esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning Model Prediction of Ionic Liquids Melting Points
## 3 Results  
### Clustering and Melting Points Distribution  
As mentioned in the previous section, 1253 different ILs have been considered in this work. To help improve the accuracy of the subsequent ML prediction on the melting points, the similarity among ILs molecules based on the filtered 137 molecular descriptors were analyzed and grouped based on the clustering method. Thus, the entire dataset was separated into several different clusters or groups based on their similarities using k-means algorithm implemented in scikit-learn libraries [36]. With the Elbow method [41], we found that the optimal number of clusters into which the dataset may be grouped  
Figure 2: The schematic plot of the deep learning (DL) model adopted in this work, which consists of one input layer, five hidden layers and one output layer.  
or clustered is 5, and a condensed view of the distribution of these clusters/groups in multidimensional descriptor space can be visualized through dimensionality reduction using principal component analysis (PCA) (Figure 3a). As shown in Figure 3, the score plot for the two principal components shows significant groupings that correspond to five distinct clusters. Therefore, throughout this work, we separated our entire dataset into five clusters or groups for the training and testing in the DL-model. Among these five clusters or groups, the size of the dataset for each cluster varies and consist of 605, 186, 134, 297, and 31 different ILs separately, which consists of with various combination of cations (e.g., ammonium, imidazolium, phosphonium, pyridinium, etc.) and anions (e.g., sulfonate, phosphate, hexafluorophosphate, borate, acetate, dicyanamide, triazolide, etc.) families (Figure 3b).  
From Figure 4, a wide distribution of melting points (\(T_{m}\)) can be found for this large diverse dataset of 1253 ILs, whose melting points vary from 30 K to 550 K. As shown, a \"bi-modal\"-like distribution in melting points of the entire dataset is found, which could be assigned to two apparently distinct ranges, i.e., low melting points (i.e., \(T_{m}\) < 273 K) and high melting points, i.e., \(T_{m}\) > 273 K (Figure 4a). Meanwhile, the distribution of the melting points among these five clusters (i.e., cluster 1-5) is different, and only cluster 1 has a similar \(T_{m}\) distribution to the entire dataset which consists of both low and high \(T_{m}\) ILs candidates (Figure 4a,b). In cluster 1, a wide variation of \(T_{m}\) for ILs can be found (i.e., from 30 K to 550 K) with a median \(T_{m}\)-228 K. For cluster 2-5, the distribution of the melting points mostly consists of high \(T_{m}\) ILs candidates, which generally yield a higher median \(T_{m}\), i.e., 317 K to 333 K (Figure 4).  
Figure 3: (**a**) The score plot for the first two principal components (PCA1, PCA2) with respect to 5 clusters that representing 1253 ILs dataset. (**b**) The randomly selected representative ILs molecules from cluster 1 to 5 dataset.
## 2 Materials and Methods
### Procedure
#### 2.2.2 EEG Data Acquisition  
To non-invasively collect neurophysiological data and measure changes in EEG spectral activity between the resting-state baseline and task phase, the Muse(tm) headband (version 2; InteraXon Inc., Toronto, ON, Canada) was adopted. This wearable device enables the detection of neurophysiological EEG activity through an integrated accelerometer, gyroscope, pulse oximeter, and seven electrodes constructed from conductive materials, specifically silver and silicon rubber. Of these electrodes, three serve as references, while the remaining four detect EEG spectral activity in the frontal and temporoarietal regions, following the international 10-20 system. Specifically, the electrodes were placed in the left and right hemispheres, respectively in AF7 and AF8 for the frontal area and in TP9 and TP10 for the temporoarietal area.  
Data was recorded at a sampling rate of 256 Hz and transmitted via Bluetooth to an associated smartphone using the mobile app Mind Monitor, whose software applied a 50-Hz notch filter. Moreover, participants were instructed to minimize movement to reduce artifacts in the EEG signal. After collecting the data, the raw EEG data were visually inspected to eliminate motor artifacts, such as jaw clenching and eye blinks, and processed using a Fast Fourier Transform (FFT) to convert the logarithm of the power spectral density into brain wave frequencies across several bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-44 Hz). Finally, the EEG activity collected during the task was normalized relative to the 120 s of neurophysiological resting-state baseline activity gathered at the beginning of the experimental phase.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology  
In this section, the methodology to enhance the multimode vibration damping of composite beam structures is presented. Initially, a finite-element model is developed to simulate the dynamic response of a laminated composite beam with surface-bounded piezoelectric patches. The formulation is based on Timoshenko beam theory, which takes into account the electromechanical coupling. Next, to simulate the electromechanical behavior of the compound system when a \"current-flowing\" shunt circuit is connected at the top piezoelectric patch, a state-space model is developed. Finally, the proposed design optimization approach is presented to determine the optimal shunt circuit parameters for enhancing the multimode vibration control of the composite piezoelectric structure.
