esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to compare the longitudinal efficacy between chlorhexidine gluconate (CHX; 0.12%) and cold atmospheric plasma (CAP) in reducing oral biofilm pathogenicity, utilizing a quantitative light-induced fluorescence-digital (QLF-D) camera. Oral microcosm biofilms were developed for 2 days on 57 hydroxyapatite disks. These biofilms were treated with distilled water for 1 min, CHX for 1 min, and CAP for 2 min over the course of 6 days. The red fluorescence intensities of the biofilms were measured using a QLF-D and expressed as pre- and post-treatment red/green ratios (Rating/G). The bacterial viability (ratio of the green-stained area to the total stained area, Ratio\({}_{\rm G/G+R}\)) was calculated using live/dead bacterial staining: the total and acidic bacterial counts were determined. A significant intergroup difference was found between Ratio\({}_{\rm R/G}\) changes according to the treatment period (\(p<\) 0.001). The Ratio\({}_{\rm R/G}\) observed within the CAP-treated group was significantly lower compared with the CHX-treated group at every interval of measurement (\(p<\) 0.001). The CAP-treated group also exhibited a lower Ratio\({}_{\rm G/G+R}\) and more weakened bacterial aggregation compared with the CHX-treated group (\(p<\) 0.05). In the group treated with CAP, the counts of both total and aciduric bacteria were substantially reduced compared with the DW group, with a statistically significant reduction (\(p<\) 0.001). Therefore, CAP may be more effective in minimizing oral microcosm biofilm pathogenicity than 0.12% CHX.  
antibacterial effect; cold atmospheric plasma; microcosm biofilm; oral biofilm; quantitative light-induced fluorescence-digital Article Efficacy of Cold Microcosm Biofilms  
[PERSON]
## 2 Materials and Methods
The remaining dried film was then shaved off from the glass plate with a razor blade and finely ground in a mortar for TEM observation. To measure the size distribution of silver nanoparticles, the diameters of four hundred particles were measured from a TEM image using image analysis software ImageJ (version 1.53; U.S. National Institutes of Health, Bethesda, MD, USA). After nanopore generation, a zeta potential analyzer (ELS-Z2 PT, Otsuka Electronics Co., Ltd., Tokyo, Japan) was used to measure the zeta potential of nano-silver dispersion and the YSZ surface, and pH was measured with a pH meter (pH METER D-51, HORIBA Ltd., Osaka, Japan). After the irradiating laser to remove the excessively dried film, the surface morphologies of the irradiated sample were observed by a field-emission scanning electron microscope (FE-SEM, Inspect F50, FEI Co., Hillsboro, OR, USA). An energy-dispersive X-ray spectroscopy (EDX, EDAX Inc., Pleasanton, CA, USA) was used to analyze the elemental distribution. For the cross-sectional investigation of pores, a focused ion beam system (FIB, Quanta 3D 200i, FEI Co., Hillsboro, OR, USA) was used to cut the sample. An FE-SEM (ZEISS GeminiSEM 500, Carl Zeiss AG, Oberkochen, Germany) was also used to observe the cross-section of the pore. Elemental distribution under the irradiated surface was investigated by glow discharge optical emission spectroscopy (GDOES, GD-Profiler2, HORIBA Ltd., Osaka, Japan). Moreover, the material phase structure was evaluated by a laser micro-Raman spectrometer (InVia Raman Microscope, Renishaw plc., Kingswood, UK) with a laser wavelength of 532 nm and a beam diameter of 1 \(\upmu\)m.  
For the quantitative evaluation, the number of pores with and without embedded material was counted. Nine SEM images of the same field of view at 40,000\(\times\) were prepared for each irradiation condition. The ratio of the number of pores with implantation to the  
Figure 2: Schematics of experimental procedure: (**a**) nanopore fabrication on entire YSZ surface by irradiating femtosecond pulsed laser; (**b**) nano-silver dispersion liquid is dropped onto the surface and dried in a vacuum desiccator; (**c**) laser irradiation is used to embed nanoparticles and remove excess dried film; (**d**) SEM images of irradiated surface in process (**a**); (**e**) overview of YSZ sample. total number of pores in the field of view was defined as the \"pore-embedded ratio\", and the average value for nine images was calculated.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 3 Simulation Results
### Position
#### 3.1.4 Comparison of the Best Positions  
Creating more asymmetry by placing the ABH further away from the center does not improve the system's frequency response in general. It is much more a question of the right placement on the panel than a measure of absolute distances. For this reason, the best three positions, P12, P21 and P22, from the previous graphs, are compared in Figure 8.  
