esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 3 Results
### Classical Pilot Placement Technique  
Figure 2 shows the pilot overhead key parameter when the pilot placement technique is applied. The results demonstrate a significant initial pilot overhead size of approximately 350 symbols at low signal-to-noise ratio (SNR) values. The pilot overhead ratio to overall symbols count is similar to the pilot overhead for downlink channel estimation in LTE-A standard with eight antennas, which exceeded 25% [23]. This overhead remains relatively constant as SNR increases until a threshold of 15 dB is reached. Beyond this point, the pilot overhead exhibits a marked decrease, reaching a minimum of around 63 symbols at an SNR of 30 dB.  
Figure 3 illustrates the BER performance versus SNR for the proposed pilot placement technique. The BER achieved at \(-10\) dB SNR is \(6\times 10^{-3}\) and decreases to \(2\times 10^{-3}\) at 30 dB SNR. While these results are comparable to those reported in [2,3], further optimiza  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Carrier frequency & \(28\times 10^{9}\) Hz \\ Sampling frequency & \(100\times 10^{6}\) Hz \\ Modulation technique & BPSK \\ Transmitter antennas & 16 \\ Receiver antennas & 4 \\ Number of streams & 4 \\ Number of symbols & 1000 \\ SNR & \(-10\) dB to 30 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation setup.  
Figure 2: Pilot Overhead vs. SNR.  
tions are necessary to mitigate the impact of un-optimized pilot placement and imperfect channel estimation.  
Figure 4 illustrates the system's spectral efficiency (SE) as a function of signal-to-noise ratio (SNR) for the conventional pilot placement technique. While the SE increases with SNR, reaching a maximum of \(12.5\) bps\(/\)Hz at \(30\) dB SNR, this performance is suboptimal. To address this limitation, we propose using AI-based methods to optimize pilot placement and enhance system performance.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 2 Related Works
### Blockchain Implementations on Medical Data  
To guarantee the integrity of healthcare data, many blockchain technologies have been employed in data management systems. MedRec provides safe storage and EHR sharing for patients served by different providers. MedRec employs Ethereum to manage access control, where only users who have access rights can access the healthcare data [16]. In addition, [PERSON] et al. incorporated the ABSC scheme to strengthen the confidentiality and integrity of medical data [18]. The symmetric key used to encrypt the medical data was encrypted using ABE, and a signature for both the encrypted data and encrypted key was created. However, these schemes are disadvantageous in that they depend on the proof of work and cryptocurrency.  
[PERSON] et al. employed the blockchain to guarantee the integrity of the EHR [30], and [PERSON] et al. proposed a healthcare record management framework to provide not only security, but also privacy. Moreover, there are several blockchain-based works for secure EHR sharing. [PERSON] et al. [31] presented a blockchain framework for managing and sharing EHR data between healthcare providers and researchers. [PERSON] et al. [32] proposed a method of sharing EHR by storing encrypted data in the cloud and saving information, including addresses, to the blockchain. Moreover, ABE and signatures were combined to achieve sharing data in many-to-many communications. [PERSON] et al. [33] presented a protocol for securely sharing EHRs of data stored in the cloud based on a consortium blockchain with searchable encryption and PRE. MeDShare [17] monitors all actions, including data transition and sharing, and records them in the blockchain. This system uses smart contracts and access control to track the behavior of the data and enables the withdrawal of dishonest access. By enabling data owners to control the anonymization process, [PERSON] et al. [34] proposed a method for sharing private data with blockchain in a cloud federation. However, problems caused by outsourcing blockchain have not been addressed.
# Using Neural Networks for Bicycle Route Planning
## 3 Materials and Methods
### Modelling Bicycle Path Using the Multicriteria Analysis
#### 3.1.2 Criteria Choice  
Considering the research [11], the criteria picked for bicycle route desirability determination are:  
* Road segment length;
* Road type;
* Slope grade;
* Distance between the Emergency unit and the road segment;
* Distance between the drinking water source and the road segment.  
Our road network topology consisted of road segments and nodes. A road segment connects two nodes; it is a part of the road between two road intersections. All five criteria used for a route desirability determination were assigned to a particular road segment.  
The length of a road segment was taken as the criteria in the route model, considering the desire to choose the shortest path. That means that the shorter road segment will more likely be chosen as the best one.  
Road type was taken as the criterion for bicycle route desirability determination based on safety reasons. Since the road infrastructure in the Imotski area is weak, it is safer for the bicyclist to ride on roads of a higher category because they are kept in a better condition than the lower category roads.  
