esa-sceva/satcom-chunk-collection huggingface dataset:
# New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks
###### Abstract  
In this work, two new self-tuning collaborative-based mechanisms for jamming detection are proposed. These techniques are named (i) Connected Mechanism and (ii) Extended Mechanism. The first one detects jamming by comparing the performance parameters with respect to directly connected neighbors by interchanging packets with performance metric information, whereas the latter, jamming detection relays comparing defined zones of nodes related with a collector node, and using information of this collector detects a possible affected zone. The effectiveness of these techniques were tested in simulated environment of a quadrangular grid of \(7\times 7\), each node delivering 10 packets/sec, and defining as collector node, the one in the lower left corner of the grid. The jammer node is sending packets under reactive jamming. The mechanism was implemented and tested in AODV (Ad hoc On Demand Distance Vector), DSR (Dynamic Source Routing), and MPH (Multi-Parent Hierarchical), named AODV-M, DSR-M and MPH-M, respectively. Results reveal that the proposed techniques increase the accurate of the detected zone, reducing the detection of the affected zone up to 15% for AODV-M and DSR-M and up to 4% using the MPH-M protocol.  
detection and mitigation jamming; performance metrics; wireless sensor networks +
Footnote †: journal: Sensors
# Measurement and Analysis of Radar Signals Modulated by the Respiration Movement of Birds
## 4 Discussion  
The above results indicated that radar signals posed by respiration were weak; thereby, it was not believed to be important for the 10 dB-level amplitude modulation on echoes from flying birds. Other reasons take primary responsibility for the 10 dB-level amplitude modulation. The most likely reason could be the contributions of flapping wings, as some studies reported that the fluctuation was related to the flapping gaits of birds. Our dynamic measurement system can also be used for capturing the radar signals modulated by different flapping gaits, so that we can continue to conduct future research on this topic and quantify the relationship between radar signals and the flapping gaits of flying birds using our measurement systems, as shown in Figures 2 and 3.  
The signal fluctuations modulated by respiration within the S-band were \(\sim\)1 dB and stronger within the X-band and Ku-band (\(>\)2 dB), as shown in Table 2. The higher the frequency, the stronger the modulation of birds' echoes. Scattering regions can explain this phenomenon. The material contents of the target mainly affect the signal fluctuation in the resonance region. In contrast, in the optic region, scattering centers (i.e., the shape of the target) contribute to the significant scattering field of the target. Although the bird is breathing, its material contents did not change much; therefore, the signal fluctuation within the S-band is small. However, due to respiration, the scattering section of the bird is changing; hence, signal fluctuations within the X-band and Ku band are more evident and larger than those within the S-band. The same reason could also explain the case in [1], where the authors found that the modulation signals within the S-band are weaker than those within the X-band. For this reason, the S-band could be a little unsuitable for detecting radar signals of birds, especially those involved in signal recognition using modulation signatures.
# Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning
## 7 Applications to Federated Learning  
### Building Decision Tree/Forest  
Decision trees [30] are hierarchical models used for classification and regression tasks, where data are recursively partitioned based on feature values to maximize a purity metric such as information gain or Gini impurity. At each node, the algorithm evaluates possible splits by considering thresholds over features and selects the one that optimally separates the data. Random forests extend this concept by constructing an ensemble of decision trees, each trained on a bootstrap sample of the data and a random subset of features, thereby improving generalization through bagging and feature randomness.  
In federated learning settings, where data are distributed across multiple clients with privacy constraints, the traditional centralized approach is infeasible. To address this, the model-building process can be transformed into multiple rounds of histogram estimation. Each client computes local histograms \(H_{f}^{(k)}\) for feature \(f\), aggregating the frequencies of feature values within predefined bins. These set-valued frequency estimations are securely aggregated and sent to a central server, which combines them to approximate the global data distribution: \(H_{f}=\sum_{k=1}^{K}H_{f}^{(k)}\). The server then determines optimal split points based on the aggregated histograms without accessing raw data, enabling the collaborative construction of decision trees and forests while preserving data privacy.  
For each histogram frequency query, one can adopt a SOTA shuffle protocol from [14; 16; 29]. For every one of them, the \(p\), \(\beta\) and \(q\), \(n\) parameters can be easily computed (see Section 8 for detail).
