esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
For this study, 90 roots of incisors (_n_ = 54) and premolars (_n_ = 36) extracted for periodontal reasons from patients aged 57 to 70 years, free of caries, restorations, and root resorption were used. Before the extraction, the patients were informed of the use of the teeth for research purposes and gave their consent.  
After extraction and during all procedures, the teeth were stored in bi-distilled water at 4 \({}^{\circ}\)C.  
The teeth were stabilized with polyvinyl siloxane (Affinis light body; Coltene, Altsattten, Switzerland) and instrumented on a precision electronic balance (TM 503 Power Module, Tektronix(r), Beaverton, OR, USA) to obtain an instrumentation standardized force of 0.1 \(\pm\) 0.05 N.  
All procedures were performed by the same operator in a single session for 30 s.  
The roots were subjected to preliminary instrumentation with a universal curette (M 23, Deppeler, Rolle, Switzerland) and ultrasound with a steel tip for the removal of tartar, stains, and periodontal ligament residues.  
Only one surface from each root was selected for the experiment, deliberately excluding extreme grooves, restoration margins, or the cementoenamel junction. A rectangular area of interest was identified and was outlined with a diamond-coated disk (Intensiv, Swiss Dental Diamond, Discoflex, 173 D, Montagnola, Switzerland) in a slow contra angle (Micro Mega, Geneve, Acacias, Switzerland) with water cooling. Only this area was subjected to sequential impressions.  
Following preliminary instrumentation, each root was brushed with a propby brush mounted on a micromotor at 4000 rpm for 30 s, then washed for another 30 s and air-dried for 10 s.  
Teeth were randomly divided into 3 experimental groups (_n_ = 30) to be then subjected to three treatments:  
* Group 1 (G1): manual instrumentation with a Gracey(r) curette (SG 5/69E2r, Hu-Friedy, Chicago, IL, USA);* Group 2 (G2): ultrasonic instrumentation (Piezon(r), EMS, Geneva, Switzerland) with a standard steel tip (Universal Perio S-SERIES: USU, Hu-Friedy, Chicago, IL, USA) at a power equal to 50%;
* Group 3 (G3): ultrasonic instrumentation (Piezon(r), EMS, Geneva, Switzerland) with a diamond tip (Punta Piezo Serie E Scaling, Hu-Friedy, Chicago, IL, USA) at a power of 20%.  
After the experimental procedures, the samples were washed for 30 s and dried with air-water spray for 10 s.  
The instrumented teeth were then homogeneously distributed in three groups (\(n=30\)) so that the three types of instrumentation were equally represented (10 from each instrumentation group) and subjected to 30 s of polishing with a rubber cup (Prophy Cups, Kerr, Switzerland) on a low-angle contra-angle (2000 rpm), with three different polishing pastes with medium grain size (compositions reported in Table 1) in single-dose cups:  
* Group I (GI): Ultrapro Tx cool mint medium(r), Ultraent;
* Group II (GII): Stomyprox media(r), Coswell;
* Group III (GIII): Nupro medium orange(r), Dentsply Sirona.
# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 3 Proposed Method
### Position-Scale Heatmap Supervision with CloU Regularization
The formulation first guides the network to localize targets with high spatial sensitivity through dense heat-map predictions and then refines their geometric structure via focused box-level feedback. This loss design supports PSHNet's core objective of achieving high recall and precision under the low-signal conditions characteristic of infrared small-target scenes.
