esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Target Response Model and Analysis  
As outlined in Figure 1, the proposed external calibration workflow starts with the analysis of reference target responses in multiple channels of acquired SAR data. The analysis is repeated throughout the calibration process to take into account each calibration correction as it becomes available.  
The notation adopted throughout the following sections uses _az_ to denote the azimuth sample index and super- and sub-scripts \(\square_{pq}^{s}\) to indicate any quantity derived from the response of reference target \(s\) in a SAR data channel that uses the antenna elements \(p\) and \(q\) on transmit and receive, respectively. An antenna element is, for the present purposes, the smallest constituent of an antenna array that can individually send and/or receive signals. In particular, polarimetric antennas are considered as two independent elements corresponding to the horizontal and vertical polarizations.  
The calibration approach is intended to be applied to a data set comprising one or more calibration data acquisitions. Such acquisitions contain reference targets that are imaged over a wide range of off-nadir angles. To avoid unnecessary complication of the notations adopted, the reference target  
Figure 1: A flowchart illustrating the calibration process. The columns, from right to left, correspond to the target analysis of Section 3.2, the estimation of individual calibration corrections described in Section 4 and the inputs used by estimation step (derived from the previous iteration of analysis).  
superscript \(s\) is specific to the data acquisition. Thus, when the same target appears in independent acquisitions, the responses are treated as though originating from different targets.
## 3 Analysis Model
### Energy Conversion Efficiency  
By using linear super-positioning [29], the mean wave energy density, including kinematic and potential energy, is obtained as follows  
\[E=\frac{1}{8}\rho gH^{2} \tag{21}\]  
where \(H\) is the wave height, and the group velocity for constant water depth \(h\) is obtained by  
\[C_{g}=\frac{1}{2}\frac{g}{\omega}D(kh) \tag{22}\]  
where \(k\) is the angular repetency and can be defined as the number of radians per unit distance in the following equation  
\[k=\frac{2\pi}{\lambda} \tag{23}\]  
where \(\lambda\) is the wavelength and \(D(kh)\) is the depth function [29] and can be defined as  
\[D(kh)=\left[1+\frac{2 kh}{\sinh(2 kh)}\right]\tanh(kh) \tag{24}\]  
Then, the mean wave power is obtained by  
\[P_{m}=EC_{g} \tag{25}\]  
Then, the absorption wave power acting on the floating buoy can be predicted in the following equation  
\[P_{w}=P_{m}d \tag{26}\]  
where \(d\) (the capture width of the device) is the horizontal extension of the floating buoy.  
Finally, the overall efficiency of the proposed WEC is defined as the ratio of the generated power and the absorption wave power, and expressed as  
\[\eta_{o}=\frac{P_{g}}{P_{w}} \tag{27}\]  
According to [29], the energy absorption efficiency can be increased by adjusting the natural frequencies to resonate with the incident waves. The wave frequencies are usually lower than the natural frequency of the PTO system. Therefore, the inertial supplementary can be added to bring the natural frequency close to the regular wave frequencies. Then, the natural frequency of the PTO system can be expressed as  
\[\omega_{n}=\sqrt{\frac{S_{b}}{m_{b}+m_{a}+\frac{L}{h}}}\rightarrow\omega \tag{28}\]
## 3 Modeling Approaches
### Evaluation of the Models' Performance
#### 3.7.2 Receiver Operating Characteristic (ROC)  
The ROC is commonly used in evaluating landslide susceptibility and other research areas. The ROC curve is obtained by plotting the false positive rate on the \(X\)-axis and the true positive rate on the \(Y\)-axis. The ROC curve reflects the change in the performance of a binary classifier learner when the threshold value is adjusted. The area under the ROC curve (\(AUC\)) is a measure of the model's reliability. The \(AUC\) can be calculated using the following formula  
\[AUC=\frac{(\sum\textit{TP}+\sum\textit{TN})}{(\textit{P}+\textit{N})} \tag{11}\]  
where \(P\) and \(N\) represent the number of landslides and the number of non-landslides, respectively.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Simulation and Calculation of Double-Balance Mixer Conversion Gain  
For the emitter degeneration case, effective \(g_{m}\) can be written as \(g_{m\_eff}=\frac{g_{m}}{1+g_{m}Z_{E}}\) while emitter degenerating impedance \(Z_{E}\) can be found using \(Z_{E}\approx SL_{E}+(R_{E}\big{|}\big{|}\frac{1}{5C_{E}}\big{)})\). The calculated value of \(Z_{E}\) is \(\approx 21.983+j19.577\) and \(of\ |Z_{E}|\ \approx 29.436\) ohm. The simulation of the circuit is carried out in PathWave Keysight ADS 2023. The layout of the measured prototype was simulated using 3D Keysight ADS 2023 Momentum tools, and simulation measurements were done using the EMcosimulation test setup method.  
