esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Our findings show that the drug has significant antimicrobial activity and can inhibit and destroy microbial mass effectively, even at low concentrations. Microscopy images support these results, verifying that sildenafil citrate successfully inhibits and disrupts biofilm formation, even at doses below the inhibitory concentration.  
It is well-documented that bacterial infections lead to biofilm formation in more than 80% of cases, causing chronic conditions and high morbidity rates, according to the National Institutes of Health [18,19]. _P. aeruginosa_ biofilm formation is linked to various chronic human infections, including chronic wound infections, otitis, prostatitis, urinary tract infections, and infections related to medical devices such as intravenous catheters and heart valves. These infections often persist and progress despite treatment, highlighting the importance of biofilm formation as a key virulence factor [6].  
Sildenafil citrate works by inhibiting the 5 PDE enzyme, which is crucial for converting cGMP to GMP and cAMP to AMP. This inhibition increases cellular cGMP levels, which affects protein kinase G (PKG) activity, influencing ion channel conductance, cellular apoptosis, and glycogenolysis [20,21]. The drug's antimicrobial and antibiofilm effects may relate to this mechanism of action.  
Our study also explored the synergistic effects of sildenafil citrate in combination with commonly used antimicrobials using the checkerboard method (FIC index assay). We discovered that when used together, these drugs act synergistically, boosting the drug's effectiveness [22,23]. This finding indicates a promising approach for treating _P. aeruginosa_ infections with sildenafil citrate, especially when combined with other antimicrobials. However, further studies are necessary for each strain to determine the impact of specific resistance mechanisms on synergy.  
Considering the decrease in newly approved antimicrobials over the past decade and the rise in drug-resistant bacteria, sildenafil citrate represents a promising approach for treating _P. aeruginosa_ infections. Although more research is needed, the drug could provide a timely and effective therapeutic option, especially in combination with approved medications.
## 4 Experiment Verification  
The proposed method in this paper is tested and verified in terms of availability and security in a low-power wireless communication IoT environment. Detailed items are as follows:  
Availability aspect  
* Communication method/step for encryption key generation (theoretical verification).
* Communication method/step for encryption key recovery (theoretical verification).
* Key generation time per packet loss (experimental verification).
* Key generation time, Memory used, CPU used for key generation operation (experimental verification).  
Figure 10: Dynamic encryption key generation.  
Security aspect  
Randomness and unpredictability of encryption key (theoretical verification).  
Prevention of key generation information exposure (experimental verification).  
Security against Encryption Key Hijacking Attack (theoretical verification).
# DTFS-eHarris: A High Accuracy Asynchronous Corner Detector for Event Cameras in Complex Scenes
## 3 Materials and Methods
### Double Threshold Filter  
The main purpose of the double-threshold filter is to reduce redundant events and noise. This step can significantly reduce the number of events to be detected, greatly improve the real-time performance of the algorithm and improve the quality of corner detection.  
A sudden and drastic brightness change would generate multiple events in one pixel almost instantaneously according to Equation (4). Due to hardware limitations, however, there exists a minimum amount of time between consecutive events triggered at the same pixel. However, such time-intensive events are not necessary for corner detection tasks.  
\[\Delta I(x,y)=I(x,y,t+\Delta t)-I(x,y,t)=pc \tag{4}\]  
The time filter proposed in [25] removes redundant events in corner detection tasks. Events are divided into two parts by a single threshold. Only events whose timestamps over the threshold will be detected, other events will be treated as redundant events.  
However, this method can only remove redundant events through a single threshold and can not remove filter noisy events. Therefore, we propose a double threshold filter to remove redundant and noisy events. Timestamps are stored in two global SAE, \(\mathcal{S}\) and \(\mathcal{S}^{*}\), the SAE \(\mathcal{S}\) is defined as \(\mathcal{S}:(x,y)\in\mathbb{R}^{2}\mapsto t_{l}\in\mathbb{R}\), where \(t_{l}\) is the timestamp of the latest event triggered at the pixel location \((x,y)\), \(\mathcal{S}^{*}\) is defined as \(\mathcal{S}^{*}:(x,y)\in\mathbb{R}^{2}\mapsto(t_{r},t_{l})\in\mathbb{R}^{2}\), and \(t_{r}\) is the reference time. \(\mathcal{S}\) will be updated for every event and \(\mathcal{S}^{*}\) will be updated only if the previous event in the same pixel was not triggered within the time window [\(t_{r}\), \(t_{l}\)], as shown in Equations (5) and (6).  
\[\Delta t=\mathcal{S}(x,y)_{i}-\mathcal{S}(x,y)_{i-1} \tag{5}\]  
\[\left\{\begin{array}{ll}\text{update $\mathcal{S}$ and $\mathcal{S}^{*}$}&\Delta t\in[t_{r},t_{l}]\\ \text{update $\mathcal{S}$}&\text{others}\end{array}\right. \tag{6}\]  
Only the events in \(\mathcal{S}^{*}\) are considered in subsequent processing, the rest of the events are treated as redundant events or noise.