## 3 Results and Discussion
### Gene Prediction of Holocellulose Degrading Enzymes in A. olivimuriae  
Genomes of _Aspergillus_ species code for a high number of CAZymes involved in the degradation of plant polysaccharides, including cellulose, hemicellulose (xylan, xyloglucuan, arabinoxylan and glucomanan) and pectin. Several glycosyl hydrolases (GH) families have been characterized and predicted in Aspergillus, along with carbohydrate esterases (CE) and polysaccharide lyases (PL) [3]. Different _Aspergillus_ species (e.g., _A. niger_, _A. fumigatus_, _A. tamarii_, _A. nidulans_) showed the occurrence of genes coding for lignin-modifying enzymes [3; 56], classified in the family of auxiliary activity enzymes (AA). These include multi-copper oxidases, such as lacases, ferro oxidases and lacase-like multi-copper (AA1), heme peroxididases, such as lignin peroxididases, manganese peroxididases and versatile peroxididases (AA2) and, finally, 1,4-benzoquinone reductases (AA6). Recently, lytic polysaccharide monooxygenases (LPMO) have also received special attention due to their boosting effect on hydrolytic enzymes' activity, suggesting a fundamental role of redox reactions in lignocellulosic conversion. In the CAZy database, LPMOs belong to six AA families: AA9, AA10, AA11, AA13, AA14 and AA15, among which, the most widely studied are AA9 and AA10 [57]. In _A. olivimuriae_, the genome annotation of CAZymes allowed identifying a total of 148 protein-coding genes involved in lignocellulosic degradation, belonging to 42 families. Among them, 109 genes were assigned to GH, 6 to CE, 3 to PL and 30 to AA. CAZymes were further subdivided based on their substrate and the family they belong to, as suggested
## 4 Gravity Field Changes and Seismicity during the Process of Water Impounding
### The 175 m Experimental Stage
It is worth mentioning that, during the three stages of water impoundment, they both achieved a correlation coefficient of about 0.4 when the time lag was 0 months, of which the result seems relatively small. However, [PERSON] used the reshuffling tests method to study the Koyna reservoir earthquake in India and pointed out that the maximum correlation coefficient between the two was merely 0.13. Still, it cannot be plainly said that the two are not relevant since they passed the reliability test [13]. The significance of the reshuffling tests method is to provide a threshold of the correlation coefficient by simulating the distribution of various types of noise. It can answer the scientific question of practical significance of how much the correlation coefficient implies a correlation between seismic activities and reservoir water level changes [24]. As the TGR enters the 175 m high-level operation stage, the annual change of the reservoir's water level is around 30-40 m. The time series cross-correlation test of the monthly frequency of earthquakes and the reservoir water level confirms that the seismic activity and the reservoir water level show negative correlation characteristics under the time lag of 4 to 5 months, indicating that the seismic activity has a lagging response to the reservoir water level change (Figure 7d). The lagging response phenomenon has been observed in many reservoir earthquake-prone areas, such as the Koyna area of India and the Aswan Dam area of Egypt [23; 27]. It usually occurs several years or even decades after the water impoundment. Although the above two still maintain the characteristic of a synchronous positive response, the new lagging response is direct evidence of the continuous infiltration of reservoir water in the study area, suggesting that its influence on the crustal stress-strain field in the study area has gradually deepened [23; 27]. This is an interesting question that is worthy of our analysis of the causing mechanism and an attempt to further explain it.
## 3 Sample, Experimental Devices, and Protocols
### Delay-and-Sum Processing (D.a.S.)  
Fluctuations in the position of the transducers during the rotation can give rise to time-shift variations that degrade the spatial resolution of USCT. To assess these biases, a sinogram was acquired in the pulse-mode configuration on the copper thread at the same height of the sample. The impulse response of the thin thread had a short duration, and could be assimilated to a geometric Dirac delta function. The corresponding USCT was considered as the point spread function (PSF) of the device:  
\[w(t)=\delta(t)\ \Rightarrow\ \left[s^{\Phi}(t)\right]^{\mathit{p}\mathit{f}}=r^{ \Phi}(t)\otimes\left[h_{T}^{*}(t)\right]^{\mathit{ref}} \tag{35}\]  
For each signal of each sinogram corresponding to each frequency obtained on the object to be imaged, the correction process consisted of a time shift in relation to the reference signal obtained at the same angle on the thread.  
Once this operation was completed, the three signals (one per frequency) were summed for the same angle, and a new sinogram was compounded by delay-and-sum processing (Figure 9). An apodization coefficient \(a(\ell_{0})\) was applied to each signal to correct the difference between the sensibilities of the three transducers, and the phase shifts (\(\tau_{\phi}^{(\ell_{0})}\)) due to the misalignment between the transducers on the array were corrected. The misalignment was measured by reflection on the reference plate (Figure 5, Configuration B) determining the times corresponding to the maximums of the signal.  
