esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Process and Result Analysis
### Obtain the Original Positioning Error  
The KF method is used to obtain the original positioning error in the first dataset. The positioning process not only includes a series of processing and analysis of satellite observation data and navigation message data, but also includes the correction and elimination of various error factors such as satellite clock deviation and ionospheric delay. The second dataset includes original positioning error data. Therefore, the step of calculating the original satellite positioning error can be ignored during the experiment using this dataset, and the subsequent steps can be directly carried out.  
According to Figure 6, the calculated positioning error fluctuates greatly, with a minimum error of about 0.1 m and a maximum error of more than 6 m. The average positioning error is calculated to be 2.840 m. Notably, the purpose of the designed method is mainly to explore positioning error estimation, which does not depend too much on the accuracy of the original positioning error. Therefore, not only the KF method can be used to obtain the value of the positioning error, but also other GNSS positioning methods can be utilized to obtain the value of the positioning error.
## 2 Background
### Motivation  
The rising global adoption of CPLS and the increasing complexity of botnet attacks leveraging these as C&C servers are the motivation for this study. Botnet creators find CPLS extremely useful for establishing hidden and robust C&C communication channels. These channels are globally accessible, easy to implement, and offer a cost-effective setup. Moreover, the user-friendly nature of most CPLS, demanding minimal technical skills, lowers the bar for botnet creators.  
These factors have turned CPLS into a fertile ground for botnet abuse, presenting substantial challenges for most intrusion detection systems. These systems often struggle to differentiate between legitimate and malicious C&C activities, leading to delayed detection and response. This delay in identifying and countering threats substantially increases the likelihood of successful cyber attacks, potentially leading to catastrophic consequences, such as data breaches, significant financial losses, and damage to organizations' reputations.
## 5 Case Study--LCA Results and Discussion
### Costing Assessment  
The main operation of the linear and circular model focuses on the production of precast concrete, but there are also operations of depositing the waste in landfill (linear model) and transporting the lime ash to the precast concrete producer (circular model). Figure 7 shows the operating costs of the linear and circular model. The use of lime ash in the production of concrete generates savings by EUR 3.30, due to the elimination of the cost of the filler acquisition by the concrete producer. In addition, transporting the lime ash to the concrete producer generates savings of EUR 0.32 compared to depositing the lime ash in the landfill. Thus, transporting the lime ash to the concrete producer (23 km) does not produce more costs than disposal of the waste in landfill.  
Circular model operations (production of precast concrete with waste and transport of lime ash to the concrete producer) generate savings of 6% compared to linear model operations (production of precast concrete and disposal of waste in landfill) because lime ash is free of cost for the precast concrete industry, and this saves on the cost of acquiring the filler. In addition, the waste producer also saves by eliminating the costs of depositing the waste in landfill. The costs of the linear and circular models (Figure 7) come from materials, transport, investment in equipment, rental of equipment, manpower and energy, as shown in Figure 8. The linear and circular model has no costs arising from waste treatment and external costs, but the value of investment in equipment and transport is the same for the linear and circular model, (EUR 0.12 and EUR 7.95, respectively). This means that the implementation of the circular model can use the equipment that was used in the linear model and does not require additional investment. However, the cost savings of renting equipment, manpower and energy are EUR 0.35, EUR 0.16 and EUR 0.76, respectively, because the tasks resulting from the disposal of waste in landfills disappear in the circular model.  
Furthermore, the use of lime ash as filler in concrete generates savings in transport distance (49 km) compared to the use of natural filler. Therefore, transporting lime ash consumes less manpower and energy than transporting natural filler. Finally, the biggest savings (EUR 2.33) appear in the purchase of materials from the circular model compared to the traditional model, because lime ash constitutes no cost for the concrete producer.
## 1 Introduction
It can achieve a satisfactory accuracy and resolution in a compact and simple design. The measurement principle will be described in detail in Section 2, and the factors that affected the design of this type of precision level are discussed in Section 3. The calibration and application experiments of the prototype sensor will be presented in Section 4. At the end of this paper, some directions to improve the performance of the proposed precision level are discussed.
