esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm
## 3 Proposed Framework and Methods
### Overview  
In this section, we describe the proposed FedGSA framework in detail. Figure 2 shows the overall model framework of FedGSA; specifically, FedGSA has the following steps.  
1. First, clients share local augmented data to form an augmented dataset based on global incremental integration, and a central aggregation server is trained to generate a quasi-global model based on this dataset and apply it to each client.  
2. After each client applies the quasi-global model locally, a sliding window scheme is used to generate local two-channel network traffic data for each client, and then the client executes a local training procedure and passes the local model parameter information to the central aggregation server after the local training is completed.  
3. Finally, the central server performs a two-level weighted aggregation of each client's network model based on the gradient similarity of each client, and finally generates a global model.
# Simulation Study on Nanofluid Heat Transfer in Immersion Liquid-Cooled Server
###### Abstract  
In order to study the application of nanofluids for enhancing heat transfer in the field of liquid-cooled data centers, a mathematical and physical model of liquid-cooled servers was established in this paper. FC-40 was used as the server cooling liquid base, and simulation studies were conducted on the flow and heat transfer of five types of nanofluid: Cu-FC40, CuO-FC40, Al-FC40, Al\({}_{2}\)O\({}_{3}\)-FC40, and TiO\({}_{2}\)-FC40. The results showed that using Al-FC40 nanofluids as the cooling medium had the best heat transfer effect. Under the same operating conditions, the average Nussel number \(Nu\) and friction resistance coefficient/\(f\) of five types of nanofluid were analyzed, and the heat transfer state of Al-FC40 nanofluid had the smallest \(f\). Further analysis was conducted on the influence of 'nanoparticle volume fraction' \(a\) and'server inlet flow rate' \(u\) on fluid flow and heat transfer. Our research found that an increase in \(a\) and the acceleration of \(u\) can effectively reduce the surface temperature of server components. As \(u\) increases, \(Nu\) gradually increases and \(f\) generally decreases, but the amplitude of the increase and decrease becomes smoother.  
immersion liquid cooling; nanoparticles; enhanced heat exchange; numerical simulation
## 3 Results and Discussion
### Preparation and Characterization of the Material
#### 3.1.3 Hydrodynamic Size  
The hydrodynamic size of the materials was evaluated with the NanoSight LM10 apparatus, and the results are presented in Figure 2 and Table 1. The assay portrays the aggregation behavior of the composite material within water suspension. While SEM provides insight into the size and morphology of the material in dry form, the results might differ in a water suspension due to factors such as aggregation. In addition, before NTA, solid materials have to undergo treatment such as sonicating and vortexing to allow uniform distribution of the particles within the suspension for accurate measurements. Based on the results, it can be concluded that all materials show a high tendency for aggregation, especially evident in the case of TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\), which presents an average particle size of around 369.5 nm. This fact could explain the high standard deviation values of 232.9 nm noted for TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\) particles. Polydispersity indices were calculated for both TiO\({}_{2}\) and TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\) materials. In all cases, PDI exceeds the value of 0.2, which is treated as the threshold for labeling material as monodisperse; as such, all materials were considered polydisperse.  
Figure 1: SEM micrographs of bare TiO\({}_{2}\) and TiO\({}_{2}\)–Fe\({}_{3}\)O\({}_{4}\) composite material. (**A**) Microscale image of bare TiO\({}_{2}\) (magnification 10,000\(\times\)), (**B**) nanoscale image of TiO\({}_{2}\) (magnification 200,000\(\times\)), (**C**) microscale image of TiO\({}_{2}\)–Fe\({}_{3}\)O\({}_{4}\) (magnification 10,000\(\times\)), and (**D**) nanoscale image of TiO\({}_{2}\)–Fe\({}_{3}\)O\({}_{4}\) (magnification 200,000\(\times\)). Red boxes in A and C show the enlarged areas in B and D, respectively.
