esa-sceva/satcom-chunk-collection huggingface dataset:
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 3 Methods  
In Federated Learning, non-independent and identically distributed (non-IID) data is a highly challenging issue. The data across different clients may exhibit significant differences in aspects such as class distribution and feature distribution, which can lead to various difficulties during the global optimization process. These challenges include slow convergence rates and unstable model performance, significantly hindering the effectiveness of Federated Learning.  
To address the issues related to data imbalance and distribution discrepancies among clients, we utilize a CVAE-GAN network to generate synthetic data. Figure 2 illustrates the exact process of generating data using the CVAE-GAN network. This network consists of five components: (1) Encoder network E; (2) Label correction module; (3) Generator network G; (4) Discriminator network D; (5) Classifier network C.  
The functions of networks \(E\) and \(G\) are analogous to the components in a conditional variational autoencoder (CVAE). Specifically, the encoder network \(E\) maps a data sample \(x\) to a latent vector \(z\), which represents the data category, by learning the underlying  
Figure 2: Illustration of CVAE-GAN structure.  
distribution. Next, the label noise correction module applies an autoregressive generative model to precisely estimate the distribution of the limited but high-dimensional training data. Based on the distribution characteristics of the training samples, it corrects their labels accordingly.  
Subsequently, the generator network, denoted as \(G\), creates synthetic data \(x^{\prime}\) resembling the original dataset by drawing samples from the latent distribution it has learned. Simultaneously, the roles of the networks \(G\) and \(D\) correspond to those of the generator and discriminator within a generative adversarial network (GAN). Using gradient information provided by the discriminator network \(D\), the generator \(G\) refines its approximation of the actual data distribution. On the other hand, the discriminator \(D\) is trained to differentiate between authentic data instances and synthetic ones produced by \(G\), thereby establishing an adversarial learning dynamic.  
The primary role of the network \(C\) is to evaluate the posterior probabilities, enabling a more precise classification of data categories across the entire system.
## 2 Track Modeling
### Model Components and Mesh  
The track model consists of:  
* Wheel with a diameter equaling 920 mm.
* Modified Rail 54E1 (UIC54).
* A total of 21 timber sleepers, where the width equals 250 mm and the thickness equals 150 mm, with 600 mm spacing between each other.
* A total of 21 rail pads and tie plates.
* The thickness of the ballast, subballast, and subgrade layers are 300 mm, 300 mm, and 500 mm, respectively.  
The mesh of the wheel grades from the minimum element size at the outer edge, which equals 6 mm, to the maximum element size, as depicted in Figure 5(a). There are 14,521 and 4691 wheel nodes and elements, respectively. The element size of the rail mesh, as depicted in Figure 5(b), is 5 mm and 8 mm in the horizontal and vertical directions, respectively. There are 134,814 and 43,231 rail nodes and elements, respectively. Figure 5(c) shows the mesh of the rail pad, tie plate, and sleeper as the minimum element sizes equal 5 mm, 6 mm, and 27 mm for these components, respectively. Moreover, Figure 5(d) shows a part of the ballast layer mesh, as the minimum element size equals 27 mm. There are 11,501 and 3560 ballast layer nodes and elements, respectively.  
By decreasing the mesh size of the wheel from 6 mm to 4 mm and the rail from 5 mm to 4 mm, it was found that the results do not show a great difference, as shown in the results of the railhead stresses and wheel/rail contact pressure.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Element** & **Poisson’s** & **Young Modulus,** & **Mass Density, \(\rho\)** & **Damping** & **Friction** & **Dilation** & **Cohesion,** \\  & **Ratio, \(v\)** & **E (Mpa)** & **(Kg/m3)** & **Factor, \(\beta\)** & **Angle, \(\varphi\) (**)** & **Angle, \(\varphi\) (**)** & **Angle, \(\varphi\) (**)** & **\(c\) (Mpa)** \\ \hline Rail, wheel and tie plate & 0.3 & \(2\times 10^{5}\) & 7850 & & & & & \\ Rail pad & 0.45 & 850 & 950 & & & & & \\ Sleeper & 0.4 & 1000 & 1084 & & & & & \\ Ballasted layer & 0.37 & 200 & 2000 & 0.01 & & & & \\ Subballast layer & 0.37 & 138 & 1920 & 0.01 & & & & \\ Subgrade soil & 0.3 & 19 & 1800 & 0.01 & 35 & 5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters of the track model.  
