esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Software Architecture and Bidirectional Communication
* Anyone: Combination of all the previous three features that supports convenient, comprehensive plus cost-effective and continuous monitoring device make the prototype applicable by users from different categories. Due to the high accuracy requirements of the data in motion tracking, no sensor activation is applied for gyroscope, accelerometer, and magnetometer. Here, the data are transmitted to the smartphone, immediately after BLE establishes the channel. As the air pressure, temperature, and humidity values are used for calibration, their data are also transmitted continuously without sensor activation/deactivation. Furthermore, we strictly aim at protecting measured data. We integrated memory (256 kB) to buffer data when BLE is temporarily unavailable. Due to the limited capacity of the memory, it is used only if necessary. A specific scenario for retrieving data from the internal memory has been implemented, too. So far, the transmission of buffered data requires a user decision. Once the user has pressed the \"Data log\" button on the smartphone, data are transmitted in a first-in-first-out (FIFO) mode as they have been stored. The last packet of stored data is followed with an empty packet that indicates the re-start of real-time data acquisition (Algorithm 1). Figure 8: Developed application for bidirectional communication between smartphone and the prototype.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 2 Antenna Design
### The Proposed Single Microstrip Monopole Antenna
#### 2.1.2 The Proposed Ground Antenna Structure
The different thicknesses were tested, and the results are given in Figure 8. It could be demonstrated from Figure 8 that the best performance was achieved with thickness R\({}_{1}\) = 0.1 mm since the antenna resonated at 58.9 GHz with a \(-\)10 dB return loss and a bandwidth of 2.38 GHz. After carrying out the necessary parametric study on the proposed antenna, Table 3 lists the optimum dimensions that qualify the antenna for 5G applications. Figure 6: Simulated return loss for the proposed antenna with various radii of the circular slot (unit in mm). Figure 7: Simulated return loss for the proposed antenna with various distances between the double T-shapes (unit in mm).
# Compressive Behavior of Inconel 625 and Ti-6 Al-4V Strut Lattices Fabricated by LPBF
## 2 Experimental Procedure
### Compression Testing Assisted by Static Finite Element Analysis  
#### 2.2.1 Static Finite Element Analysis  
The nTop software (version 4.9) was used to perform static finite element analysis to evaluate the compression performance of the strut lattice configurations manufactured from the two materials. The aim was to compare the simulation results with those obtained from experimental testing. This type of numerical analysis was performed for each material and lattice structure. The nTop implicit alloy properties were used, with the material considered to be isotropic and linear in the simulations; for Ti-6 Al-4V they were a density of 4.43 g/cm\({}^{3}\), [PERSON]'s modulus of 113.8 GPa, a Poisson ratio of 0.342, thermal conductivity of 6.7 W/m/K, and specific heat of 0.5263 J/g/\({}^{\circ}\)C. For Inconel 625, the properties used were a density of 8.44 g/cm\({}^{3}\), [PERSON]'s modulus of 208 GPa, a Poisson ratio of 0.2, a thermal conductivity of 9.7 W/m/K, and specific heat of 0.41 J/g/\({}^{\circ}\)C. The mesh was robust tetrahedral with a 0.2 mm tolerance. The mesh nodes and elements recorded for each type of specimen were as follows: for the octet specimen, FE model 347,246 mesh nodes and 1,260,004 mesh elements; for the truncated octahedron specimen, FE model 245,175 mesh nodes and 881,398 mesh elements; for the Kelvin cell specimen, FE model 188,727 mesh nodes and 665,928 mesh elements; and for the re-entrant specimen, FE model 236,937 mesh nodes and 843,958 mesh elements. For the mesh, the edge length selected was 0.5 mm, the shape selected was a triangle, span angle was 30\({}^{\circ}\), growth rate was 2, feature angle was 45\({}^{\circ}\), edge length was 0.5 mm, and regularity factor was 10.  
In the simulations, the boundary conditions used were force (loading force) of \(-\)50 kN on the Z-axis (for compression), matching the maximum force of the universal testing machine's loading cell used during the experimental procedure, and the displacement restraint used was the FE face boundary condition where the top face of the specimen was selected. Similar numerical simulations were performed by the authors using nTop to assess the software capabilities regarding the static finite analysis and performance of the four lattice types under flexural load (determined by three-point bending) [37].
