esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Simulations
### Experimental Results and Analysis
#### 4.3.3 Experiment Comparing Single Model with Hybrid Model  
In order to verify the effectiveness of the model proposed in this paper on intrusion recognition, this section sets up performance analysis experiments on the intrusion detection model fusing CNN and GRU: under the same experimental conditions, the dataset was processed by sampling the preprocessing methods mentioned in Section 3.1 of the text, after which CNN, GRU and CNN-GRU were tested through the UNSW-NB15, NSL-KDD and CIC-IDS2017 datasets to obtain their classification accuracy, precision, recall and F1-score values as shown in Table 9. The bold text indicates the evaluation indicators of the proposed model.  
As can be seen from the table, compared to using a single model of CNN and GRU, the CNN-GRU model can effectively extract the features of the original data and then effectively perform intrusion detection. The detection accuracy, recall, precision, and F1 score of dataset NSL-KDD reached 99.69%, 99.65%, 99.69%, and 99.70%, respectively; the detection accuracy, recall, precision, and F1 score of dataset UNSW_NB15 reached 86.25%, 86.92%, 86.25%, and 85.59%, respectively; and the detection accuracy, recall, precision, and F1 score of dataset CIC-IDS2017 reached 99.65%, 99.63%, 99.65%, and 99.64%, respectively. The reason for this is that the CNN structure can learn spatial features effectively by deepening the width of the network, while the GRU structure can extract temporal features of the data better. The model in this paper fuses CNN and GRU to learn both spatial and temporal features of the data, and introduces the attention mechanism to learn features comprehensively and effectively, thus achieving better results.
###### Abstract  
This paper proposes a personalized head-related transfer function (HRTF) estimation method based on deep neural networks by using anthropometric measurements and ear images. The proposed method consists of three sub-networks for representing personalized features and estimating the HRTF. As input features for neural networks, the anthropometric measurements regarding the head and torso are used for a feedforward deep neural network (DNN), and the ear images are used for a convolutional neural network (CNN). After that, the outputs of these two sub-networks are merged into another DNN for estimation of the personalized HRTF. To evaluate the performance of the proposed method, objective and subjective evaluations are conducted. For the objective evaluation, the root mean square error (RMSE) and the log spectral distance (LSD) between the reference HRTF and the estimated one are measured. Consequently, the proposed method provides the RMSE of \(-18.40\) dB and LSD of \(4.47\) dB, which are lower by \(0.02\) dB and higher by \(0.85\) dB than the DNN-based method using anthropometric data without pinna measurements, respectively. Next, a sound localization test is performed for the subjective evaluation. As a result, it is shown that the proposed method can localize sound sources with higher accuracy of around \(11\%\) and \(6\%\) than the average HRTF method and DNN-based method, respectively. In addition, the reductions of the front/back confusion rate by \(12.5\%\) and \(2.5\%\) are achieved by the proposed method, compared to the average HRTF method and DNN-based method, respectively.  
head-related transfer function; audio rendering; personalization; deep neural network; convolutional neural network; anthropometric measurement; ear image; sound localization +
Footnote †: journal: Applied
###### Abstract
Focusing to this problem, the aim of this paper is (i) to examine certain constraints in the output and the applicability of GNSS bridge monitoring imposed by structural data; (ii) recognize certain pitfalls of the GNSS monitoring, mostly in form of  
Figure 1: Response of the Humber Bridge, UK, with a 1400 m-long span, to strong wind, derived from GNSS data. (**top**) Long-period component of wind record (no gusts shown) during an extraordinary wind event. (**bottom**) Recorded lateral deflections (deviations from the red line) indicate a combination of semi-static and dynamic oscillations at mid-span, with a total amplitude of about 2 m. Modified after [11]. These GNSS-derived deflections have a small SNR (signal-to-noise ratio) and are a priori reliable. This is not, however, the case with small deflections of short, stiff bridges. apparent excessive deflections of stiff bridges; and (iii) explain the causes of such pitfalls and (iv) propose some practical rules to avoid them. These results are very important because short span bridges represent the striking majority of highway and railroad bridges all over the world, little attention is usually paid to their structural health, the percentage of damage in relatively high (cf. [19]) and the problem of pitfalls in the estimations of dynamic deflections of stiff bridges has hardly been understood.
