esa-sceva/satcom-chunk-collection huggingface dataset:
# Developments in Contact Lens Imaging: New Applications of Optical Coherence Tomography
###### Abstract  
Optical coherence tomography (OCT) is a high-speed and non-contact optical imaging technology widely used for noninvasive cross-sectional imaging of biological objects. Two main OCT technologies have been developed: time domain and Fourier domain technologies. The latter can be further divided into spectral domain OCT, which uses a broadband light source and a spectrometer as a detector, and swept source OCT, which employs a quickly-rotating laser source. Advances in OCT technology have made it one of the most helpful devices in ophthalmic practice. Fourier OCT has revolutionized imaging of the posterior segment of the eye, as well as of anterior structures and has enhanced the ability to diagnose and manage patients. It provides high-resolution information about the tear film, contact lens (CL), a qualitative and quantitative assessment of the anterior eye that is important in CL fitting, and allows possible eye surface changes while wearing CLs to be monitored. Potential swept source OCT technology applications include industrial processes of lens design and quality control. In this paper, we describe clinical applications ant outline a variety of multifunctional uses of OCT in the field of refractive error correction with CLs.  
contact lens imaging; optical coherence tomography +
Footnote †: journal: Medical Imaging
## 3 Principals of Routing Protocols and Their Breakdowns
### Data-Based Routing Protocols
#### 3.4.2 SPIN [66]  
Sensor protocols for information via negotiation (SPIN), a sensor protocol for data by way of arrangement, is a major information-driven steering convention. Before transmitting data, the hubs will haggle to ensure data transfer legitimacy and avoid dazzle spread. They utilize meta-information structures to avoid sending repeated data across the business. There are three types of messages in SPIN: a new data advertisement (ADV), a request for data (REQ), and data. The ADV message is used to alert a neighbor hub that data should be sent. The neighbor hub sends the REQ message to request information, and the neighbor hub sends the Information message to deliver the initial information. Because they only include meta-information, the ADV and REQ signals are less potent than their Information counterparts.
## 3 The Selection Parameters of the MPPT Algorithms
### Circuitry  
Circuitry refers to the hardware required to implement the MPPT algorithm. The complexity and efficiency of the circuitry can influence the overall performance and cost of the MPPT system. MPPT algorithms can be implemented using analog or digital circuitry. Analog implementations are simpler and faster but may lack precision and scalability. Digital implementations, often using microcontrollers or DSPs, offer flexibility, higher precision, and easier integration with other systems.
## 3 Results and Discussions
### Conversion Equations from Dynamic to Static Modulus of Elasticity  
The assessment of the dynamic modulus of elasticity has the advantage that involves a non-destructive method, and it can be performed, in general, directly on site. Since, for design and technical assessment purposes, the values of the static modulus of elasticity are considered, conversion equations were proposed. This section is dedicated to checking whether the readily available equations could also be suitable for rubberized concrete.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & **Length** & **Diameter** & **Mass** & **Freq of Vibration** & **Ed** \\ \cline{2-6}  & **[m]** & **[m]** & **[kg]** & **[Hz]** & **[MPa]** \\ \hline Ref & 0.2 & 0.1 & 3.51 & 10,019 & 35,863 \\ \hline RuC40 & 0.199 & 0.1 & 3.23 & 8278 & 22,381 \\ \hline RuC60 & 0.2 & 0.1 & 3.13 & 7339 & 17,128 \\ \hline RuC80 & 0.199 & 0.1 & 3.01 & 6343 & 12,298 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic modulus.  
Figure 9: Shift of the fundamental frequency of vibration as a function of rubber content.  
The considered equations are the one proposed by [PERSON] [23] (Equation (3)), the equation proposed by [PERSON] and [PERSON] [24] (Equation (4)) and the equation available in BS EN 1992-1-1:2004 [25] (Equation (5)).  
\[\mathrm{E_{c}}=\frac{446.09\times\mathrm{E_{d}^{1.4}}}{\mathrm{\rho_{c}}} \tag{3}\]  
\[\mathrm{E_{c}}=0.83\times\mathrm{E_{d}} \tag{4}\]  
\[\mathrm{E_{c}}=1.25\times\mathrm{E_{d}}-19 \tag{5}\]  
where \(\mathrm{E_{c}}\) is the static modulus of elasticity (GPa), \(\mathrm{E_{d}}\) is the dynamic modulus of elasticity [GPa] and \(\mathrm{\rho_{c}}\) is the density of concrete (kg/m\({}^{3}\)). The obtained results are summarized in Figure 10.  
