esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Construction Technique for Curved Structures
## 7 Conclusions  
The present paper presents a new constructive technology for curved masonry elements by using externally bonded FRP materials instead of traditional temporary scaffolds (namely, centering). A calculation model for the design of the proposed technique was developed and illustrated. Furthermore, an experimental prototype was built using limestone blocks. As a consequence of the obtained results, the following conclusions are reported in order to highlight the advantages of the proposed technique:  
* It is possible to build arches in situ by using FRP materials and lifting techniques, avoiding the use of traditional methods, such as temporary scaffolding;
* The proposed construction technology has several advantages in terms of construction time-saving, ease of installation, safety, lower construction site costs, and the possibility of constructing masonry arches even at high altitudes. In addition to this, the seismic capacity of the arch is increased thanks to the presence of the FRP;
* The calculation model for the design of the construction technique has the advantage of being applicable to any curved structure;
* The experimental prototype confirmed the validity of the analytical procedure and the fast and inexpensive application of the technique.  
On the other hand, the bond between the FRP and the substrate should be carefully assessed, requiring skilled expertise. Other limitations could be found by means of further experimental investigations. In this perspective, possible future research developments are proposed, such as:  
* Further optimizations regarding the construction and installation of arches using the lifting technique, with particular attention to the required materials and tools;
* Application of the technique to curved glulam timber structures as well;
* Application to 3D structures such as vaults and domes;
* Development of software applications for the design;
* Mechanical testing under different load conditions, as typically performed for FRP-strengthened masonry arches (_in progress_).  
Figure 13: Data elaboration: (**a**) symmetry level of the lifting and (**b**) FRP-strains with respect to the lift of the key-block.
## 3 Methodology
### Framework Overview
#### 3.2.1 Association Graph Generation  
Simple distance-based graphs can easily ignore related information between non-adjacent spatio-temporal information, so we propose a new spatio-temporal multi-association graph generation method. In this section, we will show how to use multiple graphs to capture static and dynamic spatio-temporal information in traffic.  
Spatial static graph \(G_{ss}(V,A_{ss})\): It is generated based on the adjacency of the spatial distance of nodes in the road network, where the spatial structure of the road network can be captured through \(G_{ss}\). Two adjacent nodes in the spatial dimension are more likely to have similar flows because of the connectivity of the road network. We generate a matrix  
Figure 3: The framework of MFDGCN.  
Figure 2: According to historical traffic flow time series graphs, future traffic flow time series graphs can be predicted (here \(t\) is time, \(m\) is the number of historical time steps, and \(n\) is the number of prediction time steps).  
\(A_{ss}\) based on the node distance and set a certain threshold \(\theta\) to filter further information. If there are nodes \(v_{i}\) and \(v_{j}\), \(A_{v_{i}v_{j}}\) is the value of two nodes in matrix \(A_{ss}\), which is defined as:  
\[A_{v_{i}v_{j}}=\left\{G,dis\big{(}v_{i},v_{j}\big{)}\leq\theta\right\} \tag{2}\]  
where \(dis\big{(}v_{i},v_{j}\big{)}\) is the distance between nodes.  
Spatial dynamic graph \(G_{sd}(V,A_{sd})\): It searches for nodes with similar traffic flow to generate a graph according to the dynamic changes within a certain period of time in the road network. Spatial nodes with similar functions can be captured through \(G_{sd}\). We select the \(k\) nearest neighbor nodes to generate a matrix \(A_{sd}\) according to the similarity of traffic flow of each spatial node in a certain period using KNN [22]. If there are nodes \(v_{p}\) and \(v_{q}\), \(sim(v_{p},v_{q})\) is the similarity between the two nodes, and \(A_{v_{p}v_{q}}\) is the value of the two nodes in the matrix \(A_{sd}\) then:  
\[A_{v_{p}v_{q}}=\left\{\begin{array}{ll}1,&sim(v_{p},v_{q})ek\\ 1,&v_{p}=v_{q}\\ 0,&otherwise\end{array}\right. \tag{3}\]  
The temporal dynamic graph \(G_{td}(T,A_{td})\) (\(T\) is time series) is based on dynamic changes in time to find time nodes with similar traffic flow to generate graphs. Time nodes with similar functions can be captured through \(G_{td}\). For example, if there are two time points \(T_{p}\) and \(T_{q}\) which are both in the peak period, traffic flow may be equally large and the similarity is also large. We generate a matrix \(A_{td}\) based on the similarity of traffic flow between time nodes and set a certain threshold \(\theta\) to filter information that is far apart. Temporal static coding divides the time series into daily and weekly periods, which can capture the static adjacent time relationship of traffic flow.
