esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 3 Mechanical Passive Constant Flow Regulators
### Pillar Valves in Parallel
At low pressure, the main hydraulic resistance is no longer a tiny hole but a long microchannel with a well-controlled cross-section obtained by anisotropic wet etch (potassium hydroxide (KOH)) of (100) Si wafers. A \(50\) um SOI with a \(2\) um buried oxide was used to form the circular membrane (\(3.5\) mm in diameter). A \(20\) um borosilicate thick spacer was deposited to form the gap and to anodically bond the Si membrane wafer to the Si middle wafer. Two different devices dedicated to implantable infusion systems were designed to deliver a constant flow rate of \(1\) mL/h (High Flow (HF) design) and \(1\) mL/day (Low Flow (LF) design), respectively. Figure 5: Cross-section of a passive flow regulator with a top SOI wafer (membrane) and a bottom wafer (substrate) made of Pyrex or Si. The fluid flows through the inlet holes of the membrane, the radial diffuser (annular restriction between the membrane and the pillar), and the outlet hole of the substrate. As the pressure increases, the deflection of the membrane induces an increase in the hydraulic resistance of the radial diffuser to keep the flow rate constant. The flow direction is indicated by red arrows. The samples were manufactured in a clean room and tested using water at 20 \({}^{\circ}\)C. The HF and LF samples exhibited a constant flow rate of 1 mL/h \(\pm\) 10% and 1 mL/day \(\pm\) 10% respectively, in the pressure range [200; 800] mbar. Replacing the fluidic restriction of a fixed-rate implantable pump by a passive constant flow regulator would enable a reduction in flow variability by up to a factor 2.4, and, for a safer refill procedure, a lowering of the reservoir pressure by a factor 5, from 2.5 to 0.5 bar [5; 9].
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Metals  
Metals, such as copper, silver, and steel, are the most electroconductive materials, and are extensively used in smart textile applications such as copper yarns and silver-coated fibers [1; 50]. Silver (Ag), gold (Au), copper (Cu), titanium dioxide (TiO\({}_{2}\)), zinc oxide (ZnO), nanolayered clay, and other organic and inorganic nanoparticles and their nanocomposites are applied to textile materials to pass on different multifunctional properties [51]. The conductivity of metallic-nanoparticle-coated threads is low because their small size leads to many interparticle junctions [52].  
A few metals and metal-derived oxides used in textile finishing are discussed here.
# Valorization Potential of Oilseed Cakes by Subcritical Water Extraction
## 4 Discussion
### Total Lipid and Protein Content
[21] detected 17 amino acids in subcritical water hydrolysates of fish proteins, whereas [PERSON] et al. [22] recovered amino acids from squid viscera and monitored theinfluence of the treatment temperature on the yield. According to these authors, the highest yield of amino acids in raw and de-oiled squid viscera hydrolysates were 23.3% and 53.3% at 180 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. The subcritical water extraction has also been applied in the production of protein hydrolysates from several vegetable sources. In rice bran hydrolysates obtained by subcritical water extraction more than 14 essential and non-essential amino acids were detected [23]. The optimum production temperature for most of the amino acids was 127 \({}^{\circ}\)C and, at temperatures higher than 227 \({}^{\circ}\)C, no amino acids were detected. In the case of soy proteins, the hydrolysis under subcritical water extraction was most effective at 190 \({}^{\circ}\)C [24]. This processing temperature resulted in the highest yield of low molecular weight proteins and the highest free amino acid group contents. The authors concluded that hydrolysates undergo structural disruption at temperatures above 210 \({}^{\circ}\)C, indicated by the decrease in the free amino group content. Molecular size distribution of peptides highly depended on temperature, which is important considering that peptides with molecular weight between 1000 and 6000 Da give rise to bitterness, a highly undesirable property for further use in food industry. Above 210 \({}^{\circ}\)C the distribution of peptide molecular size was shifted to smaller fragments of \(\sim\)200 Da [23]. Hydrolysis of carbohydrate fraction required higher temperatures, beginning at 190 \({}^{\circ}\)C.  
All these studies support the growing interest in the production of protein hydrolysates from different food sources and agricultural waste, with perspective of their use in energy drinks, weight control products and nutritional supplements for elderly and immuno-compromised users. Since hydrolysates are complex mixture of peptides, oligopeptides and amino acids, much better nutritional value is obtained. Many of the produced peptides exhibit different biological activities, such as antihypertensive, antidiabetic, antioxidant, anticancer and antioxidant [18]. Considering their high market value, different plant (soy, pea, maize, amaranth), marine (sponges, algae, molluscs) or animal (casein) proteins are hydrolyzed with methods usually involving enzymatic (papain, pepsin, protease, pancreatin, trypsin, chymotrypsin, alkalis, thermolysin) hydrolysis or microbial fermentation. Subcritical water treatmentsignificantly lowers the price in comparison to enzymes, as well as providing a short process duration, compared to competitive techniques, and therefore may represent a good alternative for the production of protein hydrolysates from different sources.
