esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Case One  
First of all, we aimed for the moving table to move from the starting coordinates \(x=0,\ y=0,\ z=0\), and \(\theta=0^{o}\) to the reference coordinates \(x=0,\ y=0,\ z=15\) cm, and \(\theta=0^{o}\) in 1.5 s. In other words, the movable table was intended to move 15 cm upwards in the z coordinate without any orientation changes. In order for this reference to be realized by the system, according to the kinematic equations, the first and third motors must turn to the right at an angular position value of 12.54 radians, and the second and fourth motors must turn to the right in a similar manner at an angular position value of 19.32 radians. According to kinematic analysis, the reason why the first and third motors rotate less than the second and fourth motors is that the connection points of the second and fourth motors to the moving table are different compared to the first and third motors. According to the experimental results, the angular position changes of the motors are presented in Figure 9a,b. In all the drawn graphs, zoom images were included in order to clearly show the system settling time, steady-state error, and corresponding controller performance. When Figure 9a,b is examined, it is seen that if the motors in question are moved with sliding mode control instead of PID control, the table reaches the reference value faster. In other words, while the settling time of the motors was faster with the sliding mode controller, the steady-state error values were lower. In addition, in order to compare the performance of the proposed sliding mode controller with the classical PID controller, the root-mean-square error (MSE) and integral-square error (ISE) values of the system response occurring after the settling time were calculated, and these values are presented in Table 2. Since the sliding mode controller was based on the system model, a faster and more robust response was achieved. In addition, lower average error and integral error values were obtained in the SMC controller compared to the PID controller. The sliding mode controller produced better results than the traditional PID controller because it was based on the dynamic model of the system and was robust against disturbance effects during the motion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case 1** & **Settling Time (s)** & **MSE** & **ISE** \\ \hline Motor 1 SMC & 0.512 & 6.94 \(\times\) 10\({}^{-4}\) & 4.82 \(\times\) 10\({}^{-7}\) \\ Motor 3 SMC & 0.511 & 6.49 \(\times\) 10\({}^{-4}\) & 4.21 \(\times\) 10\({}^{-7}\) \\ Motor 1 PID & 0.557 & 0.002 & 4.15 \(\times\) 10\({}^{-6}\) \\ Motor 3 PID & 0.551 & 0.0022 & 4.94 \(\times\) 10\({}^{-6}\) \\ Motor 2 SMC & 0.786 & 0.002 & 3.90 \(\times\) 10\({}^{-6}\) \\ Motor 4 SMC & 0.778 & 0.002 & 3.97 \(\times\) 10\({}^{-6}\) \\ Motor 2 PID & 0.818 & 0.0062 & 3.81 \(\times\) 10\({}^{-5}\) \\ Motor 4 PID & 0.805 & 0.0056 & 3.09 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Outcomes of the case one applications.  
Figure 8: The controller block diagram of the proposed system.
# Plugging Small Models in Large Language Models for POI Recommendation in Smart Tourism
## 3 Preliminaries
### Check-In Behavior Data Analysis  
In order to explore the rationality of the POI recommendation in mining time slot patterns and POI transfer relationships, we performed data analysis based on the NYC dataset from the Foursquare platform and the CA dataset from the Gowalla platform. Firstly, we investigated the activity of user check-in behavior under different check-in periods. Dividing the data into 24 groups based on user check-in times in UTC, we calculated the check-in proportion for each time slot. As shown in Figure 2, the changes in check-in activity across different time slots are evident in both the NYC and CA datasets. Obviously, the frequency of check-in behavior is closely related to the users' daily routines.  
Furthermore, based on common knowledge, different POI categories correspond to different peak visit hours. For instance, bars have their peak visiting hours concentrated between midnight and early morning locally, while restaurants typically experience peak times during local lunch and dinner hours. Therefore, we further analyze the impact of different time slots on the popularity of various POIs, as shown in Figure 3. It can be observed that, regardless of the dataset, there is a significant variation in user preferences for POIs across different time slots. Consequently, incorporating visiting hours into recommendations can lead to more targeted and accurate suggestions for POIs.  