## 4 Fiber Sensing Technologies
### Optical Sensing for Gas Detection  
Optical gas sensing is mostly associated with the detection of optical absorption by the targeted gas when an optical beam propagates in a region where the gas is present. Several techniques with such a purpose have been developed over the years; the most successful one is the so-called Wavelength Modulation Spectroscopy (WMS). It is a rather sensitive technique for detecting atomic/molecular species, with the advantage that it can be implemented in the near-infrared region by considering optical telecommunications technology [6].
###### Abstract  
The interface between nanof fillers and matrix plays a key role in determining the properties of nanocomposites, but the interfacial characteristics of nanocomposites such as molecular structure and interaction strength are not fully understood yet. In this work, the interfacial features of a typical nanocomposite, namely epoxy resin (EP) filled with boron nitride nanosheet (BNNS) are investigated by utilizing molecular dynamics simulation, and the effect of surface functionalization is analyzed. The radial distribution density (RDD) and interfacial binding energy (IBE) are used to explore the structure and bonding strength of nanocomposites interface. Besides, the interface compatibility and molecular chain mobility (MCM) of BNNS/EP nanocomposites are analyzed by cohesive energy density (CED), free volume fraction (FFV), and radial mean square displacement (RMSD). The results indicate that the interface region of BNNS/EP is composed of three regions including compact region, buffer region, and normal region. The structure at the interfacial region of nanocomposite is more compact, and the chain mobility is significantly lower than that of the EP away from the interface. Moreover, the interfacial interaction strength and compatibility increase with the functional density of BNNS functionalized by CH\({}_{3}\)-(CH\({}_{2}\))\({}_{4}\)-O- radicals. These results adequately illustrate interfacial characteristics of nanocomposites from atomic level.  
nanocomposite; epoxy resin; boron nitride; interface characteristics; surface functionalization; molecular dynamics Article
## 3 Results and Discussion  
### Choice of the Best Performing Carbon-Based By-Product Filler  
The results obtained for the REF mortar and those manufactured with UFS and GCH fillers after 28 days of curing are reported in Table 3.  
Figure 5: Curves of Bode-\(|Z|\) and Bode-phase obtained as an example in (**a**) reference mortar (REF) mortar and (**b**) GNP mortar.  
It was observed that the addition of UFS filler decreases both the flexural and compressive strength of mortars compared to REF by about 13% and 6%, respectively. On the contrary, the use of GCH filler only slightly affects the R\({}_{\text{f}}\) and does not modify the R\({}_{\text{c}}\) values. This effect is probably related to the better interfacial transition zone (ITZ) between GCH particles and cement paste compared to that of UFS particles. [PERSON] and [PERSON] [33] reported that the ITZ between biochar particles and cement paste is more compact than that between natural sand and cement paste. This is related to the porous surface of biochar, which provides an interlocking mechanism caused by the migration of water from the internal reservoirs of the particles to the surrounding paste. This migration promotes hydration reactions and results in a denser and less porous ITZ. Probably, the pore structure of GCH, which is similar to that of biochar, generates the same effect [34]. On the contrary, UFS particles do not have a porous surface, therefore their ITZ with cement paste, even if still quite good, is weaker than that of GCH.  
Results obtained for the electrical resistivity show that DC and AC measurements follow the same trend. However, the addition of UFS and GCH has a different effect on the mortars. In particular, UFS always causes an increase in the \(\rho\) values of mortars, which increase by 43% and 28% in DC and AC, respectively. Conversely, the use of GCH decreases the electrical resistivity of mortars by 31% and 42% in DC and AC, respectively. This is probably related to the higher carbon content of GCH compared to UFS, which is equal to 77% and 33%, respectively [21].  
The results obtained for both the mechanical and electrical properties of mortars show that GCH gives a better performance than UFS as a conductive filler in mortars.
