esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Cement Paste Properties with Biochar: Mechanical and Rheological Insights
## 2 Materials and Methods  
### Materials  
The cement composite samples were made using a type I cement 52.5 R purchased from Buzzi-Unicem, Roccavione, Italy. According to the cement manufacturer, the cement had a Blaine specific surface area of 0.4-0.55 m\({}^{2}\)/g, an initial setting time > 90 min, a specific gravity of 3.14 g/cm\({}^{3}\) and a mean particle size of about 16 \(\upmu\)m [38]. The composition of the cement is given in Table 1. Biochar (B) (NERA Company s.r.l, Torino, Italy) was employed in the sample's preparation. Superplasticizer (SP) provided by Master Builders Solutions, Treviso, Italy, was used to reduce the water-to-cement ratio (w/c) and achieve good workability and biochar particle dispersion in the mix. Biochar was ground for 7 h using the ball milling method in a ceramic jar with agate balls. Then, the ground biochar was sieved for 30 min using an ASTM mesh 80 (180 microns) sieve employing a short-period oscillatory movement produced by the compact vibration sieve. This procedure was carried out because previous research indicated that the smaller the average size of the biochar particles, the better the performance of the cement composites, as the surface area of the particles will increase. For B, the reinforcement percentages added to the mix by weight of cement were 1, 2, 3, 5 and 7%. These percentages were chosen to verify the possibility of using higher percentages of biochar as a reinforcement after the excellent results obtained from former studies [39; 30; 31; 32; 33].  
After the milling process, the biochar was characterized by means of laser granu-lometry (Malvern Mastersizer 3000, Malvern, UK with Aeros S dry cell measurement), X-ray diffraction (XRD, Pan'Analytical X'Pert Pro, Eindhoven, the Netherlands, Cu K\(\alpha\) anticathode, \(\lambda\) = 0.154056 nm) and X-ray fluorescence (XRF, Rigaku NEX CG II, Cedar Park, TX, USA). Field emission-scanning electron microscopy (FE-SEM, Hitachi S3800, Tokyo, Japan) was used to observe the microstructure of the powder.
## 2 Materials and Methods
### Antioxidant Activity and Total Polyphenol Content Evaluation  
The antioxidant activity of the extracts was assessed using the DPPH and FRAP methods, and the total polyphenol content was assessed using the Folin-Ciocalteu technique. The analyses were performed on a Hitachi U-5100 UV-Vis spectrophotometer (Tokyo, Japan) according to the methodology described by [PERSON] et al. (2018). In the DPPH technique, 150 uL of the tested extract was mixed with 2850 uL of a DPPH radical (0.3 mM) solution prepared in concentrated ethanol. The DPPH working solution was diluted with ethanol until the absorbance at 517 nm was 1.00 \(\pm\) 0.02. Concentrated ethanol was used as a blank zero. The incubation time of the samples in the dark was 10 min. Results based on the standard curve (y = \(-\)0.9795x + 0.9342, R\({}^{2}\) = 0.977, standard concentration 0-0.9 mmol/L) are expressed as Trolox equivalents [mmol Trolox/L]. The working solution for the FRAP method was made up of 10 volumes of acetate buffer (pH 3.6), 1 volume of 10 mM TPTZ (dissolved in 40 mM HCl), and 1 volume of 20 mM FeCl\({}_{3}\) (aqueous solution). A total of 2320 uL of this solution was mixed with 80 uL of the tested extract, and then the absorbance at 593 nm was measured after 15 min of incubation. Results based on the standard curve (y = 0.6362x + 0.0929, R\({}^{2}\) = 0.998, standard concentration 0-2.25 mmol/L) are expressed as FeSO\({}_{4}\) equivalents [mmol FeSO\({}_{4}\)/L]. The Folin-Ciocalteu technique was used to evaluate the total polyphenol content. A total of 150 uL of the tested extract, 150 uL of Folin-Ciocalteu reagent (tenfold diluted with water), 1350 uL of water, and 1350 uL of 0.01 M sodium carbonate solution were mixed. After 15 min of incubation at room temperature, the sample absorbance was measured at 765 nm. Results based on the standard curve (y = 0.9747x \(-\) 0.0032, R\({}^{2}\) = 0.998, standard concentration 0-0.6 g/L) are expressed as  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Raw Material Content**} & \multicolumn{4}{c}{**Ethanol Concentration**} & \multicolumn{4}{c}{**Extraction Time**} \\ \cline{2-10}
