esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mitigation Scheme for SAR
### Discussions  
It should be noted that no universal method exists for post mitigating all RFI in SAR data. Current techniques work best for interfering signals that have sparse spectral or temporal or spectral-temporal occupancy with the target echoes. This means these techniques are most effective for narrow band RFI and pulsed wide band RFI, while it is rather difficult to deal with the broadband continuous wave RFI. The effectiveness of any given mitigation technique depends on many factors, including:  
* System architecture of the SAR instrument: if SAR has multiple antennas, the sniffing pulse and spatial filtering could be employed. If not, only other signal processing techniques can be applied.
* Configuration or mode for a particular observation: the scanning mode of the data acquisition like stripmap mode, spotlight mode, scan mode, etc., defines the interfering geometry, which determines whether the RFI directs into the SAR receiver from the terrestrial RFI sources or scattered by multi-path from spaceborne RFI sources (as discussed in Sections 2.2 and 2.3). Different observed modes will lead to different ISR, which is a key factor affecting the algorithm performance.
* Nature of the RFI: for example, whether the RFI source is persistent or intermittent emitted, whether the bandwidth, polarization type is temporally or spatially correlated with the transmitted signal of SAR system, as discussed in Section 2.4.
* The availability of computational resources.  
Current mitigation methods depend on expert knowledge for maximizing the difference between the RFI and useful echoes, like energy, statistical difference, etc. During the implementation of these algorithms, some tuned parameters are chosen rather empirically, and only applicable for specified modulation types of interference. However, heterogenous RFI environment would be a challenging task, where various RFI types could be encountered simultaneously. Mitigation techniques should be adaptive, practical, and applicable in routine operations, thus minimizing the expert inference as much as possible.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 4 Case Study of Transmission Line Insulator Defect Detection
### Result Analysis
#### 4.3.3 Comparison with Different Detection Algorithms  
In order to verify the effectiveness of the proposed method, four types of object detection models were also constructed, including Faster RCNN, SSD, YOLOv3, and YOLOv4. The transmission line insulator image dataset was also used for training and test of these object detection models. The detection results are shown in Table 3.  
From the results in Table 3, the YOLOv4 detection algorithm achieves high-precision detection of insulators and its defects relying on its advantages of structure and training method. The \(mAP\) can reach 96.21%, which is higher than that of Faster RCNN, SSD, and YOLOv3, while the detection speed (_FPS_) of YOLOv4 is slow. The improved YOLOv4 model proposed in this paper can improve the detection speed to 53 pictures/s with the cost of 2.4% precision; that is, the time required to detect one image is about 18.87 ms, which is nearly 2.8 times the detection speed of YOLOv4. The detection accuracy is better than Faster RCNN, SSD, and YOLOv3. Moreover, the \(F_{1}\) scores of improved YOLOv4 are also superior to Faster RCNN, SSD, YOLOv3, and the \(F_{1}\) score of improved YOLOv4 in the category of the insulator defect is only 0.01 lower than YOLOv4. The above results verify  
Figure 11: Performance indexes under different width multipliers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{\(AP\) (\%)} & \multicolumn{2}{c}{\(F_{1}\) **Score**} & \multirow{2}{*}{_FPS (pictures/s)_} \\ \cline{2-2} \cline{4-6}  & **Insulator** & **Defect** & & & **Insulator** & **Defect** \\ \hline Faster RCNN & 87.27 & 92.54 & 79.91 & 0.79 & 0.92 & 1 \\ SSD & 93.81 & 75.03 & 84.42 & 0.81 & 0.72 & 28 \\ YOLOv3 & 89.00 & 87.33 & 88.16 & 0.88 & 0.89 & 24 \\ YOLOv4 & 96.14 & 96.43 & 96.21 & 0.96 & 0.95 & 19 \\ Improved YOLOv4 & 94.53 & 93.09 & 93.81 & 0.96 & 0.94 & 53 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance indexes of different detection models.  
that the proposed method can realize the defect detection of transmission line insulators with high precision and fast detection speed.