It can be observed that both Equations (3) and (4) give quite accurate results in terms of the predicted value of the static modulus of elasticity of the reference mix, although they tend to slightly overestimate the experimental results by 6.6% and 5.5%, respectively. When it comes to RuC mixes, Equation (3) underestimates the experimental results by 3.3% for RuC40 up to 20.1% for RuC80. On the other hand, Equation (4) tends to slightly overestimate the experimental results by almost 7% for RuC40 and improve its accuracy with only 4.1% overestimation for RuC80.  
Based on the obtained results, it can be concluded that even though Equations (3) and (4) were developed for regular concrete, they are both accurate for 40% replacement of sand by rubber aggregates with Equation (4), keeping its accuracy even for 80% replacement. Equation (5) is very conservative when it comes to the reference mix and quite inaccurate for rubberized concrete.
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 3 Design Framework
### Topology Selection  
The voltage multiplier, the bridge of diodes, and the single diode are three basic topologies for the design of a rectifier. We designed a Greinacher voltage multiplier because this circuit not only converts the AC input to DC output but also amplifies it [56]. This topology consists of two capacitors and two Schottky diodes. Figure 3 illustrates the Greinacher topology. We see two diodes (D1, D2), two capacitors (C1, C2), an AC source, and the ground GND. In this topology, a half-wave of input sinusoidal voltage passes through one diode. Then, the opposite half-wave surges through the other. Repeating the process doubles the voltage and charges the final capacitor. As a result, the output load has a double voltage [57].  
Figure 2: Design methodology.
## 2 Materials and Methods
### Reference UM-VLC Channel Model
#### 2.3.7 Reference Channel Impulse Response  
If we define \(H_{\text{NLOS},w}\) as the single-hop NLOS link formed by light reflecting off the \(w\)th reflector such that  
\[H_{\text{NLOS},(1)}=\sum_{w=1}^{W}H_{\text{NLOS},\;w}\,, \tag{61}\]  
and \(H_{\text{scatter},n}\) as the NLOS link formed by light reflecting off the \(n\)th scatterer such that  
\[H_{\text{scatter}}=\sum_{n=1}^{N}H_{\text{scatter},\;n}\,, \tag{62}\]  
then the channel impulse response (CIR) for the reference model, \(h_{\text{ref}}(t)\), can be expressed as [7]  
\[\begin{split} h_{\text{ref}}(t)=R\Bigg{[}H_{\text{LOS}}\cdot \delta\bigg{(}t-\frac{d}{c}\bigg{)}+\sum_{w=1}^{W}H_{\text{NLOS},\;w}\cdot \delta\bigg{(}t-\frac{d_{1,w}+d_{2,w}}{c}\bigg{)}\\ +\sum_{n=1}^{N}H_{\text{scatter},\;n}\cdot\delta\bigg{(}t-\frac{d _{n}}{c}\bigg{)}\Bigg{]}\,,\end{split} \tag{63}\]  
where \(\delta(\cdot)\) denotes the Dirac delta function. It is shown in [7; 18] that the scattering component \(H_{\text{scatter}}\) is about three orders of magnitude smaller than the single-hop NLOS component and about four orders of magnitude smaller than the LOS component. Consequently, \(H_{\text{scatter}}\approx 0\) is a good approximation. In the present work, the CIR of the reference model is considered to be  
\[h_{\text{ref}}(t)=R\Bigg{[}H_{\text{LOS}}\cdot\delta\bigg{(}t-\frac{d}{c} \bigg{)}+\sum_{w=1}^{W}H_{\text{NLOS},\;w}\cdot\delta\bigg{(}t-\frac{d_{1,w}+ d_{2,w}}{c}\bigg{)}\Bigg{]}\,. \tag{64}\]
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 5 Bacteria HKs in Phage-Bacteria Interactions
### The Cpx Envelope Stress Response System
#### 5.3.2 Recent Update of Cpx in Antibiotic Hetero-Resistance and Resistance  
The first study [86] demonstrates that hyperactivation of the Cpx pathway, through a gain-of-function mutation in the sensor kinase CpxA (Y144N), confers resistance to \(\beta\)-lactams (e.g., imipenem) and aminoglycos (e.g., amikacin). This resistance arises from two key mechanisms: downregulation of outer membrane porins like OmpF, which reduces \(\beta\)-lactam influx, and upregulation of efflux pumps such as AcrD, which expels aminoglycos. Notably, this protection is antibiotic-specific, with no observed resistance to fluoroquinolones, underscoring the Cpx system's role in targeted envelope remodeling. The Y144N mutation disrupts the inhibitory interaction between CpxA and its periplasmic repressor CpxP, leading to constitutive activation of the response regulator CpxR (CpxR-P). This persistent activation drives envelope stress responses, including peptidoglycan repair and efflux pump expression, highlighting Cpx's adaptability to cell wall damage caused by \(\beta\)-lactams.  
