esa-sceva/satcom-chunk-collection huggingface dataset:
# Lifetimes of Used Nuclear Fuel Containers Affected by
## 3 Sulphate-Reducing Bacteria Activity
### SRB-Driven Sulphate Reduction  
Data was used from [18; 19], who performed an experiment to study copper corrosion on the presence and absence of SRB. Copper specimens were exposed to biotic and abiotic artificial anoxic groundwaters for periods of 4 and 10 months and then examined their chemical and electrochemical changes. The microorganisms used in the experiment came from the deep bedrock of Olkiluoto, Finland.  
A particular featured of the results is that pH decreased after the experiment, meaning that the concentration of hydrogen ions increased, since \(\left[\text{H}^{+}\right]=10^{-\text{pH}}\) (from pH = \(-\text{log}_{10}\)[H\({}^{+}\)]). A decrease in hydrogen ions was expected in reaction (3) under controlled conditions, indicating that likely other reactions took place which increased the hydrogen ion concentration.  
Therefore, since hydrogen ions were not limiting the reaction, it was assumed that hydrogen ions would be in abundance for groundwater with similar characteristics. This is a conservative, bounding scenario as the real rate of sulphide production will be significantly less than this. Additionally, the electrolysis of water into hydrogen gas (H\({}_{2}\)) and oxygen (O\({}_{2}\)) was assumed to be faster than the sulphate reduction to represent a conservative scenario; therefore, hydrogen gas was also considered in abundance inside the DGR. However, the use of HCB would prevent the formation of a biofilm on the surface of the UFCs as it did in the experiments; therefore, this sulphide production rate is an  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Dry Density** & **Porosity** & **Saturation** & **Sulphide** & **Sulphate D\({}_{\text{S}}\)** \\  & **[kg/m\({}^{3}\)]** & **\%** & **\%** & **D\({}_{\text{H}}\) [m\({}^{2}\)/s]** & **[m\({}^{2}\)/s]** \\ \hline Gapfill & 1410 & 48.6 & 6 & \(1.43\times 10^{-11}\) & \(7.15\times 10^{-12}\) \\ HCB & 1700 & 38.2 & 67 & \(6.81\times 10^{-12}\) & \(3.40\times 10^{-12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bentonite clay properties.  
extreme bounding case representing the worst-case scenario in which every engineered barrier system fails [20]  
The conservative bounding upper limit of the reaction rate is described by  
\[\frac{\partial S}{\partial t} =-k_{\rho}S \tag{4a}\] \[\frac{\partial H}{\partial t} =\chi\,k_{\rho}H \tag{4b}\]  
where \(k_{\rho}\) is the reaction rate coefficient as a function of the dry density \(\rho_{dry}\), \(\chi\) is the efficiency of the reaction, and \(S\) and \(H\) are concentrations in [mol/L]. If concentrations were in [mg/L], then the right-hand side of Equation (4b) must be multiplied by the factor 33.07/96.06 to account for the mole conversion.
# An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments
## 6 Performance Evaluation
### Quality of Images  
The image quality or the frame resolution is the main cause of performance degradation and higher error rates. The low-resolution frames affect the YOLO and CSRT tracker simultaneously. Hence, the accuracy of the proposed algorithm will drop with low-resolution images. These low-resolution frames cause the YOLO to miss more objects and the system will not be able to count the objects accurately. Even if the YOLO can detect objects in low-resolution environments, CSRT might fail to do the same, resulting in the less accurate object count, as illustrated in the right image of Figure 5. The low image quality affects the accuracy by two means, either YOLO is not detecting the object in the first place, or by the CSRT tracker failing to track. Video number 15 displays a low-resolution and complex environment, the accuracy result is the worst in the whole dataset.  
Figure 5: Object density (**Left**) and Image Quality (**Right**).
