esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discussion  
It can be seen from the above results that once the trained model is available, the PQDs can be easily classified with a high classification rate. The feature maps can be extracted from the disturbance signals automatically without human intervention. Besides, the novelties of this paper are listed as follows:  
1. The PQD classification can transform a complicated 1D signal processing problem into a simpler 2D space image classification problem. A CNN model-based method was established to achieve this. This idea may be applied to similar research fields.
2. The 2D images transformed from 1D voltage disturbance signals are in grayscale which has only one color channel. That is, the input data used in this paper is much simpler than traditional image classification methods, which use color graphs with three color channels.
3. A high classification rate can be obtained with a more succinct CNN model (three-level) by the proposed method.
4. An average pooling layer is used in the CNN model instead of the traditional max pooling layer because the 2D PQD images as the inputs of the model are sparse. In the grayscale, high values in the image matrix are presented in white and low values are in black. By doing so, key information in the 2D image can be preserved in this method.  
Figure 8: Comparison of different numbers of operations chosen for fine-tuning the model.  
Figure 9: The validation confusion matrix of real-world voltage disturbance.  
From real-world signals, the classification rate can be improved by adding a small amount of real-world data into the training process to fine-tune the parameters of the model. It proves that the proposed method has an excellent capability of learning and adaptation.
6. In addition, sag, swell, and interruption were considered in this paper. These kinds of disturbances have similar shapes, but with different amplitudes. For accurate classification, the coordinate information is reserved in their 2D images. Through this operation, the three types of voltage disturbance can be effectively distinguished.
7. The proposed method can be implemented very quickly for classification after the training process. It is convenient for end users without requiring specialist knowledge. Whilst this work is based on the offline tests, it can be applied in online tests. This will be implemented in future work.
## 6 Future Works  
In the near future, several interesting investigations should be established in the context of the physical layer security field, especially in FD short-packet transmission. First, the location of the eavesdropper will be considered to emphasize the outperformance of the proposed algorithm with the conventional algorithm. Moreover, a hardware implementation based on Software-Defined Radio (SDR) will be considered to emphasize the performance of the proposed schemes in realistic transmission scenarios for IoT applications and green communications.  
Figure 22: \(S_{g}\) versus \(\rho_{SI}\) in case of an active eavesdropper.  
**Author Contributions:** Conceptualization, B.Q.V., A.F. and R.G.; methodology, B.Q.V., A.F. and R.G.; software, B.Q.V. and C.D.-S.; validation, M.M., A.F. and R.G.; formal analysis, B.Q.V., A.F. and R.G.; investigation, B.Q.V., R.G. and A.F.; resources, B.Q.V. and C.D.-S.; data curation, B.Q.V. and A.F.; writing--original draft preparation, B.Q.V. and A.F.; writing--review and editing, B.Q.V., R.G., A.F., M.M. and C.D.-S.; visualization, B.Q.V., A.F. and M.M.; supervision, R.G. and M.M.; project administration, R.G.; funding acquisition, R.G., A.F. and M.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is jointly supported by the IBNM (Brest Institute of Computer Science and Mathematics), CyberIoT Chair of Excellence at the University of Brest (UBO), and the Brittany region--Pole d'Excellence Cyber.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank to the University of Brest (UBO), the Direction Europe and International (DEI), the IBNM (Brest Institute of Computer Science and Mathematics) CyberIoT Chair of Excellence, and the Brittany region--Pole d'Excellence Cyber for their funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results
### Round-Trip Time (RTT)
#### 5.1.1 Laboratory  
The results are arranged from left to right in ascending order of frequency. As shown in Figure 5, the highest RTT levels are observed in the 2.4 GHz band, which has the highest number of interfering signals. This saturation of the spectrum is observed both in high values of the mean deviation of the RTT in Figure 5 and in the spectrogram in Figure 4 for the laboratory environment.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \begin{tabular}{c} **Frequency** \\ **Bands** \\ \end{tabular} & \begin{tabular}{c} **Spectrum Characteristics** \\ \end{tabular} & \begin{tabular}{c} **Relevant Environments** \\ **Residential** \\ \end{tabular} & \
\begin{tabular}{c} **E** **Laboratory** \\ \end{tabular} &
\begin{tabular}{c} **Factory** \\ \end{tabular} \\ \hline
2.4 GHz & Free spectrum & 91.33 \% & 50.89 \% & 62.13 \% \\  & Peak signal interference & \(-\)73.7 dBm & \(-\)48.75 dBm & \(-\)73.19 dBm \\ \hline
5 GHz & Free spectrum & 99.71 \% & 67.13 \% & 87.14 \% \\  & Peak signal interference & \(-\)63.81 dBm & \(-\)40.63 dBm & \(-\)64.71 dBm \\ \hline
6 GHz & Free spectrum & 99.97 \% & 99.81 \% & 99.97 \% \\  & Peak signal interference & \(-\)60.17 dBm & \(-\)62.81 dBm & \(-\)60.72 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spectrum analysis in the relevant environments.  