## 3 Results
### Horizontal Flow Patterns
#### 3.1.2 Flow Fields of Two Alternating Groins with Various Spacing
In this subzone, the length of the recirculation zone gradually increases again from 0.187 to 0.236 m, about 20%, which was similar to that for cases 1-5. In downstream far-field cases, the flow reattachment point behind Groin A moves further downstream with increasing spacing between groins, except for case 13, which was almost same as that of case 7, case 14 and the experimental results observed by [PERSON] and [PERSON] [39]. Two fitted curves of recirculation zone length for cases 1-5 and 9-13 in Figure 8 were presented for the relationship between the spacing of alternating groins and recirculation behind groin A with a correlation coefficient \(R^{2}\) over 0.96. The result also suggests that as Groin B moves upstream and downstream from Groin A, the magnitude of impact Groin B has is similar to that on the wake flow behind Groin A; however, the flow direction is opposite. Figure 6: Time-averaged horizontal streamlines and velocity distribution of alternate groins configuration with Groin B at the opposite bank at 5 to 55 cm upstream (left column) and downstream (right column) from Groin A. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Spacing**} & \multicolumn{4}{c}{**Recirculation Zone Behind the Fixed Groin A**} \\ \cline{2-7}  & **between** & **Length/L** & **Width/W** & **Normalized** & **Normalized Width** & **Feature** \\  & **Groins/x (m)** & **(m)** & **(m)** & **Length (L/b)** & **(W/b)** & \\ \hline \(1\) & \(-0.55\) & 0.314 & 0.073 & 6.276 & 1.465 & \\ \(2\) & \(-0.45\) & 0.325 & 0.077 & 6.506 & 1.534 & \\ \(3\) & \(-0.35\) & 0.345 & 0.078 & 6.903 & 1.552 & \\ \(4\) & \(-0.25\) & 0.355 & 0.081 & 7.063 & 1.616 & \\ \(5\) & \(-0.15\) & 0.384 & 0.082 & 7.683 & 1.630 & \\ \(6\) & \(-0.05\) & 0.350 & 0.075 & 6.999 & 1.504 & \\ \hline \(7\) & 0.00 & 0.245 & 0.063 & 4.905 & 1.265 & \begin{tabular}{c} Symmetrical groins \\ (both banks) \\ \end{tabular} \\ \hline \(8\) & 0.05 & 0.170 & 0.056 & 3.395 & 1.121 & \\ \(9\) & 0.15 & 0.160 & 0.057 & 3.193 & 1.149 & \\ \(10\) & 0.25 & 0.187 & 0.060 & 3.745 & 1.194 & \
\begin{tabular}{c} Groin B moving \\ downstream \\ \((alternate layout)\) \\ \end{tabular} \\ \(11\) & 0.35 & 0.212 & 0.064 & 4.234 & 1.276 &
\begin{tabular}{c} downstream \\ (alternate layout) \\ \end{tabular} \\ \(12\) & 0.45 & 0.226 & 0.067 & 4.526 & 1.344 & \\ \(13\) & 0.55 & 0.236 & 0.067 & 4.721 & 1.334 & \\ \hline \(14\) & — & 0.250 & 0.066 & 5.002 & 1.329 & Single Groin A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of the horizontal recirculation zone behind Groin A for different cases. Figure 7: Normalized length L/b (left \(y\)-axis) and width W/b (right \(y\)-axis) of the recirculation zone behind a fixed Groin A as a function of the spacing (Top \(x\)-axis) between alternate groins on either side of river bank. The blue and gray circles are the normalized length of the recirculation zone for a single groin.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### Equipment of Ground Station
#### 5.2.1 L-Band Antenna System for GEO Satellites  
This system is designed to receive signals from GEO satellites, which are located more than 35,800 km away, resulting in a free space path loss greater than 180 dB at the L-band. The signals received on Earth are quite weak. According to calculations in Table 3, receiving signals from FY-2H satellite requires an antenna dish with a C/N higher than 10 dB. The antenna must have a gain of at least 28.62 dB.  
Therefore, this research chose a 1.9 m antenna dish with an F/D ratio of 0.36, using a self-developed feed antenna with left-hand circular polarization (LHCP) that has a half-power beamwidth (HPBW) of 138.8 degrees to ensure signal coverage over the entire dish antenna. The feed is a 2.5-turn helical antenna with a diameter of 60 mm, as shown in Figures 7a and 6, item 15. The antenna provides a system gain of 27 dBi. This feed antenna is used with a bandpass filter and an LNA with a gain of 38 dB, supporting frequencies of 1653-1723 MHz (Figure 6, item 16).  