## 3 Explicit Solution to Rotation Angle for Large Deformation of Cantilever Beams by IHAM  
### Problem Description  
We consider the large deformation of a cantilever beam under point load at the free end as shown in Figure 1. The bending equation of a uniform cross-section beam with a large deformation is: [40; 41]  
\[\frac{\mathrm{d}\theta}{\mathrm{d}s}=\frac{F}{EI}(l-x) \tag{45}\]It complies to the boundary conditions:  
\[\theta(0)=0,\ \theta^{\prime}(L)=0, \tag{46}\]  
where \(s\) is the arc-coordinate of the neutral axis of the beam, \(x\) is the horizontal coordinate from the fixed end, \(L\) stands for the length of the beam, \(F\) denotes the point load at the free end, \(EI\) specifies the bending stiffness of the beam, \(\theta\) represents the rotation angle of cross-section of the beam, and \(l\) specifies the unknown horizontal distance of two ends. Differentiating the equation with respect to \(S\) and then using the dimensionless variables \(\xi=S/L\), the original Equation (45) becomes  
\[\theta^{\prime\prime}+\alpha\cos\theta=0 \tag{47}\]  
in compliance to boundary conditions of  
\[\theta(0)=0,\ \theta^{\prime}(1)=0, \tag{48}\]  
The prime in Equations (47) and (48) denotes differentiation with respect to \(\xi\), and  
\[\alpha=\frac{EI^{2}}{EI} \tag{49}\]  
The rotation angle of cross-section plane at the free end is denoted by \(\theta_{b}=\theta(1)\).  
For infinitesimal deformation, the below linear equation will be sufficient  
\[\theta^{\prime\prime}+\alpha=0 \tag{50}\]  
which is subjected to the boundary conditions  
\[\theta(0)=0,\ \theta^{\prime}(1)=0, \tag{51}\]  
The corresponding solution is:  
\[\theta(\xi)=\frac{\alpha}{2}(2-\xi)\xi \tag{52}\]  
which gives the linear result of ration angle at the free end for \(\xi=1\)  
\[\theta_{b}=\frac{\alpha}{2} \tag{53}\]  
Figure 1: Large deformation of beam under point load at free end.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 4 State Estimation
### Joint Model Estimation
#### 4.2.1 Gyroscope Model  
Figure 16 shows the estimates of the joints' angles obtained with the joint model using only gyroscope measurements. There is clearly an aberrant behavior that manifests at the first sign of movement. When \(\theta_{2}\) changes at 5 s, the estimate of \(\theta_{2}\) (\(\theta_{2 est}\)) falls short of the baseline (\(\theta_{2b}\)), and the estimate of \(\theta_{3}\) (\(\theta_{3 est}\)) also changes. This error in \(\theta_{3 est}\) persists after rotation of that joint at 10 s. Later, when \(\theta_{1}\) changes at 15 s, the error in both estimates (\(\theta_{2 est}\) and \(\theta_{3 est}\)) is corrected.  
These errors are the first sign of a flaw in the joint model. This model assumes that the pose of the manipulator is fully describable by the angles of each joint. However, as the results of the link model show, there can be slight misalignments between the links that result in a slightly different pose. These misalignments are not accounted for by the joint model. In this case, the consequence is that the small difference of the rotation axes of joints 2 and 3 results in a different amount of rotation measured in the axes of the inclined joints.  
The errors are corrected when \(\theta_{1}\) changes because the different IMUs measure that rotation in different axes. Assuming \(\theta_{2}\approx 90^{\circ}\) and \(\theta_{3}\approx-90^{\circ}\), the rotation of Joint 1 should be measured mostly in the \(y\) axis of IMU A and on the \(x\) axis of IMU B. Therefore, the only way the model can justify this change is by quickly altering its estimates to the actual values of \(\theta_{2}\) and \(\theta_{3}\). The full model avoids this flaw using the accelerometers to correct the steady-state value of these estimates.