## 3 Results and Discussion
### Method Performance--Matrix and Analyte Extension
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \multirow{2}{*}{**Analytic**} & \multirow{2}{*}{**LOD**} & \multirow{2}{*}{**LOLO**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**Intercept**} & \multirow{2}{*}{**\(\mathbf{r^{2}}\)**} & \multicolumn{3}{c}{**Intraday Precision (\(\mathbf{r=9}\) \%*RSD**} & \multicolumn{3}{c}{**Accuracy**} \\  & & & & & & & & & & & & & (\(\mathbf{\mu=9}\) \%*RSD** & & & & (\(\mathbf{\mu=9}\) \%*Revery**) \\ \cline{2-13}  & \(\mathbf{\mu_{9}}\)/mL & \(\mathbf{\mu_{9}}\)/mL & \(\mathbf{\times}\)10\({}^{-3}\) & & & & & & & & & & & & \\ \hline Formaldehyde & 0.022 & 0.671 & 24.90 & 2.3749 & 0.9997 & 3.9 & 2.6 & 2.9 & 2.4 & 1.1 & 1.5 & 102.6 & 98.5 & 98.5 \\ Malondialdehydedehyde & 0.008 & 0.195 & 2.979 & 0.027 & 0.9999 & 4.7 & 1.9 & 2.2 & 4.3 & 1.7 & 1.1 & 100.9 & 98.5 & 99.4 \\ Acetaldehyde & 0.089 & 0.340 & 3.968 & 0.0981 & 0.9999 & 2.0 & 5.8 & 0.4 & 4.6 & 0.9 & 1.2 & 98.0 & 99.2 & 100.0 \\ Acrolein & 0.017 & 0.061 & 1.999 & 0.0087 & 0.9989 & 5.5 & 4.2 & 2.7 & 3.1 & 3.5 & 1.5 & 100.4 & 99.4 & 98.6 \\ Methyloglycan & 0.083 & 0.229 & 0.574 & 0.0499 & 0.9990 & 3.4 & 2.8 & 6.9 & 2.4 & 0.9 & 3.4 & 98.0 & 95.8 & 93.1 \\ Dacetyl & 0.065 & 0.184 & 1.552 & 0.0889 & 0.9997 & 1.3 & 2.1 & 1.5 & 1.7 & 2.2 & 1.0 & 97.2 & 97.2 & 98.2 \\ \end{tabular}
\end{table} 
Table 1: Analytical characteristics of matrix and analyte extension study for the US-DLLME-GC-MS method. Overall, while combining derivatization with HPLC-MS produced the lowest detection limits, the extended method reported here achieved similar or lower detection limits compared to previous GC-MS and HPLC-UV methods for milk, dairy products, and infant formula, with more accurate recoveries (closer to 100%) and comparable precision, proving the suitability of this method for simultaneous monitoring of MDA, FCHO, ACE, ACRL, MGO, and DA in adult formulas.
## 3 Experimental Test Results
### Impulse Test Results
#### 3.2.2 Impulse Impedance, \(Z_{imp}\)  
From the voltage and current traces, the impulse impedance (\(Z_{imp}\)) values were measured by dividing the voltage magnitude at the peak current by the peak current. A graph illustrating impulse impedance against increasing current magnitudes for all ground electrodes was plotted, as shown in Figure 9. It was noticed from the figure that the \(Z_{imp}\) of ground electrode (i) exhibits a slight dependency on current magnitudes, indicating the occurrence of a potential soil ionisation process in the soil, where high potentials may build up within the air voids and interfaces between the soil and ground electrodes. On the other hand, the \(Z_{imp}\) of the other ground electrodes (ii) and (iii)a-(iii)d with lower \(R_{DC}\) values was independent of the current magnitude, indicating that the current may reach a saturation point, where the free electrons produced may diffuse due to the low soil resistivity, preventing voltage build up in the air voids. The \(Z_{imp}\) values of all the ground electrodes were found to be lower than the \(R_{DC}\) values by approximately 30% for electrode (i) and 23% for electrodes (ii) and (iii)a-(iii)d, which may be due to possible ionisation in the soil. Furthermore, these results were found to be independent of impulse polarity.
## 4 Performance Metrics  
The performance indicators covered in this paper are PU1 packets' blocking rate and throughput; PU2 packets' blocking rate, loss rate, and throughput; and SU packets' blocking rate, loss rate, and throughput. Blocking rate, loss rate, and throughput reflect the loss and successful transmission of packets. We note that other indicators in network analysis, such as average queue length, average delay, and interruption rate, cannot visually represent the \"in and out\" of the system. Therefore, we focus on blocking rate, loss rate, and throughput to show the performance of the system in this paper.
