esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions  
### Comparisons between Two Solutions  
#### 4.1.1 Load vs Central Deflection  
First, let us compare the relation of loads vs central deflection. Note that we may directly obtain the relation of loads vs central deflection from the perturbation solution based on \(W_{m}\). After substituting the known \(P_{1}\) and \(P_{3}\) into the first formulas of Equation (49), we easily have  
\[\frac{P}{16}=\frac{4K}{2\lambda_{1}+1}W_{m}+\frac{V}{1080(2\lambda_{1}+1)^{4}} \Bigg{[}\begin{array}{c}360\lambda_{1}{}^{4}+840\lambda_{1}{}^{3}+825 \lambda_{1}{}^{2}+388\lambda_{1}+73\\ +10\lambda_{2}(108\lambda_{1}{}^{4}+162\lambda_{1}{}^{3}+108\lambda_{1}{}^{2}+ 35\lambda_{1}+5)\end{array}\Bigg{]}W_{m}^{3}. \tag{74}\]  
For the perturbation solution based on \(P_{m}\), the relation is obtained indirectly. For this purpose, we substitute the known \(\overline{W}_{1}\) and \(\overline{W}_{3}\) into the first formulas of Equation (61),  
\[W=\frac{\eta^{2}+2\lambda_{1}\eta}{4K}P_{m}-\frac{V}{276480K^{4}}\left\{ \begin{array}{l}2\eta^{6}+6(3\lambda_{1}+1)\eta^{5}+15(5\lambda_{1}{}^{2}+4 \lambda_{1}+1)\eta^{4}\\ +20[6\lambda_{1}{}^{3}+9\lambda_{1}{}^{2}+5\lambda_{1}+1+\lambda_{2}(6\lambda _{1}{}^{2}+4\lambda_{1}+1)]\eta^{3}\\ +30[6\lambda_{1}{}^{3}+9\lambda_{1}{}^{2}+5\lambda_{1}+1\\ +\lambda_{2}(18\lambda_{1}{}^{3}+18\lambda_{1}{}^{2}+7\lambda_{1}+1)](\eta^{ 2}+2\lambda_{1}\eta)\end{array}\right\}P_{m}^{3}. \tag{75}\]  
Letting \(\eta=1\), we may have the relation of central deflection vs loads as follows  
\[W_{\eta=1}=W_{m}=\overline{W}_{m1}P_{m}+\overline{W}_{m3}P_{m}^{3} \tag{76}\]  
where  
\[\overline{W}_{m1}=\frac{2\lambda_{1}+1}{4K}\]  
\[\overline{W}_{m3}=-\frac{V}{276480K^{4}}\Bigg{[}\begin{array}{c}360\lambda_{ 1}{}^{4}+840\lambda_{1}{}^{3}+825\lambda_{1}{}^{2}+388\lambda_{1}+73\\ +10\lambda_{2}(108\lambda_{1}{}^{4}+162\lambda_{1}{}^{3}+108\lambda_{1}{}^{2}+ 35\lambda_{1}+5)\end{array}\Bigg{]}. \tag{77}\]The inverse transform of above formulas will give  
\[P_{m}=\frac{P}{16}=\frac{4K}{2\lambda_{1}+1}W_{m}+\frac{V}{1080(2\lambda_{1}+1)^ {4}}\left[\begin{array}{c}360\lambda_{1}{}^{4}+840\lambda_{1}{}^{3}+825\lambda _{1}{}^{2}+388\lambda_{1}+73\\ +10\lambda_{2}(108\lambda_{1}{}^{4}+162\lambda_{1}{}^{3}+108\lambda_{1}{}^{2} +35\lambda_{1}+5)\end{array}\right]W_{m}^{3} \tag{78}\]  
which is the same as Equation (74), and also demonstrates the relation of loads vs central deflection based on \(W_{m}\) or on \(P_{m}\) is equivalent.
