esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Strongest AoA Components Estimation Algorithm
### The Design of Quantization Tiresholds
```
0: Pre-designed analog beamforming vectors for the BS, \(f_{\text{RF},k}\), \(k\in\{1,\dots,N\}\)
1: Initialize \(n=1,k_{i,n}=0\), the number of detection repetition \(L=1\), and trigger condition \(\varpi=1\)
2:while\(\varpi==1\)do
3: Search for \(M+1\) beam space
4:for\(i=1:M+1\)
5: Receiving signal with \(f_{\text{RF},i}\), for the \(i\)-th direction:
6:\(y_{k,i}=f_{\text{RF},k}^{H}y_{\text{IR}}\mathcal{Q}_{\text{users}}(s_{k})+f_{ \text{RF},k}^{H}\mathbf{z}\)
7: Received signal after 1-bit quantization:
8:\(|\widehat{y}_{k,i}|=\text{sign}(|y_{k,i}|-\tau)\)
9:if\(|y_{k,i}|-\tau\geqslant 0\)
10:\(k_{i,n}=k_{i,n}+1\)
11:end
12: Calculation detection probability:
13:\(\widehat{p}_{i,n}=k_{i,n}/L\)
14:end
15: Sort the detection probability outputs \([\widehat{p}_{1,n},\cdots,\widehat{p}_{M+1,n}]\) in descending order and obtain the ordered detection probability output vector as \(\left[\widehat{p}_{(1),n},\cdots,\widehat{p}_{(M+1),n}\right]\), where \(\widehat{p}_{(i),n}\geqslant\widehat{p}_{(i+1),n}\), \(\forall i\)
16: Calculate the Wald-type confidence interval by Equation (9)
17: Reward function:
18: Case1: Low SNR regimes
19:if\(\widehat{p}_{(i),n}>\widehat{p}_{(i+1),n}\)
20:\(\lambda=\lambda+1\)
21:if\(\widehat{p}_{(i),n}-\zeta_{(i),n}\geqslant\widehat{p}_{(i+1),n}+\zeta_{(i+1 ),n}\)
22:\(\lambda=\lambda+1\)
23:end
24:end
25: Case2: Medium-to-high SNR regimes
26:if\(\widehat{p}_{(i),n}>\widehat{p}_{(i+1),n}\)
27:\(\lambda=\lambda+1\)
28:end
29: Stop condition:
30:if\(\lambda>T\)
31:\(\varpi=0\)
32:end
33: Repetition calculation:
34:\(L=L+1\)
35:endwhile
```  
**Algorithm 1** Beam Searching for Strongest AoA Components Estimation
# Data Privacy and Ethical Considerations in Database Management
## 5 Results and Analysis
### Data Encryption  
In pgAdmin4, we used the Advanced Encryption Standard (AES) algorithm with the help of the _pgcrypto_ extension in PostgreSQL, ensuring that only authorized users with access to the private key could view or modify the information.  
We executed the query \"SELECT * FROM PatientRecord ORDER BY id\" to analyze the database's performance and the response time in milliseconds. Before data encryption, we observed a response time of less than 1 s, as shown in Figure 5:  
After encrypting the data in the columns referring to the patient's medical condition and medication, we can observe a response time of approximately 11 s, as illustrated in Figure 6:  
Data encryption resulted in a considerable increase in database response time, indicating a compromise between security and performance that must be carefully evaluated to ensure the appropriate balance between the two aspects. This suggests that encryption may harm system performance, although it provides greater security in protecting sensitive data, as seen in Figure 7.  
Figure 5: Query execution before encryption.  
Figure 6: Query execution after encryption.  
Data encryption, although essential for protecting privacy and data security, resulted in a loss in the performance of our database queries. This was because the additional time required to encrypt and decrypt information increased the system's response time, making it less efficient. While security is paramount, it is important to consider performance and find a balance between these opposing needs.  
Ethical Implications of Data Encryption  
The implementation of data encryption raises several ethical considerations:  
* Security vs. Accessibility: While encryption enhances data security, it may limit data accessibility for legitimate uses, potentially impacting patient care or research [19].
* Performance Trade-offs: The significant increase in query execution time could affect the efficiency of healthcare operations, potentially impacting patient care. This presents an ethical dilemma between data security and operational efficiency [45].
* Key Management: The ethical responsibility of managing encryption keys is crucial, as loss of keys could result in permanent data loss [35].