An LC-tuned circuit load is connected between two outputs at a constant IF of 145 MHz, and each output will have about half of the load connected to the ground. The following formulas can be used to determine the total load impedance: \(Z_{L}=(SL|\big{|}\frac{1}{5C})\) and \(re\{Z_{L}\}=\sqrt{\frac{k}{c}}\approx 503.94\) ohm.  
Figure 7 shows the simulated load presented at the IF port without matching the 50-ohm system. If we only consider the real part of the load impedance, then \(R_{L}\approx 227\) ohm. This will approximate the mixer's conversion gain at the IF port.  
Figure 6: Collector current waveform of LO differential pair.  
Figure 8 shows the simulated AC transconductance \(g_{m}\) of the double-balanced differential mixer. At the same time, the table shown in Figure 8 represents a mixing index of RF and LO signals. The IF frequency of 145 MHz is at index 2. The value of transconductance is about 0.182 S. It can also be calculated from the voltage and current waveform of either Q2 or Q3. The transconductance of the BJT device is shown in Figure 9, and the value can be calculated using \(\frac{\Delta F_{L}}{\Delta V_{\mathrm{Je}}}\), which is also frequency dependent. In our case, it is 0.189 S.  
Substituting the value of \(g_{m,eff}\) into Expression (8) yields the following.  
Voltage conversion gain \(A_{v}=\frac{2}{\pi}g_{m,eff}R_{L}=\frac{2}{\pi}\frac{g_{m}}{1+8g_{m}R_{E}}R_{L }=\frac{2}{\pi}\frac{0.182\ast 227}{1+(0.182\ast 29,436)}\approx 12.338\) dB.  
The RF frequency is 1 GHz at \(-40\) dBm, while the LO is 855 MHz at \(-5\) dBm. The IF frequency is down-converted to 145 MHz. Figure 10 shows a simulated frequency spectrum at the IF port. It demonstrates that the LO and RF signals are substantially attenuated, and the simulated conversion gain is around 11.49 dB.  
Figure 8: AC transconductance of BJT mixer.  
Figure 7: Simulated load impedance at 145 MHz of IF frequency.
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 3 Preliminaries
### Cluster Head Selection
#### 4.2.4 Average Distance  
To calculate the average distance, the UAV nodes attach the GPS location information to the HELLO message and send it to neighbor nodes. The average distance between \(i\) and its neighbors can be calculated as  
\[d_{i}=\frac{1}{Nd_{i}}{\sum\limits_{j=1}^{Nd_{i}}\sqrt{(x_{j}-x_{i})^{2}+(y_{j }-y_{i})^{2}+(z_{j}-z_{i})^{2}}} \tag{29}\]  
where \((x_{i},\ y_{i},\ z_{i})\) is the 3D coordinate of the \(i\), and \(\big{(}x_{j},\ y_{j},\ z_{j}\big{)}\) is the position of its neighbor node \(j\). The average distance is normalized [5] by  
\[D_{i}=\log(\frac{d_{i}}{R}+1) \tag{30}\]  
Based on the definition of the four factors, the cluster head selection is proposed, of which the pseudocode is listed in Algorithm 2.  
```
Input: cluster numbers \(K\), clusters set \(CL=\{CL_{1},CL_{2},\ldots CL_{i}\ldots,CL_{K}\}\). Output: cluster head \(CH_{i}\), \(i=1,2,\ldots,K\) /*Initialization*/
1: Each UAV node broadcasts a HELLO message to its neighboring set \(\varphi\). /*Computation*/
2:for\(i=1:K\)
3:for each UAV node \(je\ CL_{i}\)
4: calculate the weight \(W_{j}\) by using Equation (9)
5:endfor
6:\(CLbest=argmax\{W_{j}\}\)
7:\(CH_{i}=CLbest\)
8: broadcast \(CH_{i}\) claim
9:endfor
```  
**Algorithm 2**Cluster Head Selection Algorithm
# Diabetic Retinopathy Improved Detection Using Deep Learning
## 2 Related Works
### Diabetic Retinopathy  
Depending on the stage of diabetic retinopathy, it can be classified into different classes, from the earliest to the most advanced. This classification depends on the condition of the retina in its fundus oculi evaluation, divided into two main categories, Nonproliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) [15].  
