esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison with Previous Plasma Catalytic Studies and Other Conversion Technologies  
As shown in Table 5, the CH\({}_{4}\) and CO\({}_{2}\) conversions achieved in this study using the 5 Ni-5 Fe/Al\({}_{2}\)O\({}_{3}\) catalyst (34.8% and 19.7%, respectively) are significantly higher than those reported in previous plasma-assisted biogas conversion studies. For instance, inert SiO\({}_{2}\) support showed only 17.0% CH\({}_{4}\) and 10.9% CO\({}_{2}\) conversion, highlighting the limited activity of non-catalytic or passive supports in such systems. Catalysts based on noble metals like Ag/CZSM5 and Ir/CZSM5 demonstrated slightly improved conversion rates, with Ag/CZSM5 reaching 18.7% CH\({}_{4}\) and 13.6% CO\({}_{2}\) conversion, and Ir/CZSM5 achieving 21.2% and 12.8%, respectively. However, these values still fall short of the performance obtained in this work. In addition to higher conversions, the 5 Ni-5 Fe/Al\({}_{2}\)O\({}_{3}\) catalyst also demonstrated superior liquid product yield. While the SiO\({}_{2}\) support resulted in 0% liquid yield and Ag/CZSM5 and Ir/CZSM5 yielded 33.1% and 25.0% liquid products, respec  
Figure 4: (**A**) Influence of CO\({}_{2}\)/CH\({}_{4}\) molar ratio, (**B**) influence of input power, and (**C**) stability study of Ni-Fe/Al\({}_{2}\)O\({}_{3}\) catalyst on CH\({}_{4}\) and CO\({}_{2}\) conversion. tively, the catalyst developed in this study achieved a notably higher yield of 38.6%. This improvement can be attributed to the synergistic interaction between Ni and Fe species on the alumina support under plasma conditions, which effectively promotes both conversion and selectivity toward valuable liquid products. These findings underline the enhanced catalytic performance of the 5 Ni-5 Fe/Al2O3 system, both in terms of reactant utilization and product formation, compared to previously reported plasma-catalytic configurations. Conventional dry reforming of methane (DRM) via thermal catalysis requires high operating temperatures (typically \(>\)700 \({}^{\circ}\)C) due to the endothermic nature of the reaction and the stability of CH4 and CO2. Although effective at high temperatures, these systems often suffer from catalyst deactivation caused by sintering and coke deposition. While noble metals exhibit better resistance, their high cost limits large-scale applications, leading to greater interest in transition metal catalysts like Ni, which are more economically viable but more susceptible to deactivation. Photocatalytic DRM, on the other hand, offers the advantage of operating under milder conditions by utilizing solar energy. However, low light absorption, poor charge separation, and low conversion efficiencies hinder its practical application, and significant advances in photocatalyst design are still required [16]. In contrast, non-thermal plasma (NTP)-assisted DRM enables chemical reactions at near-ambient conditions by producing energetic electrons that can activate stable molecules like CH4 and CO2 without the need for bulk gas heating. This not only reduces energy
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 7 Kaolin in Petrochemical Catalysis: From Fundamentals to Innovations  
The petrochemical industry underpins much of modern life and the global economy, driving advancements in everything from everyday consumer products to high-tech innovations. The utilization of kaolin in catalytic processes holds substantial relevance for the petrochemical industry, offering promising advancements in reaction efficiency and selectivity.  
This section delves into the significant role of kaolin in catalytic operations relevant to this critical industry. Initially, an overview of the current state of the global petrochemical industry will be provided. Subsequently, the petrochemical processes designed to transform heavy feedstocks into light distillates will be described. Finally, we will detail the specific catalytic reactions--such as cracking, isomerization, and alkylation--in which kaolin is utilized. By analyzing recent studies and industrial implementations, we aim to elucidate the practical impacts and enhancements that kaolin contributes to these crucial petrochemical processes.
# Effect of Mineral Additives on the Behavior of an Expansive Soil for Use in Highway Subgrade Soils
###### Abstract  
Black cotton soil (BCS) forms a major soil group in Kenya and is characterized by high shrink/swell potential when exposed to water. A comprehensive series of laboratory tests were performed on BCS treated with lime (0-9%), volcanic ash (VA, 0-25%), and their combinations in order to study the physical-mechanical properties and mineralogical changes of the stabilized BCS. Moreover, a test road which replaced the BCS with the lime-VA-stabilized BCS was constructed to investigate the moisture change and soil movement in the BCS foundation. The results revealed that BCS stabilized with combinations of lime and VA shows larger California bearing ratio (CBR) and unconfined compressive strength (UCS) values when compared with a single stabilizer. BCS stabilized with 3% lime + 15% VA meets the performance requirements of roadbed materials in accordance with JTG D30-2015. The increase of pH and electrical conductivity (EC) in the stabilized soil promotes chemical reactions between the stabilizers and BCS to form new cementing agents, which are confirmed by X-ray diffraction (XRD) and transmission electron microscope (TEM) findings. The replacement of BCS with 3% lime + 15% VA-stabilized BCS shows an obvious effect on controlling the moisture change and soil movement in the foundation BCS. This research provides a low-cost strategy for making use of the vast resources of BCS in Kenya obtained from foundation excavation.  