## 3 Case-Study Building and Retrofit
### Lighting System  
The case-study building currently has two types of luminaires installed as lighting systems: fluorescent devices on the central zone, and compact fluorescent (CFL) devices in each individual room. The replacement luminaire for these lighting systems is an LED luminaire of both tube and bulb types with more luminous efficiency than the currently used luminaire. The specifications of the current luminaires and their replacement for the sake of energy efficiency are summarized in Table 10. The illuminance on the working plane of both the currently used and replacement luminaire provides a similar light output, that is, 200-300 k in rooms and 100 k on corridors, according to CIE (International Commission on Illumination).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Composition** & **Detail** & **Thickness (m)** & **U-Value (W/m\({}^{2}\)K)** \\ \hline \multirow{4}{*}{Base case} & Reinforced concrete & 0.15 & \\  & Air Gap & 0.1 & 1.368 \\ \cline{1-1}  & Gypsum plates & 0.015 & \\ \hline \multirow{4}{*}{Retrofitted case} & Reinforced concrete & 0.15 & \\  & Air Gap & 0.1 & 0.445 \\ \cline{1-1}  & Fiberglass insulation & 0.05 & \\ \cline{1-1}  & Gypsum plates & 0.015 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Composition of material and thermal transmittance of the roof in the case-study building.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Material** & **Composition** & **Thickness (m)** & **U-Value (W/m\({}^{2}\)K)** & **SHGC** \\ \hline \multirow{4}{*}{Base case} & Clear float glass & 0.06 & 5.74 & 0.82 \\ \cline{1-1}  & Clear colour single silver & 0.06-0.06 * & 2.73 & 0.4 \\ \cline{1-1}  & low-e coat on ocean green & & & \\ \hline \hline \end{tabular}  
* Thickness value of double paned glass consists of outer glass-air gap-inner glass.  
\end{table}
Table 9: Thermal properties of transparent materials employed in the envelope of the case-study building. SHGC: Solar heat gain coefficient.  
Note from Table 10 that the replacement LED luminaires provide similar light output in terms of lumen compared to the currently used fluorescent luminaires. However, the energy consumption is significantly lower than that of conventional fluorescent devices, thereby achieving a higher luminous efficacy. This also results in a significant reduction in power consumption in lighting systems and improved lighting aspects of the building energy code in terms of LPD compared to previously installed luminaires.
## 3 RR-FRC Under Chloride Ingress
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & **d** & **NaCl** & **Material** & **Dimensions** & **\(w/b\)** & \(V_{t}\) & \(l_{t}\) & \(d_{t}\) & \(c\) & \(f_{c}\) & \(w\) \\  & **[d]** & **[\%]** & **Material** & **[mm]** & **[-]** & **[\%]** & **[mm]** & **[mm]** & **[mm]** & **[MPa]** & **[mm]** \\ \hline \multirow{2}{*}{[66]} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & RC & 450 \(\times\) 450 \(\times\) & - & - & - & - & & 0.21–0.58 \\  & & & RR-SFRC & 15,000 & - & 0.50 & 0.550 & 0.550 & 45 & 40 & 0.21–0.27 \\  & & & RR-SFRC & 15,000 & - & 1.00 & 35.0 & 0.550 & 0.500 & 0.13–0.15 \\ \hline \multirow{2}{*}{[67]\({}^{(\ref{eq:m})}\)} & \multirow{2}{*}{72\({}^{(\ref{eq:m})}\)} & \multirow{2}{*}{3.5} & RC & 120 \(\times\) 180 \(\times\) & \multirow{2}{*}{0.47} & \multirow{2}{*}{-} & - & - & 52.8 & \multirow{2}{*}{0.00–0.14} \\  & & RR-SFRC & 1750 & - & 0.75 & 35.0 & 0.630 & & 47.5 & & 0.00–0.14 \\ \hline \multirow{2}{*}{[68,69]\({}^{(\ref{eq:m})}\)} & \multirow{2}{*}{280} & \multirow{2}{*}{5.0} & RC & 90 \(\times\) 90 \(\times\) & \multirow{2}{*}{0.49} & \multirow{2}{*}{-} & - & - & - & 0.19–0.23 \\  & & RR-SFRC & 830 & & 0.64 & 33.0 & 0.600 & 39 & 38 & 0.09–0.15 \\ \hline \multirow{2}{*}{[70]\({}^{(\ref{eq:m})}\)} & \multirow{2}{*}{560} & \multirow{2}{*}{3.5} & RC & 100 \(\times\) 100 \(\times\) & 0.45 & - & - & - & 25 & 48 & \multirow{2}{*}{0.40} \\  & & RR-SFRC & 500 & - & 0.65 & 0.50 & 0.550 & 45 & 29 & & \\ \hline \multirow{2}{*}{[71]} & \multirow{2}{*}{28–180} & \multirow{2}{*}{2.7 \({}^{(\ref{eq:m})}\)} & PC, RC & 200 \(\times\) 200 \(\times\) & \multirow{2}{*}{0.44} & \multirow{2}{*}{-} & - & - & - & 55 & 30 & 0.35 \\  & & RR-SFRC & 750 & & 0.50 & 50.0 & 0.800 & 55 & 30 & & \\ \hline \multirow{2}{*}{[72]\({}^{(\ref{eq:m})}\)} & \multirow{2}{*}{6–32} & \multirow{2}{*}{3.5} & RC & \multirow{2}{*}{RC-RFRC (\(\ref{eq:m})}\) & RC & - & - & - & - & 36 & \multirow{2}{*}{-} \\  & & & RR-SFRC & 1200 & & 0.50 & 10.0–60.0 & 0.250–0.350 & - & 50 & & 47 & \\ \hline \multirow{2}{*}{[40]\({}^{(\ref{eq:m})}\)} & \multirow{2}{*}{28} & \multirow{2}{*}{5.1} & RC & 150 \(\times\) 200 \(\times\) & \multirow{2}{*}{0.51} & \multirow{2}{*}{-} & - & - & 20 & 28 & \multirow{2}{*}{-} \\  & & RR-SFRC & 2050 & & - & - & - & - & & 48 & \\ \hline \multirow{2}{*}{[73]\({}^{(\ref{eq:m})}\)} & \multirow{2}{*}{-} & \multirow{2}{*}{5.0} & RC & 100 \(\times\) 150 \(\times\) & \multirow{2}{*}{0.45} & \multirow{2}{*}{-} & - & - & - & 48 & \multirow{2}{*}{-} \\  & & RR-SFRC & 1000 & & & 15.0 & 0.200 & & 52 & & \\ \hline \multirow{2}{*}{[74]\({}^{(\ref{eq:m})}\)} & \multirow{2}{*}{20} & \multirow{2}{*}{20.0} & RC & 600 \(\times\) 200 \(\times\) & \multirow{2}{*}{0.39} & - & - & - & - & 0.01 & \\  & & RR-SFRC & 1360 & & 0.50,1.00 & 60.0 & 0.750 & 50 & 70 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental setups and concrete properties considered in the reviewed campaigns regarding RR-FRC.
## 2 Results and Discussion
### C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) Mag-dSPE Procedure and Its Optimization
#### 2.2.1 Optimization of Elution Solvent  
The selection of a suitable elution solvent was an important aspect of improving the extraction efficiency. The ideal elution solvent can completely elute the analyte from the surface of the extraction material. In this study, several solutions of formic acid/methanol (1.0%, \(v/v\)), ammonia/methanol (1.0%, \(v/v\)), 2.0 mmol/L ammonium acetate in methanol, 5.0 mmol/L ammonium acetate in methanol, 10.0 mmol/L ammonium acetate in methanol, 15.0 mmol/L ammonium acetate in methanol, 20.0 mmol/L ammonium acetate in methanol, and 25.0 mmol/L ammonium acetate in methanol were studied. The results are shown in Figures 7 and 8. The elution effect of the 2.0 mmol/L ammonium acetate-methanol solution was obviously better than formic acid/methanol (1.0%, \(v/v\)) and ammonia/methanol (1.0%, \(v/v\)). Using 2.0 mmol/L ammonium acetate in methanol, the recoveries of 11 food additives were 15.62-24.3%. The possible reason was that the mixture of ammonium acetate and methanol destroys the electrostatic force between C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) and analytes. Compared with the ammonium acetate solution of 2.0 mmol/L, using the ammonium acetate solution of 20.0 mmol/L as the eluent, the elution effect of 11 kinds of food additives was the best, and the recoveries were 89.8-108.0%. It may be explained that the higher the concentration of ammonium acetate, the greater the destruction intensity of the electrostatic force between C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) and the analyte.
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 5 Discussion
### Effect of Visual Guidance  
Interestingly, a previous study revealed that introducing a flickering dynamic visual effect to arrow guidance could potentially slow down the target search performance [2]. Conversely, our study revealed that the dynamic movement of an arrow oscillating back  
Figure 8: Mean user preference and NASA TLX of Study 2.  
and forth significantly accelerated both reaction and selection times, compared to guidance provided by a Static Arrow, and it also reduced the angle of head rotation (as summarized in Table 2). The results affirm that the Dynamic Arrow could yield a quicker reaction time for the Secondary Task.  
