esa-sceva/satcom-chunk-collection huggingface dataset:
# AI Makes Crypto Evolve
## 8 Future Roadmap: The Promise of Secure AI
### Quantum-Inspired AI  
In this section, we provide insights into quantum-inspired AI. The capacities of quantum computing can be adopted by AI to perform learning techniques. We analyze how the capabilities of quantum computing would improve the learning performance of AI.  
A study reported in [212] presented a Quantum-inspired Reinforcement Learning (QiRL) solution to the problem of Unmanned Aerial Vehicle (UAV) trajectory planning. This QiRL used a novel collapse behavior selection strategy inspired by quantum mechanics, which provided a natural balancing approach for exploration and development by ranking the collapse probabilities of different behaviours. The proposed QiRL method was proven to be effective, and the proposed method was shown to be capable of balancing convergence speed and learning quality better than the traditional Q-learning method.  
Moreover, an algorithm for navigation control of autonomous mobile robots was presented in [213] using QiRL. In quantum information retrieval, probabilistic behavior selection and probabilistic enhancement strategies were applied, which were influencedby the amplitude amplification and collapse phenomenon of quantum measurements and computation. Several simulation experiments on the Markov state transfer revealed that QiRL was more robust in terms of learning rate and the initial state than traditional reinforcement learning. Simulations and experimental results demonstrated the effectiveness of the QiRL-based navigation control method when applied to a real mobile robot.  
Another study proposed a Quantum Fuzzy Neural Network (Q-FNN) classifier that was able to solve the overlapping sample classification problem [214]. According to empirical results, Q-FNN outperforms existing classifiers in terms of classification accuracy. The model used a tenfold cross-verification scheme, and generated accurate results. In a second experiment, the classification model proposed was applied to 11 of 15 state-of-the-art datasets. Another study also aimed to develop a framework for Deep Reinforcement Learning (DRL) supported by quantum computation based on the results of an empirical replay [215].  
Furthermore, self-convergent iterative learning model algorithms were notably improved using quantum computing in [216]. Quantum-inspired Hopfield Associative Memory (QHAM) demonstrated quantum information processing in neural structures. In this study, a new Quantum-inspired Multidirectional Association Model was introduced (QMAM), combining one-off learning and self-convergent iterative learning methods. The simulation results show that the proposed model was of acceptable stability, memory capacity and recall reliability.
# Loop-Type Field Probe to Measure Human Body Exposure to 5G Millimeter-Wave Base Stations
## 4 Discussion on Reactive near Field Measurement  
According to [11], the region where EMF is radiated from a source is divided into three regions: source regions I, II, and III. Source region III corresponds to the far-field region, while source regions I and II correspond to the near-field region. Source regions I and II have a different property with respect to the radiation characteristic: source region I is the reactive near-field region, while region II is the radiative near-field region. Ref. [11] provides equations to calculate the distances from a source to each region when the source is a linear array antenna. The horn antenna with the slot array corresponds to a linear array antenna, so the distances for the source regions can be calculated using the equations in [11]. According to those equations, the boundary distance between source regions I and II is about 2 mm from the horn antenna while the distance between source regions II and III is about 470 mm.  
Considering this point, the measurement regions in Figures 7 and 9 are included in the radiative near field region. The measurement results in Figures 7 and 9 show that the proposed probe accurately measures the radiative near-field region. This is owing to the function of the proposed probe that measures the phase difference between the E- and H-fields as described in (2). As shown in Figure 1, the proposed probe provides the simultaneous measurement of the E- and H-fields using, respectively, the loop and the gap. As depicted in (2), this simultaneous measurement makes the phase difference be included at the output voltage \(V_{PD}\) of the probe. As a result, the proposed probe is useful in that PD can be measured even in the radiative near-field region with a single measurement, and consequently, it is not necessary to measure the E- and H-fields separately using two different probes like in [14].