Finally, we analyze the potential transfer patterns of successive check-in pairs. Specifically, on the one hand, we select the 50 most frequent POIs from the NYC dataset and  
Figure 3: (**a**) Check-in frequency of some POIs at different time slots in the NYC dataset. (**b**) Check-in frequency of some POIs at different time slots in the CA dataset.  
Figure 2: (**a**) Proportion of check-ins at different time slots in the NYC dataset. (**b**) Proportion of check-ins at different time slots in the CA dataset.  
analyze their transfer heat values, as shown in Figure 4a, where the heat values are the results of a logarithmic transformation using \(\log_{10}\left(n+1\right)\). We can draw the following two conclusions: First, the heat values along the main diagonal are significantly higher compared to the surrounding heat values, indicating that users generally exhibit a pronounced tendency for repeated check-ins. Second, the transfer preferences for POIs are not uniformly distributed, but show a higher preference for certain types of POIs or specific POIs. On the other hand, we analyze the temporal relationship between two successive check-ins, as shown in Figure 4b. It can be clearly observed that the heat values near the main diagonal are relatively higher, meaning that the transfer heat values for POIs in similar time slots are higher. Furthermore, the transfer heat values are generally lower or higher during specific time slots, for example, showing lower transfer heat values between time slot 9 and time slot 12.
## 2 System Model and Problem Formulation  
In this section, we first introduce the UAV-assisted Internet of Vehicles (IoV) system, followed by a detailed description of the system model, including the caching model, the computation model, and the communication model. Then, we propose a joint data caching and computation offloading optimization problem for UAV-assisted IoV. To quickly clarify some variables, we summarize the key symbols used in Table 2.
# Repair Performance of Self-Healing Microcapsule/Epoxy Resin Insulating Composite to Physical Damage
## 3 Results and Discussion
### Repair Performance of Mechanical Damage
#### 3.1.2 Structural Change of Cross-Section  
The SEM results of cross-section are showed in Figure 5. From Section 3.1.1, the repair behavior was concentrated in the first 30 min. So, the samples of cross-section were heated for 30 min. Compared with the pure epoxy resin, there are obvious microcapsules in the composite. And the dispersion of microcapsules is great, which is similar to the OM results in Figure 1. Moreover, the microcapsule  
Figure 4: Infrared spectroscopy analysis of the filler in scratch.  
Figure 3: Repair behavior of composite.  
has obvious core-wall structure and it can be damaged in the cross-section operation (as shown in Figure 5c). It indicates that the wall structure of microcapsule has the rupture response to the stress change of insulating material.  
The reaction effect of the repairing agent is shown in Figure 5d. When the microcapsule was damaged, the core material flowed out and reacted in the damage area. And the PDCPD product appeared as the irregular flocculent material at the cross-section. The flow of DCPD due to the gravity influence is the main reason for the concentration of repair product at the section bottom. This phenomenon also proves that he fluidity of core material is well, which meets the design requirement of repair behavior.  
The repair mechanism can be analyzed by combining the capillary flow theory and the molecular diffusion model on defect surface [27]. With the appearance of the minor damage in matrix, the pressure difference would be formed on both sides of the defect. And due to the DCPD is the Newtonian liquid between 32.5-172 \({}^{\circ}\)C, the repairing agent would continuously diffuse into the defect depth under the capillary effect. Meanwhile, the core material covers the defect interface to form the surface or body containing the repairing agent. And then the repairing agent molecules attract and react with each other. Although the molecular weight of DCPD is small, its molecular space structure is complex. So, the arrangement of molecule in the reaction process is irregular, resulting in the irregular morphology of repair product. Therefore, the core material of microcapsule can achieve the better repair effect to the physical damage, but the regularity of product is still needed to be improved.  
All in all, the experimental results intuitively demonstrate that the doping effect of microcapsule in epoxy resin insulating material is excellent. The wall structure of microcapsule has timely response  
Figure 5: Cross-section characteristics of epoxy resin. (**a**) Pure epoxy resin; (**b**) and (**c**) Composites before reaction; and (**d**) Repair effect.  
to the stress change in matrix. And the fluidity and reactivity of core material is great. Therefore, it is proved that the epoxy resin insulating composite based on microcapsule system can repair the damaged parts quickly and automatically.