This improvement in reaction time could be attributable to the dynamic visual effect making it easier for users to notice the guidance, subsequently reducing response time. It was also easier to identify the target direction due to the arrow's motion. The Dynamic Arrow further resulted in faster selection times and smaller head rotation angles in the Secondary Task. This could be credited to the more noticeable and clearer indications provided by the Dynamic Arrow, which allowed users to move their heads more efficiently and accurately.  
In addition to enhancing reaction time for the out-of-view target search task behind the user (the Secondary Task), the Dynamic Arrow also improved selection times for the Primary Task, with the target positioned in front of the user. Given that no visual guidance is provided for the Primary Task, this improvement can be construed as a result of the overall task execution speed being faster, thus facilitating a smoother transition from the back to the front with Dynamic Arrow guidance.  
In contrast, under the category of Visual Guidance Continuity, no significant difference in reaction time, selection time, or user head rotation and movement was noted between the Transient and Persistent Arrows in both the Primary and Secondary Tasks. This may be due to users' ability to actively locate the target behind them after discerning its direction, thereby diminishing the need for arrow guidance. User feedback suggested that the continuous movement of the arrow could potentially impede visual task searching, which is an outcome that aligns with findings indicating that visual cues can be beneficial in complex tasks but might degrade performance in simpler ones [65]. The Secondary Task, which contains only 10 targets in a sparsely populated environment, makes target acquisition a relatively easy task. In this scenario, arrow guidance may not be a crucial component in enhancing user target location performance.
## 1 Introduction
Coagulation-flocculation is considered to be one of the most essential methods for wastewater treatment, being applied in different industrial sectors as demonstrated in the reviewer research of [PERSON] et al. [12], especially in aquaculture systems [7; 13]. Conventional coagulants, such as those based on aluminum and iron, have notable drawbacks. These include the production of large volumes of sludge, strong pH dependency--which raises operational costs--and the presence of residual metals like aluminum and iron in the treated water [14]. As a result, there is growing interest in exploring alternative solutions, particularly those involving natural coagulants. Environmentally friendly coagulants are designed to support sustainable development and green technology principles. Sourced from renewable biological origins such as plants, animals, fungi, and bacteria, these coagulants are typically biodegradable, non-toxic, non-corrosive, and economically viable [15]. Among them, tannin-based coagulants have garnered considerable scientific attention over the past decade [16]. Traditionally obtained from tree-derived biomass, tannin production can face certain limitations, including seasonal variability, storage difficulties, land use conflicts that may impact food production, and high water demands for cultivation [17]. Therefore, identifying alternative sourcesparticularly those derived from agricultural or vegetable residues--not only adds value to waste materials but also reinforces the circular economy framework. Aquaculture is one of the main sectors where natural coagulants have been tested and applied for research purposes [14; 18; 19; 20; 21; 22]. However, there is a research gap due to the predominance of lab-scale experiments conducted in batch mode, with most results obtained from jar tests. This study aims to evaluate the performance of a tannin-based coagulant derived from chestnut shells to treat real aquaculture wastewater under continuous flow conditions. The treatment was conducted in a laboratory-scale continuous coagulation/floculation system, focusing on the removal of color, organic matter, phosphorus, and nitrogen from the final effluent of the _Clarias gariepinus_ (African catfish) production line, which is recirculated back to the fish tank. For comparison, the chestnut shell-based coagulant was assessed alongside one commercial natural coagulant (Tanfloc SG) and two conventional chemical coagulants (Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\cdot\)14H\({}_{2}\)O and FeCl\({}_{3}\cdot\)6H\({}_{2}\)O).
# Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
## 4 Scheme Overview
### Security Requirements  
This paper assumes that the server \(S\) is an honest but curious party, whose protocol can be trusted, but, at the same time, who will attempt to obtain as much information as possible from the stored data. The security requirements are as follows:  
1. Document collection: The server \(S\) is not allowed to know the contents of _[PERSON]_'s collection.
2. Query document: Query document for the authorized user _[PERSON]_ should not be revealed to server \(S\), and the server \(S\) cannot generate a valid fingerprint query without knowing the secret key. A provided query should not leak any information about its underlying fingerprint. However, fingerprints are generated deterministically, so the server must not know whether the same query has been presented before; such leakage is known as query patterns.
3. Index security: The secure index does not leak any information about its contents.
4. The server must not know the search results for multiple searches or it may learn whether multiple searches have the same fingerprint; this is known as access patterns leakage.
