esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Test Vehicle
### Physical Implementation of the New Differential Final Ratio  
The final differential ratio is changed after proving that a lower final differential ratio improves the consumption and performance of the electric vehicle. However, manufacturing and cost constraints hindered the development of a dedicated prototype. Specifically, the development of a prototype cone and differential crown and the manufacturing process was very high. Therefore, developing a prototype without considering mass production in the future was not profitable, even more, when considering the lack of machinery to manufacture helical gears.  
For this reason, we searched the market for an axle with a final differential ratio lower than 4.3, as this improves energy efficiency according to the simulations. Thus, an axle with a final ratio of 3.54 was implemented in the electric vehicle. Therefore, the only change was the final differential final ratio; the vehicle's weight remained constant and the tire's dynamic radius. A comparison between original and new axles is depicted in Figure 4.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Description** & **Symbol** & **Unit** & **Value** \\ \hline \multirow{4}{*}{Powertrain} & \multirow{4}{*}{\(-\)} & \multirow{4}{*}{\(-\)} & Central electric motor with \\  & & & five-speed gearbox and \\  & & & final ratio \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & 1370 \\ \cline{1-1}  & Battery pack capacity & \(-\) & kWh & 32 (Li-ion cells) \\ \cline{1-1}  & Nominal voltage & \(E_{0}\) & V & 105 \\ \cline{1-1}  & Maximum capacity & \(Q\) & Ah & 304 \\ \cline{1-1}  & Initial state-of-charge & \(-\) & \% & 100 \\ \cline{1-1}  & Internal resistance & \(\Omega\) & Ohms & 0.07 \\ \cline{1-1}  & Nominal current discharge & \(i\) & A & 160 \\ \cline{1-1}  & Exponential voltage & \(a\) & V & 106 \\ \cline{1-1}  & Exponential capacity & \(\beta\) & Ah & 260 \\ \cline{1-1}  & Frontal area & \(A\) & m\({}^{2}\) & 2.15 \\ \cline{1-1}  & Drag coefficient & \(C_{d}\) & \(-\) & 0.436 \\ \cline{1-1}  & Rolling resistance coefficient & \(\mu_{rr}\) & \(-\) & 0.015 \\ \cline{1-1}  & Gearbox efficiency & \(\eta_{g}\) & \(-\) & 0.95 \\ \cline{1-1}  & First shift ratio & \(\epsilon_{g,1}\) & \(-\) & 3.636 \\ \cline{1-1}  & Second shift ratio & \(\epsilon_{g,2}\) & \(-\) & 1.9641 \\ \cline{1-1}  & Third shift ratio & \(\epsilon_{g,3}\) & \(-\) & 1.428 \\ \cline{1-1}  & Fourth shift ratio & \(\epsilon_{g,4}\) & \(-\) & 1 \\ \cline{1-1}  & Fifth shift ratio & \(\epsilon_{g,5}\) & \(-\) & 0.801 \\ \cline{1-1}  & Final ratio efficiency & \(\eta_{d}\) & \(-\) & 0.95 \\ \cline{1-1}  & Final ratio & \(\epsilon_{d}\) & \(-\) & 4.3 \\ \cline{1-1}  & Maximum torque & \(-\) & Nm & 691.51 \\ \cline{1-1}  & Maximum power & \(-\) & kW & 25.92 \\ \cline{1-1}  & Maximum speed & \(-\) & km/h & 117 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle features.  
Figure 4: Axles with **(a)** original final ratio (4.3), **(b)** new final ratio (3.54).