\begin{table}
\begin{tabular}{l l l l l l} \hline
16 & LNA and SAW filter & Noolece LNA SAWbird+ GOES & 34.95 & 1 & 34.95 \\ \hline
17 & Rotator & Yaesu G-5500 with rotation of azimuth 450\({}^{\circ}\) and elevation 180\({}^{\circ}\) & 950 & 1 & 950 \\ \hline
18 & Tripod mount & Tripod mount for dish antenna & 80 & 1 & 80 \\ \hline \multicolumn{5}{c}{Total cost of ground station—GEO satellite} & 1124.35 \\ \hline \multicolumn{5}{c}{SDR server} & \\ \hline
19 & RTL-SDR & Noolece NESDR SMArIee(R820T2 and RTL832U chip) & 33.95 & 5 & 169.75 \\ \hline \multirow{2}{*}{20} & \multirow{2}{*}{
\begin{tabular}{l} Suge protection \\ device (SPD) \\ \end{tabular} } & Coaxial surge protection with 90 V DC breakdown voltage gas & 1.29 & 5 & 6.45 \\ \hline
21 & Computer & PC using on SDR server w/o screen monitor & 400 & 1 & 400 \\ \hline
22 & Rotator board control & Developed independently using the ESP32 microcontroller & 55 & 2 & 55 \\ \hline \multicolumn{5}{c}{Total cost of SDR server} & 631.20 \\ \hline
23 & Camera & IP camera 2 MP, POE & 30 & 1 & 30 \\ \hline
24 & Coaxial line & LMR-400 & 1.50 & 40 m & 60 \\ \hline
25 & USB isolation & USB isolation/hub & 2.8 & 5 & 14 \\ \hline \multicolumn{5}{c}{Other} & 10 & 1 & 10 \\ \hline \multicolumn{5}{c}{Total cost of miscellaneous} & 114 \\ \hline \multicolumn{5}{c}{Total cost of ground station} & 3999.73 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 7: L-band antenna system: (**a**) helical feed 2.5 turns with LHCP and LNA with SAW filter and (**b**) 1.9 m dish antenna setup.  
The parabolic antenna used in this system is a mesh dish antenna, which is lightweight and has a low wind load, as shown in Figures 7b and 6, item 14. This makes it suitable for low-budget stations and temporary, less stable installations. However, the dish antenna requires frequent maintenance due to its susceptibility to damage and lack of robustness.
## 3 Results and Analysis
### Geothermal Characteristics of Lushan Geothermal Springs
#### 3.1.2 Chemical Characteristics of Geothermal Water  
In Table 1, the analysis results of water quality are presented. As illustrated in the table, pH value of hot spring water is 7.36-9.08, and the mineralization degree is 0.355-0.705 g/L. It belongs to weak alkaline low mineralized HCO\({}_{3}\)-Na or HCO\({}_{3}\)-SO\({}_{4}\)-Na type water. The content of Cl\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), K\({}^{+}\), Na\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\) in Shentang is significantly higher than that in other springs, and has a trend of gradually increasing from west to east. The content of Cl\({}^{-}\) in Xiatang and Shentang is significantly higher than that in other springs. The content of SO\({}_{4}\)\({}^{2-}\) in Shentang is significantly higher than that in other springs, followed by Shangtang. Geothermal water contains copper, zinc, arsenic, and other trace elements, including radioactive elements radium and uranium. The content of other microelements is low. From Shangtang-Xiatang-Shentang, the mineralization degree, total hardness, and various ion contents gradually increased from west to east.  
Compared with the chemical composition of nearby river water and shallow groundwater, the content of Ca\({}^{2+}\) and Mg\({}^{2+}\) in hot springs is generally lower than that of river water and regional shallow groundwater, while the content of HCO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), Cl\({}^{-}\), K\({}^{+}\), Na\({}^{+}\), F\({}^{-}\) is generally higher than that of river water and regional shallow groundwater. The average content of Na\({}^{+}\) is 11.3 times higher than that of river and regional shallow groundwater, while the content of Mg\({}^{2+}\) is small or not. It can be seen from the hydrochemical characteristics that the geothermal water in Lushan hot springs belongs to uplift fracture type geothermal water, which is recharged by atmospheric precipitation.
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 2 Production of Valeric- and Propionic-Rich VFA Streams from Agri-Food Wastes
### Dark Fermentation: Method to Transform Agri-Food Waste into VFA  
As previously stated, VFAs represent one of the final products of dark fermentation (DF). The dark fermentation follows a similar pattern to that of anaerobic digestion (AD), with the exception that it eliminates the final stage of methanogenesis [32], thereby enabling the accumulation of VFAs and the generation of CO\({}_{2}\) and H\({}_{2}\), that could be used as a biofuel [33].  