## 3 Detailed Analysis of Maximum Power Point Tracking (MPPT) Methods
### Conventional MPPT Method
#### 3.3.1 Algorithm and Working Principle  
The conventional MPPT method used in the design is the Perturb and Observe (P and O) algorithm.
## 3 Quality of Service
### Evaluation of the Selected Specifications
#### Superframe Management  
Furthermore, the WirelessHART, ISA100.11a and the ZigBee standards allows optimizing the transmission of superframes to fit the real-time communication constraints. The ISA100.11a standard supports insertion, removal and activation of superframes during the operating process, whereas additional superframes can be transmitted in parallel to the mandatory superframe in the WirelessHART standard. In the _without beacon_ mode of the ZigBee standard, no superframe is transmitted and the devices do not have to wait for the next timeslot to transmit their data. However, this solution has also drawbacks. If one device has a great amount of data to send, the channel would be occupied for a long period of time and data requiring real-time transmission could not be send during this period. Moreover, the time to get access to the channel may be longer than in case of dedicated slots, if all the devices have data to be sent. The management of dedicated and shared timeslots has therefore to be tailored to application characteristics and the traffic patterns.
## 2 Principle of Microtremor Exploration
### Spac  
The SPAC method is based on two basic assumptions: (1) the microtremor accords with a stationary random process in time and space; and (2) the fundamental order surface wave is dominant among all kinds of waves included in the microtremor. The basic principle of the SPAC method is as follows.  
The SPAC method is a data-processing method to obtain a microtremor signal and extract the phase velocity dispersion curve of the Rayleigh wave from the vertical component of the microtremor signal by deploying the observation array [15]. It requires a circular observation array; four microtremor acquisition stations are located in the array (Figure 1), one of which is located at the center of the circle (S1), with the other three (S2, S3, S4) evenly distributed on the circumference of the radius (r), forming an equilateral triangle [16].  
Taking S1 (0, 0) and S2 (r, \(\theta\)), the surface wave signals are \(u\) (0, 0, \(\omega\), \(t\)) and \(u\) (r, \(\theta\), \(\omega\), \(t\)). The spatial autocorrelation function can then be expressed as:  
\[\varphi(r,\ \theta,\ \omega)=\overline{u(0,\ 0,\ \omega,\ t)u(r,\ \theta,\ \omega,\ t)} \tag{1}\]  
In (1), \(\varphi\) is the spatial autocorrelation function; \(r\) is the polar diameter of point S1, the polar diameter here being the observation radius; \(\theta\) is the polar angle of point S1; \(\omega\) is the angular frequency; \(u\) is the surface wave signal; and \(t\) is the collection time. The arithmetic mean of the spatial autocorrelation function in all directions of the circular array is the spatial autocorrelation coefficient (\(\rho\)).  
\[\rho(r,\ \omega)=\frac{1}{2\pi\varphi(0,\ \omega)}\int_{0}^{2\pi}\varphi(r,\ \theta,\ \omega)d\theta \tag{2}\]  
Figure 1: Illustration of the single-circle observation array for the SPAC method.  
The integral result of spatial autocorrelation coefficient can be expressed as:  
\[\rho(r,\ \omega)=J_{0}(\frac{\omega r}{c(\omega)})=J_{0}(\frac{2\pi f{r}}{c( \omega)}) \tag{3}\]  
In (3), \(J_{0}(\chi)\) is the first kind of zero-order Bessel function; \(c(\omega)=\omega/k\) is the surface wave phase velocity; and \(k\) is the wave number. By calculating the spatial autocorrelation coefficient, the relationship between the angular frequency and phase velocity can be obtained, and then the dispersion curve at this point can be obtained.
## 4 Conclusions  
Our experiments showed that strongly acidified solvents (pH 1.5) allow high extraction yields using ultrasound-assisted extraction (UAE). The best solvent for the UAE of anthocyanins from freeze-dried blackcurrant pomace was a mixture of water and ethanol (50/50 \(v/v\)) acidified with HCl. The most favorable and efficient extraction parameters combined with the water-ethanol solvent were a material/solvent ratio of 1:7, a temperature of 35 \({}^{\circ}\)C, and a time of 15 min. Increasing the amount of solvent or raising the process temperature did not lead to an improvement in the extraction efficiency.  
