esa-sceva/satcom-chunk-collection huggingface dataset:
# An 8 kb RRAM-Based Nonvolatile SRAM with Pre-Decoding and Fast Storage/Restoration Time
## 3 nvSRAM Peripheral Circuit Design
### Read Drive Circuit  
As shown in Figure 7, the read drive circuit includes a sensitive amplifier, latch and three-state gate [29]. The sensitive amplifier adopts a latch structure, which has faster speed compared with the traditional one. The voltage difference of the two bit lines is amplified by the sensitive amplifier, and the data are output through the latch and the three-state gate. SAE is the control signal. When SAE is high, the tail current source transistor turns on, and the sensitive amplifier starts to work. In read operation stage, when Q = 0 and QB = 1, EQB is low, and DL and DLB are pre-charged to high, DL and DLB output to BL and BLB by the three-state gate. When WL is on, because BL is high and storage node Q is low, BL forms a discharge path with the access and pull-down transistor, which will cause BL to produce a voltage drop. The voltage of BLB and QB remains the same. After rapid amplification by the sensitive amplifier, BL is low and BLB is high. When the BL and BLB voltages are transmitted to the input of the latch, the three-state gate outputs low, which completes the operation of reading 0. Similarly, when Q = 1 and QB = 0, after discharge of the memory  
Figure 5: Row decoding circuit.  
Figure 6: Column decoding circuit.  
cell, DL is high and DLB is low. After the two signals pass through the input of the latch, the latch outputs low, and the three-state gate outputs high, which completes the operation of reading 1. At the same time, when the latch input is high, the output maintains the data of the previous clock cycle until the next reading cycle. The latch of the next stage ensures the reliability of data read out by the memory cell in each clock cycle.
###### Abstract  
Background: Clear aligner therapy (CAT) has become increasingly popular for treating mild to moderate malocclusions. However, discrepancies between predicted and achieved tooth movement remain a concern, partly due to the limited understanding of aligner material behavior under clinical conditions. Since these materials must deliver controlled and sustained forces, their flexural properties are critical for treatment efficacy. Objective: To identify and analyze in vitro studies investigating the flexural properties of thermoplastic clear aligner materials, summarize their testing methodologies, and examine the factors that may influence their clinical performance. Methods: A scoping review was conducted following the PRISMA-ScR guidelines. Three electronic databases (PubMed, Scopus, and Web of Science) were systematically searched. Studies were screened based on predefined eligibility criteria, and data extraction included testing methods, materials, and clinically relevant variables. Risk of bias was assessed using the QUIN tool. Results: Seventeen studies published between 2008 and 2024 were included. All studies used three-point bending to assess mechanical properties. Common influencing factors included thermoforming, liquid absorption, temperature changes, loading conditions, and material thickness. Most studies reported that these factors negatively affected force delivery. The most frequently tested material was Duran (PET-G). Polyurethane-based materials, such as Zendura, showed comparatively better stress relaxation properties. Conclusions: Thermoforming, intraoral temperature changes, liquid exposure, and prolonged or repeated loading can compromise the mechanical properties and force delivery capacity of aligner materials. Standardized testing methods and further investigation of newer materials are essential to enhance the predictability and performance of clear aligner therapy.  
clear aligners; Invisalign; thermoplastic materials; orthodontic appliances; flexural strength; force +
Footnote †: journal: Applied Sciences
# Trepenoids in Marine Heterobranch Molluscs
## 4 Diterpenoids
### Linear Detrepenoids  
Among the sea slugs, the chromodorid nudibranch _Goniobranchus splendidus_ from Australia presents 31 different terpenes, including the linear furanoditerpene ambliofuran (39) (Figure 6) [154], while _Chromodoris reticulata_ (also from Australia) presents this compound in its viscera [175].  