# The Impact of Stimulation Intensity on Spatial Discrimination with Multi-Pad Finger Electrode
## 2 Methods  
### Setup  
The experimental setup consisted of a custom-made multi-pad electrode prototype, a stimulator unit, and a tablet PC with dedicated LabVIEW 2020 (National Instruments, 78759 Austin, Texas, United States) applications. The electrode prototypes were produced by screen printing of conductive and dielectric inks for biomedical applications on a flexible substrate. The electrode comprised six rectangular conductive pads that acted as active electrodes. The surface of each pad was 11 mm\({}^{2}\). This size was selected in accordance with the hardware limitations of the stimulation unit, while following general recommendations from [PERSON] et al. [39], suggesting that the best sensation quality was obtained when stimulating mechanoreceptors with an active electrode surface ranging from 7 mm\({}^{2}\) to 15 mm\({}^{2}\). The minimal distance between two pads of 1.6 mm (4.6 mm center-to-center) was in accordance with the electrotactile two points discrimination threshold (2.0-4.0 mm) for the adult fingertip [40]. The reference electrode had an \"H\" shape and a significantly larger area (150 mm\({}^{2}\)) compared to the active pads, as shown in Figure 1a (marked with \"R\"). The electrode was designed to fit on the solar side of the finger's phalanx. Vertical lines of the reference electrode were placed laterally, and central active electrodes were aligned with the midline of the phalange (Figure 1b).  
The stimulation unit generated trains of rectangular biphasic symmetric pulses [41]. The stimulator output was current controlled, and the pulse amplitude could be modulated between 0 mA and 9 mA in steps of 0.1 mA. Bluetooth communication between the stimulator unit and the tablet PC allowed online control of the stimulation parameters and the selection of active electrode pads. Stimulation frequency and pulse width were constant during the experiment, and set to 30 Hz and 400 us, respectively. It is shown that pulse polarity plays a role in the activation of mechanoreceptors in the human skin, i.e., cathodic pulses result with pressure-like stimuli, while anodic pulses provoke vibratory sensations [42]. Although this approach enables better selectivity in terms of mechanoreceptors activation, monophasic pulses are prone to damaging the tissue and the stimulating electrode [43]. Our system relies on charge-balanced biphasic pulses to ensure there is no uncompensated charge transfer and allow for safer prolonged use of the stimulation. Although the system is designed to have cathodic-leading pulses, it should be noted that the compensatory pulse may also influence the neuronal response to the stimulus [44]. Considering that in the presented electrode the referent pad is more than 10 times larger than each of the active electrode pads, the higher current density below an active pad will ensure that there is no activation below the referent pad as a result of the compensatory pulse.
###### Abstract  
Carbon fiber-reinforced plastic/titanium alloy (CFRP/Ti) stacks are widely used in the aerospace field based on their high strength to weight ratio and heat resistance. High-quality bolt hole assembly is critical for the safety of the aerospace industry. Reaming is a crucial process in precision machining and is extensively used to improve the quality of bolt holes. Due to the different properties of the material, machining with conventional reaming (CR) presents some challenges, such as tolerance variations across the hole group and difficulty in controlling thrust. In this paper, ultrasonic vibration is applied to the reaming process. A geometrical model of ultrasonic vibration reaming (UVR) was established to analyze its kinematic law. UVR experiments on CFRP/Ti stacks were carried out to study the influence of different ultrasonic amplitudes on reaming thrust and the influence of tool speed on thrust, dimensional accuracy, and surface roughness under optimal ultrasonic amplitude. The average thrust forces in UVR decreased by over 57% (Ti) and 40% (CFRP), respectively, compared to CR. The roughness of CFRP is reduced by 20% with UVR and 28% for titanium alloys. The surface topography of the holes is significantly improved by UVR. This work guides the manufacture of high-quality bolt holes for CFRP/Ti stacks.  
CFRP/Ti stacks; ultrasonic vibration reaming; thrust force; surface finish Article
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 2 Materials and Methods
### Broadband Interferometer (ITF)  
Figure 1 shows the proposed lightning mapping general procedures consisting of three stages: (1) Preprocessing to assess the VHF signal quality, by removing noise components and performing amplitude normalization. (2) A piecewise procedure is used to segment the lightning signals into overlapped sliding windows to perform the cross-correlations and calculate the phase difference from overlapping VHF signals time difference at each pair of antennae. Different cross-correlation techniques in time-domain (TD), frequency-domain (FD) and wavelet-domain (WD) were applied. (3) The lightning maps are derived by calculating the azimuth and elevation angles from the ITF geometry.
