esa-sceva/satcom-chunk-collection huggingface dataset:
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 3 Experimental Procedures and Methods
### SAR Image Processing Procedure
#### 3.3.1 Point Target Polarization Matrix Extraction  
After performing Fast Fourier Transform (FFT) interpolation on the point targets [48], the amplitude values were extracted using the peak method. This was followed by normalization with the maximum amplitude value of the quad-polarization of the point target [40]. According to the radiometric calibration formula for C-SAR satellites, the amplitude value was obtained by Equation (5):  
\[Amp=10*\log_{10}\Bigl{(}DN^{2}*(QualifyValue/32767)^{2}\Bigr{)} \tag{5}\]  
Polarization calibration does not correct the absolute phase but only calculates the relative phase between the quad-polarization channels. Therefore, using the polarization channel with the largest amplitude as the reference, the phase differences between the other polarization channels and the reference polarization channel were calculated. The calculation of the phase angle was used to obtain the phase difference between the different polarization channels.  
The phase angle of a complex number is defined within the interval \((-\pi,\ \pi]\), as shown in Figure 7:  
The phase angle difference between two complex numbers \(u_{1},u_{2}\) was obtained by multiplying one complex number by the conjugate of the other [49]:  
\[u_{int}=u_{1}\cdot u_{2}^{*}=|u_{1}||u_{2}|e^{j(\varphi_{1}-\varphi_{2})} \tag{6}\]  
At this point, the principal phase angle of \(u_{int}\) was calculated, resulting in the relative phase. The principal phase angle falls within the range \((-\pi,\ \pi]\). This method of calculating the relative phase is borrowed from the interferometric phase calculation method.  
Figure 6: Characteristics of four ARCs on images of different polarizations. (**a**) HH-polarization. (**b**) HV-polarization. (**c**) VH-polarization. (**d**) VV-polarization.
## 2 A Proposed ASPC Approach for OCRs in a DG-Integrated Distribution Network
### Coordination Constraints of OCRs
#### 2.3.4 Selection of Pick-Up Currents for OC Relays  
The pick-up current \(I_{pu}\) of OCR should be selected to be higher than the maximum possible load current and lower than the minimum fault current with a reliable security margin. The measurement error of current transformers (CTs) should also be considered for the pick-up current selection. The boundary of pick-up current of the \(r\)-th OCR, \(I_{pu,r}^{\min}\) and \(I_{pu,r}^{\max}\), is determined as below:  
\[\left\{\begin{array}{l}I_{pu,r}^{\min}=\left(OLF+K_{L}\frac{c_{CT \%}}{100}\right)I_{OLF}^{\max}\\ I_{pu,r}^{\max}=\left(\frac{1}{3}-K_{SC}\frac{c_{CT\%}}{100}\right)\frac{I_{r \%}^{min}}{CTR}\\ I_{pu,r}^{selected}=\frac{I_{pu,r}^{\min}+\frac{\max}{B_{pu,r}^{\min}}}{2}\end{array}\right. \tag{37}\]  
where \(OLF\) is the overload factor selected to 1.15 [91]; \(K_{L}\) and \(K_{SC}\) are the security factors to be higher than 1; \(c_{CT\%}\) is the measurement error of current transformers selected to 10%; \(I_{load,r}^{\max}\) is the maximum load current seen by the \(r\)-th relay based on the power-flow analysis results in Section of The Current-Injection-Based Power Flow Analysis; \(CTR\) is the current transformer ratio; \(I_{f,r}^{\min}\) is the minimum total fault current observed by the \(r\)-th relay (normally considering a phase-to-phase (ph-ph) fault located at a remote end of the protected line) as presented in Section of An Adaptable Fault Analysis Technique; a 1/3 factor in Equation (37) is referred by the references [92; 93]. In this study, the selected pick-up current \(I_{pu,r}^{selected}\) of the \(r\)-th OCR will be the average of \(f_{pu,r}^{\min}\) and \(I_{pu,r}^{\max}\).