## 3 Active Fault-Tolerant Control Method for Configuration Transformation  
### Framework  
This section proposes an active fault-tolerant control framework for dynamically adjusting the tension of hangers and the SLB during the main cable spatial configuration transformation. The framework focuses on dynamically adjusting the fault-tolerant inversion model based on data feedback from each construction stage. This results in a dynamic optimization mechanism that incorporates data feedback, verification, and secondary optimization. The framework is highly effective in addressing assembly failures, improving fault tolerance, and preventing hanger bending damage, ultimately enhancing the constructability of the construction process. The proposed framework is shown in Figure 4, and the key steps in the proposed framework are as follows:  
Figure 4: Active fault-tolerant control framework.  
Figure 3: Suspended lateral bracing.  
Step 1: Determine the initial design space of optimization parameters based on engineering experience and fault tolerance requirements defined by the construction team. This step sets the boundaries within which the construction parameters can vary.  
Step 2: Use Latin Hypercube Sampling (LHS) [23] to extract a set of input samples within the design space. These samples are then used to compute the corresponding structural responses using the FEA method, which provides a comprehensive understanding of how the structure will behave under different conditions.  
Step 3: Establish a fast mapping relationship between the optimization variables and multiple performance objectives using an artificial rabbit optimization-backpropagation neural network (ARO-BP) surrogate model [24; 25]. The worst reliability prediction method from Section 3.3 is applied to formulate the worst reliability constraint function and the worst hanger angle deviation constraint. This step effectively decouples the reliability calculation from the multi-objective optimization process.  
Step 4: Apply the NSGA-II [26] multi-objective optimization algorithm to solve the fault-tolerant inversion model for each construction stage. The Pareto solution set is generated for the current stage, and the solution with the highest fault tolerance is selected for guiding construction. Nominal values are used for subsequent construction parameters until real-time feedback is obtained.  
Step 5: Perform construction quality testing for the \(i\)-th construction stage and provide feedback based on the measured data. If the measured data \(d_{i}\)' fall within the fault-tolerant range, the assembly is considered successful, and the process moves to Step 6. If the measured data fall outside the fault-tolerant range, rework is required, and the stage undergoes re-testing.  
Step 6: Based on the measured values from the \(i\)-th construction stage, reconstruct the worst hanger angle deviation constraint for the next stage (\(i+1\)) and repeat Steps 3-5 to complete fault-tolerant analysis for subsequent construction stages.
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 4 Experiments and Results
### Dynamic Rerouting Algorithm Experimental Results
Figure 7: Comparison when using DR-RA, without DR-RA, and without the proposed VQoSRR. (**a**) Comparison of VMAF; (**b**) comparison of SSIM metrics. Furthermore, Figure 9a,b also show the effects of executing the dynamic rerouting algorithm at different time intervals on HD video quality measured by SSIM every 10 frames in Figure 9a and every 200 frames in Figure 9b. As can be seen from the figures, the DR-RA improved the SSIM when interval (t = 7s). Figure 8: Effects of executing dynamic rerouting algorithm at different times in SD video resolution. (**a**) SSIM values are measured every 10 frames; (**b**) SSIM values are measured every 200 frames.
## 3 Results and Discussion
### PdM Assessment of Case Study
#### 3.1.5 Step 5: Detection Techniques and Priority Index  
The assessment of the detection effectiveness of each monitoring technique is supported by two measures, _detectability percentage_ and _detection earliness_, as shown in the priority index in Table 3. These refer to how many cases were detected compared to all the cases and their earliness of detection, respectively. The ranking is based on the detection failure stage (incipient, degraded, or critical) and how easily the specific failure mode is detected. The detection level is a product of both earliness and detectability.
## 4 Methods
### Volcanic Ash Deposits Mapping  
For mapping volcanic hazards in vegetated areas, a _dNDVI_ analysis was performed. The _NDVI_ describes the normalized ratio of reflectance in the NIR and red (Equation (1)) and provides information about the photosynthetic activity or health of plants [55],  
\[NDVI=\frac{\rho_{NIR}-\rho_{RED}}{\rho_{NIR}+\rho_{RED}} \tag{1}\]  
where \(\rho_{\rm NIR}\) and \(\rho_{\rm RED}\) represent the corresponding reflections in the NIR and red wavelength ranges.  
In this work, _dNDVI_ analyses were performed for the Agung I and Krakatau II eruption events to identify and quantify ash and tephra deposits. In addition, _dNDVI_ layers were created for the Karangetang I, Karangetang II, and Krakatau I eruption phases, which were subsequently incorporated into the object-oriented methodology for lava flow mapping (Section 4.3).  