Figure 6: Mesh of: (**a**) wheel; (**b**) modeled rail 54E1 (UIC54); (**c**) rail pad, tie plate, and sleeper; (**d**) ballast layer.
## 2 Materials and Methods  
A-MFM measurements were performed by using the L-trace II (Hitachi High tech) scanning probe system in ambient conditions. Tip-sample distance in a lift mode was \(\approx\)5 nm while oscillation amplitude was set up to 20% during the lift scan. The basic setup is described in the works [1, 4, 5] and the current version is shown in Figure 1. Si cantilever with spring constant of \(\approx\)40 N/m and resonant frequency of \(\approx\)300 kHz was coated by 100 nm thick Co\({}_{0.43}\)(GdO\({}_{3}\))\({}_{0.57}\) superparamagnetic film [5, 6]. Co and Gd\({}_{2}\)O\({}_{3}\) targets were used for DC and RF co-sputtering under an Ar atmosphere.  
Amplitude modulated HF voltage \(V_{0}(1+\alpha\cos(\omega_{m}t))\cos(\omega_{c}t)\) was applied to PMR magnetic head by using a HF signal generator (8657A Hewlett Packard) for varying the carrier frequency of \(f_{c}(=\omega_{c}/2\pi)\) between 0.1 and 1000 MHz with an amplifier (Keysight 83020A). To generate AM input of the head, we used a low-frequency signal generator (NF Wave Factory WF1974) with the \(f_{m}(=\omega_{m}/2\pi)\) frequency 89 Hz as an external signal source of the HF signal generator. The AM depth \(\alpha\) of the HF signal generator was set up to 90%.  
To detect the degree of frequency modulation \(\Delta\omega\) in \(\omega=d\phi/dt=\omega_{0}+\Delta\omega\sin(\omega_{m}t)\) from the induced frequency modulated oscillation of \(A\sin\left(\omega_{0}t+\frac{\Delta\omega}{\omega_{m}}\cos(\omega_{m}t)\right)\), we firstly carried out the frequency demodulation of cantilever oscillation (\(\omega-\omega_{0}=\Delta\omega\sin(\omega_{m}t)\)) by (Phase Locked Loop circuit) PLL and then measured \(\Delta\omega\) by lock-in detection with \(\omega_{m}\)
# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 2 Related Work
[42] introduced the ImGAGN model, generating nodes to emulate the attribute distribution and network topology of minority classes, which then facilitates the training of a Graph Convolutional Network (GCN) discriminator on a synthetic balanced network to distinguish between real and synthetic nodes, as well as between minority and majority nodes. In the context of semi-supervised learning with imbalanced data, a novel approach for semi-supervised medical image classification, named Adaptive Blended Consistency Loss (ABCL), has been proposed, effectively addressing class imbalance by adaptively blending the target class distribution according to the class frequency obtained by [PERSON] et al. [43]. [PERSON] et al. [44] tackled the challenge of class imbalance in semi-supervised learning (SSL) by introducing Class-Imbalanced Semi-Supervised Learning (CISSL) and proposing a new Suppressed Consistency Loss (SCL) that is robust to class imbalance in both labeled and unlabeled data. Currently, there is sparse research on rule-based oversampling algorithms, and the gap in this research field deserves further exploration. Among the few existing studies, [PERSON] et al. [45] proposed an oversampling algorithm based on fuzzy rules to address the issue of class imbalance. This method generates fuzzy rules and synthesizes new minority class instances to tackle the class imbalance problem. Moreover, it can handle imbalanced data with missing values. However, this method is limited to imbalanced data with numerical variables and cannot handle categorical variables. Additionally, it requires manual selection of the number of fuzzy partitions, lacking the ability for adaptive selection. In summary, harnessing data relationships and rules for synthetic data generation is a promising strategy that warrants deeper investigation. Exploring this approach could offer novel alternatives to current oversampling techniques.