# A Study of a Cornering Braking Control System for a Motorcycle
## 5 Conclusions  
A motorcycle is a commonly used and convenient means of transportation in our daily life. Its inherent instability leads to hidden safety hazards during turning and braking. Therefore, it is meaningful to research and develop an active safety system for it. This study systematically analyzed the tire model characteristics and created an optimization procedure for the optimal slip ratio, and proposed a cornering braking control system based on Fuzzy PID. The control efficiency was verified by a co-simulation, and a comparative study was conducted with a traditional ABS. This research achieved the following:  
1. The kinematic influences on the characteristic of a tire were identified: the slip ratio, the sideslip angle, and the vertical force could affect the longitudinal and lateral characteristics. The slip ratio was selected as the manipulated variable for the braking control system in consideration of its operability.  
2. Based on the kinematic influences on tires, a constrained optimization model was created and solved. The obtained slip ratio was the optimal solution that could pursue excellent braking performance and ensure vehicle stability. It could change with the change in the motorcycle kinematic parameters, contributing to an improvement in the braking efficiency at any time.  
3. The proposed cornering braking control system based on Fuzzy PID could perfectly follow the changing optimal slip ratio. The motorcycle would stop steadily and promptly, and the sideslip angles for the tires would be very small, meaning that the direction stability would be excellent during braking. The comparative study results verified the feasibility of the proposed cornering braking control system further.  
The proposed braking control system required several motorcycle kinematic parameters, such as the roll angle, vehicle velocity, sideslip angle, and vertical forces. Obtaining these variables was the prior condition of the experiment. However, it is difficult to accurately measure some kinematic parameters directly using traditional sensors. For example, the roll angle, a crucial variable that determines a motorcycle's behavior, need to be obtained via a state-estimation method. After obtaining the kinematic parameters via measurement or the state-estimation method, experiments should be conducted in the following research to test the proposed control system.  
Conceptualization, X.-D.Z. and C.-K.C.; methodology, X.-D.Z.; formal analysis, X.-D.Z.; investigation, C.-K.C.; writing--original draft preparation, X.-D.Z.; writing--review and editing, X.-D.Z. and C.-K.C.; supervision, C.-K.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the Ministry of Science and Technology of Taiwan, ROC, under grant number MOST 110-2622-E-027-008. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable.  
I would like to thank my supervisor, [PERSON], for his guidance through each stage of the process. The authors declare no conflict of interest.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 3 Privacy-Preserving Distinct Counting Computation
### Overview of PPDC  
At a high level, PPDC works as follows. First, each user independently prepares his dataset and transforms the sensing data into a specific form through their smart devices. Then, the trusted authority calculates and distributes a pair of key seeds to each user. Combining with key seeds and a nonce information which is different in each time of aggregation process, each user encrypts his transformed sensing data for this process and sends the ciphertexts to the aggregator. Since all the data are encrypted and there is no information of keys published, the aggregator has no way to decrypt received data, thus, the privacy of users is protected. In the end of PPDC, FM sketches are applied by the aggregator to acquire the count of distinct elements in all users' sensing datasets based on the judgement of ciphertexts. The main idea and essential part of PPDC will be described below.
## 2 Materials and Methods
### Deformable Registration of Point Clouds
#### 2.2.1 Energy Model  
The total energy function \(\mathcal{E}_{\text{total}}\) guiding the deformation is composed of three terms:  
\[\mathcal{E}_{\text{total}}=\mathcal{E}_{l}+\mathcal{E}_{i}+\mathcal{E}_{g}, \tag{2}\]  
where \(\mathcal{E}_{l}\), \(\mathcal{E}_{i}\), and \(\mathcal{E}_{g}\) are the local, intermediate, and global energy terms, respectively. The core idea is to combine local and global geometric information with an intermediate representation to ensure accurate and smooth deformations.  
Local Energy \(\mathcal{E}_{l}\)  
Local energy operates at the finest level, focusing on small neighboring regions (rings) around each key point. For the \(i\)-th patch, we define the \(j\)-th neighboring ring \(\mathcal{D}_{i}^{j}\) as the set of vertices at the geodesic distance \(j\) from the key point \(\eta_{i}\). The goal is to find the local transformation \(\mathcal{T}_{ij}\) for each ring that aligns \(\mathcal{D}_{i}^{j}\) with the corresponding ring \(\mathcal{S}_{i}^{j}\) in the moving point cloud.  