## 6 Simulation and Experimental Results  
### Simulation  
Previously to the inverter assembly, the full system behaviour has been analysed in the PSCAD simulation environment. A model of the bidirectional converter and its controller has been simulated connected to a PV array. In this way, the PV array has been modelled according to the pilot plant characteristics, so that the obtained results can be extrapolated to the experimental set-up.  
Current EL legislation [64] establishes that two tests must be completed in order to detect defects of different nature. A current equal to 10% and 100% of \(I_{sc}\) must be injected during the first and second test, respectively. Starting from a scenario where the PV panels are connected to the inverter without current flowing between them, the current controller has been tested under different irradiance levels and current set-points, always within the limits set in legislation.  
Figure 8 shows the PID controller response to a reference in the form of 200 ms pulse. Thus, voltage, current and power results are summarised in Table 1 for the analysed cases, allowing the calculation of power consumption required during the inspection.  
Figure 6: Panels with several defects used in the pilot plant (**a**), and connection diagram between panels and the inverter (**b**).  
Figure 7: Bidirectional inverter developed for the direct polarization of the panels (**a**), and inverter installed in the demo site facilities (**b**).  
On the one hand, the simulations conclude that irradiance is not strongly related with voltage and power levels required to achieve the current reference. On the other hand, a slight increase in voltage produces an important current variation. Consequently, if current set-point is increased, the voltage increases slightly also, and therefore the needed power variations are approximately linear with current. In conclusion, power consumption and, consequently, inspection cost, depends strongly on the current level necessary to achieve the light emission required to the defect detection.
## 6 Conclusions and Future Directions
### Main Conclusions
#### 6.1.1 Method Effectiveness Validated  
Through processing and analyzing actual trajectory data containing over 2.48 million records, the effectiveness of the proposed method was validated. The data preprocessing phase successfully eliminated 30.80% of redundant data, improving data quality. Thisresult not only demonstrates the necessity of systematic data cleaning but also provides a quantitative reference for trajectory data quality control.  
The path identification algorithm successfully processed trajectory data from 7252 vehicles, accurately identifying node sequences traversed by vehicles. Through parameter optimization, 49 m was determined as the optimal radius for polygon circumscribed circles, with this setting yielding the minimum number of abnormal vehicles and optimal path identification results. This achievement indicates that parameter optimization based on data characteristics is key to improving algorithm performance.  
The research contributes to scientific knowledge through (1) the novel regular polygon coverage algorithm that solves trajectory point-labeling challenges more effectively than traditional nearest-neighbor approaches; (2) the systematic five-module processing framework addressing fragmentation in existing research; (3) the multi-source data fusion approach combining instantaneous speed and time difference calculations for enhanced reliability; and (4) the highly automated toolchain enabling practical engineering applications. Social implications include providing cost-effective alternatives for regions lacking traditional detection infrastructure, supporting intelligent traffic management through comprehensive parameter extraction, and facilitating data-driven traffic control optimization that can reduce congestion and improve urban mobility.  
Traffic parameter extraction covered multiple dimensions, including link flows, average speeds, travel times, and OD matrices. These parameters were not only complete in quantity but also ensured accuracy through multi-source fusion calculations.