## 3 Data Collection and Observation Quality Assessment
### The Number of Satellites and PDOP  
There is a relationship between tracked satellites and the observation data quality. When the number of tracked satellites is larger, it illustrates a better observation quality [31]. Figure 5 shows the number of satellites that can be observed by the Huawei Mate40, P40 and Novatel receiver, as shown in the figure, and the total number of satellites that can be observed is nearly 25. Overall, the number of observed BDS satellites is larger than that of GPS, while the number of satellites observable by the Galileo system is relatively small. The reason can be attributed to the BDS-2 and BDS-3 ability to provide services, and many satellites can be used to conduct Positioning, Navigation and Time (PNT) experiments. Our experiment was conducted in China, so the number of observed BDS satellites is high. The average number of observed BDS satellites for smartphones is around 15, while it is 8 for GPS satellites. Moreover, the fluctuations in the number of observed satellites for smartphones are larger.  
The PDOP can reflect the strength of the satellite geometric distribution between the ground-tracked station and the satellite; moreover, the feasibility and accuracy of the positioning can also be reflected, and it is an important indicator to determine the reliability of the GNSS measurements. The smaller the PDOP, the stronger the satellite geometry configuration and the smaller the positioning error, i.e., the higher the accuracy, and vice versa. Figure 6 shows the PDOP of the Huawei Mate40, P40 and Novatel receiver. As can be seen from the figure, the PDOP of the Huawei P40 is slightly better than that of the Huawei Mate40, indicating the better satellite geometry configuration for Huawei P40. The BDS PDOP for smartphones is superior to GPS, and the mean PDOP for both GPS/BDS and GPS/BDS/Galileo is less than 1.8.  
Figure 5: Number of satellites for Huawei Mate40 and P40.
## 3 Results
### Improvement of Piezoresistive Characteristics by Using Sensor Pattern  
Based on the first experiment described in Section 3.2, we hypothesized that the sensitivity of the NCSS is closely related to its length because structural deformation brings whole piezoresistive change from entire units of the sensor. We designed three grid sensors via a thin polyimide film mask. The mask pattern was designed by CAD (Computer Aided Design) program and was fabricated by laser cutting process. The CNT ink was manually printed through the mask on the cantilever, as shown in the lower right-hand corner of Figure 5. To secure sensor electrical stability with better conductivity, we conservatively used the 0.5 wt.% to fabricate the grid sensor samples.  
We measured each resistance change with respect to beam bending five times and averaged the measured values after eliminating the maximum and minimum data points. Figure 5 shows the results. When comparing the amount of change in the normalized resistance with respect to the strain of the sensor samples shown in Figures 3 and 5, it can be seen that the 40-mm pattern type (G.F.: 0.87) has a larger change in the normalized resistance for the same strain than the 40-mm bulk type (G.F.: 0.22).  
As we expected from the above, the NCSS sensitivity improvement is exactly proportional to its incremental length ratio. We obtained higher sensitivity in cases where the sensor had a longer length or denser pattern per unit area. We also achieved more linear, higher sensitivity and consistent data from the grid-type NCSS than from the bulk-type sensor. Additionally, we deduced that NCSS strain output may be expressed by the summation of the entire sensor covered surface. According to our literature survey, the  
Figure 5: Grid-type NCSS experiment: (**a**) grid-type NCSS samples and (**b**) sensitivity characteristic of the grid-type NCSS with respect to length pattern change.  
strain sensitivity of a conventional foil strain gauge is only related to its material properties and is not much affected by its length [32], which is a notable piezoresistive feature of the NCSS. We conclude that since NCSSs are affected by the overall strain change in a given direction, the geometric pattern design is one of the most important variables in axial load and deformation measurements.
## 2 Materials and Methods
### Tunnel Point Cloud Displacement Detection
#### 2.3.2 Displacement Detection  
To monitor tunnel deformation, this study performed a detailed displacement analysis for the shield ring within each tunnel cross-section. The methodology involves superimposing point cloud data collected over different time intervals to track shield ring-level displacements accurately. Each cross-section in the tunnel lining is composed of multiple shield rings, and analyzing displacement at the ring level allows for precise detection of localized deformations and stresses within the tunnel structure. This process uses the tunnel point cloud data from individual shield ring separation processes. Using the ICP algorithm, point clouds from different epochs are aligned, ensuring accurate positional correspondence between points across time intervals. The displacement calculation provides a quantitative measure of each shield ring point cloud movement, helping identify areas experiencing higher displacement at the cross-section level.  