A second study [93] demonstrates that CpxRA mediates transient aminoglycosoxide hetero-resistance in _Enterobacter cloacae_ by activating cell envelope stress responses, leading to the formation of small colony variants (SCVs) with significantly increased minimal inhibition concentration (MIC) for gentamicin and related aminoglycosotides but not other antibiotic classes. Genetic evidence shows that deletion of _cpxRA_ abolishes both SCV formation and aminoglycosoide resistance, while a constitutively active _cpxA_ allele induces high-level resistance, confirming CpxRA's essential role. Importantly, environmental stressors like copper exposure activate this Cpx-mediated resistance pathway in both laboratory strains and clinical bloodstream isolates, revealing a conserved mechanism of phenotypic resistance. These findings highlight how non-genetic, stress-induced activation of the Cpx system can drive antibiotic hetero-resistance through transcriptional reprogramming, potentially contributing to treatment failures and suggesting that common antimicrobial metals like copper may inadvertently promote aminoglycosoide resistance in this important ESKAPE pathogen.
## 3 Results
### Impact of Beam Translation Rate along the Surface of the Sample When Cutting in Water
Since the ablation rate is highest in this case and the ablated channels also exhibit a blunt apex, as described previously, it is believed that the transformation of the Gaussian shape going through a medium with lower (as compared to water) non-linear losses is the main reason behind the shape of the curve depicted in Figure 11. It is known that the coefficient of nonlinear losses, as well as n\({}_{2}\), are lower for water vapor [49; 54; 55], however that depends on various conditions of the water vapor. By taking a three-times lower coefficient of non-linear losses and a 10-times lower coefficient for n\({}_{2}\) (a possible scenario for the transformed water layer), the propagation of the beam through the modified medium was studied numerically. The choice of how much these coefficients were lowered remains somewhat arbitrary, since the concentration, as well as the constituent species within the transformed region remain unknown. It is likely, that much lower values are achieved in the experimental case. In order to qualitatively demonstrate the result of beam propagation, these settings yield an adequate representation. The results are shown in Figure 12. The left side (a) of the image shows the beam as it propagates through water. This was described previously, and will not be discussed in order to avoid redundancy. The right side (b) shows the beam as it propagate through a modified layer. The most important consideration is that a semi flat-top intensity formation still occurs, whereas, the beam loses energy at a slower rate. In addition, when the beam passes through 1.5 mm of the medium, breakup of the beam was not observed. Since self-focusing still occurs, only the two protrusions around the corners of the distribution are present. This demonstrates that, indeed, lower non-linear losses and n\({}_{2}\) are favorable for thick material processing, since a similar (as compared to water) semi-flat distribution is achieved and is sustained for a longer distance. It is our belief that this is the main reason for the peak of the cutting speed graph shown in Figure 11. In fact, the deepest crater that we were able to experimentally micromachine in glass reached 8 mm, suggesting that while micromachining is carried out and the crater becomes deeper, the properties of the modified layer also change, which ultimately lead to a severe prolongation of the semi flat-top intensity distribution and enable macroscale micromachining in the Z coordinate while the dimensions in the transverse direction remain in the microscale.
## 3 Results
### Dsc  
The DSC profiles of ProFile and 2 Shape show single endothermic and exothermic peaks below 22 \({}^{\circ}\)C (Figure 6A,F). The DSC curves of ProTaper Gold contain single endothermic and exothermic peaks (Figure 6B), while those of ProfaTaper Gold contain two endothermic peaks and a single exothermic peak above 22 \({}^{\circ}\)C (Figure 6C). HyFlex CM and V Taper 2H display a single endothermic and two exothermic peaks (Figure 6D,E). The first exothermic peak in the curve corresponds to the austenite to R-phase transformation, which occurs above 22 \({}^{\circ}\)C. The starting temperatures of the martensite to austenitic transformation--which was represented by the endothermic curves--of HyFlex CM and V Taper 2H were above 22 \({}^{\circ}\)C (Figure 6D,E). The DSC curves of K3 XF present two consecutive endothermic peaks below 22 \({}^{\circ}\)C (Figure 6G): the first represents the initial transformation from martensite to the R-phase, and the second indicates the transformation from the R-phase to austenite. The exothermic curve of K3 XF presents a single peak below 22 \({}^{\circ}\)C (Figure 6G). Table 3 lists the phase transformation temperatures and associated enthalpy changes.  