* Transparency: It is important to be transparent about encryption practices to maintain trust with data subjects and comply with regulations [74].
###### Abstract  
We propose a novel authoring and viewing system for generating multiple experiences with a single 360\({}^{\circ}\) video and efficiently transferring these experiences to the user. An immersive video contains much more interesting information within the 360\({}^{\circ}\) environment than normal videos. There can be multiple interesting areas within a 360\({}^{\circ}\) frame at the same time. Due to the narrow field of view in virtual reality head-mounted displays, a user can only view a limited area of a 360\({}^{\circ}\) video. Hence, our system is aimed at generating multiple experiences based on interesting information in different regions of a 360\({}^{\circ}\) video and efficient transferring of these experiences to prospective users. The proposed system generates experiences by using two approaches: (1) Recording of the user's experience when the user watches a panoramic video using a virtual reality head-mounted display, and (2) tracking of an arbitrary interesting object in a 360\({}^{\circ}\) video selected by the user. For tracking of an arbitrary interesting object, we have developed a pipeline around an existing simple object tracker to adapt it for 360\({}^{\circ}\) videos. This tracking algorithm was performed in real time on a CPU with high precision. Moreover, to the best of our knowledge, there is no such existing system that can generate a variety of different experiences from a single 360\({}^{\circ}\) video and enable the viewer to watch one 360\({}^{\circ}\) visual content from various interesting perspectives in immersive virtual reality. Furthermore, we have provided an adaptive focus assistance technique for efficient transferring of the generated experiences to other users in virtual reality. In this study, technical evaluation of the system along with a detailed user study has been performed to assess the system's application. Findings from evaluation of the system showed that a single 360\({}^{\circ}\) multimedia content has the capability of generating multiple experiences and transfers among users. Moreover, sharing of the 360\({}^{\circ}\) experiences enabled viewers to watch multiple interesting contents with less effort.  
authoring system; 360\({}^{\circ}\) video experiences; object tracking; virtual reality; experience transfer; focus assistance; visualization; human-computer interaction (HCI)
# Enhanced Proton Acceleration from Laser Interaction with a Tailored Nanowire Target
###### Abstract  
Target normal sheath field acceleration via laser interaction with structured solid targets has been widely studied for its potential use in a wide range of applications. Here, a novel nanowire target with a corrugated front surface is proposed to improve the proton acceleration by a target normal sheath field. Two-dimensional particle-in-cell simulations demonstrated that with the existence of the corrugated surface, the cut-off energy of accelerated protons nearly doubles compared to the planar nanowire target. When interacting with the corrugated surface, the incident laser pulse is reflected multiple times, focused and reinforced in each cavity near the front surface, which leads to suppression of the reflectivity and an improvement in the absorption rate. Electrons are heated more efficiently and the sheath field at the target rear side is naturally enhanced. To further investigate the performance of this novel target, a series of simulations with various laser intensities and target sizes were also carried out. This simple target design may provide insights for experiments in the future and should arouse interest because of its wide application.  
target normal sheath acceleration; nano-structured target; laser plasma interaction +
Footnote †: journal: Applied Physics Letters A  
Article
## 5 Cryptosystems Choice
### Cryptosystem for Vendor'S Key-Pair
#### 5.1.1 RSA Signatures  
Given an RSA [21] private key, \(d\), and the corresponding public one \((N,e)\), an RSA digital signature over \(M\) is computed from secret key \(d\) as \(S=M^{d}\pmod{N}\). The resulting \(\{M,S\}\) digest-signature tuple is verified by checking whether \(M\) equals \(S^{e}\pmod{N}\).  
RSA blind signatures [1] can be issued through the following protocol:  
[PERSON] chooses a random \(R\in\mathbb{Z}_{N}\) and computes \(\overline{M}=M\cdot R^{e}\pmod{N}\) and sends \(\overline{M}\) to [PERSON] (operator \(\cdot\) denotes the integer modular multiplication).  
[PERSON] computes \(\overline{S}=\overline{M}^{d}\pmod{N}\) and sends \(\overline{S}\) to [PERSON].  
[PERSON] computes \(S=\overline{S}\cdot R^{-1}\pmod{N}\) obtaining signature \(S\) on \(M\).  