Figure 7: Comparison of the normalized mean surface velocity \(\frac{\overline{v}_{x,y}}{p}(f)\) for the diagonal row of different ABH positions P11, P22, P33.  
The most effective normalized mean surface velocity \(\overline{v}_{x,y}\) frequency response is observed when the ABH is positioned at P22. At this location, both even modes, \(f_{2,1}\approx 150\) Hz and \(f_{1,2}\approx 190\) Hz, are excited in a manner similar to the single excitation observed at positions P12 and P21. As a result, the resonance peaks are evenly distributed along the logarithmic frequency axis. Considering the circular ABH mode \(f_{\rm ABH}\approx 120\) Hz, the average distance between the peaks below \(f<200\) Hz is approximately one third of an octave. This characteristic enables the DMA to effectively absorb sound in a uniformly spaced manner.
# An Application of Manifold Learning in Global Shape Descriptors
## 4 Experiments
### Retrieval Results
Models of the same class with articulating parts are in different scales, shape, and structure. The 2D PCA projections of 33-dimension descriptors from all four algorithms are shown in Figure 5.  
As illustrated in Figure 5, the original Shape-DNA is highly sensitive to scales. Multiple methods are presented in [20] to make the descriptor normalized to scale. cShape-DNA represents a normalized version of it by multiplying the descriptor with the surface area. Although cShape-DNA can separate models from each other, classes are not separated efficiently. LESI outperforms the other algorithms by providing distinct descriptors, which can separate classes. Shape descriptors offered by LESI prove superior to the other algorithms in the shape retrieval and classification tasks, as described below and in the next section, respectively. To examine the superiority of LESI quantitatively, we computed multiple standard retrieval measures including Nearest Neighbor (NN), First Tier (FT), Second Tier (ST), e-Measure (E), and Discounted Cumulative Gain (DCG). These measures represent state-of-the-art quality metrics used when evaluating matching results for shape-based search engines [56]. Table 1 reports the results of shape retrieval. Boldface numbers indicate the highest value for each measure per each dataset. In Table 1, it is clear that the LESI descriptor outperforms all other methods concerning all measures in retrieving models from the McGill dataset. When retrieving models of the TOSCA dataset, LESI  
Figure 4: Experiment on separating different models of men: (**a**) a sample of [PERSON] (left) and [PERSON] (right) models; and (**b**) PCA projection of LESI descriptors of different men models using \(t=2d_{max}^{2}\), \(t=5\), and \(t=15\). Figure 5: 2D PCA projection of shape descriptors computed from: (**a**) original Shape-DNA; (**b**) cShape-DNA; (**c**) GPS; and (**d**) LESI algorithms on McGill dataset. outperforms all methods concerning FT, ST, and E measures. Shape-DNA outperforms LESI by a higher value for NN and DCG measures, due to the poor discrimination between [PERSON] and [PERSON] performed by the LESI descriptor. However, it does not diminish the validity of our claim that LESI performs well for meshes with non-uniform sampling or peculiarities.
## 4 Results and Discussion
### Trade-Off Analysis  
The resulting Pareto front reveals the characteristic trade-off between maximizing operational efficiency (f\({}_{1}\)) and minimizing environmental impact (f\({}_{2}\)) in the tube-making process. Starting from a baseline efficiency of 87.2% with 2007.5 kg CO\({}_{2}\)/day emissions, optimization identified solutions ranging up to 95.2% efficiency. The convex shape indicates an initial region (87-91%) where significant environmental improvements can be achieved with minimal efficiency sacrifice, followed by a steep region (91-94%) where marginal efficiency gains require proportionally smaller environmental compromises, and finally a plateau region (94-95%) where further efficiency improvements lead to increased emissions due to resource intensification.