## 4 Discussion
### Evaluation on Edge Detect Accelerate Strategy  
According to the proposed edge detection strategy, the detection module does not work during the certain idle periods. There are 30 periods of 5-min non-operating apron monitoring video to validate the strategy in this experiment. The acceleration of each period is shown in Figure 12. The experimental results show that this strategy can effectively avoiding idle detection. The failure of this strategy is when the detection is active because of the staff or vehicles passing through the operation area of the apron.  
There is another experiment to validate the acceleration strategy 2, with two apron operation videos from Obihiro Airport in Japan and Guiyang Airport in China. As shown in Table 6, the duration of the two videos is 49 min 28 s and 57 min 35 s, respectively. Since the video frame rates of these two videos are 30 and 25, respectively, there are 89,029 frames in Obihiro Airport video and 86,395 frames in Guiyang Airport video. Both of the videos record the whole process from flight taxiing into the parking bay to departure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **[EMAIL_ADDRESS] (\%)** & **mAP @50:595) (\%)** \\ \hline SSD [19] & 0.927 & 0.915 & 90.15 & 72.3 \\ YOLOv5s [3] & 0.946 & 0.925 & 94.9 & 78.6 \\ MobileNet-SSD [20] & 0.896 & 0.878 & 88.6 & 69.4 \\ YOLOv4-tiny [23] & 0.898 & 0.877 & 92.2 & 73.9 \\ Edge-YOLO & 0.931 & 0.884 & 90.79 & 72.5 \\ Edge-YOLO-CA & 0.94 & 0.899 & 91.37 & 73.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model accuracy performance with state of art algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Name** & **CPU** & **GPU** \\ \hline Cloud Server & \(\backslash\) & Intel Core i5-10400F & NVIDIA Telsa V100 \\ Edge Embedded & & & 16 GB \\ Device & Raspberry 4B & ARM [EMAIL_ADDRESS] & \(\backslash\) \\  & & & GHz & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment platform.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicator** & **SSD [19]** & **YOLOv5s [3]** & \begin{tabular}{c} **MobileNet** \\ **SSD [20]** \\ \end{tabular} & \begin{tabular}{c} **YOLOv4** \\ **Tiny [23]** \\ \end{tabular} & \begin{tabular}{c} **Edge-YOLO** \\ \end{tabular} & \begin{tabular}{c} **Edge-YOLO-CA** \\ \end{tabular} \\ \hline \begin{tabular}{c} FPS \\ Model Size \\ 640 \(\times\) 640 \\ 320 \(\times\) 320 \\ \end{tabular} & \begin{tabular}{c} 0.48 \\ 14 MB \\ 131.31 \\ 320 \(\times\) 320 \\ \end{tabular} & \begin{tabular}{c} 1.25 \\ 14 MB \\ 131.31 \\ 32.11 \\ 34.76 \\ \end{tabular} & \begin{tabular}{c} 1.84 \\ 22.1 MB \\ 21.11 \\ 3.87 \\ \end{tabular} & \begin{tabular}{c} 1.13 \\ 1.21 M \\ 8.1 \\ 8.1 \\ 3.20 \\ \end{tabular} & \
\begin{tabular}{c} 4.29 \\ 14 MB \\ 2.11 \\ 34.87 \\ \end{tabular} &
\begin{tabular}{c} 4.21 \\ 1.18 M \\ 8.1 \\ 8.1 \\ 3.42 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: FPS on raspberry 4B and model performance comparison.
