esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methodology
### Background on Categories
#### 2.1.2 Energy Harvesting Techniques  
Battery-operated WSN platforms face some serious power consumption issues. The life span of a battery-powered WSN platform is not enough to operate for longer durations. To address this drawback, certain maintenance operations are carried out on a regular basis. An example of these maintenance operations is the recharging of the battery-operated system. In some cases, the batteries are entirely replaced. As a result, the operational cost of the systems has increased significantly. A promising alternative to the costly manual maintenance procedure (such as recharging or replacing batteries) is energy harvesting. During this phenomenon, the energy is taken from an ambient source. Typical examples of ambient energy sources are sunlight, wind, vibration, sound, heat, and radio frequency (RF) [26]. The research articles in which an energy harvesting technique is deployed for the SHM process are included in this category. The energy harvesting techniques considered in this SLR include solar, wind, thermal, RF, electromagnetic vibration-based, and PZT vibration-based.
## 2 Literature Review
### EVRP Research with CI Capacity Constraints  
In traditional EVRP studies, CI capacity constraints were historically treated as secondary considerations, with early research often assuming infinite capacity to prioritize route optimization and vehicle scheduling. However, growing urban logistics demands and charging needs have highlighted the critical role of capacity constraints on operational efficiency, giving rise to two key research paradigms. The first focuses on capacity constraint modeling and operation research optimization, employing mixed-integer programming and approximate methods. For instance, [PERSON] and [PERSON] [19] proposed an MIP model for optimizing electric bus CI economics, which are solved via a heuristic algorithm. [PERSON] et al. [20] combined iterative local search and branch-and-cut algorithms to solve EVRP with nonlinear charging and capacity constraints, updating 80 benchmark instances. [PERSON] et al. [21] used a branch-and-price algorithm to solve 34% of 100-customer instances under charger availability and nonlinear charging constraints, while [PERSON] et al. [11] addressed capacity constraints through a charging pump reservation mechanism, which were solved using the cutting-plane method and GRASP algorithm. The second paradigm adopts stochastic and dynamic approaches, integrating queuing theory and Markov decision processes to address random queuing issues. [PERSON] et al. [22] embedded charging station queuing models into multi-graph frameworks, showing that alternative routes mitigate cost increases from extended waits. [PERSON] et al. [23] applied the M/G/1 queuing model and ALNS algorithm to solve EVRP with limited charging capacity. [PERSON] et al. [24] proposed a Markov decision process-based framework by using a binary occupancy indicator (OI) to reduce charging wait times by 23.7-95.4% and the total travel time by 1.4-18.5%.
# The Loop-Mediated Isothermal Amplification for the Rapid Detection of Porphromonas Gingivalis
###### Abstract  
Periodontitis is a highly prevalent oral disease occurring across the globe, resulting from an interaction of many predisposing factors. Our study aimed to determine some chosen periodongens (_Porphyomonas gingivalis_, _Fusobacterium mucatumatum_, _Tannerella forsythia_) using three different methods: loop-mediated isothermal amplification (LAMP), polymerase chain reaction (PCR) and real-time PCR (qPCR), and to compare their sensitivity and specificity. The study included 62 patients with diagnosed periodontitis. Gingival crevicular fluid was collected from all patients to take samples of bacterial DNA for the further laboratory molecular analyses. In comparison to the gold standard (qPCR), the best diagnostic quality parameters were achieved for LAMP using the TE buffer for the _P. gingivalis_ determination. Therefore, the LAMP is an analytical technique that could be used to quickly assess the presence of periodongtogens in an outpatient setting.  
loop-mediated isothermal amplification; periodontal disease; periodontal pathogen; periodontal bacteria; periodontal diagnostics; _Pothyomonas gingivalis_; dentistry
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 5 Conclusions  
Past research into neural-network-based aeromagnetic compensation methods usually employed normal convolution structures, resulting in low generalization of the algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **WilliamsNN** & **BPNN** & **ResNet** & **MagTCN** \\ \hline Average Time Cost (ms) & 0.428 & 0.046 & 1.240 & 1.837 \\ FLOPs (MB) & 0.238 & 0.684 & 105.541 & 95.722 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Resource consumption of several compensation algorithms.  