## 4 Numerical Results and Discussion
In order to verify the advantage of the proposed network, we make the comparison of our network and the existing BWSN proposed in Reference [35]. In Reference [35], a joint source and relay design for MIMO two-way relay networks with SWIPT considering a perfect CSI is proposed. In the network, the sources are equipped with PS receivers. The comparison is implemented under the same parameters for the two systems, and the results are as follows. According to the results shown in Figure 6, it can be observed that the performance of the same algorithm based on the proposed system is preferred to that based on the BWSN, which verify the superiority of the proposed system. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**SNRs (dB)**} & \multicolumn{5}{c}{**FPP-SCA Scheme**} & \multicolumn{5}{c}{**Low-Complexity Scheme**} \\ \cline{2-10}  & \(A^{M,N}\) & \(B^{M,N}\) & \(C^{M,N}\) & \(D^{M,N}\) & \(E^{M,N}\) & \(F^{M,N}\) & \(G^{M,N}\) & \(H^{M,N}\) \\ \hline
**0** & 0.39 & 0.25 & 0.29 & 0.20 & 0.44 & 0.77 & 0.35 & 0.64 \\
**5** & 0.18 & 0.06 & 0.09 & 0.04 & 0.21 & 0.62 & 0.12 & 0.40 \\
**10** & 0.04 & 0.003 & 0.012 & \(5.0\times 10^{-4}\) & 0.05 & 0.47 & 0.02 & 0.26 \\
**15** & 0.003 & \(1.0\times 10^{-5}\) & \(6.5\times 10^{-6}\) & 0.0 & 0.004 & 0.33 & 0.002 & 0.14 \\
**20** & 5.0 \(\times\) 10\({}^{-6}\) & 0.0 & 1.6 \(\times\) 10\({}^{-7}\) & 0.0 & \(4.5\times 10^{-5}\) & 0.22 & \(5.7\times 10^{-6}\) & 0.07 \\
**25** & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.11 & 0.0 & 0.02 \\
**30** & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.04 & 0.0 & 0.0045 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The effects of antennas variation. Figure 5: The antennas versus SNR for 50 iterations.
# Recent Developments in Plasmonic Sensors of Phenol and Its Derivatives
## 5 Plasmonic Resonance-Based Raman Sensors for Detection of Phenolic Compounds  
Raman spectroscopic technologies--including micro-Raman mapping, imaging, and SERS spectroscopy--have been widely adopted in many fields such as food safety [97], the monitoring of contaminants [98], and the diagnosis of disease biomarkers [99]. Raman substrates may be divided by several kinds of pure noble metal nanostructured materials, hybrid nanomaterials, and MOF structures.
## 3 Related Work and Methodology
### Global and Local Moran's I  
Spatial autocorrelation is an important index to express the spatial behavior of geographical elements, reflecting the interdependence between some elements in the study area [30]. The global indicators of spatial association, such as Global Moran's I, and the Local Indicators of Spatial Association (Local Moran's I, also known as LISA), are frequently employed to quantify spatial autocorrelation [33]. These metrics, Global and Local Moran's I, are robust analytical techniques that offer valuable insights into the spatial structures and associations inherent in geospatial datasets. Their application extends across various domains, offering a quantitative basis for spatial pattern analysis and decision making [39]. Both Global and Local Moran's I play a pivotal role in spatial analysis, contributing to a comprehensive understanding of the distribution of earthquake epicenters. They assist in unraveling the spatial dynamics and patterns, which are crucial for informed policy making and resource allocation decisions [40].
## 2 An Isolated, High-Power, Photovoltaic Pumping System (Monitored System)
### Induction Motor  
The centrifugal pump is powered by a 50 Hz, 3-phase (400 V) induction motor [15] of 30 kW. The induction motor has 2 poles, leading to an angular velocity of 2870 rpms. The current consumption of the motor is 101 A, considering a 0.85 power factor and an efficiency of 84% [18]. Table 13 from Appendix A presents the AC induction motor technical specifications [15].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sector** & **Htotal (mca)** & **Q (m\({}^{3}\)/h)** & **Pressure Reference (bar)** \\ \hline
1 & 68 & 55.5 & 3.5 \\
2 & 72 & 85.5 & 2.5 \\
3 & 76 & 90 & 2.1 \\
4 & 76 & 94.5 & 1.9 \\
5 & 76 & 85.5 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sector operating points of the irrigation (hydraulic) network.  
Figure 2: Isolated, high-power, photovoltaic pump system layout [13].
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 3 Background  
### Blockchain  
To date, numerous blockchain-based systems have emerged, including Bitcoin as well as platforms like Ethereum [27; 28; 29] and Hyperledger Fabric [30; 31; 32]. Based on the level of decentralization, participant permissions, and application scenarios, blockchains can be categorized into three main types: public blockchains [33; 34; 35], consortium blockchains [36; 37; 38], and private blockchains [39; 40; 41]. Public blockchains allow any node globally to freely join, exit, read, and send transactions. Consortium blockchains lie between public and private blockchains; they are primarily used among specific groups such as organizations and businesses, where node permissions are managed to ensure data transparency and security. Private blockchains, typically developed and controlled by individual companies, have stricter access controls, with each node representing the company itself.  
