esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Traffic Filtering-Based Techniques
### Quick Removing (QR)
#### 4.1.2 QR Operations  
To understand the QR operation, we consider a single-ring network as shown in Figure 3. In that case, source node 2 has sent two copies A and B to all the ring nodes as the duplicated copies for a frame generated in the upper layer. Within four clocks, each of the frame copies, A and B, will pass each other at the link between nodes 5 and 6, and then travel towards nodes 5 and 6, respectively. At this time, HSR nodes will take the following actions.  
* If both frame copies are error-free: Nodes 5 and 6 have already received a copy of the frame. Node 5 has received copy B from the right side and then received copy A from the left side. The same is true for node 6; it has received copy A from the left side and then received copy B from the right side. In that case, nodes 1, 7 and 6 have received copy A and there is no need for copy B. The same is true for nodes 3, 4 and 5; they have received copy B and do not need to receive copy A. Therefore, node 6 will remove copy B from the ring and node 5 will remove copy A from the ring.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Traffic Filtering Features** & **QR** & **TC** & **PL** & **EPL** & **FHT** \\ \hline Filtering traffic for DANH rings & No & No & Yes & Yes & Yes \\ Filtering traffic for QuadBox rings & No & No & No & Yes & Yes \\ Removing duplicated and circulated traffic & Yes & Yes & No & No & Yes \\ \hline \end{tabular} Notes: Quick removing (QR), Traffic control (TC), Port locking (PL), Enhanced port locking (EPL), Filtering HSR traffic (FHT), Doubly attached node for HSR (DANH).  
\end{table}
Table 1: Traffic filtering features of techniques.  
2. If a frame copy is corrupted as soon as it leaves the source node: We assume that copy A was corrupted when it left the source node 2, moving towards node 1. In this case, node 1 will remove copy A from the ring as soon as it receives copy A, and all the nodes in the rings, except the source node, will wait for copy B. Therefore, each node will receive a copy and forward it to the next node until it reaches the source node.
3. If a frame copy is corrupted during transmission: We assume that copy A was corrupted during transmission between nodes 1 and 7. In that case, node 7 will remove copy A from the ring, and node 6 will not receive copy A. Therefore, node 6 will forward copy B as soon as it receives copy B from the right side. Node 7 will also forward copy B to node 1, but node 1 has already received copy A, so it will remove copy B from the ring.  
Figure 4 shows the process of removing duplicated and circulated frames under the QR approach in a sample network with six DANH rings and two QuadBox rings. In that case, the source node in DANH ring 1 sends unicast frames to the destination node in DANH ring 6. The QR approach removes duplicated and circulated traffic from all DANH and QuadBox rings. However, the traffic is forwarded into all rings of the network.
## 6 Sensor Location and Spatial Aliasing  
Efficient sensor location and possible spatial aliasing errors for risers are conveniently evaluated by examining the \(N\times N\) orthogonality matrix, \(\mathbf{R}\), defined in terms of \(N\) modes of interest [14]--i.e., \(\mathbf{R}_{i,j}=|\varphi_{i}^{\mathrm{T}}\varphi_{j}|\) (\(i,j=1,\ldots,N\)) where, for example, if sinusoidal mode shapes are assumed, \(\varphi_{i}=[\sin(n_{i}\pi z_{1}/L),\sin(n_{i}\pi z_{2}/L),\ldots\sin(n_{i} \pi z_{M}/L)]\) when \(M\) loggers are employed on a riser of length, \(L\). For an array of sensors that must avoid aliasing errors, off-diagonal terms of \(\mathbf{R}\) are relatively small (the diagonal terms are less important but are generally close to unity). Figure 13a shows off-diagonal terms of the orthogonality matrix computed for the twelve modes used with the WWA method and based on twenty-three sensors (sensor no. 4 is the target) for the NDP2350 sheared current data set. Figure 13b is a similar plot where eight sensors (associated with combination no. 34) are used and six modes are employed with the WWA method. The relatively low values of the off-diagonal elements of \(\mathbf{R}\) in the figures confirms that spatial aliasing is not of great concern in the choice of sensor locations in the WWA and modified WWA method results presented. Note that the orthogonality matrix, \(\mathbf{R}\), for the modified WWA method can be constructed in a similar manner as for the WWA method.  