In the context of the AD process, the conversion of organic substrates into CH\({}_{4}\), CO\({}_{2}\), and water is a complex process that involves four sequential biochemical steps. These steps can be broadly categorized as follows [21]:  
1. Hydrolysis. Complex organic polymers--carbohydrates, proteins, and lipids--are converted into simpler monomers: monosaccharides, amino acids, fatty acids, and glycerol. To do this, microorganisms secrete hydrolytic enzymes that break down the polymers.
2. Acidogenesis. The soluble monomers produced in the previous stage are fermented by acidogenic bacteria to produce VFAs, alcohols, CO\({}_{2}\), and H\({}_{2}\), among other compounds.
3. Acetogenesis. The VFAs and alcohols produced in the previous phase are converted to acetic acid, hydrogen, and CO\({}_{2}\).
4. Methanogenesis. The compounds formed in acetogenesis are converted to biogas (mainly CH\({}_{4}\) and CO\({}_{2}\)) by methanogenic archaea.  
Steps (1) and (2) correspond to VFA production [34]. Thus, the DF process is commonly defined as the biotransformation of a carbohydrate-rich substrate into H\({}_{2}\) and other by-products, such as organic acids and CO\({}_{2}\), in the absence of light and oxygen.  
In DF, as outlined by [PERSON] et al. [35], complex sugars such as cellulose, hemicellulose, starch, and sugar can be obtained from organic waste, wastewater, or other types of waste that have undergone prior pretreatment (to ensure the elimination of harmful compounds),and subsequent transformation into simple sugars that will give rise to different VFA. A summary of this synthesis pathway is presented in Figure 1.  
The findings of the study carried out by [PERSON] et al. indicate that the DF process represents an effective process for VFA production. In order to ensure optimal VFA production, instead of maximizing H2 production, it is essential to control a range of critical parameters, including the substrate, the reactor, the microbial culture, the pH, the temperature, the solids retention time (SRT), the hydraulic retention time (HRT), and the organic loading rate (OLR). The composition of the VFA is determined by the operational parameters [36].
## 3 Inherent Flow Characteristics and Numerical Analysis
### Cavitation  
Complicated flow paths in valves often causes cavitation due to rapid changes in the pressure and velocity of the working fluid. Cavitation occurs when the fluid pressure falls below the saturated vapor pressure, and the vapors that are generated tend to form cavities. The cavities move to an area above the saturated vapor pressure. At that point, they collapse rapidly and create shock waves. These shock waves disturb not only flow control but could even damage the inside of the valve [15].  
The vapor volume fraction, \(VVF\), has been used to quantify the degree of damage due to cavitation, as shown in Equation (3).  
\[VVF=\frac{V_{vapor}}{V_{element}} \tag{3}\]  
In Equation (3), \(V_{vapor}\) is the volume occupied by vapors, and \(V_{element}\) is the volume of one element in the finite element model. The \(V_{vapor}\) was calculated using a form of multi-phase CFD analysis that considers both liquid and gaseous phases.  
To account for the turbulence characteristics of cavitation at the rear portion of the flow path and the turbulent stress that occurs due to the perturbation of the fluid velocity, the Reynolds-averaged Navier-Stokes (RANS) equation [16] was adopted. For the turbulence model, the SST k-\(\omega\) model [17] was used, because both flow separation and multi-phase flow at the wall could be implemented. To calculate cavity size, such as the radius of vapors, \(R_{B}\), from the results obtained by the CFD analysis via the RANS equation, the Rayleigh-Plesset equation was applied, as shown in Equation (4). Subsequently, the \(V_{vapor}\) was determined by the \(R_{B}\)[18].  
\[R_{B}\frac{d^{2}R_{B}}{dt^{2}}+\frac{3}{2}\left(\frac{dR_{B}}{dt}\right)^{2}+ \frac{2\sigma}{\rho_{f}R_{B}}=\frac{p_{\varphi}-p}{\rho_{f}} \tag{4}\]  
Figure 3: Features of the finite element model.  
In Equation (4), \(\sigma\), \(p_{v}\), \(p\), and \(\rho_{f}\) are the surface tension coefficient, saturated vapor pressure (Pa), fluid pressure (Pa), and fluid density (kg/m\({}^{3}\)), respectively.