## 2 System Model and Problem Formulation
### Problem Formulation  
In this section, the Multi-Objective Optimization Problem (MOOP) is realized by concurrently minimizing the AoI, maximizing the CAM message Delivery Rate Ratio (DRR) probability, and minimizing the latency. The MOOP is precisely defined as  
\[min\ \text{AoI},max\ \text{DRR},min\ \text{L}\]  
subject to  
\[C_{1}: \quad C_{i,\mathcal{E}}^{t}[b]\geqslant C_{i,\mathcal{E}}^{min} \forall i\in\mathcal{P},\forall b\in\mathcal{B} \tag{13}\] \[C_{2}: \quad\xi_{i,b^{\prime}}^{t}\varrho_{i}^{t}\in\{0,1\} \forall i\in\mathcal{P},\forall b\in\mathcal{B}\] \[C_{3}: \quad\sum_{b\in\mathcal{B}}\xi_{i,b}^{t}\leqslant 1 \forall i\in\mathcal{P},\forall t\in\mathbb{V}\] \[C_{4}: \quad V_{i}\in er_{n} \forall i\in\{1,2,\dots,I\},n\in\{1,2,\dots,N\}\] \[C_{5}: \quad\sum_{i=1}^{I}ct_{i}\leqslant\sum_{n=1}^{N}es_{n}; \forall i\in\{1,2,\dots,I\},n\in\{1,2,\dots,N\}\] \[\quad\sum_{i=1}^{I}cr_{i}\leqslant\sum_{n=1}^{N}er_{n}\]  
The objective functions are estimated to minimize the AoI and maximize the delivery ratio rate probability of CAM messages for every T seconds amid the platoon vehicles while minimizing the latency as a consequence of computing task offloading. The data rate between the CV and RSU should not be significantly less than the minimum required capacity of the CV taken into consideration in c1. In the estimated optimization function, the mode and subchannel selection criteria are implied as Boolean factors in \(C_{2}\). According to constraint \(C_{3}\), one particular subchannel can be utilized by every platoon over a given period of T. The actual amount of computational resources and caching spaces needed for the task execution are beyond the resources delivered by the edge nodes and are ensured by constraint \(C_{5}\).  
It becomes challenging to address the mixed-integer nonlinear programming problem that was presented in (13) due to the following factors. Binary integers are used for denoting the decision of utilizing the allocated resources, representing the chosen mode of communication, discovering the location of the vehicle in the vicinity area, and indicating the vehicle destination in the V2V communication. These factors can lead to a combinatorial decision issue. Furthermore, the defined constraints are non-convex problems offering local numerical optimum solutions. The centralized approach employed in C-V2X-based communication in the literature [30; 31] involves high computational complexity restricting the scalability of vehicular networks.  
However, the objective function becomes a challenging, non-convex, and NP-hard optimization problem. In order to combat the complexity of the suggested optimization techniques, the cutting-edge Multi-Agent Deep Deterministic Policy Gradient approach is analyzed.
## 3 Problem Formulation for Kronecker-Product-Based Frames
### Problem Formulation  
In this paper, we consider a unitary matrix \(\mathbf{U}=[u_{ij}|1\leq i,j\leq p]\in\mathbb{C}^{p\times p}\), where \(|u_{ij}|=1\). Let \(\mathbf{K}=\mathbf{U}\bigotimes\mathbf{F}_{q}\in\mathbb{C}^{N\times N}\), where \(\mathbf{F}_{q}\in\mathbb{C}^{q\times q}\) is a Fourier matrix. We define a row selection operator \(\mathbf{R}_{\Omega}\in\{0,1\}^{m\times N}\) to select \(m\) rows out of \(\mathbf{K}\), where the selected row indices are specified by \(\Omega\subset\{1,\cdots,N\}\) with \(|\Omega|=m\). Let \(\mathbf{R}_{\Omega}=[\mathbf{R}_{\Omega_{1}},\cdots,\mathbf{R}_{\Omega_{p}}]\), where \(\mathbf{R}_{\Omega_{i}}\in\{0,1\}^{m\times q}\). Selecting the row indices of \(\Omega\) from \(\mathbf{K}\), we obtain \(\mathbf{K}_{\Omega}=\mathbf{R}_{\Omega}\cdot\mathbf{K}\in\mathbb{C}^{m\times N}\), where  
\[\mathbf{K}_{\Omega}=[\sum_{k=1}^{p}u_{k,1}\mathbf{R}_{\Omega_{k}}\cdot\mathbf{ F}_{q},\ \ \cdots,\ \ \sum_{k=1}^{p}u_{k,i}\mathbf{R}_{\Omega_{k}}\cdot\mathbf{F}_{q},\ \ \cdots,\ \ \sum_{k=1}^{p}u_{k,p}\mathbf{R}_{\Omega_{k}}\cdot \mathbf{F}_{q}]. \tag{8}\]  
