esa-sceva/satcom-chunk-collection huggingface dataset:
# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
## 2 Methodology
### Classification Results  
The results obtained from the proposed VTA model are shown in Table 1. From the test set compounded of 1000 samples, 997 samples were correctly classified. From these 997 samples, 498 samples correspond to the healthy class, and 499 correspond to the unbalance class. From the remaining samples, two samples corresponding to the healthy class were misclassified to the unbalance class. One sample corresponding to the healthy class was misclassified to the unbalance class. According to these results, the average accuracy obtained with our VTA approach was 99.7%. The remaining 0.3% corresponds to the misclassified samples. Figure 7 shows the learning curve from the VTA model. From this figure, batches of samples of the test set are used to measure and evaluate the performance of the SVM. Note, the performance of the VTA model is increased according to the number of samples used in the test set.  
Figure 6: Decision boundary generated for two linear models. The models were trained with the same dataset. (**a**) Logistic regression, (**b**) Simple perceptron.  
Figure 7: VTA model learning curve.  
Finally, the time consumed to train the VTA model using an Intel core i9 processor to 2.6 GHz was approximately 0.55 min.
# Macroporous Activated Carbon Derived from Rapeseed Shell for Lithium-Sulfur Batteries
###### Abstract  
Lithium-sulfur batteries have drawn considerable attention because of their extremely high energy density. Activated carbon (AC) is an ideal matrix for sulfur because of its high specific surface area, large pore volume, small-size nanopores, and simple preparation. In this work, through KOH activation, AC materials with different porous structure parameters were prepared using waste rapeseed shells as precursors. Effects of KOH amount, activated temperature, and activated time on pore structure parameters of ACs were studied. AC sample with optimal pore structure parameters was investigated as sulfur host materials. Applied in lithium-sulfur batteries, the AC/S composite (60 wt % sulfur) exhibited a high specific capacity of 1065 mAh g\({}^{-1}\) at 200 mA g\({}^{-1}\) and a good capacity retention of 49% after 1000 cycles at 1600 mA g\({}^{-1}\). The key factor for good cycling stability involves the restraining effect of small-sized nanopores of the AC framework on the diffusion of polysulfides to bulk electrolyte and the loss of the active material sulfur. Results demonstrated that AC materials derived from rapeseed shells are promising materials for sulfur loading.  
Keywords: biomass; activated carbon; sulfur hosts; lithium-sulfur battery +
Footnote †: journal: Applied Physics Letters
## Materials and Methods
### Sorption Experiments  
The batch process was used to investigate the metal ions sorption ability of the modified chitosan from aqueous solution. In a typical procedure, 25.0 mL of metal solution was taken in a different polyethylene flask, and 20 mg of the newly prepared material was added to it. The concentration of the metal solution used in the experiment ranged from 7.0 \(\times\) 10\({}^{-4}\) to 2.0 \(\times\) 10\({}^{-3}\) mol dm\({}^{-3}\) at neutral pH. In order to obtain the period associated with the saturation of the isotherms, kinetic experiments were previously performed using similar cationic solutions. The suspension was shaken by an orbital apparatus at 298 \(\pm\) 1 K for 24 h to reach isothermal saturation. This time was previously established by using the same procedure to obtain an isotherm with a well-defined plateau, indicating that all basic centers in each biopolymer were saturated by cations. An isotherm having a well-defined plateau was  
Figure 1: A stepwise illustration of the chemical reaction performed to modify the chitosan (CH). Step 1 involves the reaction between CH and glycidyl methacrylate, while step 2 shows the reaction between CH modified in step 1 and 1,4-bis (3-aminopropyl) piperazine. At the end of this two-step reaction, the requisite products of modified chitosan, i.e., CH-gly (from step 1) and chemically modified chitosan (CH-ANP, from step 2) were obtained.  
obtained by a similar method after 4 h of contact time, demonstrating the saturation of basic centers on chitosan. The number of cations sorbed was determined by separating the supernatant solution from the solid material through decantation. The aliquot obtained was analyzed using ICP-OES (Inductively coupled plasma-optical emission spectrometry) for the presence of cations, and the quantity of the cations sorbed in the sorption process was calculated (mmol g\({}^{-1}\)) by Equation (1).  
\[Nf=\frac{ni-ns}{m} \tag{1}\]  
where _Nf_, _ni_, and _ns_ represent the number of mole of cations at the equilibrium stage, while \(m\) represents the mass of the sorbent [33]. In addition, during the whole sorption process, the linear and nonlinear methods were applied to study the Langmuir, the Freundlich, and the Temkin isothermal behavior.