# Application of Plasma Technology in Bioscience and Biomedicine
###### Abstract  
Plasma technology has been an integral part of research in life sciences for decades through its role in the manufacture and modification of material surface characteristics of many common laboratory consumables, and it is still of interest in many fields, including the treatment of biomaterials and implants. In recent years, the use of plasma at room temperature and atmospheric pressure (cold atmospheric plasma) has led to a shift in the field by putting the focus on biomedicine due to its applicability to heat-sensitive materials, including biomaterials, and expanding the traditional applicability of plasmas to cells and tissues.  
Consumption, biomedical applications of cold plasmas have experienced a surge in recent years due to both the evolution of plasma sources to be used under atmospheric pressure and in handy devices, and the increasing need of our society to find novel solutions to unresolved health problems.  
The versatility of plasmas and the vibrant advances in the field are reflected in the high number of papers collected in this Special Issue and their wide scope of topics and applicability. Cold plasmas, both at low pressure and at atmospheric pressure, are reflected in the contributions, and the latter being employed both directly on materials and cells, as well as being used to produce plasma-treated liquids which find equivalent applications to plasmas in the different areas mentioned.  
[PERSON], [PERSON], [PERSON], C.  
pressure and also aerosol-assisted atmospheric pressure PE-CVD. The atmospheric pressure coatings required an intermediate gradient layer to grant proper adhesion. An accurate modulation of the experimental conditions, both at low and atmospheric pressure, allowed control of the film chemical structure and surface properties, to permanently promote or discourage the cellular adhesion on the surfaces of biomaterials [2].  
Focusing on surface functionalization of poly(l-lactic acid) (PLLA), different methods were compared to oxygen plasma by [PERSON] et al., showing that the oxygenated carboxyl and hydroxyl groups favored endothelial cell adhesion and spreading. This approach was applied to producing 3D printed PLLA bioresorbable stents, which may improve endothelialization after implantation [3].  
An interesting work by [PERSON] et al. investigated the application of collagen coatings onto polystyrene microplates for cell culture using a cold atmospheric pressure plasma process. Compared to standard wet chemical collagen thin films, plasma coatings were thinner and appeared equivalent in terms of coverage, porosity, structure, and chemistry. Significantly, the cold plasma method preserved both the primary and secondary structure of the protein and this resulted in high biocompatibility and cell activity that was at least equivalent to the standard wet chemical technique. The significance of these results is discussed in relation to the benefits of a single step plasma coating in comparison to the traditional multi-step aseptic coating technique [4].
## 3 Results and Discussion
### Stability Study  
To confirm the stability of the target functional nano-Bi\({}_{2}\)O\({}_{3}\) films, the extra-long exposure experiments were conducted as follows. All samples were divided into seven groups (I-0 day, II-30 days, III-60 days, IV-90 days, V-120 days, VI-150 days, and VII-180 days), each going through a different exposure time. Figure 5 shows the relationship betweeen the CA of C with the fresh samples in group I. When the exposure time continued increasing, the CA of samples gradually decreased with an infinitesimal range, even for group VII after 180 days of exposure. The difference in value of CA between samples in group I and VII was only ca. 2\({}^{\circ}\), which indicated that the product after a half-year exposure test still possessed great superhydrophobicity, furthermore demonstrating the outstanding stability of functional nano-Bi\({}_{2}\)O\({}_{3}\) films with wide application in lots of domains.