# A Blockchain-Based Auction Framework for Location-Aware Services
## 3 The Proposed Blockchain-Based Auction Framework
### Auction
#### 3.2.1 Initialization  
Algorithm 1 illustrates the auction initiation process. To initiate an auction, the buyer must submit a request along with the required fields summarized in Table 1, including paying the auction registration fee. This fee deposited into the administrative account is necessary to ensure the buyer's commitment. This fee is refundable to the user only if no offer is received from the bidders within the bidding timeline. The service location represents the physical address of the requested service (e.g., city and street name). In the service type field, the buyer determines the service category that he/she would like to rent (storage area, apartment, etc.). In the service description field, the buyer determines the duration of the required service. For instance, the user may specify that he/she wants to rent the described service for one month. In the maximum price field, the buyer determines the maximum price he/she is willing to pay for the service. The bidders' SI field represents the seller's SI requested by the buyer. Accordingly, a bidder with an SI that does not match the requested SI will not be notified about the auction. They can choose whether all bidders (undecided, positive, and negative) may participate in the auction or only sellers with a positive SI can participate. Using the additional requirements input, the buyer can describe requirements, such as the temperature of the storage area.  
```
Data:\(L\), \(T\), \(sd\), \(max_{p}\), \(SI\), and \(ad\)
1\(P\gets determineEligibleBidders(L,T,SI)\)
2 if\(|P|\
eq zero\)then
3\(r\gets createRequest(L,T,sd,max_{p},SI,ad)\)\(notifyBidders(P,r)\)
4 else
5\(cancelRequest()\)
6
7 end if
```  
**Algorithm 1**initializeAuction  
Based on the service location, a submitted request can be accepted or rejected by the auction initialization process. This process is done by determining if there is any eligible registered bidder in the specified service location. If no bidder is found, the buyer will be notified and the request will be canceled. However, in other cases, the auction request is accepted, and the corresponding bidders who satisfy the requirements are notified about the auction.  
Figure 2: The interactions between the auction stage processes.
# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
###### Abstract
The simultaneous research on multiple decision-making methods shows the advantages of multi-modal signal research and makes up for the flaw of single model signal diagnosis. Therefore, this paper integrates various technologies to realize the judgments of cardiac health statuses via ECG and PCG signals. This paper proposes the classification method of ECG and PCG signals based on BiLSTM-GoogLeNet-DS (combined with the fusion technology for deep learning). This method can extract the features of long-distance dependent information based on BiLSTM, which can improve the training time and learn the features better, and can produce higher accuracy for long-span time series data. The GoogLeNet model has also achieved excellent results in the field of image recognition with its multi-layer convolutional neural network structure and inception structure. This paper, combined with the advantages of these two deep learning networks, uses the BiLSTM network and GoogLeNet network to extract and classify the features of ECG and PCG signals. The final classification results were obtained by using the fusion technology twice with the help of the improved D-S theory. The experiments show that the proposed algorithm improves the classification accuracy of ECG and PCG signals effectively, and is more accurate than single-modal cardiac function signals. The structure of this paper is organized as follows: the methodology of the proposed algorithm is presented in Section 2. Section 3 presents the related theory about the BiLSTM network and GoogLeNet model. Section 4 presents the implementation steps of the proposed algorithm. Section 5 presents the experiments and results. The experimental dataset and the parameter settings of the network model are presents. The comparative experiments are set. The evaluation criteria of the classification accuracy, sensitivity, specificity, precision, and F1 score are given. The comparison results with other algorithms are provided. Finally, the conclusions are drawn in Section 6.