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### The Analytical IT System Framework as a Foundation for AloT in Aviation Health Monitoring
By seamlessly integrating IoT data collection capabilities with AI-powered analytics, this system offers a comprehensive solution for improving aircraft maintenance, reliability, and operational efficiency. The framework enables a shift from reactive to proactive maintenance strategies, potentially reducing costs, improving safety, and optimizing aircraft availability. The inclusion of OEM data in the analytical process suggests a collaborative approach to aircraft health monitoring, where manufacturers' expertise is leveraged alongside real-world operational data. This synergy between operators and manufacturers can lead to continuous improvements in aircraft design and maintenance practices. As AIoT technologies continue to evolve, frameworks like this will play an increasingly crucial role in enhancing aviation safety, reducing costs, and optimizing performance across the industry. The ability to process vast amounts of data in real-time, make intelligent predictions, and provide prescriptive maintenance recommendations represents a significant leap forward in aviation maintenance and operations management.
# Emerging Diagnostics in _Clostridioides difficile_ Infection
## 1 Introduction
difficile_ endospores are highly resilient, surviving various stressors inside and outside hosts until conditions allow for germination [28; 29; 30; 31; 32; 33]. Various transmission pathways for CD endospores in the mammalian infection cycle are shown in Figure 1[34]. Given the ubiquitous nature of _C. difficile_ in food and medical settings, recent concerns have focused on identifying hyperviulent strains (RT027) using biochemical, molecular, and immunological techniques [35; 36; 37; 1]. Rapid testing methods like PCR tests provide results within hours. Enzyme immunoassays and lateral flow assays are now more user-friendly and cost-effective, suitable for multiple settings [38; 39; 40; 41]. Figure 1: The infection cycle of _Clostridioides difficile_ (_C. difficile_) in a mammalian host. _Clostridioides difficile_ infection in a mammalian host begins with ingesting _C. difficile_ spores (yellow spots). These spores travel through the esophagus to the stomach. From the stomach, the spores move to the duodenum and ileum, where they encounter various metabolic factors, such as the ratio of primary to secondary bile acids and short-chain fatty acids. These factors induce the spores to transition into vegetative cells. The vegetative cells then journey through the gastrointestinal tract, reaching the ileum and occur, settling in the vacated anaerobic folds. Some of these cells proceed to the colon. In the colon, nutrient deprivation triggers the release of a battery of toxins (tcdA, tcdB, and cdt (CDT)), potentially causing prolonged inflammation (indicated by the red color) of the epithelial layer. Alternatively, the vegetative cells may enter sporulation and are excreted with feces. Without proper sanitation, these spores can adhere to various inanimate and animal surfaces, including clothing, metals, and dogs, thereby perpetuating the infection cycle.
## 2 Materials and Methods
### Scale-Up and Biomass Production  
All experiments described were performed at Allmicroalgae industrial facilities between January and June 2018. A concentrated culture medium (Sameca, Porto, Portugal) based on Guillard's F/2 and supplemented with 25 \(\upmu\)M of iron (Hubel Olhao, Portugal) was added until a final concentration of 10 mM of nitrate was reached.  
Microalgal cultures were grown in the laboratory in 5-L bubble column reactors at 24 \(\pm\) 1 \({}^{\circ}\)C with continuously injected compressed 0.2-\(\upmu\)m filtered air, under a continuous photon flux density of 100 \(\upmu\)mol-m\({}^{-2}\)s\({}^{-1}\). The pH of the culture medium was kept between 7.5 and 8.0 by periodical CO\({}_{2}\) injection.  
The scale-up to the outdoor pilot and industrial-scale reactors was performed by using five laboratory reactors to inoculate a 0.25-m\({}^{3}\) flat panel (FP) (Figure 1a). Cultures were aerated with 0.2-\(\upmu\)m filtered air, the pH was also maintained between 7.5 and 8.0 by CO\({}_{2}\) pulse injection and the temperature inside the FP was maintained below 28 \({}^{\circ}\)C by a sprinkler-like irrigation system. Thereafter, the 0.25-m\({}^{3}\) FP was used to inoculate a tubular photobioreactor (PBR) of 2.5-m\({}^{3}\) (Figure 1b) and about 80% of this was used as inoculum for a 10-m\({}^{3}\) PBR (Figure 1c). The pH was maintained under 8.0 through an automated CO\({}_{2}\) injection system and the temperature was kept under 28 \({}^{\circ}\)C by a sprinkler-like irrigation system.  