**No.** & **Observed** & **Expected** & **Residual** & **Observed** & **Expected** & **Residual** & **Observed** & **Expected** & **Residual** \\
**Experiment** & **Values** & **Values** & **Values** & **Values** & **Values** & **Values** & **Values** & **Values** & **Values** \\ \cline{2-10}  & & \multicolumn{4}{c}{**mmol Trolox/L**} & \multicolumn{4}{c}{**mmol FeSO\({}_{4}\)/L**} & \multicolumn{4}{c}{**g GA/L**} \\ \hline
1 & 8.99 & 8.56 & 0.44 & 10.57 & 10.24 & 0.33 & 1.34 & 1.30 & 0.04 \\
2 & 11.53 & 10.71 & 0.82 & 16.59 & 14.44 & 2.16 & 1.91 & 1.82 & 0.09 \\
3 & 1  
gallic acid equivalents [g GA/L]. All results are the arithmetic mean of three independent samples \(\pm\) SD (standard deviation).
# Day and Night: Locating the General Practitioner's Panel after Hours
## 4 Discussion
### Limitations  
There are some limitations of the data used in the case study that could be improved upon for more accurate modelling of primary care needs in Nova Scotia. One problem is how the cost parameter \(c_{ij}\) assumes that travel distance is an equal measure of access for all patients under all circumstances. The values of \(c_{ij}\) are based on minimum road distance from one DA centroid to another, but the time and location are factors that may change how accessible this distance is for a patient. For example, travelling across a dense city during rush hour is likely to take much longer than travelling the same distance in a rural setting late in the evening. A measure of time travelled may be a better metric for measuring access, including the time taken for different forms of available transport in the area.  
Another issue with the \(c_{ij}\) values is that demand assigned to the same node is assumed to travel no distance. That is, \(c_{ii}=0\ \forall i\in I\). This assumption may be robust in the case of very small DAs where the distance from the centroid to any edge is negligible. However, as DA size increases, the distance that a patient might have to travel within the DA also increases. A possible improvement may be to calculate the average distance within a DA from the centroid to each other point and to use this value as the intranodal travel distance for each node. While the number of facilities to be placed is fixed in the p-Median formulations used in this study, this would be particularly important to address in a model that considers the number of facilities as a decision variable. Since every additional facility allows demand at the same node to travel no distance when \(c_{ij}=0\), this may unrealistically incentivise adding facilities to reduce distance.  
Finally, an analysis for \(p=5\) was excluded from the Halifax case study. This is because the results for these tests were highly variable. It is likely that this is a discretisation effect and that the low number of facilities yielded very different adjacent solutions. Sampling more cases may improve the interpretability of the results, but this effect may be unavoidable for low values of \(p\).
## 7 Phase One Testing and Results
### Method Sensitivity to Size of Space and Objects  
Without considering the hybrid results, it can be observed from the mean completion time in Table 1 for Tests 1 to 3 that IVI and MOD IVI significantly outperformed 3 DTouch and HOMER-S; the maximum completion time of the former two was shorter than the minimum completion time of the latter two. It can be further observed that, excluding the anomaly of Test 5 results, the method and completion time relationship was swapped for results from Tests 4 to 7, where Touch3D and HOMER-S significantly outperformed IVI and MOD IVI.  
Recalling that Tests 1 to 3 were designed for small-scale objects whereas Tests 4 to 7 were for larger-scale augmented space and virtual objects, these results suggest that users welcomed the stateless simplicity and one-to-one proxy manipulation of IVI and MOD IVI for small-scale objects. However, users reported difficulties in maintaining finger contact with the touch screen while moving their entire body to simultaneously translate and rotate objects in large-scale tests.  
