esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network-Based Reference Block Quality Enhancement for Motion Compensation Prediction
## 3 Proposed Method
### The Construction of an RBENN  
Figure 6 depicts the overall framework of the RBENN. The network consists of 10 residual modules and 2 convolutional layers at the beginning and end. The network takes the padded reference CU before MC as input and passes it through a \(3\times 3\) convolution layer, which is followed by a rectified linear unit (ReLU) [26]. This process can be expressed as  
\[\mathbf{F}_{1}=\max{(0,\mathbf{W}_{0}\cdot\mathbf{X})}, \tag{1}\]  
where \(\mathbf{W}_{0}\) represents the convolution kernel of the first layer, \(\mathbf{X}\) is the input of the RBENN, and \(\mathbf{F}_{1}\) is the output of the first layer.  
The main body of the RBENN is composed of multiple residual convolution blocks (RCB), and the composition of the residual module [27] is also shown in Figure 6. It is composed of two convolutional layers, an ReLU, and a shortcut. The purpose of using a short cut is to make the network converged quickly. This process can be expressed as  
\[\begin{cases}\mathbf{F}_{i,1}=\max{(0,\mathbf{W}_{i,1}\cdot\mathbf{F}_{i})},\\ \mathbf{F}_{i,2}=\mathbf{W}_{i,2}\cdot\mathbf{F}_{i,1},\\ \mathbf{F}_{i+1}=\mathbf{F}_{i}+\mathbf{F}_{i,2},\end{cases} \tag{2}\]  
where \(1\leq i\leq 10\), \(i\in\{1,2,\ldots,10\}\), \(\mathbf{F}_{i}\) is the input of the \(i\)-th module, \(\mathbf{W}_{i,1}\) is the first convolution kernel of the \(i\)-th module, and \(\mathbf{W}_{i,2}\) is the second convolution kernel of the \(i\)-th module, \(\mathbf{F}_{i+1}\) is the output of the \(i\)-th module and also the input of the \((i+1)\)-th module.  
Finally, a \(3\times 3\times 1\) convolution kernel is used to compress all feature maps into a residual image, which is added to the input image to obtain the final output:  
\[\begin{cases}\mathbf{F}_{12}=\mathbf{W}_{12}\cdot\mathbf{F}_{11},\\ \mathbf{Y}=\mathbf{F}_{12}+\mathbf{X},\end{cases} \tag{3}\]  
where \(\mathbf{Y}\) is the output, \(\mathbf{F}_{11}\) is the output of the last RCB, and \(\mathbf{W}_{12}\) is the last convolution kernel.  
The number of channels of convolution kernels in the whole network is 64, except in the last layer where it is 1.  
Figure 5: The detail of an RBENN integrated before MC.
## 3 Architecture and System Evaluation
### Inference Metrics  
Metrics for measuring machine learning inference performance are presented in recent works [35; 36]. In our study, the following metrics were used to conduct our experiment:  
* **Transmission delay:** is the length of time needed to transfer the images to a destination device.
* **True Positive (TP):** In the context of image classification, a true positive is when the model correctly predicts that an image belongs to a certain class, and the image actually belongs to that class.
* **True Negative (TN):** A true negative is when the model correctly predicts that an image does not belong to a certain class, and the image actually does not belong to that class.
* **False Positive (FP):** A false positive is when the model incorrectly predicts that an image belongs to a certain class, when in fact it does not.
* **False Negative (FN):** A false negative is when the model incorrectly predicts that an image does not belong to a certain class, when in fact it does.
* **Precision:** is the fraction of total True positives predictions of our model divided by the sum of True Positive and False Positive predictions, as presented in the following equation: \[Precision=\frac{TP}{TP+FP}\] (1)
* **Recall:** is the fraction of total True positives predictions of our model divided by the sum of True Positive and False Negative predictions, as presented in the following equation: \[Recall=\frac{TP}{TP+FN}\] (2)
* **Mean Average Precision (mAP):** is a metric used in object detection tasks to evaluate the model's performance. More specifically, mAP is calculated by finding the Average Precision (AP) for each class \((i)\) and then the average over a number of classes. N is the total number of classes. The accuracy of the object detector is obtained by this metric: \[mAP=\frac{1}{N}\sum_{i=1}^{n}AP_{i}\] (3)
* **Execution Time:** is the time for processing every image set.