Figure 1: Different scale systems used for outdoor scale-up: (**a**) 0.25-m\({}^{3}\) flat panel photobioreactor (FP); (**b**) 2.5-m\({}^{3}\) pilot-scale tubular photobioreactor (PBR); (**c**) 10-m\({}^{3}\) industrial PBR. Pictures were provided by Allmicroalgae, Research and Development Department, Pataís, Portugal.  
For the assessment of growth performance and biomass production, three 0.25-m\({}^{3}\) FP were cultivated between January and February at an average temperature of 13.6 \({}^{\circ}\)C and light irradiance of 14.06 MJ\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\). The 2.5-m\({}^{3}\) PBR triplicates were grown from March to May at an average temperature of 17.7 \({}^{\circ}\)C and light irradiance of 19.04 MJ\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\), while the industrial PBR of 10-m\({}^{3}\) triplicates were grown from May to June at an average temperature of 15.5 \({}^{\circ}\)C and light irradiance of about 20.10 MJ\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\).
## 1 Introduction
It was through a patent published in 2006 that early investigations into the use of limestone as a precursor, activated by sodium hydroxide without the addition of silicate phases, were initiated [16]. Studies concerned with recent cement production have increasingly focused on its minimal environmental impact. Limestone  
Figure 1: Additive manufacturing processes for concrete fabrication. offers considerable advantages due to its structural purity, availability, and cost-effectiveness compared with conventional cement. Many researchers have explored its potential to act as a substitute for cement for this reason. An inceptive study considered aspects such as CO2 emissions, temperature effects, workability, density, shrinkage, compressive strength, mortar strength, and porosity [17]. From the results, it was concluded that higher percentages of limestone contributed largely to CO2 emission reductions, and with a 10% increase in limestone powder, the percentage reduction in emission was about 8-9%. It was also found that the replacement strengthened the distribution and workability while reducing the porosity and shrinkage, the compressive strength anyway decreasing. In addition, for mortar and concrete, the limestone particle size had a minor effect on all the levels of replacement. Nevertheless, studies on the development of alkali activation of calcium carbonate are few [18; 19; 20] and have mainly focused on conventional casting methods. However, their applications in 3D printing remain largely unexplored. The present study investigates the properties of 3D-printed limestone material activated with different NaOH water solutions. The phase stability, mineral composition, and mechanical properties were studied. The results obtained from this work allow for a better understanding of the influence of NaOH concentration on properties that make printed limestone materials suitable for applications in the construction industry.
# Improved Anticancer Activity of the Malloapelta B-Nanoliposomal Complex against Lung Carcinoma
## Materials and Methods
### In Vivo Antitumor Activity  
The experiment was carried out using 36 healthy BALB/c mice at 20-25 g weight. Mice were subcutaneously injected with \(1\times 10^{6}\) LLC cells to induce tumors. After 5 days, tumorized mice were randomly distributed to 6 groups (n = 6). Group 1 served as the negative control that received normal saline. Group 2 was treated with blank nanoliposome (with lipid components only). Groups 3 and 4 received malB-nanoliposome at doses of 5 mg/kg and 2.5 mg/kg body weight (b.w.) by intraperitoneal injection (i.p.) every 2 days for 14 days continuously. Group 5 was i.p. injected with unconjugated malB compound at a dose of 5 mg/kg b.w. every 2 days for the same duration. Group 6 was treated with the reference control (doxorubicin 5 mg/kg b.w. i.p. injection). The tumor size of each mouse was measured every 7 days for 28 days and tumor volume was calculated by the following equation:  
\[\text{V}=(\text{W}^{2}\times\text{L})/2,\]  
where V is the volume of the tumor, W is the width of the tumor, and L is the length of the tumor.  