# Oxidation-Reduction of Ti-6 Al-4V in Direct Energy Deposition Subject to Minimum Argon Consumption
## 2 Material and Methods
### Shielding Algorithm  
With eight nozzle attachments in the system, it is crucial to precisely control the flow of the shielding gas into each attachment for effective shielding. Additive manufacturing (AM), which is known for generating complex shapes, does not always have a well-defined or linear path, making it challenging to provide conformal shielding consistently. Continuous monitoring of the tool path is therefore essential to ensure that the correct attachment is activated in real time. Given the high working temperatures, turning on the wrong attachment or failing to activate any attachment can lead to the oxidation of the alloy within minutes, compromising the quality of the deposition. To address this challenge, an algorithm was developed to ensure that the correct attachment is activated during the deposition process.  
In this study, a P1 AM-100 CPU controller was used to interface with the AM machine, acquiring the position of the nozzle at high rates. The controller continuously monitors a shielding range of 100 mm, based on observations from the contour plot in Figure 1, where the shielding range for laser powers between 800 and 1200 W is noted to be approximately 100 mm. The controller stores every previous position of the system up to a distance of 100 mm, allowing it to regenerate the deposited path in real time. Each stored former location is denoted as point \"P\", with P\({}_{0}\) representing the current position and P\({}_{  
The algorithm is designed such that an attachment is activated or deactivated when its corresponding cosine value reaches 0.819 (which corresponds to \(\cos\) (35\({}^{\circ}\))). This configuration ensures that each nozzle attachment covers a shielding range of 35\({}^{\circ}\), meaning that when the deposition line falls between two nozzles, shielding coverage is provided by two adjacent attachments for better protection.  
Future improvements could involve integrating automated adaptive shielding, where machine learning or predictive algorithms anticipate the required shielding configurations, further reducing response time and enhancing shielding precision. Real-time monitoring techniques such as infrared cameras or laser-based oxygen sensors could be added to measure oxidation levels or temperature variations directly, providing feedback to adjust the shielding gas flow dynamically. This would enable more precise control and optimize gas usage, further minimizing argon consumption.  
The system can also be adapted for other materials or larger parts, as the concept is not limited to Ti-6 Al-4V. It could be scaled for different reactive metals or even non-metallic materials where contamination is a concern. Retrofitting older DED machines with this attachment system could enhance their shielding capabilities and bring them up to modern standards.
## 4 Experimental Results
### Proposed JD Framework and HD Video Sequences  
In this section, the proposed JD framework is tested with HD video sequences in order to evaluate its effectiveness at various video resolutions. The HD video sequence selected contains both low content and moderate content types. A block size of \(16\times 16\) and GoP size of 8 is adopted.  
The PSNR and SSIM results presented in Tables 5 and 6 respectively, clearly show that the proposed JD framework also performs well for HD video sequences compared with conventional BCS framework. A similar trend as that of CIF video sequence can be noticeable using JD reconstructions. For low motion video (Lovebird) the gain in term of PSNR and SSIM is more significant than moderate motion videos (Newspaper, Book arrival).
## 8 Potential Implementation Constraints  
Although QR decomposition-based detection improves the MIMO system's accuracy, there are several challenges that make it difficult to use in practice, especially in large-scale MIMO. High processing and memory requirements are imposed by the computationally demanding matrix operations used in the decomposition process, such as Givens rotations, Householder reflections, and Gram-Schmidt orthogonalization. Due to these criteria, real-time QR decomposition is challenging to do in high-throughput environments where latency must be kept to a minimum, such as massive MIMO. Additionally, QR-based techniques are frequently inappropriate for systems with limited power or resources, like Internet of Things devices, where memory availability and computational overhead are constrained. Furthermore, QR decomposition presents more hardware implementation complexity than more straightforward detection techniques like Minimum Mean Square  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Technique I** & **Technique II** & **Technique III** & **ML** \\ \hline Search Times & 4 \(\times\) (\(\xi\)) \({}^{\dagger}\) & 2 \(\times\) (\(\xi+(\xi)^{2}\)) \({}^{\dagger}\) & 6 \(\times\) (\(\xi\)) \({}^{\dagger}\) & 2\({}^{m}\) \\ \hline \hline \end{tabular} \({}^{\dagger}\xi\) show the sample space.  