The displacement of each matched point is calculated using the Euclidean distance formula [38; 39]. If \(x_{0},y_{0},z_{0}\) are the coordinates of a point in the tunnel shield ring, \(P_{0}\), and \(x_{i},y_{i},z_{i}\) are the coordinates of the point in the \(P_{i}\), the displacement, \(d_{i}\), is given by Equation (6).  
\[d_{i}=\sqrt{\left(x_{i}-x_{0}\right)^{2}+\left(y_{i}-y_{0}\right)^{2}+\left(z _{i}-z_{0}\right)^{2}} \tag{6}\]  
where \(x_{0},y_{0},z_{0}\) and \(x_{i},y_{i},z_{i}\) are the coordinates of the matched points in \(P_{0}\) and \(P_{i}\), respectively.  
This formula measures how much each point on the tunnel surface moves between the two measurements, as shown in Figure 4.  
Additionally, by examining the changes in ring orientation and alignment, the analysis captures linear displacements and any rotational misalignments that may signal structural instability. This allows for a smoother and more interpretable visualization in the 3D model [40]. The ring-level displacement monitoring highlights potential structural vulnerabilities and stress points within the shield rings, enabling targeted maintenance interventions [21; 41]. By focusing on the ring within each cross-section, the methodology provides a highly localized deformation assessment, ensuring that even subtle shifts and rotations within the tunnel lining are detected early. The results demonstrated the robustness of MLS in capturing accurate deformation metrics, thus offering a valuable tool for ongoing tunnel infrastructure monitoring.  
Figure 4: Example point cloud displacement visualization. (**a**) Shield ring MLS point cloud. (**b**) Point cloud displacement magnitude (3D view).
# Fault-Tolerant Resolvability and Extremal Structures of Graphs
## 2 Preliminaries
### Anti-Prism Graphs
\begin{array}{ll}\ell-k+2,&1\leq k\leq\ell;\\ 1,&s=\ell+1;\\ k-\ell-1,&\ell+2\leq k\leq\leq n.\end{array} \right.\]  
Also, \(d(z_{\ell+1},y_{1})=n-\ell\). So, from the above discussion, we see that \(z_{\ell+1}\) distinguishes the vertices of \(\Gamma\). Hence, \(R=\{z_{1},z_{3},z_{\ell+1}\}\) is a resolving set of \(\Gamma\). This shows that \(\beta(\Gamma)\leq 3\). **Claim 2:** There exists a fault-tolerant resolving set of cardinality \(14\) in \(\Gamma\). \(\Gamma\) contains a resolving set \(R\) of order \(3\). We show that \(\Gamma\) has a fault-tolerant resolving set of \(14\). Now, we can see from Figure 4 that \(N[z_{1}]=\{z_{1},z_{2},z_{n},y_{1},y_{2}\}\), \(N[z_{3}]=\{z_{2},z_{3},z_{4},y_{3},y_{4}\}\), and \(N[z_{\ell+1}]=\{z_{\ell},z_{\ell+1},z_{\ell+2},y_{\ell+1},y_{\ell+2}\}\). Moreover, we find that \(\lambda(N_{\Gamma}(z_{1}))=\lambda(N_{\Gamma}(z_{2}))=\lambda(N_{\Gamma}(z_{ \ell+1}))=\emptyset\). Thus, by using Lemma 3, we find that \(R^{\prime}=\{y_{1},y_{2},y_{3},y_{4},z_{1},z_{2},z_{3},z_{4},y_{\ell+1},y_{ \ell+2},z_{\ell},z_{\ell+1},z_{\ell+2},z_{n}\}\). Thus, there exists a fault-tolerant resolving set of \(\Gamma\) with cardinality \(14\) when \(n\geq 10\). By using Proposition 4, the above discussion, and Inequality (1), we find that \(\beta^{\prime}(\Gamma)\geq 4\). As a result of Theorem 10, we present the following corollary. It provides a fault-tolerant metric dimension analog of Proposition 5. Corollary 2: _The anti-prism graph \(A(n)\) has a constant fault-tolerant metric dimension._  
Proof: By Theorem 10, we have \(4\leq\beta^{\prime}\big{(}A(n)\big{)}\leq 14\). This implies that the fault-tolerant metric dimension of \(A(n)\) does not depend on the defining parameter \(n\). Thus, by definition, \(A(n)\) is a family of graphs with a constant fault-tolerant metric dimension. In view of Lemma 3 and Proposition 4, we propose the following conjecture on the greatest lower bound on the fault-tolerant metric dimension for the anti-prism graph \(A(n)\). Conjecture 2: _Let \(\Gamma\) be an anti-prism graph \(A(n)\) and \(n\geq 10\); then, \(\beta^{\prime}(\Gamma)\geq 14\), and thus, \(\beta^{\prime}(\Gamma)=14\)._  
Figure 4: The anti-prism graph \(A_{3}\).
