esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Anticancer Activity  
The \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles were tested for cytotoxicity against human A549 cancer cells at a range of doses (1-100 mg/mL). A phase-contrast inverted microscope was used to examine the cell growth. After receiving treatment with \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles, A549 cells shrank and their nuclei became fractured and compacted, as shown in the microscopy images in Figure 2b. At lower hematite concentrations (1-10 mg/mL), significant morphological alterations were not seen. According to our research, the substance has a concentration-dependent cytotoxic effect on A549 lung cancer cells (Figure 2c). At a concentration of 15.6 \(\mu\)g/mL, which is regarded as the IC50 value, \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles demonstrated 50% cell killing. According to the designated activity levels of the US National Cancer Institute's Center For Cancer Research, the produced nanoparticles are moderately active [23].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline S. No. & \multicolumn{2}{c}{**Name of the Organism**} & \multicolumn{4}{c}{**Zone of Inhibition (\(\mu\)g/mL\({}^{-1}\)) a**} \\ \hline  & & **PE** & **Fecl3.6H2O** & **\(\alpha\)-Fe2O3** & \
\begin{tabular}{c} **Positive** \\ **Control** \\ \end{tabular} &
\begin{tabular}{c} **Negative** \\ **Control** \\ \end{tabular} \\ \hline
**1.** & _Streptococcus pyogenes_ & \(9\pm 1.37\) & \(14\pm 1.26\) & \(25\pm 2.56\) & \(20\pm 1.32\) & \(0\) \\
2. & _Staphylococcus aureus_ & \(7\pm 1.24\) & \(12\pm 1.43\) & \(23\pm 2.23\) & \(17\pm 1.55\) & \(0\) \\
4. & _Escherichia coli_ & \(6\pm 0.76\) & \(7\pm 0.20\) & \(23\pm 1.34\) & \(13\pm 1.22\) & \(0\) \\
3. & _Pseudomonas aeruginosa_ & \(8\pm 1.23\) & \(10\pm 1.21\) & \(22\pm 1.23\) & \(15\pm 1.35\) & \(0\) \\
5. & _Candida albicans_ & \(7\pm 1.19\) & \(11\pm 0.17\) & \(19\pm 0.98\) & \(11\pm 1.12\) & \(0\) \\
6. & _Aspgillus niger_ & \(5\pm 0.99\) & \(6\pm 1.27\) & \(17\pm 1.62\) & \(9\pm 0.75\) & \(0\) \\ \hline \hline \end{tabular}  
* Mean values of three triplicates \(\pm\) standard deviation. **PE**—plant extract, **Fecl3.6H2O**—ferric chloride hexahydrate, \(\alpha\)-**Fe2O3**—biosynthesisized iron oxide nanoparticles, **Control**—ethyl acetate.  
\end{table}
Table 1: Antimicrobial activity of biosynthesised \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles against microorganisms.  
Figure 2: Bio-fabricated \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles: (**a**) antimicrobial activity, (**b**) MTT assay, (**c**) anticancer activity.
## 3 Method
### Data Preprocessing
In this paper, considering that it takes a minimum of 30 s to successfully measure oxygen saturation [23], signals from 10, 20, and 30 s were used, resulting in dataset lengths of 300, 600, and 900, respectively. As mentioned in Section 3.1, the oxygen saturation values were measured at a sampling rate of 1 Hz during image acquisition. Therefore, the length of the generated data needed to be adjusted accordingly. For example, when using the signals from a 10-second period, only one of the 10 oxygen saturation values measured during that time should be selected. In this paper, to best represent the decreasing trend of oxygen saturation while preserving the original values, the average of the median values was used. To calculate this, the \(n\) oxygen saturation values (in this case, \(n\) = 10, 20, 30) were first sorted, and then the average value of the oxygen saturation values corresponding to the third interval, divided into five intervals, was taken. Figure 5 shows the resulting signals when the original signal and the oxygen saturation values for 10, 20, and 30 s were calculated using the average of the median values for one of the participants.