For the identification of volcanic induced vegetation loss, the _NDVI_ layers of the pre- and post-scenes (exact dates of imagery are reported in Sections 5.1 and 5.2) of the respective eruption phase were differentiated using Equation (2):  
\[dNDVI=NDVI_{post}-NDVI_{pre} \tag{2}\]  
The direction of change can then be inferred from the resulting _dNDVI_ layer [60], whereby areas of negative _dNDVI_ values represent volcanic deposits that cover or destroyed vegetation. A threshold classification of the _dNDVI_ layer was then used to classify the intensity of change. In doing so, the events used to analyze ash and tephra deposits (Agung I and Krakatau II) were classified with \(\left|\,0.2\,\right|\) intervals. For the analysis of eruption events with the lava flows (Karangetang I, Karangetang II, Krakatau I), the _dNDVI_ change areas were classified using a \(-0.3\) threshold and added to the object-oriented change detection method as an input dataset.
###### Abstract  
In recent years, edge computing has emerged as a new concept in the computing paradigm that empowers several future technologies, such as 5G, vehicle-to-vehicle communications, and the Internet of Things (IoT), by providing cloud computing facilities, as well as services to the end users. However, open communication among the entities in an edge based IoT environment makes it vulnerable to various potential attacks that are executed by an adversary. Device authentication is one of the prominent techniques in security that permits an IoT device to authenticate mutually with a cloud server with the help of an edge node. If authentication is successful, they establish a session key between them for secure communication. To achieve this goal, a novel device authentication and key management mechanism for the edge based IoT environment, called the lightweight authentication and key management scheme for the edge based IoT environment (LDAKM-EIoT), was designed. The detailed security analysis and formal security verification conducted by the widely used \"Automated Validation of Internet Security Protocols and Applications (AVISPA)\" tool prove that the proposed LDAKM-EIoT is secure against several attack vectors that exist in the infrastructure of the edge based IoT environment. The elaborated comparative analysis of the proposed LDAKM-EIoT and different closely related schemes provides evidence that LDAKM-EIoT is more secure with less communication and computation costs. Finally, the network performance parameters are calculated and analyzed using the NS2 simulation to demonstrate the practical facets of the proposed LDAKM-EIoT.  
Internet of Things (IoT); edge computing; authentication; key management; security; AVISPA; NS2 simulation +
Footnote †: journal: Sensors
## 6 A Cube Satellite with Elastic Plate Boundaries and its Dynamic Responses
Thus, the results presented in this section indicate that the simplified 3-stage model established in Section 4 is valid in determining the boundary stiffness and damping parameters of the structural plate. In this section, the effect of the elastic plate boundary is verified using a relatively simple cube satellite. FEA results demonstrate that the adoption of an elastic boundary for the structural plate with flywheels (vibration source) can significantly reduce the in-orbit micro-vibration response of the satellite structure. For a satellite with a more complex structure and layout, the elastic boundary of the structural plate should be further designed in detail according to the flywheel (vibration source) layout and the shape of the structural plate. Furthermore, as the primary objective of this paper is to introduce the basic principle and verify its feasibility, the implementation of the elastic plate boundary is not considered in this paper. It is certain that the elastic plate boundary will introduce extra weight, and it will be a challenge to design a boundary spring-dashpot component with low weight and limited size. Nevertheless, an elastic spring with limited size can be achieved such as by using sheets [35], and the piezoelectric materials can be utilized as the damper to harvest or dissipate energy [36].
# A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories
## 4 Methods
### Image Preprocessing  
In the preprocessing stage, speckle noise reduction based on anisotropic diffusion (SRAD) [17] was conducted to remove the speckle noise caused by different diffusion signals. It reduced the interference of noises on the breast tissues, as shown in Figure 5b. Next, intensity normalization was applied to decrease the brightness differences among all images acquired by different types of imaging instruments. This operation preserves image quality, and eliminates variabilities from different radiologists who fetch images with different ultrasound machines, as shown in Figure 5c. Finally, a contrast enhancement method based on histogram equalization was performed to increase the contrast between the tumor regions and the non-tumor (background) regions as shown in Figure 5d.
## 2 Methodology
### Instance Definition  
The base scenario in which to develop the algorithm and measure the associated indicators is described next.  
In this research, we employ a system wherein a set of preventive maintenance activities is defined, of which 6 activities must be performed under a certain periodicity for each one of them over a time horizon \(T=26\) weeks. These activities are scheduled and grouped (if feasible) by the algorithm considering the use of time tolerance for each execution. Perfect maintenance is assumed for all executions, and non-negligible duration is assumed for the preventive maintenance activities.  