The distance to the emergency unit is also a safety-related criterion. Therefore, the model will produce paths gravitating to emergency units; in case of an accident, the distance to the emergency unit will be minimised.  
The length between the source of drinking water and the road segment is primarily a leisure-related criterion, even though it can be partly considered as a safety-related criterion. Since there are many drinkable water sources in the lowland part of the Imotski area, it can be used to provide better security and comfort in riding a bicycle.  
ESRI ArcGIS software was used to build the bicycle route model. The primary sources of information were topographic maps in scale 1:25,000 and maps in the scale of 1:5000 collected from the Croatian State geodetic administration.  
Using the data containing information about the height of the terrain from maps, the relief model of the Imotski area (Figure 1) has been reconstructed.  
Triangulated irregular network (TIN) relief model was reconstructed using layers containing height data obtained from a topographic map on a scale of 1:25,000. The height data resolution is 50 m. TIN is vector-based data generated by triangulating vertices connected with a series of edges and then forming a triangle network, with resolution higher in the more dynamic areas.  
The TIN model was used to calculate the slope grade, which could have both negative and positive effects on the bicycle path decision. The most professional bicyclist would instead pick bicycle routes that are more challenging, such as longer and steeper routes. Contrarily, some people use a bicycle irregularly, and they do not have enough endurance. Therefore, two different models for each type of bicyclist will be built.
## 3 Multi-Exposure Fusion via Sparse Representation of Gradient Features
### Final Weight Map Estimation and Fusion  
Since the noises in the initial weight maps have greater influence on the fused image, the maps must be refined further. Considering the availability and real-time capability, the recursive filter [31], which can perform high-quality edge-preserving filtering of images in real time, is used to refine the initial weight maps:  
\[\hat{W}_{i}(x,y)=RF(W_{i}(x,y),I_{i}(x,y)), \tag{12}\]  
where \(RF(\bullet)\) presents the recursive filter operation, and \(W_{i}\) denotes the initial weight map, and \(I_{i}\) are the multi-exposure source images which can intensify the edge information during filtering.  
After the final weight map has been normalized, the fused image, \(I_{F}\), can be obtained by:  
\[\widetilde{W}_{i}(x,y)=\frac{\hat{W}_{i}(x,y)+\tau}{\sum_{i=1}^{N}\left(\widetilde {W}_{i}(x,y)+\tau\right)}, \tag{13}\]  
and  
\[I_{F}(x,y)=\sum_{i=1}^{N}\widetilde{W}_{i}(x,y)\times I_{i}(x,y). \tag{14}\]
## 4 Data Preprocessing
### Privacy Analysis of the BDP-LR Algorithm  
\(A\) and \(A^{\prime}\) are two neighboring datasets, \(F(A)\) and \(F(A^{\prime})\) denote the set of all outputs of the neighboring datasets after the BDP-LR algorithm, respectively. \(S\) denotes all outputs of the algorithm, then:  
\[\frac{\Pr[F(A)=S]}{\Pr[F(A^{\prime})=S]} =\frac{\Pr\bigl{[}f(A)+N\bigl{(}\sigma^{2}\bigr{)}=S\bigr{]}}{\Pr [f(A\prime)+N(\sigma^{2})=S]}=\frac{\Pr\bigl{[}N\bigl{(}\sigma^{2}\bigr{)}=S-f( A)\bigr{]}}{\Pr[N(\sigma^{2})=S-f(A^{\prime})]}\] \[=\frac{\exp\bigl{(}-\frac{[S-f(A)]^{2}}{2\sigma^{2}}\bigr{)}}{\exp \bigl{(}-\frac{[S-f(A)]^{2}}{2\sigma^{2}}\bigr{)}}=\frac{\exp\bigl{(}-\frac{[S -f(A)]^{2}}{2\sigma^{2}}\bigr{)}}{\exp\bigl{(}-\frac{[S-f(A)]^{2}}{2\sigma^{2} }\bigr{)}}\] \[=\exp\biggl{(}\frac{[S-f(A^{\prime})]^{2}}{2\sigma^{2}}-\frac{[S- f(A)]^{2}}{2\sigma^{2}}\biggr{)}\] \[=\exp\biggl{(}\frac{1}{2\sigma^{2}}\Bigl{\{}[S-f(A)+\Delta f]^{2}- [S-f(A)]^{2}\Bigr{\}}\biggr{)}\] \[=\exp\biggl{(}\frac{1}{2\sigma^{2}}\Bigl{[}\Bigl{(}s+\Delta f \Bigr{)}^{2}-s^{2}\Bigr{]}\biggr{)}\]  
Due to the constant positivity of the probability values,  
\[\biggl{|}\ln e^{\frac{1}{2\sigma^{2}}\bigl{[}(s+\Delta f)^{2}-s^{ 2}\bigr{]}}\biggr{|}= \biggl{|}\frac{1}{2\sigma^{2}}\Bigl{[}\Bigl{(}s+\Delta f\Bigr{)} ^{2}-s^{2}\Bigr{]}\biggr{|}\] \[=\biggl{|}\frac{1}{2\sigma^{2}}\Bigl{(}2s\Delta f+\Delta^{2}f \Bigr{)}\biggr{|}\]  
When \(\sigma^{2}\geq c\Delta^{2}f/\epsilon^{2}\), \(c^{2}\geq 2\ln(1.25/\delta)\), it can be proven that \(\frac{1}{2\sigma^{2}}\bigl{(}2s\Delta f+\Delta^{2}f\bigr{)}\leq\epsilon\), then \(\frac{\Pr[F(A)=S]}{\Pr[F(A^{\prime})=S]}\leq e^{\epsilon}\).  
