esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Different Deployments and Resolution of Antenna Arrays in SWIPT
###### Abstract  
In this paper, three different deployment antenna arrays with circular, triangular and rectangular shapes were used to optimize the simultaneous wireless information and power transfer (SWIPT) system for the Internet of Things (IoT). Ray-tracing was employed to channel the model for a real environment. Self-adaptive dynamic differential evolution (SADDE) was used to optimize the harvesting power ratio with bit error rate constrained by the two different resolutions of feed length (high resolution and low resolution). Numerical results show that those three antenna arrays can achieve the goal for information quality in both resolutions. The harvesting power ratio for the circular array is the best and the harvesting power ratio for the rectangular array is the worst. The harvesting power ratio for the low-resolution case is 25% lower than the high-resolution case. However, the circular antenna array is the best deployment in those three different arrays for both high and low resolutions.  
simultaneous wireless information and power transfer (SWIPT); millimeter wave (mmWave); ray-tracing; bit error rate (BER); Internet of Things (IoT) Article
## 4 Applications of Integrated Photonics
### Sensing Applications
In industrial automation, integrated photonic sensors play a crucial role in enhancing precision and efficiency [78]. These sensors can be integrated into manufacturing processes to monitor and control various parameters with high accuracy. For example, in the semiconductor industry, integrated photonic sensors are used for lithography and inspection processes, ensuring that components meet stringent quality standards. The ability to detect minute defects and variations in materials and products can lead to significant improvements in yield and product quality [79]. Furthermore, integrated photonics can facilitate the development of smart manufacturing systems, where sensors provide continuous feedback to optimize production processes and reduce downtime [80].
# _Bacillus_ sp. GLN Laccase Characterization: Industry and Biotechnology Implications
## 2 Materials and Methods
### Characterization of the Produced Lacase  
#### 2.8.1 Determination of pH Optimal and Stability Profile of the Lacase  
Lacase activity was evaluated in various buffers ranging from pH 2.0 to 10.0, with a strength of 100 mM. Different buffer solutions employed were glycine-HCl (pH 2.0-3.0), sodium acetate (pH 4.0-5.0), potassium phosphate (6.0-7.0), Tris-HCl (8.0-9.0), and sodium carbonate-bicarbonate (pH 10.0), as reported elsewhere [23]. The salts and acid used to prepare the buffers were purchased from Merck chemicals (Pty) Ltd. (Modderfontein, Gauteng, South Africa). For the pH stability study carried out over 180 min, pH values were selected based on the relative enzyme activity observed under various conditions, and they included 2.0, 5.0, 8.0, 9.0, and 10.0. The bacterial lacase was mixed with the corresponding buffer and incubated at 30 \({}^{\circ}\)C. Samples were collected at regular 30 min intervals to assess the remaining lacase activity. The standard enzyme activity determined before incubation was taken as 100%.
## 3 Results
### All- and CNR-Selected Channels  
The compositions with all channels (FC-1 to 3 and FC-7 to 9) performed better than the ones with CNR-selected channels (FC-4 to 6 and FC-10 to 12). Specifically, the all-channels compositions increased MCC\({}_{\text{RS}}\) by 1.86% (\(p=0.11\)) and curtailed detection latency by 23.05% (\(p<0.05\)) (Table 2). Finally, we carried out one-way ANOVA tests to examine if the 12 different feature vector compositions affected classification accuracy and detection latency, respectively. The results indicate that the compositions significantly improved both classification accuracy (for MCC\({}_{\text{RS}}\), F = 4.69; \(p<0.00005\)) and detection latency (F = 2.5; \(p<0.05\)).  
Figure 4: MCC\({}_{\text{RS}}\) Comparison according to feature vector compositions. MCC\({}_{\text{RS}}\) (seven participants) results are displayed after being grouped. ChPrsv means ‘channel-preserving, and ChAvg means ‘channel-averaging’. All indicates ‘using all channels’, whereas Contrast-to-Noise Ratio (CNR) means ‘CNR-selected’. The values used for the bar graph are accessible in Table S1 (Supplementary Materials).