It should be noted that as we increase the frequency band of the Wi-Fi network, we decrease \(RTT_{mean}\) value. This is because most of the adjacent Wi-Fi networks are configured in 2.4 GHz and 5 GHz bands. However, as seen in Figure 5, the 6 GHz band experiences the least interference, reflected in \(RTT_{max}\) values below 5 ms. Additionally, the measured \(mdev\) remains below 0.5 ms, indicating a stable and consistent performance.  
These practical measurements indicate that the RTT performance achieved by the Wi-Fi 7 standard not only fails to surpass previous versions but, in some cases, actually falls below them, contradicting theoretical expectations of improvement. Specifically, tests conducted using an access point fully compliant with the 802.11 be (Wi-Fi 7) specification yielded mean \(RTT_{mean}\) values below 15 ms in the 2.4 GHz band and 10 ms in the 5 GHz band, with a higher level of dispersion as well. These findings suggest that, although the latest Wi-Fi standards theoretically have the potential to meet the strict latency requirements of industrial applications, Wi-Fi 7 devices have yet to deliver the expected benefits. This is mainly due to incomplete software and driver-level integration, which currently limits the full exploitation of the advanced features and capabilities of Wi-Fi 7.
# A High-Fidelity Energy Efficient Path Planner for Unmanned Airships
## 5 Trajectory Objectives
### Collision Avoidance  
In this paper, the collision avoidance objective function, \(\mathbb{C}_{a}\), considers the terrain, the altitude ceiling and static obstacles (including airspace constraints and no-fly-zones). Each risks is considered to have a finite minimum separation zone (MSZ) based on aircraft visual flight regulations or risk minimization. The MSZ for each risk is defined as a distance from the risk boundary to the obstacle. The collision objective cost is normalized from zero to one while operating outside any MSZ and is equal to \(\infty\) inside the MSZ. The collision avoidance objective function for the proposed vehicle is taken to be the maximum of the terrain, altitude ceiling and static obstacle components such that:  
\[\mathbb{C}_{a}\left(n\right)=\max\left(\mathbb{C}_{a}^{l}\left(n\right), \mathbb{C}_{a}^{m}\left(n\right),\mathbb{C}_{a}^{o}\left(n\right)\right) \tag{14}\]  
The terrain component, \(\mathbb{C}_{a}^{l}\), adopted is,  
\[\mathbb{C}_{a}^{l}\left(n\right)=\begin{cases}\left(\frac{MSZ_{l}}{d_{ol,z}} \right),&z_{v}>z_{l}\\ \infty,&z_{v}\leq z_{l}\end{cases} \tag{15}\]  
The pressure altitude ceiling component, \(\mathbb{C}_{a}^{m}\), is taken as,  
\[\mathbb{C}_{a}^{m}\left(n\right)=\begin{cases}\left(\frac{MSZ_{m}}{d_{om,z}} \right),&z_{v}<z_{m}\\ \infty,&z_{v}\geq z_{m}\end{cases} \tag{16}\]  
The static obstacles component including buildings, \(\mathbb{C}_{a}^{o}\), is defined as,  
\[\mathbb{C}_{a}^{o}\left(n\right)=\begin{cases}\max\left(\frac{MSZ_{o}}{d_{o,z} },\frac{MSZ_{o}}{\left|d_{o,x},d_{o,y}\right|}\right),&\left(d_{vo,x}\,,\,d_{vo,y}\,,\,d_{vo,z}\right)>MSZ_{o}\\ \frac{MSZ_{o}}{\left|d_{vo,x},d_{o,y}\right|},&\left(d_{vo,x}\,,\,d_{vo,y} \right)>MSZ_{o}\,\&\,d_{vo,z}<MSZ_{o}\\ \frac{MSZ_{o}}{d_{vo,z}},&\left(d_{vo,x}\,|\,d_{vo,y}\right)>MSZ_{o}\,\&\,d_{vo,z}>MSZ_{o}\\ \infty,&\left(d_{vo,x}\,,\,d_{vo,y}\,,\,d_{vo,z}\right)<MSZ_{o}\end{cases} \tag{17}\]  
where \(d_{i,j}\) are the distances between the vehicle \(v\) and the terrain \(l\), altitude ceiling \(m\) and blocks of buildings \(o\), along the \(x\),\(y\),\(z\) axis in Cartesian coordinates. The selection of the avoidance term \(\mathbb{C}_{a}^{o}\) depends on the vehicle position with respect to the obstacle. As an example, the second term of (17) would be used for the vehicle position shown in Figure 6.  