## 2 Related Work
### Medical Image Classification Based on Supervised Learning  
In recent years, the field of deep learning has experienced significant advancements, leading to remarkable medical applications for addressing classification problems. Within the medical imaging domain, numerous outstanding studies have emerged, demonstrating the efficacy of supervised learning in developing high-performance models [17; 18; 19]. For instance, [PERSON] et al. [17] introduced a cooperative training paradigm encompassing both global and local representation learning for medical image classification. [PERSON] et al. [18] leveraged deep learning methods on clinical data obtained from the Danish National Patient Registry (DNPR) comprising 6 million patients (including 24,000 pancreatic cancer cases), as well as data from the United States Veterans Affairs (US-VA) involving 3 million patients (including 3900 cases). Their findings exhibited improved capabilities in designing realistic surveillance programs for high-risk patients. Furthermore, [PERSON] et al. [19] integrated deep learning techniques into MRI analysis to predict survival outcomes in patients with rectal cancer, utilizing segmented tumor volumes extracted from pre-treatment T2-weighted MRI scans. These studies serve as compelling examples of the successful application of deep learning and supervised learning approaches in various medical image classification tasks, highlighting promising advancements and potential avenues for future research.  
In supervised medical image classification, feature fusion has emerged as a pivotal factor in enhancing the performance of medical image classification in recent years. Notably, [PERSON] et al. [20] leveraged neural network principles, streamlined feature vectors, and a variety of machine learning techniques to distinguish between mitotic and non-mitotic cells in breast cancer histology images. Their approach assigns differential weights to various features, thereby boosting the model's efficiency. Similarly, [PERSON] et al. [21] introduced a novel three-branch hierarchical multi-scale feature fusion network architecture tailored for medical image classification. This model underscores the significance of integrating both global and local multi-scale features, affirming its critical role in the domain of medical image classification.  
It should be emphasized that supervised learning approaches are significantly dependent on extensive labeled datasets, a dependency that necessitates resource-intensive and costly efforts. This typically involves the manual annotation of data by domain experts. In the realm of medical imaging, the acquisition of labeled datasets is further complicated by privacy considerations, culminating in a scarcity of publicly available labeled medical datasets.
## 2 Related Works
### Fractional-Order Neural Networks  
Fractional-order neural networks (FONNs) represent an extension of traditional neural networks, incorporating fractional calculus to enhance their modeling capabilities. This approach allows FONNs to handle complex, dynamic systems with greater precision, especially in scenarios where traditional integer-order networks may struggle [29]. FONNs have found applications in diverse fields, including system identification, control systems [30], and signal processing [31], demonstrating their utility in addressing a wide range of engineering challenges.  
A notable application of fractional calculus in neural networks is the fractional-order backpropagation (BP) neural network. By leveraging fractional derivatives, such as the Caputo derivative, these networks optimize the learning process more effectively, leading to faster convergence and better overall performance compared to standard BP networks [32; 33].  
Recurrent neural networks (RNNs) have also seen significant improvements through the application of fractional calculus [34]. Researchers have introduced fractional stochastic gradient descent methods that utilize fractional derivatives like Caputo and Riemann-Liouville [34]. These methods make them more robust in managing complex, dynamic time-series data. Additionally, fractional-order Hopfield neural networks have been developed, leveraging the advantages of fractional calculus, such as long-term memory and nonlocality, to enhance their performance in capturing complex dynamics and improving stability [35]. By incorporating fractional-order activation functions, LSTMs have been optimized to improve predictive accuracy, even in challenging scenarios like wind power forecasting with missing data [36]. For instance, a hybrid LSTM-based FONN employing a fractional arctan activation function has demonstrated enhanced performance in handling complex, nonlinear time-series data [36]. These advancements underscore the potential of fractional-order techniques in boosting the effectiveness of neural networks, particularly for sequential and time-series applications.  
Overall, the use of fractional derivatives in neural networks has significantly advanced their capability to model and optimize complex systems. These improvements are not limited to traditional networks but extend to CNNs as well. Fractional-order CNNs, by integrating fractional calculus, could offer enhanced feature extraction capabilities [19] and are likely to play a critical role in applications requiring detailed analysis of spatial and temporal patterns.