## 2 Materials
### Data Acquisition and Preprocessing
#### 2.2.4 SRTM DEM Data  
The Shuttle Radar Topography Mission (SRTM) was a collaborative project involving NASA, the U.S. Department of Defense, and the German and Italian space agencies. The mission collected satellite data over a 10-day period from 11 February to 22 February 2000, producing two types of digital elevation models with spatial resolutions of 30 m and 90 m. In this study, 30 m resolution SRTM DEM data were utilized. The SRTM data were employed to extract key topographic parameters, including ground elevation, slope, and slope aspect, which were used as input variables in the model to account for and mitigate the influence of topography on AGB estimations. These data are also available in the GEE platform.
## 2 Related Work
### Graph Neural Networks  
Deep learning has demonstrated successful performance in recent years in different domains. Research efforts have proven the powerfulness of this machine-learning approach to extracting complex patterns from massive amounts of irregular data [13]. Therefore,using these machine-learning techniques to analyze graphs has been frequently proposed in recent years. The objective of this task was to overcome the challenges that remain in applying traditional deep learning to graphs [14]. These challenges included the irregular structures of graphs, the heterogeneity and diversity of graphs, and large-scale graphs.  
Graph neural networks (GNNs) were promoted after the success of neural networks and their ability to learn to extract latent representation from complex relations and interactions of a given network. GNNs are categorized into: recurrent graph neural networks (RGNNs), convolutional graph neural networks (CGNNs), graph auto-encoders (GAEs) and spatial-temporal graph neural networks (STGNNs) [15]. GNNs can graph structured data to perform tasks such as node classification, link prediction, and graph classification. Therefore, GNNs can be trained in a semi-supervised manner by using the labeled nodes to classify the unlabeled ones in the graph [16].  
Spatially based GNN models such as GAT are preferred, as they maintain efficiency, generalizability, and flexibility of implementation compared to spectral models. This is because spatial models can handle multi-source graph inputs, in contrast to spectral models that only handle undirected graphs [15]. In addition, spatial models are more scalable, as they perform graph convolutions locally on individual nodes, and share weights across different locations and structures. GAT models as a spatial model are considered a GCN model with an attention mechanism, and have achieved good performance in the literature [17]. This has encouraged research to expand this model into multi-view perspectives, where different views of data are integrated [18; 19].  
Recent studies have proposed using GCNNs to enhance bot detection using relational graph convolution networks (BotRGCN) [20] and GCNNs for spam bot detection [21]. In [20], the authors aimed to construct a heterogeneous graph of Twitter, and to apply relational GCN to detect bot communities. In [21], the authors deployed graph convolutional neural networks to a well-known spam bot dataset that outperformed previous methods with regard to the results. Hence, the issue with previous datasets is that they were easily detected and did not include recent generations of bots.
## 4 Analysis and Discussions
### Influence of Loss on Temperature Rise  
In the coupling iterative process, losses of different components in the LG vary with the temperature field distribution and the impact of loss variation on the temperature field is investigated.  
Figures 12 and 13 illustrate the changes in the losses and temperature with iterations, respectively. After each iteration, the temperature field results of the windings become higher than the previous ones, hence the next higher initial value of temperature is set. As a result, the resistance of the windings along with the copper loss continuously grows in the iterative process, which conversely increases the calculated temperature. The copper loss as the main part of loss, increased by 28.1 W, corresponds to an increasing ratio of 24.5%. Contrary to the upward trend of copper loss, the residual flux density and magnetic coercivity decrease with increasing temperature of the PMs, which leads to the reduced loss of the PMs.  
As the iterative process progresses, changes in calculated temperature get smaller. After five iterations there is almost no change in the temperature field and relative temperature errors are within the error range. The maximum temperature of the windings increases by 15.2 \({}^{\circ}\)C from the beginning to the last iteration, accompanied by a 14.9 \({}^{\circ}\)C rise in the final average temperature. The final maximum temperature of the PMs increases by 11.1 \({}^{\circ}\)C, with the final average temperature increasing by 8.8 \({}^{\circ}\)C. In addition, the temperature results of the first iteration and the final iteration reflect the gap between one-way coupling and bidirectional coupling.  