\end{table}
Table 4: Complexity of detection for various detection methods.  
Error (MMSE) or Zero-Forcing (ZF). As a result, it is less suitable for deployment on FPGA or ASIC platforms where timing, power, and area constraints are crucial design factors.
## 2 Materials and Methods
### Microbial Electrosynthesis Reactor Configuration and Operation  
A double-chamber H-type reactor was used for the microbial electrosynthesis (MES) reaction (working volume: 350 mL) (Figure 2). The anode and cathode chambers were joined by a glass arm (38 mm diameter) and separated using a proton exchange membrane (PEM, Nafion 117; DuPont Ltd., Wilmington, DE, USA). The anode and cathode electrodes were pieces of graphite felt, and were 4 cm \(\times\) 10 cm and 4 cm \(\times\) 5 cm, respectively. The thickness of each electrode was 0.3 cm (GF030, FuelCellStore, College Station, TX, USA), and each one was connected to titanium wire which acted as the current collector from anode to cathode. The reference electrode, Ag/AgCl (in 3.0 M NaCl, 0.209 V vs. NHE) was placed in the cathode chamber. All bottles and reactors were sterilized at 121 \({}^{\circ}\)C for 15 min by autoclaving. After autoclaving, the cathode chamber was flushed with filtered 5% CO\({}_{2}\) (balanced with Ar) for at least 1 h to remove any oxygen remaining in the graphite felt.  
The cathode electrode was poised with \(-\)0.6 V (vs. Ag/AgCl) using a potentiostat (WMPG1000; WonAtech, Seoul, Korea). The experiments were carried out under anaerobic conditions in batch mode. The cathode chamber was supplied with 5% CO\({}_{2}\) (\(v/v\)) balanced with Ar at a rate of 17.5 mL min\({}^{-1}\) (0.05 vvm). All MES reactors were placed under white LED lamps in the range of 450-475 nm (www.photonic.com) at 28 \(\pm\) 3 \({}^{\circ}\)C, which provided 50 \(\mu\)mol photons m\({}^{-2}\) s\({}^{-1}\). The reactors were operated in triplicate.  
Figure 2: (a) Schematic representation and (b) Photograph of the microbial electrosynthesis reactor used for conversion of CO\({}_{2}\). CO\({}_{2}\) conversion was performed in growth medium (pH 7.0) without a carbon source other than supplementation with 5% CO\({}_{2}\) at an applied potential of \(-\)0.6 V (vs. Ag/AgCl).
## 6 Solutions to Optimization Problems
### Time (Computational) Complexity  
An important consideration in the trajectory optimization of UAVs for IoVs is the time complexity associated with the formulated optimization problem and its solution. UAVs are designed to navigate autonomously and support IoT and IoV systems [117]. This necessitates that the optimization process be executed in real-time to enable UAVs to perform their missions autonomously, without reliance on external computation or memory resources. While convex programming solutions offer the advantage of real-time implementation, they do so at the expense of solution optimality. As demonstrated by Lemma 1, trajectory optimization involving valid LoS paths is NP-hard, making real-time solutions computationally prohibitive.  
Sampling-based techniques, such as rapidly exploring random trees (RRT), are widely used in the robotics field for trajectory optimization in complex environments [148]. However, the computational cost of these methods is significant when applied to wireless communication scenarios, where each sample must be evaluated against multiple objectives. This further complicates their real-time applicability, especially for dense urban environments where maintaining LoS links for IoV support is crucial. Therefore, AI-based agents, if properly trained offline, can be the best candidates for real-time trajectory generation and communication control. Alternatively, they can be deployed to address the nonconvexity of the optimization problem, thereby overcoming the limitations of convex programming, such as sub-optimality.