Keywords:black cotton soil; natural pozzolans; physical-mechanical properties; mineralogical change; deformation control +
Footnote †: journal: Applied Sciences
## 3 Current Signal Processing and Feature Extraction
### Feature Extraction of Low-Frequency Current Signal  
The low-frequency current signal is denoted as \(LF(n)\), where \(n=1\), \(2\), \(\ldots\), \(N\), and \(N\) is the length of the signal, which is 4000 here.  
A signal can be defined as:  
\[I=\{\;I_{wave}(e)|e=1,2,\cdots,E\} \tag{6}\]  
The number of sampling points for each full wave content is:  
\[Q=0.02f_{s2} \tag{7}\]The average AC current _IMean(e)_ in the \(e\)-th full wave is:  
\[IMean(e)=\frac{1}{Q}\sum_{q=1}^{Q}\left|LF(e,q)\right|^{2} \tag{8}\]  
In the formula, \(LF(e,q)\) is the \(q\) sampling points of the \(e\)-th full wave.  
The amplitude symmetry factor of the \(e\)-th full wave is defined as:  
\[AmpSymIndex(e)=\frac{\left|\max_{q=1,2,\cdots,Q}LF(e,q)-DC\right|}{\left|DC- \min_{q=1,2,\cdots,Q}LF(e,q)\right|} \tag{9}\]  
In the formula, \(DC\) is the DC component of the current signal.  
The phase symmetry factor of the \(e\)-th full wave is defined as:  
\[PhaseSymIndex(e)=\frac{\left|MaxLoc\_LF(e,q)-MinLoc\_LF(e,q)\right|}{Q/2} \tag{10}\]  
In the formula, \(MaxLoc\_LF(e,q)\) and \(MinLoc\_LF(e,q)\) are the positions of the maximum and minimum values in the e-th full wave, respectively.  
(1) The mean value of the average current of \(E\) full waves _IMean_Mean_ is:  
\[IMean\_Mean=\frac{1}{E}\sum_{e=1}^{E}\frac{1}{Q}\sum_{q=1}^{Q}\left|LF(e,q) \right|^{2} \tag{11}\]  
(2) The instantaneous current _IIN_ of \(E\) full waves is:  
\[IIN=IMean(E) \tag{12}\]  
(3) The peak value of \(E\) full wave average current _IMean_Max_ is:  
\[IMean\_Max=\max_{e=1,2,\cdots,E}[\frac{1}{Q}\sum_{q=1}^{Q}\left|LF(e,q)\right| ^{2}] \tag{13}\]
# Modeling Driver's Real-Time Confidence in Autonomous Vehicles
## 3 Modeling Confidence
### Model Framework  
The calculation of the confidence value can be expressed by the following equation. The operating logic of the VC model can be understood by combining the content of Figure 2 with Equation (1).  
\[Conf_{t+1}=\begin{cases}Conf_{t}-Of_{t}-Sf_{t}+Re_{t}\\ Mf,\quad\text{if }Conf_{t+1}>Mf\\ 0,\quad\quad\quad\text{if }Conf_{t+1}<0\end{cases} \tag{1}\]  
In the equation, (\(Conf\), \(Of\), \(Sf\), \(Re\), \(Mf\)) refers to (Virtual confidence value, Outside influencing value, Self influencing value, Recovery value, Maximum virtual confidence value). Where \(t\) and \(t+1\) indicate two adjacent moments in terms of frequency. \(Conf\) is the specific confidence value at the corresponding moment. In the operation process, this confidence value is controlled to be no more than a maximum value of \(Mf\) and no less than 0. The confidence value is specified to be the maximum value at \(t=0\) when the vehicle system starts. We specify the \(Mf\) as 100 when the surrounding complexity is in the most stable state. \(Of_{t}\) for the vehicle's out factors indicates that the vehicle at the time of \(t\) is affected by the external potential threat. This value is the sum of the influence value of the diverse disturbance that the vehicle is subjected to, including the influence of other vehicles' lane changing, the influence of vehicles' trajectory overlapping, etc. \(Sf_{t}\) is the self-factor of the ego vehicle, which indicates the sum value of the abnormal behavior's impact or unusual state of the vehicle at the moment \(t\), including continuous violation of regulations or interference with other traffic participants. \(Re_{t}\) is the amount of confidence value's recovery. The value of \(Re_{t}\) is obtained by calculating the complexity of the driving environment around the ego and converting it using the entropy method. The calculation details of the factors in Equation (1) will be fully described in the following sections. Section 3.2 is for \(Of_{t}\), Section 3.3 is for \(Sf_{t}\) and Section 3.4 is for \(Re_{t}\) and \(Mf\).  