## 4 Blind Schnorr Signatures--Results and Discussion
### Establishing a Shared Secret by \(\mathcal{S}\) and \(\mathcal{D}\)
#### 4.1.1 Transmitting a Diffie-Hellman Ephemeral Share from \(\mathcal{S}\) to \(\mathcal{D}\)  
The trick is to use the element \(R=g^{r}\) as the share of \(\mathcal{S}\). This may seem to contradict what we have said above (\(r\) can be derived by \(\mathcal{J}\) when \(\sk_{\mathcal{S}}\) is surrendered). However, the strategy of \(\mathcal{S}\) is to interrupt the protocol execution and report an error for some reasonable reason (e.g., \"server overloaded\"). In this case, the corresponding value \(s\) is not transmitted to \(\mathcal{D}\) and the only element dependent on \(r\) and visible to \(\mathcal{J}\) is \(R=g^{r}\). So, we are in the situation of the DH key-exchange protocol. The details are given in Figure 6.  
Figure 6: Protocol transmitting an ephemeral share \(R\) for DH key exchange from \(\mathcal{S}\) to \(\mathcal{D}\).
## 3 Results and Discussion
### Performance Evaluation of the Coatings
#### 3.2.2 Carbonation Resistance  
The carbonation depth (mm) and digital images of the coatings after four and eight weeks of exposure are listed in Table 3. The concrete surface (without coating) exhibited 14.4 and 18 mm penetration of CO\({}_{2}\), and discoloration was mostly observed on the outer surface. With the increase in the exposure periods, the penetration depth increased because of the porous nature of the concrete surface, owing to the easy ingress of CO\({}_{2}\). Moreover, an interesting observation in the Cu coating was that the CO\({}_{2}\) penetration depth was higher than that without coating (concrete) even after four and eight weeks, which was attributed to the lower bond adhesion, the greater affinity of Cu with CO\({}_{2}\) (chemical reactivity), and porous surface morphology, which causes CO\({}_{2}\) to easily penetrate and reach beneath the coating. This result suggests that the Cu coating cannot function as a barrier to the ingress of CO\({}_{2}\) even though it accelerates the carbonation reaction. The Cu coating had a penetration depth of 20.2 mm after eight weeks of exposure. In comparison, the Ti coating exhibited the lowest penetration depth, at 9.2 mm after four weeks and 12.5 mm after eight weeks. This is attributed to its dense and compact morphology, as shown in Figures 2b and 3b, and oxide formation, which restricts the movement/diffusion and chemical reaction of CO\({}_{2}\). The carbonation depth results were in good agreement with those of the surface morphology, where the Ti coating exhibited the least porosity and a dense microstructure (Figure 3b), resulting in the least penetration of CO\({}_{2}\). The 85 Zn-15 Al coating after four weeks of exposure showed a 13.3 mm penetration, which was lower than that without coating (only concrete); however, once the exposure period was extended to eight weeks, it exhibited the highest penetration, that is, 21.2 mm. This might be attributed to the porous nature of the corrosion products, as CO\({}_{2}\) easily penetrates and reacts with Zn or Al, thus forming oxides, which later initiate the corrosion of the steel rebar embedded in the concrete. Owing to the high reaction affinity of Zn with CO\({}_{2}\) to form hydrozinctic (Zn\({}_{5}\)(CO\({}_{3}\))\({}_{2}\)(OH)\({}_{6}\)), it showed the highest CO\({}_{2}\) penetration depth after eight weeks of testing. The present findings suggest that Cu and 85 Zn-15 Al cannot be used as repair materials due to their high CO\({}_{2}\) penetration. By contrast, the Ti coating exhibited an almost 1.5-2 times lower CO\({}_{2}\) penetration even after four and eight weeks of exposure. This means that Ti can be used as a repair material for concrete and can extend the service life of exposed structures in industrial areas.