In the batch gradient algorithm, a Gaussian noise mechanism with a privacy budget of \(\epsilon\) is executed on each disjoint gradient according to the parallelism and combinatoriality [51] of differential privacy, so that the algorithm satisfies (\(\epsilon\),\(\delta\))-differential privacy in each round of iterations.
# A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
## 4 System Measurement  
Figure 6a shows the evaluation platform with a die photo of the fabricated transceiver chipset in Figure 6b. The transceiver's die size is 4 mm \(\times\) 5 mm, including 64 ESD-protected I/O pads. As shown in Figure 6b, the transmitter and receiver sub-systems are placed on the bottom and top sides of the chip for maximum signal isolation, and the PLL is placed in between. Figure 6a shows the PCB with the transceiver chip assembled on the RF board and is integrated with two on-board antennas. A motherboard powered with an NXP 8-bit MC9S08 JM60 MCU controls the RF front end's working conditions and monitors its power consumption. Windows-based software is used to configure the working condition of the RF chipset through a GUI interface. A mini-USB port works as both the control bus and the power supply of the platform. There are two quadrature differential baseband interfaces matched to 100 \(\Omega\) impedance, and they are available on the RF board as the transmitter input and receiver output. A 108 MHz TCXO crystal board works as a reference for the RF platform.  
Figure 7 shows the measurement setup of the transceiver system. One set of DUTs is configured as the transmitter. A Keysight M8199 arbitrary waveform generator (AWG) with corresponding software features (81199A) is used to generate a baseband test pattern. A horn antenna is connected to the Rohde and Schwarz FSW67 spectrum analyzer to check the transmitter power. The single-tone baseband signal is used to measure the maximum output power of the PA and channel flatness over the specified frequency band. The power plot is calculated based on the measured power, amending with the estimated setup losses.  
Another set of DUTs is configured in the full-duplex mode to down-convert the RF signal transmitted by the first set of DUTs with its own local leakage. The single-tone signal helps analyze the relationship between the received signal and local SI from the transmitter. Furthermore, the 802.11 ad-based package frame support measures the signal quality through the demodulated EVM. A Tektronix AWG 7122C is used as the second baseband source. The AWG has dual-channel 12 GSym/s digital-to-analog converters (DACs) to support the 802.11 ad standard. A Rohde and Schwarz RTO 1044 oscilloscope captures the baseband signal for further analysis and calculates the EVM. It captures the received baseband data at 10 GSym/s. MATLAB is used to resample the data to four times oversampling and remove the out-band noise as well. The virtual storage-extended (VSE) software V1320.7500.06 with 802.11 ad features from Rohde and Schwarz was used to demodulate the received baseband quadrature signal and plot the EVM. For the full-duplex mode, the captured data are filtered by a digital filter in MATLAB to reduce the SI signal power through the DSIC module. The generated data are demodulated and analyzed by Keysight VSA software version V2019.1.3.  
Figure 6: Photos of the fabricated chip die with the RF platform: (**a**) the 60 GHz RF platform with the motherboard and (**b**) the transceiver die.