###### Abstract  
As a fundamental part of ground penetrating radar (GPR) data processing, reverse time migration (RTM) can correctly position reflection waves and focusing diffraction waves on the proper spatial position. Least-squares reverse-time migration (LSRTM) is widely used in the seismic field for its ability to suppress artifacts and generate high-resolution images in comparison to conventional RTM. However, in the particular case of GPR detection methods, LSRTM is extremely susceptible to aliasing artifacts caused by under-sampling. In pursuit of enhanced precision in underground structure characterization, this paper presents the development of a new LSRTM based on modified total variation (MTV) regularization to improve imaging resolution. Initially, the objective function of LSRTM is derived by combining the Born approximation in 2-D transversal magnetic mode. Next, the adjoint equations and their gradients are solved using the Lagrange multiplier method. The objective function is then constrained by MTV regularization to ensure the precision and convergence of the LSRTM, which delivers a refined edge with reconstruction details. In the numerical experiments, in comparison to the conventional LSRTM method, the LSRTM-MTV algorithm demonstrated a 30.4% increase in computational speed and a 21.1% reduction in mean squared error (MSE). The outperformance of the proposed method is verified in detail through the image resolution and amplitude preservation in the test of synthetic data and laboratory data. Future research efforts will center on applying the proposed method to models featuring dispersive or anisotropic media that closely mimic real-world conditions and extending the application to various imaging techniques involving objective function minimization.  
Keywords:ground penetrating radar; least-squares reverse-time migration; modified total-variation regularization +
Footnote †: journal: Applied
## 5 Conclusions and Future Work
### Future Work  
In future work, there are two main directions to further improve this work. The first direction is to examine the conclusions of this work with more datasets. Although the corpora used in this work cover two main languages (English and Chinese) and the two most common forms of Internet text (serious text from Wikipedia and free text from social media), the experimental results of data from more different languages and sources are expected to provide more evidence for the conclusions of this work and help to understand the explicit and implicit bias of people from different cultural backgrounds. Another important future work is to design algorithms to automatically discover the concepts on which people have bias instead of selecting the concepts manually. Our work in this paper manually adopts the specified concepts to facilitate comparison with existing research in psychology to verify our method. Once the effectiveness of our method is verified, in practical applications, we need to be able to automatically discover more biased concepts that have not been addressed in existing research by improving our method.  
Conceptualization, B.W.; Methodology, B.W., R.Z. and B.X.; Software, B.X.; Validation, B.X.; Writing-original draft preparation, Y.Z.; Writing-review and editing, L.Y. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China(2022 YFC3301900; 2022 YFC3301901), National Natural Science Foundation of China (62376188, 62272340, 62276187, 62376192).  
The original data presented in this study are openly available for Chinese Wikipedia at [[https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2](https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2)]([https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2](https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2)) (accessed on 12 March 2024) and for English Wikipedia at [[https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2)]([https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2)) (accessed on 12 March 2024). Due to the large scale of the datasets collected from Weibo and Twitter, interested readers may request access to them by contacting the corresponding author.  
The authors declare no conflicts of interest.
## 2 Instrument Design
### Radiometer Assembly
#### 2.1.2 Front-End Stage  
The front-end stage is composed by several sub-stages whose purpose is to condition the signals collected by the antenna set, and then, to guide them to the back-end stage. These sub-stages are depicted in the overall diagram in Figure 1. The first sub-stage is used to calibrate the drifts of the amplifiers with the help of matched loads with known physical temperature. For the L-, S-, and C-band, the calibration stage is composed by a Single-Pole Dual-Through (SPDT) switch, that commutes between the signal coming from the antenna, and the matched load, followed by a circulator that acts as an isolator. For the X-, K-, Ka-, and W-band, the scheme is similar but simplified due to the existence of the latching circulators. There is one matched load per band, and polarization, and their physical temperature is measured by means of a digital temperature sensor attached to each one of them.  