Figure 12: Compensation results within the bandpass of the flight 1006.06 signal.  
In this article, we propose an aeromagnetic compensation method, MagTCN, based on temporal convolutional networks, to improve both generalization and compensation accuracy. In the proposed method, a neural network based on separable convolution with a residual connection is employed to improve the compensation accuracy and convergence stability, and a gradient correction loss function based on the Tolles-Lawson model is used to improve the generalization ability. We conducted experiments based on both simulated and real datasets and compared typical neural network compensation methods proposed by previous researchers. The results indicate that the method proposed in this article can achieve a lower standard deviation of residual magnetic interference than other neural network methods, demonstrating a better compensation performance and generalization ability. In future work, we will further study the relationship between the input sequence length, convolution kernel receptive field, and compensation accuracy in temporal convolutional networks to achieve higher compensation performance.  
Conceptualization, B.Z.; methodology, B.Z. and H.W.; software, H.W.; validation, B.Z. and H.W.; formal analysis, B.Z.; investigation, B.Z. and H.W.; resources, B.Z.; data curation, B.Z.; writing--original draft preparation, H.W.; writing--review and editing, B.Z. and H.W.; visualization, H.W.; supervision, B.Z.; project administration, B.Z.; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank the School of Computer Science, China University of Geosciences (Wuhan), for providing the deep learning servers.  
The authors declare no conflicts of interest.
## 4 Result Analysis
### Experimental and Case Studies
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**System** & **Freq.** & **Perc.** & **References** \\ \hline Virtual learning environments & 17 & 47.2\% & [99; 102; 104; 105; 107; 111; 114- \\ Mobile applications & 9 & 25.0\% & [98; 100; 101; 109; 112; 120; 124; 126; 132] \\ Digital games & 6 & 16.7\% & [103; 108; 113; 118; 121; 130] \\ Platforms & 3 & 8.3\% & [106; 131; 133] \\ Sandbox & 1 & 2.8\% & [110] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: System distribution. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Research method** & **Freq.** & **Perc.** & **References** \\ \hline Quantitative & 27 & 75.0\% & [100; 102; 105; 107; 119; 121; 122; 123; 124; 125; 126; 127; 128; 129; 133] \\ Mixed & 8 & 22.2\% & [98; 99; 101; 103; 104; 106; 118; 120] \\ Qualitative & 1 & 2.8\% & [117] \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Research methods. Finally, the experimental design adopted was also explored, as can be seen in Table 13. Specifically, the majority of studies (n = 29, 80.6%) focused solely on an experimental group, while seven (19.4%) studies used both an experimental and a control group in their experiments. Given the ability to collect data in several stages of the experimental process, due to the technologies and methods used, studies focus on collection data, before, during, and after the interventions. Most studies capitalized on the data generated during the use of the applications, systems, or games and the ability to analyze them in real time and as a result, the studies focused on collecting data during the intervention. Finally, the variables that were mostly examined in the studies are summarized below:  
* Performance, score, and time. * Decisions, choices, and responses. * Number of correct and wrong responses, number of tries, and amount of assistance received. * Time-on-task, dropout rate, task completion rate, and overall completion rate. * Usefulness, satisfaction, enjoyment, and realism. * Ease of learning and ease of use. * Learners' knowledge, skills perspectives, and progression. * Behavioral, cognitive, and affective states. * Learners' actions, engagement, experiences, focus, and stress. * Learning gains, learning effectiveness, and learning behaviors. * Movement and position. * Head-, hand-, and eye-tracking, visual attention, and regions of interest. * Interactions with virtual content, virtual objects, and educational material.