RSA digest-signature tuples can be simulated by taking a random \(S\in\mathbb{Z}_{N}\) and then computing \(M=S^{e}\pmod{N}\). In a typical signature, \(M\) is the hash digest of the piece of data to be signed. Hence, obtaining a message signed by simulation further requires inversion of such hash function which is unfeasible.  
We can enable RSA signatures with the construction presented in Section 3.3. By signing the \(Y\) component of an OAEP-encoded message, we allow simulated signatures over pseudo-random pieces of data.
## 6 Visualization, Limitations and Future Directions
However, the platform is capable of following a partial large-scale processing strategy by evaluating tiles with a lower stride (e.g., \(128\times 128\) pixels), and subsequently overlap the central parts of adjacent images, resulting in predictions with no obvious division boundaries. Figure 9: Per-pixel predictions of the best performing hybrid segmentation model covering an area of 5.24 km\({}^{2}\) (160 tiles). In the map scheme’s representation, bright red means a higher probability of the pixel belonging a road while transparent is used for pixels labelled with “No road”. Another solution to this challenge would be working with larger tiles to provide more semantic context (e.g., training with tiles of \(512\times 512\) or \(1024\times 1024\) pixels). The goal of this study was not to create of the best possible road maps, but to demonstrate that hybrid segmentation models implemented on a flexible web platform component can improve the road surface area's extraction from remotely sensed data. We have seen that a solution based on a combination of frameworks can be more efficient for large-scale extraction operations when compared to single end-to-end deep learning models.
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 4 Results  
The GIMAD system was tested by capturing real GNSS signals and combining them with a generated RFI with an HP Agile signal generator. Testing included both injected (I-SAM, O-SAM, and MAM) and radiated (C-SAM only) RFI for a GIMAD station. The full capabilities of the system will be available after deploying more stations. In the following subsections, the key results for I-SAM, O-SAM, MAM, and C-SAM modules are presented hereafter.
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 3 Methodology  
### System Architecture and Block Diagram  
The overall block diagram including system architecture is presented in Figure 1. The study begins by defining the coverage area, user density, and quality of service requirements for the proposed network. It collects geographical and user-related data, including terrain maps, population distribution statistics, and details of existing infrastructure. A network model is built to depict the coverage area accurately, considering obstacles, propagation models, and signal strength requirements. Network model is a detailed framework that combines system modeling and path loss calculations with metaheuristic algorithms (PSO, GWO, GA, and SA) to optimize base station locations and configurations. Four metaheuristic algorithms--PSO, GWO, GA, and SA--are utilized. PSO and GWO are population-based, SA uses a probabilistic approach, and the GA employs evolutionary principles.  
System modeling includes path loss models for link budget design, covering calculations for uplink and downlink transmission. After calculating the Radius from the Link budget analysis, constraints are established, focusing on maximizing data rates and coverage while minimizing the cost of deploying multi-tier remote radio units. These constraints are integrated into the chosen algorithm to optimize network design. Performance evaluation involves metrics like coverage, signal quality, and data transfer rates. Comparative analysis of the algorithms determines the most efficient solution in terms of convergence speed and solution quality. The algorithm best meeting the study's objectives and requirements is selected as the optimal solution.
# PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model
## 3 Methods
### Handcrafted Features  
This section extracts the trend coefficients of the mean and linear regression through mathematical statistics. The introduction of these handcrafted features aims to provide more intuitive and interpretable information through mathematical statistics, enhancing the model's capability in learning the original sequence shapes. In the experimental process, we applied the mean convolution kernel method to process the dataset containing multidimensional features such as fuel cell voltage, current, power, and total battery current and voltage. Specifically, we treated the multidimensional time series data as a combination of several univariate time series. To ensure the integrity of the boundary data, we performed padding on each time series by repeating the first and last values to extend the sequence length. Based on this, we applied a mean convolution kernel of size 3 to the time series to extract feature information reflecting the trend in the data. Figure 6 illustrates the mean of the original data. The mean serves as a measure of the overall level of the data, representing the average level of the data over a given time period. Through the introduction of this feature, the model is better able to grasp the overall performance of the system, capturing the overall trends and fluctuations in the data. This is crucial for predicting the RUL of a vehicle PEMFC, as the overall operation status of the system directly relates to changes in its RUL. During model training and inference, as shown in Figure 3, the decoder output is directly added to the handcrafted features before being fed into the RUL prediction module. This module consists of a single linear layer that ultimately generates the RUL prediction results.  