## 3 Test Results
### Test Results of Edge Cases  
The data of all edge void specimens are summarized and presented in Figure 8 and Table 7. Among them, only Case 6 did not experience failure, while the remaining specimens all suffered from failure. Among these five specimens, Cases 9-10 exhibited the smallest ultimate stress, and their stress-strain curves maintained a high degree of consistency. Case 6 was least affected by the angle, and Cases 6, 7, and 8 were essentially identical at the starting position. Case 7 exhibited the highest ultimate stress. Overall, the ultimate stress decreases with the increasing angle. For all specimens, the strain exceeded 0.1 at the point of failure.  
Figure 7: Stress–strain curve under working Case 1–5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Case** & **Stress** & **Strain** & **Case** & **Stress** & **Strain** \\ \hline \multirow{4}{*}{\(1\)} & 0.0 & 0.00 & & 0.0 & 0 \\ \cline{2-6}  & 17.0 & 2.39 & & 5.7 & 0.71 \\ \cline{2-6}  & 34.1 & 5.40 & & 11.4 & 1.27 \\ \cline{2-6}  & 51.2 & 8.81 & & 17.1 & 2.27 \\ \cline{2-6}  & 60.2 & 11.04 & 2 & 22.9 & 3.66 \\ \hline \multirow{4}{*}{\(3\)} & 0.0 & 0.00 & & 28.6 & 5.19 \\ \cline{2-6}  & 7.1 & 1.75 & & 34.3 & 6.45 \\ \cline{2-6}  & 14.3 & 2.75 & & 40.0 & 7.77 \\ \cline{2-6}  & 21.4 & 3.58 & & 51.4 & 8.98 \\ \cline{2-6}  & 28.6 & 4.88 & & 0.0 & 0.00 \\ \cline{2-6}  & 35.7 & 6.38 & & 9.5 & 0.65 \\ \cline{2-6}  & 42.9 & 7.96 & & 19.0 & 3.45 \\ \cline{2-6}  & 50.0 & 9.18 & & 28.6 & 4.43 \\ \hline \multirow{4}{*}{\(5\)} & 0.0 & 0.00 & & 38.1 & 6.58 \\ \cline{2-6}  & 10.0 & 2.21 & & 47.6 & 8.97 \\ \cline{2-6}  & 20.0 & 3.87 & & & \\ \cline{2-6}  & 30.0 & 7.20 & & / & \\ \cline{2-6}  & 40.0 & 9.28 & & & \\ \hline \end{tabular}
\end{table}
Table 6: Stress (MPa) and strain (%) data for various cases (middle case).  
Figure 8: Stress–strain curve under working Cases 6–10.
## 6 Instance Analysis and Experiment Results
The data of time slices between 10:00 am and 13:58 pm are selected for noon peak prediction, and we suppose the noon peak appears at 14:00 pm. The data of time slices between 14:00 pm and 17:58 pm are selected for evening peak prediction and we suppose evening peak appears at 18:00 pm. Then, we compare the prediction value with real value. The results are shown in Figure 8. As shown in Figure 8, the predicted value is consistent with the actual value, but some points fluctuate. The reason is the influence of outliers, which will affect the \"day-hour trend\" features and the \"hour-minute trend\" features to affect the predicted value. In addition, the prediction value and real value at the morning peak is closer than that of the noon peak and the evening peak. Since it is the first day of the May Day holiday, people's travel habits are different from the usual. As Table 2 shown, we compare the prediction Algorithm proposed in this paper with gray model, CP-WOPT, and uncompleted-missing-data-based prediction. The experimental results show that completing the missing data is helpful to improve the prediction accuracy. Moreover, the prediction accuracy of the proposed Algorithm is better than gray model and traditional CP-WOPT decomposition method. The reason is that the proposed Algorithm makes full use of \"temporal-spatial-periodic\" multi-mode characteristics based on the completed data, and the tensor window makes the data of time slices that close to the forecast point play a greater role. Figure 8: \(Cont.\)
## 2 FM-ACSK Communication System  
This section describes the structure of the FM-ACSK communication system with the focus on the key parts of the transmitter and receiver. As this work is essentially a continuation of the previous research involving ACSK communication system development and evaluation, some core elements, namely, the chaos generator, chaotic synchronization method, and the ACSK modulation part, are similar to the ones used in the previous prototype described in [21]. Hence, these parts of the communication system will only be described here briefly.  