## 2 Experimental Details  
### Preparation of Si/C Nanocomite  
#### Preparation of Nano-Scale Si Powder  
The nano-scale Si powder was synthesized from commercially obtained micro-sized (\(\sim\)100 \(\mu\)m) Si powder by a high-energy mechanical milling system (Simolayer CM01, Zoz GmbH, Wenden, Germany) controlled by MALTOZ-software (Zoz GmbH, Wenden, Germany). The milling was carried out in an Argon gas filled stainless steel jar (chamber) containing stainless steel balls (diameter \(\sim\)5 mm) as milling media. The weight ratio of stainless-steel balls to powder was 15:1. The stearic acid (5 wt % of total Si weight) was used as a processing control agent to avoid the agglomeration and contamination caused by balls and jar. The silicon powders were milled for 8 h with the milling conditions of impeller speed of 1300 rpm for the first 45 s and 700 rpm for the next 15 s to achieve a homogeneous nano-scale Si powder.
## 1 Introduction
The proposed fabrication method starts with the production of metal (stainless steel) interlocking structures by LPBF, followed by the deposition by FFF of the melted polymer (PLA or PET) on the top of the metallic substrate. During the FFF process, the substrate is heated to decrease the polymer's viscosity and facilitate its flow into those metal interlocking structures. In the final step of the process, the melted plastic material is compressed and the laser is scanned, boosting the inter-molecular adhesion between the metal and polymer. The use of the FFF process is of great interest given its widespread use in commercial 3D printers. However, the application of FFF technology with the use of PMHM still requires further study. In 2017, [PERSON] et al. [21] demonstrated the deposition of PMHM with the FFF process using polymer-metal hybrid filaments, and PLA reinforced with bronze and iron. The mechanical properties of the parts were studied, and the authors concluded that although the tensile strength of parts produced with hybrid materials were similar to the ones of PLA, the interfacial adhesion of PMHM was reduced when compared with the PLA. The FFF process was also employed to fabricate PMHM joints between aluminium and ABS [22; 23], or between aluminium and alternate layers of polyamides (PA) and continuous carbon fiber (CF) reinforced PA [22], in a process referred to as the Addjoining technique. The authors observed that the mechanical behavior of the Addjoining parts is comparable to the joints produced with state-of-the-art adhesive bonding. Due to the popularity of FFF equipment and the need to understand the mechanisms of joining dissimilar materials, in this work the feasibility of adhesion between aluminium and three polymer-based materials: ABS, PA and PA reinforced with short CF, was studied. Single-lap joints of aluminium substrate with polymer-based adherents were produced by FFF using a customized printing bed. The effect of surface roughness and the presence of fibres on the adhesion strength between each pair of materials was also studied. The experimental work includes standard tensile tests, optical profilometry measurements, and morphological observations using scanning electron microscopy (SEM). The analysis of the fractured surfaces and failure modes of the single-lap joints was also performed. To the best of our knowledge, this is the first time that the adhesion of single-lap joints of aluminum-PA and aluminum-(PA-CF) produced by FFF was reported, and it correlated with the aluminum surface roughness and the failure modes of the joints.
# Evaluation of Metal-Organic Frameworks as Potential Adsorbents for Solar Cooling Applications
## 3 Physical Adsorbents and Desired Adsorption Behavior  
The adsorption process is categorized as physical and chemical adsorption and for adsorption-based cooling; the phenomenon of physical adsorption takes place [32]. The materials used for physical adsorption are known as physical adsorbents. The ideal adsorbents should have a higher enthalpy of adsorption, high porosity, large surface area, and high uptake capacity [33; 34].  
Regardless of the type of adsorbent, material with high uptake at lower partial pressure, and having stepwise isotherm is preferred as this will give better efficiency and performance [35; 36]. Furthermore, the phenomenon of hysteresis is undesired for the adsorption process as this will cause a loss of inefficiency. Many researchers have used silica gel [37; 38], zeolite [39; 40], and functional adsorbent material such as FAM Z05 [41], Z01 [42], Z02 [43], and [44] for different applications. All these adsorbents show good adsorption characteristics, especially silica gel and zeolites. It is found that the use of zeolite in natural gas-driven heat pumps can reduce the energy requirements of a conventional household boiler by up to 30% [40].  