In sacoglossans, only shell-less species contain linear diterpenoids. The Mediterranean _Thuridilla hopei_, a beautifully colored species, contains the three diterpenoids thuridillins A-C (40) (Figure 6) [176] and three nor-thuridillonals--nor-thuridillonal (41), dihydroron-thuridillonal, deacetyl-dihydro-nor-thuridillonal (Figure 6)--suggested to derive from the common dietary precursor epoxylactone (42) (Figure 6) [177]. _T. hopei_ feeds and lives on the caulerpal green alga _Derbesia tenussima_[178], also containing the active diterpenoid epoxylactone. The related compounds thuridillins D-F are also present in the Australian species _T. splendens_[179]. Thuridillins from _T. splendens_ did not deter feeding by the sympatric shrimp _Palaemon serenus_[90]. The conversion of the precursors to thuridillins and nor-thuridillonals is suggested to be a detoxification mechanism by oxidation or reduction processes [176; 177]. Contrastingly, _Elysia translucens_ presents udoteal from the green algae _Udotea_[178; 180; 181].  
Figure 6: Structures of selected diterpenoids from Heterobranch molluscs: (**39**) ambliofuran; (**40**) thuridillin C; (**41**) nor-thuridillonal; (**42**) epoxylactone; (**43**) spurillin A; (**44**) rubifolide; (**45**) pukalide; (**46**) pilosarcenone; (**47**) halimedatrial.
## 4 Deep Reinforcement Learning-Based Efficient Federated Learning Policy Optimization
### Aggregation Policies  
The aggregation process will be executed after the local models from selected participants are offloaded and obtained in the FL parameter server within each iteration of the communication round. By using DRL-based algorithms, device selection and update scheduling are positively optimized as a primary contribution to the final learning performance. Another aspect to improve the efficiency of FL is an adaptive aggregation policy for massive IoT services. Each FL service in heterogeneous networks requires the label ing of its criticality and satisfactory expectation to ensure a reliable and on-time model construction. An advanced approach can be migrated from different improving factors such as significance-aware modeling, edge-assisted global aggregation, and a DRL-based control mechanism. Figure 10 presents an eFL procedure transition between clients, edge aggregators, and parameter server throughout the initialization, global model distribution, local model training, model update scheduling, and aggregation policies. DRL-based approaches are greatly assured to be deployed in processes of controlling local training, selecting clients, and adjusting the aggregation [84; 85; 86].  
In [84], a cooperative approach for scheduling and aggregation was proposed by evaluating several policies in asynchronous FL. In terms of client scheduling, three significant policies were studied, namely (1) a random policy in which the selection process decides randomly and chooses a subset of qualified participants in the accessible batches, (2) a significance-based algorithm in which a norm formulation of gradient update is used to identify the most suitable client, and (3) a frequency-based algorithm in which the relativity between each communication round is considered by tackling how often the particular client contributes to the aggregation. Aggregation policies considered two detailed approaches, namely (1) the equal weight method in which the weights rely on data contribution and (2) an age-aware aggregation in which the primary weights are based on the metric of the local update's age. In [85], a dynamic scheduling algorithm, termed as DISCO, was proposed by aiming for learning and energy-constrained metrics. The evaluation between multiple policies has been discussed in depth, which leads to a major contingency of applying a DRL agent in a formulated specific-reward assessment. Each objective can be specified into the agent's reward functions, where overall states are considered.  
An aggregation approach with an intelligent decision-making agent and self-organizing capabilities will prioritize the mission-critical class in a real-time AI-enabled environment. A weighted-sum model for specific objectives can be deployed as a reward function to evaluate the rightful actions in particular network states.
# The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review
## 2 Axial Compressive Stress
### Geological Movements  
For thin pipes, radial and shear stresses are neglected, as circumferential and axial stresses are the main stresses affecting the pipe [32]. Ground movement due to earthquakes (e.g., active reverse fault), landslides, and frost heaves induce external axial force on the pipelines. Generally, a steel pipeline in direct tension due to fault rupture can safely accommodate a larger fault offset value compared to when it is strained in direct compression [18; 19]. Although good engineering practice is not to lay pipelines on volatile soils, nevertheless, adjacent soil movements affect a pipeline. Often, these soil movements are relatively small, but the effects may be large enough to affect the pipeline performance [33].  
Under compressive geological movement, pipeline behavior would be dominated by global and local buckling [34]. Axial bending of the corroded pipe region results from a differential settlement, or in the Arctic regions, the freeze-thaw action of permafrost regions coupled with axial compressive loading from the constraint due to the thermal expansion that exists when transporting high-temperature petroleum products [35]. A report by the Transportation Safety Board of Canada [36] revealed that some steel pipeline failures caused by axial compressive stress are due to slope movements and landslides. An example of such failure is one in 1997 when a pipeline in British Columbia, Canada ruptured, as it was subjected to excessive axial compressive stress due to soil movement in the axial direction [36]. The external loads due to soil often pose several types of stresses on pipes, namely radial, shear, circumferential, and axial stresses.