# Optimal Piezoelectric Potential Distribution for Controlling Multimode Vibrations
###### Abstract  
Vibration damping is prominent in engineering; in fact, vibrations are related to many phenomena (e.g., the fatigue of structural elements). The advent of smart materials has significantly increased the number of available solutions in this field. Among smart materials, piezoelectric materials are most promising. However, their efficiency depends on their placement. There are many studies on their optimal placement for damping a particular mode, but few account for multimodal vibrations damping. In a previous work, an analytical method was proposed to find the optimal placement of piezoelectric plates to control the multimode vibrations of a cantilever beam. In this study, the efficiency of the above method has been improved, considering all plates active simultaneously, regardless of the eigenmodes that are excited, and changing, instead of the plates, the potential distribution. The method results indicate the optimal potential distribution for different excited eigenmodes. The results have been compared with those obtained by experimental tests and numerical simulations, exhibiting very good agreement.  
p ezoelectric; vibrations; active control; potential distribution Article
## 3 Results and Discussion
### Detection of Flooded Plots  
Thanks to the high spatial resolution of the selected radar images (3 m \(\times\) 3 m for TSX, and 8 m \(\times\) 8 m for CSK), photo interpretation makes it possible to detect which parts of the plots are flooded by gravity irrigation. The \"Domaine du Merle\" grassland plots are irrigated every 10 days on average, for between 10 and 30 h. The interpretation of SAR X-band images shows that the X-band allows for the tracking of irrigation practices. An analysis of the radar signal was conducted on plots under  
Figure 7: Sensitivity of X-band to soil moisture for biomass lower than 1 kg/m\({}^{2}\) ((**a**) HH, (**b**) HV) and for biomass higher than 1 kg/m\({}^{2}\) ((**c**) HH, (**d**) HV).  
irrigation at the time of the SAR acquisitions (Figure 8b-d) and on plots where irrigation was completed a few hours earlier (Figure 8a) _In situ_ observations showed that water bodies present on irrigated plots in some locations varied from a few centimeters to thirty centimeters. In plot 6i, _in situ_ observations showed that the plot was entirely irrigated during the SAR overpass (the SAR image was acquired 5 h after irrigation) with the presence of two water bodies (Figure 8a). The presence of water could be explained by a leveling defect in some areas and low hydraulic conductivity preventing the quick infiltration of water.  
The analysis showed a higher radar signal at locations with water bodies than at locations without water bodies. The brightest radar returns were caused by double-bounce scattering between the water surface and the vertical stems and leaves of the vegetation. The difference in the radar signal level (\(\Delta\)) between the flooded areas and the unfolded areas is generally two times greater in HH compared to HV (\(\Delta\)HH-5.5 dB and \(\Delta\)HV-3.5 dB). This is due to the attenuation of the backscattered radar signal by the vegetation, which is more significant at HV polarization than at HH polarization. [PERSON] _et al._[83] found also that the potential of HH polarization is higher than HV and VV polarizations in a study mapping wetlands from C-band SAR data. Our results also showed that the penetration depth of the radar wave in the X-band is high, even for dense and tall vegetation. For HVE between 20 and 55 cm and water bodies with depths between 4 and 10 cm, flooded areas are clearly visible on the images (Figure 8a,c). A strong penetration was also observed in other training grassland plots with HVE between 71 and 102 cm and water bodies with depths of approximately 30 cm (Figure 8b,d). These plots corresponded to wet biomass (BIO) values up to 3.9 kg/m\({}^{2}\).  