The parameters for the instance used in this article are as follows:  
The values for periodicity and maintenance task duration for each activity are listed in Table 1; these parameters are the same as in [7]. Furthermore, the parameters for the Weibull distribution of each activity are shown in Table 2 and were set considering a high shape parameter to simulate a high failure rate at late times and scale parameter equal to five times the periodicity of the preventive maintenance to ensure high levels of reliability between interventions. These parameters are selected with the aim of reducing focus on corrective maintenance, instead focusing on the performance of the grouping strategy.  
The system parameters for setup cost (\(cs\)) and inefficiency cost (\(uw\)) are presented in Table 3. These values were chosen to depict the behaviour of the indicators related to cost and should be further studied to improve the accuracy of the report.  
Finally, the parameters assumed for the calculation of the indicators defined above are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activity (\(i\))** & **Periodicity (\(T_{i}\))** & **Duration (\(p_{i}\))** \\ \hline
1 & 3 & 0.2 \\
2 & 5 & 1 \\
3 & 7 & 0.5 \\
4 & 11 & 1.5 \\
5 & 13 & 1.1 \\
6 & 17 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Instance parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activity (\(i\))** & **Shape (\(\beta_{i}\))** & **Location (\(\gamma_{i,j}\))** & **Scale (\(\eta_{i}\))** \\ \hline
1 & 2 & 0 & 15 \\
2 & 2 & 0 & 25 \\
3 & 2 & 0 & 35 \\
4 & 2 & 0 & 55 \\
5 & 2 & 0 & 65 \\
6 & 2 & 0 & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weibull parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(cs\) & \(uw\) \\ \hline
500 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: System parameters.  
These values are selected considering that:  
* The mean time for a corrective maintenance intervention (\(cnt_{i}\)) is 10% of the periodicity of the activity;
* The workforce needed for an activity (\(r_{i}\)) is 10 times the duration of the activity;
* The cost of the unemployed useful life of the asset (\(cul_{i}\)) and cost of corrective intervention (\(cc_{i}\)) are set to 100 and 1000, respectively, for all activities.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 3 Preliminaries
### Additively Homomorphic Encryption  
The homomorphic encryption performs a set of mathematical computations on plain-text and derives a new cipher-text which presumably same as the plain text after decryption. Meanwhile, additively homomorphic encryption performs the additivity on multiple cipher-texts and decrypts the encrypted result at the same time [13]. Therefore, local users can send this encrypted data for processing on the cloud server without revealing the private information. For instance, consider two plain-texts \(\xi_{1}\), \(\xi_{2}\), such that  
\[\begin{array}{l}E_{\delta}(\xi_{1}+\xi_{2})=\tau_{1}\oplus\tau_{2}\\ E_{\delta}(\alpha\times\xi_{1})=\alpha\otimes\tau_{2}\end{array} \tag{2}\]  
where \(E_{\delta}\) represents the encrypted-secret text, \(\tau_{1}\), \(\tau_{2}\) denotes the cipher-text of \(\xi_{1}\), \(\xi_{2}\), respectively, and \(\alpha\) is a constant for any encrypted text.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) is considered as a prospective indicator of vegetation photosynthetic activity and the ecosystem carbon cycle. The current coarse spatial-temporal resolutions of SIF data from satellite missions and ground measurements still cannot satisfy the corroboration of its correlation with photosynthesis and carbon flux. Practical approaches are needed to be explored for the supplementation of the SIF measurements. In our study, we clarified the diurnal variations of leaf and canopy chlorophyll fluorescence for a subtropical evergreen coniferous forest and evaluated the performance of the canopy chlorophyll concentration (CCC) approach and the backward approach from gross primary production (GPP) for estimating the diurnal variations of canopy SIF by comparing with the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. The results showed that the canopy SIF had similar seasonal and diurnal variations with the incident photosynthetically active radiation (PAR) above the canopy, while the leaf steady-state fluorescence remained stable during the daytime. Neither the CCC nor the raw backward approach from GPP could capture the short temporal dynamics of canopy SIF. However, after improving the backward approach with a correction factor of normalized PAR incident on leaves, the variation of the estimated canopy SIF accounted for more than half of the diurnal variations in the canopy SIF (SIF\({}_{\text{SIF}}\): R\({}^{2}\) = 0.53, \(p\) < 0.001; SIF\({}_{\text{SIF}}\): R\({}^{2}\) = 0.72, \(p\) < 0.001) for the subtropical evergreen coniferous forest without water stress. Drought interfered with the utilization of the improved backward approach because of the decoupling of SIF and GPP due to stomatal closure. This new approach offers new insight into the estimation of diurnal canopy SIF and can help understand the photosynthesis of vegetation for future climate change studies.  
chlorophyll fluorescence; model comparison; SCOPE model; evergreen conifer; water stress