# Focusing 3D Small Objects with Object Matching Set Abstraction
## 2 Related Work
### Set Abstraction Algorithms  
The set abstraction algorithm contains sampling, query, and feature learning. FPS is usually employed to select the key points from the raw point clouds. This method mines key points according to the distance. As a result, these mined points contain a considerable number of background points. To mitigate this effect, [PERSON] et al. propose a weighted FPS with Euclidean distance to raise the sampling probability of foreground points [29]. To further reduce the introduction of irrelevant points that do not belong to the corresponding object, semantic information supervision is employed in the subsequent work; 3 DSSD develops a Feature-FPS strategy to preserve the points in the objects with the feature distances [14]. In contrast to 3 DSSD, SASA presents a more direct heuristic strategy to identify the foreground points [26]. Specifically, a binary segmentation module is added to estimate point-wise foreground scores. This method only classifies foregrounds and backgrounds, neglecting the small objects. Motivated by this research, we have added another segmentation module designed specifically to distinguish the small objects. Accordingly, the probability of sampling small objects has been significantly enhanced. A ball query is used to mine the context for the key points, in which the ball is difficult to match the objects [30,31]. Thus, a certain amount of key point losses and a certain number of irrelevant points are introduced, ultimately lowering the detection performance. Aiming at the issue, we propose a geometric-prior method, i.e., cube query. Unlike the ball, the cube aligns more closely with objects such as pedestrians, bicycles, and cars, enabling an accurate feature extraction for points.
###### Abstract  
The presented research examines the performance characteristics of Brazed Plate Heat Exchangers through computational fluid dynamics (CFD), focusing on pressure drop calculations for single-phase flow within full channels of plates featuring dimpled corrugation. This work aims to bridge gaps in the literature, particularly regarding the underexplored behavior near the ports for the studied technology and establishing a framework for future conjugate heat transfer studies. A methodology for the domain generation was developed, integrating a preliminary forming simulation to reproduce the complex plate geometry. Comprehensive sensitivity analyses were conducted to evaluate the influence of different parameters and identify the optimal settings for obtaining reliable results. The findings indicate that the \(k-\epsilon\) realizable turbulence model with enhanced wall treatment offers superior accuracy in predicting pressure drops, with errors within \(\pm\)4.4%. Additionally, leveraging the information derived from CFD, a strategy to estimate contributions from different channel sections without a direct reliance on those simulations was developed, offering practical implications for plate design.  
heat exchanger; dimple pattern; CFD; pressure drops Article
# Transmission Delay-Based Uplink Multi-User Scheduling in IEEE 802.11 ax Networks +
## 5 Proportional Fair-Based Transmission Scheduling
### Re-Clustering Condition Based on Transmission Delay  
The performance of the proposed scheduling method depends on the difference in the transmission delays of the stations. Hence, if data sizes or transmission rates for uplink transmissions considerably change after the clustering, the channel use lowers, and the performance of the proposed method eventually degrades. In order to compensate for the performance degradation, the stations should be re-clustered when the difference in transmission delays exceeds a certain threshold.  
The re-clustering condition of the proposed method occurs on the basis of the changes in transmission delays. Let the time that stations are clustered be \(t^{\prime}\). At time \(t^{\prime}\), maximum transmission delays of each cluster can be calculated, and we denote the difference in transmission delay of the cluster with the largest value as \(\tau^{\prime}_{max}\). At every time \(t\), the cluster for performing uplink multi-user data transmission is selected by solving (5), and we denote the cluster selected at time \(t\) as \(C^{t,t}_{*}\). Before notifying the stations of scheduling information by transmitting a triggering frame, the AP compares the maximum transmission delay of the currently selected cluster \(\mathcal{C}_{*}^{UL,t}\) with \(\tau^{\prime}_{max}\). The proposed method performs re-clustering when the following condition is satisfied:  
\[\max\{\tau(d_{s_{i}}^{t},d_{s_{j}}^{t})\}>\lambda\cdot\tau^{\prime}_{max},\ \ \text{for}\ \ s_{i},s_{j}\in\mathcal{C}_{*}^{UL,t}, \tag{6}\]  
where \(\lambda\) is an adjustable parameter, which can be changed depending on the network topology or channel environments. In the proposed method, re-clustering is performed by comparing the current transmission-delay difference with the one for the initial clustering to maintain reasonable channel use for uplink multi-user transmission without considerable performance degradation. Note that \(\lambda\) is supposed to be greater than or equal to 1. This is because the amount of uplink data of stations and channel states can be changed, and the transmission-delay difference at time \(t>t^{\prime}\) is likely to be larger compared to the one at time \(t^{\prime}\). In the following section, we set \(\lambda\) as 1.5 or 1.9 for the evaluations. The re-clustering is performed more frequently when \(\lambda\) is 1.5 than 1.9.