After that, the signal power is boosted with tuned amplifiers, one for each band and polarization. All these tuned amplifiers have a gain of at least 60 dB [22] in the band-pass frequency, with gain flatness \(\leq\) 1.5 dB, noise figure \(\leq\) 2.4 dB at the highest frequency band, and 1 dB compression point \(\geq\) 5 dBm for the worst case.  
Then, the output of each amplifier of the seven lower frequency bands (L-, S-, C-, X-, K-, and Ka-band) are combined using two different septuplexors (7-to-1 tuned power combiners), one for the vertical and another for the horizontal polarization. After that, a SPDT switch commutes between the output of the two septuplexors, and finally, the combined signal reaches the back-end stage.  
Furthermore, the W-band signal is treated in a different way as compared to the signal of the lower bands after the amplification stage. Since the bandwidth of the back-end stage can reach up to 40 GHz, the W-band signal is first down-converted to an Intermediate Frequency (IF) much lower than 40 GHz. This process is done using two specific harmonic mixers, one for the vertical, and another one for the horizontal polarization, whose features are detailed in the next section. After the down-conversion, a SPDT switch selects the polarization that is connected to the back-end stage.  
Figure 1 summarizes the connections between the antenna set, the front-end stage and the back-end stage.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 3 Robust Matrix Factorization with Robust Adaptive Structure Learning
### The Proposed RMFRASL Model
#### 3.1.1 RMFFS Model  
In order to take the correlation of features into consideration, the self-representation of data or features is widely used for feature representation learning [30]. Therefore, a new feature selection model named robust matrix factorization-based feature selection (RMFFS) is proposed. In the proposed RMFFS model, we first utilize an indicator matrix to find a suitable subset of features for capturing the most critical information to represent the original features approximately. Then, unlike the previous methods, the \(L_{21}\)-norm metric is imposed on the reconstruction errors to improve the effectiveness and enhance robustness of feature selection. As a result, the objective function of the RMFFS model is defined as follows:  
\[\begin{array}{c}\text{min}\phi(Z,A,S)=||Z-ZSA||_{2,1}\\ \text{s.t.}\ S\geq 0,A\geq 0,S^{T}S=I_{k\times k}\end{array}, \tag{9}\]  
where \(I_{k\times k}\) is an identity matrix with the size of \(k\), \(A\in R^{k\times d}\) is a coefficient matrix which maps the original features from high-dimensional space into a new low-dimensional space, \(S=[s_{1},s_{2},\ldots,s_{k}]\in R^{d\times k}\) is defined as a feature weight matrix, and \(k\) is the number of selected features. The constraint conditions of \(S^{T}S=I_{k\times k}\) and \(S\geq 0\) are to force each element of matrix \(S\) to be either one or zero and at most a nonzero element in any row or column during the iterative update process. Thus, the matrix \(S\) can be considered as an indicator matrix for the selected features under these constraint conditions.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 3 Improved Ant Colony Algorithm
### Improved Pheromone Update Rules  
In the classical ant colony optimization algorithm, as the iteration count increases, the chemical signal levels on the map gradually rise, prompting ants to increasingly follow paths with higher pheromone concentrations. This phenomenon can lead to the algorithm becoming trapped in local optima. To address this issue, this paper proposes an adaptive pheromone update mechanism. Specifically, in addition to the original pheromone update increment, the proposed approach incorporates the disparity between the total pheromone levels on the most effective route from the previous generation and the total pheromone levels on the least optimal route from the same generation. The adaptive pheromone update \(\omega_{ij}(t)^{*}\) is expressed as follows (11):  
\[\omega_{ij}(t+1)*=(1-\sigma)\cdot\omega_{ij}+\Delta\omega_{ij}(t)+\Delta \omega_{ij}^{*}(t) \tag{11}\]  
\[\Delta\omega_{ij}^{*}(t)=\sum_{k=1}^{m}\Delta*\omega_{ij}^{k}(t) \tag{12}\]  
\[\Delta*\omega_{ij}^{k}(t)=\vartheta\times\frac{\mathcal{Q}}{L_{min}}-\
u\times \frac{\mathcal{Q}}{L_{max}} \tag{13}\]  
In the formula, \(\Delta\omega_{ij}^{*}(t)\) represents the disparity between the sum of chemical signals deposited by ants on the ideal path and the aggregate of chemical signals deposited by ants on the least effective path, serving as a supplementary pheromone increase. \(\Delta^{*}\omega_{ij}^{k}(t)\) is the pheromone increment left by the kkth ant on paths \(i\) and \(j\). \(\vartheta\) denotes the quantity of ants on the best route for this iteration, \(\
u\) is the quantity of ants on the least optimal route for this iteration, \(L_{min}\) is the distance of the optimal trajectory for this round, and \(L_{max}\) is the length of the least optimal route for this round. The pseudocode for the improved ant colony path planning algorithm is shown as Algorithm 1.  
