esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ultrasonic Neuromodulation of the Peripheral Nervous System
US neuromodulation of peripheral nerve trunks and axons has been investigated by a handful of studies summarized in Table 2, which give seemly contradictory results, with reports of enhanced nerve activities, suppressed activities, and mixed effects. Nonetheless, there are two consistent observations. First, all studies document no direct activation of peripheral nerves by US stimulation alone, except for a recent abstract report lacking technical details [78]. Second, high-intensity US stimulation (>200 W/cm\({}^{2}\)) causes a nerve conduction block likely from a local thermal effect, and the nerve blocking effect can last for days to weeks and even be completely irreversible [50; 54; 79]. [PERSON] et al. did report a reversible conduction block with a proper selection of the stimulus parameters [80]. The contradictory results occur with US intensity in the intermediate range, with enhanced nerve conduction velocity by some reports [43; 51; 81] and suppressed conduction velocity by others [47; 52]; [PERSON] also reported that US enhanced CAP amplitude, thus providing mixed outcomes [52]. The difference in US neuromodulation effects could be attributed to the difference in the types of nerves studied (e.g., sciatic vs. vagus), preparations (e.g., ex vivo vs. in vivo), and animal species (e.g., mammals vs. non-mammals). CAP recordings are used to evaluate US effects except for one study in which single-unit recordings from individual axons are implemented [43]. As discussed earlier, single-unit recordings are more sensitive in detecting neuromodulatory effects than CAP recordings. Collectively, the study by [PERSON] et al. conducted on harvested nerve ex vivo with single-unit recordings has the least confounding factors and thus provides the most convincing results: US stimulation of intermediate intensity enhances peripheral nerve activity by increasing the conduction velocity in both A- and C-type axons [43]. This is further supported by a clinical study showing increased conduction velocity in human median nerves following US stimulation [81]. The DRG that contains sensory afferent somata has emerged as a promising target for neuromodulation [82; 83; 84]. To the best of our knowledge, all existing DRG neuromodulations implement electrical stimulation, and the modality of US has yet to be investigated on DRG. A recent pilot report with patch-clamp and calcium imaging recordings on dissociated DRG neurons shows that US evokes action potentials in 33-40% of DRG neurons, which may involve the activation of sodium, calcium, and non-selective ion channels [69].
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 2 Related Work
### Aspect Based Sentiment Analysis  
Aspect-based sentiment analysis (ABSA), as one of the more challenging subtasks in sentiment analysis, has been widely studied. Its two fundamental tasks are aspect terms extraction (ATE) and aspect sentiment classification (ASC) [26; 27]. The early text-based studies mainly use bidirectional LSTM (BiLSTM) and conditional random field (CRF) to extract the opinion information through the hidden vectors [28; 29]. Although the ability of CNN used for learning the local features of text has been verified in many studies [14; 30; 31], it might obtain the local features expressing different sentiments simultaneously and build the false correspondences between opinion words and emotional entities. To this end, [PERSON] et al. [32] and [PERSON] et al. [33] applied capsule networks on ABSA, which distinguished overlapping features by clustering feature capsules. The existing studies based on capsule networks mainly weight the activation values of capsules by calculating the gated attention between different contexts and affective entities. This kind of method may lead to the following problems: (1) The input of the gated attention is from the hidden vectors and the weighted objects are the capsules on the next layer, so hidden vector correlation does not mean capsule correlation. (2) The gated attention requires that the length of the weighted capsule sequence be the same as that of the hidden sequence, which will lead to the degradation of the network into a fully connected network, thus limiting the representation ability of the high-level capsules. (3) The gated attention directly weights the activation value of a capsule without considering its credibility, and it is obviously unreasonable to assign a higher attention to a capsule with low credibility. To solve the above problems, we propose a MemCapsNet model for extracting the aspect emotional features of text, which uses a mask memory attention mechanism on the pose matrixes of capsules. Extensive experiments on the public datasets verified the effectiveness of the network.
## 2 Cable Model and Effective Length in 3D Twisted Conductors
### Frequency Range and Material Properties  
All cable cores are assumed to be made of copper, with electromagnetic properties detailed in Table 1. The table lists the relative magnetic permeability (\(\mu_{r}\)), electric resistivity (\(\rho\)), and electric conductivity (\(\sigma\)), for both copper and air. All insulating volumes are considered to be air, which is similar to standard poly insulators in terms of electromagnetic properties. These parameters are used to define the materials in all simulations.  