## 3 Results and Discussion
### Effect of Membrane Materials on the Permeate Flux of the DCMD Process
The thermal stability of PP membrane at high temperatures is average, with a surface energy of 3.0 \(\times\) 10\({}^{-3}\) N/m, with lower membrane performance. Additionally, PTFE membrane has good thermal stability, with the lowest surface energy of 9.0 \(\times\) 10\({}^{-3}\)\(-\)2.0 \(\times\) 10\({}^{-2}\) N/m [53]. In addition, it has high vapor permeability and high moisture resistance. Therefore, the highest permeate flux is produced by PTFE membranes with low surface energy, high porosity (narrow size of pores dispersion, small average size of pores), and low thermal conductivity. \begin{table}
\begin{tabular}{c c c c} \hline
**Scenario** & **Pore Size A (\(\upmu\)m)** & **Material B** & **Average Permeate Flux/J kg/(m\({}^{2*}\)h)** \\ \hline
1 & 0.22 & PTFE & 8.62 \\
2 & 0.45 & PTFE & 11.42 \\
3 & 1.0 & PTFE & 12.87 \\
4 & 0.22 & PVDF & 8.40 \\
5 & 0.45 & PVDF & 10.25 \\
6 & 1.0 & PVDF & 11.64 \\
7 & 0.22 & PP & 2.00 \\
8 & 0.45 & PP & 1.16 \\
9 & 1.0 & PP & 4.00 \\ \hline \end{tabular}
\end{table} 
Table 3: The performance of different membrane materials in DCMD experiments. Figure 6: Comparison of membrane permeate fluxes with different pore sizes (**a**) PTFE, (**b**) PVDF, (**c**) PP. Figure 7: Relationship between membrane thickness and permeate flux for different membrane materials.
# Experimental Study of the Behavior of Rectangular Excavations Supported by a Pipe Roof
## 1 Introduction  
Road-box jacking (RBJ) technology is used for the construction of shallow underground passages in dense urban areas (Figure 1). The advantage of this technology lies in its effective role in the reduction of the soil settlement and the improvement of the excavation stability in soft soils [1]. The performances of the pipe-roof excavation depend on the interaction between the soil, the pipe-roof and the excavation. This interaction is particularly complex and largely affects the contribution of the pipe roof to the reduction of the soil settlement as well as to the improvement of the excavation stability.  
Studies using limit equilibrium or limit analysis methods were used for the analysis of the tunnel face stability [2; 3; 4]. Experimental studies were also conducted to explore excavation stabilities [5]. [PERSON] et al. [6] analyzed the evolution of arching effects on the soil mass surrounding tunnels. [PERSON] et al. [7] conducted small-scale centrifuge experiments to study the arching effects. [PERSON] et al. [8] recognized that soil dilation may have an adverse influence on the observed deformation and failure mechanisms at low stress levels. [PERSON] et al. [9] determined that shear wedge damage in the direction of the minimum principal stress can easily lead to collapse in tunnels. Little attention was given to the analysis of the role of a pipe-roof in reducing soil settlement and providing stability improvement. This paper presents an experimental study of the mechanical behavior of a rectangular excavation supported by a pipe-roof. The study uses an advanced monitoring system, which allows tracking of the soil settlement, the pipe deformation and the soil pressure. Analysis of these tests will enhance theunderstanding of the role of the pipe-roof in reducing both the soil settlement and the soil pressure on the excavation. It will also show the influence of pipes on the deformation mechanism of the soil, which is useful for the development of limit-analysis design methods for pipe-roof excavations.
## 2 Detection Principle
### Propagation Model of Pulse-Echo Method  
In this study, a pulse-echo method was used with one ultrasonic probe. The piezoelectric chip of the probe converts electric waves into ultrasonic waves. When the ultrasonic wave encounters the back wall of the sample, the wave is reflected and received by the probe. The ultrasonic wave is then converted back into an electric wave by the piezoelectric chip. The propagation time of the ultrasound is measured using the time difference of the back wall echoes, which eliminates the influence of the coupling agent thickness [11; 24].  
The ultrasonic propagation path of the ultrasonic reflection and the diagram of the detected waveform are schemed in Figure 1a,b.  
The piezoelectric chip of the probe emits the ultrasonic wave \(E\). When the wave \(E\) reaches the surface of the sample, a portion of the ultrasonic wave is reflected and returns along the same path to be detected by the probe (indicated as wave \(F\) in Figure 1). The other part of the wave \(E\) continues into the sample (of a thickness H) and propagates until reflected by the back wall surface. The reflected wave travels to the sample-probe interface where it is again transmitted and reflected. The transmitted ultrasonic wave is detected by the probe as wave \(B_{S1}\), and the reflected part of the ultrasonic wave continues to propagate in the sample. It is reflected at the back wall surface for the second time. The secondary reflected wave is detected at the sample-probe interface and is detected by the probe as wave \(B_{S2}\). The \(T\) is the time difference between the first wave peak of the reflected wave \(B_{S1}\) from the back wall of the epoxy sample and the first wave peak of the second reflected wave \(B_{S2}\), as shown in Figure 1b [11; 24].