Activated carbon (AC) is an effective adsorbent which has a surface area of 500-1500 m\({}^{2}\)/g [45] and it can adsorb refrigerants such as R134a, ammonia, ethanol, and methanol with a high uptake rate [46; 47; 48]. AC has a low adsorption heat (1800-2000 kJ/kg) and poor thermal conductivity (0.4 W/(m\(\cdot\) K)) [49]. The surface area of silica gel is 100 to 1000 m\({}^{2}\)/g and it has a regeneration temperature of 50-120 \({}^{\circ}\)C depending on its type. It has large adsorption capacity, low mass transfer, and large adsorption heat of 2500-2800 kJ/kg. Low mass transfer is not a desirable phenomenon for thermally activated cooling systems. The zeolites have a high adsorption heat (3000-4500 kJ/kg) and poor thermal conductivity (0.2 W/(m\(\cdot\)K)) which reduces its heat transfer performance [50; 51]. Zeolites can be regenerated at a temperature of 200-300 \({}^{\circ}\)C which is not desirable for cooling applications. The porosity and surface area of commercially used adsorbents along with their potential applications are presented in Table 1.  
Figure 6: Advantages of thermal cooling technology.
## 2 Experimental Method
### Data Reduction  
The convective heat transfer coefficient (\(h\)) on the air side can be expressed as:  
\[h=Q/(A\cdot\Delta T_{ln}) \tag{8}\]  
where \(Q\) is the average capacity of heat transmission for both the air side and the water side, and \(\Delta T_{ln}\) is the average logarithmic temperature difference. The specific expressions for \(Q\) and \(\Delta T_{ln}\) are calculated as follows:  
\[Q=(Q_{a}+Q_{w})/2 \tag{9}\]  
where  
\[Q_{a}=\dot{m_{a}}c_{p,a}\Delta T_{a} \tag{10}\]  
\[Q_{w}=m_{w}c_{p,w}\Delta T_{w} \tag{11}\]  
\[\Delta T_{ln}=\frac{(T_{a1}-T_{w2})-(T_{a2}-T_{w1})}{ln\frac{T_{a1}-T_{w2}}{T _{a2}-T_{w1}}} \tag{12}\]  
In the above equations, \(\dot{m}\) is mass flow rate, \(\Delta T_{a}\) is temperature difference on the air side between the inlet and outlet and \(\Delta T_{w}\) is temperature difference on the water side between the inlet and outlet.  
Reynolds number in the equal cross-section flow channel is \(Re_{m}\):  
\[Re_{m}=\frac{u_{m}d_{h}}{\mu/\rho} \tag{13}\]The flow condition of trapezoidal and circular-arc-shaped flow channels can be evaluated by the maximum Reynolds number (\(Re_{max}\)):  
\[Re_{max}=\frac{u_{in}d_{in}}{\mu/\rho} \tag{14}\]  
In the above two equations, \(Re_{m}\) is calculated by using mean velocity (\(u_{m}\)). \(Re_{max}\) is calculated by using maximum velocity (\(u_{in}\)) at the inlet of curved flow channels.  
The uncertainties of the velocities \(u_{m}\) and \(u_{in}\) calculated by using Equation (11) are about 1.54% and 2.1%, respectively.  
The same flow channel form and different structural parameters were evaluated by the overall heat transfer evaluation index \(JF\) factor, and the expression formula of the \(JF\) factor was as follows [28]:  
\[JF=\frac{j_{i}/j_{0}}{\left(f_{i}/f_{0}\right)^{1/3}} \tag{15}\]  
where the subscript \"\(i\)\" stands for different channels, for example, different circular radii \(R\) (\(R\) = 200 mm, \(R\) = 250 mm and \(R\) = 300 mm), in the circular-arc-shaped flow channel, and \"0\" represents the reference duct with the equal cross-section flow channel (\(R\)\(\rightarrow\)\(\infty\)).