Spatial distribution of loggers may, in general, also be of concern when the MPR and POD methods are employed. This is not so much a spatial aliasing issue then; rather, these two methods rely on interpolation from discrete sensor locations (from relevant POD or MPR modes) to any target location while reconstructing strains there. If the target location is very far from the closest sensors used in the interpolation, reconstructed strains can be quite inaccurate. Even if higher modes are well  
Figure 12: Variability factors estimated by the hybrid method based on thirty-four combinations involving eight strain sensors: (**a**) two uniform current data sets; and (**b**) two sheared current data sets.  
Figure 13: Off-diagonal elements of orthogonality matrices, \(\mathbf{R}\), computed with assumed sinusoidal mode shapes for the NDP2350 (sheared current) data set: (**a**) twenty-three sensors and twelve WWA modes used (sensor no. 4 is the target); and (**b**) eight sensors and four WWA modes (combination no. 34).  
represented in the POD or MPR modes derived, because these modes are discrete, poor interpolation might lead to spurious consideration of any higher harmonics at the target location since these are associated with smaller wave lengths, which will accentuate problems due to interpolation when sensor distribution is not dense.
## 3. Results and Discussion
### Intercomparison with VGT-C3 and PV-C2 LTS for P1, P2 and P3
These consistency levels are, however, in line with the initial expectations, based on the simulated Medium Resolution Imaging Spectrometer (MERIS) and Advanced Along-Track Scanning Radiometer (AATSR) data, which state that, depending on the spectral band, an accuracy of 3 to 5% could generally be expected [18].
## 3 Test Results with Discussions
### Recovery Capacity of NiTi Foam Subjected to Static Load with Discussions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Dimension of Specim**} \\ \cline{3-8}
**Test No.** & **Deformation** & \multicolumn{2}{c}{**Before Test**} & \multicolumn{2}{c}{**After Test**} & \multicolumn{2}{c}{**After Recovery**} \\ \cline{3-8}  & & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Diameter** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Diameter** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Diameter** \\ **(mm)** \\ \end{tabular} \\ \hline
1 & 20\% & 12.75 & 40.00 & 12.10 & 40.30 & 12.39 & 40.10 \\
2 & 20\% & 12.39 & 40.10 & 11.25 & 41.10 & 11.65 & 40.90 \\
3 & 20\% & 11.65 & 40.90 & 10.65 & 42.12 & 11.18 & 41.57 \\
4 & 20\% & 11.18 & 41.57 & 10.32 & 42.25 & 10.80 & 42.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test record for quasi-static test: 20% strain. Figure 5: Stress–strain relationship of quasi-static failure test. Figure 6 shows the failure pattern of the crushed specimen after the test. Foam parts with various sizes at the specimen fringe fall off, while the central part of the specimen remains intact. With such a failure mode, it is obvious that even if all the fallen parts and the main part can recover completely when heated, the load-bearing capacity of the NiTi foam specimen permanently degrades. The fall-off mass during loading at 20%, 25%, and 37% are 0.61 g, 1.13 g, and 4.25 g, respectively. Finally, the loading stopped at 45% strain, where some parts amounting to 5.58 g were observed. The fall-off mass measuring method is that the fall-off parts were removed at regular strain intervals, and the masses were weighed and recorded. Subsequently, the mass to a certain strain can be determined by gradually adding the incremental fall-off mass.
# Revalorization of _Posidonia oceanica_ Waste for the Thermochemical Production of Biochar
## 1 Introduction
### Washing of the Posidonia oceanica Samples  
The samples were intensively washed with a continuous and constant flow of tap water to remove as much salt content as possible. This process is key when using synthesized biochar to apply to the soil. A volumetric flow of 8.2 mL/s of water via a peristaltic pump was used. It operated at 150 rpm. The sample stirring rate was 300 rpm. The process was controlled by measuring the water's ionic conductivity before and after the washing. When the water's ionic conductivity before and after the treatment was approximately equal, the treatment terminated, and the sample was dried at 105 \({}^{\circ}\)C for 24 h then ground to 1 mm.  
