esa-sceva/satcom-chunk-collection huggingface dataset:
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods  
### Antenna 1 Design  
A simple, low-cost, compact, and flexible Vivaldi UWB antenna is designed. The antenna has a radiating triangular shape on the top side and a slotted ground plane on the bottom side. It has a relatively small size of 25 \(\times\) 20 mm\({}^{2}\) compared with conventional Vivaldi antennas presented in the literature. It is fabricated using standard printed circuit board (PCB) manufacturing processes with polyimide flexible material. To the best of our knowledge, the compact size of the flexible Vivaldi antenna presented here can be considered one of the smallest sizes that have been reported in the literature. Figure 1 illustrates the geometry of the proposed flexible Vivaldi antenna 1. The antenna is fabricated on an inexpensive Polyamide substrate with a thickness of 0.1 mm, relative permittivity of 3.5, and loss tangent, tan\(\delta\) = 0.02. The radiating side of the antenna on the top layer consists of a microstrip feed line, a horizontal line that passes above the parabolic notch on the GND plane, and a triangular shape to enhance propagation. The feeder line of the antenna consists of two segments of the line where the first segment has a width of 0.21 mm, which results in a 50 \(\Omega\) matching impedance. The second segment of the feeder has a width of 0.28 mm to match the first segment of the feeder to the horizontal line of the radiation side. The horizontal line extends through the triangular shape of the radiating side of the antenna. The antenna has a 50 \(\Omega\) CPW feeder line on the top side, and an SMA connector is connected at the end of the line. The proposed antenna underwent many design stages. Starting with a 45 \(\times\) 40 \(\times\) 1.5 mm\({}^{3}\) Vivaldi antenna, the parameters were optimized to yield the proposed final shape of 25 \(\times\) 20 \(\times\) 0.1 mm\({}^{3}\). The radiating shape of the antenna  
The simulation results show that the antenna has two resonant frequencies: the first resonant frequency is at 4.0 GHz with a 1 GHz impedance bandwidth, and the second is located at 9.4 GHz with a 1 GHz impedance bandwidth. Figure 2 shows the reflection coefficient of this antenna against frequency.
## 3 Results and Discussion
### Analysis of Single-Mode OAM Mode Detection Based on AP-CNN
After plane wave interference, the plane wave interferogram collected at the receiving end has more distinguishable characteristics, and the OAM mode detection accuracy of the ResNet18 demodulator is significantly improved compared with no interference. The AP-CNN adds a dual-path structure combined with an attention pyramid after the ResNet18 network. As such, the OAM mode detection accuracy is further improved compared to that of ResNet18. This reveals that the dual path with an attention pyramid is beneficial for single mode detection accuracy.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 3 Proposed Method
### Three-Dimensional Point-Cloud Map Reconstruction  
The 3D point-cloud map consists of a number of points of the 3D vector coordinate Data Point. Therefore, we can obtain the R-distance and the \(\alpha\)-horizontal angle after decoding the LiDAR packet, and find the corresponding Channel Data to know directly that \(\omega\) is the vertical angle. With this information, the XYZ coordinates are calculated and the location of the Data Point is known, and the point-cloud map information is reconstructed according to the Data Point of each point.  
LiDAR is an adjustable design with 5-20 turns per second. This paper uses 10 rotations per second and obtaining a complete frame requires 75 packets of information, each having a horizontal angle of 4.8 degrees. After all the packet information is decoded, a complete picture can be obtained. Figure 4 shows the 3D point-cloud image information obtained after 75 packets are decoded.  
Figure 3: LiDAR decoding flow chart.
## 3 Results and Discussion
### Experiment 3. Two Stage Classification  
Once that the classifiers yielding the best accuracy were identified as the set \(q_{2}\), in this third experiment the classification process was divided into two stages. This division is depicted in Figure 3. In the _pre-classification_ stage, the objective is to segregate the chromosomes into wide groups, and use this output as the input of a final classification into 24 classes. In Reference [39], the authors propose applying a post-classification process to re-assign a chromosome to a different class when a wrong number of chromosomes is found in some class. In this work, a post-classification process is executed to reassign the misclassified chromosomes to their correct class.  