Blockchain consists of three fundamental components: blocks, chains, and a consensus mechanism. A block serves as the basic unit of storage, containing a set of related transaction data. Each block is uniquely identified by several attributes, including block height, timestamp, current hash, previous block hash, and the Merkle tree root. These attributes not only provide essential information about the block but also ensure its uniqueness within the blockchain. The chain is the structural backbone that connects individual blocks. In a blockchain network, blocks do not exist independently; rather, they are linked together in a specific manner to form a continuous chain. Each block contains a block header that references the address of the previous block. This linking mechanism is what gives the blockchain its inherent continuity, immutability, and indivisibility, ensuring that all blocks are securely and sequentially connected. The consensus mechanism is a critical element in maintaining the security and functionality of the blockchain. In a decentralized distributed network, the consensus mechanism ensures that all participating nodes agree on the state of the blockchain, preserving data consistency across the network. By achieving consensus, the mechanism protects the blockchain from attacks and tampering, safeguarding it from malicious actors who might attempt to alter the data or disrupt the network. This process guarantees the security, reliability, and integrity of the entire blockchain system.
# An Intelligent Real-Time Object Detection System on Drones
## 2 Related Work
### FPGA Acceleration  
FPGAs are widely used for AI task acceleration. We find three studies that are closely related to our proposed design and they are summarized in Table 1.  
[PERSON] et al. [9] propose an FPGA accelerator for Yolov2-tiny, where the Xilinx Virtex-7 485T FPGA is used for development. They support operators of convolution, pooling, ReLU, and fully connected layers. The input size is \(448\times 448\) and the system generates \(7\times 7\times 2\) bounding boxes. The essential contribution is that they optimize the hardware implementation of the fully connected layer and the detection speed is fast. It is worth mentioning that in the new Yolov3, the fully connected layer is not used.  
[PERSON] et al. [10] develop an FPGA accelerator using Xilinx Zynq 7020 FPGA board. This design can be applied to various neural networks and it supports Yolov2, whose operators are the same as those in [9]. Its detection speed is low compared to the work in [9].  
[PERSON] et al. [11] achieve high-speed detection using a Xilinx Virtex-7 690T FPGA. They accelerate the convolution by utilizing FFT. The operators supported include convolution, max pool, leaky ReLU, etc. The main contributions are that they implement the fully  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Virtex-Yolo** & **Zynq-Yolo** & **Req-Yolo** \\  & [9] & [10] & [11] \\ \hline Advantages & High speed & Multiple NN support & High speed \\ \hline Disadvantages & Yolov3 not supported & Yolov3 not supported & Yolov3 not supported \\  & Few operator support & Few operator support & Few operator support \\  & No post-proc. & No post-proc. & No post-proc. \\  & High power & High power & High power \\  & & Low speed & Compressed Yolov2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of state-of-the-art object detection using FPGA accelerators.  
connected layer using convolution and compress the model weight to 4.95 MB. This is much smaller than the original and it comes with a cost of accuracy  
In addition, the related studies all accelerate Yolov2--which consists of nine convolutional layers--and do not support Yolov3. The operators in Yolov2 are limited. Our design can accelerate Yolov3, which includes 13 convolutional layers and new operators such as up-sampling and concatenation. The previous studies generate bounding boxes which are then processed by CPUs and our design includes a post-processing module in the FPGA, which greatly accelerates the post-processing. Since the previous studies all include SoCs that run an operating system, the power consumption of these studies is inevitably high and they are not suitable for lower power-consumption scenarios.