# Formation of a Colloidal CdSe and ZnSe Quantum Dots via a Gamma Radiolytic Technique
## 4 Results and Discussion
### Optical Properties  
The optical band gap energy of the CdSe and ZnSe nanoparticles was estimated through the Tauc equation [35]:  
\[\left(\alpha lvv\right)^{1/n}=B\left(lv-E_{g}\right) \tag{10}\]  
where \(\alpha\) is the absorption coefficient, \(hv\) is the photon energy of the incident light, \(Eg\) is the band gap energy, \(B\) is a constant and \(n=1/2\), \(1\), \(3/2\) or \(2\) for allowed direct, allowed indirect, forbidden direct and forbidden indirect, respectively. The absorption band gap of prepared samples was estimated by extrapolating the linear portion of \((\alpha lvv)^{2}\) as a function of hv. Then, the intercept of the extrapolated line on the \(x\)-axis indicates the band gap energy of the sample. The values of \((\alpha lvv)^{2}\) versus \((lv)\) were plotted using the UV-Visible spectra of the samples as illustrated in Figure 6. Straight lines were drawn to fit the experimental band gap curves and were extended intersect the \((lv)\) axis to determine the optical band gap values of the CdSe and ZnSe nanoparticles.  
Careful observation of Figure 6 shows that the band gap energy for the CdSe and ZnSe nanoparticles can be extrapolated to 2.87 eV and 3.58 eV respectively. These results shows that the magnitudes of the obtained band gap energies for both CdSe and ZnSe nanoparticles are higher than the band gap energies of their bulk counterparts of 1.74 and 2.7 eV, respectively--the blue shift phenomenon.  
Figure 5: Transmission electron microscopy (TEM) for as prepared (**a**) CdSe and (**b**) ZnSe nanoparticles.
# Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
## 1 Introduction
As pointed out by [PERSON] [5], any security analytics solution must deal with (1) an active adversary, (2) the base rate fallacy, (3) the asymmetrical costs of misclassification, (4) potential poisoning of datasets, and (5) the attack time-scale. Furthermore, they must handle sensitive or confidential information used as input (e.g., in ML-based anomaly detection) effectively. In this work, we address the fourth and fifth challenge with a blockchain-based federated deep learning method. A key property of federated learning is that it does not require the centralization of training data, which has the advantage of increased the efficiency and confidentiality of the training data. We leverage the distributed ledger to record updates to machine learning models in a manner that is secure, transparent, highly resistant to outages, and auditable. By combining federated learning with blockchain technology, we enable accountability for collectively-learned machine learning models. The non-repudiation property of blockchains increases the reliability of the federated learning solution. Figure 1: Big Data analytics and security intelligence for Industry 4.0 applications. The mere adoption of blockchain technology does not provide this capability out of the box, as the only thing a blockchain offers is a distributed immutable ledger. With our solution, the traceability of transactions--in our case, updates to a deep neural network model--is exploited for the detection of tamper attempts or fraudulent transactions of adversaries aiming to evade anomaly detection. Indeed, every model update--or local weight or gradient updates for deep learning--can be cryptographically associated with an individual. Furthermore, it guarantees the integrity of incrementally-learned machine learning models by cryptographically chaining one machine learning model to the next. The main contribution of this work is evaluating the practical feasibility of auditing a federated deep learning process by using blockchains, demonstrated in a realistic use case on intrusion detection and a real-world dataset (i.e., CICIDS2017). We find that our proof-of-concept implementation--on top of the MultiChain ([[https://www.multichain.com](https://www.multichain.com)]([https://www.multichain.com](https://www.multichain.com))) open source blockchain platform--has a limited relative performance impact and network overhead, while providing full transparency over the distributed deep learning process. In Section 2, we review relevant related work and identify the gap in the current state-of-the-art. Section 3 describes our novel approach towards accountable federated learning using blockchain technology to ensure non-repudiation with respect to machine learning model updates. We present the evaluation results of our proof-of-concept implementation using an anomaly detection use case in Section 4. We conclude this work in Section 5, summarizing our main findings and highlighting opportunities for future work.