Additional \(\mathbb{C}_{a}\) terms can be added to (14) to include restricted airspace, dynamic objects such as other UAVs and ground vehicles. The planner's ability to avoid dynamic obstacles depends on the the object's speed relative to the UAV, the frequency, range and uncertainty of the available on-board sensor information and the frequency of the re-planning process.
## 3 Coherence Detection and Inland Water Body Monitoring
### Coherence Detection
& \\  & Kurtosis: the measurement of the “peak” of an angle dataset: & \\  & This detector relies on the generalized & \\  & eigendecomposition (GED) of the correlation matrix & \(\mathbf{R}=\frac{1}{2}\mathbf{Z}\mathbf{Z}^{H}\) \\  & \(\mathbf{R}\) of complex zero-Doppler delay waveforms. & \\ \cline{1-1}  & Detector [80] & To detect coherence in GNSS-R signals, the concept of von Neumann entropy is used to measure the amount of information contained in & \(H=-\mathrm{Tr}\left(\frac{\Delta}{\mathrm{Tr}(\Delta)}\log\left(\frac{\Delta}{ \mathrm{Tr}(\Delta)}\right)\right)\) \\ \cline{1-1}  & generalized eigenvalues. & \\ \cline{1-1}  & & where \(T_{lat}\) is the total signal length and \(S_{i}\) is a sequence of complex samples taken from the DDM after the initial cross-correlation of the measured signal with a local replica. The coherence metric is bounded by [0, 1], where a coherence value of 0 represents a noncoherent signal and a value of 1 represents a fully coherent signal. The typical value of noncoherent reflection is approximately between 0 and 0.2. & \\ \cline{1-1}  & & \\ \cline{1-1}  &  
The coherence of GNSS-R signals is influenced by various factors, including surface water, soil moisture, terrain, and vegetation. Studies have shown that coherence is most dependent on surface water, but not on terrain or soil moisture [81; 82]. The signal-to-noise ratio is also closely related to signal coherence. Therefore, optimizing coherence integration time and selecting appropriate GNSS systems can significantly improve signal quality [83; 84; 85]. In addition, it was found that most of the current development of conventional GNSS-R (cGNSS-R) is based on processing GPS L1 C/A signals, and the utilization of modern GNSS signals and the development of future spaceborne GNSS-R payloads are crucial to overcome this limitation. Therefore, in the latest study, [PERSON] et al. [86] coherently combined GPS III L1 C/A and L1C signals, which significantly improved the signal-to-noise ratio of the reflected signal. The effectiveness of this method was verified through CYGNSS raw IF data, which can provide a reference for the design of future GNSS-R instruments.
# Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
## 2 Experiments  
LIVE lidar has been designed for 3D wind profiling from an aircraft for meteorological applications. The wind measurement requirements are a horizontal resolution of 3 km, a vertical resolution of 100 m and an accuracy on each of the three wind vector components better than 0.5 m.s\({}^{-1}\).