## 1 Introduction
[15] analyzed underwater communication using quasi-static electric fields, validating the quasi-static model through measurements. [PERSON] et al. [16] wirelessly transmitted text and images at 16 bits/s underwater using Maxwell displacement currents from triboelectric nanogenerators. Herein, we established an underwater electric field communication system based on biomimetic principles and explored ways to improve its communication reliability. The remainder of this paper is organized as follows. Section 2 describes the theory of underwater electric field communication and performs underwater electric field modeling and simulation using CST 2021 software to determine the parameters for underwater electric field communication. Guided by the theoretical parameters from the previous section, Section 3 presents the design of a DSSS BPSK underwater electric field communication system; one significant advantage of DSSS technology is its strong resistance to interference. Section 4 provides comparative experiments and discusses the experimental results. Finally, Section 5 summarizes the conclusions. The results indicate that the constructed electric field communication system qualitatively validated the simplified discharge organ model and achieved error-free communication at a rate of 11.2 m/1.2 kHz under low power consumption of 5 W. Low-power and compact systems are expected to be mounted on small underwater robots for operations.
# Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance
## 3 Materials and Methods
### Generalized Mean Information Coefficient  
The generalized mean information coefficient (GMIC), proposed by [PERSON] and [PERSON] [35], extends the maximal information coefficient (MIC) by introducing a more flexible framework for detecting associations, particularly in finite sample settings. The GMIC enhances the sensitivity of MIC by incorporating a generalized mean approach, which allows for a more nuanced detection of statistical dependencies between variables.  
The core innovation of GMIC lies in its use of a maximal characteristic matrix, defined as follows:  
\[C^{*}(\mathbf{x},\mathbf{y})_{i,j}=\max_{kl\leq ij}\{C(\mathbf{x},\mathbf{y})_ {kl}\}, \tag{13}\]  
where \(i,j\geq 2\). This matrix captures the highest normalized mutual information that can be obtained with grid sizes up to \(ij\), providing a robust measure of the strongest possible association achievable for each grid resolution.  
GMIC itself is calculated using a generalized mean, which is defined as follows:  
\[GMIC_{p}(\mathbf{x},\mathbf{y})=\left(\frac{1}{Z}\sum_{ij\leq B(n)}(C^{*}( \mathbf{x},\mathbf{y})_{i,j})^{p}\right)^{1/p}, \tag{14}\]  
where \(p\) is a tuning parameter that adjusts the measure's sensitivity to different aspects of the data, and \(Z=\#\{(i,j):ij\leq B(n)\}\) denotes the number of grid sizes considered. By varying \(p\), GMIC can emphasize different features of the data's structure; for example, when \(p\rightarrow\infty\), it approaches the original MIC, while for \(p\rightarrow-\infty\), GMIC converges to the minimal value from the maximal characteristic matrix  
The introduction of the tuning parameter \(p\) provides flexibility, allowing GMIC to detect a broader range of complex associations across various data types. In simulations, GMIC has demonstrated improved power over MIC for detecting associations, particularly in finite samples. It retains desirable properties, such as convergence to zero for independent variables as the sample size increases, and convergence to one for noiseless, monotonic relationships.  
Overall, GMIC enhances the capabilities of the MINE suite by offering a more adaptive and sensitive approach to uncovering diverse relationships in complex datasets, making it a valuable tool for exploratory data analysis in high-dimensional contexts.
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 1 Introduction
### Mode Shapes by Demodulation of Uniform--Rate Scanning CSLDV Output Signals  
#### 2.3.1 Measured Mode Shapes of the Damaged State  
For a linear structure subjected to sinusoidal excitation at the \(r^{th}\) modal frequency \(\omega_{r}\), the measured velocity vibration \(\mathbf{V}(x,y,t)\) of a specific point \(P(x,y)\) perpendicular to the surface can be expressed as  
\[\mathbf{V}(x,y,t)\ =\ \mathbf{V}_{R}(x,y)\cos(\omega_{r}t)+\mathbf{V}_{I}(x,y)\sin(\omega_{ r}t) \tag{9}\]  
A convenient method for demodulation is simply to multiply the CSLDV output signals by in-phase and quadrature signals at the excitation frequency, respectively, and given as  
\[\mathbf{V}(t)\cos(\omega_{r}t) =\mathbf{V}_{R}(t)\cos^{2}(\omega_{r}t)+\mathbf{V}_{I}(t)\sin(\omega_{r}t )\cos(\omega_{r}t) \tag{10}\] \[=\tfrac{1}{2}\mathbf{V}_{R}(t)+\tfrac{1}{2}\mathbf{V}_{R}(t)\cos(2\omega_ {r}t)+\tfrac{1}{2}\mathbf{V}_{I}\sin(2\omega_{r}t)\] \[=\dots(LPF)\dots\ =\ \tfrac{1}{2}\mathbf{V}_{R}(t)\]  
\[\mathbf{V}(t)\sin(\omega_{r}t) =\mathbf{V}_{R}(t)\cos(\omega_{r}t)\sin(\omega_{r}t)+\mathbf{V}_{I}(t)\sin ^{2}(\omega_{r}t) \tag{11}\] \[=\tfrac{1}{2}\mathbf{V}_{I}(t)+\tfrac{1}{2}\mathbf{V}_{R}(t)\sin(2\omega_ {r}t)-\tfrac{1}{2}\mathbf{V}_{I}(t)\cos(2\omega_{r}t)\] \[=\dots(LPF)\dots\ =\ \tfrac{1}{2}\mathbf{V}_{I}(t)\]  
where the LPF represents the low-pass filter. Because the scan frequency is slow enough, the signal components at frequency \(2\omega_{r}\) are removed with the LPF and the real and imaginary parts of ODS can be obtained. Practically, due to the mechanical delay of the scanning mirror, the actual measurement position of the laser spot deviates from the ideal position given by the control signal. Before the mode  
Figure 1: Two different scan strategies of area scanning. (**a**) Lissajous strategy with a sinusoidal rate scan. (**b**) Zigzag strategy with a uniform rate scan.  