NPDR is subdivided into slight, medium, and severe, and the changes it produces are limited to the retina and do not go beyond the inner retinal limiting membrane. PDR can be subdivided into early, high-risk, and advanced, with ophthalmoscopic alterations produced by ischemia, result in blood vessels neoformation, which proliferate beyond the retina. For this work, PDR is used as a single class containing the early, high-risk, and advanced subtypes.  
* **No DR:** patient without retinal alterations due to diabetes.
* **Slight NPRD:** in this case, microaneurysms, exudates, cotton-wool spots, and retinal hemorrhages are considered.
* **Medium NPRD:** the fundus oculi present hemorrhages, severe microaneurysms, or venous rosaries in some retina quadrants.
* **Severe NPRD:** two of three criteria from the medium NPDR exist in the fundus oculi.
* **Proliferative DR:** proliferative retinal neovessels appear in the retina's image.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 4 Result Calculation and Analysis
### Effect of Inner Ring Rotational Speed Variations on Bearing Fatigue Life  
The rotational speed of cylindrical roller bearings directly influences the entrainment velocity between rollers and bearing rings, thereby affecting the formed film thickness and ultimately impacting the system's overall fatigue life.  
Table 6 shows the influence of bearing speeds from 500 r/min to 1200 r/min on the overall fatigue life when the parameters of the cylindrical roller bearing in Table 3 are used, the pure radial force is 160 KN, and the initial viscosity of lubricating oil is 0.193 Pa-s.  
Figure 11: Results and relative deviations of the three models under different loads.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Load/N** & **De Mul Model** & **This Article Model** & **ISO/TS 16281:2008** \\ \hline \(1.0\times 10^{5}\) & 660.1 & 594.1 & 570.3 \\ \(1.2\times 10^{5}\) & 318.5 & 281.2 & 269.8 \\ \(1.4\times 10^{5}\) & 172.0 & 147.8 & 141.9 \\ \(1.6\times 10^{5}\) & 100.9 & 85.3 & 81.8 \\ \(1.8\times 10^{5}\) & 62.9 & 52.5 & 50.4 \\ \(2.0\times 10^{5}\) & 41.3 & 34.1 & 32.9 \\ \(2.2\times 10^{5}\) & 28.2 & 23.2 & 22.3 \\ \(2.4\times 10^{5}\) & 19.9 & 16.2 & 15.6 \\ \hline \end{tabular}
\end{table}
Table 5: Fatigue life of bearings under different loads (\(\times 10^{6}\) r).  
As demonstrated in Table 6, (1) the De Mul model's calculated bearing fatigue life exhibits minimal sensitivity to rotational speed variations, as its lubrication-free assumption restricts speed effects to centrifugal force changes. (2) The results from the proposed theoretical model reveal a progressive reduction in system fatigue life with increasing rotational speed. This occurs because rotational speed severely affects oil film thickness calculations across lubrication states, which subsequently governs the magnitude of elastic deformation and thereby influences contact load distribution and fatigue life.  
Figure 12 provides the lifespan calculation results from three models, along with the relative deviations between the proposed theoretical model and the De Mul model, as well as between the proposed model and ISO/TS 16281:2008-based calculations.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 1 Introduction
### Key Contributions  
The main contribution of this paper is the presentation of a detailed and comprehensive account of the research and development process of intelligent MTD. Our work provides MTD-related researchers with the following new perspectives:  
* We conducted a thorough survey on MTD, as shown in Figure 3. We analyzed the development and deficiencies of MTD techniques, highlighting the emergence of reinforced MTD techniques such as intelligent MTD and SDN-based MTD. Then, we focused on techniques related to intelligent MTD, categorizing them based on their characteristics towards _affordable_, _optimized_, and _self-adaptive_, and formed a systematic organization, aiming to provide researchers with a more intuitive understanding of current intelligent MTD techniques.
* During our research, we introduced a new classification method for MTD techniques, SDR/Five layers, which aligns more closely with existing MTD development. This approach has practical significance and offers a more detailed classification for all the MTD techniques proposed so far.
* We discussed the practical conclusions that could be obtained from our survey and identify existing limitations. From these insights, we offer suggestions for future developments in intelligent MTD techniques.  
Figure 3: Structure of our survey.