Figure 11: Maximum and average temperatures of the components in the TPMLG (in \({}^{\circ}\)C).
# Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
## 2 Model and Analysis  
In this paper, we construct a gold wire bonding model in HFSS software, and optimize the S-parameter, which greatly improves the microwave transmission characteristics.
# Development and Validation of Universal 3D Blast Fragmentation Model
## 4 Case Study
### Burden Analysis and Model Pre-Calibration  
In situ production blasts use the explosives and pattern described in previous paragraphs. The burden for the explosive charges is 2 m. The fragmentation model determines the burden by the following expression [22]:  
\[B=\frac{0.17\cdot P_{h}\cdot r_{h}}{k\cdot\sigma_{t}}\]  
where  
\(P_{h}\)-- borehole pressure;  
\(r_{h}\)-- borehole radius;  
\(k=\frac{(1-\
u)}{(1+\
u)(1-2\
u)}\);  
\(\
u\)--Poisson's ratio;  
\(\sigma_{t}\)-- tensile strength.  
Borehole pressure is determined according to the Chapman-Jouguet detonation theory [32; 33], and expression for explosives with density above 1 g/cm\({}^{3}\) is as follows:  
\[P_{d}=\frac{\rho_{e}\cdot D^{2}}{8}\]  
where  
\(\rho_{e}\)--density of explosive (g/cm\({}^{3}\));  
\(D\)--detonation velocity of explosive (km/s).  
Figure 17: Fragmentation model setup.  
For explosives with a density below 1 g/cm\({}^{3}\), the pressure on the blasthole walls is calculated as follows:  
\[P_{d}=\frac{\rho_{e}\cdot D^{2}}{4.5}\]  
Therefore, for rock with a tensile strength of 5 MPa that is blasted with 76 mm ANFO charge with VOD of 2000 m/s and density of 0.9 g/cm\({}^{3}\), the burden equals approximately 1 m. This is two times smaller than the actual case. With such burden value and model setup, as previously explained, the fragmentation model would compute blast-induced fractures that are too small, and complete fragment computation would not be possible. The reason for such discrepancy is within the pressure calculation formula.  
To overcome pressure formula limitations, the model was pre-calibrated by increasing borehole pressure to achieve a burden of the same size as was used in production. This means that borehole pressure increased from an initial value of 0.8 GPa to 1.68 GPa, corresponding with a burden of 2.01 m in the model.
## 6 Verification and Results
### Episode 1: Packet Forwarding and Provisioning (req: i, iii, iv, vi)
#### 6.1.1 Service Provisioning Times
It is expected that most web applications based on TCP, such as Youtube and Netflix, are resistant to these network outage times that are less than 100 ms. Re-using an EF in order to reconnect it to a different EF peer has a provisioning time of 1.2 s (1.b-1). Hence, this result indicates that it is more efficient to pre-instantiate and pre-authenticate a new pair of EFs instead of reusing any existing EFs during re-provisioning. This pre-authentication consequently sets the soft provisioning time and the network outage time to only include the time it takes to distribute the flow rules. It is assumed that the network outage time caused by the re-distribution of the flow rules is not perceived as network outage by most end-users.
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
###### Abstract  
A printed monopole antenna for millimeter-wave applications in the 5G frequency region is described in this research. As a result, the proposed antenna resonates in three frequency bands that are designated for 5G communication systems, including 28 GHz, 38 GHz, and 60 GHz (V band). For the sake of compactness, the coplanar waveguide (CPW) method is used. The overall size of the proposed tri-band antenna is 4 mm \(\times\) 3 mm \(\times\) 0.25 mm. Using a watch strap and human tissue, such as skin, the proposed antenna gives steady results. At 28 GHz, 38 GHz, and 60 GHz, the antenna's gain is found to be 5.29 dB, 7.47 dB, and 9 dB, respectively. The overall simulated radiation efficiency is found to be 85% over the watch strap. Wearable devices are a great fit for the proposed tri-band antenna. The antenna prototype was built and tested in order to verify its performance. It can be observed that the simulated and measured results are in close contact. According to our comparative research, the proposed antenna is a good choice for smart 5G devices because of its small size and simple structure, as well as its high gain and radiation efficiency.  