Figure 6: Original data and mean.  
In Equation (20), \(L_{a}\big{(}Y,\hat{Y}\big{)}\) employs autocorrelation and cross-correlation functions to assess the similarity in shape between the actual values and the predicted values, as shown in Equation (24).  
\[L_{a}\big{(}Y,\hat{Y}\big{)}=\big{\|}\mathcal{R}(Y,Y)-\mathcal{R}\big{(}Y,\hat{ Y}\big{)}\big{\|}_{p} \tag{24}\]  
In this equation, \(R(Y,Y)\) represents the autocorrelation function of the actual values, measuring the similarity within the actual values themselves, while \(R(Y,\hat{Y})\) represents the cross-correlation function between the actual values and the predicted values, measuring the similarity between the predicted and actual values.
## 4 Discussions, Conclusions, and Future Directions
### Discussions  
#### 4.1.1 Discussion: Channel Measurements  
In the corridor setting, the variability of root mean square (RMS) delay spreads was evident across different testing times, ranging from 0.12 ns to 0.67 ns. This variation is attributed to fluctuations in the channel characteristics, particularly noticeable during afternoon data collection sessions. Furthermore, the movement of individuals in the corridor introduces additional variability, impacting the RMS delay spread.  
Conversely, in the meeting room environment, the RMS delay spread values remained relatively consistent, ranging from 0.14 ns to 0.63 ns. The absence of dynamic elements in the room minimized the fluctuations in the measurement results.  
However, it is important to acknowledge that the corridor environment featured metal rods positioned parallel to the testing path. This could potentially influence the attenuation of the direct propagation path compared to other multipath components, thus affecting the RMS delay spread values.  
The reasons for the observed variations in the RMS delay spread are multifaceted. In the corridor environment, fluctuations in the channel characteristics were noted due to factors such as the time of day and the presence of moving individuals, introducing variability into the channel response and leading to changes in the RMS delay spread values. In contrast, the controlled environment of the meeting room, with minimal movement and disturbances, resulted in more stable RMS delay spread measurements. Additionally, the presence of metal rods along the corridor potentially altered the propagation characteristics, affecting the observed RMS delay spread values.
## 4 Simulation Verification and Analysis  
To ascertain the efficacy of the control strategy introduced in this paper, a simulation model of the grid-connected wind power system utilizing MMC-HVDC technology was developed on the PSCAD/EMTDC platform, as illustrated in Figure 1. The model parameterswere the same as those in Table 1. Firstly, the validity of the wind farm grid-connected system model via MMC-HVDC under conventional control strategies was confirmed through steady-state simulation. Subsequently, the enhanced inertial response strategy proposed in this study underwent comprehensive simulation verification and comparative analysis, demonstrating its superior performance in inertial response effectiveness. Further analysis compared the fault characteristics between traditional and coordinated control strategies during transient grid faults, providing valuable simulation references for investigating transient behaviors in wind farms integrated with MMC-HVDC transmission systems.  
This paper focuses on the relationship between wind farms and the MMC-HVDC system rather than the interaction between wind farms. Therefore, it was assumed that the wind speed distribution across the farm was uniform, the turbine control parameters were homogeneous, and electrical characteristics were aggregated. The wind farm adopted a single-machine equivalent model. In the model, the equivalent simulation method was used to construct a wind farm with a rated active power of 280 MW, which was composed of 140 wind turbines with a rated power of 2 MW and an AC combiner bus voltage of 380 kV. The power grid was simulated by an ideal voltage source and an inductor series circuit. In the simulation implementation, in order to simulate the frequency fluctuation caused by a load increase or other reasons, the frequency change in the power grid was directly set.
## 3 Results
### Correlations  
In order to gain insight into the therapeutic qualities of the method studied, a two-tailed Pearson correlation coefficient was computed to assess the linear relationship between PSS-10 results, which indicate the stress level of the participant, and the following variables from the research data: grade given, differences of CSOM values before and after the intervention, and differences of HRV data variables before and after the intervention.  