Figure 8: Detection of flooded parts of irrigated plots (in red) (**a–d**). \(\Delta_{\rm b}\) (in hours) = SAR acquisition time–start irrigation time; \(\Delta_{\rm e}\) (in hours) = SAR acquisition time–end irrigation time; \(\rm{Wd}=\) Water body depth. Black arrows indicate open canal locations used for border irrigation. Images are in RGB colors (R: HH, G: HV, B: HH-HV).
## 4 Experimental Results and Discussion
### Evaluation Indexes  
In this experiment, the mean average precision ([EMAIL_ADDRESS]), parameters (Param), Giga Floating-point Operations Per Second (GFLOPs), precision (P), recall (R), and Frames Per Second (FPS) were used as the evaluation indexes of model performance. P and R were found as follows:  
\[\mathrm{P}=\frac{\mathrm{T_{P}}}{\mathrm{T_{P}+F_{P}}} \tag{8}\]  
\[\mathrm{R}=\frac{\mathrm{T_{P}}}{\mathrm{T_{P}+F_{N}}} \tag{9}\]  
where \(\mathrm{T_{P}}\) (true positive) indicates that the object to be detected in the image is correctly identified and the IoU is greater than the threshold; \(\mathrm{F_{P}}\) (false positive) indicates that the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Data** & \multirow{2}{*}{**P (\%)**} & \multirow{2}{*}{**R (\%)**} & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\  & **Volume** & & & **(\%)** & **(\%)** \\ \hline Original—horizontal & 2235 & 90.6 & 92.6 & 95.1 & 68.7 \\ Enhanced—horizontal & 19,815 & 99.8 & 95.4 & 98.3 & 82.7 \\ Original—vertical & 1155 & 91.8 & 91.8 & 94.5 & 75.5 \\ Enhanced—vertical & 18,434 & 97.4 & 98.8 & 98.8 & 90.0 \\ Original—scratch & 497 & 85.1 & 83.3 & 87.2 & 33.6 \\ Enhanced—scratch & 2322 & 92.8 & 95.0 & 96.6 & 57.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The data sheet of ablation experiment 1.  
Figure 6: Example dataset diagram. (**a**) Scratches (vertical angle). (**b**) Qualified (vertical angle). (**c**) Tears (vertical angle). (**d**) Qualified (horizontal angle). (**e**) Tears (horizontal angle).  
detection object is not correctly identified and the IoU is less than the threshold; and F\({}_{\text{N}}\) (false negative) indicates that the target is not detected.  
The calculation process of mAP is as follows:  
\[\text{mAP}=\frac{1}{\text{n}}{\sum_{0}}\int_{0}^{1}\text{P}_{\text{n}}(\text{r })\text{d}\text{r}. \tag{10}\]  
N is the number of target categories detected in the dataset and the mAP value of a certain category. In particular, [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 are commonly used to evaluate the model performance; [EMAIL_ADDRESS] focuses on the variation trend of model accuracy with the recall rate, and [EMAIL_ADDRESS]:0.95 pays more attention to the comprehensive performance of the model under different IoU thresholds, reflecting the fitting range between the detection and real frames. Unless otherwise specified, mAP in subsequent sections refers to [EMAIL_ADDRESS].
# Bidirectional Atmospheric Channel Reciprocity-Based Adaptive Power Transmission
## 1 Introduction
To lessen the negative effects of the turbulence effect and enhance the performance of the transmission system, the channel state information in instantaneous transmission may be measured and extracted in transmission [21]. Then, the channel compensation scheme can be employed. Since there are currently fewer methods that combine adaptive transmission and reciprocity [22], the bidirectional atmospheric channel that uses adaptive transmission needs further study. In this paper, we propose a bidirectional atmospheric channel reciprocity-based adaptive power transmission technique. According to the reciprocity property of a bidirectional atmospheric channel, while CSI estimation is performed at the receiving end of forward transmission using the received signal, adaptive power transmission is used at the transmitting end of inverse transmission in order to lower the BER of the transmitted signal. To test the usefulness of the technique, a bidirectional atmospheric channel with various turbulence strengths is built in the simulation software, and various background sounds are introduced to the channel to vary the channel reciprocity. Based on experimental data gleaned via simulation, the impact of reciprocity on adaptive power transfer is researched, and the effectiveness of adaptive power transfer technology is assessed. In order to examine the viability of this technique in various contexts, Fixed threshold decision (FTD) and conventional ATD techniques are included in the simulation studies for horizontal comparison.