Keywords:tri-band antenna; mm-wave antenna; 5G antenna; wearable antenna +
Footnote †: journal: Sensors
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 5 Design of the DNN Accelerator and Acceleration Framework Based on FPGA
### FPGA Accelerator for Optimization Strategy
#### 5.3.2 Optimization for Storage  
Due to the large amount of data and parameters in neural networks, problems of parameter and data storage are inevitable when neural networks are deployed. In the optimization of storage, the strategy of reducing data precision is usually used to reduce the amount of data, so as to improve the operation speed. The data reuse method can be adopted in order to greatly reduce the data storage and the memory space occupation. For example, ShortcutFusion, an accelerator optimization tool proposed in [161], reduces memory footprint by maximizing on-chip data reuse under given resource constraints. The data processing time was found to be 2.8 times faster than NVIDIA RTX 2080 Ti, and the energy efficiency ratio was nearly 10 times higher on a Xilinx KCU1500 FPGA. Some scholars have used a memory management framework to realize automatic data layout and to solve the memory competition problem of the CPU-FPGA heterogeneous system by analyzing cross-layer memory competition. By automatically generating the optimal data placement strategy and the cache partition mechanism of the parallel execution kernel, the performance of both the FPGA kernel and the CPU kernel in the heterogeneous system is improved [115].
## 4 Conclusions  
Vermicasts of the recently described indigenous earthworm species, _P. losbanosensis_, were investigated and compared with _E. eugeniae_ vermicasts. _P. losbanosensis_ vermicasts harbor diverse bacterial communities comprising 153 different species belonging to the phyla Proteobacteria, Bacteroidetes, Acidobacteria, Actinobacteria, Chloroflexi, Firmicutes, Gemmatimonadetes, Nitrospirae, Planctomycetes, Spirochaetes, Thermodesulfobacteria, and Verrucomicrobia. The Shannon index confirmed that more diverse species of bacteria were found in _P. losbanosensis_. Physiological profiles of the bacterial communities in the vermicasts of _P. losbanosensis_ were shown through the utilization of all 31 carbon substrates, primarily the amines and amides, phenolic compounds, polymers, and carboxylic and acetic acids, with higher AWCD and H index indicating higher functional diversity or metabolic potential. Among the 45 isolates, bacteria from the vermicasts of _P. losbanosensis_ were found to be more efficient in plate-based NPK mineralization, showing the largest blue coloration zone, thus confirming nitrogen fixation and the highest phosphate and potassium solubilization index. Overall, this study unveiled that vermicasts produced by indigenous earthworms are better than those of non-native species in terms of having (1) more diverse bacterial communities, (2) higher functional diversity or metabolic potential, and (3) higher NPK mineralization activities. These results highlight the potential of _P. losbanosensis_ vermicasts as an organic input to promote plant growth and improve soil substrate. Hence, Philippine earthworms should be further explored and harnessed for the production and utilization of vermicasts to mitigate the probable threats of invasive non-native species.  
Conceptualization, M.R.F.M., N.M.A. and M.C.M.O.; Methodology, validation, formal analysis, data curation, and writing--original draft preparation, M.R.F.M.; Writing--review and editing, M.R.F.M., N.M.A. and M.C.M.O.; Supervision and funding acquisition, M.C.M.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Commission on Higher Education (CHED) through the K to 12 DARE TO Research, grant number 9610145-499-416.  
Not applicable.  
The data presented in this study are openly available as the content of reported tables and figures.  
This research was conducted at the Microbial Ecology of Terrestrial and Aquatic Systems (METAS) Laboratory of the Institute of Biology, College of Science, University of the Philippines, Diliman, Quezon City, Philippines.  
The authors declare no conflict of interest.