We formulate this as a weighted least squares problem:  
\[\mathcal{E}l=\sum i=1^{k}\sum_{j=1}^{r}\xi_{ij}\sum_{\mathbf{p}\in\mathcal{D} ^{j}}\left|\mathcal{T}ij\mathbf{p}-\mathbf{q}_{\text{corr}(\mathbf{p})} \right|^{2}, \tag{3}\]  
where \(\xi_{ij}\) is an influencing factor based on the surface area or other criteria, and \(\mathbf{q}_{\text{corr}(\mathbf{p})}\) is the corresponding point in \(\mathcal{S}_{i}^{j}\). The local transformations are assumed to be locally rigid and independent.  
Intermediate Energy \(\mathcal{E}_{i}\)  
We aim to integrate the deformed neighboring rings at the intermediate level to obtain a coherent transformation for each patch \(\mathcal{D}_{i}\). We consider the deformed vertices from the local transformations and model the problem as fitting a Gaussian Mixture Model (GMM), where the deformed vertices \(\hat{\mathcal{V}}\mathcal{D}_{i}\) are the centroids. The corresponding vertices in \(\mathcal{S}_{i}\) are observations.  
We minimize the negative log-likelihood  
\[\mathcal{E}i=-\sum i=1^{k}\ln p\big{(}\mathcal{V}_{Si}\mid\hat{\mathcal{V}} \mathcal{D}_{i},\Theta_{i}\big{)}, \tag{4}\]  
where \(\Theta_{i}\) represents the GMM parameters for patch \(i\).  
Global Energy \(\mathcal{E}_{g}\)  
Global energy corrects any residual misalignments on a global scale. It refines the overall pose of the moving point cloud by finding a global rigid transformation \(\mathcal{T}\) that minimizes  
\[\mathcal{E}g=\sum i=1^{n}\epsilon_{i}|\mathcal{T}\hat{\mathbf{p}}_{i}-\mathbf{ q}_{i}|^{2}, \tag{5}\]  
where \(\hat{\mathbf{p}}_{i}\) are the deformed points after the intermediate step and \(\epsilon_{i}\) is a weighting factor that may depend on the distance between corresponding points.
## 1 Introduction
### Dry and Variably Wet scCO\({}_{2}\)  
Dry and variably wet CO\({}_{2}\)-rock interactions have historically been overlooked, and, among the limited investigations, most are directed at host rock, rather than caprock, reactivity [28; 29; 30; 31]. However, a number of recent studies have shown this CO\({}_{2}\)-rich phase to be geochemically significant [21]. Within the traditional carbon storage environment, these interactions occur in two scenarios. The first is close to the wellbore and within the host rock, and the second is at the caprock interface where migrated CO\({}_{2}\) will accumulate. In the case of Sleipner, accumulation occurs within the body of the reservoir as well as at the upper limit due to the shale interlayers. Dry and variably wet CO\({}_{2}\)-rock interactions also have the potential to occur within the interlayers themselves.  
Wet scCO\({}_{2}\) can react with metal silicates, such as feldspars, to form carbonate minerals. A possible reversible reaction of this type, which might also be seen in the Sleipner caprock, is:  
\[\text{NaAlSi}_{3}\text{O}_{8}+\text{CO}_{2}+\text{H}_{2}\text{O}\Leftrightarrow \text{NaAl}(\text{CO}_{3})(\text{OH})_{2}+3\text{SiO}_{2} \tag{2}\]  
where NaAlSi\({}_{3}\)O\({}_{8}\) (felspar: albite), NaAl(CO\({}_{3}\))(OH)\({}_{2}\) (dawsonite), and SiO\({}_{2}\) (silicon dioxide) are solids, CO\({}_{2}\) is a gas, and H\({}_{2}\)O is either as gas or liquid. The new products will precipitate on the surface of the rock and on the walls of the pores [32]. Dry scCO\({}_{2}\) reactions can also occur via this process due to water occurring within the mineral structures themselves [33].  
Adsorption partitioning of water, from wet scCO\({}_{2}\) to silicate surfaces in the host rock, can lead to the formation of films of water, which can facilitate carbonation [21]. The water film can acidify via CO\({}_{2}\) dissolution, as described previously, thereby enabling the formation of bicarbonate ions, metal cation dissolution, and carbonation, followed by the precipitation of secondary minerals [34].  
