esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Ethical-Regulatory Validation During Laboratory Testing  
The NeuroPredict platform effectively demonstrated the incorporation of ethical and regulatory safeguards in all simulated use scenarios during laboratory testing. Each stage of the ethical-regulatory pipeline, which was established during system development,was operationalized in practice and incorporated into the platform's everyday operations, according to the validation process. To ensure that data streams contained only anonymized session-level identifiers, the platform implemented encryption and pseudonymization mechanisms across all test sessions. Through internal audit logs and network monitoring tools, secure data transmission via HTTPS (TLS 1.3) [69] and encrypted data storage based on AES-256 standards [68] were verified, ensuring that end-to-end data protection measures were operating as planned.  
Only authorized development experts could see sensitive metadata thanks to role-based access controls that were properly tested and implemented. This verified that the platform could maintain data confidentiality in the controlled testing environment while limiting access in accordance with GDPR regulations. Digitally signed forms for fictitious patients and clinicians were used to validate the consent management workflow concurrently. Each session was programmatically associated with these forms, and pertinent consent metadata, such as timestamps, document versions, and related policy references, were captured in the system logs. This confirmed that the platform can support compliance with ethical review processes and enforce traceable and verifiable consent documentation, as mandated by GDPR Article 7.  
The effective application of these safeguards in a controlled setting shows that adherence to ethical and legal requirements is ingrained in the NeuroPredict system and is not an afterthought. In addition to meeting future certification requirements under the GDPR, MDR, and the upcoming AI Act, these results offer a solid basis for transitioning to semi-clinical trials.
## 2 Methodology
### Ultrasonic Methane Detection Principle in a Sewer Environment  
Sewage mainly contains methane, carbon monoxide, hydrogen sulfide, ammonia, and carbon dioxide [22]. Most sever explosions are methane explosions. Carbon monoxide and hydrogen sulfide are toxic gases and occur in concentrations far lower than those of methane and carbon dioxide. Meanwhile, ammonia is extremely soluble in water, and has a concentration of approximately zero. Therefore, the effects of carbon monoxide, hydrogen sulfide, and ammonia can be ignored. However, the content of carbon dioxide in mixed gases is relatively high, and the sound velocity \(c\) will decrease by 0.89 m/s for each 1% increase in its concentration. Therefore, the impact of carbon dioxide cannot be ignored. In addition, if the concentration of carbon dioxide is at a high level, it can lead to suffocation and death, so the real-time detection of carbon dioxide is necessary.  
Humidity varies over time due to the influence of weather, and the content of water vapor directly affects the molar mass of a mixed gas, which, in turn, affects the speed of sound. Thus, the influence of humidity should be considered, and water vapor should be treated as a part of the mixed gas. In this study, we used a humidity sensor to monitor the environmental humidity of a Sewer in real time, and we converted the humidity into the proportion of water vapor in the mixed gas.  
According to the above analysis, a Sewer can be simplified as a mixture of methane, carbon dioxide, and water vapor, with air as the background gas. According to the general equation of sound velocity, the relationship among sound velocity \(c_{\rm mix}\), the concentration of methane \(x\), the concentration of carbon dioxide \(y\), and the concentration of water vapor \(z\) can be expressed as  
\[c_{\rm mix}=\left(\frac{\gamma RT}{xM_{\rm CH_{4}}+yM_{\rm CO2}+zM_{\rm H_{2}O }+(1-x-y-z)M_{\rm air}}\right)^{0.5} \tag{3}\]
## 3 Simulation Results and Discussion
### Reconfiguration with DGs
#### 3.2.2 Standard IEEE 69-Bus Distribution System  
This standard test system is also proposed to have the three distributed generations in the systems which are at 11, 18, and 61 with the sizing as provided in Table 5. We observe that the voltage profile before reconfiguration (i.e., 0.9793) is higher than reconfiguration (i.e., 0.9690 pu). However, the power loss is dramatically reduced to 39.64, which equals to 42.88 % reduction compared to the base case as seen in the table. In addition, the MSSOE is computed fastest compared to SSOE, i.e., it is 0.1291 s faster. Figure 10 shows the voltage profile before and after the reconfiguration with integrated distributed generations.  