5. Proposed Scheme Details  
The following sections describe our suggested scheme. To enable effective document similarity detection, _Bob_ creates a secure inverted index and outsources it to the remote server along with the encrypted documents. The server performs an index comparison according to data users' queries with no information about the data other than that which _Bob_ has made accessible.  
The secure DSD mechanism over encrypted data consists of two stages; namely, initialization, followed by private similarity computation and search queries. The \"Initialization stage\" subsection covers the configuration phase which includes index-generating, secure, searchable inverted index building and document encryption. The second subsection, \"Private similarity computation and search queries\", describes the encrypted document search and retrieval which is built on top of an inverted index. Figure 1 shows the mechanism of the secure and private DSD.
## 4 Maintaining Maximum Coverage and Connectivity Scheme  
### Design Principles  
Ant colony optimization algorithms were proposed based on inspiration from actual research on ant colony behavior. These algorithms are some of the most successful examples of swarm intelligence systems. Ant colony optimization algorithms have been applied to many types of problems such as typical tourist agency problems in order to optimize the problems of communication networks [20; 24; 25; 26].  
Ant colony optimization algorithms have demonstrated good performance when dealing with the traveling salesman problem (TSP). In this study, we introduced an ant colony optimization algorithm into the node deployment problem of sensor networks in order to propose a maximum coverage and connectivity self-deployment algorithm that was based on ant colony optimization (MCC-ACO). Furthermore, the grid point of each deployed candidate sensor node was analogous to the city points in the TSP problem, and all of the covered events were analogous to the task termination of postmen.  
The primary design consideration for MCC-ACO was to ensure that every sensor node in the deployment could be connected to the sink node in order to ensure network connectivity. The method used in this paper involved enabling the initial site deployment of the ants to be within the communication radius of the sink. Following this deployment, the movement radius of the ants was limited in order to make this radius lower than the communication radius and to ensure that the grid point that every ant moved to was directly connected to the sink node (or through other deployed sensor nodes). In this way, when the ants continuously moved, the sensor nodes in the deployment continuously increased. In the end, the set of sensor nodes in the deployment could cover all of the monitoring points, and the first iteration of ants was completed.  
The movement process of the ants is explained by Figure 2(a)-c. As shown in Figure 2(a), Node 1 in the deployment was directly connected to the sink. After the deployment of Node 1, the candidate network area of the deployment sensor nodes was further expanded to the blue sensing region in Figure 2(a). Any sensor node deployed in this region could connect to the sink. After a grid point was selected in the blue sensing region in Figure 2(a) for the deployment of the sensor node, the candidate network area in the blue sensing region was further expanded, as shown in Figure 2(b). This deployment was successively carried out for Sensor Nodes 3, 4, and 5 until all events (stars) were completely covered, as shown in Figure 2(c). Through this node deployment, we could ensure that all of the deployed nodes were connected to the sink. The following section explains the problem of ensuring that the minimum number of nodes was used to achieve coverage, which was a problem that could be solved using the ant colony optimization algorithm.  
Figure 3: _Cont_.
## 2 Optimization Design Process and Modeling Method  
### Optimization Process  
In traditional design of fluid machinery, taking pumps for example, research procedures involve manufacturing prototypes, performance tests, results analyses, and optimal design. The design is mostly two-dimensional and relies on the experience of the designer. However, creating prototypes may cost a lot of time and money. Moreover, a large number of prototype tests would inevitably cause significant manufacturing and testing errors. The automatic design with the intelligent optimization method of fluid machinery presented in this study is based on the combination of CFD technology and optimization theory. An accurate CFD simulation method needs to be established first, which could provide virtual experimental results for optimization. Meanwhile, an automatic optimization platform including all CFD process such as fluid domain modeling, meshing, solving, post-processing, and design of experiment (DOE) was built for future optimization processes. The modeling of the pump geometry is based on the three-dimensional parametric design method, which means the fluid domain could be modified by DOE arrangements. The main parameters that influence the hydraulic performance of centrifugal pumps were screened out, and orthogonal design was applied to obtain the spacing-filling sample points. Significance test of optimization variables was conducted based on analysis of variance (ANOVA). Further, the MIGA optimization method was utilized to drive the platform for improving the efficiency of the pump automatically under three flowrates. Finally, an optimized scheme was obtained and the prototype was 3D printed to experimentally validate the optimal results. The whole optimization process is shown in Figure 1.  
Figure 1: The flowchart of optimization process. CFD, computational fluid dynamics.