```
initialize environment and parameters for each node i in the environment:  calculate distance between node i and the target node E  calculate potential attraction fa = sigma * (distance squared) if node i is not the target node:  Eta(i) = (0.5 ^- \(\epsilon\) fa)/sqrt(distance squared) else:  Eta(i) = a very large number for each ant k in K ants:  for each iteration m:  if ant k completes a path in iteration m:  calculate path length PL_km  update pheromone matrix Delta_Tau considering path length PL_km, best path length  minkl, and worst path length maxkl  for each step s in the path:  x, y = current step, next step  Delta_Tau(m, n) += \(\mathcal{Q}\) / PL_km + b * \(\mathcal{Q}\) / minkl-w * \(\mathcal{Q}\) / maxkl  Tau = (1-Rho) * Tau + Delta_Tau
```  
**Algorithm 1** Improved ant colony path planning algorithm
## 3 Results and Discussion  
### Characterization of Monoclonal Antibodies by the ELISA  
The quantitative characterization of the interaction of MG with specific antibodies was carried out using a flow-through biosensor system, Biacore X, based on the registration of a surface plasmon resonance. Kinetic curves of association and dissociation processes of binary antibody-MG complexes at different concentrations of antibodies were obtained. On the basis of the experimental data, the corresponding kinetic (\(\mathrm{k_{a}}\), \(\mathrm{k_{d}}\)) and equilibrium (\(\mathrm{K_{A}}\), \(\mathrm{K_{D}}\)) constants of association and dissociation of antibody-MG complexes were calculated (Table 1). It was demonstrated that the 1B4, 4E2 cc, MyoD4, and MyoF5 clones of antibodies possessed the highest affinity (\(\mathrm{K_{D}}\) values varied in the range \(2.9\)-\(6.2\times 10^{-10}\) M). The 7C3, MyoA6, MyoE4, MyoF7, MyoH9, and MyoH11 clones were characterized by an average affinity level (\(\mathrm{K_{D}}\) values varied in the range \(1.8\)-\(9.6\times 10^{-9}\) M). The MyoB1, MyoB12 clones had the lowest affinity properties (\(\mathrm{K_{D}}\) were \(9.6\times 10^{-8}\) M and \(1.8\times 10^{-7}\) M, respectively). For both the ELISA and LFIA, a sandwich assay format was used, which was suitable for assessing the preservation of the protein native structure and commonly used for the detection of antigens having at least two epitopes on their surface [30]. To perform the ELISA, conjugates of monoclonal antibodies with biotin were synthesized, in accordance with the method described in Section 2.3. For all 12 antibody-biotin conjugates obtained, a reliable binding with MG immobilized in the wells of the microplate at a concentration of \(2\)\(\mathrm{\SIUnitSymbolMicro mL^{-1}}\) was confirmed, and therefore they were further used in the ELISA. Testing of the immune properties of antibodies and their conjugates with biotin in the ELISA allowed for assessment of the ability to form triple antibody--MG--biotinylated antibody complexes, which was important for further development of the LFIA. On the basis of the results of this testing, combinations that did not lead to a reliable binding in the ELISA or provided it only at extremely high (\(>\)100 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\)) MG concentrations, were excluded. After this exclusion, only eight combinations were selected for further studies. The analytical parameters of the ELISA using these reagents are summarized in Table 2.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## Appendix C Proof of Lemma 3  