Figure 8: Comparison of the temporal representations (**top**) and frequency spectra (**bottom**) of the theoretical analyzing wavelet (solid line), the transmitted pulse signal (dashed line), and the pseudo-wavelet (dotted line) for the 2.25 MHz-transducer.  
Figure 9: A flow chart of the USCT processing.
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 3 Methodology
### Recovery Strategies
#### 3.3.2 Recovery Process Based on Critical Links  
We assume that recovery is performed after the cascading failure ends, as shown by the green line in Figure 3. The edges in the network are classified into three states: normal, failed, and preparing for recovery. The higher-ranked edges are prioritized in the recovery sequence. The number of normal edges at each recovery time step is recorded, with the iteration concluding when the network state stabilizes. The changes in the network state at each time step during the recovery process are then outputted.  
The proposed recovery strategy is defined as a step-by-step recovery approach. In this strategy, failed edges are restored sequentially based on their importance. Each set of critical edges is fully restored and verified to be functioning normally before proceeding to the next set of edges. This process is repeated until the entire system is fully recovered.  
Figure 4: The overall architecture of the process for identifying critical links.  
This feature makes the recovery process computationally efficient and straightforward to implement.
## 3 Results
### Issues Remaining after MOXIE
The field of SOEC technology for terrestrial applications is worldwide and funded at much higher levels than NASA could consider. MOXIE demonstrated that this technology is also applicable to Mars ISRU technology. Post-MOXIE activities by OxEon Energy LLC showed that this technology might be applied to lunar ISRU as well via electrolysis of water. MOXIE left behind it a well-equipped state of the art laboratory for testing SOEC devices and systems. It seems likely that NASA could leverage the field of SOEC technology with a relatively small investment, by continuing to adapt advances in terrestrial SOEC technology to space applications. While MOXIE was a small-scale demonstration, [PERSON] (2022) developed a detailed model of a full-scale version of MOXIE, including a very detailed analysis of requirements and expected performance of all subsystems. He optimized the design based on mass, power, risk,and ancillary factors [50]. [PERSON] and [PERSON] (2023) tested how [PERSON]'s design would perform with several alternative control schemes against 10,000 h of Mars atmospheric data at half-hourly intervals [51]. They evaluated power requirements and determined the most efficient control scheme based on power and avoidance of carbon formation. These results provided further evidence that an extension of MOXIE technology would prove effective on Mars.
## Appendix A Continuities of Probability and Phase Distributions  
Consider a general quantum state in mono-electronic system represented by the wavefunction of Equation (1). The total time derivative of the particle probability density \(p(t)=p[\mathbf{r}(t),t]\) reads  
\[dp(t)/dt=\partial p(t)/\partial t+(d\mathbf{r}/dt)\cdot\partial p(t)/\partial\bm {r}=\partial p(t)/\partial t+\mathbf{V}(t)\cdot\
abla p(t)\equiv\sigma_{p}(t). \tag{10}\]  
It determines the local probability \"source\" \(\sigma_{p}(\mathbf{r},\,t)\equiv\sigma_{p}(t)\), which measures the time rate of change in an infinitesimal volume element around \(\mathbf{r}\) in the probability fluid, moving with the local velocity \(d\mathbf{r}/dt=\mathbf{V}(t)\), while the partial derivative \(\partial p(t)/\partial t\) refers to the volume element around the fixed point in space:  