## 4 Numerical Results  
### Parameter Setting  
The simulation uses the channel model of single input and three outputs, and then we get three different CIR of underwater acoustic channel [33]. The generation process of channel is as follows. We assume the number of paths in three channels is 6, the average multipath delay is 14 ms, and the time delay of adjacent paths obeys the exponential distribution. We can achieve six random variables which are negative exponential distributed, then we calculate the sampling point positions delay_p corresponding to six paths. We also assume the amplitudes of paths are Rayleigh distributed with the average power decreasing exponentially with delay, where the difference between \(T\_guard=20\) ms is \(p=20\) dB. Therefore, we can conclude that:  
\[delta\_k=ae^{(-delay\_p/(fset\_\{rms\}))} \tag{29}\]  
where \(\alpha=1\), \(t\_\{rms\}=T\_guard/\log(10^{p/10})\). Now, we can achieve random variables that are Rayleigh distributed in each path, and then we assume that the value of random variable is \((a+bj)\). The amplitude of each path can be expressed as:  
\[delta=\left|\sqrt{0.5*delta\_k}*(a+bj)\right| \tag{30}\]  
where \(||\) is the modulus of complex number. Finally, the sparse multipath channel is generated by normalizing the amplitudes of all paths [34; 35]. The noise included in simulation is the gaussian white noise in band, and a fixed frequency offset of 300 Hz is added in simulation in order to verify the phase compensation capability of the algorithm, while the constellation may rotate by down sampling, which make the receiving signal become the constellation. The sampling frequency of the communication system is 48 kHz, the carrier frequency is 8 kHz, and the bandwidth is 6 kHz. We select 100 groups of signals from BPSK, QPSK, and 8 PSK, respectively, to calculate the identification rate. The simulation parameter settings are shown in Table 1.
## 1 Introduction
Therefore, we advocate the use of complementary validation techniques for connectivity restoration algorithms, especially in safety-critical applications. Formal methods are advanced mathematics-based techniques, having computer tool support, used for modeling and formal specification of complex and critical systems. Z notation [6] is a model-centered approach based on sets, sequences, bags, relations, functions and predicate logic which is used at an abstract level of specification of systems. Z can be used for specifying behavior of distributed as well as sequential programs because of its accommodation of abstract data types. This paper presents a **P**artitioning detection and **C**onnectivity **R**estoration (PCR) algorithm that proactively identifies critical/non-critical nodes and rapidly repairs the topology in case of critical node failure. Each actor determines whether it is critical or not based on localized information and designates a suitable neighbor as a backup that continuously monitors and triggers a recovery in case of primary failure. The algorithm is recursively executed until all actors become connected. We use formal and non-formal techniques for correctness and performance validation. We model a WSAN network as a dynamic graph and transform PCR into a corresponding formal specification using Z notation. This formal specification is analyzed and validated using the Z Eves tool [7]. Moreover, the performance of PCR is validated through extensive simulations. The simulation results confirm the effectiveness of PCR and the results are shown to outperform contemporary schemes found  
This paper is organized as follows: Section 2 discusses the system model and problem statement. The related work is discussed in Section 3. The proposed PCR algorithm is detailed in Section 4. Section 5 presents the formal specification and analysis of PCR. The performance evaluation of PCR is presented in Section 6. Section 7 concludes the paper.
# Displacement Monitoring of Subway Tracks and Tunnels According to Adjacent Construction
## 2 Displacement Monitoring of Tunnel Structures
### Measurement Results  
Figure 3 shows the measurement results for the first and 18 th months of the adjacent excavation work. Six sensors were installed in the cross-section of each tunnel: TL-1, left wall; TL-2, left shoulder; TL-3, left crown; TL-4, right crown; TL-5, right shoulder; and TL-6, light wall.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Construction Step** & **Construction Condition** & **Monitoring Months** \\ \hline Step—0 & General condition & 0 \\ \hline Step—1 & Retaining wall construction & 1–4 \\ \hline Step—2 & Stage 1 excavation & 5–7 \\ \hline Step—3 & Stage 2 excavation and raker construction & 8–11 \\ \hline Step—4 & Stage 3 excavation and slab construction & 12–13 \\ \hline Step—5 & Stage 4 excavation and slab construction & 14–15 \\ \hline Step—6 & Stage 5 excavation and slab construction & 16 \\ \hline Step—7 & Stage 6 excavation and slab construction & 17–18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adjacent construction steps and measured months.  
Figure 3: Tunnel displacement measurements: (**a**) Vertical displacement (1 st month); (**b**) Vertical displacement (18 th month).  