# Optimization Model of Express-Local Train Schedules Under Cross-Line Operation of Suburban Railway
## 7 Computational Results
These advantages make E-ALNS suitable for solving large-scale or practically constrained scheduling problems under cross-line operation. As shown in Figure 4, which demonstrates the curve of the objective function value with the number of iterations in the case of a fluctuation coefficient of 1, the objective curve decreases rapidly and steadily during the iteration process. The algorithm demonstrates significant optimization performance, with a rapid decline in the objective function value observed within the first 150 iterations. In accordance with the predefined stopping criterion, the algorithm terminates at the 300 th iteration, after 50 consecutive iterations without any improvement in the best-found solution.
## Results
### Effect of T. spicata on Fecal Microbiota after Simulated Digestion and Colonic Fermentation  
In vitro simulated digestion was performed to inspect the physiological effects of _T. spicata_ on the viable cell density of the main cultivable bacterial groups belonging to the intestinal microbiota. The differences observed were determined through the simulated digestion of _T. spicata_, comparing it against the same medium and culturing conditions without the presence of the plant's macro and micronutrients. While a similar trend characterized the experiments, indicating a decreasing density from FS to FSTD, no significant differences were detected based on the presence of _T. spicata_ in the medium. In fact, the densities of both total aerobes (\(\sim\)9 logs CFU/mL) and anaerobes (\(\sim\)8 logs) were comparable between FS and FSTD samples (Table 3). Consistently with these findings, even _Lactococci_ and _Bifidobacterial_ cells did not exhibit substantial variations. Although not statistically significant (\(p\) > 0.05), lactobacilli and _Enterobacteriaceae_ showed a \(\sim\)0.5 log higher density in FS compared to FSTD. The most notable difference was observed in the density of _Enterocci_, reaching approximately 5 logs in FS, while reporting mean values of 3.5 logs in FSTD. _Clostridi_, on the other hand, demonstrated the lowest \(p\)-value (\(p\) = 0.09) among the sample comparisons, with a higher density in FS (\(\sim\)8.8 logs) compared to FSTD (\(\sim\)8.3 logs).  
In addition, to better inspect the effects of _T. spicata_ on microbiota composition in the fecal batch, a panel of microbial genera and species was investigated by qPCR analysis (Figure 2). In line with the viable microbial cells count, the Log (Copy Number) values for all tested bacteria, which were calculated based on the DNA concentration and amplicon length, were comparable between FS and FSTD.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Viale Microbes** & **FS** & & \\ **(Log UFC/mL)** & **(Control)** & **FSTD** & \(p\)**-Value** \\ \hline Total aerobes & 9.3 \(\pm\) 0.3 & 9.0 \(\pm\) 0.4 & 0.61 \\ \hline Total anaerobes & 8.0 \(\pm\) 0.5 & 8.0 \(\pm\) 0.4 & 0.99 \\ \hline _Lactobacillus_ & 5.0 \(\pm\) 1.0 & 4.4 \(\pm\) 1.0 & 0.69 \\ \hline _Lactococci_ & 7.8 \(\pm\) 0.4 & 7.6 \(\pm\) 0.5 & 0.81 \\ \hline _Bifibobacteria_ & 7.1 \(\pm\) 0.7 & 6.9 \(\pm\) 0.5 & 0.86 \\ \hline _Enterobacteriaceae_ & 8.8 \(\pm\) 0.2 & 8.2 \(\pm\) 0.6 & 0.39 \\ \hline _Enterococci_ & 4.9 \(\pm\) 0.1 & 3.5 \(\pm\) 1.4 & 0.46 \\ \hline _Clostridi_ & 8.8 \(\pm\) 0.1 & 8.3 \(\pm\) 0.2 & 0.09 \\ \hline \end{tabular} Data are expressed as Mean \(\pm\) SEM; \(p\)-value obtained by Student’s \(t\)-test.  