## 3 Design of the Compound Controller
### Neural Network Extend State Observer
#### 3.2.2 Stability Analysis of Neural Network Extend State Observer  
For the convenience of the following analysis, the two following lemmas are given:  
**Lemma 1**[30].: _Given a matrix \(A=\big{[}a_{ij}\big{]}\), the Frobenius norm is defined as the root of the sum of the squares of all elements_  
\[\|A\|_{F}^{2}\equiv\sum a_{ij}^{2}=tr(A^{T}A), \tag{26}\]  
_it is compatible with the vector 2-norm in that_  
\[\|Ax\|_{2}\leq\|A\|_{F}\|x\|_{2},\]  
**Lemma 2**[31].: _A mapping \(H:L_{p,c}\to L_{p,c}\) is said to be stable if there exist positive numbers \(\gamma\) and \(\xi\) such that \(\|(Hu)_{j}\|\leq\xi+\gamma\|u_{t}\|_{p}\) for all \(u\in L_{p,c}\) and all \(t\in[0,+\infty)\)._  
**Theorem 3.1**.: _The state estimation error approaches zero asymptotically \((\lim_{t\to\infty}\overline{X}=0)\), provided_  
1. \(\gamma<\lambda_{\min}(Q)/2\lambda_{\max}(P)\)_, where_ \(P,Q\) _are positive definite symmetric matrices and_ \(\lambda_{\min}\) _and_ \(\lambda_{\max}\) _represent the minimum eigenvalue and the maximum eigenvalue of the matrix respectively,_
2. _the Lyapunov function_ \((A-LC)^{T}P+P(A-LC)=-Q\) _holds._  
**Proof.** Choose the Lyapunov function for the observer as  
\[V_{o}=\frac{1}{2}\overline{X}^{T}P\overline{X}+\frac{1}{2}tr\big{(}\widetilde {W}^{T}\kappa^{-1}\widetilde{W}\big{)}, \tag{27}\]Differentiate Equation (28) and we get  
\[\begin{array}{ll}\dot{V}_{o}&=\frac{1}{2}\dot{\overline{X}}^{T}P\overline{X}+ \frac{1}{2}\overline{X}^{T}P\dot{\overline{X}}+\text{tr}\Big{(}\dot{\overline{ W}}\kappa^{-1}\overline{W}\Big{)}\\ &=\frac{1}{2}\overline{X}^{T}Q\overline{X}+\overline{X}^{T}P\Big{[}\overline{X }^{T}P\overline{W}\sigma\Big{(}\hat{X}\Big{)}+\omega(t)\Big{]}\\ &+\text{tr}\Big{[}\overline{W}^{T}R\overline{X}_{o}T\Big{(}\hat{X}\Big{)}+ \widetilde{W}^{T}\|C\dot{\overline{X}}\|\big{(}W-\overline{W}\big{)}\Big{]}\\ &\leq-\frac{1}{2}\|\vec{X}\|\lambda_{\min}(Q)\|\vec{X}\|+\|\vec{X}\|\cdot\|P\| \big{(}\|\vec{W}\|_{\partial M}+\overline{\omega}\big{)}\\ &+\sigma_{M}\|\overline{W}\|\cdot\|R\|\cdot\|\vec{X}\|+\Big{(}W_{M}\|\overline {W}\|-\|\overline{W}\big{)}^{2}\|C\|\cdot\|\vec{X}\|\end{array}, \tag{28}\]  
According Equation (29), when \(\dot{V}_{o}\leq 0\) the inequality holds  
\[\|\vec{X}\|\geq(2\|P\|\overline{\omega})+\Big{(}\sigma_{M}\|P\|+W_{M}\|C\|+ \sigma_{M}\|R\|^{2}/2\Big{)}/\lambda_{\min}(Q), \tag{29}\]  
Invoking Equation (26), the ideal weight tuning law of RBF neural network is as follows:  
\[\dot{\overline{W}}+\alpha\overline{W}=\phi(\overline{X})+\alpha W, \tag{30}\]  
where the bounded function \(\phi(\overline{X})=\eta\Big{(}\overline{X}C^{T}C(A-LC)^{-1}\Big{)}\sigma\Big{(} \hat{X}\Big{)}\), \(\alpha=\kappa\|C\overline{X}\|\). As shown in Equation (30),
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 2 Methodology  
In the handling with LiDAR data, we have difficulties to use filters that consider prevailing measurement influences. While the assumption of the LiDAR data behaviour included in every LiDAR data filter may appear to be uncritical for some applications, it seems paradox to filter this data for scientific studies investigating this behaviour. In order to filter LiDAR data in an adaptive dynamic way, we developed two methodologies based on the same approach to identify valid and invalid measurement points in an adaptive, dynamic way. Below, these filters are described along other filters found in the literature.