Figure 6: Cont.  
Figure 6: _Cont_.  
Figure 6: DSC curves of **(A)** ProFile, **(B)** ProTaper Gold, **(C)** ProfaTaper Gold, **(D)** HyFlex CM, **(E)** V **Taper 2H**, **(F)** 2 Shape, and **(G)** K3 XF. The upper and lower curves represent the cooling and heating curves, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Heating (Endothermic) Curve**} \\ \cline{2-6}  & **R\({}_{\text{s}}\) (\({}^{\circ}\)C)** & **R\({}_{\text{f}}\) (\({}^{\circ}\)C)** & **\(\Delta\)H (J/g)** & **A\({}_{\text{s}}\) (\({}^{\circ}\)C)** & **A\({}_{\text{f}}\) (\({}^{\circ}\)C)** & **\(\Delta\)H (J/g)** \\ \hline ProFile & & & \multicolumn{2}{c}{–3.06} & 19.27 & 2.37 \\ \hline ProTaper Gold & & & 35.20 & 47.92 & 4.77 \\ \hline ProfaTaper Gold & 15.01 & 36.46 & 1.89 & 37.85 & 56.25 & 2.83 \\ \hline HyFlex CM & & & 30.92 & 58.33 & 18.31 \\ \hline V **Taper 2H** & & & 22.77 & 41.67 & 15.74 \\ \hline
2 Shape & & & 1.27 & 29.69 & 2.64 \\ \hline K3 XF & 2.08 & 18.43 & & 18.43 & 30.21 & 11.44 \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Phase transformation temperatures and associated enthalpy changes derived from the DSC curves.
## 3 The Proposed Approach
### Dataset Preparing  
The preparation of the dataset is firstly initialized with an appropriate feature selection where only the most significant and sensitive features will be chosen. Each one must describe a certain attitude of degradation level or gradual time-varying behavior in each time cycle. Measurements that do not represent any variation during operating cycles will not contribute to the learning process. In fact, their noncorrelation with the desired RUL can disturb the training model and makes it more sensitive to over-fitting and other ill-posed problems. In our experiments, we chose only 10 parameters for RUL prediction whose indices are: [7, 8, 9, 12, 14, 16, 17, 20, 25, 26]. After that these features are scaled in the interval [0-1] using a min-max normalization procedure before training or testing. Finally, each time series will be labeled according to the piecewise linear RUL function as proposed in [1,22] and shown previously in Figure 1.  
Figure 1: (**a**) Normalized sensors measurement. (**b**) Corresponding RUL to sensors measurement.  
Figure 2: Flowchart of the proposed RUL prediction approach.
# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 3 Materials and Methods
### Decentralized BF Algorithm
In other words, the worker's filter is out-of-date and has to be updated, as the submitted reading is redundant in this period. An ME host can be configured to require multiple results of the same type in the _small_ MGRS area before updating its BF structure, but after a certain amount of time, the data production will be stopped on all workers as inserted elements cannot be removed from a BF structure. Therefore, a BF maintained by an ME host must be periodically reset and delivered to possible workers in the area in a _BFreset_ message to avoid a scenario in which a filter membership test always finds that a received reading is redundant. Note that by sending the reset filter structure to favored workers first, an ME host can preference some workers  
Figure 2: A setup for decentralized energy-efficient MCS from an ME host’s perspective. based on predetermined parameters. The BF's validity period (i.e., the time between two consecutive filter resets) is strongly dependent on the type of MCS service and can be defined in the configuration prior to the deployment. Figure 3 illustrates a scenario in which two workers enter the system and ask the coordinator about the ME host responsible for their geographical location. As they are in the same \(km\) MGRS area, the coordinator directs them to the \(ME_{1}\) host. After announcing their capabilities to the \(ME_{1}\) host, it finds currently available tasks to which they can potentially answer and assigns those tasks to workers, together with the current state of the BF for the area. First, worker \(u_{w_{n}}\) produces a result and queries its BF structure to decide whether to transmit this result to the \(ME_{1}\) host or not. As this is the first result in the area, the filter membership test reports negative, and \(u_{w_{n}}\) inserts \(r_{n}\) in the filter and submits \(r_{n}\) to the \(ME_{1}\) host. After receiving the result, the \(ME_{1}\) host queries its filter, decides that this result is not redundant, and inserts \(r_{n}\) in the filter. At the same time, worker \(u_{w_{1}}\) produces a new result \(r_{1}\) and queries its BF structure to decide whether to transmit this result to the edge server or not. As its filter is not up-to-date, it decides that \(r_{1}\) should be submitted to the \(ME_{1}\) host, although it is redundant. Upon receiving \(r_{1}\), the host realizes that the worker's filter should be refreshed and sends the current filter state to \(u_{w_{1}}\). Afterwards, worker \(u_{w_{1}}\) updates its BF.