Figure 6: Voltage profile of IEEE 69-bus before and after the reconfiguration without DGs.
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
## 4 Results and Discussion  
For the sake of analyzing the orbit determination accuracy of the HY2D satellite, we selected spaceborne BDS data from 6 July to 19 July, and GPS data from 20 July to 10 August for determination orbit. Then, we analyzed the residual variation of the POD processing for the HY2D satellite, presented the overlap orbit precision and compared the calculated orbits with precise orbit products provided by CNES, and showed the SLR validation results.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 6 Numerical Analysis
### Minimum-Cost Decision Strategy
Similarly, in Figure 8c,d, we can see that more tasks are offloaded to the MEC server compared with that in Figure 8a. This is because, as \(\alpha_{\text{MEC}}\) getting smaller, more of the tasks are offloaded to the MEC server for saving the cost. The proposed algorithm in this paper considers the tasks offloading strategy with minimum cost. As \(\alpha_{\text{MEC}}\) becomes smaller, the cost of offloading tasks to the MEC server decreases gradually, so that more tasks will be offloaded to the MEC server.
## 2 Methods
### Preprocessing
#### 2.1.4 Continuous Wavelet Transforms  
CWT stands as widely adopted approach for analyzing signals in the time and frequency domain [3; 16; 17]. Initially introduced by [PERSON] and [PERSON] in 1987, CWT involves the dissection of a signal into wavelets across various components. This decomposition enables the examination of diverse frequency scales embedded within the data. Unlike traditional frequency analysis methods, the CWT affords a comprehensive analysis. Consequently, it provides researchers with a time-frequency representation of data, facilitating a deeper comprehension of their intricacies and temporal dynamics. Equation (6) presents the formulation for the CWT.  
\[w_{s}(a,\tau)=a^{\frac{1}{2}}\int s(t)\phi(\frac{t-\tau}{a})\mathrm{d}t \tag{6}\]  
where the scaling of the wavelet transform is denoted as \(a\), while the input signal is represented as \(s(t)\), the wavelet basis function is denoted as \(\phi\), and the time offset is denoted as \(\tau\).  
Among the array of five wavelet basis functions commonly employed in contemporary signal processing, the Morlet wavelet holds a prominent position [3]. We have selected the Morlet wavelet as the basis wavelet for our study. Its expression in the time domain is given in Equation (7).  
\[\phi(t)=(\frac{2}{\pi T^{2}})^{\frac{1}{4}}exp(-\frac{t^{2}}{T^{2}}+iw_{c}t) \tag{7}\]  
The expression of frequency is shown in Equation (8).  
\[\phi(w)=(\frac{T^{2}}{2\pi})^{\frac{1}{4}}exp(-\frac{(w-w_{c})^{2}}{4T^{2}}) \tag{8}\]  
From the above equation, \(T\) and \(w_{c}\) are determined. The resulting time-frequency domain image, generated from the CWT mapping process, is employed as the input for our model.
## 3 Results  
The initial automated search of the databases yielded 3097 titles from PubMed, 4409 titles from ScienceDirect and 97 titles from GoogleScholar. After thoroughly reviewing all abstracts, we concluded that 54 PubMed, 4 ScienceDirect and 4 GoogleScholar articles were of relevance. Among the 62 identified articles, 5 were indexed in more than one database and had to be excluded, leading to a total of 57 original studies as candidates for inclusion in the review. Furthermore, we searched all references lists from these 57 articles in order to detect any unidentified literature; this yielded 4 additional articles, raising the total number of potentially eligible manuscripts to 61. Subsequently in the selection procedure, the full-texts of the 61 articles were carefully and critically evaluated according to the review's inclusion criteria. This process led to the final selection of 11 studies which fulfilled inclusion criteria and entered the review. It is worthy of note that the 11 selected studies were published in journals with an Impact Factor ranging from 2 to 4 (except for [PERSON] et al. IF = 0.802), Figure 1 depicts a flow diagram analytically describing the process of article selection.