## 3 Results
### Usability  
Usability includes the analysis of learnability, operability, and user error protection.  
Learnability defines the ability to learn the software, which can be easily operated after having been learnt. The evaluation of learnability is based on the time register, marking the time for training. The easiest software to learn was AST due to its simplicity and clarity. Further, CitySim also has a simple interface, requiring less time than ENVI-met. The most complicated software tools were GreenScenario and Ladybug, as they require knowledge of visual scripting.  
Operability evaluates how the software operates after the learning phase. The most complex software tool is ENVI-met as it integrates several applications and complex settings and requires knowledge of climatology. Similarly, Ladybug requires knowledge of climate calculation formulas, which has a high impact on results. GreenScenario has an integrated formula to calculate the results from the model. The CitySim and AST software tools can be distinguished as the easiest to use.  
User error protection evaluates the software's protection against errors by employing the model checker function. ENVI-met integrates the model checking function comprehensively to analyse the failures in the model before the simulation is started. CitySim scans for potential model failures when reopening the file. Ladybug and GreenScenario have anautomatic error finder for the failures in the script, but not in the model. Meanwhile, AST does not contain error protection measures.
## 3 Results
### Simulations in Semi-Infinite Phantoms
#### 3.1.5 Scaling Factors: Linearly Correcting Retrieved \(D_{b}\) Coefficients  
Given relative changes in \(D_{B}\) could be computed across optically identical media but with different flow coefficients, we derived correction factors (for each medium with given optical coefficients and required SDS). These correction factors were calculated as the ratio of the simulated (input) Brownian flow coefficients simulated, to the flow coefficient retrieved via fitting the simulation using Equation (2). Thus, retrieved \(D_{B}\) coefficients, for any medium, could be appropriately scaled and corrected to estimate an absolute flow coefficient. Figure 7 shows the correction factor maps obtained (across the 35 simulated media) at two different input \(D_{B}\) coefficients (Figure 7a,c: \(D_{B}=1\times 10^{-8}\) cm\({}^{2}\)/s; Figure 7b,d: \(D_{B}=3\times 10^{-8}\) cm\({}^{2}\)/s) for two different SDS (Figure 7a,b: 0.25 cm; Figure 7c,d: 1.05 cm). The similarity of these derived correction factors in Figure 7a-d indicate that they were nearly identical for either flow coefficient. Since a correction factor of unity would indicate that retrieved flow coefficient was the same as that modeled, the correction factors in Figure 7a,b (short SDS) are correspondingly lower, than those for Figure 7c,d (longer SDS).  
Figure 8a shows the average absolute values of retrieved \(D_{B}\) coefficients, across the 6 different sets of simulations (with each set having fixed \(D_{B}\) ranging from \(5\times 10^{-9}\) cm\({}^{2}\)/s through \(3\times 10^{-8}\) cm\({}^{2}\)/s in intervals of \(5\times 10^{-9}\) cm\({}^{2}\)/s), for four SDS (squares: 0.15 cm; triangles: 0.55 cm; circles: 1.05 cm; asterisks: 1.95 cm). The error bars show the standard deviation in retrieved \(D_{B}\) values across the 35 simulated media with varying optical properties (the dashed line is \(y=x\)). Figure 8b shows the average absolute value of the corrected \(D_{B}\) coefficients, for the same data shown in Figure 8a where each retrieved flow coefficient was corrected by multiplication with correction factors derived. Correction factors for each tissue model and SDS were obtained from the set simulations with input \(D_{B}=3\times 10^{-8}\) cm\({}^{2}\)/s. As was seen previously in Figure 6, corrected flow coefficients yielded values that were near exactly identical to the true (input) value for \(D_{B}\), across all \(D_{B}\) coefficients.  