The frequencies of 0 Hz and 50 Hz are of principal interest in the twisted models:  
* _DC Condition_ (0 Hz) to confirm that twisting does not change the purely ohmic resistance. \(R_{\text{DC-2D}}\) is obtained from simulations at this frequency which will then be compared to the numerically calculated DC resistance \(R_{\text{DC-2D-cal}}\);
* _AC Condition_ (50 Hz) as the fundamental cable frequency, representing the predominant frequency in most of the power systems globally. Skin and proximity effects are expected to increase the ohmic power loss in these simulations. These simulations will yield \(R_{\text{AC-2D}}\) which will then be compared to \(R_{\text{DC-2D}}\).
# Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring
## 2 Related Work
### Feature Selection  
The selection of input features is an important part of model building. Typically, the process aims to ensure optimum model performance with minimum features. This reduces noise, data costs, and the risk of overfitting. Feature-selection methods are generally classified into wrapper, filter, and embedded methods [2]. With wrapper methods, models are fit with subsets of the features, and the resulting model performance is evaluated. However, due to the high computational cost, they are difficult to run on datasets with a large number of features. Examples include backward and forward selection. For filter methods, features are selected based on inherent properties, such as variance. Analysis of variance (ANOVA) is an example of this [19]. Finally, embedded methods, such as LASSO [20] and ridge regression, perform feature selection and model fitting simultaneously.  
Feature selection has been conducted by using support vector machines based on EMP in an embedded method [4]. A profit-based measure was applied with a Holdout Support Vector Machine (HOSVM) to extract the features with highest profitability [5]. Feature selection was conducted by using mixed-integer linear programming models with varying acquisition costs as constraints [15]. The orthogonal transform was used for dimensionality reduction, thus reducing the number of features for model training, leading to faster convergence and better performance [21]. Feature selection is carried out by integrating a multicriteria optimization classifier (MCOC) with a one-norm regularization term inspired by the LASSO regression method to create a sparse feature vector [22].  
Multiple objectives have also been optimized in feature selection through multi-objective feature analysis. Existing literature has several examples of optimizing the feature-selection process with two objectives. A non-dominated sorting genetic algorithm-II (NSGA-II) fitted to maximize the expected maximum profit (EMP) and minimize the number of features was demonstrated [7]. Mutual information and entropy were optimized for filter-based feature selection with a non-dominated sorting binary Particle Swarm Optimization (NSBPSO) [23]. Binary Grasshopper Optimization Algorithms were applied for filter-based feature selection based on error rate and number of features in References [10; 11]. A wrapper based multi-objective evolutionary algorithm optimized feature selection with three objectives: default prediction, exposure at default, and number of features [24]. Two objectives, number of features and root mean square error (RMSE), were optimized in feature selection with multi-objective genetic algorithm and neurofuzzy models [25].
# Power Quality Fault Identification Method Based on SDP and Convolutional Neural Network
## 5 Results
### CNN Recognition Result  
CNN identified the eigenvalues of various accidents, and the result was compared with other recognition methods (HOG+SVM, HOG+Ensemble, and HOG+KNN). Table 1 shows the simulation results. The recognition accuracy of SDP+CNN is 94.2%, the accuracy of SDP+HOG+SVM is 89.0%, and the recognition rates of SDP+HOG+Ensemble and SDP+HOG+KNN are 85.4% and 84.4%, respectively. According to training time, the SDP+CNN needs 39 s, which is quite time-consuming with the highest test accuracy of 94.2% and 0.034 s for execution. The test environment was MATLAB 2020a, using an Intel(r) Core(tm) i5-10400 [EMAIL_ADDRESS] GHz processor, NVIDIA GeForce GTX 1660 SUPER graphics adapter, and Windows 10 professional 64-bit operating system.  