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
## 4 Experimental Test and Comparison
### Performance of the Round-trip Motion  
Another two repeated experiments with longer running times were conducted to confirm the performance of the proposed RBF-CKF method. The motion trajectory of the moving object is shown in Figure 11, which has a round-trip motion. The pitch and yaw axes range from 0\({}^{\circ}\) to +40\({}^{\circ}\) in round-trip form. Two groups of experiments were carried out and the attitude angle errors of pitch and yaw angles are shown in Figure 12. The optimal estimation of RBF-CKF can be displayed in pitch and yaw angle errors with long data. It is clear that the performance of RBF-CKF is superior to the other four methods with long data. During the period when the errors of the other four methods are relatively large, such as 40-60 s and 120-140 s for the pitch axis as well as 20-40 s and 100-120 s for the yaw axis, the RBF-CKF remains stable. Actually, RBF-CKF possesses the good convergence during the
## 3 Results and Discussion
### Hydrolysis Kinetics
#### 3.4.2 Comparison of ILs, Dilute Sulfuric Acid and PEK-C-ILs Catalyst  
The kinetic parameters of inulin hydrolysis by PEK-C-ILs was calculated from the experimental results and the contrasting results of hydrolysis kinetics parameters by PEK-C-ILs, VImaILs and dilute sulfuric acid were listed in Table 4. All experiments were carried out in the equimolar ILs catalyst condition. It is noted that the hydrolysis rate constants by PEK-C-ILs were greater than by VImaILs and dilute sulfuric acid, which was attributed to the activation energy of PEK-C-ILs was smaller than that of latters. Furthermore, for PEK-C-ILs powder catalyst, the ILs immobilized on the surface and inner pore wall of PEK-C has a high-density spatial distribution. Thus, with the reaction temperature increased, the viscosity of inulin solution decreases and the effective collision probability between glycosidic bonds and active sites of ILs was strongly enhanced. However, the spatial distribution distance of equimolar homogenous ILs was greater than that of PEK-C-ILs powder catalyst. So in the homogenous ILs catalysis system, the probability of synergistic catalytic effect of cation and anion is decreased. It was concluded by simulation study that this synergistic effect could decreased  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**PEK-C-ILs** & \multicolumn{5}{c}{**k (min\({}^{-1}\))**} & \multicolumn{5}{c}{**\(E_{\text{a}}\) (kJ/mol)**} & **InA** & \(R^{2}\) \\ \cline{2-7}
**Concentration (\(\mathbf{r}\)/\(\mathbf{w}\) \%)** & **50 °C** & **55 °C** & **60 °C** & **65 °C** & **70 °C** & \\ \hline
1 & 0.00325 & 0.00541 & 0.00881 & 0.01410 & 0.02242 & 89.01 & 27.39 & 0.9967 \\
2 & 0.00678 & 0.01188 & 0.0  
the activation energy. Thus, this chemical and macroporous structures of PEK-C-ILs catalyst made it better hydrolysis active.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 2 FRA, Frequency Bands, and Their Impact on Fault Detection  
The influence of frequency bands on fault detection in power transformers is profound, as different frequency ranges can reveal distinct diagnostic information about the condition of the equipment. By analyzing resonant frequencies generated by various electromagnetic disturbances, practitioners can identify specific fault signatures that correlate with malfunctions. For instance, low-frequency signals are often associated with electrical anomalies, while higher-frequency signals may indicate physical deterioration such as insulation breakdowns. The integration of advanced machine learning models can further enhance the sensitivity of fault detection systems by leveraging these frequency-specific characteristics. Recent studies underscore the importance of this multidimensional approach, highlighting how diverse algorithms can optimize fault diagnosis across multiple frequency bands. This alignment of machine learning techniques with dedicated frequency analysis creates a more accurate and responsive diagnostic framework, ultimately leading to improved maintenance strategies and reduced downtime for power transformers [9; 10]. In an offline test known as FRA, a sweep frequency and low voltage are injected into one transformer winding terminal, and the response across the other terminal is measured in relation to the tank [11]. The FRA response, expressed in decibels (dB), is the ratio of the source voltage, Vi, to the magnitude of the response signal, Vo, as a function of frequency [12]. The FRA signature is thought of as the transformer's fingerprint, which can