The washed _Posidonia_ samples were dried and then analysed by X-ray fluorescence to verify washing efficiency. Determining the reduction of the halogen emitted during combustion allowed a comparison with the non-washed samples. The experiments were conducted following the SW-846 Test Method 5050 of the U.S. Environmental Protection Agency (EPA) [42]. Fluoride, chloride, nitrite, bromide, nitrate, phosphate, and sulphate contents were defined using calorific bomb and ionic chromatography as a detection method. In short, 10 mL of a NaHCO\({}_{3}\)/Na\({}_{2}\)CO\({}_{3}\) (0.03 M/0.024 M) aqueous solution and 0.5 g of biomass were introduced into the calorific bomb for combustion. After combustion, the resultant solution was collected and diluted with distilled water in a 100 mL flask. An aliquot of this solution was analyzed using a Dionex DX-500 ionic chromatograph (Thermo Fisher Scientific Inc., Waltham, MA, USA). Each halogen's concentration was determined in ppm. The concentration of the ion per gram of sample in ug/g, \(C_{o}\), was calculated with the Equation (1):  
\[C_{o}=\frac{C_{comb}\times V_{comb}\times DF}{W_{o}}. \tag{1}\]  
where \(C_{comb}\) is the ion concentration in the solution in ppm, \(V_{comb}\) is the collected volume in the calorific bomb after combustion (10 mL), \(DF\) is the dilution factor (10), and \(W_{o}\). is the sample mass.
## 3 Materials and Methods
### Determination of Total Phenolics  
Total phenolic content (TPC) was determined by the Folin-Ciocalteu colorimetric method [63] with some modifications. Strawberry wine was centrifuged at 4000\(\times\) g (4 \({}^{\circ}\)C) for 15 min (5810 R, Eppendorf, Germany), and 25 \(\upmu\)L of diluted supermates or standard  
Figure 5: Winemaking procedure scheme. Notes: The procedure in the dotted box depended on the specific pre-treatment process in Table 1.  
solutions were mixed with 125 \(\mu\)L Folin-Ciocalteu reagent. After incubation for 5 min at 50 \({}^{\circ}\)C (HCM 100-Pro, Dargon, China), 100 \(\mu\)L Na\({}_{2}\)CO\({}_{3}\) solution (75 g/L) was added and kept in the dark for 30 min (26 \({}^{\circ}\)C) before the absorbance was measured at 760 nm (Infinite 50, Tecan, Mannedorf, Switzerland). The result was expressed as the gallic acid equivalent (GAE) in mg GAE/L, with the following standard curve regression equation: y = 0.0095x + 0.0257 (R\({}^{2}\) = 0.999).  
Total flavonoid content (TFC) was determined by the aluminum nitrate colorimetric method [64] with some modifications. Briefly, 1.0 mL of diluted strawberry wine was reacted with 0.3 mL of 50 g/L sodium nitrite for 6 min. Then, 0.3 mL of 100 g/L aluminum nitrate was added and maintained for 6 min. Finally, 2.4 mL of 40 g/L sodium hydroxide was added. The absorbance was measured at 510 nm after a reaction time of 15 min with a spectrophotometer (Specord 50, Analytic Jena, Jena, Germany). The result was expressed as rutin equivalent (RE) in mg RE/L, with the standard curve regression equation: y = 0.0024x + 0.0225 (R\({}^{2}\) = 0.999).  
Total anthocyanin content (TAC) was determined using the pH difference method [65] and expressed as cyanidin-3-O-glucose chloride equivalent (CGE) in mg CCE/L. The 0.5 mL diluted strawberry wine and 2.0 mL of two different buffers (0.025 mol/L KCl pH 1.0 and 0.4 mol/L CH\({}_{3}\)COONa pH 4.5) were mixed. After incubation for 15.0 min in the dark, the absorbance was measured at 510 and 700 nm. The total anthocyanin was (mg CGE/L) = [(A/(\(\varepsilon\)\(\times\) L)] \(\times\) MW \(\times\) DF, where A = (Abs510 \(-\) Abs700)pH1.0 \(-\) (Abs510 \(-\) Abs700)pH4.5, \(\varepsilon\) = 26,900 L/mol-cm (molar absorptivity of cyanidin-3-Oglucose), where L is the correction factor for a 1 cm optic path length, MW is 449.2 g/mol (molecular mass of cyanidin-3-O-glucose), DF is the dilution factor.