## 4 Discussion  
The performed review aimed at systematically analyze the current state-of-the-art related to conductive materials usable for tissue engineering purposes, i.e., sensing and stimulating supports such as scaffolds. In this perspective, it is fundamental not only underline the types of materials available, but it is also mandatory to stress out the main specific applications. For this reason, the discussion section is addressed towards the identification of the most promising application thus to discuss the practical advantages and disadvantages of each conductive biomaterial. Furthermore, this approach might give useful guidelines to researchers for future implementations of conductive biomaterials for scaffolds development. As summarized in Table 4, three most relevant fields of applications were highlighted in the literature: neural, cardiac and bone tissue engineering. Applications of conductive biomaterials encompassing the use of electroactive cells (neurons and myocytes) require the use of biomaterials that could interact with the cells ensuring both monitoring and stimulation. In order to ensure this interaction, high electrical conductivity in biological environment, high thermal and electrical stability and tunable electrical properties represent fundamental and mandatory key requirements. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fillers** & **Manufacturing Techniques** & **Conductivity (Scm\({}^{-1}\))** & **References** \\ \hline Gold nanofillers & Electrospinning, capillary force lithography & \(10^{-4}\)–\(2\times 10^{5}\) & [92; 93; 94; 95] \\  & + electron beam evaporation & \(10^{-4}\)–\(2\times 10^{5}\) & [92; 93; 94; 95] \\ Silver nanofillers & Capillary force lithography + electron beam evaporation & 4 & [97; 98] \\ Platinum nanofillers & Heat sintering & / & [96] \\ Zinc nanofillers & Electrospinning & 0.01–0.08 & [99; 100] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Properties of different metal-based fillers and corresponding techniques used in manufacturing scaffolds. Focusing on neural tissue engineering, where nerve regeneration represents the most thrilling challenge, different approaches were adopted with promising effects demonstrated on cells development. Among conductive polymers, PPy and PEDOT were shown as the most suitable choice, often combined with different kinds of materials. PPy provides from one side excellent mechanical, electrical and stimulus-responsive characteristics, and from the other the highest electrical
# The Basis for Reliability-Based Mechanical Properties of Structural Aluminium Alloys
## 3 Methodology for Data Analysis
### Material Partial Factor Determination  
The structural design principle adopted in the Eurocodes applies the partial factor format in combination with the concept of limit states [18]. The partial factors, \(\gamma_{\text{MI}}\), ensure that in all the relevant design situations no relevant limit state is exceeded and are calibrated to achieve the desired level of the reliability of a structure. Design values take different uncertainties into account applying partial factors, characteristic values, and other measures of reliability [5]. The design value \(R_{\text{d}}\) can be defined using the following simplified expression [18]:  
\[R_{d}=\frac{R_{k}}{\gamma_{M}} \tag{1}\]  
In Equation (1), \(R_{k}\) is the characteristic value, and \(\gamma_{M}\) is the partial factor that includes uncertainties in the mechanical properties (\(\gamma_{M}\)) as well as model uncertainties in the structural resistance (\(\gamma_{Rd}\)) [18]:  
\[\gamma_{M}=\gamma_{Rd}\cdot\gamma_{m} \tag{2}\]  
Such a format of partial factor \(\gamma_{M}\) defines uncertainties of the structural resistance and uncertainties of mechanical properties as independent variables, thus allowing for a separate calculation of the material variability. Accordingly, the design value of a mechanical property can be defined with the following expression:  
\[X_{d}=\frac{X_{k}}{\gamma_{m}} \tag{3}\]  
Unless stated otherwise in EN 1991 to EN 1999, the characteristic value of a mechanical property \(X_{k}\) should be defined as a 5% fractile value where a low value of a mechanical property is unfavourable, or as the 95% fractile value where a high value of a mechanical property is unfavourable. The design value of a mechanical property is defined in EN 1990 [18] and ISO 2394 [33] as a 0.1% fractile value in order to reach a target reliability level. Therefore, the partial factor for mechanical properties can be directly calculated:  
\[\gamma_{m}=\frac{X_{k}}{X_{d}} \tag{4}\]  
Nominal values of material properties \(X_{tk}\) can be taken as the characteristic values when there are insufficient statistical data available to establish the characteristic values [18]. Such an approach is adopted in the current version of Eurocode 9 [15] regarding mechanical properties of aluminium. Consequently, the partial factor for mechanical properties related to the nominal values \(\gamma_{mk}\) takes the following form:  
\[\gamma_{mk}=\frac{X_{tk}}{X_{d}}=\gamma_{m}\cdot\Delta_{k}=\gamma_{m}\cdot \frac{X_{tk}}{X_{k}} \tag{5}\]  
Note that the factor \(\Delta_{k}\) indicates if the nominal value is safe compared to the characteristic value (less than 1 is safe).
# FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot
## 3 System Implementation
### Edge Detection Algorithm Module Design
Then, use the maximum between-class variance method, i.e., [PERSON]'s method, to adaptively determine the high and low thresholds and use this to perform threshold segmentation and edge linking. This method ensures that the divided two classes have the largest inter-class variance, which means that they have the smallest probability of misclassification. The calculation circuit diagram of this algorithm is shown in Figure 9.  
The clock cost of the inter-class variance calculation is 6, and the threshold calculation needs to traverse all gradient levels. Therefore, if the OTSU threshold of this frame of image is used as the basis for gradient image segmentation, an additional 1082 beats of delay will occur. In the inspection process, the edges and textures of the working image of the belt conveyor will not have a sudden short-term change, so the threshold value obtained by the gradient histogram of the previous frame is used as the basis for the segmentation of this frame. Figure 9: OTSU calculation circuit diagram. In summary, two-level first-in-first-out (FIFO) buffers, combined with registers, are used in FPGA to construct a 3 \(\times\) 3 convolution window for the result of non-maximum suppression, and then the threshold determined by the OTSU algorithm is used as the high threshold, and one-half of it is used as the low threshold. Threshold segmentation and edge linking are based on three judgments:  
1. When the central element is greater than the high threshold, it is judged as an edge;
2. When the central element is less than the low threshold, it is judged as non-edge;
3. When the central element is between the two thresholds, if there are elements higher than the high threshold in the neighborhood, it is judged as an edge. Moreover, the pixels judged to be edges also need to satisfy the LLA in the interval [\(\pi/4\), \(3\pi/4\)].
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 2 Numerical Modeling
### Boundary and Loading Conditions  
The DMA's resonant panels are attached to a frame that is fixed to a housing with screws. This leads to the assumption of a fixed support boundary condition at all four edges of the panel for the model.  
To simplify the computation, the panel is driven by a uniform harmonic pressure of \(\hat{p}=1\,\text{Pa}\) normal to the panel surface. This simplification corresponds to a plane wave that hits the surface vertically with an SPL = 94.5 dB. This condition is e.g., achieved in the far field of a centered mono-pole sound source.  
The panel motion is coupled with the air in the back cavity. The numerical coupling between the panel (solid) and the air (fluid) is done using the ANSYS Fluid-Solid Interface. The Fluid-Solid Interface also closes the air volume on the front side. The back cavity (BC) housing is made of 19 mm MDF for the side walls and 30 mm MDF for the rear wall. This prevents the housing from vibrating. They are simulated as a rigid wall, so, a perfect reflection boundary with reflection coefficient \(\Gamma=1\) is used. The modeling approach is depicted in Figure 2.  
Figure 2: Model details: Red represents the areal loading force in negative \(z\)-direction, the black lines represent the clamped/fixed support boundary condition for the front plate, yellow represents the Fluid–Solid Interface between the two physics regions, grey represents the rigid wall boundary condition for the air volume.
# Volumetric Analysis of Aortic Changes after TEVAR Using Three-Dimensional Virtual Modeling
## 2 Materials and Methods
### Volumetric Analysis
#### 2.3.2 Volume Computation  
The segmented aorta was then divided into the same 5 parts, as previously described for the diameter measurements (Figure 2).  
The volume of each region was then calculated in Matlab R2019a using the stlVolume function, which, given a surface triangulation, computes the volume enclosed using the divergence theorem. The assumption for this function is having triangle nodes of the surface ordered correctly (i.e., the computed normal is outward). The function outputs the total volume enclosed and the total area of the surface [16].  
These segments were then evaluated both separately and grouped as global descending thoracic aorta. All these volumes were analyzed at discharge (DIS) and at the 12-month (12 M) follow-up.  
Volumetric analysis was performed blinded to the outcomes and presence of complications.  