A strong relationship (r > 0.991) was also found between anthocyanin content and color saturation or hue in the obtained extracts. Based on the E1% coloring strength results, all the extracts obtained can be considered strong colorants, especially those obtained with the water-citric acid solvent. Decreasing the ratio of the mass of the pomace to the volume of the solvent resulted in a greater increase in pH during UAE. To obtain a strong red color, the pH of the extracts must be kept below 3 during the entire process.  
Our study was limited to evaluating the effect of only one value of ultrasound frequency and intensity. Future research should focus on the impact of different ultrasound parameters on anthocyanin extraction yield.  
Conceptualization, B.K. and E.B.; methodology, B.K.; software, B.K.; validation, B.K.; formal analysis, B.K.; investigation, B.K. and E.B.; resources, B.K.; data curation, B.K.; writing--original draft preparation, B.K.; writing--review and editing, E.B.; visualization, B.K.; supervision, B.K.; project administration, B.K.; funding acquisition, B.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material/Solvent** & **Time** & **Temperature** & \multicolumn{2}{c}{**Solvent**} \\
**Ratio** & **(min)** & **(\({}^{\circ}\)C)** & **Water–Ethanol with HCl** & **Water–Citric Acid** \\ \hline \multirow{8}{*}{1:20} & 15 & & \(1.6\pm 0.1\)\({}^{\text{a}}\) & \(1.7\pm 0.1\)\({}^{\text{a}}\) \\  & 30 & 25 & \(1.6\pm 0.0\)\({}^{\text{a}}\) & \(1.7\pm 0.0\)\({}^{\text{a}}\)  
**Acknowledgments:** Research for this publication was conducted using research equipment purchased as part of the \"Food and Nutrition Centre--modernisation of the WULS campus to create a Food and Nutrition Research and Development Centre (CZiZ)\" co-financed by the European Union from the European Regional Development Fund under the Regional Operational Programme of the Mazowieckie Voivodeship for 2014-2020 (Project No. RPMA.01.01.00-14-8276/17).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 5 Experimental Analysis
### The Effect of LSTM Unit Size  
In Figure 5, it can be seen the larger the LSTM unit size, the higher the classification accuracy. When the size of LTSM has been doubled, the recognition accuracy is improved. However, with the increase in LTSM size, the training time has also increased. Therefore, the best trade-off of 256 is selected.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Kernel Size** & **Loss** \\ \hline \(1\times 2\) & 1.2986183166503906 \\ \(1\times 3\) & 1.2517382701237996 \\ \(1\times 4\) & 1.2592326800028484 \\ \(1\times 5\) & 1.1835391124089558 \\ \(2\times 2\) & 1.18420636665390015 \\ \(2\times 3\) & 1.145102779075364 \\ \(2\times 4\) & 1.1322169701258342 \\ \(2\times 5\) & 1.1263486941655476 \\ \(3\times 3\) & 1.1229949394861858 \\ \(4\times 4\) & 1.1136138439178467 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The impact of the size of the CNN kernel on classification accuracy.  
Figure 4: The impact of the size of the CNN kernel on classification accuracy.  
Figure 5: The impact of the size of the LSTM unit on classification accuracy.
# Quantum Neural Networks Approach for Water Discharge Forecast
## 1 Introduction
This study applies quantum neural networks (QNNs) to river discharge prediction. Although classical neural network techniques have achieved significant success in this field, the core value of this research lies in exploring the feasibility of quantum intelligence methods in this domain and conducting an in-depth analysis of their predictive performance, mainly because the approaches using classical NNs failed to capture the extremes in the data series. The proposed approach is not only executable in quantum simulation environments on classical computers but also has the potential to be implemented on actual quantum hardware, leveraging unique quantum advantages such as ultrafast computation, parallel processing, and computational acceleration enabled by quantum superposition and entanglement. Results show significant performances of this approach in forecasting three data series.