The Gram matrix of \(\mathbf{K}_{\Omega}\) is \(\mathbf{G}=\mathbf{K}_{\Omega}^{H}\cdot\mathbf{K}_{\Omega}=\mathbf{K}^{H} \cdot\mathbf{R}_{\Omega}^{T}\cdot\mathbf{R}_{\Omega}\cdot\mathbf{K}\in \mathbb{C}^{N\times N}\). By definition of (2), the mutual coherence of \(\mathbf{K}_{\Omega}\) is clearly determined by the maximum magnitude of off-diagonal elements of the Gram matrix \(\mathbf{G}\). Therefore, we can formulate a problem to find a subsampling index set \(\Omega\) for the Kronecker-product-based frame \(\mathbf{K}_{\Omega}\) with low mutual coherence by  
\[\min_{\Omega\subset\{1,\cdots,N\}}\mathcal{M}(m^{-1}\mathbf{K}^{H}\cdot \mathbf{R}_{\Omega}^{T}\cdot\mathbf{R}_{\Omega}\cdot\mathbf{K}-\mathbf{I}). \tag{9}\]
# Remanufacturing of Industrial Electronics: A Case Study from the GCC Region
## 5 Revitalizing the Oil and Gas Sector in GCC through Remanufacturing
Furthermore, incorporating new technologies such as RFID, digital twins, and machine learning can significantly enhance the remanufacturing process, ensuring that it meets industry standards and that the remanufactured products are reliable and safe [26]. Radio frequency identification (RFID) technology can track and monitor components throughout remanufacturing, ensuring traceability and accurate inventory management [26]. In addition, RFID tags can store crucial information, such as part numbers, manufacturing dates, and maintenance history, allowing for efficient and streamlined remanufacturing operations. Digital twins, which are virtual representations of physical assets, can simulate the behavior and performance of remanufactured equipment in the oil and gas, petrochemical, and power generation sectors. By creating a digital replica of the remanufactured product, engineers can identify potential issues, optimize the design, and validate the product's performance before it is reintroduced into service. Machine learning (ML) algorithms can be utilized to analyze large amounts of data generated during the remanufacturing process, enabling predictive maintenance [28] and improving the overall efficiency of the process. In addition, ML can identify patterns and trends that may indicate potential issues or areas for optimization, allowing for proactive measures to be taken to ensure the quality, reliability, and safety of the remanufactured products. Despite the benefits of remanufacturing in the oil and gas industry, some limitations exist. One limitation is the availability of spare parts, as some components may be obsolete or difficult to source. This can lead to increased lead times and higher costs for remanufactured equipment. Another limitation is the potential for regulatory and compliance challenges as remanufactured equipment must meet stringent safety and environmental standards set by industry regulators. Furthermore, remanufacturing processes can be complex and the required expertise and knowledge may be lacking in some cases, resulting in sub-par quality and performance of the remanufactured products. In summary, integrating RFID, digital twins, and machine learning can significantly enhance the remanufacturing process in the oil and gas sector, ensuring that the remanufactured products are reliable, safe, and meet industry standards. However, it is essential to be aware of the limitations, such as spare part availability, regulatory compliance, and the complexity of the remanufacturing process.
# Surgical Phase Recognition: From Public Datasets to Real-World Data
## 2 Methods
### Datasets  
We curated a large private dataset that comprises 384 videos provided by our partners at the Department of Visceral Surgery and Medicine at Inselspital in Bern, Switzerland. During video collection, the institutional video archive was screened for video recordings of laparoscopic cholecystectomies performed between January 2014 and May 2019. All possible video recordings, including additional procedures, emergency surgery, or teaching cases, were included in our dataset.  