## 1 Introduction
### Wearable Sensing Devices for Core Temperature Measurement
* H1b: There is a linear association between SS and ICTS, \(\rho\
eq 0\). * H1c: Individual differences have a significant effect on the SS CT measurement. * H2a: ZephyrTM wearable sensor CT data are within the clinically acceptable limits. * H2b: There is a linear correlation between ZephyrTM and ICTS, \(\rho\
eq 0\). * H2c: Individual differences have a significant effect on the Zephyr CT measurement. This study will contribute to the existing knowledge on CT monitoring, investigate how individual differences impact CT measurements, and provide insight for the development of effective heat stress preventive measures. Additionally, evaluating the effectiveness of wearable sensors for monitoring core body temperature aligns with the Occupational Safety and Health Administration (OSHA) goal of preventing and lowering the number of work-related injuries, illnesses, and deaths caused by hazardous heat exposure. By using wearable sensors, workers can be closely monitored for changes in their CT, allowing for early detection and intervention to prevent heat-related illnesses and fatalities [7]. These wearable sensors are especially critical in industries where workers are exposed to high heat stress, such as construction, manufacturing, and agriculture.
# Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics
## 3 Physics-Informed Neural Networks for Wave-Based Room Acoustics
### Multi-Scale Fourier Feature Networks  
The multi-scale Fourier feature architecture (see Figure 4) was proposed in [28] to address the spectral bias and low convergence rates observed in conventional PINN models [47]. The spectral bias expresses that the target function is first learned along the eigendirections connected to the larger eigenvalues of the neural tangent kernel and gradually the remaining components of smaller eigenvalues. Translated to conventional, fully connected neural networks, the eigenvalues are lowered by the increasing frequency of the corresponding eigenfunctions. As a result, the convergence rate of high-frequency components of \(p(x)\) is low [48; 49]. This learning tendency is a significant challenge when dealing with multi-scale problems like localized acoustic sources featuring space-dependent high-frequency solution features. The challenge is addressed by embedding multiple Fourier features into the network to learn features across scales and to mitigate the spectral bias behavior. Firstly, the input coordinates are transformed using multiple Fourier feature mappings. The forward pass of \(M\) Fourier features with \(m=1,\ldots,M\) is defined by  
\[\text{Input layer: }\mathcal{N}^{0}(\mathbf{x})=\mathbf{x}\in\mathbb{R}^{d_{ \text{in}}}\] (21) \[\text{Fourier layer: }\gamma^{(m)}(\mathbf{x})=\begin{bmatrix}\cos(2\pi B ^{(m)}x)\\ \sin(2\pi B^{(m)}x)\end{bmatrix}\in\mathbb{R}^{2d_{\text{in}}\times M}\] \[\text{Firstst hidden layer: }\sigma\Big{(}\mathcal{N}^{1,(m)}(\mathbf{x})\Big{)}= \sigma\Big{(}\mathbf{W}^{1}\gamma^{(m)}(\mathbf{x})+\mathbf{b}^{1}\Big{)}\in\mathbb{R }^{N_{1}},\] \[\text{Hidden layers: }\sigma\Big{(}\mathcal{N}^{\ell,(m)}(\mathbf{x})\Big{)}= \sigma\Big{(}\mathbf{W}^{\ell}\sigma\Big{(}\mathcal{N}^{\ell-1,(m)}(\mathbf{x}) \Big{)}+\mathbf{b}^{\ell}\Big{)}\in\mathbb{R}^{N_{\ell}},\quad\text{ for }\quad 2\leq\ell\leq L-1,\] \[\text{Output layer: }\hat{\rho}(\mathbf{x})=\mathbf{W}^{L}\cdot\Big{[} \sigma\Big{(}\mathcal{N}^{L_{\text{(}1})}\Big{)},\sigma\Big{(}\mathcal{N}^{L _{\text{(}2})}\Big{)},\ldots,\sigma\Big{(}\mathcal{N}^{L_{\text{(}M)}}\Big{)} \Big{]}+\mathbf{b}^{L}\in\mathbb{R}^{d_{\text{out}}}\,;\] \[\text{where }B^{(m)}\text{ represents the preselected matrices with entries randomly sampled from a Gaussian distribution }N(0,\hat{s}_{m}^{2})\text{. The }\hat{s}_{m}\text{ values are chosen because they are additional  
Figure 3: Schematic representation of a neuron-wise LAAF-PINN with slope recovery that learns the mapping \(x\to p(x)\).  
hyperparameters that may be optimized, and typical values should be in the range of the expected frequency (e.g., \(k=1/\lambda\)) [28]. Overall, no additional training parameters \(\theta\) are introduced by this architecture compared to a standard fully connected neural network.
# Implementation of Explosion Safety Regulations in Design of a Mobile Robot for Coal Mines
## 4 Implementation of IEC 60079 for TeleRescuer
### Classification  
IEC 60079 classifies devices into two groups:  
Figure 5: TeleRescuer—cameras and lights on the sensory head.  