## 3 Results  
### Structural and Electronic Properties  
Among the three different crystal structures of TiO\({}_{2}\) (brookite, rutile, and anatase), anatase is considered the most suitable for photocatalytic devices [45]. It belongs to the tetragonal I4/amd space group and its lattice parameters are measured to be a = 3.782 A, b = 3.782 A, and c = 9.502 A [46]. Herein, the DFT calculated lattice parameters are a = 3.804 A, b = 3.804 A, and c = 9.729 A, which agree with previous theoretical studies [8; 34; 47]. The dopants' percentage was 1 X (=Cr, Mo, W) atom per 108 TiO\({}_{2}\) atoms, which results in a 0.92% doping percentage. In our work, all the dopants were examined in interstitial and substitutional positions. We discuss the effect of the above dopants for interstitial and Ti-substitutional doping. Each case produces a small distortion in the lattice, which changes the crystal parameters and the supercell volume (Table 1). The increase of the unit cell volume is due to the electronegativity differences between Mo, W, and Ti. This is in turn reflected on the larger Ti- X(=Mo, W) distance as compared to the Ti-Ti distance of the undoped TiO\({}_{2}\).
## 2 Materials and Methods
### Prototyping and Compression Tests
#### 2.3.1 Preliminary Prototyping and Compression Tests  
Figure 4 shows the CAD models prepared for the sample manufacturing using BAMBU STUDIO software (BAMBU LAB, Version 1.10.1.50, Shenzhen, China). The first prototyping trial included the reference PLA specimen (Figure 4a,b), which was designed with a target weight of 155.7 g and resulted in a final printed weight of 156.5 g, with a 4% grid infill density. Two alternative samples (Figure 4c,d) were produced from LW-PLA using the fabrication settings described in Section 2.1, which were selected to activate the material's foaming behavior. To achieve the designed target weight of 155.7 g, Sample 1 was fabricated with a 13% grid infill density, while Sample 2 was produced with an increased 24% grid infill density. This variation was necessary because of the material's sensitivity to the printing conditions mentioned above. Table 3 shows the fabrication settings for all fuselage prototypes.  
Figure 4: Fuselage front fragment models prepared for the FFF: (**a,b**) reference PLA model and its internal pattern; (**c–f**) internal patterns of LW-PLA Samples 1, 2, 3, and 4 (refer to Table 3 for the sample notations).  
Figure 5 shows the first three specimens from Table 3 prepared for the test and the test setup. Figure 6 illustrates the failure shapes of the tested prototypes, with a particular focus on the fracture defragmentation of the Reference fuselage part. Figure 7a shows the load-vertical displacement diagrams of the test samples shown in Figure 6a. Differences in the mechanical performances are apparent, but there is no definite criterion to compare the structural resistances of these prototypes. Therefore, this study employed the deformation energy approach [35] for comparison purposes, using the impact energy value determined in Section 2.2. This energy determines the area under the load-displacement diagram. Thus, Figure 7b highlights the deformation energy amount corresponding to a 300 J impact effect; the vertical dashed lines indicate the deformation value necessary to release the target impact energy.  
Thus, Figure 7b illustrates the enhancement in the energy absorption capacity of the LW-PLA samples compared to the Reference PLA specimen. In this figure, shaded areas under the load-displacement curves represent the deformation energy equivalent to a 300 J collision energy, with vertical dashed lines marking the corresponding displacement thresholds. However, this improvement may result from the weight increase in the front part of the fuselage prototypes (Table 3). Therefore, a further optimization procedure focused on the internal prototype structure, using the mechanical resistance of Sample 2 as the reference and reducing its weight to achieve the target value. Figure 8 shows the cutting place and deformed shapes of the internal lattice patterns of the LW-PLA prototypes shown in Figure 6a. The following sub-section presents the compression test results of the modified prototypes.