Figure 9 is the confusion matrix, which shows the recognition result of power quality. As seen, the x-axis is the fault type, and the y-axis is the predicted fault type. The numbers of accurate recognitions and misrecognitions are denoted by the green and red grids in the confusion matrix, respectively. The green and red values on the x-axis' light gray grids are the recognition accuracy and misrecognition rate of specific fault types, respectively. The green and red values in the lower rightmost dark gray grids are the overall recognition accuracy and misrecognition rates, respectively. The overall recognition accuracy can be obtained by dividing the total value of green grids by the total value of green and red grids. As shown in Figure 9, the proposed method identified 19 out of 20 test data as Interruption and misidentified only one as Swell. Therefore, the recognition rate of Interruption is 95%. Similarly, the recognition rates of Flicker, Harmonic, and Normal of the proposed method are all 100%, while the Sag and Swell recognition rates are 80% and 90%, respectively. Finally, the total recognition accuracy of 94.2% was obtained by dividing the total value of green grids by the total value of green and red grids.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Testing Method** & **Training Time (s)** & **Execution Time (s)** & **Test Accuracy** \\ \hline SDP+CNN & 39 & 0.034 & 94.2\% \\ SDP+HOG+SVM & 3.1 & 0.012 & 89.0\% \\ SDP+HOG+Ensemble & 11.3 & 0.062 & 85.4\% \\ SDP+HOG+KNN & 7.0 & 0.008 & 84.4\% \\ \hline \end{tabular}
\end{table}
Table 1: Test accuracy results of different methods.  
Figure 8: (**A**) Normal energy eigenvalue curve. (**B**) Sag energy eigenvalue curve. (**C**) Swell energy eigenvalue curve. (**D**) Interruption energy eigenvalue curve. (**E**) Flicker energy eigenvalue curve. (**F**) Harmonic energy eigenvalue curve.
## 3 Results and Discussion
### Characterization of Effluents Generated in Electro-Coagulation and Electro-Oxidation Experiments
#### 3.2.3 Kinetic Analysis  
Using the generated data, the chemical kinetics of the decreases in COD, DOC, NH\({}_{3}\)-N, color, and Cl\({}^{-}\) were analyzed for the EC and EO processes and it was found that, under conditions of maximum COD removal, the evaluated parameters followed pseudo-first-order kinetics. The results are listed in Table 5, with determination coefficients greater than 0.9, indicating good adjustment of the experimental data to the proposed kinetic model.  
Figure 6: Eh-pH diagram for the electro-coagulation experiments. Eh—oxidation/reduction potential; E—experiments.  
The parameter for which the highest value of the kinetic constant was obtained was color, which was attributable to the removal of 95% and 98% for the EC and EO processes, respectively. In the case of kinetic constants for NH\({}_{3}\)-N, the EO process obtained a value of more than double the EC process (0.2475 min\({}^{-1}\) compared with 0.1095 min\({}^{-1}\)), a situation attributable to the EO process having a greater ability to degrade NH\({}_{3}\)-N; in the case of the EC process, the decrease in the concentration of NH\({}_{3}\)-N was due to aeration generated by the agitation in the process and in addition to the indirect EO mediated by Cl\({}^{-}\). The higher kinetic constant value for Cl\({}^{-}\) in the EO process compared with that in the EC process (0.1715 min\({}^{-1}\) versus 0.1048 min\({}^{-1}\)) was explained by the fact that the indirect EO in the EC process (which consumed Cl\({}^{-}\)) was lower than that in the EO process.
## 5 Experimental Verification  
The proposed \((N_{\rm P}+1)/2\) hall effect current sensors-based current detection scheme for OMSRMs is further verified on an experimental platform, as shown in Figure 21. The three-phase SRM in the simulations is used as the experimental motor. A magnetic powder brake is used to provide the load torque. A resolver is installed to measure the absolute rotor position. Two hall effect current sensors (LA-50P) are utilized to measure the phase  
Figure 19: Simulation waveforms at 500 rpm (\(i^{*}=50\) A). (**a**) With the proposed \(N_{\rm P}/2\) sensors-based current detection scheme. (**b**) With the baseline scheme.  
Figure 20: Maximum torque-speed characteristics of the three-phase SRM drive (**a**). Optimal turn-on and turn-off angles with the proposed scheme (**b**), and the baseline scheme (**c**).  
current differences as illustrated in Figure 18a. Six IGBT modules (2 MBI75 VA-120-50) construct the AHBC. TMS320F28335 is selected as the controller.  