# Compatible Ground Motion Models for South Korea Using Moderate Earthquakes
## 2 Materials and Methods
### Earthquake Data Sources  
The ground motion data were sourced from KIGAM. KIGAM operates a network of ground motion recording stations across South Korea, with each station assigned an abbreviated name, suggesting the location [36]. A list of KIGAM recording stations is provided in Table 2. Also listed in the table are the measured shear wave velocities in the upper 30 m (V\({}_{\text{S30}}\)) for certain stations. Note that not all stations have a measured V\({}_{\text{S30}}\). This parameter is typically used to describe local-site effects in many GMMs and is required in several NGA GMMs. For purposes of GMM computations, results from another study utilizing geologic and topographic features to estimate V\({}_{\text{S30}}\) are used [37]. Each station recorded data in three directions, indicating the axis of movement: east-west (E), north-south (N), and up-down (Z). For each azimuth, the recorded data underwent essential processing and baseline correction procedures. These included downloading an unprocessed earthquake acceleration record with 10 s of data before and 200 s of data after the listed start time. The instrument response was removed and a cosine taper of 5% was applied to both the front and back of the series. The data were then filtered using a 4 th-order low-pass filter with a corner frequency of 0.1 Hz and a 5 th-order high-pass filter with a corner frequency of 50 Hz, which happened to be the Nyquist frequency. These resultant frequencies were the result of varying the corner frequencies and performing a visual analysis of the plotted ground motions and their corresponding Fourier spectra. An additional cosine taper of 5% was applied to both the front and back of the filtered data. Baseline correction involved fitting and removing a 4 th-order polynomial to the \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Date**} & **Time** & **Lat.** & **Lon.** & **Depth** & \multirow{2}{*}{**M\({}_{\text{W}}\)**} & **M\({}_{\text{L}}\)** & \multicolumn{3}{c}{**Fault Plane (\({}^{\circ}\))**} \\  & & **(UTC)** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **(km)** & & & **Strike** & **Dip** & **Slip** \\ \hline Ulsan & 2016/07/05 & 11:33:07 & 35.51 & 129.99 & 12.0 & 4.5 & 5.0 & 114 & 87 & \(-\)15 \\ \hline Gyeongju FS & 2016/09/12 \({}^{\text{F}}\) & 10:44:34 & 35.77 & 129.19 & 14.0 & 5.1 & 5.1 & 26 & 65 & \(-\)179 \\ \hline Gyeongju & 2016/09/12 & 11:32:57 & 35.76 & 129.20 & 13.6 & 5.5 & 5.8 & 26 & 69 & 171 \\ \hline Pohang & 2017/11/15 \({}^{\text{P}}\) & 05:29:33 & 36.12 & 129.36 & 4.6 & 5.5 & 5.4 & 222 & 60 & 136 \\ \hline Pohang AS & 2018/02/10 \({}^{\text{A}}\) & 20:03:04 & 36.08 & 129.33 & 4.2 & 4.7 & 4.6 & 21 & 55 & 116 \\ \hline \hline \end{tabular} \({}^{\text{F}}\) foreshock. \({}^{\text{A}}\) aftershock. \({}^{\text{P}}\) possible induced or triggered earthquake.  
\end{table} 
Table 1: Earthquake events considered in this study.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 4 Results
### Evaluation of the Computational Complexity  
The evaluation of the computational complexity in this study focused on the static tensor calculation model used for real-time update passage records into the historical data and global data calculations. For our ETC gantry tensor model, the number of gantries is denoted as \(m\), the number of vehicles recorded historically is \(n\), the number of updated vehicles is \(\Delta n\), and the number of core tensor and factor matrix iterations in the static high-order ETC gantry tensor model is \(o\left(m^{2}\left(n+\Delta n\right)^{2}\right).\) The daily traffic volume on the G50 Nanjing highway is more than 20,000 vehicles; therefore, the calculation volume under the static tensor model is extremely large. If \(D^{(i)}\) is the number of features of each point in the \(i\)th-dimensional space in the improved dynamic tensor decomposition model for ETC gantries, the high sparsity of the ETC gantry data leads to \(D^{(i)}\ll\prod n^{i}\). Thus, the computational complexity of the factor matrix increment in each dimension in the conventional calculation is \(o\left(n^{(i)}D^{(i)}\right)\), whereas the computational complexity of the factor matrix increment in the approximate tensor decomposition is only \(o\left(D^{(i)}\right)\). The computational time complexity for updating the core tensor corresponding to the static high-order tensor decomposition and IDTD are \(o\left(T\sum_{i=1}^{3}\left(D^{(i)}\left(n^{(i)}\right)^{2}+\left(n^{(i)} \right)^{3}\right)\right)\) and \(o\left(T\sum_{i=1}^{3}r^{(i)}\left(n^{(i)}+1\right)D^{(i)}\right)\), respectively, which show that the computational complexity is significantly reduced.