Two variables were considered for the volumetric analysis: the net difference between the absolute values of the computed volume at the 12 M follow-up and at discharge (Volume\({}_{\text{change NET}}\), see Equation (3)), and the corresponding percentual difference, referred to as the volume at discharge (Volume\({}_{\text{change REL}}\), see Equation (4)),  
\[Volume_{change_{NET}}(\text{mL})=Volume_{12M}-Volume_{DIS} \tag{3}\]  
\[Volume_{change_{REL}}(\%)=(Volume_{12M}-Volume_{DIS})/Volume_{DIS} \tag{4}\]
###### Abstract  
The monitoring of irrigated areas still represents a complex and laborious challenge in land use classification. The extent and location of irrigated areas vary in both methodology and scale. One major reason for discrepancies is the choice of spatial resolution. This study evaluates the influence of spatial resolution on the mapped extent and spatial patterns of irrigation using an NDVI threshold approach with Sentinel-2 and operational PROBA-V data. The influence of resolution on irrigation mapping was analyzed in the USA, China and Sudan to cover a broad range of agricultural systems by comparing results from original 10 m Sentinel-2 data with mapped coarser results at 20 m, 40 m, 60 m, 100 m, 300 m, 600 m and 1000 m and with results from PROBA-V. While the mapped irrigated area in China is constant independent of resolution, it decreases in Sudan (\(-29\%\)) and the USA (\(-48\%\)). The differences in the mapping result can largely be explained by the spatial arrangement of the irrigated pixels at a fine resolution. The calculation of landscape metrics in the three regions shows that the Landscape Shape Index (LSI) can explain the loss of irrigated area from 10 m to 300 m (\(r>0.9\)).  
irrigation mapping; land use classification; Sentinel-2; NDVI; rescaling technique; spatial resolution; scaling relation; land monitoring; sensor resolution; landscape metrics 2023
## 3 Results
### Biochemical Composition of Biomass  
In this trial, the quality of the biomass obtained was compared regarding proximate composition (Table 2) and FAME profile (Table 3), in order to identify the effect of the optimization on the biochemical composition of _Aurantiochytrium_ sp.  
Figure 4: Batch growth of _Aurantiochytrium_ sp. AF0043, with MB10 medium (control), inorganic optimized medium (IOM), and waste-based optimized medium (WOM). Error bars represent the standard deviation between three biological replicates.  
The biomass grown with standard growth medium (MB10) presented the highest protein content (24% of DW), followed by the WOM (21% of DW), while IOM led to the lowest protein content (14% of DW) (\(p<0.05\)). Regarding total lipids, _Aurantiochytrium_ sp. grown in WOM presented the highest values, 31% of DW, compared to 23 and 15% of DW in MB10 and IOM, respectively (\(p<0.05\)). The carbohydrate content was significantly higher in cultures grown in IOM (59% of DW) (\(p<0.05\)), than in MB10 (28% of DW) and WOM (33% of DW). MB10 promoted the highest ash content in _Aurantiochytrium_ sp. cultures, 26% of DW (\(p<0.05\)), while IOM- and WOM-grown cells presented similar ash values of 12 and 15% of DW, respectively.  
The FAME profile (Table 3) of _Aurantiochytrium_ sp. was mainly composed of myristic (C14:0), palmitic (C16:0), docosapentaenoic (C22:5 n-3, DPA) and docosahexaenoic (C22:6 n-3, DHA) acids. Other FAs common in this species were also detected in minor abundances, such as pentadecanoic (C15:0), heptadecanoic (C17:0) and stearic (C18:0) acids.  
When the different media are compared among each other, it is clear that MB10 led to a higher amount of saturated fatty acids (SFA) in the biomass of _Aurantiochytrium_ sp., 87.65%, mainly hexadecanoic acid (C16:0) and a lower amount of DHA, which accounted for 10.12% of total FA. On the other hand, both IOM and WOM led to a steep increase in the contents of DHA and to lower contents of SFA in the final biomass. Overall, the optimization allowed a significant improvement in the DHA content of produced biomass, from 10.12% to 17.05% and 29.66% with IOM and WOM, respectively.