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 4 Test Case Simulations and Results
### Results: Trapezoidal-B Shape  
Table 5 reports the efficiency calculated for all the dynamical shading scenarios characterized by Trapezoidal-B shape's objects. Figure 9 shows, as example, the diagrams of the harvested power (solid blue line) and maximum harvestable power (dotted red line) obtained in case of dynamical shading caused by the trapezoidal-A shape 4 \(\times\) 1 shading object and controlling the off grid PV system,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Efficiency**} \\ \cline{2-4}
**MPPT** & **2 \(\times\) 1** & **3 \(\times\) 1** & **4 \(\times\) 1** \\ \hline Perturb \& Observe—A & 95.37\% & 95.82\% & 95.95\% \\ Perturb \& Observe—B & 97.59\% & 94.68\% & 95.30\% \\ Perturb \& Observe—C & 93.28\% & 94.26\% & 93.69\% \\ Incremental Conductance & 96.03\% & 94.73\% & 94.72\% \\ Constant Voltage & 69.90\% & 66.41\% & 62.19\% \\ Open Voltage & 69.22\% & 73.29\% & 72.17\% \\ Short Current Pulse & 93.75\% & 93.47\% & 91.42\% \\ PSO—3 particles & 92.77\% & 93.44\% & 89.71\% \\ PSO—4 particles & 87.89\% & 90.91\% & 90.64\% \\ PSO—5 particles & 90.34\% & 91.57\% & 91.36\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Efficiency—Trapezoidal-A shapes (simulations).  
Figure 8: Trapezoidal-A shape—3 \(\times\) 1: (a) P&O—A; (b) PSO 3 particles.  
with the best performing traditional MPPT algorithm (P&O--A) and the best performing PSO-based MPPT algorithm (PSO--3p).  
The obtained results are very similar to those obtained in the previous cases of dynamical shading. The efficiency of the best PSO-based MPPT algorithm ranges from a 3.42% (4 \(\times\) 1, P&O--A vs. PSO--3p) to a 4.51% (2 \(\times\) 1, P&O--B vs. PSO--3p) lower than the best hill climbing technique.
## 2 Ontology Alignment Techniques
### Definitions  
Given two ontologies, O1 and O2, ontology alignment is defined as the process of creating mappings in the form (c1, c2, s), where c1 \(\in\) O1 and c2 \(\in\) O2 are concepts from the two ontologies and s \(\in\) [0,1] is the estimated similarity between the two concepts (also called the confidence of the mapping). Alignment A between two ontologies O1 and O2 is a set of mappings defined as: A(O1, O2) = {(c1, c2, s) \(|\) c1 \(\in\) O1, c2 \(\in\) O2, s \(\in\) [0,1]}. Mappings may also have the extended form (c1, c2, s, r), where r is the type of the relation such as equivalence or generalization, or a restricted form (c1, c2), where the matching coefficient is not graded (see _Figure 1_ for a graphical illustration).
# Multi-Band Polarization Imaging in a Harsh Sea Fog Environment
## 2 Multi-Band Polarization Imaging Principle
### The Stokes Vector Method
 \tag{7}\]  
Then, the new Stokes vector of incident light obtained after the detector and the liquid crystal phase delayer can be expressed as  
\[S_{out}=M_{P90}\times M_{LCVR}\times S_{in} \tag{8}\]  
Determine the \(\beta\) value of the liquid crystal phase delay, alter the voltage value of LCVR (Liquid Crystal Variable Retarder), and get four groups of LCVR phase delay, with each group corresponding to a system Mueller matrix \(M\) and a detectable light intensity \(I\). After solving and synthesizing the inverse matrix, it is possible to extract the Stokes parameters of the incident light. \[ \begin{bmatrix}S_{0}\\ S_{1}\\ S_{2}\\ S_{3}\end{bmatrix} = \begin{bmatrix}A_{00}&A_{01}&A_{02}&A_{03}\\ A_{10}&A_{11}&A_{12}&A_{13}\\ A_{20}&A_{21}&A_{22}&A_{23}\\ A_{30}&A_{31}&A_{32}&A_{33}\end{bmatrix} \times \begin{bmatrix}I_{1}\\ I_{2}\\ I_{3}\\ I_{4}\end{bmatrix}  \tag{9}\]The Stokes vector for this beam is generally found by knowing the four intensity measurements, giving the values of the four Stokes parameters in the following form:  
\[\left\{ \begin{array}{l}S_{0}=I_{x}+I_{y}\\ S_{1}=I_{x}-I_{y}\\ S_{2}=2I_{p}-\left(I_{x}+I_{y}\right)\\ S_{3}=2I_{r}-\left(I_{x}+I_{y}\right)\end{array} \right. \tag{10}\]  
where \(I_{x}\), \(I_{y}\), and \(I_{p}\) indicate the intensity of light measured when the measured beam travels through a polarization analyzer (line polarization) with \(0^{\circ}\), \(90^{\circ}\), and \(45^{\circ}\), respectively, and \(I_{r}\) represents the intensity detected when the beam passes through a \(1/4\) waveplate with the orientation set to zero. Using the calculation formula, the polarization degree \(DOP\) and polarization angle \(\theta\) of the incident light are determined, and full polarization characteristics for the incident light are detected. \[\left\{ \begin{array}{l}DOP=\frac{\sqrt{S_{1}^{2}+S_{2}^{2}+S_{3}^{2}}}{S_{0} }\\ \theta=\frac{1}{2}\text{arctan}\left(\frac{S_{2}}{S_{1}}\right)\end{array} \right. \tag{11}\]  