\end{table}
Table 3: Viable microbes of fecal slurry (FS) and fecal slurry and _T. spicata_ digest (FSTD).
# Model Test on Grouting Properties of Alluvial Filler Soil
## 2 Test Methods and Test Scheme
### Test Scheme  
In this section, the grouting model test scheme is illustrated. The model test conducted in this work contains the following steps: (1) The grouting materials and devices were prepared for the grouting model test; (2) Subsequently, the physical and mechanical properties of the soil sample were tested by the methods of an indoor soil test, dynamic penetration test, and geological radar test, respectively; (3) Then, the cement slurry was grouted into alluvial soil by the grouting pump; (4) After grouting, the grouting effect was detected by the methods of indoor soil test, dynamic penetration test, and geological radar test, respectively; (5) Finally, the diffusion pattern of grouting slurry was observed by the excavation of the grouting model container. The detailed procedures are given as follows:  
(1) Preparation of the grouting model test: as shown in Figure 1, the model test container, which was made of bricks, was filled with alluvial soil with 30 cm per layer.  
(2) Initial physical and mechanical properties tests of the alluvial soil: Three group soil samples before grouting were taken in the container by handheld drill. Then the physical and mechanical properties of soil samples were tested in a laboratory. The initial state tests of alluvial soil are shown in Figure 5.  
(3) The grouting pipe was inserted in the middle of the model container. The model grouting test was carried out with a maximum grouting pressure of 2.5 MPa. An automatic monitoring device was installed at the outlet of the grouting pump to record the grouting pressure and the amount of grouting. During the grouting process, the relationship between the grouting pressure and the grouting time was recorded by a camera device.  
(4) The density distribution of grouting soil was measured by a geological radar after grouting since the geological radar can detect the location of the slurry.  
(5) The static load test and light dynamic penetration test were conducted to test the bearing capacity of grouting soil.  
(6) Three groups of soil samples after grouting were taken in the model test container by handheld drill, and the soil samples were tested in the laboratory to measure the shear strengths of the grouting soil.  
(7) After grouting, a full-section excavation of grouting soil was carried out to study the diffusion law and the diffusion range of slurry.
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 4 Mathematical Model
### Proposed Controller Design  
Fuzzy controllers are designed for use in current and DC voltage loops. The fuzzy system's first and second inputs are the current/DC voltage error and its derivative. The triangle membership function was selected for the Fuzzy type 2 controller because it is simple to construct and produces good results with accepted precision.  
There are seven linguistic tags to denote the entries: High Negative (HN), Medium Negative (MN), Low Negative (LN), Zero (Z), Low Positive (LP), Medium Positive (MP), and High Positive (HP). The following Member Functions (MFs) are used for the inputs: labels indicating HN and PH are trapezoidal, while those for MN, LN, Z, LP, and MP are triangles. Figure 4 depicts the input representing MFs.  
The variability of the desired control is the fuzzy system's output. This output is denoted by 7-linguistic labels: Steep Drop (SD), Medium Drop (MD), Low Drop (LD), Zero (Z), Low Growth (LG), Medium Growth (MGH), and High Growth (HG). The MFs representing the output are shown in Table 1. The 49 rules needed to account for all possible inputs are based on the seven error (E) values and seven error change (\(\Delta\)E) values. These are typically presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**MFs** & **Interval** \\ \hline SD & [\(-1.00\), \(-0.40\)] \\ MD & [\(-0.60\), \(-0.20\)] \\ LD & [\(-0.50\), \(0.00\)] \\ Z & [\(-0.20\), \(0.20\)] \\ LG & [\(0.00\), \(0.40\)] \\ MGH & [\(0.20\), \(0.60\)] \\ HG & [\(0.40\), \(1.00\)] \\ \hline \end{tabular}
\end{table}
Table 1: Output memberships.  