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Reality
#### 4.4.3 Augmented Reality (AR)  
The limitation of VR systems in the medical industry comes mostly from the user's concern about the surrounding physical world in reverse of the virtual world. Augmented Reality (AR) overwhelms these matters by delivering an immediate and simple user interface on top of the electronically enhanced physical world.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **DoF** & **Max. Force/Torque** & **Stiffness** \\ \hline Touch/Geomagic Touch & 6 & 3.3 N & 2.13 N/mm \\  & & & 1.02 N/mm \\  & & 8.5 N & 3.5 N/mm \\ Phantom Premium & 6 & 37.5 N & 3.5 N/mm \\  & & 22 N & 1 N/mm \\ Virtuose 6D & 6 & 35 N & 30 N./rad \\ omega.7 & 7 & 12 \(\pm\) 8 N & 14.5 N/mm \\ sigma.7 & 7 & 20 \(\pm\) 8 N & Unequal closed \\  & & & loop stiffness \\ \hline \hline \end{tabular}
\end{table}
Table 2: Haptic devices with their features.  
AR offers a straightforwardly functional space for the medical staff where the consultation and diagnosis of the 3D model produced from the medical results can be achieved effortlessly. With this technology, the virtual elements of the system can be visualized in the real world and accomplish an efficient interaction by utilizing a haptic device. This entails that the surgeon may be controlling the device in the operating room and interact, with smooth actions, with the patient. This interaction is incredibly essential to avoid any danger of contamination, as it is not obligatory to operate any external device [64].  
In medical applications, when there is a need of pre-planning, intraoperative procedures, or a physical examination that demands medical imaging, the real-world image can be the actual patient's body. AR is broadly used in both medical imaging systems and surgery procedures. Some of these produced promising results, such as the interaction between physicians and overdue 3D virtual data. The main goal of visualization during surgical procedures is to make the medical staff feel like they are in a real operation room. Reliability, usability, and interoperability are three essential requirements for any system based on the visualization of AR. As the persuasive power of this type of visualization is exceedingly high, the visualization software is imperative to guarantee specific accuracy margins by following it during the operation time.
## 3 Results
### Characteristics of Spatial Differences in Corn Production Cost  
The CPC of the 20 major maize producing provinces in 2008, 2013, and 2018 is spatially displayed (Figure 2). The results show that the CPC presents a trend of gradual increase from east to west. From the provincial level, we observe the following: (1) Relatively high cost: In 2008, the regions with high production costs are Gansu and Yunnan, accounting for 10% of the studied provinces. In 2018, the high-cost regions are Gansu, Yunnan, Guangxi, and Hebei, accounting for 20% of the examined regions. Among them, the Gansu and Yunnan provinces exceed the national average for 11 consecutive years, ranking among the top costs in all Chinese provinces. (2) Relatively medium cost: In 2008, 8 provinces, including Guangxi, Sichuan, and Shanxi, have moderate production costs, accounting for 40% of the total number of examined provinces. In 2018, there are 7 provinces, including Sichuan, Shanxi, and Shaanxi, which account for 35% of mid-cost regions. (3) Relatively low cost: In 2008, 10 provinces, such as Heilongjiang, Henan, Shandong, and Jiangsu, have lower CPC, accounting for 50% of the studied area. In 2018, there are a total of 9 provinces, including Heilongjiang, Shandong, Anhui, and Jiangsu, with low cost that account for 45% of the total number of examined regions.  
In general, provinces with relatively high costs have gradually increased over time. In addition, it can be observed that all increased provinces are situated in the southern mountainous and hilly corn area. Their positioning indicates that the cost gap in this region is widening in comparison to other regions. The main reason for this may be the existence of numerous hills in the southern mountain and hilly provinces, scattered arable land, and the difficulty of mechanization transformation. Although agricultural mechanization has developed rapidly in various regions of China, the process of agricultural mechanization in this region is slow. With the exception of Hubei province, the comprehensive mechanization level of corn cultivation and harvest in 2018 in the southern mountainous and hilly region is below 35%, while the index in most provinces in the northern spring corn region and the Huang-huai-hai summer corn region is 70%. Therefore, agricultural machinery in the southern mountain and hilly area has a lower replacement of labor compared with other  
Figure 2: Corn production costs in various regions of China in 2008, 2013 and 2018.  
regions. In recent years, labor prices have soared, causing the gap in the CPC between this and other regions to widen further.