# Brain Activity Recognition Method Based on Attention-Based RNN Mode
## 3 The Proposed Method
### EEG Signal Preprocessing  
Primitive EEG samples are preprocessed for erasing DC offset. Different EEG acquisition devices will introduce constant noise components when recording signals. Thus, in the preprocessing stage, a constant DC offset should be subtracted from primitive signal E. Data normalization generates a significant impact on data processing at each unit and feature scale.Normalization is a linear operation, which makes EEG data more in line with independent and identical distribution conditions, and reduces the offset caused by internal corrugate shift, so that the input data of (0, 1) standard normal distribution is converted into slightly deviated (0, 1) standard normal distribution data. This operation can ensure that the input data basically maintain a normal state, and the input is nonlinear, which keeps the data away from the saturation region of the activation function and speeds up. Z-score normalization will be used in this paper. Preprocessed brain wave data E' is computed as below(1),  
\[E^{\prime}=\frac{(E-DC)-\mu}{\sigma} \tag{1}\]  
where \(DC\) is direct current noise offset, \(\mu\) suggests \(E\)-\(DC\) average, and \(\sigma\) represents standard deviation of \(E\)-\(DC\).  
The EEG signal belonging to the delta band (0.5-4 Hz) is the most stable and accurate for emotion recognition. In order to separate the signals in the delta band, we use a filter, in which the order is set to 3, the low cut-off is 0.5 Hz and the high cut-off is 4 Hz. All  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Explanation** \\ \hline E & EEG raw data \\ E\({}^{\prime}\) & Preprocessed EEG data \\ X\({}^{i}\) & Data in the \(i\)-th layer in attention-based RNN \\ I & The number of layers in attention-based RNN \\ N\({}^{i}\) & The number of dimensions of \(X^{i}\) \\ Y & The one-hot label of emotion \\ Y\({}^{\prime}\) & The predicted status form attention-based RNN \\ K & The number of categories of subject status \\ \(f(\bullet)\) & The linear function \\ C & The intermediate code \\ \(T(\bullet)\) & The output calculation procedure of LSTM cell \\ \(T^{\prime}(\bullet)\) & The final hidden state calculation procedure of LSTM cell \\ \(W_{a}^{\prime}\) & The unnormalized attention weights \\ \(W_{a}\) & The normalized attention weights \\ \(C_{a}\) & The attention-based intermediate code \\ \(X_{D}\) & The feature of deep learning \\ \(S_{D}\) & The final status of subject \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation.  
dimensions of the preprocessed data are sent to the band-pass filter in turn, and finally the decomposed delta mode is obtained \(\delta\).  
Since human consciousness is naturally related to the mental and physical state of each person, and the delta band contains the most stable EEG consciousness record, the features here refer to the data frequency corresponding to the EEG signal in the delta band under different reactions.
# A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7
## 3 Results and Discussion
### The Results of Comparing the Detection Performance of the Proposed Network with Other Networks
There are obvious differences between ripe and unripe pineapples, but the difference between ripe pineapples and semi-ripe pineapples was not obvious. If the model had poor differentiation ability for ripe pineapples and semi-ripe pineapples, the \(AP\) value of both would be reduced at the same time, resulting in the \(AP\) value of unripe pineapples being higher than that of ripe and semi-ripe pineapples. With a better feature extraction network, YOLOv5s could better distinguish ripe and semi-ripe pineapples to prevent the confusion between them and reduce the detection performance. Therefore, the detection \(AP\) of both ripe pineapple and semi-ripe pineapple was higher than that of unripe pineapple. Although the detection \(AP\) value of YOLOv7 for ripe pineapple was higher than that of YOLOv5s, its detection \(AP\) for semi-ripe pineapple was lower than its own detection \(AP\) for unripe pineapple. It shows that YOLOv7 had a relatively weak detection ability for semi-ripe pineapple. The improved model proposed in this paper achieved the best results on the three maturities for pineapple detection \(AP\), which were 94.4% for unripe pineapple, 95.5% for semi-ripe pineapple, and 97.5% for ripe pineapple. Among them, compared with the original YOLOv7, the biggest improvement was the detection \(AP\) for semi-ripe pineapple. This proves that the improved strategy in this paper is effective for improving the ability of the model to distinguish ripe pineapple and semi-ripe pineapple. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Maturity**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Ground Truth Count**} & \multicolumn{2}{c}{**Correctly Identified**} & \multicolumn{2}{c}{**Falsely Identified**} \\ \cline{4-7}  & & & **Amount** & **Rate (\%)** & **Amount** & **Rate (\%)** \\ \hline \multirow{3}{*}{Unripe} & YOLOv7 & 64 & 53 & 82.81 & 5 & 7.81 \\  & Ours & 64 & 57 & 89.06 & 2 & 3.12 \\  & YOLOv7 & 73 & 60 & 82.19 & 3 & 4.10 \\  & Ours & 73 & 66 & 90.41 & 1 & 1.37 \\  & YOLOv7 & 75 & 68 & 90.66 & 2 & 2.66 \\  & Ours & 75 & 69 & 92.00 & 1 & 1.33 \\ \hline \end{tabular}
\end{table} 
Table 5: The comparison results of the proposed model and YOLOv7 in detecting pineapple under different maturities. In addition, [PERSON] et al. [31] proposed a pineapple detection method based on Mobilenet-SSD, which could not distinguish the maturity of pineapple, [PERSON] et al. [32] proposed a pineapple detection method based on improved YOLOv3, which can only distinguish ripe and unripe pineapples, the method proposed in this paper can distinguish ripe, semi-ripe and unripe pineapples. In comparison, our model has richer maturity resolution, which will lead to more accurate detection results.
# An Experimental Investigation of Passive Jet Control Method on Bridge Tower Wake
###### Abstract  
In this study, we employed a four-hole cobra probe to measure the wake characteristics of a rec-tangular bridge tower model in a wind tunnel. The scale of the model was 1:30, and the Reynolds number varied from \(1.38\times 10^{5}\) to \(2.27\times 10^{5}\) by changing the yaw angle. A measurement plane with \(9\times 19\) measurement points was horizontally set at the middle height behind the model. The wake characteristics of the test model without control, i.e., the baseline case, was first tested in the yaw angle range from \(0^{\circ}\) to \(90^{\circ}\); then, four kinds of passive jet control cases were tested to study their control effects on the bridge tower wake. To evaluate the wake characteristics, three main aspects, i.e., mean velocity, turbulence intensity, and velocity frequency, were investigated. The meas-urement results indicate that the passive jet control method can achieve an effect in suppressing the turbulence of the wake but can slightly modify the mean velocity distribution. The dominant frequency distribution region was eliminated when the yaw angle was small but slightly expanded at a large angle. The differences between cases show a trend that the larger the suction coefficient is, the better the control effects are.  
passive jet control; tower wake characteristics; cobra probe +
Footnote †: journal: Applied sciences
## 4 Field Campaign  
To evaluate the calibration procedures, which define the performance of the radar, a field campaign for the radar was implemented by comparing its response with the response obtained with a pattern instrument, a Lidar (VAISALA CL31 Ceilometer, e.g., [21]), which provides range corrected attenuated backscatter profiles at 910 nm, with a resolution of 5 m and up to 7.6 km. The campaign took place in the coast of Papudo, Chile (Location of the experiment in GPS coordinates: 32\({}^{\circ}\)30\({}^{\prime}\)09.7\({}^{\prime\prime}\)S, 71\({}^{\circ}\)27\({}^{\prime}\)35.6\({}^{\prime\prime}\)W), on 20 January 2019. Figure 7 (Left) and (Right) shows the experiment setup and an aerial view of the experiment taken by the drone, respectively.  
Figure 8 shows the Lidar height profile of the back-scattered intensity (The Lidar back-scattered intensity is extracted from CL-VIEW, which is the Lidar vendor software.) and height profile of the radar reflectivity, from 11:07 a.m., until 11:41 a.m., local time. The Lidar and the radar, after the UAV based distance calibration, detect the cloud base at the same altitude of approximately 400 m (a.g.l).  
Figure 7: **(a)** the experimental setup for measuring clouds with the radar and Lidar; **(b)** image taken with a drone that shows the location of the experiment and in the top left corner you can see marine stratus clouds moving towards the sea. Close to the bottom right corner, the Radar and Lidar position (32\({}^{\circ}\)30\({}^{\prime}\)09.7\({}^{\prime\prime}\)S, 71\({}^{\circ}\)27\({}^{\prime}\)35.6\({}^{\prime\prime}\)W) is marked with a red circle.