## 3 Results and Discussion
### C. vulgaris Sedimentation Kinetics after the use of O. cochenillifera
A possible explanation for these results is that with the acid eluent, the biopolymer could have been partitioned into molecules with a wide range of molecular weights, which contributed to the formation of flocs with a less uniform size distribution. Hence, the larger flocs \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Eluent**} & **Extract Coagulant** & \multicolumn{2}{c}{**Average Speed of**} & \multirow{2}{*}{**Correlation**} \\  & **Dosage (g L\({}^{-1}\))** & & \\  & & **(cm s\({}^{-1}\))** & & \\ \hline \multirow{2}{*}{-} & 0 (control) & 0.163 \(\pm\) 0.008 & 0.98 \\ \cline{2-4}  & 3.5 & 0.30 \(\pm\) 0.05 & 0.95 \\ \cline{2-4}  & 5.9 & 0.29 \(\pm\) 0.03 & 0.97 \\ \cline{2-4}  & 8.2 & 0.25 \(\pm\) 0.03 & 0.95 \\ \hline \multirow{2}{*}{NaOH} & 3.5 & 0.181 \(\pm\) 0.008 & 0.99 \\ \cline{2-4}  & 5.9 & 0.172 \(\pm\) 0.007 & 0.99 \\ \cline{2-4}  & 8.2 & 0.166\(\pm\) 0.004 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average speed of flake sedimentation over time in a measuring cylinder. might have a higher sedimentation speed, but a lower removal harvesting efficiency was obtained after sedimentation for 30 min since the smaller particles remained in suspension, influencing the optical density. On the other hand, using the alkaline eluent, the polymer might have been broken into molecules with a lower molecular weight. Therefore, smaller flocs might have been formed with a more uniform size distribution, taking more time to settle, which could explain the lower sedimentation speed. Hence, after sedimentation for 30 min, most of these particles were settled, leading to a higher harvesting efficiency. In the experiments with the dosages of acid coagulant, the biomass sedimented faster than with the dosages of alkaline coagulant. This can be explained by the formation of well-defined flocs, which influences their mass and gravitational force. In addition to the action of the gravitational force on the flakes, the formation of better-defined colloids also implies a smaller number of particles dispersed in the solution, which facilitates the descent of the flocs with less friction [12]. This formation of colloids is given by the formation of bonds between the coagulants and the biomass.
## 2 Materials and Methods
### Chemicals  
Analytical grade standards of phenolic compounds were obtained from Extrasynthese (Genay, France). HPLC grade purity Methanol (MeOH) and acetonitrile (AcN) were obtained from Merck (Darmstadt, Germany), and phosphoric acid was obtained from Sigma-Aldrich (St. Louis, MO, USA). Hydrochloric acid (Suprapure) was purchased from Merck (Darmstadt, Germany). HPLC grade deionised water was obtained from Siemens UltraClear (Siemens AG, Munchen, Germany). The multielement standard solution was obtained from Perkin Elmer (NexION Setup Solution, Waltham, MA, USA). Argon used to form plasma for ICP-MS was supplied by Messer (Messer Croatia Plan d.o.o., Zapresic, Croatia) and was of purity 6.0 as well as acetylene.  
Identification and Quantification of Phenolic Compounds by High-Performance Liquid Chromatography (HPLC)  
High-performance liquid chromatography analysis was performed for the analytical qualification and quantification of phenolic compounds in each olive leaf extract according to our previously published method [26]. Briefly, ground leaves (30 mg) were suspended in methanol/water (80:20 \(v/v\), 1.5 mL) and sonicated in an ultrasonic bath (Sonorex Digitec; Bandelin electronic, Berlin, Germany) for 15 min, centrifuged for 5 min at 5000 rpm (Domel Centric 350; Zelezniki, Slovenia), and filtered through 0.45 \(\upmu\)m filters before HPLC analysis. The separation of phenolic compounds was performed on the HPLC instrument (Shimadzu Nexera LC-40 DX3, Kyoto, Japan) using a C18, 2.1 mm \(\times\) 150 mm, 2.7 \(\upmu\)m core-shell column (Agilent, Palo Alto, CA, USA) held at 30 \({}^{\circ}\)C. The elution was carried out in gradient mode using a binary solvent mixture composed of water acidified with 0.1% formic acid (solvent A) and acetonitrile containing 0.1% formic acid (solvent B). A linear gradient was run as follows: 95% (A) and 5% (B) was held for the first 2 min., from 95% (A) and 5% (B) to 50% (A) and 50% (B) gradient changed during 18 min; then, it changed to 5% (A) and 95% (B) in 1 min and remained unchanged for the next 2 min, after which the system was reequilibrated for 7 min to initial composition. The mobile phase flow rate was 0.35 mL/min, and the injection volume of each sample was 5 \(\upmu\)L. All phenolic compounds were identified and quantified by comparing their retention times and peaks area with those of analytical standards. The UV/Vis detection was set at 360 nm for luteolin-4-O-glucose, luteolin-7-O-glucose, apigenin-7-O-glucose, apigenin, luteolin, and rutin, at 280 nm for deuropein, oleuppein aglycone, oleacein, catechin, tyrosol, hydroxytyrosol,and verbascoside, and at 210 nm for oleanolic acid. Quantification was performed using the external standard method, while the calibration curves for individual polyphenols were obtained by serial dilutions of the corresponding stock standard solutions [26].