be compared to its earlier signatures to detect any alterations and, consequently, any mechanical deformation. However, transformer fingerprints are not often available, especially for older transformers. Other comparison techniques, including comparing sister transformer signatures or comparing phases, can be used in this situation. FRA signature interpretation is still a highly specialized field that requires skilled professionals to identify the type and likely location of the fault, even if the testing technique has become relatively straightforward due to the development of FRA test equipment. Particularly for young and inexperienced power engineers, this is not always a straightforward task. This work presents an effort to use sensitivity analysis to quantify and categorize transformer FRA signatures. A transformer distributed parameters model is subjected to a thorough simulation analysis to examine and categorize the effects of internal defects on the FRA signature. By suitably altering the transformer distributed parameters, mechanical failures such axial displacement, radial buckling, disk space variation, loss of clamping, bushing, and leakage faults are simulated.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 5 Power-Optimized Cooperative MAC Protocol
### Cooperative Node Selection Phase  
In the cooperative node selection phase, the nodes satisfying the constraint conditions of cooperation contend to be cooperative nodes by sending help-to-send (HTS) packet to the sender. There are two issues to be addressed in this process, i.e., the contention access delay of the HTS packet transmission and the method to determine the optimal group of cooperative nodes.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 2 Petroleum Products  
The term petroleum was derived from the words petra (stone) and oleum (oil), which occurs naturally and is generally processed into various products by the refinery [11]. Crude oil is known as a petroleum hydrocarbon, generally composed of hydrocarbons, heteroatom compounds, and relatively small concentrations of metallic components. The aliphatics, aromatics, asphaltenes, and resins could be classified as general classes of petroleum derivatives. The aliphatic fraction consists of straight-chain alkanes, branched alkanes (isoalkanes), and cycloalkanes (naphthenes). The aromatic fraction is the volatile monoaromatic hydrocarbons such as benzene, toluene, and xylenes; polycyclic aromatic hydrocarbons (PAH); naphthalene aromatics; and aromatic sulfur compounds such as thiophenes and dibenzothiophenes. The asphaltene (ketones, phenols, fatty acids, esters, and porphyrins) and resin (sulfoxides, amides, pyridines, quinolines, and carbazoles) parts, which are composed of polar molecules with N, S, and O\({}_{2}\)[12]. These fractions are distributed relative to one another depending on the crude oil's source, age, geological history, migration, and other factors [13]. Despite the complexity, petroleum compounds can be broadly classified into two main categories: hydrocarbons (measured as THP) and non-hydrocarbons. Figure 1 summarises the different categories and subclasses of total petroleum hydrocarbons. Crude oil has been separated in several ways, yet it is often a complex mixture of molecules such as B. Jet fuels contain more than 300 different hydrocarbon compounds. Its spills are generally the most severe type of pollutants that pollute the environment, such as soil, water, and ocean. In this overview, much emphasis is placed on soil and water. Leaking and routine washing have been identified as common sources of contamination. Others result from underground storage tanks, offshore platforms, wells, shipping accidents, ruptured pipelines, and natural oil spills.
# Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
## 4 Implementation of the V2x Data Dissemination over diSRsVN and Its Results
### Simulation Scenario
#### 4.2.2 Update Flow Rules Based on Vehicle's Mobility and Topology Discovering  
According to the SUMO model, the vehicle placed based on the SUMO traffic model, the vehicle speed and the vehicle starting position in the simulation are based on the SUMO configuration file. The vehicle node sends the packet with the speed limit to the nearest nodes, based on their mobility speed and also the moving speed and moving direction based on the SUMO configuration. Compare the speed limit with the flow table in the small cell RSU switch. If it does not match with flow table, the small cell RSU switch updates the flow rules into the SDN controller, distributes to all nodes, and makes an upload.