The variably wet CO\({}_{2}\)-rich phase is expected to reach the caprock, resulting in salt precipitation and mineral dehydration. Drying out of swelling clays such as smectites can cause shrinkage, resulting in fracturing and therefore creating higher-permeability leakage pathways in the seal [19]. However, these fractures may'self-heal' after the scCO\({}_{2}\) phase becomes saturated with brine and the clays rehydrate. Variably wet CO\({}_{2}\)-shale interactions are the focus of this review, and further detail relating to mineral reactions found in the Sleipner caprock are presented below.
## 5 Discussion and Analysis
### Handover Latency  
Figure 11 shows the handover latency versus variation in mobile producer speed from 0 ms to 100 ms after the simulation was run using the NS3 software based on the parameter set.  
The handover latency of all schemes continued to increase, due to the increase in interest arrival rate. In addition, for the hybrid NeMO scheme, the handover latency was higher than that of KITE and the PMSS, because of the fast handover messages sent by the mobile producer as a pre-process of handoff. Moreover, the signaling cost of KITE was between that of hybrid NeMO and the PMSS, while hybrid NeMo had the lowest handover latency of 46% compared with KITE and 60% compared with the PMSS.  
On the basis of a mathematical equations that were constructed for cost signaling and handover latency, a simulation was conducted by using the NS3 simulator to generate network performance with the use of a certain parameter. The techniques were compared to determine the hop count over speed. Figure 12 shows that while the mobile process increased the speed, the hop count to reach data for hybrid NeMo was less than that for the PMSS and KITE, because BIT decreased the usage of FIB, thereby reducing the hop count. This feature was an advantage of hybrid NeMO over the other two methods.  
Handover latency can be measured by the time a mobile producer requests I_packets that were received from the last content router and the transmission of new I_packets after the handover operation. When more content needs to be traversed, the latency is increased because of the long distance from the consumer to the producer. The hop count between the consumer and the producer was examined. Figure 12 shows that when the speed of mobile producers increased, the hybrid method reduced the number of hops from the consumer to the producer. The average hop count for hybrid NeMo was, at most, 4 per hop, compared with the average hop count for the PMSS, which was 10 per hop; KITE had a maximum of 8 per hop while the mobile producer speed increased. Hybrid NeMO showed that having a lower average hop count could reduce handover latency, thus resolving the issues of path stretching for KITE.  
Figure 11: Handover latency (hop count) versus speed.
## 5 Spectroscopy Analysis  
### Dielectric Properties and Absorption Characteristics  
The interaction between THz wave and material could result in individual responses according to the chemical components and microstructure, thus making THz spectroscopy a powerful tool to analyze substances and detect responses. THz-ATR spectroscopy is very convenient and suitable for obtaining dielectric properties and absorption characteristics for the substance in the form of liquid, powder, or thin film. We concluded the samples with the parameter calculated in Table 1. All the measurements were taken under room temperature. The following are the reports on the dielectric properties and absorption characteristics of the testing substance. [PERSON] et al. [47] derived the dielectric functions of InAs and distilled liquid water through the measured ATR spectra. [PERSON] et al. [51] obtained the dielectric properties of porcine tissues, including dermis, subcutaneous tissue, and muscle. [PERSON] et al. [48] studied the ATR spectra of nine kinds of crystalline solid materials and three kinds of liquids, including L-ascorbic acid (vitamin C), citric acid, L-tartaric acid, D-mannitol, D-sorbitol, xylitol, pyridoxine, riboflavin, thiamine hydrochloride, water methanol, and 2-propanol. All the solid materials showed distinct absorption peaks in the 0.3-3.6 THz region, which arise from low-frequency vibrations and phonon modes. Unlike the solid materials, the liquids, water methanol and 2-propanol, showed unstructured absorption. [PERSON] et al. [49] obtained and analyzed the spectra of the aqueous glycine solution and glycine powder in the 0.5-12 THz region. The differences of absorption peaks between powder and solution could be explained by that the low frequency vibrational mode in glycine molecules changed with the hydrogen bonding in water. [PERSON] et al. [50] promoted a method to monitor the crystallization of NaCl and NaI out of watery solution through the variation of the absorption coefficient and refractive index. Their absorption coefficient shows a similar decrease with the increase of the concentration, while the change in the refractive index is completely opposite. Based on this, crystallization of tartaric acid in the act was investigated [32]. Three different stages in the process of crystallization (supersaturated solution, nucleation stage, and final crystal) were changed in the relative contributions to the total volume, showing varied absorption character in each stage, which could be caught by ATR continuous measurements. The absorption characteristics of six prospective explosive materials