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 2 Synthesis Methods for Copper- and Iron-Based Nanoflowers
### Sonochemical Method  
The sonochemical method is another technique for producing NFs, utilizing ultrasonic waves ([PERSON] et al., 2013 [86]). This environmentally friendly approach avoids the use of highly toxic chemicals and does not necessitate high-temperature conditions. By optimizing the energy of the ultrasonic waves and the exposure duration, desired shapes and yields of nanomaterials can be achieved [33]. This method offers several advantages, making it a leading technique: it is simple, environmentally friendly, cost-effective, and safe. The chemical and physical effects of ultrasound are not caused by direct interactions between chemical species and sound waves but rather by the physical phenomenon of acoustic cavitation. This process involves the formation, growth, and eventual explosive collapse of bubbles in the liquid. These stages generate localized hot spots with temperatures around 5000 \({}^{\circ}\)C and pressures of 500 atm, lasting only a few microseconds. Consequently, when a liquid is exposed to high-intensity ultrasound, high-energy chemical reactions are triggered [87]. The sonochemical method induces intense local heating, high pressure, and extremely rapid cooling rates, all due to acoustic cavitation--the continuous formation, growth, and implosive collapse of bubbles in the liquid [88].  
The sonochemical method was developed to further optimize the one-pot method and facilitate even more HNF production. The use of a sonicator bath accelerates the formation of HNFs from three days of incubation to under ten minutes. In a similar way, [PERSON] et al., used a CuSO\({}_{4}\) SH\({}_{2}\)O solution and a hemoglobin solution and incubated their mixture with sonication for ten minutes [89]. In both cases, the refining of the produced HNFs was carried out according to the conventional method. It should be noted that enzyme-free HNFs, containing the typical inorganic part (metal phosphates) and an organic part (pieces of DNA sequence or antigens but not enzymes), are considered to be the second generation of HNFs [90] and are generally synthesized with similar methods as the ones mentioned above [91; 92].
# The Effects of _Anthyllis vulneraria_ Hydroalcoholic Leaf Extract as an Adjuvant in Wound Healing
## 4 Discussion
Regarding the histopathological findings, the lack of significant differences between the four groups across all parameters shows that in our experiment, the _A. vulneraria_ extract did not significantly alter the structural aspects of wound healing at the microscopic level under the tested conditions. Several factors may explain the absence of histological differences. First, the small sample size (\(n=8\) per group, with only four rats analyzed at each time point) may have limited the statistical power. Second, basic hematoxylin-eosin (H and E) staining may have been too crude to detect subtle changes in tissue structure or cell populations. More sensitive methods, such as immunohistochemistry for proliferation (Ki-67), angiogenesis (CD31), or collagen deposition (Masson's trichrome), could reveal changes missed by H and E. Third, the selected time points (days 7 and 14) may not align with the extract's peak activity, which likely occurs earlier (days 5-7), as suggested by the macroscopic data. By day 14, the remodeling phase may mask earlier differences. Finally, the extract appears to act mainly through biochemical pathways by reducing oxidative stress and inflammation, rather than by inducing structural changes. This is supported by the significant improvements in markers like MDA, GSH, SOD, and GPX. Thus, _A. vulneraria_ may promote healing at a molecular level, creating favorable conditions for repair. Future studies should increase the sample size and incorporate advanced histological and gene expression analyses (e.g., TGF-\(\beta\), VEGF, Ki-67) to better elucidate the mechanisms of action and capture potential long-term effects on tissue remodeling. We acknowledge the limitations of the study. The sample size was reduced due to ethical constraints; the microscopic measurement methods were limited. As for the oxidative stress quantification, although many markers were quantified, many more remain to be measured to better characterize the exact healing capacities of _A. vulneraria_, including more cytokine profiling, as well as multi-omics analysis. Additionally, the lack of significant histopathological findings underscores the need for further investigation into the structural effects of _A. vulneraria_ extract, potentially using more sensitive techniques and larger sample sizes to detect subtle differences in tissue-repair processes.