## 1 Introduction
### Research Gap  
Section 1.1 reveals that the performance enhancement of point multiplication on BECs has optimized latency, area, and throughput. Nevertheless, the following points need further exploration:  
* **Scalability and Adaptability:** It is not well understood how existing architectures perform when dealing with various sizes of curves as well as different levels of security.
* **Resource Efficiency versus Speed Trade-off:** Balancing computational speed and hardware resource utilization needs further exploration, especially for resource-constrained devices.
* **Detailed Performance Metrics:** Many studies lack an evaluation of performance metrics including speed, which is crucial when making design choices.  
Consequently, a hardware architecture is needed that can effectively utilize BEC for PM operation through some algorithmic and architectural innovations.
# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
###### Abstract  
The rapid expansion of economic globalization and trade has led to a sharp increase in the shipping investment risks currently faced by cities around the world. This study aims to explore the risk warning mechanism of shipping input under public health events to establish an effective risk warning method. This would enable the rapid identification of potential risk inputs and the implementation of targeted prevention and control measures to ensure public health and safety. This study investigates the mechanisms of both intra-regional and cross-border risk transmission within shipping networks. It establishes a transmission dynamics model (termed the SEIR-SEI model) incorporating climatic, economic, and health factors to analyze the potential inherent risks of regional shipping nodes. It uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model, modified by the entropy weight method, to calculate the importance of nodes in the shipping network. This comprehensive approach considers the network's clustering coefficient, betweenness centrality, closeness centrality, degree centrality, and eigenvector centrality. To validate the practicality of the model, this study selects shipping data with Shanghai, China, as the destination node to conduct simulation computations of different risk propagation chains. The findings demonstrate that overall risk transmission is determined by the joint influence of a node's inherent risks and propagation probabilities. This study not only clarifies the process of cross-border transmission of public health events through the shipping network between cities of different countries, but also provides insights for the application of shipping input risk assessment systems, enriching the academic research on logistics network propagation.  
shipping input; complex network; risk transmission; warning system Article
## 2 Road Traffic Situation Assessment Model
### Road Traffic Situation Assessment  
The collaborative monitoring method proposed in this paper is an approach to assess the urban road traffic situation under emergencies. This approach accomplishes two parts of the calculation: one is road traffic situation awareness, and the other is the road traffic situation prediction. It provides data support for road traffic diversion and management and relieves road pressure.
## 4 Discussion
### Model Verification  
In this study, we tested the predictive ability of our process-based model by comparing the time series of simulated NPP at each plot with available plot-specific annual tree-ring data. Previous studies have shown that estimated NPP does not necessarily show a one-to-one correlation with data from annual tree-rings, as the latter may be formed from both the current years' assimilated carbon as well as from carbon stored from previous years [70; 71]. Across our study sites, the Pearson correlation coefficient between the simulated NPP and the observed ring width index was always positive and ranged from 0.34 to 0.50, suggesting that the general patterns of interannual C-fluxes are captured by the process-based model. Other studies that have used annual tree-ring width indices to compare independent model NPP output for three forest types found statistically significant results only in two of them, with an R\({}^{2}\) = 0.12-0.21 [72]. On the other hand, a study that used annual tree-ring data to estimate NPP found a statistically significant correlation between NPP with RWI, with \(\rho\) ranging from 0.109 to 0.804 from the smallest to the largest tree diameter class, respectively [69].  
It should be noted that, as our model does not specifically simulate between-tree competition, recruitment and/or mortality, but rather uses a static stand structure, these results mainly verify the realism of the temperature and water availability effects on primary productivity. Thus, our model is suitable for simulating C-fluxes in stands with a known structure, which should be provided as a model input from either forest inventory or remote sensing data.