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 8 Conclusions  
A stand-alone robust algorithm (and variations) for real-time open loop tracking of the code phase delay and Doppler of GNSS reflected signals has been proposed and validated. The algorithms were demonstrated on example tracks using CYGNSS raw sampled data as well as over a large CYGNSS data set. The demonstrated open loop tracking mean delay accuracy over ocean was found to be 0.13 L1 C/A code chips, with a mean Doppler error of 78 Hz. The algorithm was found to converge 100% of the time with an average of 8.6 iterations of the specular point solver, which would allow implementation on an on-board satellite instrument. The paper provides a baseline publically available open loop GNSS-R tracking algorithm for use by the scientific community.  
**Funding:** This research was funded by NASA grant number NNL13 AQ00C, Cyclone Global Navigation Satellite System (CYGNSS).  
**Acknowledgments:** Thanks is due to the NASA CYGNSS project and science team members for making available the sampled raw data and for useful suggestions and feedback.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 4 Removal of Pharmaceuticals during Wastewater Treatment
### Electrocoagulation Process  
Electrocoagulation is a treatment process in which cations are formed by metal electrodes in an electrical field [68; 69]. It produces coagulants using metal electrodes that are more likely to encounter pharmaceutical products with several advantages, including sludge minimization, automatic treatment, and efficient and low operating costs [69; 70]. The removal efficiency of the three most consumed pharmaceutical products such as carbamezapine (70%), diclofenac (90%), and amoxicillin (77%) was investigated by [PERSON] et al. [71]for a density of 0.5 mA/cm\({}^{2}\), an initial concentration of 10 mg/L, and a hydraulic retention time HRT of 38 h. Oxytetracycline, an antibiotic drug, was analyzed by [PERSON] et al. (2018). The optimum current density was 20 mA/cm\({}^{2}\) for both anodes: iron and aluminum had a removal efficiency of 93.2% and 87.75%, respectively. The initial concentration on removal efficiency was also studied: increasing the initial concentration of oxytetracycline hydrochloride up to 200 mg/L did not have a significant impact on its removal. The pH, Eh, and dissolved oxygen of all samples were measured during the experiments: with both anode-cathode combinations, pH was seen to increase considerably, while Eh and dissolved oxygen decreased substantially. Furthermore, the total removal of ciprofloxacin using electrocoagulation process was achieved at a density of 15 mA.cm\({}^{-2}\), pH 7.5, initial CIP concentration of 60 mg.L\({}^{-1}\), electrolyte dose of 0.07 M NaCl, and inter-electrode distance 1.58 cm within the equilibrium time of 20 min ([PERSON] et al., 2018; [PERSON] et al., 2018).
# Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser
## 6 Image Quality Improvement Experiment Based on Inertial Reference Laser
### Image Quality Improvement Based on Inertial Reference Laser  
The original image shown in Figure 12 was used for the restoration experiment aiming at one architectural image. In order to calculate the MTF of the restored image conveniently, a black-and-white edge was added to the image.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{2}{c}{**12.5 Hz**} & \multicolumn{1}{c}{**20 Hz**} & \multicolumn{1}{c}{**50 Hz**} & \multicolumn{1}{c}{**110 Hz**} & \multicolumn{1}{c}{**Random**} \\  & \multicolumn{1}{c}{**Sinusoidal**} & \multicolumn{1}{c}{**Sinusoidal**} & \multicolumn{1}{c}{**Sinusoidal**} & \multicolumn{1}{c}{**Sinusoidal**} & \multicolumn{1}{c}{**Vibration**} \\ \hline \multirow{3}{*}{Difference} & \(Y\)-axis & 0.07 pixel & 0.07 pixel & 0.12 pixel & 0.09 pixel & 0.08 pixel \\ \cline{2-7}  & \(Z\)-axis & 0.06 pixel & 0.12 pixel & 0.11 pixel & 0.07 pixel & 0.06 pixel \\ \hline \hline \end{tabular}
\end{table}
Table 2: The difference between the two curves (RMS).  
Figure 11: Image restoration method and process based on an inertial reference laser.  
Figure 16: The MTF value is increased from 0.048 to 0.090.  
Figure 14: The MTF value is increased from 0.045 to 0.080.  
Figure 15: The MTF value is increased from 0.044 to 0.071.  
The visual qualitative evaluation revealed that both the contrast and sharpness of the restored image were significantly improved without obvious ringing, and that the noise was within a controllable range. The MTF of the restored image could be increased to 1.61-1.88 times of the original image. When the image motion was less than 3.5 pixels, the image motion measurement method based on an inertial reference laser could significantly improve the image quality.