In contrast, 3 DTouch and HOMER-S allowed users to separate virtual object manipulation into distinct and logical steps: first, select an object; second, choose a transformation mode; and finally, focus on the actual manipulation of the currently selected object.
# Joint Use of Spaceborne Microwave Sensor Data and CYGNSS Data to Observe Tropical Cyclones
## 6 Conclusions  
This paper presented the joint use of a spaceborne microwave sensor and the CYGNSS constellation to observe TCs. The observation results demonstrated that compared to the case where only the spaceborne microwave sensor data or the CYGNSS data were used to observe TCs, the proposed method obtains a more complete structure of the TC and can measure the surface wind speed around the TC inner core at more frequent intervals. Furthermore, the TC tracks obtained by the proposed method are in better agreement with the NHC best tracks, and the maximum wind speeds around the TC inner core obtained by the proposed method are more consistent in most cases with the best track peak winds estimated by the NHC. In future work, more observation data from the microwave sensors, such as the SMOS radiometer and the spaceborne SAR, and the wind direction parameter will be combined to further improve the spatial coverage, the temporal resolution, and the measurement accuracy of the maximum wind speed around the TC inner core. Furthermore, for the observation results obtained by fusing the data from the CYGNSS and meteorological satellites, a spatial interpolation method and an automatic TC center-fixing algorithm based on deep learning will be developed to solve the problems related to the sparse tracks of the GYGNSS satellite and to improve the accuracy of tracking TCs.  
Conceptualization, S.S.; methodology, S.S. and S.W.; data collection and data analysis, S.W.; writing--original draft preparation, S.W. and S.S.; writing--review and editing, B.N. All authors have read and agree to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (41504007).  
The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.  
The authors declare no conflict of interest.
# Enhanced Flow Control for Low Latency in QUIC
## 4 Evaluation
### Two-Stream Connection with Small Buffer  
We simulated and measured the throughput of a connection with static, autotuning, and fast autotuning allowances. The number of streams, the maximum receive win dow, and the upper bound are listed in Table 1. First, we evaluated the throughput of a two-stream connection with static allowance. The average throughput was 234.56 Kbps, and Figure 6(a) depicts the bytes received by the receiver over time. Because the sum of the maximum stream receive window was 8 KB, approximately 7.32 KB of data were sent in the initial transmission. Since the buffer update occurs when the consumed bytes exceed half of the receive window (Figure 6(b)), approximately 4.3 KB to 5.1 KB of data were transferred in the second transmission.  
In a two-stream connection with autotuning allowance (Figure 8), the sender sends 4.39 KB and 5.12 KB of data after the first and second buffer updates, respectively, and the receive window is kept at 8 KB, as with the static allowance. The receive window grows to 16 KB when the third buffer update happens at around 0.5 s. The receive window offset rises by around 12.39 KB, with 4.39 KB of consumed bytes (half of the previous receive window) and 8 KB of a new receive window increment. When the fourth buffer update happens at 0.68 s, because the receive window abruptly increases to 24 KB instead of 32 KB, the sender transmits 16.35 KB (8.35 KB for consumed bytes and 8 KB for increment). This is because one of two streams extends the receive window to 16 KB, while the other maintains an 8 KB receive window. The receive window of the stream with the 8 KB window grows to 16 KB in the fifth buffer update, and the aggregate grows to 32 KB. The sum of the streams' receive windows is kept at 32 KB.  
Figure 9 shows the simulation results for fast autotuning allowance. Fast autotuning achieves approximately 7% of throughput gain compared to autotuning (Figure 8(a)). The throughput gain by fast autotuning is achieved by rapid growth of the receive window. During the two buffer updates, the aggregate of the receive windows of two streams is maintained at 8 KB. The receive window is extended by four times to 32 KB after the third buffer update in 0.5 s. Because a stream's receiver window is 4 KB and the stream upper bound is 16 KB, the increase factor is 8. \(W_{max}*F_{inc}\) is 32 KB, but \(W_{max}\) is calculated as 16 KB because the stream upper bound is 16 KB. Because the aggregate receive window is 32 KB, 28.39 KB of data (24 KB for new increment and 4.39 KB for consumed bytes) are transmitted, and fast autotuning maintains the aggregate receive window at 32 KB.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of streams & 2 \\ Maximum connection receive window & 8 KB \\ Maximum stream receive window & 4 KB \\ Connection upper bound & 64 KB \\ Stream upper bound & 16 KB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting for a two-stream connection.  