## 4 Damping Force Characteristics
### Damping Coefficient Control  
As mentioned in Section 2, the damping coefficient change can be achieved by changing the effective orifice area of the main orifice before the SBP. The damping coefficient control is realized through the input current applied to the main orifice control part \(I_{M}\) as shown in Figure 13. At this time, the current applied to the bypass orifice control part \(I_{B}\) is set to 2A so that the bypass orifice is initially closed. It is seen that the damping force is degressively increased before the SBP. This is because the damping coefficient can be controlled critically when the main orifice is partially open with the closed bypass orifice. After the main orifice is fully opened, the degree of damping coefficient change is reduced. When \(I_{B}\) is 2A, the point (damping force) where the bypass orifice is open is evaluated by 1326 N. Here, in order to evaluate the linear damping characteristics, the equivalent damping coefficient is defined as the linear gradient up to the SBP. When are 3, 2, 1, and 0 A, the equivalent damping coefficients are evaluated by 10,763, 8220, 4688, and 2295 Ns/m, respectively. And it is seen that the noticeable change in the damping coefficient occurs before the SBP. In additional, this tendency depends on the pole shape function. On the other hand, the damping coefficient is almost constant after the SBP. This is because the effective area of the bypass orifice is significantly larger than the effective area of the main orifice.
## 4 Review of Microgrid Control and Design
### Secondary Control Level
#### 4.4.2 Distributed Secondary Control  
Distributed secondary control approaches are often employed due to the inherent limitations of centralized control, such as the vulnerability to communication failures. In distributed secondary control, each DER has its own local controller that communicates with neighboring DERs to coordinate voltage and frequency regulation. In this context, [PERSON] et al. [106] proposed an approach that involves the implementation of a distributed control strategy, where necessary control signals are locally transmitted to the primary controllers. In this configuration, every DG unit comes with a local controller. Consequently, in the event of a failure in one unit, only that specific unit experiences a breakdown without impacting the functionality of other DG units [106]. Distributed control methods manage both voltage regulation and power sharing, and numerous methods have been presented in the literature [107]. For instance, the use of input-output feedback linearization for voltage control at the secondary level of MGs has been demonstrated by [PERSON] et al. [108]. This method requires a distributed communication network with unidirectional communication, proving more efficient than centralized controllers at the secondary level.
## 4 Data Analysis of the Tangential Contact Stiffness Measuring and Model Validation
### Experiments with Different Radius  
Although these experiments' designs depend on the principle of single-parameter control, the comparing experiments mainly include but are not restricted to, Groups 7 and 8.  
In Figure 5, the meaning of the symbols is the same as in Figure 4, and, in general, similar to the phenomena above, the relative error between the measured tangential stiffness and the predicted one is less than 10%. More specifically, to estimate the contact object radius's role in the model's accuracy, two experimental groups, including two different radii (8 and 9 mm), have been arranged, accompanying the relevant groups: Groups 2 and 5.  
Figure 5: The comparison of tangential contact stiffness between measured ones and predicted ones among different radii.  
In Group 7, the materials of the contact pair are Cu and Fe, with the radii of 8 mm and 8 mm. Compared with Group 2, only one radius of half of the contact pair has been changed, while the other parameters are fixed. Putting together these two groups' experiment results, there are inconspicuous differences between them, which is the same phenomenon of experiments with different contact materials above.  
The analogic conclusion is that the change of the radius has almost no influence on the model's accuracy. In Group 7, the largest relative error is 9.9%, while in Group 2, the largest relative error is 9.9% which is almost the same as Group 7. The comparison between Group 8 and Group 5 has the analogous situation to Group 7 and Group 2: in Group 8, the largest relative error is 9.8%, while in Group 3, the largest relative error is 9.9%, which is almost the same as Group 8. However, the only difference between Group 8 and Group 3 is the radius, which verifies that the change of the contact object's radius plays an infinitesimal role of the tangential contact stiffness model's accuracy.