The overall structure of FM-ACSK, which consists of a transmitter and a receiver, is shown in Figure 1. In the transmitter, binary data modulate the chaotic output \"R_out\" by passing it through directly or inversely, depending on the data bit value (the switch is regulated by a multiplexer with a register). This is why the ACSK modulation has \"antipodal\" in its name. The frequency modulation and demodulation ensure viable passband transmission of the ACSK-modulated signal and are discussed in more detail in Section 2.2. Once the retrieved ACSK signal is filtered from the channel noise, the chaotic demodulation begins. The signal is passed to both slave chaos generators in direct and inverted form. Depending on the prior inversion state of the ACSK signal, only one of the slave generators achieves chaotic synchronization. The synchronization errors, obtained by comparing the input and output of the slave chaos generators, are averaged and compared among themselves. The result signal of this comparison, which will be further referred to as \"\(\Delta\)sync\", is used to decide the transmitted data bit value. Additionally, the symbol phase synchronization module ensures timing recovery for the transmitted bits.  
To illustrate the ACSK detection principle, both the slave chaos generator's synchronization errors are shown on Figure 2 for the \"10101010\" data bit transmission example, where the \(\Delta\)sync signal's sign correlates with the values of the transmitted data bits.
###### Abstract  
Using an optical freeform surface to realize the precision measurement of displacement has become a research focus in the present day. However, the measurement range of this method is limited by the size of the freeform surface processed. In order to overcome this difficulty, this paper presents a two-dimensional displacement measurement system with a large range, which is composed of a slope sensor and a rotating paraboloid array. The slope sensor utilizes the optical structure of an autocollimator with minor optimization, and the rotating paraboloid array expands the measurement range of the system in a discrete manner. The experimental results showed that the optimized optical system enhanced the measurement accuracy to \(\pm\)0.4 \(\upmu\)m within the range of 1500 \(\upmu\)m and the overall measurement error was approximately \(\pm\)2 \(\upmu\)m when measured within the range of 450 mm. The developed measurement system has potential applicability for detection of errors, such as the position error and straightness error of multi-axis systems.  
rotating paraboloid array; two-dimensional; displacement measurement; optical structure optimization; large range; stability; calibration +
Footnote †: journal: Applied Physics Letters
## 5 Conclusions  
Removal of soluble Mn (II) present in partly processed synthetic mine water was effectively removed in column bioreactors packed with pebbles coated with Mn (IV) biofilms by a combination of chemical adsorption and microbial oxidation. The Mn (II)-oxidizing microflora were predominantly heterotrophic, and provision of small amounts of additional organic substrates appeared to be required to sustain their activities and to effectively remove manganese. Under optimized conditions, >99% of Mn removal was obtained, lowering concentrations of soluble Mn to below regulatory discharge levels. Microbiological remediation greatly reduced the requirement for large amounts of alkali addition associated with chemical mitigation of Mn-rich waters. Future work should consider the effect of both continuous input of organic carbon and fluid shear stress on biofilm growth and manganese removal. At least two species of Mn-oxidizing microorganisms were identified--the bacterium _L. discophora_ and a novel fungus that was isolated and characterized in vitro.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11094287/s1](https://www.mdpi.com/article/10.3390/app11094287/s1)]([https://www.mdpi.com/article/10.3390/app11094287/s1](https://www.mdpi.com/article/10.3390/app11094287/s1)), Figure S1: Image of the Mn (IV)-coated pebbles collected from a catchment stream in North Wales, UK, Figure S2: Summary of the integrated system protocol, Figure S3: Fixed bed bioreactor packed with Mn (IV) biofilm-colonized pebbles, Figure S4: Schematic representation of the in-line 2-column Mn (II)-oxidizing bioreactor, Figure S5: Images of isolate MnII grown on solid media, Figure S6: Terminal restriction enzyme fragment length polymorphism (T-RFLP) analysis (HaellII digests) of amplified 188 rRNA eukaryotic genes from the biofilm-colonized pebbles, Figure S7: Micrographs of the isolated MnII, Table S1: Summary of the forward and reverse primer sequences used in this study, Table S2: Identity of cloned archaeal genes (168 rRNA gene) obtained from the biofilm-colonized pebbles collected from the stream in Snowedonia, Table S3: Identity of cloned bacterial genes (168 rRNA gene) obtained from the biofilm-colonized pebbles collected from the stream in Snowedonia, Table S4: Identity of cloned eukaryotic genes (188 rRNA gene) obtainedfrom the biofilm-colonized pebbles collected from the stream in Snowdonia, Table S5: Utilization of organic substrates by MnI1.  