## 5 Evaluation  
This chapter focuses on evaluating the performance enhancement of DC-SoC in the Fabric federation chain system. In addition to the native Gossip protocol, this paper selects BlockP2P-EP and DC-Gossip for experimental comparison. The former utilizes the K-Means algorithm for peer clustering, while the latter, which is the predecessor of this paper, employs the OPTICS algorithm for peer clustering. Additionally, this paper employs the DC-SoC-DB algorithm. To objectively evaluate the performance differences among the different methods in Fabric network communication, this paper compares and validates the time consumed for peer clustering, the number of malicious transactions, the propagation delay, and the rate of incomplete block propagation across multiple dimensions. Among these, the incomplete propagation rate measures the proportion of blocks lost due to the propagation mechanism during a broadcast round.
###### Abstract  
A robustness measure is an effective tool to evaluate the anti-interference capacity of the construction schedule. However, most research focuses on solution robustness or quality robustness, and few consider a composite robustness criterion, neglecting the bounded rationality of subjective weights and inherent importance and nonlinear intercriteria correlations of objective weights. Therefore, a construction schedule robustness measure based on improved prospect theory and the Copula-criteria importance through intercriteria correlation (CRITIC) method is proposed. Firstly, a composite robustness criterion is established, including start time deviation \(r_{s}\) and structural deviation \(r_{p}\) for measuring solution robustness from project execution and completion probability \(r_{c}\) for measuring quality robustness from the project result. Secondly, bounded rationality is considered, using prospect theory to calculate subjective weights, which is improved by the interval distance formula. Thirdly, the Copula-CRITIC method is proposed to determine objective weights incorporating both inherent importance and nonlinear intercriteria correlations. Finally, an information-entropy-based evidence reasoning method is applied to combine subjective and objective weights together while identifying their validity. An underground power station in China is used for a case study, whose robustness is measured using the proposed methods, single robustness criterion, and composite robustness criterion using traditional weighting methods. The comparison results verify the consistency, representativeness, and advantage of the proposed criterion and methods.  
**Keywords:** construction schedule robustness measure; solution robustness; quality robustness; prospect theory; Copula-CRITIC; evidence reasoning +
Footnote †: journal: Applied
# Pro-Pre and Postbiotic in Celiac Disease
## 8 Clinical Trials Using Pro- and Pre-Biotic in CD
### Probiotics and celiac Disease
#### 8.1.2 Effect of Pro-Biotics in High-Risk Individuals  
Subjects with positive genetics and first degree familiarity for celiac disease are regarded as \"at risk\".  
The European multicentre Prevent Celiac Disease project investigated the possible primary prevention of celiac disease [58]. What they wanted to test was whether the frequency of celiac disease could be reduced by exposing children at high risk of the disease to small amounts of gluten between 16 and 24 weeks of age, preferably while still breastfeeding. The intervention consisted of randomly administering participants 200 mg of gluten mixed with 1.8 g of lactose daily for 8 weeks. The treatment did not reduce the risk of disease. In second study 832 infants were randomly assigned to two groups: Group A: gluten introduction at 6 months of age; Group B: gluten introduction at 12 months of age. Delayed gluten introduction did not change the risk of celiac disease among at-risk children, but delayed gluten introduction was associated with a delayed onset of the disease [59]. The Environmental Determinants of Diabetes in the Young (TEDDY) study evaluated whether the intake of _L. reuteri_ and _L. rhamnusus_, during the first year of life, could influence the development of CD. The treatment had no protective effect [60].
# Simulation Analysis and Experiment of Piezoelectric Pump with Tapered Cross-Section Vibrator
## 2 Structure and Working Principle  
The structure of the piezoelectric pump with a tapered cross-section vibrator is shown in Figure 1. It is composed of the tapered cross-section vibrator, cover, base, inlet and outlet fluid tubes, and connectors and seals. The sealed pump chamber is formed by the cover and the pump base.  