\[ \begin{array}{c}\partial p(t)/\partial t=\sigma_{p}(t)-\mathbf{V}(t)\cdot \
abla p(t)\\ =\sigma_{p}(t)-(\ln/2m\mathrm{i})[\psi(t)^{*}\Delta\psi(t)-\psi(t)\Delta\psi(t )^{*}]=\sigma_{p}(t)-\
abla\cdot j(t)\\ =\sigma_{p}(t)-[\mathbf{V}(t)\cdot\
abla p(t)+p(t)\;\
abla\cdot\mathbf{V}(t)]\\ =\sigma_{p}(t)-(\ln/m)\;[\
abla\phi(t)\cdot\
abla p(t)+p(t)\;\
abla^{2}\phi(t )].\end{array}  \tag{11}\]  
One also recalls that probability dynamics from SE expresses the sourceless continuity relation for the particle probability \"fluid\", \(\sigma_{p}(t)=0\), or  
\[\partial p(t)/\partial t+\
abla\cdot j(t)=\partial p(t)/\partial t+\
abla p \cdot\mathbf{V}(t)+p\;\
abla\cdot\mathbf{V}(t)=0. \tag{12}\]  
This relation thus implies the vanishing divergence of effective velocity field determined by the phase-Laplacian:  
\[\
abla\cdot\mathbf{V}(t)=(\ln/m)\;\
abla^{2}\phi(t)=0. \tag{13}\]  
The partial derivative of Equation (11) expressing the probability dynamics thus reads:  
\[\partial p(t)/\partial t=-\;\mathbf{V}(t)\cdot\
abla p(t)=-\;(\ln/m)\;\
abla\phi( t)\cdot\
abla p(t) \tag{14}\]  
or  
\[dp(t)/dt=\partial p(t)/\partial t+\
abla\cdot j(t)=\partial p(t)/\partial t +\mathbf{V}(t)\cdot\
abla p(t)=0. \tag{15}\]  
The probability continuity also determines the dynamics of the state modulus component:  
\[\partial R(t)/\partial t=-\;(\hbar/m)\;\
abla\phi(t)\cdot\
abla R(t). \tag{16}\]  
For example, for a general local-phase expression [86; 87]  
\[\phi(\mathbf{r})=\mathbf{k}\cdot\mathbf{f}(\mathbf{r})+C, \tag{17}\]  
where the constant \(C\) remains unspecified in QM, one determines the probability-velocity field at point \(\mathbf{r}=\{x_{\alpha}\}\): \(\mathbf{V}(\mathbf{r})=(\hbar/m)\;\mathbf{k}\;\
abla\cdot\mathbf{f}(\mathbf{r})\). Its vanishing divergence then implies a local condition  
\[\mathbf{k}\cdot\
abla[\
abla\cdot\mathbf{f}(\mathbf{r})]\equiv\mathbf{k}\cdot\
abla^{2}\mathbf{f} (\mathbf{r})=\sum_{\alpha}\sum_{\beta}k_{\alpha}\;[\partial^{2}f_{\beta}(\mathbf{r})/ \partial x_{\alpha}\;\partial x_{\beta}]=0. \tag{18}\]The effective velocity \(V(t)\) also determines the phase current, the flux concept associated with the state phase: \(J(t)=\phi(t)\)\(V(t)\).
## 6 Conclusions  
Si lattice-shifted photonic crystal slow-light modulators were constructed using a 180 nm CMOS process, with a phase shifter length as short as 200 \(\upmu\)m. The frequency response, \(ER\), and \(BER\) were measured in wide wavelength ranges, with fluctuations observed, perhaps due to the oscillation in the group index \(n_{\mathrm{g}}\) spectrum. The device with \(n_{\mathrm{g}}\approx 20\) showed a wide working spectrum of 20 nm and a sufficient frequency response for the 25 Gbps modulation. A higher \(n_{\mathrm{g}}\) up to 60 is available by modifying the lattice shift but the working spectrum is comparably narrowed, which is the major constraint of the slow-light effect. Even for the moderately low \(n_{\mathrm{g}}\approx 20\), the \(\pm 10\%\) fluctuation was observed in the \(n_{\mathrm{g}}\) spectrum, which was reflected in \(ER\) and \(BER\). Therefore, a margin larger than 1 dB is necessary in \(ER\) for stable, error-free operation, which can be achieved by adding a 0.5 dB modulation loss, comparably increasing the light source power and/or reducing passive losses. The \(ER\)  
Figure 10: The \(\Delta\Phi\) estimated from \(ER\) as a function of \(n_{\mathrm{g}}\).  