The SCCC is applied to regulate the phase currents. The current sampling rate and the hysteresis band are set to 20 kHz and 5 A, respectively. The turn-on and turn-off angles are set to 22.5\({}^{\circ}\) and 44\({}^{\circ}\), respectively. The dc-link voltage is set to 72 V. The experimental waveforms at 500 rpm (\(i^{\star}=50\) A) are shown in Figure 22. It can be seen that the difference between the phase B current and the phase A current is measured accurately by the hall effect sensor HS\({}_{1}\), and the difference between the phase B current and the phase C current is detected precisely by the sensor HS\({}_{2}\). All the phase currents are maintained well around the current reference of 50 A during the conduction region. Thus, the practicality of the proposed scheme is demonstrated.
## 4 Discussion
### Total Liquid and Vapor Flux  
From the above research, we conclude that using soil water content and temperature data simultaneously to invert hydrothermal parameters can effectively simulate soil water and heat movement. Therefore, to better understand the soil water-vapor-heat coupling process, we establish a soil water-vapor-heat coupling model based on the optimal parameters obtained with \(\theta+T\) as objective variables, and plot the changes in total liquid water flux (Figure 10a) and total vapor water flux (Figure 10b) over time. Overall, as the depth increases, the fluctuations in total liquid water flux and total vapor flux gradually decrease. Meanwhile, during precipitation events, the peak of the liquid water flux significantly increases, and the daily evaporation rate notably decreases. Additionally, we observe that with an increase in evaporation, the total vapor flux in the shallow soil layer increases. This also validates that plotting the water and vapor flux and further analyzing the relationship between flux and precipitation-evaporation processes, we can gain a deeper understanding of soil water-heat transfer in arid and semi-arid regions. This reinforces the necessity of developing a coupled water-vapor-heat model.  
Figure 10: The changes in total liquid water flux (**a**) and total vapor water flux (**b**), with \(\theta+T\) as the objective variables. The red, blue, pink, black, and green lines represent observation depths of 0 cm, 5 cm, 10 cm, 20 cm, and 30 cm, respectively. In (**a**), the blue bars represent daily precipitation, while in (**b**), the black dashed line represents daily evaporation.
## 2 Concept of ARCP  
When the tensile stress exceeds the tensile strength of concrete, a crack occurs to relieve the stress. CRCP is known for its randomly occurring cracks at an early age. The volume changes in the concrete slab due to environmental loading are restrained by a huge amount of continuous longitudinal reinforcement, leading to the development of internal stresses in both concrete slab and longitudinal steel bars [38; 41; 42; 43]. The reinforcement in CRCP is required only at the crack locations. Figure 1 shows the tensile stresses in the steel bar and concrete at the cracks in CRCP at early age. The stress values are larger during the initial cracking and an ample amount of reinforcing steel is required to avoid the failure of steel. As more cracks appear with the passage of time--in other words, when the crack spacing decreases--the stresses in concrete and continuous steel bar decrease, as shown in Figure 1b,c. This implies that, when cracking initiates in CRCP with large crack spacing, an ample amount of steel is required, but, when the CRCP fully cracks with small crack spacing, the required steel is much lower than the initial amount of steel.  
When cracks are induced at the designated locations, the same number of continuous steel bars in CRCP at the crack interfaces could be replaced with the partial length steel bars, as shown in Figure 2a. The stresses in partial steel bars are basically the same as those in continuous steel bars, as shown in Figure 2b. The continuous steel bars in between the intentionally induced cracks are intended to hold freely formed cracks between the induced cracks, as shown in Figure 3. This type of pavement structure is called the advanced reinforced concrete pavement (ARCP). Hence, a considerable amount of reinforcement can be reduced by eliminating the unnecessary continuous steel bars in CRCP without compromising the fundamental behavior of CRCP. One of the major drawbacks of CRCP, the high initial construction cost, could be eliminated with the choice of ARCP.  
Figure 1: Cracking development in continuously reinforced concrete pavement (CRCP): (**a**) crack initiation; (**b**) crack propagation; and (**c**) fully developed crack pattern.