According to the International System of Human Cytogenomic Nomenclature (ISCN) [40], chromosomes can be grouped by shape and area. In Reference [25], the authors propose a hierarchical classification approach, where they divide the chromosomes into seven groups. For the G-band chromosomes images used in this work, this preclassification into seven groups gave accuracy results lower than the single phase classification. It was found that the preclassification in three groups worked better for the G-band dataset, then the pairs are divided as followsThe 3 pre-classification groups are defined according to the shape and area of the chromosome. The 23 chromosome pairs are divided as follows:  
**Group 1:**: Chromosomes 1 to 7.
**Group 2:**: Chromosomes 8 to 15.
**Group 3:**: Chromosomes 16 to 23.
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 4 Experimental Results and Analysis
### Average Energy Consumption Analysis
#### 4.5.1 Static EDs Scenario  
In general, the energy consumption of all schemes in the confirmed mode shows an increasing trend as the number of EDs increases, as shown in Figure 12. However, in the proposed schemes, the energy consumption is lower than the typical ADR and ADR+, owing to the small number of retransmissions. However, in typical ADR and ADR+, many EDs transmit packets with high parameters, including SF = 12 and TP = 14 dBm. The maximum number of packets is lost due to increased interference, resulting in EDs retransmitting packets with higher settings. Therefore, high energy consumption is observed for typical ADR and ADR+, because the transmit energy consumption is primarily based on the values of SF, TP, and ToA, and the retransmissions [23].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**N** & **ADR** & **ADR+** & **G-ADR** & **G-ADR (I-SFA)** & **EMA-ADR** & **EMA-ADR (I-SFA)** \\ \hline
200 & - & 12.3 & 28.2 & 30.8 & 29.5 & 31.9 \\
400 & - & 9.8 & 21.6 & 24.6 & 23.6 & 27.8 \\
600 & - & 7.4 & 12.5 & 16.0 & 15.0 & 19.7 \\
800 & - & 4.5 & 7.3 & 11.2 & 9.4 & 13.6 \\
1000 & - & 1.8 & 3.8 & 8.6 & 6.1 & 12.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: PSR improvement for mobile EDs.  
Figure 12: Average energy consumption for static EDs (uplink period = 24 packets/day, total simulation time = 4 days).
## 4 Cluster-Based Distributed Compressed Sensing for QoS Routing
### Distributed QoS Routing
#### 4.3.3 Local Delay Guarantee  
Local delay requirement also needs to be considered in data transmission. A node knows its neighbors, available channels and transmission rates when it chooses a next-hop node. Thus, it can choose one from a set of channel transmission rates \(\{R_{1},R_{2},\ldots,R_{n}\}\). The higher the channel transmission rate, the shorter the time data transmission spends.  
A geographic mechanism is used to map end-to-end delay requirements to local delay requirement. Suppose a packet with a length of \(L\) at \(v_{i}\) needs to be delivered to the sink within delay \(T\). The local delay constraint, denoted by \(T_{i,j}\), is expressed as  
\[T_{i,j}=\frac{T}{\widehat{N}_{i,s}}. \tag{31}\]  
The transmission delay for a packet from \(v_{i}\) to \(v_{j}\) is \(\frac{L}{p_{i,j}^{*}T_{i,j}}\), where \(\frac{L}{p_{i,j}^{*}}\) is the packet length after redundancies are added.
# An Accelerating Method of YOLOv7 Based on Lightweight Network Architecture
## 5 Discussion  
In this paper, we addressed the challenge that slow detection speeds pose for hardware platforms with limited computing resources. The hardware configuration of the testing environment is a Jetson TX2, and the deep learning framework used is PyTorch 1.8.0. By incorporating depthwise separable convolutional layers into the YOLOv7 backbone network, we were able to significantly enhance the detection speed while retaining a high level of detection accuracy. Depthwise separable convolutions effectively reduce computational complexity by decoupling the spatial and depthwise convolutions, leading to faster image processing. The improvements to the receptive field allowed the model to maintain detection performance, even in resource-constrained environments.  