## 3 Experiment Procedure
### Weighting Objective Method  
The weighting objective method is selected to use in this situation by building a weighted sum of the two objectives (\(R_{a}\) and \(Z_{w}\)) [21,22] to form the unified objective function \(Y\) as:  
\[Y(\mathbf{x})=\lambda_{a}R_{a}(\mathbf{x})-\lambda_{w}Z_{w}(\mathbf{x}) \tag{1}\]  
where  
\[\lambda_{a}+\lambda_{w}=1 \tag{2}\]  
and  
\[\mathbf{x}=[P\quad F\quad V_{\mathrm{s}}\quad V_{\mathrm{w}}]^{T} \tag{3}\]  
where \(\lambda_{a}\) and \(\lambda_{w}\) are the weighting coefficients that represent the relative importance of each criterion and reflect closely importance of objectives.  
Figure 5: Single-factor experiment. **(a)** Abrasive size (\(R_{a}\): roughness; \(Z_{w}\): MRR; \(P\): abrasive size); **(b)** Contact force (\(R_{a}\): roughness; \(Z_{w}\): MRR; \(F\)/\(N\): contact force); **(c)** Belt linear speed (\(R_{a}\): roughness; \(Z_{w}\): MRR; \(V_{\mathrm{s}}\): belt linear speed); **(d)** Feed speed (\(R_{a}\): roughness; \(Z_{w}\): MRR; \(V_{W}\): feed speed).  
As the surface roughness and MRR are two different goals, so it is necessary to evaluate them at the same level. Generally, normalization is the best way to solve this problem as:  
\[\hat{Y}(\mathbf{x})=\lambda_{a}\hat{R}_{a}(\mathbf{x})-\lambda_{w}\hat{Z}_{w}( \mathbf{x}) \tag{4}\]  
where \(\hat{Y}\) is the new comprehensive goal that can be seen as normalized \(Y\). \(\hat{R}_{a}\) is the normalized surface roughness and \(\hat{Z}_{w}\) is normalized MRR. Since the surface roughness is more important than MRR for products, it is reasonable to set the value of weighting coefficient \(\lambda_{a}\) be larger than \(\lambda_{w}\).  
In the experiment, \(R_{a}(\mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{3},\,\ldots,\,\mathbf{x} _{n})\) and \(Z_{w}(\mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{3},\,\ldots,\,\mathbf{x} _{n})\) is the measured data, \(\hat{R}_{a}(\mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{3},\,\ldots,\, \mathbf{x}_{n})\) and \(\hat{Z}_{w}(\mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{3},\,\ldots,\, \mathbf{x}_{n})\) is normalized. The normalized method can be described as follows:  
\[\hat{R}_{a}(\mathbf{x}_{i})=\frac{R_{a}(\mathbf{x}_{i})-\text{min}R_{a}( \mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{3},\,\ldots,\,\mathbf{x}_{n})}{ \text{max}R_{a}(\mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{3},\,\ldots,\, \mathbf{x}_{n})-\text{min}R_{a}(\mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{ 3},\,\ldots,\,\mathbf{x}_{n})}\hskip 56.905512 pt(i=1,\,\,2\ldots\,\,n) \tag{5}\]  
\[\hat{Z}_{w}(\mathbf{x}_{i})=\frac{Z_{w}(\mathbf{x}_{i})-\text{min}Z_{w}( \mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{3},\,\ldots,\,\mathbf{x}_{n})}{ \text{max}Z_{w}(\mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{3},\,\ldots,\, \mathbf{x}_{n})-\text{min}Z_{w}(\mathbf{x}_{1},\,\mathbf{x}_{2},\,\mathbf{x}_{ 3},\,\ldots,\,\mathbf{x}_{n})}\hskip 56.905512 pt(i=1,\,\,2\ldots\,\,n) \tag{6}\]
# Specific Resonant Properties of Non-Symmetrical Microwave Antennas
## 3 Results and Discussion  
In this section, we firstly present the well-known results of measuring resonant properties of classical cylindrical antennas. Then we present our new findings based on experimental measurements of the resonant properties of flat microwave antennas.