The existing subway tunnel structure monitoring system simultaneously monitored data from all sensors installed onsite, as shown in Figure 3. However, visually evaluating the measurement results proved challenging. In addition, the tunnel structure safety evaluation results were numerically evaluated based on the maximum value during the measurement period. In this study, time-series data were analyzed based on sensor TL-2 in Section C, where the maximum tunnel displacement occurred. Figure 4 shows the time-series data from sensor TL-2 in Section C, considering the adjacent excavation work.  
An analysis of the vertical displacement of the tunnel structure (Section C, sensor TL-2) due to the adjacent excavation work showed a vertical displacement of \((-)0.49\) mm for the first month of measurement, as shown in Figure 4. After 10 months of monitoring, the vertical displacement for 1 month was \((-)1.64\) mm. The vertical displacement decreased by approximately 2.35 times after 10 months of construction compared with that in the first month of monitoring. The vertical displacement for 1 month after 18 months of construction was \((-)1.81\) mm. The vertical displacement 18 months after construction increased by approximately 10.37% compared with that at 10 months after construction. Therefore, the vertical displacement of the tunnel structure decreased by approximately 1.33 mm owing to the adjacent excavation work.
# Synthesis of Polystyrene Particles with Precisely Controlled Degree of Concaveness
## 1 Introduction  
Anisotropic polymeric particles have attracted great attention due to their importance in both fundamental research and potential applications. On the one hand, anisotropic particles can be used as experimental models to study the structure and dynamics of condensed states such as crystal and liquid crystal. On the other hand, anisotropic particles have begun to be used as novel materials in molecular recognition, self-assembly, and Pickering emulsion, to name a few applications [1; 2; 3]. With the development of synthesis strategies, a variety of polymeric particles with anisotropic shapes have been synthesized [4; 5; 6; 7; 8], but precise control of the shape is still very challenging. Shape is one of the most basic and important properties of polymeric particles. Common anisotropic shapes include concave, mushroom, dumbbell, snowman, ellipsoid, etc. [9; 10; 11; 12]. Among them, concave-shaped particles have shown great potential in orientational self-assembly, targeted bonding and nano-/microchemical reactors, and hence attracted intensive research effort in recent years [13; 14; 15; 16]. In general, the preparation of concave polymeric particles falls in the following three categories: microfluidics, solvent swelling and core-shell shrinkage [15; 16; 17; 18; 19]. These methods have disadvantages, such as sophisticated procedures, low-throughput production, and polydispersity in size and/or shape. Recently, seeded emulsion polymerization has made a breakthrough in the preparation of anisotropic particles, enabling thepreparation of monodispersed concave particles in large quantity [20; 21; 22]. However, a simple method for systematic control of the degree of concaveness is still rare, which not only greatly limits the applications of the particle as polymeric materials, but also prevents the particle from being used as a model system where a tunable degree of concaveness is crucial for any quantitative research [22; 23].  
In this paper, a simple method for the synthesis of polymeric particles with a controlled degree of concaveness is proposed. PS particle with one single dimple is synthesized by a well-documented seeded emulsion polymerization procedure reported by our group and others [23; 24]. Then the particles are dispersed in a solvent mixture of DMF/Water, a good--poor solvent combination for PS. Some of the non-crosslinked PS chains inside the particles are dissolved by DMF and removed by subsequent wash, leading to buckling of the dimpled particles [25]. By simply change the fraction of DMF in the solvent mixture, we can control the amount of PS dissolved. And consequently, we can precisely tune the buckling process to produce polymeric concave particles with well-defined degree of concaveness. Moreover, the polymeric concave particles are found to self-assemble, driven by depletion attraction when their size and shape match.