When the pump is working, the pump chamber is fully filled with fluid. The surface of the piezoelectric vibrator is coated with an insulating layer to isolate it from the fluid. There are two wires connected to the electrodes of the vibrator for power supply. Due to the inverse piezoelectric effect of piezoelectric ceramics pasted on both sides of the vibrator substrate, the ceramic sheets will then drive the vibrator substrate to bend under a certain voltage. With AC supply, the bending direction of the vibrator will alternate. Because one end of the vibrator is fixed, the free end of the vibrator will swing up and down like a fish caudal fin. Thus, the vibrator will create a driving force to move the fluid nearby, especially near the tail, to form the unidirectional flow. It means that the fluid will be drawn in from the inlet tube and forced out from the outlet tube, which creates the pump flow rate.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 5 Discussion
### Model Limitations  
While the CNN-LSTM-SAM-Attention model provides significant improvements over traditional machine learning models, it is not without limitations. One of the primary constraints is the model's reliance on a comprehensive dataset of landslide occurrences and related factors. The performance of the model is directly tied to the quality and quantity of the input data. Although we have taken extensive measures to clean, standardize, and balance the dataset, any inconsistencies or biases in the dataset could affect the model's accuracy and generalizability.  
Furthermore, the model assumes that spatial and temporal relationships remain relatively consistent within the training data's geographic region. This assumption may limit the model's ability to predict landslide susceptibility accurately in areas with significantly different environmental, climatic, or geological conditions. The CNN and LSTM components, while powerful, may also struggle with spatial data that does not exhibit clear local patterns or temporal sequences. In such cases, the convolutional layers may not extract meaningful features, and the LSTM may fail to capture temporal dependencies.  
Additionally, despite the integration of the Spatial Attention Mechanism (SAM), which helps focus on relevant spatial regions, the model may still be sensitive to background noise and less significant features in certain cases. This can lead to occasional misclassifications in complex terrains where multiple variables interact in unpredictable ways.
# Feasibility of Wave Simulation in Typhoon Using WaveWATCH-III Forced by Remote-Sensed Wind
## 2 Data and Method
### Description and Settings of WW3  
Both WW3 and SWAN are third-generation wave models (WAMs) [34] because they solve the wave equations of energy conservation. The balance equation in the direction of wave propagation is expressed as  
\[\frac{\partial N}{\partial t}+\
abla\cdot\left[\left(c_{g}+c\right)\times N \right]=S_{in}+S_{bot}+S_{nl}+S_{other} \tag{3}\]  
where \(N\) represents the wave action density spectrum; \(t\) is the time dimension; \(\
abla\) is the Hamiltonian operator interpreting the spatial dimension; \(c_{g}\) is the speed of the wave group; \(c\) is the background current speed; \(S_{in}\) is the input source produced by wind stress; \(S_{bot}\) is the wave-bottom interaction; \(S_{nl}\) represents non-linear triad and quadruplet wave-wave interactions; and \(S_{other}\) is the other sources, i.e., wave breaking and wave-current and wave-ice interactions. WW3 uses several parameterizations for these sources. In this study, the parametrization sets are selected according to previous studies, i.e., parametrizations of ST6+FLD2+BT1 for the input-dissipation source [16], parametrizations of TR1 for the source of wave-current interaction [14], and parametrizations of GMD2 for the source of wave-wave interactions [25]. The computational grid covers the region from 0\({}^{\circ}\) N to 60\({}^{\circ}\) N and from 100\({}^{\circ}\) E to 180\({}^{\circ}\) E, and the grid spacing is 0.1\({}^{\circ}\).  
The forcing fields include winds assembled using multiple remote-sensed products, currents, and sea levels. Here, the daily average sea surface current and sea level data from the Copernicus Marine Environment Monitoring Service (CMES) [35] are employed with 0.08\({}^{\circ}\) grid spacing. The water depth is based on the General Bathymetry Chart of the Oceans (GEBCOs) [36], with a horizontal resolution of 1 km. Other settings are listed as follows: frequency bin \(f\) is logarithmic, ranging from 0.04118 to 0.7186 at a 0.01 interval of \(\Delta f/f\); the directional resolution is 15\({}^{\circ}\); and the time step is 300 s. The outputs from the WW3 model are SWH, peak wave period (PWP), and wave spectrum, with a spatial resolution of 0.1\({}^{\circ}\) at intervals of 1 h. In our work, the wave simulations of 30 typhoons continuously run with a 'cold start' regime from January 2021 to December 2022. As for the  
Figure 3: Diagrammatic sketch of this study.  
'cold start' regime, several initial months of simulation results were used to establish the boundary conditions of the WW3 model. Thus, there is no prior information on the initial and boundary conditions for the wave spectrum simulation. In our study, we removed the initial 3 months simulation results to ensure the model accuracy of WW3.