# Improved Branch-and-Bound Antenna Selection Algorithm for Massive MIMO
## 2 Representative Methods in Antenna Selection  
Consider a MIMO system where the base station (BS) is equipped with \(N_{t}\) transmit antennas and the user terminal has \(N_{r}\) receive antennas. To optimize reception, a subset of \(M_{r}\) (\(M_{r}<N_{r}\)) antennas is selected from the \(N_{r}\) available antennas. The channel model is defined as:  
\[\mathbf{Y}=\mathbf{H}\mathbf{x}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{Y}\in\mathbb{C}^{N_{r}\times 1}\) is the received signal vector; \(\mathbf{H}\in\mathbb{C}^{N_{r}\times N_{t}}\) is the channel matrix; \(\mathbf{x}\in\mathbb{C}^{N_{t}\times 1}\) is the transmitted signal vector; \(\mathbf{n}\) is the additive complex Gaussian noise vector following \(\mathbf{n}\in\text{CN}\ (0,\ \mathbf{I}_{N_{r}})\).  
Antenna selection algorithms aim to choose \(M_{r}\) antennas from the available \(N_{r}\) antennas at the receive side to optimize system performance. In ideal independent and identically distributed (i.i.d.) Rayleigh fading channels, all antennas contribute approximately equally to system performance, resulting in limited antenna selection gains [23]. However, in practical propagation environments, significant asymmetry in channel gains arises due to factors such as large-scale fading, spatial correlation, and variations in antenna directivity [24; 25]. These channel variations have motivated the development of various antenna selection algorithms, which can be classified based on their underlying search methodologies.
# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
## 5 Experimental Setup  
### Dataset  
The used dataset consists of a set of recordings related to five machines working in a real-world construction site. Sounds have been recorded with a Zoom H1 digital recorder with a sampling frequency of 44,100 Hz and saved as wave files. The five classes considered in this work are related to three excavators (two compact excavators and a large one), a compactor, and a concrete mixer. For each class, 15 min of recordings are available. The recording of each piece of machinery has been made by placing the recorder about 5-6 m in front of the activities of interest, without any obstacle in the middle. The recorded sounds are related to normal construction site activities (i.e., excavation and concrete mixing work) performed in outdoor scenarios. The subset of sounds considered in this work is related to a single source at a time; segments where more than one piece of equipment is active at the same time have been preventively removed from the dataset. Additional details on the operating scenario can be found in [5].  
Figure 1: A picture of the proposed idea.  
Figure 2: A step-by-step flowchart of the proposed methodology.  
Each file has been split into chunks of 30 ms each. The entire dataset has been split into a training and a test set, with proportions of 75% and 25%, respectively. In addition, 10% of the training set has been devoted as the validation set to check the convergence performance during the training phase. Details of the used dataset, along with the number of chunks and related training/test splits, are reported in Table 1.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 6 Conclusions  
We have evaluated the system performance of the RIS-aided system by exploring NCCS and \(K_{G}\) distributions of channels. We compared system performance under the impact of IQI and the number of meta-surface \(N\) of a RIS. More importantly, we derived expressions of outage probability and ergodic capacity in closed-form. Simulation results showed that the significant improvement of outage probability can be obtained at high SNR and high meta-surface \(N\). Moreover, there should be a limited impact of IQI levels on such a system. Furthermore, both schemes can provide guidelines to employ the RIS system under the impact of IQI. Finally, in terms of ergodic capacity, although SNR at the source can be increased, the ergodic capacity faces an upper limit. Despite this limitation, the proposed system can work well once some parameters are controlled well, such as the transmit SNR, levels of IQI, and the number of RIS components. As a potential future work, other distributions of channel should be examined; for example, Rician fading can be taken into account, and more system metrics can also be explored.  
[PERSON] introduced the idea, contributed to developing some mathematical analysis; [PERSON]-T.L. performed the simulation experiments; [PERSON] contributed to prepare manuscript; [PERSON] and E.P. provided valuable comments. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by Van Lang University under Project 1000.  
This work has been partly supported by Van Lang University under Project 1000.  
The authors declare no conflict of interest.