## 2 Basic Equations
\begin{array}{l}\sum\limits_{m=1,3}^{\infty}\frac{2}{b}(-1)^{\frac{m-1}{2}} \Big{[}S_{2 mn}\chi_{2 mn}-(-1)^{\frac{m-1}{2}}\mu\Big{]}h_{m}-\sum\limits_{m=1,3} ^{\infty}\frac{2}{b}(-1)^{\frac{m-1}{2}}S_{2 mn}\frac{\lambda_{n}}{2}i_{m}\\ +\sum\limits_{m=1,3}^{\infty}\frac{2}{b}\Big{[}(-1)^{\frac{m-1}{2}}S_{2 mn}\chi _{1 mn}-1\Big{]}d_{n}\\ -\sum\limits_{m=1,3}^{\infty}\frac{2}{a}(-1)^{\frac{m-1}{2}}S_{2 mn}\frac{\delta _{m}}{2}e_{n}=0\ \ \ \ \ n=1,3,5\cdots\end{array}  \tag{25}\]\[ \begin{array}{l}\sum\limits_{n=1,3}^{\infty}\frac{2}{b}S_{3 mn}\chi_{2 mn}h_{m}- \left[2b\big{(}1-r_{y0}\big{)}+\sum\limits_{n=1,3}^{\infty}\frac{2}{b}S_{3 mn} \frac{\chi_{2}}{2}\right]i_{m}+\sum\limits_{n=1,3}^{\infty}\frac{2}{a}S_{3 mn} \chi_{1 mn}d_{n}\\ -\sum\limits_{n=1,3}^{\infty}\frac{2}{a}S_{3 mn}\frac{\delta_{3}}{2}e_{n}=0\qquad m =1,3,5\cdots\\ \\ \sum\limits_{n=1,3}^{\infty}\frac{2}{b}\Big{[}(-1)^{\frac{n-1}{2}}S_{4 mn}\chi _{2 mn}-1\Big{]}h_{m}-\sum\limits_{n=1,3}^{\infty}\frac{2}{b}(-1)^{\frac{n-1}{2 }}S_{4 mn}\frac{\lambda_{3}}{2}i_{m}\\ +\sum\limits_{n=1,3}^{\infty}\frac{2}{a}(-1)^{\frac{n-1}{2}}\Big{[}S_{4 mn} \chi_{1 mn}-(-1)^{\frac{n-1}{2}}\mu\Big{]}d_{n}\\ -\sum\limits_{n=1,3}^{\infty}\frac{2}{a}(-1)^{\frac{n-1}{2}}S_{4 mn}\frac{ \delta_{3}}{2}e_{n}=0\qquad m=1,3,5\cdots\end{array}  \tag{26}\]  
where  
\[ \begin{array}{l}S_{1 mn}=\frac{3r_{y0}\delta_{m}}{\left(\frac{\sigma_{0}^{2}} {4}+\frac{\lambda_{3}^{2}}{4}\right)^{-\frac{\rho h\omega^{2}}{D}}}\,\ S_{2 mn}=\frac{\frac{\sigma_{0}^{2}}{2}+\mu\frac{ \lambda_{3}^{2}}{4}}{\left(\frac{\sigma_{0}^{2}}{4}+\frac{\lambda_{3}^{2}}{4} \right)^{2}-\frac{\rho h\omega^{2}}{D}}\\ S_{3 mn}=\frac{3r_{y0}\lambda_{n}}{\left(\frac{\sigma_{0}^{2}}{4}+\frac{\lambda _{3}^{2}}{4}\right)^{2}-\frac{\rho h\omega^{2}}{D}}\,\ S_{4 mn}=\frac{\mu\frac{ \delta_{3}^{2}}{4}+\frac{\lambda_{3}^{2}}{4}}{\left(\frac{\sigma_{0}^{2}}{4}+ \frac{\lambda_{3}^{2}}{4}\right)^{2}-\frac{\rho h\omega^{2}}{D}}\end{array}  \tag{27}\]  
The complicated title problem can be turned into dealing with sets of infinite linear algebra equations composed of Equations (24)-(27) via the above derivations. A zero determinant of above matrix is needed since the requirement of non-zero solutions, which gives the natural frequencies. Subsequently, the constants \(d_{n}\), \(e_{n}\), \(h_{m}\) and \(i_{m}\left(m=1,3,\cdots,M;\ n=1,3,\cdots,N\right)\) can be acquired by substituting the obtained natural frequencies into the corresponding matrix. Finally, the associate free vibration mode shapes will be determined through using the \(w_{mn}\) determined by the acquired constants. It is theoretically possible for one to obtain exact results when \(m\) and \(n\) are close to infinity. Actually, convergent results with desired accuracy can be obtained by solving sets of finite equations containing the identical count of unknowns. In the present study, the identical terms \(s\) is selected for \(m\) and \(n\), with their upper limit setting at \(M=\frac{2s-1}{2}\) and \(N=\frac{2s-1}{2}\).
## 4 Results and Discussion
### Modal Analysis of Numerical Results: Identification of the Excited Modes
#### 4.1.3 Effect of Source Rise Time  
We study the influence of the source rise time in order to evaluate the consequences of a rapid or slow event on the 2 DFT. Three sources with three different rise time: 0.1 us, 1 us and 10 us, modelling a mode I crack opening D\({}_{1}\), are placed 0.25 mm from the midplane of the sample in the gauge section.  