## 4 Experimental Results
### Critical Analysis and Discussion  
In conclusion, the proposed approaches presented in this paper offer a promising solution to the challenges of DL-based machining feature recognition. There are some advantages to be highlighted. The proposed semi-supervised learning framework is able to leverage both labeled and unlabeled data to improve machining feature recognition. With the help of the framework, the MsvNetLite and the FeatureNetLite obviously surpassed the large models such as MsvNet and FeatureNet. The proposed framework can be a promising approach to improving the recognition performance of user-defined machining feature while minimizing the need for manual labeling. Moreover, the utilization of model compression techniques can significantly reduce the model parameters and computing complexity, making the models CPU-friendly and more suitable for real-world industrial applications. This can lead to improved software execution efficiency and lower hardware costs.  
However, there are also limitations of the proposed approaches presented in this paper, which must be taken into consideration when applying them to real-world applications. First, the evaluation of the proposed approaches was limited to the FeatureNet dataset, which may not necessarily represent the complexity and diversity of other datasets. Therefore, further investigation and validation on a wider range of datasets is necessary to generalize the effectiveness of these approaches. Second, the requirement of a considerable amount of training time for the current self-supervised stage may incur additional costs in terms of time and resources. Hence, it is important to assess the training budget for implementing the approach in real-world CAPP.
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## System Model
### Problem Formulation  
In this section, we propose a resource allocation problem to satisfy the traffic demand of each cell and maximize the total system throughput. The specific form is as follows:  
\[\begin{split}\max_{\mathbf{A},\mathbf{P}}&\sum_{j= 1}^{J}\sum_{m=1}^{M}R_{m}^{j}\\ s.t.& R1:R_{m}^{j}\geq T_{m}^{j},\forall j,\forall m,\\ R2:\sum_{m=1}^{M}\sum_{k=1}^{K}\sum_{n=1}^{N}p_{m,nk}^{j}\leq P_{ max},\forall j,\\ R3:p_{m,nk}^{j}\geq 0,\forall j,\forall m,\forall n,\forall k, \end{split} \tag{5}\]  
where \(T_{m}^{j}\) represents the traffic demand of the \(m\)th cell served by the satellite \(j\), and \(R_{m}^{j}\) denotes the throughput, which can be expressed as  
\[R_{m}^{j}=\sum_{k=1}^{K}\sum_{n=1}^{N}B_{sc}\log_{2}\Bigl{(}1+\gamma_{m,nk}^{j }\Bigr{)}, \tag{6}\]  
where \(\gamma_{m,nk}^{j}\) represents the SINR for users of the \(m\)th cell served by the satellite \(j\) covered by the beam using the \(n\)th subcarrier during the \(k\)th time-slot, and it can be expressed as  
\[\gamma_{m,nk}^{j}=\frac{a_{m,nk}^{j}p_{m,nk}^{j}H_{jm,k}^{j}}{\sum\limits_{(i, d)\
eq(j,m)}a_{q,nk}^{q}p_{q,nk}^{j}H_{jm,k}^{j}+N_{0}}, \tag{7}\]  
where \(H_{jm,k}^{j}=|h_{jm,k}^{j}|^{2}\) represents the channel gain and \(N_{0}\) represents the noise power.  
In (5), constraint \(R1\) guarantees that the throughput in each cell satisfies the respective demand. Constraint \(R2\) guarantees that the total transmission power of the satellite is less than \(P_{max}\). Constraint \(R3\) guarantees that the allocated power is non-negative.  
Next, we propose two different algorithms based on the availability of the information to solve problem (5). The information required to solve the problem optimally is the channel gain of the previous, current, and future time slots. In the offline scenario, it is assumed that the precise channel gains within the future time segment have been obtained in advance. Although this assumption is difficult to achieve, it provides the optimal solution performance of the problem (5). The second scenario is the online case, where it is assumed that only instant channel gains are known.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 1 Introduction
In Section 2, the system model is presented for the air vehicle, and, in Sections 3 and 4, the model of sensors' error, the Kalman filter, and the SOP receiver are considered. Subsequently, in Section 5, the rotation algorithm and the SOP/INS integration are presented. Finally, in Section 6, the result of the simulation and the interpretation of the results are propounded as a conclusion.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 2 Background on Federated Learning
### Handling Non-IID Data  
In FL, the training data of each client are highly dependent on the usage patterns of specific local devices, leading to potentially significant differences in data distribution across connected clients. This phenomenon, known as \"Non-independent and identically distributed data\" (non-IID), can result in severe model divergence [10; 11]. For each data sample, \(x\) indicates the input attribute or features, and \(P_{k}(x)\) is the local distribution of \(x\) for client \(k\). We can formally define non-IID as the variation of \(P_{k}\) across all the clients. There are different categories of non-IID data (see [10; 11]).  
