the presence of low (1 DisA/100-nt) or high DisA (1 DisA/12-nt) concentrations, the ATPase activity of PrcA (15 nM, 1 PrcA/ 660-nt) did not significantly vary (kcat of 1722 \(\pm\) 332 and 1736 \(\pm\) 297 min\({}^{-1}\) vs. 1750 \(\pm\) 382 min\({}^{-1}\), \(p\) > 0.1) (Supplementary Figure S3B, green and red vs. blue line). This confirms that the inhibition of the ATPase activity of RecG by DisA is a genuine and specific activity of DisA.
## 4 Evaluation  
For evaluation and validation, we have implemented the optimization problem and its variants on MATLAB (R2022b) [30] software platform with CVX (v2.1) [31] and Gurobi (v9.1.0) [32] for modeling and solving the proposed optimization problems, respectively. Gurobi is a widely used computer solver that can be used for convex optimization problems and non-convex ones such as mixed-integer linear/quadratic programming. In the case of integer programming, Gurobi can efficiently compute optimal solutions by using advanced algorithms such as branch-and-bound and cutting planes [33]. In addition to evaluating the performance on short- and long-term urban road scenarios, we have also implemented a discrete-time simulator on Matlab which is also used for visualization and result reporting. Throughout the simulation and optimization, we have used a high-performance computer with Intel(R) i9-13900 KF CPU and 32 GB memory. The processor has 8 performance cores and 16 efficient cores, making 32 threads in total, and the clock speed can go up to 5.8 GHz.
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 2 Experimental Procedure
### Catalytic Tests of Stearic Acid Conversion  
The catalytic experiments were performed in a stainless-steel autoclave furnished with a stirrer and heated by an external oven. In a typical reaction, the corresponding amount of catalyst was loaded (0.4 g) together with 10 wt % stearic acid solution (2 g, 98% Aldrich) in n-dodecane (18 g, \(\geq\)99%, Sigma-Aldrich, St. Louis, LA, USA) under 6 bar of nitrogen. Then, the reaction was performed at the desired temperature for a fixed time with stirring. Once the reaction time was reached, the reactor was cooled down to 80 \({}^{\circ}\)C. Subsequently, an ice-cooled condenser separated the reaction products into gaseous and liquid fractions which were collected in a gas bag and a vial, respectively. Additionally, the liquid product present in the reactor was collected and separated from the catalyst by filtering under vacuum in a device previously heated to 110 \({}^{\circ}\)C.
## 4 Proposed Scheme  
The proposed scheme executes the task in two phases: (i) suppressing the noise by allocating an adequate number of meta-atoms (one RIS for one user) to each user of a TDMA-RIS-NOMA cluster, (ii) finding the power coefficients to maximize the SINR.
## 5 Conclusions  
This study has proposed a methodology for the diagnosis of bearing failures in TIMs fed by frequency inverters. The proposed approach is based on information measurements, artificial bee colony algorithm, and intelligent pattern classifiers. The experimental tests considered different operating conditions that the TIMs might face in industrial environments, such as variations in the load, supply frequency and four bearing failure configurations.  
In the feature extraction stage, the Delayed Mutual Information was used to estimate the similarity measurements between two phases of the stator current to extract the bearing failure signatures. Subsequently, these signatures were evaluated by the pattern classifiers, C4.5 DT, and MLP ANN. The experimental results confirmed that the MLP ANN is a suitable classifier for the feature extraction approach, as it provides promising results for the sample classification, regardless of the operating conditions. However, it is important to highlight that using the C4.5 decision tree, the methodology also presents satisfactory results, achieving a classification rate of 94.3% of all data sets.  
When the ABC algorithm was used to select the most relevant attributes of the DMI signatures, it was verified that the C4.5 and MLP meta-heuristic classifiers present promising results, higher than 93.6%. Using this ABC algorithm, the 23 most relevant attributes of the DMI signatures were selected, achieving results close to the tests without the ABC algorithm. This shows that, using the ABC algorithm for selecting the most significant attributes, satisfactory results can be achieved with a lower computational cost since less time is spent to build the classifier model.  
All of the authors equally contributed to the conception of the idea, the design of experiments, the analysis and interpretation of results, and the manuscript's writing. Writing--original draft preparation, G.H.B., A.G. and O.D.-P.; writing--review and editing, M.F.C., W.F.G. and D.M.-S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Council for Technological and Scientific Development (CNPq) (processes no. 474290/2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0 and 405228/2016-3), the Coordination for the Improvement of Higher Level Personnel (CAPES), the Federal University of Technology--Parana and the University of Valladol.  
Not applicable.  
Figure 7: Performance comparison of the proposed method using meta-heuristic classifiers—Motors 1 and 2.