# Dual-Band Single-Layer Fractal Frequency Selective Surface for 5G Applications
## 6 Measurements  
Practical validation of the proposed dual-band single-layer FSS was obtained from specific measurements carried out inside an anechoic chamber. The FSS prototype used for the measurements is shown in Figure 11. The measurement geometry is depicted in Figure 12, in which the FSS under test is placed on a mechanical test fixture (sample holder) whose dimensions are 1.8 by 2 m (height \(\times\) width), and its center was cut in a square shape (window) with 41 by 41 cm\({}^{2}\). The sample holder is covered with pyramidal RF absorbers and aluminum foil on one side, as depicted in Figure 13. The purpose of this fixture is to minimize the contamination of measurement results due to diffraction and transmission, mainly occurring through the center square window.  
Figure 11: FSS prototype used for the measurements.  
Figure 10: Frequency response for different incidence angles.  
Wideband directional log-periodic antennas used in measurements were placed at a 2 and 1.1 m distance from the test fixture, respectively, for the transmitter and receiver. In the former, an Aaronia Hyperlog 30,100 antenna, covering the 300 MHz to 10 GHz frequency band was used, whereas in the latter, an Aaronia Hyperlog 60,100 antenna (600 MHz-10 GHz) was employed. The two antennas were connected to a Vector Network Analyzer (VNA) and placed on top of support mats.  
In particular, the complex frequency response was obtained from 10 MHz to 6 GHz, with 1601 points and averaged out from 20 complete frequency sweeps, for both vertical and horizontal polarizations. Although the discrimination of both antennas for frequencies below 600/300 MHz falls within the available dynamic range of the measurement system, significant noise in the measurement results up to 600 MHz was observed, and thus measurement results are presented only for frequencies above 600 MHz.  
An initial reference measurement was obtained without any sample (open window), which was used for normalization purposes. The normalized response of the FSS structure for both vertical and horizontal polarizations, assuming normal incidence, is depicted in Figure 14. A relatively good agreement is observed between simulation and measurement results, in which the two resonant frequencies appear around 800 and 3.5 GHz, with similar reject levels to that of the simulations. The insertion loss in the pass-band was measured to be below 2 dB, which is also in good agreement with the simulations.  
Figure 12: Measurement setup inside the anechoic chamber for FSS characterization. (**a**) Side view. (**b**) Top view.
## 6 Conclusions  
In this study, a new approach was developed for the deformation reliability analysis of retaining structures in deep excavations, making use of the in situ monitored data. The method expands the classical first-passage based time-dependent reliability problem into the spatial scale, and explicitly incorporates the potential impact of measurement error. The new method considers the deformation scenario of the whole structure rather than limited points, and thus moves from the conventional \"point control\" method to \"surface control\". The proposed method only involves the calculation of a one-fold integral, with which the deformation reliability assessment can be conducted efficiently compared with the traditional finite element-based approaches. An illustrative example is presented in this paper to demonstrate the applicability of the proposed method. The following conclusions can be drawn from this paper.  
1. The accuracy and applicability of the proposed deformation reliability method were verified by comparing the results with those obtained from a Monte Carlo simulation.
2. If only taking into account the deformations at the depths monitored, the deformation reliability of retaining structures could be overestimated, due to the potential failures at the non-monitored points.
3. If ignoring the impact of ME, the deformation reliability could be overestimated significantly if the deformation scenario passes the \"point control\" examination. This suggests the importance of carefully identifying the measurement error of the equipment (inclinometer) in an attempt to achieve a reliable estimate of the safety level of retaining structures.  
The proposed method can be further extended for the deformation reliability assessment of excavation surfaces containing multiple retaining structures. Further work is underway regarding the deformation reliability assessment of deep excavations on both the spatial and the temporal scales.  
Conceptualization, L.X. and C.W.; methodology, Y.X. and C.W.; analysis, Y.X.; drafting and editing, L.X., Y.X., C.W., and K.F.; supervision, L.X. and C.W.; funding acquisition, L.X.  
This research was funded by the Science and Technology Bureau of Fuzhou, China (grant number 2018-G-88) and the Provincial Department of Science and Technology of Fujian, China (grant number 2017J01698).  
The authors declare no conflict of interest.