Among these categories, the label distribution skew may cause severe learning divergence for parametric models such as Deep Neural Networks (DNNs), mainly in a horizontal FL setting [11]. The label distribution skew can be attributed to the sharing of a conditional feature distribution, represented by the function expressed as \(P_{k}(x|y)\), among the clients. This phenomenon is typically attributable to discrepancies in the geographical locations of clients that store analogous types of local training data.  
The FedAvg algorithm cannot deal with model divergence problems caused by non-IID data, especially when complex models such as neural networks are used. Current approaches to counter the impact of non-IID data can be classified into _data-based_, _algorithm-based_, and _system-based_ approaches [10].
# A Tunable Microstrip-to-Waveguide Transition for Emergency Satellite Communication Systems
## 5 Conclusions  
In this paper, we propose a novel tunable microstrip-to-waveguide transition for the ground station of emergency satellite communication systems. Both simulated and measured results confirm that the transition can effectively suppress the amplified noise signals within the receiving frequency band. Importantly, tuning screw technology has been introduced into the proposed transition to achieve the tunability of the transmission zero in the receiving frequency band. This is particularly crucial in the actual debugging process, as the frequency of the amplified noise signals randomly distribute within the receiving frequency band. Specially, three tuning metal screws are incorporated to enhance the out-of-band suppression within the entire receiving frequency band of 12.25-12.75 GHz. In addition, in order to enhance the match in the transmitting frequency range of 14-14.5 GHz, three matched patches and three waveguides are involved in the transition. This work is instrumental in advancing the miniaturization of ground stations for emergency satellite communication systems. In the future, we will promote the system test of the ground station with the proposed microstrip-to-waveguide transition.  
Conceptualization, Y.X.; methodology, Y.X.; software, Y.X. and X.T.; formal analysis, Y.X. and D.G.; data curation, Y.X.; writing--original draft preparation, Y.X. and X.T.; writing--review and editing, Y.X., D.G. and X.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the School-Level Scientific Research Project of Chengdu College of University of Electronic Science and Technology of China (No. 2024-XXY)-03), and the Sichuan Key Research and Development Program (No. 2022 YFG0226), National Natural Science Foundation of China (No. 62301459).  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
## 3 Results
### FEM Test Case  
The generated signal in the FEM test case has the excitation frequency of 30 kHz, as shown in Figure 5. This setup of the model was previously explained in [25]. Modelling of guided-wave signals using FEM have been widely used and validated in the literature [2; 26; 27; 28]. However, the major difference in this model is that the excitation sequences have variable transfer functions and fewer reception points are used; this tends to add flexural noise in data. In this figure, for better illustration of the signal, the received signal from (forward) the pipe end and the starting dead zone are removed. The defect signal is expected to be received at 3-3.5 m. Since highly variable transfer functions have been used, the backward cancellation algorithm is not able to cancel the backward going signal perfectly. This reversed wave reflected from the pipe end is received at 1-1.5 m. Due to this backward leakage, another signal of interest, which is a torsional wave, is expected to be received at approximately 4-4.5 m. This signal is generated when the echo of the backward leakage and the defect signal (in the forward testing direction) is received at the test tool. All other regions are the noise caused by the flexural waves.  
Figure 5: Synthesised data generated from finite element model (FEM) test case.
## 4 Selection of FEC Codes for the Railway Application
### POLAR Codes
#### 4.1.1 Encoding  
The encoder is essentially the polarization transform that the kernel provides, as  
\[\mathbf{F}=\begin{bmatrix}1&0\\ 1&1\end{bmatrix} \tag{1}\]  
The Kronecker product of this kernel with itself yields the transform for a larger input size, resulting in POLAR codes with lengths that are powers of two. For a code of length \(N\) and \(n=\log 2(N)\), the encoder is  
\[\mathbf{G}=\mathbf{F}^{\oplus n}, \tag{2}\]  
where \(\mathbf{F}^{\oplus n}\) is the Kronecker product of \(\mathbf{F}\)\(n\) times with itself. Thereafter, the encoding occurs in a similar way such as  
\[\mathbf{c}=\mathbf{u}\mathbf{G}, \tag{3}\]  
with \(\mathbf{c}\) being the output codeword, \(\mathbf{u}\) being the input block, and \(\mathbf{G}\) being the generator matrix.