Figure 9: The \(n_{\mathrm{g}}\) spectrum of PCW device without lattice shifts and \(ER\) at 25 Gbps modulation. Set voltages were the same as those for Figure 5.  
was simply increased by increasing \(n_{\mathrm{g}}\); however, the frequency response was degraded simultaneously. This degradation was considered to be due to the phase mismatch between slow light and RF signals, which will be more problematic beyond 25 Gbps. Results suggest that \(n_{\mathrm{g}}\) should be set to less than 40 to avoid this degradation and avoid irregularly large oscillations in \(ER\), which is particularly enhanced by slow light.  
This work was partly supported by the New Energy and Industrial Technology Development Organization (NEDO), Japan.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] designed the devices; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed experiment systems, analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Damage Imaging in Lamb Wave-Based Inspection of Adhesive Joints
## 3 Results and Discussion
### WRMS Imaging  
#### 3.3.1 Influence of Time Window and Weighting Factor  
Figure 8 shows the WRMS that was calculated for specimen #5 using Equation (2). Different values of the weighting factor and time window were applied. In all calculations, the beginning of the time window \(t_{1}\) was always set as zero, so the length of the time window was \(T=t_{2}\). The obtained damage maps are presented as logarithmic values of WRMS. The analysis of the results led to the conclusion that the time window is a significant factor for the readability of visualizations. According to the visual assessment, the clearness of the WRMS maps increased with the increase in the time window. For this reason, the longest time window (\(T=3.0\) ms) was tentatively chosen for the damage imaging of the other specimens (as presented further). It is also worth noting that the weighting factor was not negligible. The constant weighting factor (\(w_{r}=1\)) was not efficient when compared with others that increased with time. The maps were not clear enough and the influence of the incident wave at the excitation point was highly visible. These effects were not observable for the weighting factors \(w_{r}=r\) and \(w_{r}=r^{2}\), as represented by the linear or square function of time, respectively. Their effectiveness was comparable and significantly higher than the constant weighting factor.  
Figure 8: Weighted root mean square (WRMS) maps (values in (m/s)) for specimen #5 for different weighting factors \(w_{r}=1\) (first column), \(w_{r}=r\) (second column), \(w_{r}=r^{2}\) (third column) and varying time window: (**a**) \(T=0.03\) ms; (**b**) \(T=0.05\) ms; (**c**) \(T=0.50\) ms; (**d**) \(T=1.00\) ms; (**e**) \(T=2.00\) ms; and, (**f**) \(T=3.00\) ms.  
To assess the clearest variant of the WRMS, damage maps for adhesive joints with internal defects (#8, #9) were prepared (Figure 9). The identification of the defect geometry for these specimens was possible, however, some differences between the WRMS maps can be observed. A comparison of the different weighting factors for both of the specimens showed the advantage of a variant with the linear function.
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 4 Proposed Blind Watermarking
### Watermarking Embedding Process  
Assume that the input color host image is \(f\), where  
\[f=\{f(m,n),0\leq m,n<N\}\]  
and the input watermark image is \(W\) of size \(P\times Q\), where  
\[W=\{w(x,y)\in\{0,1\},0\leq x<P,0\leq y<Q\}\]  
which is to be concealed in the host color image. The embedding steps are discussed as follows:  
Step 1: Scramble the binary watermark to banish the correlation between image pixels to add more reliability to the proposed method by using Arnold transformation [55] with arbitrarily chosen key (k), the watermark image is shuffled from \(W\) to \(W1\), \(W1=\{W(x,y)\in\{0,1\},0\leq x<P,0\leq y<Q\}\). Transform it into a one-dimensional bit sequence as follows:  
\[W2=\{W2(k)=w1\ (x,\ y),0\leq x<P,0\leq y<Q\,k=x\times Q+y,w2\ (k)\in\{0,1\}\}\]  
Step 2: Split the input color image,\(f\), into \(4\times 4\) non-interfering blocks, \(B_{k}\),  
\[B_{k}=\{B_{k}(m,n),0\leq m,n<3\},K=1,2,\ldots\ldots N^{2}/16 \tag{24}\]  
Step 3: Compute MfrLFMs for each block, \(B_{k}\big{(}K=1,2,\ldots\ldots N^{2}/16\big{)}\) using the maximum moment order \(P\times Q\) as shown in (Section 3) in detail.  