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 2 Efficient Fuzzy MPC Algorithms with the Advanced Construction of the Dynamic Matrix
### Generation of the Dynamic Matrix  
#### 2.2.1 Fuzzy Model Used to Obtain the Dynamic Matrix  
The fuzzy Takagi-Sugeno model used to generate the dynamic matrix has the following form:\[\text{Rule }f\text{:} \tag{23}\] \[\text{if }y_{k-1}^{j_{y}}\text{ is }B_{1}^{f,j_{y}}\text{ and }\dots\text{ and }y_{k-n}^{j_{y}}\text{ is }B_{n}^{f,j_{y}}\text{ and }\] \[u_{k-1}^{j_{u}}\text{ is }C_{1}^{f,j_{u}}\text{ and }\dots\text{ and }u_{k-m}^{j_{u}}\text{ is }C_{m}^{f,j_{u}}\] \[\text{then }\widehat{y}_{k}^{j,f}=\sum_{m=1}^{n_{u}}\sum_{n=1}^{p_{d }-1}a_{n}^{j,m,f}\cdot\Delta u_{k-n}^{m}+a_{p_{d}}^{j,m,f}\cdot u_{k-p_{d}}^{m} \text{,}\]  
where \(y_{k-i}^{j_{y}}\) is the value of the \(j_{y}\)th output variable at the \((k-i)\)th sampling instant, \(u_{k-i}^{j_{u}}\) is the value of \(j_{u}\)th manipulated variable at the \((k-i)\)th sampling instant, \(B_{1}^{f,j_{y}},\dots,B_{n}^{f,j_{y}}\), \(C_{1}^{f,j_{u}},\dots,C_{m}^{f,j_{u}}\) are fuzzy sets, \(a_{n}^{j,m,f}\) are the coefficients of step responses in the \(f\)th local model, \(j_{y}=1,\dots,n_{y}\), \(j_{u}=1,\dots,n_{u},f=1,\dots,l\), and \(l\) is number of fuzzy rules. The model is composed of local models in the form of step responses [39]; thus, it can be obtained relatively easily, because it is sufficient to collect a few sets of step responses, near a few operating points, e.g., using a nonlinear model of the process. Next, the premises of the model should be formulated using expert knowledge and then tuned, e.g., by means of a fuzzy neural network [9].
# Structure Preserving Convolutional Attention for Image Captioning
## 3 Proposed Method
### Structure Preserving Convolutional Attention  
As defined in Equation (6), attention weight \(\alpha_{ti}\) is calculated on the vector representation of each grid independently by a fully connected layer. However, this operation totally discards the spatial structure of the image regions, which are known to be significant in image captioning. To overcome such a problem, instead of calculating on vector level, our SPCA attention is computed with convolution operations on the 2D feature maps, and thus can preserve the spatial structure of the image regions. Our SPCA module is composed of two attention components, named convolutional spatial attention and cross channel attention. Figure 2 depicts the framework of our attention module.  
Note that our SPCA's input latent state \(h\), input image feature \(V\) and output feature \(C\) are all represented as 3D tensors of size \(C\times W\times H\). Such a tensor can be considered as a multi-channel map, which comprises \(C\) channels, each with a size of \(H\times W\). In general 1D-LSTM which has latent state \(h^{\prime}\in\mathbb{R}^{1\times C}\), we utilize tile operation to copy \(h^{\prime}\) into a new tensor \(h\in\mathbb{R}^{C\times W\times H}\). As for output \(C\) in our SPCA, we finally use a pooling layer to reduce dimension as \(C^{\prime}\in\mathbb{R}^{1\times C}\). However, we can omit these operations at 2D-LSTM.  
At the \(t\)-th step, given the obtained image feature whose size is \(V\in\mathbb{R}^{C\times W\times H}\). After the above mentioned dimensional change operation, we concatenate \(V\) in channel dimensions with 2D latent state \(h_{t-1}\in\mathbb{R}^{C\times W\times H}\) to form a new feature maps \(F_{t}\in\mathbb{R}^{2C\times W\times H}\). Then, our attention process can be summarized as:  
\[F_{t} = [V;h_{t-1}] \tag{7}\] \[M_{t}^{C} = SPCA^{C}(F_{t})\] (8) \[M_{t}^{S} = SPCA^{S}(F_{t})\] (9) \[C_{t} = V\otimes\left(M_{t}^{C}\otimes M_{t}^{S}\right) \tag{10}\]  
where \(\otimes\) denotes element-wise multiplication. \(SPCA^{C},SPCA^{S}\) represents the attention function for channel and spatial attention modules. \(M_{t}^{C}\in\mathbb{R}^{C\times 1\times 1},M_{t}^{S}\in\mathbb{R}^{1\times W \times H}\) are the attention  
Figure 2: The left side is an illustration of the proposed attention module. The right side is a diagram of our Structure Preserving Convolutional Attention sub-module.  
weights of resulting channel and spatial attentions, respectively. \(C_{t}\in\mathbb{R}^{C\times W\times H}\) is the resulting context representation.
# FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
## 4 Results and Discussion
### Result Discussion
#### 4.2.2 Using TON-IOT Dataset
Figure 3 shows the class imbalance issue in the original dataset, which is effectively addressed by applying the SMOTE technique (Figure 4). The confusion matrices (Figure 6) demonstrate the satisfactory performance of our model, with a low number of FPR and FNR. The ROC curve (Figure 8) exhibits good results, particularly with 99% accuracy for the KNN technique. Table 3 presents the evaluation metrics, indicating that our RF classifier achieves high accuracy (0.99), recall (0.97), precision (0.97), and F1 score (0.97) for intrusion detection, surpassing the performance of other methods such as DT and KNN. Regarding the CICIDS2017 dataset, our RF classifier once again demonstrates superior performance. Figures 15 and 16 illustrate the difference between the partitions of the attack class before and after applying the SMOTE technique, effectively resolving the class imbalance issue. The confusion matrices (Figure 18) highlight the correct implementation of our model, with 0 FPR and 0 FNR. In terms of evaluation metrics (Table 5), the RF technique achieves outstanding results, with 0.999 accuracy, recall, precision, and F1 score. In contrast, the SVM technique lags behind, with significantly lower values for these metrics. Overall, the comparison of results across the TON-IOT, NSL-KDD, and CICIDS2017 datasets consistently demonstrates the superiority of our model based on the RF classifier. It consistently achieves higher performance in terms of accuracy, recall, precision, and  
Figure 10.3 Confusion matrix of prediction for CICIDS2017 dataset. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline LR & 99.9 & 99.9 & 99.9 & 99.9 \\ KNN & 99.9 & 99.9 & 99.9 & 99.9 \\ DT & 99.9 & 99.9 & 99.9 & 99.9 \\ SVM & 18.0 & 16.0 & 34.0 & 34.0 \\ Proposed Model & 99.9 & 99.9 & 99.9 & 99.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimentation result using CICIDS2017 dataset. F1 score, outperforming other techniques such as SVM, DT, and KNN. These findings highlight the robustness and effectiveness of our proposed model in intrusion detection tasks across different datasets.
# Precise Point Positioning Using Triple GNSS Constellations in Various Modes
## 2 Un-Differenced Post-Processing PPP Models  
Traditionally, PPP has been carried out using dual-frequency ionosphere-free linear combinations of carrier-phase and pseudorange GPS measurements. Equations (1) to (6) show the ionosphere-free linear combinations of GPS, Galileo, and BeiDou observations [29; 30].
# Damage-Based Assessment of the Risk of Cut-Out in
###### Abstract  
Cut-out of the hip blade of fracture fixation implants, defined as the perforation of the femoral head by the blade due to the collapse of the neck-shaft angle into varus, is the most relevant mechanical complication in the treatment of trochanteric fractures. Among the factors that contribute to cut-out, the blade positioning in the femoral head is reported as one of the most relevant. Since the optimal blade position in the superior-inferior and medial-lateral directions is unknown, the goal of this work was to investigate the impact of blade positioning in these directions, using three-dimensional finite element models of two femora with an unstable trochanteric fracture (31-A2.2 in the Muller AO classification system with an intrusion distance of 95% of the fracture line length). The finite element models developed were coupled with a stiffness-adaptive damage model for the evaluation of the risk of cut-out. The Proximal Femoral Nail Anti-rotation (PFNA) blade was placed in each model at four discrete distances from the femoral head surface in central and inferior positions. The damage distribution in bone resulting from a gait loading condition was visually and quantitatively assessed to compare the performance of the eight positions and predict the relative risk of cut-out for each. The results suggest that the closer the tip of the blade to the femoral head surface, the lower the risk of cut-out. In the superior-inferior direction, contradicting findings were obtained for the modelled femora. The depth of placement of the blade in the medial-lateral direction and its superior-inferior position were shown to have great influence in the risk of cut-out, with the medial-lateral position being the most relevant predictor. The optimal blade positioning may be subject-specific, depending on bone geometry and density distribution.  
c Article