By substituting the signal (5) received at the \(k\)-th EN under NOMA into (11) and by following similar steps as for the proof of Lemma 2, the overall vector signal received at the BBU comprising both cable and radio channels over all ENs under NOMA by treating interference as noise yields  
\[\mathbf{r}=\tilde{\mathbf{H}}_{\text{eq}}\mathbf{x}+\beta\tilde{\mathbf{A}}_ {\text{eq}}\mathbf{u}+\bar{\mathbf{z}}_{\text{eq}}, \tag{14}\]  
where the definitions of \(\tilde{\mathbf{H}}_{\text{eq}}\) and \(\bar{\mathbf{z}}_{\text{eq}}\) are the same as in (10), \(\tilde{\mathbf{A}}_{\text{eq}}=\mathbf{A}\otimes\mathbf{H}_{\text{c}}^{\eta} \otimes\mathbf{I}_{\text{I}_{\text{F}}}\) accounts for the relay of URLLC signal over the cable, and \(\mathbf{u}\) is the vectorization of the URLLC signal matrix \(\mathbf{U}\) in (5). Similarly to the proof of Lemma 2, the eMBB per-UE rate under NOMA by treating URLLC interference as noise can be computed by \[R_{B} = \frac{1}{n_{F}M}I\left(\mathbf{r},\mathbf{x}\left|\mathbf{A}\right.\right) \tag{17}\] \[= \frac{\mu}{M}\mathbb{E}_{\mathbf{A}}\left[\log\left(\det\left( \mathbf{I}+P_{B}\mathbf{R}_{A,z_{\text{eq}}}^{-1}\mathbf{H}_{\text{eq}} \mathbf{H}_{\text{eq}}^{\text{T}}\right)\right)\right]\]  
where the average is taken over all the possible values of matrix \(\mathbf{A}\), \(P_{B}\) is the power of the eMBB user under NOMA and \(\mathbf{H}_{\text{eq}}\) is defined as in (15). Finally, the covariance \(\mathbf{R}_{A,z_{\text{eq}}}\) of the noise plus URLLC interference yields  
\[\mathbf{R}_{A,z_{\text{eq}}}=\beta^{2}P_{U}\quad\mathbf{A}_{\text{eq}}\mathbf{ A}_{\text{eq}}^{\text{T}}+\mathbf{R}_{z_{\text{eq}}},\]  
where \(\mathbf{A}_{\text{eq}}=\mathbf{A}\otimes\mathbf{H}_{c}^{\eta}\) and \(\mathbf{R}_{z_{\text{eq}}}\) is the same as in (15). The proof is completed by noticing that \(\mathbf{A}_{\text{eq}}\mathbf{A}_{\text{eq}}^{\text{T}}=(\mathbf{A}\otimes \mathbf{H}_{c}^{\eta})(\mathbf{A}\otimes\mathbf{H}_{c}^{\eta})^{H}=\mathbf{A} \otimes\mathbf{H}_{c}^{\eta}\mathbf{H}_{c}^{\eta}\), since matrix \(\mathbf{A}\) is idempotent and \(\mathbf{H}_{c}^{\eta}\) symmetric.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
###### Abstract  
Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.  
resource management; millimeter waves; beyond-5G; optical fronthaul; virtualized networks +
Footnote †: journal: Applied  
18 November 2021  
Accepted: 23 December 2021  
Published: 27 December 2021  
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**Copyright** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
**Citation:** [PERSON]; [PERSON]; [PERSON] Resource Management  
In Converged Optical and Millimeter Wave Radio Networks: A Review.  
_Appl. Sci._**2022**, 12, 221. [[https://doi.org/10.3390/app12010221](https://doi.org/10.3390/app12010221)]([https://doi.org/10.3390/app12010221](https://doi.org/10.3390/app12010221))