# New Approaches of Stochastic Models to Examine the Vibration Features in Roller Bearings
## 4 Methodology  
Experimental data were gathered from the driver end bearing under two horsepower loads, operating at a speed of 1800 rpm. The bearing exhibited an inner race fault characterized by a groove width and depth of 38 \(\upmu\)m and 12 \(\upmu\)m, respectively, along with an outer race fault featuring a groove width and depth of 425 \(\upmu\)m and 209 \(\upmu\)m, respectively. Analysis of the collected data was conducted in both the time and frequency domains to extract various features under different operating conditions. Standard formulae were applied to extract up to fifteen features from healthy bearings, inner race faulty bearings, and outer race faulty bearings. The vibration analyzer CSI 2130 was utilized for validation of extract features at different intervals, and quantification was managed to identify faults in bearings during operating conditions. Finally, ARMA (**p**, **q**) modelling techniques were employed to establish a relationship between the extracted features and those of healthy roller bearings. The subsequent sections provide detailed discussions on the modelling procedures and simulation steps. Let \(\mathrm{x_{t}}\) represent extracted features in various conditions for t = 1, 2, 3..., n. Then ARMA (**p**, **q**) can be represented as follows:  
\[\mathrm{x_{t}}=\alpha+\phi_{1}\mathrm{x_{t-1}}+\ldots+\phi_{\mathrm{p}}\mathrm{ x_{t-p}}+\varepsilon_{t}+\theta_{1}\varepsilon_{t-1}+\ldots+\theta_{q} \varepsilon_{t-q} \tag{1}\]  
with \(\varepsilon_{t}\ \sim\ iid\left(0,\sigma_{e}^{2}\right)\), where \(\alpha\) represents the features' varying intercept terms, \(\phi_{\mathrm{p}}\) and \(\theta_{q}\) represents the autoregressive and moving average parameters of the features at lag i and j in the Equation (1) for i = 1, 2, 3,..., p and j = 1, 2, 3,..., q which represents varying features for particular lag.  
The appropriate model order is determined by analyzing the autocorrelation (ACF) and partial autocorrelation (PACF) functions. The truncation of the ACF reveals the moving average order, while the truncation of the PACF indicates the autoregressive order of feature representation. Additionally, suitable orders can be identified based on the lowest values of the Akaike (AIC) [38] and Schwarz (SC) [39] information criteria. The AIC is computed using the following equation:  
\[AIC(p,q)=\ln\Bigl{(}\hat{\sigma}^{2}(p,q)\Bigr{)}+\frac{2(p+q)}{T} \tag{2}\]  
Here, \(p\), \(q\), \(\hat{\sigma}\), and \(T\) represent the AR component, MA component, the standard deviation of the residuals and the number of the non-missing values in the extracted features, respectively.  
The Bayesian information criterion for \(k\) parameters, \(L\) maximum likelihood for \(n\) number of extracted features is estimated by  
\[BIC(p,q)=2\ln(L)+k\ln(n) \tag{3}\]  
The parameters of the suitable order models were assessed by utilizing the maximum likelihood method. The optimization of the models was acquired on the base of unobservable features.
## 3 Mathematical Model of a Device for Measuring Radio Signal Samples  
Expression for the radial intensity distribution \(I_{\Sigma}(x,\ t)\) the optical field in the plane of the interferogram has the form [34; 35; 36]:  
\[I_{\Sigma}(x,t)=I_{0}+I_{1}\cdot\cos\biggl{\{}\frac{2\pi\cdot(I_{0}+\Delta I(t) )}{\lambda}[\delta_{1}(x)-\delta_{2}(x,t)]\biggr{\}} \tag{1}\]  
In expression (1) \(I_{0}\) is the intensity of a constant light background in the interference plane; \(I_{1}\) is the amplitude of the intensity of the interference term of the expression; \(x\) is the measured coordinate in the plane of the interferogram; \(I_{0}\) is the difference in the path of the rays of two light streams (diffracted from a Fourier hologram and reflected by a flat mirror) in the absence of a signal; \(\Delta I(t)\) is the change in the difference in the course of the rays when the measured signal is applied to the electrodes EOM; \(\lambda\) is wavelength of optical radiation;  
\[\delta_{1}(x)=\frac{x\cdot\cos\beta-R_{1}\cdot\cos(\beta-\alpha)}{R _{1}-x\cdot\cos\alpha_{1}};\] \[\delta_{2}(x,t)=\frac{x\cdot\cos\beta_{2}(x,t)-R_{1}\cdot\cos( \beta_{2}(t)-\alpha_{1}-2\alpha)}{R_{1}-x\cdot\cos(\alpha_{1}+2\alpha)};\] \[\beta_{2}(t)=arctg\Bigl{(}\frac{\sin(\alpha_{1}-\beta)\cdot\sin( 2\alpha_{0}+\alpha_{21}(t))-\sin(\alpha_{1}+2\alpha)\cdot\sin(\alpha_{21}(t)- \beta)}{\sin(\alpha_{1}-\beta)\cdot\cos(2\alpha_{0}+\alpha_{2}(t))-\cos( \alpha_{1}+2\alpha)\cdot\sin(\alpha_{21}(t)-\beta)}\Bigr{)};\]  