Conceptualization, D.B.J.; methodology, D.B.J. and A.L.S.; investigation, A.L.S.; data curation, A.L.S.; writing--original draft preparation, A.L.S.; writing--review and editing, D.B.J.; visualization, A.L.S.; supervision, D.B.J.; funding acquisition, A.L.S. All authors have read and agreed to the published version of the manuscript.  
A.L.S. is grateful to the National Council for Scientific and Technological Development (CNPq--Brazil) for provision of a research studentship (Grant No. 248994/2013-1).  
Data Availability Statement: Data not available in publicly archived datasets.  
The authors declare no conflict of interest.
# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
## 3 Data Analysis
### Sensitivity of L-Band to SSM for Wheat and grasslands  
Figure 2 shows the sensitivity of the normalized SAR signal in the L-band as a function of SSM for three ranges of NDVI for wheat and grasslands plots. For NDVI between 0.1 and 0.7 (well-developed canopy cover), the sensitivity of the L-band in HH polarization (L-HH) to SSM remains unchanged (Figure 2a,c), whereas as the sensitivity of the L-band in HV polarization (L-HV) to SSM decreases with vegetation growth (Figure 2b,d). The decrease in the sensitivity of the L-HV with vegetation  
growth (increase in the NDVI) is related to the attenuation of the soil scattering by the vegetation volume, which is more important in HV than in HH [41; 42; 43; 44]. Moreover, the results showed that in contrast to the L-HV, the L-HH signal is still sensitive to SSM even in the case of well-developed canopy cover (NDVI > 0.7) (Figure 2e,f). The sensitivity of the L-HH to SSM is approximately 0.27 dB/vol.% for an NDVI between 0.1 and 0.7 and decreases slightly to 0.19 dB/vol.% for an NDVI higher than 0.7 (Figure 2a,c,e). The L-HV is sensitive to SSM only in the case of NDVI values less than 0.7. The sensitivity of the L-HV is 0.20 dB/vol.% for an NDVI between 0.1 and 0.4 and 0.15 dB/vol.% for an NDVI between 0.4 and 0.7. For an NDVI above 0.7, the L-HV becomes insensitive to SSM (sensitivity = 0.04 dB/vol.%, Figure 2f). As a result, the use of the L-HH instead of the L-HV would allow better SSM estimates in the presence of vegetation cover.  
Figure 2: Sensitivity of the L-band backscattering coefficient as a function of SSM for three ranges of NDVI values. (**a**): L-HH and 0.1 < NDVI <0.4; (**b**): L-HV and 0 < NDVI <0.4; (**c**): L-HH and 0.4 < NDVI < 0.7; (**d**): L-HV and 0.4 < NDVI<0.7; (**e**): L-HH and 0.7 < NDVI < 0.9; (**f**): L-HV and 0.7 < NDVI < 0.9.