In Figure 4b, the system's error and change in error are initially fuzzified in accordance with their membership functions in the design of the fuzzy PI controller. The fuzzy reasoning is then used to calculate the membership degree for the PI controller's gain variations \(\Delta K_{\text{P}}\) and \(\Delta K_{\text{i}}\). Moreover, the proportional and integral gains of the PI controller are adjusted by the defuzzified \(\Delta K_{\text{P}}\) and \(\Delta K_{\text{i}}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(\boldsymbol{\Delta\text{E}}\)} & \multicolumn{8}{c}{**E**} \\ \cline{2-9}  & **HN** & **MN** & **LN** & **Z** & **LP** & **MP** & **HP** \\ \hline HN & SD & SD & SD & SD & MD & LD & Z \\ \hline MN & SD & SD & SD & MD & LD & Z & LG \\ \hline LN & SD & SD & MD & LD & Z & LG & MGH \\ \hline Z & SD & MD & LD & Z & LG & MGH & HG \\ \hline LP & MD & LD & Z & LG & MGH & HG & HG \\ \hline MP & LD & Z & LG & MGH & HG & HG & HG \\ \hline HP & Z & LG & MGH & HG & HG & HG & HG \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy controllers rules.  
Figure 4: Fuzzy type 2 scheme: (**a**). Membership Functions for input. (**b**) Structure of the proposed controller.
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 2 FBG Hoop Strain Sensor
### Leakage Detection by FBG Hoop Strain Sensor  
The preliminary experiment is conducted on a PVC model pipeline (shown in Figure 2) to test the developed FBG hoop strain sensor for leakage detection. The leakage process can be simulated by opening a valve pre-installed on the model. A pressure sensor is also installed as a traditional leak detection method to compare with the FBG hoop strain sensor. It can be seen that the FBG hoop strain sensor is tightly fixed onto the surface of the model pipe by using the pre-tension mechanism.  
Figure 1: Developed FBG hoop strain sensor. (**a**) Schematic diagram of an FBG hoop strain sensor; (**b**) picture of the encapsulated FBG hoop strain sensor with a pre-tension system.  
Figure 2: Picture of model pipeline and FBG sensors.  
The leakage occurrence leads to the pressure drop at the leak point and then forms a negative pressure wave (NPW), which propagates along the pipeline. When NPW arrives at a sensor, the pressure drop or the strain drop can be detected to determine the arrival time of NPW and even to calculate the location of the leak point by two sensors. Figure 3 shows the time domain signals of the FBG hoop strain sensor and the pressure sensor when a leak valve is opened. The sudden drop can be considered as the arrival of NPW at the sensors. It can be found that the FBG hoop strain sensor has a similar function in leak detection and is used as a substitute for the traditional pressure sensor. The main advantage of this FBG hoop strain sensor over the traditional pressure gauge is that it can be installed nondestructively and flexibly along the pipeline. Therefore, one potential application of the FBG hoop strain sensor is to install a series of sensors along the pipeline to acquire the characteristic information of different leakage conditions.
# AHC: Advanced Network-Hiding Access Control Framework
## 5 Experiments and Analysis
### Performance Testing and Analysis  
#### 5.2.1 Comparison of Overhead  
SPA uses the RSA encryption algorithm, while AHAC employs the ECC encryption algorithm. We compared the cost-effectiveness of RSA and ECC in terms of security strength and key length, as shown in Table 2. Under the same security standards, the key length of the ECC algorithm is significantly shorter than that of the RSA algorithm. Additionally, the ciphertext size generated by RSA message signatures is roughly equivalent to the key length. Therefore, when verifying the identity of network messages, using shorter ECC random keys (32 bytes or 64 bytes) for ECDH exchange, compared to transmitting large RSA2048 message signatures (256 bytes) for verification, AHAC not only incurs lower computational overhead but also more efficiently conserves valuable bandwidth resources. This strategy demonstrates that AHAC has significant advantages over SPA in improving system efficiency and resource utilization.  
We designed an experiment to measure the encryption and decryption time of RSA and ECC, with specific data presented in Table 3. In the experiment, we tested the performance of both algorithms in different scenarios by increasing the number of encryption and decryption cycles. It can be observed that as the number of cycles increases, the encryption and decryption time for both RSA and ECC gradually rises. However, it is noteworthy that the time overhead for ECC is significantly less than that for RSA. Particularly as the number of cycles increases, the advantage of ECC becomes more pronounced, with RSA's time overhead reaching up to approximately 800 times that of ECC. This stark contrast in data highlights the significant efficiency advantage of AHAC in encryption and decryption, providing robust data support for selecting a more efficient encryption algorithm in practical applications.