# Secure Control of Linear Controllers Using Fully Homomorphic Encryption
## 3 Preliminaries
### RLWE Problem  
The RLWE problem is the underlying mathematically difficult problem of securing cryptographic methods. Before introducing the RLWE problem, it is necessary to familiarize oneself with some of the notations in the definition that follows.  
Let \(\Phi_{M}(X)\) be the \(M\)th cyclotomic polynomial of degree \(N=\phi(M)\) for a positive integer \(M\). Let \(R=\mathbb{Z}[X]/(\Phi_{M}(X))\) be the ring of integers in the \(Q[X]/(\Phi_{M}(X))\) number field. For the residue ring of \(R\) modulo an integer \(q\), we write \(R_{q}=R/qR\). We write \(R_{q}^{\vee}=R^{\vee}/qR^{\vee}\), where \(R^{\vee}\) is the dual fractional ideal of \(R\). For a positive integer modulus of \(q\geq 2\), \(s\in R_{q}^{\vee}\), \(r\in(\mathbb{R}^{+})^{N}\), and an error distribution of \(\chi:=\lfloor\Psi_{r}\rfloor_{R^{\vee}}\).  
**Definition 1** ([34]).: _(Ring learning with errors (RLWE) distribution) We define \(A_{N,q,\chi}(s)\) as the_  
**RLWE** _distribution that is formed by uniformly sampling \(a\gets R_{q}\) at random, \(e\leftarrow\chi\) and returning \((a,a\cdots+e)\in R_{q}\times R_{q}^{\vee}\)._  
**Definition 2** ([35]).: _((Decision) RLWE) The (decision) RLWE, denoted by \(\mathbf{RLWE}_{N,q,\chi}(\mathcal{D})\), is the problem of distinguishing arbitrarily many independent samples chosen according to \(A_{N,q,\chi}(s)\) for a random choice of \(s\) sampled from the distribution \(\mathcal{D}\) over \(R^{\vee}\) from the same number of uniformly random and independent samples from \(R_{q}\times R_{q}^{\vee}\)._
## 3 Test Results and Discussion
### Sub-Instability Stage Zoning Characteristics in Pre-Fabricated Jointed Red Sandstone  
Figure 10 depicts the percentage of the OA segment and AB segment within the sub-instability stage. It is noteworthy that as the joint size increases, the proportion of the static sub-instability stage in relation to the entire sub-instability stage gradually diminishes, while the dynamic sub-instability stage gains prominence. When considering the observed failure modes of jointed red sandstone at different times, it becomes apparent that larger joint sizes lead to a prolonged dissipation of accumulated elastic energy within the red sandstone, resulting in an extended period of rock failure.  
Upon analyzing data from multiple experimental sets, it becomes evident that jointed rock masses of varying sizes exhibit distinct levels of interaction between the jointed and intact regions during different stress stages in the instability process. The instability process of jointed rock masses can be subdivided into three pivotal stages: the strong departure from the linearity stage (NO), the static sub-stability stage (OA), and the dynamic sub-stability stage (AB). During the strong departure from the linearity stage, the jointed region experiences initial failure, marked by the initiation and propagation of cracks in this area. At this stage, the intact region of the jointed rock mass remains largely undamaged, and the interaction between the jointed and intact regions is relatively weak. The intact region continues to bear the majority of the load. In the sub-instability stage, cracks in the jointed region rapidly extend and interconnect, causing damage to the intact region. During this stage, the interaction between the jointed and intact regions reaches its peak. Upon entering the fully unstable stage, the jointed rock mass experiences complete failure.  
Figure 10: The percentage of OA segment and AB segment in the sub-instability stage.
## 7 Rationale for ka-band selection  
Following is a list of items that help understanding why the Ka band has been selected for the altimeter instrument of the AltiKa payload.