shape demodulation, the position signal needs to be offset to match the vibration signal as much as possible. Moreover, we need to adjust the signal phase so that the real and imaginary parts of ODS attain their maximum and minimum amplitudes. It can be expressed as  
\[\mathbf{V}_{R}(x,y)\ =\ \mathbf{V}(x,y)\cos(\alpha),\mathbf{V}_{I}(x,y)\ =\ \mathbf{V}(x,y)\sin(\alpha) \tag{12}\]  
where \(\alpha\) is the optimized phase value. In this case, the corresponding real part of ODS \(\mathbf{V}_{R}\) is the mode shape.
## 2 Materials and Methods
### Programming and Calibration of the Device
#### 2.4.1 First Round of Calibration  
This was the first contact with the sensor. The upward and downward trend of the concentration recorded by the sensor corresponded to those obtained by the KIMO equipment. Even so, there was a large error that must be resolved by calibration. Figure 3 shows the first calibration line. This error became larger in case of high CO\({}_{2}\) concentrations. This is shown in Figure 4. However, after the adjustment, the error between the prototype device and the KIMO equipment decreased significantly, as shown in Table 3. Therefore, a further calibration had to be performed, taking into account these considerations:  
1. The atmosphere at the time of data acquisition must be as stable as possible. Sudden changes in temperature, humidity, or CO\({}_{2}\) must be avoided.
2. The clinic device and the KIMO equipment are placed elsewhere. Areas close to the window or door of the room must be avoided. This makes it possible to ensure the previous point considered.
3. The number of data collected (minimum 60) must be increased.  
Figure 3: Linear regression of the first round of calibration.
## 4 Discussion
### Known Plaintext Attack Analysis  
In addition to the exhaustive search attack, a known plaintext attack is also a common attack type [29]. The purpose of this attack is to obtain the decryption secret key through the attack, so as to crack other ciphertexts. However, this attack is invalid for our system, because our decryption secret key depends on the original image. If the original image is different, the secret key will be different. In other words, even if the attacker obtains the secret key of the known plaintext through some means, the attacker can only decipher the current plaintext and cannot decipher other plaintext. Therefore, our proposed scheme is immune to this attack scheme.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 2 Related Work
Unlike the active radio communication scenarios described above, the good performance of backscatter communication is affected by channel conditions and energy-harvesting conditions. Our work is designed to maximize goodput and to adapt to energy-harvesting and channel conditions.
## 4 Results and Discussion
### Numerical Simulation of Precipitation Infiltration in the Field Test Site
#### 4.2.4. Spatial and Temporal Discretization  
The 0-1500 cm simulated soil profile was divided into nine layers and four mass balance zones (0-200 cm, 200-500 cm, 500-1100 cm, and 1100-1500 cm) according to the soil properties and the hydrogeological conceptual model of the field test site. The soil profile was divided into 300 units at equal intervals of 5 cm, and had 301 nodes and eight observation points (20 cm, 90 cm, 200 cm, 250 cm, 500 cm, 600 cm, 1100 cm, and 1500 cm).  
The simulated period of 855 days ranged from 1 July 2018 to 1 November 2020. Time discretization was used in the simulation, and the interval of the time discretization was gradually adjusted according to the number of iterations of the convergence. The initial time interval was set to \(1\times 10^{-5}\) day; the maximum time step was set to 1 day.