Figure 7: Static allowance in a two-stream connection with small buffer.
# Simulation-Based Model-Updating Method for Linear Dynamic Structural Systems
## 2 Modelling of the Structural System
### Natural Frequencies and Mode Shapes of the Structural System  
To analytically compute modal parameters such as natural frequencies and mode shapes, eigen solutions are applied to the mass and stiffness matrices of the building model. Furthermore, the frequency domain decomposition (FDD) method is used to obtain these  
Figure 2: Displacement responses of building model for free vibration test.  
values experimentally from the free vibration measurements [20]. The natural frequency values for the initial five modes of the building model, obtained through both experimental and analytical methods, are comparatively presented in Table 1. This study only considers horizontal motion in the x-direction, and the first five mode frequencies within this direction are displayed in Table 1. It is evident from the results that natural frequencies derived from both methods are in concordance.  
Figure 3 illustrates the normalized mode shapes of the first five modes obtained through analytical and experimental methods.  
Figure 3 shows that there is an acceptable closeness between the mode shapes calculated through experimental and analytical solutions, along with the natural frequency values of the building model.
## 2 Material and Methods
### Batch Mode Adsorption Experiment  
Equilibrium adsorption experiments were conducted in a set of glass flasks with 100 mL solutions of MBR treated water and adsorbent doses of 0.1-10 g/L at room temperature (24 \(\pm\) 1 \({}^{\circ}\)C) and pH 7.0-7.5. The suspensions were agitated for 24 h in a flat shaker at a shaking speed of 120 rpm. Experiments were duplicated and the average values were taken for data analysis. The differences between duplicate values were within \(\pm\)2%. The amounts of nitrate, phosphate and sulphate adsorption at equilibrium, q\({}_{\mathrm{e}}\) (mg/g), were calculated using Equation (1):  
\[\mathrm{q}_{\mathrm{e}}=\frac{\left(\mathrm{C}_{0}-\mathrm{C}_{\mathrm{e}} \right)\mathrm{V}}{\mathrm{M}} \tag{1}\]  
where, C\({}_{0}\) = initial concentration of nitrate, phosphate and sulphate (mg/L); C\({}_{\mathrm{e}}\) = equilibrium concentration of the nitrate, phosphate and sulphate (mg/L); V = volume of the solution (L) and M = mass of adsorbent (g). The adsorption data were treated with Langmuir adsorption isotherm (Equation (2)):  
\[\mathrm{q}_{\mathrm{e}}=\frac{\mathrm{q}_{\mathrm{max}}\mathrm{K}_{\mathrm{L }}\mathrm{C}_{\mathrm{e}}}{1+\mathrm{K}_{\mathrm{L}}\mathrm{C}_{\mathrm{e}}} \tag{2}\]  
where, C\({}_{\mathrm{e}}\) = equilibrium concentration of nitrate, phosphate and sulphate (mg/L), q\({}_{\mathrm{e}}\) = amount of nitrate, phosphate and sulphate adsorbed per unit mass of Dowex 21K XLT (mg/g), q\({}_{\mathrm{max}}\) = maximum amount of nitrate, phosphate and sulphate adsorbed per unit mass of Dowex 21K XLT (mg/g); K\({}_{\mathrm{L}}\) = Langmuir constant related to the energy of adsorption (L/mg).