\(\alpha\) is the angle between the flat mirror and the plane of the Fourier hologram when exposed; \(\alpha_{0}\) is the angle between the flat mirror and the plane of the Fourier hologram when exposed; \(\alpha_{1}\) is the angle between the light beam coinciding with the generatrix of the solid angle limiting the luminous flux incident on the end face of the electro-optical crystal and the normal to this face; \(\alpha_{21}\)--the angle between the direction from the edge of the Fourier hologram to the focus point and the plane of the Fourier hologram; \(R_{1}\) is the distance from the point light source to the Fourier hologram when it is exposed; \(\beta\) is the angle that determines the direction of movement of the focus point of the light flux relative to the plane of the Fourier hologram. Figure 1: Block diagram of an optoelectronic device for measuring radio signal samples. Imagine a radio signal, the readings of which need to be measured, at a time \(t\) in the following form:  
\[U(t)=U_{0}(t)\cos[\omega\cdot t+\varphi_{0}(t)] \tag{2}\]  
In relation (2), the law of change \(U_{0}(t)\) and \(\varphi_{0}(t)\) is determined by the transmitted information and the type of manipulation used. When a signal (2) is applied to the electrodes 3, 3', the refractive index of the EOM material changes according to the law:  
\[n(t)=n_{0}+k_{\rm IT3}\cdot U(t), \tag{3}\]  
where \(k_{\rm IT3}\) is the proportionality coefficient, which determines the change in the refractive index of an electro-optical material under the action of an applied voltage.
## 3 Construction of the Control Strategy
### Fuzzy-BPNN-PID Controller Design  
Compared with the PID control algorithm, the fuzzy control algorithm has the advantages of good robustness, fault tolerance, etc. The operator can set appropriate fuzzy rules to regulate the output based on the existing knowledge and experience of fertilizer regulation, and the input signal of the system is targeted to facilitate the control link to achieve the structured regulation requirements; however, the fuzzy algorithm relies on simpler fuzzy rules due to the lack of the self-learning capability, which leads to lower control accuracy and poor dynamic quality of the system, and adaptive regulation cannot be achieved [25].  
Compared with the above two algorithms, the BP neural network control algorithm has the feature of adaptive learning, meaning that it can learn and adjust adaptively for the input, complete the adjustment of its own network parameters, and finally, realize the optimal adjustment of the output signal [26]. The block diagram of the BPNN-PID controller is shown in Figure 4.  
Figure 3: PID control schematic.  
In order to obtain better control performance, combined with the advantages of the above fuzzy algorithm and BP neural network control algorithm, we chose to use fuzzy control to fuzzify and normalize the system state variables \(e\) and \(ec\) in advance, before using the BP neural network, to avoid the situation that the output is saturated due to the excessive direct input when the neural network activation sigmoid function is sampled, and reduce the situations where the network output is no longer sensitive to the input [27]. Then, the neural network was used to learn and control the output signal of the fuzzy module, as to achieve the purpose of accurately adjusting the pH value of water and fertilizer.
# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
###### Abstract  
Aeromagnetic exploration is a magnetic exploration method that detects changes of the earth's magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magnetometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays an increasingly important role in mineral exploration and other fields due to its advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude, magnetic field interference will occur and affect the measurement of the geomagnetic field by the magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic interference and improve the magnetic measurement accuracy of the magnetometer. This paper focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy of UAV based aeromagnetic data measurement. Based on the Tolles-Lawson model, a numerical simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out, and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN, the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has higher compensation accuracy and stronger generalization ability.  
a _Applied_  
1  
Aeromagnetic compensation; radial basis function; deep learning; unmanned aerial vehicles (UAV); local minimum