Regarding PSS-10 and the grade the participant gave, there was an indication to relatively strong negative correlation between the two variables, _r(11) = \(-\)0.549_, _p = 0.052_, which was on the verge of being statistically significant at _p < 0.05_. However, neither CSOM values nor HRV data revealed significant correlation between PSS-10 and the measured effect, with _AnxietyChange r(11) = \(-\)0.172_, _p = 0.575_; _TensionChange r(11) = \(-\)0.364_, _p = 0.221_; _RelaxationChange r(11) = 0.294_, _p = 0.330_; _ResourcesChange r(11) = 0.252_, _p = 0.407_; _SDNNChange r(10) = 0.331_, _p = 0.293_; _RMSSDChange r(10) = 0.274_, _p = 0.390_; and _SIChange r(10) = \(-\)0.402_,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**HRV Variable** & \begin{tabular}{c} **Baseline** \\ **Mean Value (SD)** \\ \end{tabular} & \
\begin{tabular}{c} **VR Intervention** \\ **Mean Value (SD)** \\ \end{tabular} &
\begin{tabular}{c} **Post-Intervention** \\ **Mean Value (SD)** \\ \end{tabular} \\ \hline HR & 74.83 (\(\pm\)9.49) & 72.33 (\(\pm\)8.46) & 72.67 (\(\pm\)8.57) \\ SDNN & 33.88 (\(\pm\)11.51) & 26.58 (\(\pm\)9.15) & 40.34 (\(\pm\)11.53) \\ RMSSD & 26.12 (\(\pm\)10.09) & 23.33 (\(\pm\)9.03) & 29.50 (\(\pm\)9.54) \\ SI & 13.21 (\(\pm\)3.34) & 16.39 (\(\pm\)6.14) & 11.16 (\(\pm\)3.25) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean values of HR, SDNN, RMSSD, and Stress Index (SI) from 12 participants in three five-minute measurements, recorded during baseline measurement, while using Binaural Odyssey, and after the virtual reality intervention.  
\(p=0.195\). In other words, while it seems like more stressed participants tended to give lower grades to the application, no correlation between initial stress level and measured subjective or objective therapeutic effects of the experience could be found. This offers some encouragement for the idea that the method studied could have potential in therapeutic use, while higher stress levels could theoretically explain a more negative mindset when reviewing the experience, thus resulting in a lower grade.
# The Use of Underwater Gliders as Acoustic Sensing Platforms
## 2 The Petrel II Glider and the Sea Trial  
### The Petrel II Glider and the Self-Contained Hydrophones  
The Petrel II glider developed by Tianjin University is a buoyancy-driven underwater vehicle. Without a propeller system, the glider travels by changing its buoyancy and the barycenter [17; 18; 19; 20]. The maximum operational depth of Petrel II is 1500 m, and it can navigate by GPS, pressure sensor, compass, and altimeter. The length of the glider is about 2 m and has a weight of 70 kg in the air. The glider communicates through wireless, iridium, and RF modem. The typical cruising speed is 0.5 m/s.  
Due to it having no propeller system, the self-noise of the glider is quite low, which makes the Petrel II glider a good platform from which to study underwater acoustics. Figure 1 shows the Petrel II glider in an August 2018 experiment. Two self-contained hydrophones (SCHs) were mounted on the glider to continuously collect the underwater acoustic signal, one at the front and another at the tail. The sample rate of the SCHs is 80 kHz using 24-bit quantization. The SCHs support multiple storage cards of different specifications and can work for several months using lithium batteries.
# A Sub-50 nW Implantable Body Temperature Sensor and Its Application to a Rat
## 5 Discussion
To achieve a smaller size, the battery size should be reduced and complemented with features such as wireless power transfer or energy harvesting. This paper demonstrates the functionality of the sensor in short-term in vivo experiments. Although only results for a single rat are presented in this paper, validation in multiple individuals and larger animals will be needed. Future studies in multiple animals are needed to monitor and improve long-term biocompatibility and durability in vivo. Once long-term reliability is confirmed in these animals, the ultimate goal will be to apply it to humans. In addition to the main function of measuring nerve signals, it is said that it can additionally perform a temperature measurement function as an application example. Specifically, it can be applied to fields such as pacemakers, deep brain stimulation, and pain relief nerve stimulators. Or it can be used as a standalone device for measuring deep body temperature. For cancer treatment, heat therapy is also performed to kill cancer cells in a specific area by applying high heat [26; 27; 28]. When performing this treatment, the temperature must be high enough to kill cancer cells and low enough not to kill normal cells. Therefore, it is necessary to monitor local body temperature in real time when performing heat therapy.