The output values of the VC model are presented in the form shown in Figure 3.  
Figure 2: Architecture of driver confidence.  
It should be mentioned that the dynamic influence on a human driver's confidence usually exists within the observable range of the driver. In our confidence model, we have set a fixed computational constraint range for all calculations of influence factors, which means only the factors within the range of the ego vehicle will be considered.
## 2 Methodology
### Bending Moment Calculation Using Frequency Domain  
The above method using the zero-to-peak value is very common and makes it convenient to obtain the bending moment such as during static conditions when slow turning. However, its accuracy is lower in the case of combined vibration as a dynamic test. As explained earlier, the bending moment of the propeller shaft during ship operation is influenced by many factors such as hull deformation, engine output, propeller force and propeller eccentric thrust. Therefore, the peak values of local bending moments may be not at normal to NA, leading to slightly erroneous results of bending angle, \(\lambda\). As shown in Figure 3 and Equation (4), the nominal bending moment during one revolution is determined by the zero-to-peak value, which is the amplitude of the first-order vibration, and the bending angle can be found as the first-order phase angle. To do this, frequency domain analysis must be performed, and the Fourier transformation is the key mathematical tool here.  
In this method, the tachometer is required to determine the shaft revolution. The tacho-pulse marks the beginning and end of a revolution when the strain gauge bridge is located at the upper vertical position. The measured local bending moment in time domain is divided into each complete revolution for analysis. Next, the Fourier transformation is performed to parametrize the time domain data in terms of frequency. The fast Fourier transform (FFT) is recommended to speed up the computation. Next, the signal is converted and represented in the frequency domain, and only the first-order frequency is concerned for the nominal bending amplitude and bending angle with accuracy.
## 3 Numerical Model
### Meshing and Method of Solution  
The mathematical model described above was realized in computational fluid dynamics (CFD) software Fluent using the control volume method. Prior to the CFD calculation, the geometry with its structured grid was generated as shown in Figure 5. There are 230 nodes non-uniformly distributed along the column height and 51 nodes non-uniformly distributed along the radial direction with high grid resolution at the near wall region, resulting in about 11,730 quadrilateral cells. CFD simulations were performed using a double-precision unsteady-state implicit solver, with the coupled algorithm to solve the pressure-velocity coupling problem in the momentum equations. As for the implementation of adsorption in this study, the UDS (user-defined scalars) and UDF (user-defined functions) were applied to program the mass transfer rate Equation (1) and the source terms, etc. (Equations (2), (4), (6), (8), (11), and (14)). A PRESTO (pressure staggering option) discretization scheme was used for the pressure [33; 34; 35], whereas a quick scheme for the discretization of density. To reduce numerical diffusion, a second-order upwind scheme was selected for the discretization of the momentum and energy equations.
# Design and Development of a Wideband Planar Yagi Antenna Using Tightly Coupled Directive Element
## 4 Parametric Study and Analysis of Geometrical Antenna Parameters
Similarly, the presence of tightly coupled directive element enhances realized gain value by more than 1.5 dB. The maximum realized gain values of the Yagi without and with wide directive element are found 7.1 dBi and 8.6 dBi, respectively. To illustrate the effect on input impedance of planar Yagi antenna because of additional directive element, Z-parameter of both designs are calculated and compared shown in Figure 5. The calculated \(Z_{11}\) (real) and \(Z_{11}\) (imaginary) are shown in Figure 5a,b, respectively. It can be seen that for the antenna with wide directive element, Z\({}_{11}\) (real) values are close to 50 over a wider bandwidth as compared to the antenna without wide directive element. Similarly, the antenna with wide directive element, Z\({}_{11}\) (imaginary) values are close to \"zero\" over a wider bandwidth as compared to the antenna without wide directive element. It can be concluded that presence of additional parasitic directive element in close proximity of the active dipoles does not increase the overall antenna dimensions of a planar Yagi antenna. However, it significantly improves the bandwidth and gain parameters simultaneously. Figure 4: Comparison of (**a**) reflection coefficient (S\({}_{11}\)) and (**b**) realized gain parameters of the proposed antenna element, without and with tightly coupled wide directive element. Figure 3: Parametric study of the position of the proposed directive element based on reflection coefficient and gain performance analysis.