* Group I--equipment intended for use in underground mines and parts of surface installations of such mines, liable to be endangered by the explosion of methane and/or coal dust. Group I is further divided into Categories M1 and M2.
* Group II--equipment intended for use in other industries exposed to explosive atmospheres (further divided into Categories 1, 2, and 3).  
The above-mentioned categories of devices define the required levels of security, namely in the underground mining area:  
* Category M1--equipment designed so that it can safely operate in the presence of an explosive atmosphere. This is achieved through the use of integrated explosion protection measures selected, so that in the event of a failure of one of them, at least the second measure provides an adequate level of protection (two protections based on different principles); or, in case of two independent failures, an adequate level of protection is still assured (triple protection).
* Category M2--equipment designed to ensure a high level of safety under normal conditions, and in the case of severe operating conditions, resulting e.g., due to careless handling of the device or changing of environmental conditions.  
One of the key requirements for the TeleRescuer system was that it should be approved for Group I, Category M1. This is the highest possible level and that poses a big challenge for the implementation of the robot.
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 3 Service Composition
### Service Composition's Research Landscape
#### 3.1.3 The Efficiency of the Composition Process  
The heart of the service composition is the selection and scheduling of services to match the description of the service that we would like to compose. This description should provide the hints needed to form the composite service schema. Several approaches and technologies are possible. For this purpose, many standards have been proposed to explicitly define the description of a composition pattern, namely BPEL4 WS, BPML, and WSCI. Less explicit tools, based either on textual or graphical interfaces, have been proposed to allow for the definition of the composed service logic. For the automatic approach, the logic of composition is formed based on information taken from the user context.
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 6 Case Studies and Results
### Reliability Assessment Considering Worst-Case Attack
#### 6.1.2 The Composite Reliability Assessment  
Based on the bi-level model, the load shedding caused by a worst-case attack can be quantified. In addition, the system states, including the different combinations of single events, were evaluated by the reliability indices as listed in Table 4. \(\mathrm{S_{F}}\), \(\mathrm{S_{A}}\), and \(\mathrm{S_{R}}\) are the single events of which the system states only contain the physical failure of components, cyber-attack and WPRs, respectively. The defense capability of the system was normal and the penetration rate of the wind power was 15%. The result shows that the EENS caused by a worst-case attack was over 1000 MWh/yr, which made the EENS twice the normal rate. Furthermore, when the system was involved with cyber-attack and WPRs, the system reliability deteriorated greatly.  
Among the single events, the WPR had the greatest impact on the system reliability. The impact of WPR was close relative to the ramping rate and the available system capacity. The load shedding caused by WPRs with different load demand is presented in Figure 8. When the ramping rate was higher than 380 MWh/h, during the peak load, the system available power capacity became insufficient. If the ramping rate exceeded 760 MWh/h, all the wind farms were off-grid, as the dangerous ramping rate had reached the limitation and triggered the speed relay. The highest load shedding was 445 MW with a penetration rate of 15%. The EENS caused by cyber-attacks and WPRs are compared and listed in Figure 9. The growth rate of EENS caused by WPRs was slower than cyber-attacks because the system available capacity changed little as the penetration rate grew.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Event** & **LOLP** & **EENS [MWh/yr]** \\ \hline
1 & \(\mathrm{S_{F}}\) & \(4.09\times 10^{-3}\) & \(1.68\times 10^{+3}\) \\
2 & \(\mathrm{S_{A}}\) & \(7.99\times 10^{-3}\) & \(1.11\times 10^{+3}\) \\
3 & \(\mathrm{S_{R}}\) & \(3.20\times 10^{-3}\) & \(4.04\times 10^{+3}\) \\
4 & \(\mathrm{S_{R}}\)\(\cup\)\(\mathrm{S_{A}}\) & \(3.28\times 10^{-3}\) & \(6.08\times 10^{+3}\) \\
5 & \(\mathrm{S_{A}}\)\(\cup\)\(\mathrm{S_{F}}\) & \(4.72\times 10^{-3}\) & \(3.50\times 10^{+3}\) \\
6 & \(\mathrm{S_{R}}\)\(\cup\)\(\mathrm{S_{F}}\) & \(5.57\times 10^{-3}\) & \(9.02\times 10^{+3}\) \\
7 & \(\mathrm{S_{R}}\)\(\cup\)\(\mathrm{S_{A}}\)\(\cup\)\(\mathrm{S_{F}}\) & \(9.02\times 10^{-3}\) & \(1.17\times 10^{+4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reliability result of different system states.  
Figure 7: Reliability performances of systems with different defense capabilities.