Each video was annotated with a single surgical phase label per frame. To perform phase annotations, we adapted the guidelines provided by Endovis Surgical Workflow and Skill Analysis sub-challenge 2019 [19]. We defined additional phases, such as _gallbladder drainage_, _liver biopsy_, and _intraoperative cholangiography_, which were missing in Endovis guidelines but present in our dataset. Annotations were completed in three rounds: (1) videos were shared among 5 annotators to complete the first round of annotations, (2) a cross-check was performed to raise indecisive cases, and (3) indecisive cases were resolved in a final review round. The complete set of our annotation guidelines is illustrated in Supplementary Table S1 online.  
For model training, we randomly partitioned our dataset with 70/10/20 percent training/validation/test ratios, respectively. Partitioning was performed on the video level, i.e., frames from a single video only appeared in one of the partitions. The training, validation, and test splits included 269, 38, and 77 videos, respectively.
# Tunable Plasmonic Talbot Effect Based on Graphene Monolayer
## 3 Calculation Results and Discussion
### The FWHM Properties of the Talbot Images  
Finally, the full width at half maximum (FWHM) [32] of the Talbot image is also studied as a function of the incident wavelength as shown in Figure 5. Here, the period \(D\) is set as 160 nm and the duty cycle is 0.5. The optical field distribution behind the grating is the superposition of grating single slit diffraction and multislit interference. It can be seen from the simplest double-slit interference  
Figure 4: (a) Talbot distance varies with chemical potential of graphene and incident wavelength. (b) The imaginary part and the real part of the effective refractive index of graphene supported plasmon as a function of the chemical potential from 0.2 to 0.7 eV, where the wavelength is 7 \(\upmu\)m.  
that the intensity of light waves at a certain point depends on the phase difference between the two interference waves, which can be described as Equations (10) and (11). So, the complex amplitude distribution of light field intensity is related to the spatial optical path difference and the phase difference introduced by the incident wave. Similarly, the FWHM also can be estimated by the phase difference between two points as shown in Equation (10). For a given chemical potential, the FWHM of Talbot image decreases with the increase in incident wavelength [33]. The larger the wavelength is, the more concentrated Talbot effect can be obtained, which improves the self-imaging resolution of sub-wavelength size. However, as a result the imaginary part of the effective refractive index of graphene represents the propagation loss; it should also be considered that a long wavelength possess greater material loss denoted by the ratio of the imaginary part to the real part of effective refractive index of graphene which can be observed visually from Figure 2f. Moreover, when the wavelength is fixed, the FWHM is inversely proportional to the chemical potential, as demonstrated in Figure 5. In the ranges of the chemical potential and wavelength, the FWHM is less than 1 \(\upmu\)m, which implies the sub-wavelength imaging in the proposed structure. However, when the length of propagation of SPPs becomes smaller than the self-imaging distance, or the loss of plasmons is high enough, the phenomena are hard to obtain [34]. It should be noted that variation of the duty cycle of graphene grating has no effect on the FWHM. This may be understood by Equation (10), where no duty cycle is involved.
## 3 Results and Discussions  
### Safety Qualities  
The pH values and TA contents are important indexes to evaluate the safety of fermented foods, and it was generally believed that low pH value (<4.5) and high TA content could inhibit the growth of spoilage bacteria and ensure the safety of products [30]. Figure 3 illustrates that the spontaneously fermented Zhayu samples showed the highest pH value at 5.15 and the lowest TA content at 13.41 mg/kg. The fermentation with LAB decreased pH values and increased TA content in Zhayu products significantly (\(p<0.05\)). As the inoculation amount increased, the pH value decreased, and the TA content increased gradually, indicating the accumulation of LAB metabolites and the enhancement of the acidity of fermented products. When 10\({}^{9}\) cfu/100 g of _P. acidilactici_ and _P. pentosaceus_ were inoculated, or the inoculation amounts of _L. plantarum_ exceeded 10\({}^{7}\) cfu/100 g, pH values of the fermented Zhayu were below 4.4, which is safe for consumption [31]. Additionally, samples fermented with _P. acidilactici_ showed the lowest TA content compared with the other two starter cultures, indicating the differences in the acid production capacity of various LAB species. In other fermented fish samples, such as fermented silver carp sausage and pla-som, pH values also decreased during the fermentation process with LAB, and the decrease in pH values was related to the growth of LAB [2,32].  