## 3 System Model
### Total Waiting Time of Packet  
In a UANET, when a node sends a packet to its neighboring node, the waiting time of the unprocessed packets in the neighboring nodes increase the end-to-end delays and even increase the packet losses during heavy traffic conditions. Therefore, the current forwarding node considered the waiting time of packets in neighboring nodes to select an appropriate node while still meeting the minimum delay requirements. Point-to-point delays consist of the queue time of packets in the node's buffer, the processing delay, the transmission delay, and the propagation delay, as shown in Figure 1. Because we only wanted to estimate the waiting time of packets in the neighboring node, we ignored the packets' propagation delay over the communication channel.  
Each node was expected to have the same buffer size. The scheduling mechanism of packets in the buffer was based on first-in, first-out (FIFO). When current forwarding node \(i\) forwarded a data packet to next-hop node \(j\), the total waiting time of packets in node \(j\) at current time \(t\) could be calculated by the following:  
\[T_{j}^{\text{wait}}\left(t\right)=\left\{\begin{array}{ll}\sum_{k=2}^{m} \left(\frac{P_{\text{size}}}{R_{i,j}}+T_{\text{proc}}^{k}\,+T_{\text{queue}}^{k} \,\right)&k\geq 2\\ \frac{P_{\text{size}}}{R_{i,j}}+T_{\text{proc}}^{k}&k=1\end{array}\right. \tag{9}\]  
where \(P_{\text{size}}\) is the size of a data packet, and \(T_{\text{proc}}^{k}\) is the processing delay of the \(k\)th packet in the buffer. \(T_{\text{queue}}\) is the queue delay of the \(k\)th packet in the buffer, \(R_{ij}=W\cdot\log_{2}\bigl{(}1+SNR_{i,j}\bigr{)}\) is the packet transmission rate during the communication link \(L_{i,j}\), \(W\) is the bandwidth allocation for the communication link between node \(i\) and node \(j\), and \(m\) is the total number of packets in the buffer of node \(j\). If there was only one packet in the buffer of the candidate forwarding node, i.e., \(k=1\), the packet could be transmitted immediately. Otherwise, the packet would queue and wait to be scheduled for transmission.
# A Novel Method for Cross-Modal Collaborative Analysis and Evaluation in the Intelligence Era
## 3 Research Method
### Model Architecture
#### 3.2.2 Bimodal Feature Information Interaction  
After obtaining the above unimodal feature representation, three modalities are combined in pairs (text + audio, audio + visual, and text + visual) to obtain three sets of modal pairs. Next, we proposed a BAM-model (Bimodal Attention Mechanism model) to capture the interaction information between modalities and contexts. The model structure is shown in Figure 3. The specific process and formula are as follows:  
The first step is to multiply the feature representation matrices obtained in Section 3.2.2.1 to obtain the cross-modal information matrix, where \(u,w=\{t,a,y\}\):  
\[C_{i}^{uw}=H_{i}^{s_{u}}\cdot H_{i}^{s_{w}T} \tag{4}\]  
\[C_{i}^{wu}=H_{i}^{s_{w}}\cdot H_{i}^{s_{u}T}, \tag{5}\]  
The SoftMax function (SoftMax logical regression) is used to model multiclass classification problems where we want the output to be a probability distribution of the different possible classes. The second step is to demonstrate a SoftMax calculation on the cross-modal information matrix to obtain the attention score:  
\[\alpha_{i}^{uw}=softmax(C_{i}^{uw}),\ \alpha_{i}^{uw}\in\mathbb{R}^{q\times q} \tag{6}\]  
\[\alpha_{i}^{wu}=softmax(C_{i}^{wu}),\ \alpha_{i}^{wu}\in\mathbb{R}^{q\times q}, \tag{7}\]  
In the third step, the attention score is multiplied by the feature matrix, and the modal feature representations with information interaction are obtained, respectively:  
\[H_{i}^{d\mathbb{I}_{u}}=(\alpha_{uv}H_{i}