Figure 6: Relative change in extracted flow coefficients (relative to values obtained from media simulated with \(D_{B}=5\times 10^{-9}\) cm\({}^{2}\)/s) at three different SDS. The true relative change in \(D_{B}\) (\(x\)-axis) was obtained from input values to simulations, while the derived values (\(y\)-axis) were obtained from fitted coefficients (see text).
## 2 Problem Background
### Limitations of Existing Fair Routing Solutions for Sensed Data Collection
#### 2.4.3. Scenario-Specific Burden Measures  
Another crucial aspect of ensuring fairness is the ability to determine the burden on nodes. The existing solutions infer node burden from buffer occupancy. Unfortunately, higher buffer occupancy may not always mean higher burden, and vice versa. The three main reasons that bring about this scenario-specific performance are as follows:  
1. Changes in Data Traffic  
The first reason revolves around the differences in sending and receiving abilities of popular nodes and less popular nodes. Popular nodes are likely to have a higher buffer occupancy most of the time, since they receive more messages. However, this is assuming that a steady rate of message generation is maintained throughout, for which there is no guarantee. At the event of a drop in the rate of message generation (e.g., after traffic bursts), popular nodes are able to send more messages and free their buffers at higher rates due to higher number of encounter opportunities. In such scenarios, experiments [70] show that existing solutions may bring about false positives on less popular nodes: a higher buffer occupancy on less popular nodes will be wrongly translated as higher burden, when they have done less transmissions (i.e., sending plus receiving) and consumed less energy.
# Simulation and Design of Three 5G Antennas
## 2 Materials and Methods  
### Theoretical Analysis of Microstrip Antenna Arrays  
With the rapid development of 5G communication, there is increased demand for antennas in various aspects. Individual antenna units can no longer meet the requirements of modern production and daily life. Therefore, it is necessary to connect multiple antenna units to create higher gain and more directional array antennas. Typically, identical antenna units are used, arranged in various combinations to form array antennas with different numbers of elements, enabling the array to perform different functions. Arrays with regularly spaced identical antenna units are more convenient for design and calculations.  
The overall performance of array antennas depends not only on the characteristics of the individual antenna units but also on the number of elements, spacing, arrangement, and the amplitude and phase of the excitation [15]. The most common array antenna configurations include two types: one is characterized by the placement of multiple antenna units at equal intervals along a straight line and centered around an axis, known as a linear array antenna; the other is an extension of the linear array, forming a planar arrangement, and is known as a planar array antenna. In the following sections, we will provide a detailed explanation of linear array antennas and planar array antennas.
## 4 Conclusions  
In this paper, a low-cost and compact three-degree-of-freedom laser measurement (3 DOFLM) system with real-time wavelength correction was proposed for displacement feedback, angular error measurement, and Abbe error compensation of a nanopositioning stage. The method of wavelength correction does not rely on the Edlen equation, which was adapted to the He-Ne laser and was an off-line process. The principle of the 3 DOFLM system was expressed. It only needs to calibrate and store the nominal wavelength and the corresponding initial diffraction angle once using a reference. With these stored parameters, the system can automatically correct the wavelength by itself in the remaining measurements. A prototype was constructed and tested. It was verified by experiments that, compared with the HP5529A interferometer, the angular error measurement achieved an accuracy of \(\pm\)0.5 arcsec in a travel distance of 20 mm. The displacement measurement achieved a range up to 36 mm, and the accuracy was improved from \(\pm\)45 nm without wavelength correction to \(\pm\)25 nm with wavelength correction. The feasibility of the designed 3 DOFLM system was demonstrated. In order to further improve the real-time measured wavelength, considerations of the influences of linearity error of the Lissagous circle, incident beam angle and its drift, and thermal deformation of the grating pitch on the wavelength measurement accuracy will be carried out in future works.  
[PERSON] wrote the main concepts of the manuscript and contributed the formal analysis; [PERSON] implemented numerical experiments; [PERSON] and [PERSON] collaboratively discussed the results; [PERSON] conceived the experiments and checked the English writing.  
The authors are grateful for the financial support provided by the National Key Research and Development Program of China (2017 YFF0204800), the Fundamental Research Funds for the Central Universities (No. DUT16 TD20) and the National Natural Science Foundation of China (U1508211, 51621064).  
The authors declare no conflicts of interest.