The survival time of tumorized mice in all experimental groups was also determined. It was calculated from the day of LLC cell inoculation to the day of death and percentage increase in average life span (ILS) was calculated by the following equation:  
\[\%\text{ ILS}=(\text{A}/\text{B}-1)\times 100,\]where A is the survival time of the treated group, B is the mean survival time of the control group, and ILS is the increase in the average life span group.
# Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment
## 3 Bootstrap Methodology and Its Improvement
### RBF Neural Network  
The RBF neural network is a three-layer feed-forward neural network in which the links from the input layer to the hidden layer are typically fixed and not trained. However, the links from the hidden layer to the output layer are trained. This is a simpler training process than that of standard neural network models [23].  
The structure of the RBF neural network is shown in Figure 6.  
In the RBF network structure, the input vector of the network is \(X=\left[x_{1},x_{2},\ldots,x_{n}\right]^{T}\). The radial basis vector of the RBF network is \(H=\left[h_{1},h_{2},\ldots h_{j},\ldots h_{m}\right]^{T}\), and the basis width vector of the hidden nodes of the network is \(B=\left[b_{1},b_{2},\ldots b_{j},\ldots,b_{m}\right]^{T}\). Then, the Gaussian basis function \(h_{j}\) is  
\[h_{j}=\exp\left(-\frac{\|X-C_{j}\|}{2b_{j}^{2}}\right),(j=1,2,\ldots,m), \tag{8}\]  
where \(C_{j}\) is the center vector of the \(j\)th hidden node of the network and is determined using the k-means training algorithm [24], and \(b_{j}\) is the base width parameter of node \(j\).  
The output of the RBF neural network \(y\) is  
\[y=\omega_{1}h_{1}+\omega_{2}h_{2}+\cdots+\omega_{m}h_{m}, \tag{9}\]  
where \(W=\left[\omega_{1},\omega_{2},\ldots\omega_{j},\ldots\omega_{m}\right]^{T}\) is the weight vector of the network and is determined via least squares approximation learning.
## 3 Results and Discussions
### Characterization of HNH-rGO
transmittance bands of GO were identified, related to carbonyl stretching vibration at 1723 cm\({}^{-1}\), C-O-C stretching vibration at 1044 cm\({}^{-1}\), stretching vibration of aromatic C=C at 1587 cm\({}^{-1}\), and the stretching vibration of hydroxyl groups was observed as a broad strong band centered at 3364 cm\({}^{-1}\). After being reduced and modified by HNH, the intensities of hydroxyl groups and C-O-C stretching vibration were dramatically decreased as can be seen in Figure 6. This confirmed the successful reduction of GO. Furthermore, the appearance of stretching vibration of N-H at 3273 cm\({}^{-1}\), carbonyl of acylamino at 1634 cm\({}^{-1}\), C-N at 1175 cm\({}^{-1}\) and aromatic ring at 1450 cm\({}^{-1}\) gave adequate evidences to the successful modification that the molecular chain of HNH was grafted onto the surface of rGO. Besides, the peaks at 1560 cm\({}^{-1}\) and 739 cm\({}^{-1}\) corresponded to stretching vibration of aromatic C=C in graphene and the ortho-disubstituted aromatic ring in HNH, respectively. Figure 7 showed XPS spectra of GO and HNH-rGO. In Figure 7a, GO mainly consisted of C and O elements, and the atom percentage contents were 82.81% and 17.19%, respectively. As for HNH-rGO in Figure 7b, the main elements were C, O, N, which was consistent with the materials, and the content of C and O down shifted to 77.52% and 15.52%, respectively. Given that hydrazine group was the primary pattern of N element, the content of O element in HNH-rGO except for that in HNH was calculated as 7.57% which was similar to the result of HBS [(43)] of 7.23%. The decrease of O content from 17.19% to 7.57% provided valid evidence for the complete reduction of GO. Figure 7c,d presented the C is survey scan of GO and HNH-rGO. The O-C=O totally disappeared and the intensity of C-N was obvious due to the successful reduction and modification. The C-O and C=O in HNH-rGO were mainly related to hydroxyl on naphthalene and acylamino in HNH. Figure 6: The Fourier transform infrared (FTIR) spectra of GO and HNH-rGO. Figure 7: _Cont._