# Comparison of NeQuick G and Klobuchar Model Performances at Single-Frequency User Level +
## 2 Data and Methods  
This section explains the data and methods used to calculate the TEC accuracy, TEC availability, and SF position accuracy. The stations used by the different entities are spread worldwide as shown in Figure 1.  
These stations are therefore divided into four groups according to the different geomagnetic latitude zones. Stations considered by individual entities are marked with different colours; in the case of TUW, in the mid-latitude region, 5 stations per quarter were considered. The station choice for each quarter was mainly dependent on the site location within the mid-latitude region and RINEX data availability (2019 Q1: AJAC, GRAS, JOZ2, LEOP, NICO; 2019 Q2-2019 Q4: AJAC, GRAS, LEOP, NICO, WROC; 2020 Q1-2021 Q4: EBRE, GRAS, GRAZ, NICO, WROC). Due to urgent maintenance activities at the Data Centre of the Bundesamt fur Kartographie und Geodasie (BKG) of the International Geodesy Service (IGS), and hence RINEX data unavailability, the majority of used validation sites had to be replaced in the 1 st and 2 nd quarter of 2022 (2022 Q1: DYNG, GRAS, MELI, NICO, WARN; 2022 Q2: DYNG, EBRE, GOP6, GRAS, WSRT).  
As described in the introduction, this paper presents two ionospheric models used for SF receivers, the NeQuick G and Klobuchar models. The NeQuick G model [4] is designed to reach a correction capability of at least 70% of the ionospheric code delay in terms of root mean square (RMS), with a lower Slant total electron content (STEC) residual error bound of 20 TEC Unit (TECU) for any location, time of day, season, and solar activity, excluding periods where the ionosphere is largely disturbed due to, for instance, geomagnetic storms. On the other hand, the Klobuchar model [2] provides for GPS SF users an accuracy of about 50% RMS of the ionospheric range delay, depending on the solar activity or the region. Hence, its accuracy is often unsatisfactory even for absolute positioning, especially in the case of heightened solar activity [5].
# Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications
## 5 Analysis of RSS Measurements Using Bluetooth Mesh Network  
To conveniently conduct channel measurements using multiple ESP32 transmitter and receiver devices, in this research, we custom designed and 3D-printed a panning measurement platform with 360\({}^{\circ}\) scale for horizontal rotation. The measurement platform is installed on top of a non-reflective EMC antenna tripod as shown in Figure 6. Multiple ESP32 BLE devices with external antennas and battery packs can be easily mounted on the platform. The external antenna installation positions on the platform are shown in Figure 7. The position P0 is also the platform's center of rotation. In this section, we first present antenna radiation pattern measurement data obtained using our measurement system, which is used to characterize RSS measurements with individual BLE devices. Then, measurement data are presented to study the small-scale multipath fading effects on RSS measurements. At last, we present the path loss models derived with various system configurations, including single receiver, multiple receivers, single advertising channel, and multiple advertising channels. The path loss models are used to analyze the effects of the space-time-frequency diversity combining schemes on the modeling accuracy of the RSS-distance correlation in indoor multipath environments, which is fundamentally important for the RSS-based localization techniques.  
Figure 6: A custom designed 3D-printed panning measurement platform with 360\({}^{\circ}\) scale for horizontal rotation.  
Figure 7: Antenna positions on the measurement platform.
## 3 Materials
### Satellite Image Pre-Processing
#### 3.3.3 MCD15A2 LAI Product  
The MCD15A2 version 6 MODIS LAI product is an 8-day composite dataset with a 500 m pixel size. The algorithms include a main LUT-based procedure and a back-up algorithm based on the LAI-NDVI relationships [49]. Because retrievals of the back-up algorithm have a low quality, this study only used the values retrieved with the main algorithm [50]. The LAI product is considered to be validated to stage 2. MODIS LAI is closer to true LAI than effective LAI and the C6 product is considerably better than C5 [51]. All scenes of MOD15A2 LAI covering the study area were obtained from NASA.