## 4 Material and Methods  
### Plant Material  
Globe artichoke plants were grown at the ex-situ field collection site located in the countryside of Orte municipality (latitude 42\({}^{\circ}\)47\({}^{\prime}\) N, longitude 12\({}^{\circ}\)33\({}^{\prime}\) E, altitude 130 m). The present study involved the examination of three distinct genotypes. Two of these were chosen as representative genotypes of the two populations identified within the landrace \"Carciofo Ortano\" [35] and denoted as Orte 1 and Orte 2 genotypes for simplicity. The third genotype, used as a reference, was Grato 1, a clone selected from a progeny derived from cross-pollination of plants belonging to the \"Castellamma\", \"Campagnano\", and \"Violetto di Toscana\" landraces. Notably, Grato 1 is included in the varietal platform of the PGI \"Romanesco Artichoke of Lazio\".  
All artichoke genotypes were vegetatively propagated through offshoots and transplanted into the open field in November 2020, with rows spaced at 1.00 m and inter-row spacing at 2.5 m. The experimental design employed a randomized complete block layout with three replications for each treatment, and each experimental unit comprised five plants. Field experiments were conducted under conditions of limited resource inputs, involving two irrigations per year in April and August, each delivering 60 mm of water. Organic fertilization was performed with 50 kg of nitrogen per hectare, and the cultivation excluded the use of herbicides, pesticides, and gibberell acid, aligning with the local agronomic practices [35]. After the conclusion of the harvesting period in the 2022 growing season, a minimum of 20 disease-free leaves were harvested from three plants per genotype (one plant per genotype per replicate).  
Plant material was immediately sliced and stored at -80 \({}^{\circ}\)C, and then freeze-dried. Each lyophilized sample was stored at 4 \({}^{\circ}\)C in a sealed plastic bag protected from light and under vacuum until used for analysis. The leaves of nine plants (three genotypes x three biological replicates) were combined, for a total of three samples.
# Numerical Analysis of Laser Pattern Effects to Residual Stress on Metal 3D Printing
## 3 FEM Analysis Result
### Measurement and Comparison of Residual Stress  
To confirm the accuracy of the analysis results, the residual stress was measured using X-ray diffraction (XRD). The Debye ring was measured using XRD to measure the area and accuracy, and the residual stress was calculated by calculating the cos \(\alpha\) value [14,15]. The device used for the measurement was \(\mu\)-X360 FULL 2D from Pulstec. The measurement conditions were 30 kV and 1.2 mA. The measurement area was 2 mm in diameter, and the average value of this area was used.  
The XRD measurement was performed based on a 2 mm area in the center of the upper surface of the specimen. The residual stress was measured by calculating the Debye ring using an X-ray diffraction value of 557 points at the measurement position. The residual stress was calculated based on the measured Debye ring values and compared for each specimen.  
The average value of the 2-mm diameter area was calculated to compare the measured and analyzed values. Figure 10 shows the element corresponding to the 2-mm region of the specimen surface and its residual stress value.  
The residual stress values derived from the analysis are summarized in Table 3, in addition to the XRD measurement results. It was confirmed that the overall analysis results were consistent, except for the specimen with a 180\({}^{\circ}\) rotation angle. For the 180\({}^{\circ}\) specimen, based on previous CT scan results, cracks occurred in the lower region and affected the residual stress. As illustrated in Figure 11, it was confirmed that the remaining three specimens demonstrated relatively accurate results, with an error of approximately 8.86%, except for the 180\({}^{\circ}\) specimen, which exhibited a crack. Based on these results, it was concluded that the simulation simulated the actual process accurately.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rotation Angle**} & \multicolumn{2}{c}{**XRD Measurement**} & \multicolumn{2}{c}{**FEM Simulation**} \\ \cline{2-5}  & **Residual** & **Standard** & **Residual** & **Error from Residual** \\  & **Stress** & **Deviation** & **Stress** & **Stress Measurement** \\ \hline
180\({}^{\circ}\) & 252 MPa & 22 MPa & 513.63 MPa & 50.94\% \\
90\({}^{\circ}\) & 340 MPa & 22 MPa & 436.79 MPa & 22.16\% \\
67\({}^{\circ}\) & 464 MPa & 29 MPa & 471.99 MPa & 1.69\% \\
45\({}^{\circ}\) & 237 MPa & 39 MPa & 236.38 MPa & \(-\)0.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Residual stress results.  