The TVB-N content was closely related to the content of N-containing compounds (such as biological amines) produced by the decarboxylation of amino acids and the propagation of spoilage bacteria during the fermentation process, which could measure the spoilage of fermented fish products [33]. The contents of TVB-N of all Zhayu samples (21.0-30.8 mg/100 g) were lower than the superior limit provided by the European Union (\(\leq\)35 mg/100 g), indicating that the edible safety qualities of the products were guaranteed (Figure 3c). Zhayu samples prepared with LAB showed significantly lower TVB-N contents compared with the control (\(p<\) 0.05); however, the concentrations of _L. plantarum_ and _P. acidilactici_ showed no significant effect on the TVB-N contents in Zhayu samples (\(p>\) 0.05), and the Zhayu samples cultured with _P. pentosacous_ (10\({}^{7}\)-10\({}^{9}\) cfu/100 g) presented the lowest TVB-N contents. It was reported that the addition of LAB could increase the acid-producing metabolism, resulting in the decrease in pH value and the formation of some bacteriostatic substances which could inhibit the growth of spoilage bacteria such as _Enterobacter_, and then reduce the accumulation of TVB-N [34; 35]. Additionally, in Zhayu samples prepared with LAB, the increased acids could neutralize alkaline ammonia and amines, reducing the TVB-N contents. Among the three kinds of LAB, _P. acidilactici_ presented a weak ability to improve the safety qualities of Zhayu samples due to its poor acid production capacity, while _P. pentosacous_ could enhance the safety qualities of Zhayu.
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Feature Importance Analysis  
Feature importance analysis in the RF algorithm helps identify the relative importance of each one for the prediction results. In RF, each decision tree comprises a subset of features and samples. It is measured by fitting a Random Forest to the data and recording the out-of-bag error for each data point. To measure the importance of a specific feature, the values of that feature are permuted in the out-of-bag samples, and the difference between out-of-bag error before and after the permutation is averaged over all trees. Features with larger values have higher importance to the specific model [57]. Meanwhile, to avoid randomness in the results, this study repeats the modeling 100 times, takes the average value, calculates the standard deviation (STD), and plots the histogram of the importance of the characteristics with an error bar.
## 3 Results and Discussion
### Integration of Knowledge for Risk Assessment  
The risk assessment of pesticides to non-target organisms usually is based on standard tests. The impact of the contaminants is measured through standard endpoints, such as reproduction and survival. However, these biological responses only give information about the potential ecological impact of the selected contaminant, being the assessment of other endpoints highly recommended [58]. Indeed, data covering a larger spectrum are welcomed, but when it is not possible to obtain detailed answers about the mechanistic issues involved, there is a need to look behind the apical effects. As detected in our study, alterations in terms of biochemical responses, behavior, and growth of the organisms occurred at lower concentrations than the effects observed at the standard endpoints (e.g., hatching and reproduction). Comparing the data obtained with the previously published ones, the mechanisms of toxicity of the pesticides seem dependent on the type of the pesticide and the test organism. Although, for FOS, PECs did not cause toxic effects to the tested organisms, for other pesticides, the effects may be detected at environmentally relevant concentrations. Another important aspect is that the toxicity tests should consider longer  
Figure 5: Effects of fosetyl-Al on _Enchytraeus crypticus_ for survival (number of adults) and reproduction (number of juveniles) after 28 d of exposure (**A**); reproduction (number of juveniles) after 56 d of exposure (**B**); and adults’ sizes after 28 d of exposure (**C**). Data are expressed as average value (AV) \(\pm\) standard error (SE). * Significant differences to control (\(p<0.05\)).  
exposure periods than the ones recommended by the guidelines [59]. In the environment, pesticides (and their metabolites and transformation products) may persist for long periods of time and the organisms may be continuously exposed [57]. Indeed, the toxic effect of FOS (decrease of organisms' reproduction for the highest concentration) was only seen after a long-term exposure (56 d). This has been the case for other contaminants, e.g., multi-walled carbon nanotubes (MWCNT) [60] or tungsten carbide cobalt (WCCo) [36] materials, where the effects were minute after 28 d and significantly higher at 56 d.
## 1 Introduction
* Capitalizing on the linear differences between the ideal spectral lines and noisy spectral curves, a bidirectional LSTM denoising network is constructed for hierarchical denoising, extracting valid spectral feature information from the high-frequency components to accomplish refined adaptive denoising of the Raman spectra.