## 1 Introduction
As such, in contrast with existing schemes, our scheme can reduce both the unnecessary computation overhead on remote nodes and the total number of attestations performed by the verifier. Through a combination of extensive theoretical analysis and simulation experiments, we evaluated the efficiency and security of our proposed scheme in comparison to the existing scheme using a constant attestation rate, in terms of both the computation overhead on remote nodes and the total number of attestations performed by the verifier. Our data demonstrates that our proposed scheme is more efficient than the existing scheme. For example, our proposed scheme can reach the same efficiency as the existing scheme by using a smaller number of attestations (_i.e._, about \(10\%\) of the existing scheme). In addition, the proposed scheme can significantly reduce the computation overhead in each attestation process, while the attestation efficiency remains the constant. The remainder of this paper is organized as follows: We present the network and threat models in Section 2. We present our proposed scheme in Section 3. In Section 4, we analyze the efficiency and security properties of our scheme. In Section 5, we show experimental results to validate the performance of our proposed scheme. We review related work in Section 7, and conclude this paper in Section 8, respectively.
# Optimization of Heat Pump Systems in Buildings by Minimizing Costs and CO\({}_{2}\) Emissions
## 3 Case Study
### Assumptions for Heat Pumps and Electric Heaters  
#### 3.2.1 Types of Heat Pumps and Electric Heaters  
In this study, the buildings under analysis had high heat demands and were situated in an area without proximity to open water bodies and with limited land plots. To address these factors, larger capacity GSHPs with a vertical earth probe system, hybrid HPs, and air-to-water type HPs were selected. A total of 25 different HPs models were analyzed, including 17 GSHPs from the \"Viessmann Group\" manufacturer [39], 4 hybrid HPs from the \"Robur\" manufacturer [40], and 5 air-to-water HPs from the \"Hitachi\" manufacturer [41].  
To meet peak heat demands or produce domestic hot water, auxiliary EHs are often utilized in heating systems. This analysis included seven different electric heater models [42]. It is worth noting that these EHs should only be used as an auxiliary source that produce no more than 40% of the hourly heat demand. The optimization model being studied took into account the combination of various HPs and EHs in the general heating system, assuming that the HPs should meet at least 60% of the maximum heat demand per hour and ensuring that EHs are only utilized during hours of peak demand.
## 2 Dynamic Modeling of Trans-Medium Process
### Dynamic Modeling
#### 2.3.5 Wind, Wave, and Current Complex Interference Analysis  
When the coaxial HAUV traverses the water surface in cross-domain maneuvering, its body will be subjected to multiple marine environmental disturbances, such as wind gusts, waves, and ocean currents. The perturbation of the HAUV at the water-air boundary surface has been discussed in reference 1, but the actual marine environment is difficult to simulate. To test the robustness and effectiveness of the control algorithm, random time-varying sine or cosine functions are generally used to simulate the marine environment in the literature to realize AUV control. This approximation of the marine environment is reasonable because wind, waves, and currents are also random and irregular in the real environment. In addition to the interference of the external environment, some simplified assumptions are adopted in the modeling process, which makes the established model not strict and accurate, and there are uncertainties in the model. Considering that an actual underwater environment is more complex, the lumped uncertainties are set as follows:  
\[\begin{array}{l}d_{x}=0.2\times(cos(0.4t)+k_{s}),d_{y}=0.2\times(sin(0.5t)+ k_{s}),d_{z}=0.2\times(sin(0.7t)+k_{s})\\ d_{\phi}=0.2\times(cos(0.4t)+k_{s}),d_{\theta}=0.2\times(sin(0.5t)+k_{s}),d_{ \phi}=0.2\times(sin(0.7t)+k_{s})\end{array} \tag{13}\]
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 4 Real Ship Experiment and Its Performance Analysis  
### Real Ship Experiment Description  
A real ship experiment was executed near Dalian bay and adjacent area, China. The performance of the developed navigation solution is tested and verified by using ship-mounted experimental data. An ordinary navigation grade IMU, whose sensor specifications can be found in Table 1, and one high-grade GPS receivers were used to collect data. Figure 2 indicates sea trail trajectory in Dalian bay. The whole sailing time of sea trial was about 21 h, the ship is in continuous sailing and the distance travelled is about 200 n mile. Only a sea trial of about 50 min (red circle) is selected.  
For the sake of implement the performance assessment of the GPS/INS integrated system, the single frequency pseudo-range and Doppler measured value provided by GPS are applied. The reference solution is attained through IMU measured value combine with differential GPS (DGPS) carrier phase measured value, result in a high location accuracy level will be achieved for the algorithm evaluation.