Figure 9: CT scan results at a height of 1 mm from the base plate: (**a**) same direction (180\({}^{\circ}\)); (**b**) 90\({}^{\circ}\) rotation; (**c**) 67\({}^{\circ}\) rotation; (**d**) 45\({}^{\circ}\) rotation.
## 3 Results
### Reciprocal Sync  
Oscillator'sync' is a classic technique from modular analog systems in which a'slave' oscillator has its phase reset to 0 if another'master' oscillator's output crosses a certain threshold \(\theta\). This is a convenient way to create spectrally rich, periodic output.  
If this routine is computed with lookup oscillators, the order of operations is important. Depending on whether or not the output of the master or slave oscillator is computed first, the output waveforms will be different. This scheduling issue, which results from delay in the feedback loop (depicted in Figure 2a), suggests that lookup oscillators are not ideal for representing two oscillatorsthat sync each other in a tight feedback loop. However, if we model each oscillator as a dynamical system as in Equations (13) and (14), the discrete output of the whole system is computed at once. That is to say, there is no delay between the output of one oscillator and the input of another, as in Figure 2b.  
Using a sync rule, we may couple two digital ODE + RK4 oscillators. The rule is that if the state of \(x_{i}\) for one of the oscillators crosses a threshold, the phase of the other will re-initialize. In the case of oscillator 1:  
\[\dot{x}_{1}=\begin{cases}\omega y_{1}&\text{if }x_{2}<\theta_{1}\\ g_{1}(0-x_{1})&\text{if }x_{2}\geq\theta_{1}\end{cases} \tag{16}\]  
\[\dot{y}_{1}=\begin{cases}-\omega x_{1}&\text{if }x_{2}<\theta_{1}\\ g_{1}(-1-y_{1})&\text{if }x_{2}\geq\theta_{1}\end{cases} \tag{17}\]  
where there is another master/slave oscillator with a subscript of 2.  
This is simply a nonlinear modification to the Equations (13) and (14). Here, \(g_{i}\) is a gain factor that speeds the return to the 'zero' phase (\(x_{i}=0\), \(y_{i}=-1\)). This rule is necessary, because if we are computing our state updates by adding approximate integrations of time derivatives (which is exactly what we do with RK4), then there is no simple way to describe instantaneous jumps from one state to another one that is far away. In the regime of reciprocal sync, each oscillator is both master and slave to the other. As can be seen in Figure 3, our dynamical models shows chaotic behavior which does not appear at all in the lookup oscillator implementation.  
Figure 3: The output of (**a**) two digital lookup oscillators in a tight sync loop and (**b**) another pair given Equations (16) and (17) fed to the fourth order Runge-Kutta (RK4) method for solution. (**a**) 950 Hz and 900 Hz digital lookup oscillators in reciprocal sync. \(\theta=0.9\); (**b**) The same parameters but using the dynamical model in Equations (16) and (17).  
Figure 2: Block diagrams showing the signal flow for reciprocal sync. Using dynamical oscillators. (**a**) Using lookup oscillators; (**b**) Using dynamical oscillators.
# Integration of Geoscience Information for Disaster Resilience in Kuala Lumpur, Malaysia
## 1 Introduction
Strategic planning framed by sustainable development is fundamental to the emerging science of resilience. The purpose of the study is to highlight the role of geoscience information in supporting strategic development planning for disaster resilience in Kuala Lumpur. A novel approach is introduced for integrating geoscience information to determine engineering ground conditions and susceptibility to multiple geohazards, which are then combined to demarcate suitable zones for urban development and enhance the resilience in Kuala Lumpur, Malaysia. The capital city of Kuala Lumpur reported a population of 1.98 million in 2020 and an estimated projection of 2.43 million in 2040 [40]. A total of 76% of the area is urbanised to support its role as a global economic and tourism hub, with urban expansion at an average pace of 11% annually. The city is increasingly challenged by landslides, floods and unfavourable ground conditions. Land scarcity due to urbanisation is a major issue with increasing encroachment on land with geological constraints for development [41; 42; 43]. An expansion in the use of underground space, which is currently limited, is under consideration. This is seen as an alternative to minimise landslide risks in the rugged highlands of the city. The upcoming Kuala Lumpur Structure Plan 2040 (KLSP 2040) has highlighted strategies on climate change and disaster resilience for development planning in the city to ensure urban sustainability [40]. The findings of this study are expected to contribute to strategic development planning by demarcating areas for building disaster resilience in the city.