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 1 Introduction
Hierarchical organisation of the network allows for better exploitation of data and computation locality. We introduced a way to dynamically change the topology of the network itself, although the physical organisation of the fabric uses a fixed topology: A small group of instructions is added to the PEs' ISA to allow the software directly controlling the network during the execution. We refer to our architecture as a SDNoC [17]. With applications in which operations are hierarchically grouped and dynamically scheduled [7], our adaptation allows better exploitation of the network resources and more significant power savings. Using hierarchicaltopology based on local rings attached to mesh routers, our design allows the software to monitor and control links with fine granularity. As a result, network resources can be switched off when not used. Link usage is monitored, and internal hardware counters are read via the dedicated instructions. This information can be exploited by optimisation tools and compilers to better adapt to the communication patterns of an application. Our proposed approach aims at solving three primary challenges, such as flexibility, scalability and interoperability, leaving out security aspects [18]. Our customised architecture can route the packets as per the compiler level instructions. Productivity is also improved by introducing data-driven PXM support into a high-level programming language, thus allowing the application developer to readily exploit the benefit of interconnection reconfigurability and data-driven execution. The remainder of the paper is organised as follows. Section 2 provides an overview of the proposed reconfigurable NoC, by describing its hierarchical organisation. The most relevant works are also reviewed and the paper contribution is clearly stated in its subsections. Next, the primary architecture of the proposed interconnection fabric is presented in Section 3. Micro-architecture both of the router and the ring switch is presented along with the control flow technique used to manage the traffic. Section 4 describes the architectural extensions of the NoC to provide the needed adaptability (reconfiguration). Specifically, the communication protocol implemented to support PE-to-PE data exchange is discussed. It is followed by Section 5, where the programming interface is analysed. Furthermore, to ease the development of applications, a high-level programming interface is also described, along with the low-level set of additional PE's instructions, as well as the corresponding execution model. Section 6 provides the experimental evidence of the advantages (in terms of performance, area and power consumption) brought by the proposed design compared to the flattened 2D-mesh. Finally, Section 7 presents some future work directions and concludes the paper.
# An Investigation of Particle Swarm Optimization Topologies in Structural Damage Detection
## 4 Numerical Simulations for SDD of the Cantilever Beam
Subsequently, the success rate, as a critical indicator, can be calculated:  
\[SR=\frac{\text{No. of Success}}{\text{No. of Total Trials}} \tag{13}\]  
The value of mean iterations is the average number of iterations to achieve one successful trial. Thus, only successful trials are considered for its calculation. Tables 3 and 4 present the SR values and the average number of iterations for the eight PSO topologies, respectively. The best results in the table are highlighted in bold. For damages of type I, except for Global and Wheel topologies, the SR values of PSO topologies all exceed 95%. For the damages of type II, the minimum success rate of the Local, Von Neuman, Four Cluster, and Multi-Ring topologies are still greater than or equal to 70%. Nevertheless, starting from damage scenario 6, the SR values of all the topologies show a downward trend, and the Wheel topology has fallen to less than 30%. The Local topology always has the most significant success rate, except for damage scenarios 2 and 7. The Clan Global and Clan Local topologies simultaneously possess the shortest mean iterations for type I and II damages. The Global topology holds the smallest mean iterations for damages of type III. Subsequently, the average ranks and [PERSON]'s test [29] are employed to measure the specific differences of one topology with others. Table 5 shows the average ranks of the success rate on each type of damage. The numerical value of the overall ranking for the Local topology is the best, while the Wheel topology ranks last numerically, as stated in the table. Subsequently, the rank differences in the success rate among PSO topologies are further statistically analyzed using the [PERSON]'s test, and Figure 12 illustrates the result. The horizontal line in the figure represents the threshold for the best performing topology. The height is equal to the sum of critical difference (CD) and the lowest overall rank (Local topology), which is CD \(+\) 2.1667. The bar chart that does not exceed the height demonstrates that there is no significant difference between the Local topology and the compared one. The [PERSON]'s procedure for calculating the CD value is given in [30]. In this paper, the 95% significance level with CD \(=\) 3.1061 is considered. Figure 12 indicates that the Local, Von Neumann, Four Cluster, and Multi-Ring topologies perform best on the success rate, followed by the Clan Global and Clan Local topologies, and the Global and Wheel topologies are the last. When considering the mean iterations, Table 6 further shows the average iteration ranks of the four topologies that performed best in terms of success rate. The rank order of the mean iterations is always Four Cluster, Von Neumann, Multi-Ring, and Local, as shown in the table. A small average number of iterations means fast convergence speed and it can save the computational cost. Therefore, from this point of view, the overall performance of the Four Cluster topology is better than others.