Compared to the original YOLOv7, the improved model showed a remarkable increase in detection speed, achieving 3.77 times faster performance while reducing detection accuracy by only 3.84%. This demonstrates that the model can be effectively deployed for real-time applications in scenarios where hardware limitations are a key concern. Our results indicate that lightweight YOLOv7 with these optimizations is particularly well suited for applications in fields such as autonomous vehicles, robotics, surveillance, and healthcare.  
Despite the improvements, there are opportunities to further enhance the model's performance. Future work could focus on optimizing the model further for edge devices, such as mobile phones or IoT systems, which would benefit from reduced model size and faster inference times. Additionally, integrating advanced techniques such as knowledge  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Detection Accuracy (AP)** & **Detection Speed (FPS)** \\ \hline YOLOv7 & 94.67\% & 3.1 \\ Backbone Network [15] & 65.64\% & 16.1 \\ Float16 Weight & 86.41\% & 6.9 \\ Quantization [21] & 68.79\% & 13.6 \\ Network Pruning [23] & 73.28\% & 11.3 \\ Ours & 90.83\% & 14.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of model experimental results on accuracy and speed.  
distillation or quantization could lead to even greater improvements in efficiency without compromising performance.
## 3 Results and Discussion
### The Performance of Solution-Processed Perovskite PDs
R and \(D^{*}\) can be calculated as follows [34]:  
\[R =\frac{I_{ph}}{P_{in}}=\frac{I_{light}-I_{dark}}{P_{in}}, \tag{4}\] \[D^{*}=\frac{R\sqrt{AB}}{i_{n}}~{}, \tag{5}\]  
where \(I_{ph}\) is the photocurrent and \(P_{in}\) is the incident-light intensity, \(I_{light}\) and \(I_{dark}\) are the current with and without light illumination, and \(A\) is the device area. As shown in Figure 4c, CsPbBr\({}_{3}\) perovskite PDs can respond from 300 to 550 nm, and have a top \(R\) of 0.317 A/W at 510 nm. The high \(R\) and low \(i_{noise}\) contribute to a high \(D^{*}\) over \(10^{12}\) Jones in the spectrum range from 380 to 520 nm and a peak value of \(1.47\times 10^{12}\) Jones. The response speed is another crucial parameter of PDs for UWOC, including the rise time and fall time, which are defined as the time differences between the points at which the PD reaches 10% and 90% of its peak response. The rise time and fall time were characterized by the photocurrent response under square-wave modulated laser illumination. As shown in Figure 4d, perovskite PDs exhibit a rise time of only 82 ns and a fall time of 0.71 us, with a sensitive area of about 1 mm\({}^{2}\), which is comparable to the silicon PD, with a similar device area. The response time of perovskite PDs is generally governed by the RC time constant [35], since high-quality perovskites device have high carrier mobility and small thicknesses. In our case, when the device area decreases from 4 mm\({}^{2}\) to 1 mm\({}^{2}\), the rise time and fall time decrease from 345 ns and 1.37 us to 82 ns and 0.71 us, respectively, indicating that the response time of our PDs is mainly governed by the RC constant. The high response speed and responsivity of CsPbBr\({}_{3}\) PDs suggest their significant potential in the application of UWOC.
# LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant
## 3 Materials and Methods
### Long Short-Term Memory (LSTM) Network
In regression applications, usually, the output of the last (if there is more than one) LSTM layer is passed to a fully connected layer without an activation function, which computes a linear combination, according to the following equation:  
\[y_{k}=W_{y}h_{k}+b_{y} \tag{4}\]  
where \(W_{y}\in\mathbb{R}^{p\times n}\) is the matrix of the output weights, with \(p\) the number of outputs of the network, and \(b_{y}\in\mathbb{R}^{p\times 1}\) the bias. \(W_{y}\) and \(b_{y}\) are determined during the training of the network as well. Because of this structure, LSTMs show their strength especially when dealing with temporal data and when temporal lags must be taken into account [25; 29; 30; 31; 32]. The LSTM developed in this work considers three inputs (\(m=3\)): the ingoing oxygen flow rate \(q_{oss}(t)\), the ingoing substrate flow rate \(q_{in}(t)\), and the outgoing liquid substrate flow rate \(q_{ol}(t)\), and one output (\(p=1\)), the oxygen concentration \(O(t)\). In this case, the other inputs of the physical process, \(q_{om}(t)\) and \(COD_{in}(t)\), are not considered in input because they are daily measurements, with very little variation, which can be considered almost constant signals not significantly contributing to the model. All the signals were rescaled using mean normalization before being used to train the LSTM network. The simplest LSTM architecture, with a single layer, will be considered in order to minimize the complexity and computational burden.
## 6 Comparative Analysis  
As discussed in the introduction, a broadband antenna with CP can be achieved with ME, SIW, and DRA antennas. However, to achieve the same with the planar patch antenna is quite challenging. This article proposes a broadband antenna operating from 23 to 42 GHz with a \(-3\) dB axial ratio bandwidth of 4.2 GHz (36-40.2 GHz), which is comparable with the existing state-of-the-art designs in Table 1. Also, the proposed design is low-profile compared to others in the table. The antenna represents RHCP and LHCP behavior, with a decent gain of 4 to 6.2 dBi. The developed antenna was validated for IoT indoor conditions, for which other designs have not been performed.
## 4 Results and Discussion
### Elastic Inversion Results
However, the BLI inversion process outperforms the AVO method regarding recovery accuracy, as demonstrated by the results presented in Figure 10.  
This difference is especially noticeable at higher values where the horizon's shape is distorted, demonstrating the BLI inversion's ability to capture the fundamental features of the model. When comparing the final model performance parameters to the AVO investment method, Table 2 indicates that Bayesian inversion (BLI) performs marginally better. This is because BLI exhibits a higher R-squared value (0.78 vs. 0.68), a lower mean square error (MSE) (0.014 versus 0.020), and a lower mean root square error (RMSE) (0.118 vs. 0.141). These changes in light are probably caused by the low-frequency model's inclusion of past knowledge and data uncertainty. Particularly in cases where the data are noisy, the BLI inversion procedure is frequently more dependable and effective. This sets it apart from other techniques for seismic inversion, such as AVO inversion. Figure 11 displays a segment of the inverted seismic section, including P- and S-velocities and the density section, over 500 ms \(\times\) 100 traces using three wavelets: the Ricker wavelet, the sparse spike wavelet from well 2 (SS2), and the sparse spike wavelet from well 4 (SS4). Notable features in these images are areas of extremely low and high velocities. The figure also illustrates the results of BLI inversion with each wavelet, highlighting the SS4 wavelet us superior vertical resolution, followed by SS2. The improved model results are due to the wavelets being generated through sparse spike deconvolution from seismic data, enhancing resolution by retaining high-frequency waveforms and amplitudes during inversion.
###### Abstract  
This paper addresses the problem of optimizing the voltage profile of radially-operated distribution systems by acting on the active and reactive powers provided by distributed energy resources (DERs). A novel voltage optimization procedure is proposed by adopting a decentralized control strategy. To this aim, a centralized voltage optimization problem (VOP), minimizing the distance of all the nodal voltages from their reference values, is firstly formulated as a strictly-convex quadratic program. Then, the centralized VOP is rewritten by partitioning the network into voltage control zones (VCZs) with pilot nodes. To overcome the lack of strictly convexity determined by the reduction to the pilot nodes, the dual centralized VOP working on the augmented Lagrangian function is reformulated and iteratively solved by the method of multipliers. Finally, a fully-distributed VOP solution is obtained by applying a distributed algorithm based on the auxiliary problem principle, which allows for solving in each VCE a quadratic programming problem of small dimension and to drive the VCZ solutions toward the overall optimum by an iterative coordination process that requires to exchange among the VCZs only scalar values. The effectiveness and feasibility of the proposed method have been demonstrated via numerical tests on the IEEE 123-bus system.  