###### Abstract  
This study investigates the performance of a centrifugal radial turbine within an Organic Rankine Cycle (ORC) system, focusing on operation beyond the design point due to variable waste heat sources. With the goal of integrating the turbine into optimal ORC operating conditions, its performance was analyzed using R245 fa as the working fluid over three stages with varying numbers of blades. A detailed computational analysis was performed using Ansys CFX software (Version 2020 R2) with the k-w SST turbulence model using thermodynamic data from the NIST Refprop database. The results showed significant discrepancies when operating beyond the design point. At an inlet pressure of 780 kPa, the turbine internal power was calculated to be 120 \(k\)W--double the manufacturer's maximum of 60 \(k\)W--and the mass flow rate exceeded 6 kg/s compared to the design value of 2.72 kg/s. These results highlight the challenges of adapting the turbine to fluctuating waste heat conditions, as factors such as tip clearance, blade geometry, and high outlet pressure have a significant impact on efficiency and system performance.  
radial turbine; ORC; off-design; performance Article
###### Abstract  
Many techniques are used today to study insect morphology, including light and electron microscopy. Most of them require to specifically prepare the sample, precluding its use for further investigation. In contrast, micro-CT allows a sample to be studied in a non-destructive and rapid process, even without specific treatments that might hinder the use of rare and hard-to-find species in nature. We used synchrotron radiation (SR) micro-CT and conventional micro-CT to prepare 3D reconstructions of Diptera, Coleoptera, and Hymenoptera species that had been processed with 4 common preparation procedures: critical-point drying, sputter-coating, resin embedding, and air-drying. Our results showed that it is possible to further utilize insect samples prepared with the aforementioned preparation techniques for the creation of 3D models. Specimens dried at the critical point showed the best results, allowing us to faithfully reconstruct both their external surface and their internal structures, while sputter-coated insects were the most troublesome for the 3D reconstruction procedure. Air-dried specimens were suitable for external morphological analyses, while anatomical investigation of soft internal organs was not possible due to their shrinking and collapsing. The sample included in resin allowed us to reconstruct and appreciate the external cuticle and the internal parts. In this work, we demonstrate that insect samples destined to different analyses can be used for new micro-CT studies, further deepening the possibility of state-of-the-art morphological analyses.  
micro-CT; synchrotron radiation; 3D-models; morphology anatomy; entomology; cooleoptera; hymenoptera; diptera +
Footnote †: journal: Applied Sciences
## 3 Proposed Solution
### Proposed Lightweight Accountable Privacy-Preserving (LAPP) Protocol
#### 3.2.3 Label Generation  
Since we have issued labels for all the data subchunks, the label generation is implemented in a way that prevents injection of invalid data in the system and to mitigate any vulnerability of the system that could be introduced through the label generation. This process involves data content '\(\mathrm{D_{c}}\)' that encloses the secret hash key '\(\gamma_{1}\)' and the secret label key '\(\gamma_{2}\)'. '\(\gamma_{1}\)' and '\(\gamma_{2}\)' are used as inputs. The small chunks '\(\mathrm{C(Sd_{c})}\)' require random values, as illustrated in Equation (11):  
\[\mathrm{C(Sd_{c})}=\{\tau_{1},\tau_{2},\tau_{3},\ldots,\tau_{C(Sd_{c})}\}\ \epsilon\ Z_{p} \tag{11}\]  
Once the random values are chosen, the computing process '\(\mathrm{C_{k}}\)' is initiated as \(\mathrm{C_{k}}=z_{1}^{\mathrm{\,rk}}\ \epsilon\ Z_{1}\) for all \(\mathrm{k}\ \epsilon\ [1,\mathrm{C(Sd_{c})}]\). Thus, for each data contents (\(\mathrm{Sd_{c}}\))\(\mathrm{k}\ \epsilon\ [1,\mathrm{C(Sd_{c})}]\), the data label \(\mathrm{d_{j}}\) is determined by:  
\[dl_{j}=\ \left\{f(h)(\gamma_{1},d_{id}\|\ j)\right\}\ast\prod_{k=1}^{C(Sd_{c})}C _{k}(Sd_{c})k\right\}^{\gamma_{2}} \tag{12}\]  
Labels are a vital part and data labels must be updated automatically. Therefore, the complete set of data labels can be obtained from the data contends by substituting the \(dl_{j}\).  
\[\mathrm{S(dtj)}=\left[(\mathrm{d}\mathrm{j}\mathrm{j})\right]\ \in\{1,\ \mathrm{C(Sd_{c})}\}\]\[S(dt_{j})=\left\{(\text{f(h)}(\gamma_{1},\ d_{id}\ \ \|j)\star\prod_{k=1}^{C(Sd_{c})}C_{k }(Sd_{c})k)^{\gamma_{2}}\ \right\}j\ \in\ [1,C(Sd_{c})] \tag{13}\]  
where \(j\) is the label number for each data content, \(d_{id}\) is the data identifier, and \(S(dt_{j})\) is the complete set of data labels.