A source with a long rise time represents a ductile event, whereas a source with a short rise time can be associated with a brittle event. Figure 11a shows the 2 DFT of signals generated by the 10 us rise time source. It is clear that the frequency band stimulated by the source is narrow and the stimulated frequencies are low frequencies. As already seen in Figure 10b, the 2 DFT calculated for the 1 us rise time source shows a wider frequency band with higher frequencies. In that case, the L\({}_{01}\) mode is excited between 400 and 700 kHz, which is not the case for the 10 us-rise time source. The 2 DFT calculated for the 0.1 us-rise time source (Figure 11b) shows an even wider frequency band. L\({}_{01}\) mode is stimulated between 400 and 1200 kHz and F\({}_{13}\), L\({}_{05}\) and F\({}_{15}\) modes are also stimulated. When the rise time decreases, the frequency band stimulated by the source gets wider and the 2 DFT exhibits modes with higher frequencies. Certainly, as the rise time increases significantly, it becomes more difficult to detect the waves.  
Each sample can be seen as a propagation medium with a finite number of excitable modes. These modes will be stimulated in a different way if the source's characteristics are different. As was emphasised earlier, various parameters can affect the signals generated by the source and thus the 2 DFT, such as preferential directions of the stress field generated by the source, source position in the sample thickness and source rise time. The results of the simulations represent a useful tool to analyse the effects of the nature of the source, of its position and of the geometry of the specimen. When it comes to AE testing and classification, parameters are extracted from the measured signals, and as it is almost impossible to measure as many signals as requested to calculate the 2 DFT, this tool cannot be used to analyse the data. However, useful information can be extracted from the results presented in the above studies, especially to emphasise relevant parameters to identify AE sources.
## 1 Introduction
If this estimated probability exceeds a predefined threshold, or if the SoftMax confidence score falls below a set limit, the input is classified as unknown interference, and a broadband conservative suppression strategy is triggered. The remainder of this paper is organized as follows. Section 2 introduces typical interference signal models. Section 3 provides a detailed description of the proposed interference suppression method, covering both known and unknown interference types. Section 4 presents simulation experiments to evaluate the suppression performance of the CDDJ method under various interference conditions, including chirp, pulse, and narrowband jamming, along with an in-depth experimental analysis. Finally, Section 5 concludes the paper.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 2 The Model of Single Base Station 3D Sensing
### Differentiation Analysis  
Traditional base stations and low-altitude ISAC base stations clearly exhibit significant differences in design objectives, network coverage, and service requirements. Traditional base stations typically aim for comprehensive coverage of ground communication networks. They primarily serve large-scale mobile users and focus on providing stable communication signals. Traditional base stations typically calculate coverage radius and evaluate station spacing based on edge rate and reference signal receiving power (RSRP) metrics. In contrast,low-altitude ISAC base stations emphasize the precise identification and sensing of aerial targets, mainly playing a crucial role in detecting airborne objects, such as UAVs. Unlike traditional base stations, the low-altitude ISAC base station must further consider the sensing performance in addition to the communication coverage, especially in eliminating sensing blind spots and ensuring target sensing accuracy.  
The spacing between ground base stations in traditional cellular networks is generally about 1.5 times the cell coverage radius, enabling seamless 2D planar coverage through cellular networking. In contrast, in low-altitude 3D sensing networks, due to the limitations mentioned above imposed by the vertical FOV of antennas, sensing blind spots exist in the tower-top area. To meet low-altitude sensing requirements, the proportional relationship between station spacing and cell radius must be adjusted to ensure that the coverage between base stations eliminates blind spots and enhances sensing capabilities.  
The goal of communication services is data transmission, and both main lobe and side lobe coverage can meet the requirements as long as the communication SNR reaches the demodulation threshold of the receiver. However, for sensing modes, especially the accurate detection of target positions, reliance is typically placed on the echoes of the main lobe signals. In contrast, interference from side lobe signals must be suppressed. Therefore, sensing services impose higher demands on coverage areas. In low-altitude ISAC networks, to eliminate sensing blind spots and ensure target detection accuracy, the design of the cell radius often requires smaller station spacing compared to traditional communication networks, emphasizing signal strength and interference suppression capabilities.