distribution networks; distributed energy resources; decentralized voltage optimization; distributed algorithms; auxiliary problem principle +
Footnote †: journal: Applied Sciences  
1
## 2 Material & Methods
### Biomarker Analysis  
Blood samples were obtained one day before the TAVR procedure and at follow-up times 24 h, 96 h, 3 months, and 12 months after TAVR using a vacuum-containing system. The collection tubes were centrifuged, and the plasma was separated from the blood components and frozen at \(-\)80 \({}^{\circ}\)C to analyze all samples at similar time points under the same conditions.  
The plasma levels of sST2, GDF-15, H-FABP, IGF-BP2, and suPAR were measured using enzyme-linked immunosorbent assay (ELISA) kits (sST2: Duoset DY523, GDF-15: DY957, H-FABP: DY1678, IGF-BP2: DY674, and suPAR: DY807; R&D Systems, Minneapolis, MN, USA). The instructions were followed as described by the manufacturer. The ELISA plates (Nunc MaxiSorp flat-bottom 96 well plates, VWR International GmbH, Vienna, Austria) were pretreated overnight with a diluted capture antibody. After the appropriate washing processes and blocking with a reagent diluent, the plates were ready for the final assay procedure. After putting serum samples and standard protein onto the wells of ELISA plates (Nunc MaxiSorp flat-bottom 96 well plates, VWR International GmbH, Vienna, Austria), the probes were incubated for two hours. Afterward, the plates were washed and treated with Tween 20/PBS solution (Sigma Aldrich, St. Louis, MO, USA). A biotin-labeled antibody (detection antibody) was diluted in a reagent diluent and was added for another two hours. A further washing process was performed, and the probes were treated with streptavidin-horseradish peroxidase solution. A color reaction was generated after adding tetramethylbenzidine (TMB; Sigma Aldrich, St. Louis, MO, USA). Optical density was determined at 450 nm on an ELISA plate reader (iMark Microplate Absorbance Reader, Bio-Rad Laboratories, Vienna, Austria).
## 3 Testing Experiment  
### Study Area and Data  
The EFSDAF is proposed to accurately monitor dynamic changes of land surface including gradual change (e.g., vegetation phenology) and abrupt change events (e.g., flood). In this study, vegetation phenology and flood events are selected to evaluate the performance of EFSDAF.  
The selected two datasets were from the datasets shared by [PERSON] [10], which were collected in the Lower Gwydir Catchment in Northern New South Wales, Australia (149.2815\({}^{\circ}\) E, 29.0855\({}^{\circ}\) S, hereinafter called \"Gwydir\") and have been widely applied in the evaluation of spatiotemporal fusion methods [10; 20; 21; 32; 44]. All images were atmospherically corrected and geographically co-registered, which was resampled to 25 m with the nearest neighbor method by [PERSON]. In EFSDAF, we aggregated the MODIS pixels to 500 m resolution in the process of residual distribution. Two pairs of Landsat and MODIS images of the first dataset (vegetation phenology) were acquired on 5 July 2004 and 6 August 2004 in Gwydir, and the size of the selected images is \(1400\times 1400\) pixels. The first dataset was a typical heterogeneous region, and temporal dynamic changes were mainly caused by phenological change from 5 July to 6 August 2004. The selected Landsat and MODIS images of the first dataset are shown in Figure 2. The second dataset (flood) was acquired on 11 November 2004 and 12 December 2004, and the size of selected images is \(800\times 800\) pixels. A large flood occurred from 26 November 2004 to 12 December 2004, and the dataset was a typical abrupt change area. The selected Landsat and MODIS images of the second dataset are shown in Figure 3.  
Figure 2: Test data with gradual change (vegetation phenology) in Gwydir. (**a**) Landsat image of 5 July 2004; (**b**) Landsat image of 6 August 2004; (**c**) and (**d**) MODIS images of the same periods as Landsat images. All images use NIR-red-green as R-G-B.