## Abstract  
It is well known that the effect of shale hydration causes wellbore instability due to water phase invasion of drilling fluid to lamellar shale rich formations. This is because of that the mechanical properties (compressive strength, elastic modulus, etc.) of lamellar shale surrounding the borehole, which is rich in clay minerals, will decrease significantly after hydration. In this study, using the lamellar shale in the continental stratum of the southern Ordos Basin, the mechanical properties of lamellar shale were studied by compression tests considering the effect of lamellar structure and hydration from a macroscopic point of view. In addition, the mechanical mechanism was discussed combined with the CT scanning tests results from a microscopic point of view. The results demonstrate the following points. Lamellar shale has stronger anisotropy than bedding shale, the compressive strength (deviatoric stress) and elastic modulus of lamellar shale are both lower than those of bedding shale, and it is more prone to tension fracture. With the increase in the angle (\(\beta\)) between the lamina and the axial direction from 0\({}^{\circ}\) to 90\({}^{\circ}\), the compressive strength of lamellar shale decreases when \(\beta<30^{\circ}\) and then increases, the elastic modulus of lamellar shale decreases greatly when \(\beta<30^{\circ}\) and then tends to flatten. With the increase in hydration time, the compressive strength and elastic modulus of lamellar shale both gradually decrease, and the rates of their decrements reduce. The mechanical properties of lamellar shale are more affected by hydration than those of bedding shale. The hydration of lamellar shale leads to the formation of new fractures and the expansion of existing fractures in the junction area between the laminae and rock matrix, resulting in easy tension fracture along the laminae of shale.  
Keywords:lamellar shale; mechanical properties; anisotropy; hydration; failure characteristics +
Footnote †: journal: Applied Sciences
# Improving Adversarial Robustness of CNNs via Maximum Margin
## 3 Proposed Method
### Theories  
#### 3.2.1 Linear Binary-Classification Task  
For a binary linearly separable classification problem, there exist many hyperplanes that might classify the data. SVM is designed to compute the classification hyperplane which represents the largest separation, or margin, between two classes. The max-margin hyperplane is also known as the optimal classification hyperplane.  
The main idea of finding the optimal classification hyperplane is to maximize the distance between different classes. Because of the possible inseparable points in data, slack variable \(\zeta\) and penalty coefficient \(C\) are generally added. So the essence is to solve the following optimization problems, as shown in Equation (3)  
Figure 1: Two-dimensional visualizations of the model’s output distribution of natural examples and adversarial examples from two separated classes from the CIFAR-10 dataset. For each subfigure, the left channel indicates the result from the original model and the right channel indicates the result from the SVM auxiliary classifier. First, we train the original model using SAT. Then we insert our SVM auxiliary classifier and train it but with the original model fixed. The classifying results are shown in (**a**,**b**). The loss of them is shown in (**c**). Last, we use our AT-SVM to train a model, the result is shown in (**d**).  
\[\begin{split}\min&\frac{1}{2}\|\mathbf{\omega}\|_{2}^{2}+C \sum_{i=1}^{m}\zeta_{i}\\ s.t.& y^{(i)}\Big{(}\mathbf{\omega}^{T}\mathbf{x}^{(i)}+b \Big{)}\geq 1-\zeta_{i}\\ &\zeta_{i}\geq 0,\quad i=1,2,\ldots,m.\end{split} \tag{3}\]  
where \(y^{(i)}=\pm 1\) indicate that \(\mathbf{x}^{(i)}\) is a positive or negative example.  
These constraints of the optimization problem in Equation (3) can be solved by mathematical methods such as the sequential minimal optimization (SMO) [37] algorithm. However, in order to replace the original linear FC layer in the training of CNN, this formula cannot be directly used as a new loss function, as there are inequality constraints. So the following content will deform this optimization problem.  
The constraints are equivalent to Equation (4).  
\[\zeta_{i}\geq\max\Big{(}0,1-y^{(i)}\Big{(}\mathbf{\omega}^{T}\mathbf{x}^{(i)}+b\Big{)} \Big{)} \tag{4}\]  
The inequality constraint in Equation (4) shows the lower bound of \(\zeta_{i}\). When solving the minimization problem outside, we approximately replace \(\zeta_{i}\) with the lower bound of it. Let \(C^{\prime}=1/2C\). Equation (3) can be written as Equation (5).  
\[\min\sum_{i=1}^{m}\max\Big{(}0,1-y^{(i)}\Big{(}\mathbf{\omega}^{T}\mathbf{x}^{(i)}+b \Big{)}\Big{)}+C^{\prime}\|\mathbf{\omega}\|_{2}^{2} \tag{5}\]  
Since there is no inequality constraint, the new formula can be directly used as the loss function. Notice that \(\sum_{i=1}^{m}\max\Big{(}0,1-y^{(i)}\Big{(}\mathbf{\omega}^{T}\mathbf{x}^{(i)}+b\Big{)} \Big{)}\) is the hinge loss function. The other item \(C^{\prime}\|\mathbf{\omega}\|^{2}\) can be regarded as a regularization term added to improve the generalization performance of the model.