## 3 Materials and Methods
### Overall Approach to Evaluate the Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Response** & \multicolumn{5}{c}{**Dataset**} \\ \hline
**Variable (\(\mathbf{y}\))** & **TROP1** & **TROP2** & **LUS1** & **LUS2** & **THEIX** & **WAL** \\ \hline ADF & 1530 (8.8, 66.9) & 1126 (12.4, 61.1) & 1310 (10.3, 36.5) & 1355 (17.4, 50.8) & 1507 (15.0, 46.5) & – \\ ADL & 1423 (0.7, 43.1) & 1126 (0.4, 13.6) & – & 1139 (3.0, 10.9) & 1620 (2.7, 27.1) & – \\ ASH & 1597 (1.5, 66.4) & 1476 (0.4, 57.4) & 3526 (4.5, 15.8) & 1242 (5.8, 17.7) & – & – \\ CF & – & 1302 (7.5, 57.3) & – & – & – & 797 (12.0, 42.1) \\ CP & 1564 (1.6, 32.3) & 1389 (0.7, 28.5) & 4029 (3.1, 24.9) & 1612 (2.4, 39.2) & 1564 (3.9, 37.8) & 797 (4.0, 34.2) \\ DM & 1607 (72.2, 97.7) & 1481 (84.7, 98.8) & – & – & – & 797 (89.3, 98.4) \\ DMDCELL & 1459 (9.9, 95.0) & 1137 (14.6, 93.3) & 5194 (41.0, 95.0) & 1584 (38.7, 87.3) & 1386 (20.7, 91.4) & – \\ NDF & 1529 (16.0, 85.7) & 1119 (26.3, 88.0) & 3948 (20.6, 68.4) & 1386 (26.0, 67.8) & 1672 (27.6, 76.9) & – \\ OMDCELL & 1459 (8.6, 94.3) & 1137 (10.9, 90.0) & – & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of observations by dataset and response variable (minimum and maximal values of the response variables \(y\) are in brackets).
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Implementation of Image-Based Devices and Systems
#### 4.2.2 3D Vision System for Digital Image Correlation  
DIC has gained recognition as a structural monitoring method for evaluating bridge performance [62; 63; 64], as well as other structures that might eventually undergo permanent displacements, or changes in the displacement behavior under loads [16].  
Founded from the study of [58], using cameras positioned near windows on various levels of a neighboring structure, 3D DIC may assess the deflection brought on by a static load on a pedestrian bridge. Two Bosch DINION IP Ultra 8000 MP cameras with a 75 mm lens were used in the 3D system configuration. These cameras were mounted on tripods and placed next to their region of interest; in this case, near the windows on the third and fourth floors of the building next to the pedestrian bridge. Both the computer and the cameras are linked to a single Ethernet switch, which is used to operate the cameras and capture images. The 3D system's cameras were placed 4 m apart and roughly 30 m from the area of interest, giving rise to a stereo angle of about 3 degrees and a field of vision of about 3.5 m along the length of the bridge. The 3D-DIC process also heavily relies on stereo calibration, which establishes the intrinsic (such as focus length) and extrinsic (such as relative position and orientation) aspects of the camera system. This calibration is presented here [47]. Figure 3b reveals a setup of an internet protocol (IP) camera for a 3D system.
# Surface-Modified Ceramic Boron Carbide as a Platform for Specific Targeting in Tumour Environments
###### Abstract  
Boron Neutron Capture Therapy (BNCT) is a therapeutic approach used to treat malignancies that are difficult to localise and typically inoperable. This therapy involves two stages: the administration of the boron (\({}^{10}\)B) isotope, which selectively enters cancer cells without affecting healthy tissue, followed by irradiation of the tumour with a neutron beam. In this study, boron carbide (B\({}_{4}\)C), a ceramic material with exceptional physical and chemical properties, was used as a nanoparticle platform for BNCT. The surface of the boron carbide nanoparticles was optimised by modifying them with compounds such as dextrin, dextran T70, sorbitol, lysine, and arginine. Boron carbide was synthesised directly from boron and carbon and then subjected to grinding, washing, and centrifugation. The unmodified and modified samples were analysed for their particle size, zeta potential, and toxicity against glioblastoma T98G cells. Additionally, FTIR spectroscopy confirmed the successful surface modifications. The results demonstrate that boron carbide, as a ceramic material, can be effectively functionalised with biocompatible compounds. Among the tested modifications, B\({}_{4}\)C-dextrin and B\({}_{4}\)C-dextran T70 exhibited the highest toxicity towards cancer cells, demonstrating the potential of ceramic platforms in biomedical applications.  
Boron Neutron Capture Therapy (BNCT); B\({}_{4}\)C; functionalization; saccharides; aminoacids +
Footnote †: journal: Applied Sciences