It is known that the modulation properties of light may affect the polarization angle and polarization degree of the target's reflected light, which can be used to describe the target's polarization properties. And since the polarization state of reflected light penetrating different media is distinct, the polarization angle image and polarization degree image of the target can be obtained by resolving the polarization state of reflected light, so as to achieve the target survey and identification purposes in order to conclude the detection and extraction of effective information on the target.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### Energy Readrage Techniques
#### 2.3.1 Battery Power  
Around 90% of UAVs are equipped with lithium polymer (LiPo) batteries, which provide an average of up to 90 min of autonomy. This battery technology is ideal for small UAVs weighing less than 2 kg or for short-duration operations [26]. Table 1 details thevarious batteries that can be used and their characteristics; these include specific energy, energy density, and specific power [20]. However, to extend the life of the UAV, the simplest solution would be to increase the battery capacity (i.e., use larger batteries), which would mean increasing the weight of the UAV, which in turn would require more power from the UAV engine due to the additional weight. These considerations would result in higher power consumption without addressing the issue of battery life.
## 4 Conclusions  
In this study, we investigated the interaction of three variables (pH, the manganese sand dosage, and the initial concentration ratio of Fe/Mn) on the removal of iron and manganese ions using RSM. We first determined the optimal experimental range of the three factors through single-factor experiments. ANOVA explained the significance of the factors, and the results proved the accuracy of the model. To determine the interactions between the input variables, we developed a three-dimensional response surface. The isothermal model of the adsorption process and the adsorption mechanism were also investigated.  
(i) The Box-Behnken experimental design results showed that the interaction between the pH value and the dosage was the most obvious for iron removal. The interaction between the dosage and the initial concentration ratio of iron to manganese was the most obvious for manganese removal;  
(ii) The results of response surface optimization showed that when pH was 7.20, the amount of adsorbent was 3.54 g/L, and when the initial concentration ratio of iron and manganese ions was 3.80, the adsorption rates of iron and manganese ions by manganese sand was higher, reaching 83.62% and 76.10%;  
(iii) The adsorption of iron and manganese ions by manganese sand followed the Langmuir isotherm adsorption model and the quasi-second-order kinetic model. The modified manganese sand had a remarkable adsorption effect on iron and manganese ions and thus can be used as a good material for a new type of ion adsorbent.  
Conceptualization, H.K. and Y.L.; methodology, D.L.; software, D.L.; validation, H.K. and L.X.; formal analysis, L.X.; investigation, H.K.; resources, Y.L.; data curation, L.X.; writing--original draft preparation, D.L. and Y.L.; writing--review and editing, H.K.; visualization, Y.L.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Science and Technology of Liaoning province (Grant No. 20170520224).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Langmuir**} & \multicolumn{3}{c}{**Freundlich**} \\ \cline{2-7}  & \(\mathbf{K_{L}}\) & \(\mathbf{q_{m}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{K_{F}}\) & \(\mathbf{1/n}\) & \(\mathbf{R^{2}}\) \\ \hline Fe & 1.97 & 0.395 & 0.9908 & 0.2148 & 0.395 & 0.9670 \\ Mn & 2.05 & 0.444 & 0.9918 & 0.2505 & 0.337 & 0.9759 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Constants of Langmuir and Freundlich isotherms.  
Figure 10: Adsorption isotherm models of iron (**a**) and manganese (**b**) ions.