Step 4: Select robust and accurate MfrLFMs to carry the watermark; this is done by computing the magnitude values of MfrLFMs to ensure RST invariances. In the proposed method, we selected MfrLFMs with positive reiterations \(q\
eq 4m,m\in Z\) to avoid redundancy as follows:  
\[S=\big{\{}\ \big{|}FrM_{pq}\big{|},\ q\
eq 4m,m\in Z\big{\}}\]  
By utilizing the key \(k_{2},P\times Q\) coefficients of MfrLFMs, which denote the positions of the selected pixels in the image blocks, are selected randomly from the delicate set depending on the watermark bits to get the feature vector magnitude.  
Step 5: Apply fast Walsh-Hadamard transform (FWHT) on selected MfrLFM coefficients using Equation (1).  
Step 6: Embed the scrambled binary watermark, \(W1\) bit information in the transformed MfrLFM magnitudes using dither modulation [56] using the following equation:  
\[\begin{split} m_{FrLFM^{\prime}}(M^{\prime})&= \Big{[}\frac{m_{FrLFM}(x)-d_{x}(W_{1}(x))}{\Delta}\Big{]}*\Delta+d_{x}(W_{1} (x)),0\leq x<P\times Q\\ d_{x}(1)&=\frac{\Delta}{2}+d_{x}(0),d_{x}(0)\ \in[0,1]\end{split} \tag{25}\]  
Figure 3: Proposed watermark extraction.  
The \(m_{FrLFM}(M^{\prime})\) denotes the modulated blocks. The mathematical symbols, \([\.\ ]\), \(\Lambda\) and \(d_{i}(.)\) refer to the rounding factor, quantization step, and dither modulation.  
Step 7: Apply inverse fast Walsh-Hadamard transform (IFWHT) using Equation (4).  
Step 8: Reconstruct the color watermarked image \(f_{w}(r,\theta)\) as  
\[f_{w}(r,\theta)=f_{c}(r,\theta)-f_{c}^{M}(r,\theta)+f_{c}^{M\prime}(r,\theta) \tag{26}\]  
where \(f_{c}(r,\theta)\) is the host color image, \(f_{c}^{M}(r,\theta)\) is the modified part, and \(f_{c}^{M\prime}(r,\theta)\) is the unmodified part.  
Step 9: Merge all \(4\times 4\) sub-blocks to get the color watermarked image finally \(f_{w}(m,n)\).
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.15. ANK Repeats  
Ankyrin (ANK) like repeats mediated protein-protein interactions between diverse groups of proteins [134; 135] have been reported in almost all species [136]. The ANK proteins exhibit a domain shuffling via a horizontal gene transfer [137]. A protein may have several numbers of ANK repeats per protein [134; 138]. [PERSON] et al. [138] demonstrated the association of a specific 33 residue erythrocyte ankyrin repeat with an anion exchanger. A stack of ANK repeats has a superhelical arrangement with four consecutive repeats, and each unit contains two antiparallel helices and a beta-hairpin. ANK repeats may also occur in combinations with other types of domains [134].
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 3 Simulation Results and Analysis  
This section presents the simulation setup and compares the performance of the proposed RHLP-18T cell with that of reference designs, with an emphasis on key SRAM metrics. All simulations were conducted using a standard 90 nm CMOS process technology with a supply voltage (VDD) of 1 V and an operating temperature set to 27 \({}^{\circ}\)C. To ensure a fair comparison, the reference RHBD cells [7; 8; 9; 10; 11; 12; 13; 14; 15] were reconstructed based on the schematics and transistor dimensions described in their respective publications. Any dimensions not specified in the original papers were set to the minimum feature size allowed by the 90 nm process. The proposed RHLP-18T cell was implemented according to the design details presented in Section 2.