esa-sceva/satcom-chunk-collection huggingface dataset:
# XCT and DLW: Synergies of Two Techniques at Sub-Micrometer Resolution
## 1 Introduction
Another driving force behind this work is the actual lack of cheap and commercially available 3D XCT testing structures (phantoms) at their resolution limit (below 2 \(\upmu\)m feature size). Very few manufacturers that offer 3D phantoms for sub-micrometer XCT imaging are available on the market today. Two examples are presented as a reference, the Micro-CT Bar Pattern Phantoms by QRM [34] and the Spatial Targets by Aletheia Imaging Solutions [35]. The XCT spatial resolution is commonly referenced to as 2D line pair patterns, also known as Double Wire Image Quality Indicators (IQI), Siemens Star, 2D meshes, single-wire phantoms or bulk (big) 3D metrology objects [36; 37]. To evaluate the complete XCT imaging process, including sample preparation, imaging (radiography), reconstruction and subsequent image analysis, an effective and more practical system resolution limit may be observed at real 3D nano- to micrometer-sized structures only. The 3D XCT phantoms available on the marked are mostly standardised and well-known since the 1980s in the field of medical XCT, and later, in industrial XCT, as well. The following standards are referred to as related: ASTM E1441 [38], ASTM E1695 [39], ASTM E1935-97 [40] and ISO 15708-series [41]. However, such available phantoms address another length scale in the (sub-)millimeter range. Mostly, disk-like structures with different-sized cylinders, special geometrical features (e.g., crosses, stars) or material inclusions (bulk or air) are used [42; 43]. The most prominent example is the Shepp-Longan medical phantom [44; 45]. This work highlights the detectability of previously non-detectable flaws inside DLW fabricated structures. We want to adress the lack of studies combining both methods and outline the information that can be gathered from XCT measurements to improve the DLW production process. Additional remarks are made on the practical aspects (sample preparation) of such sample scans and the option to produce novel 3D testing structures (phantoms) for XCT/XRM.
## 4 Array Calibration  
The realization of a digital beamforming phased array has several technological challenges. The most important one is phase synchronization and amplitude equality across the RF transmitting channels. For engineering calibration methods, at present, there are two major types of array calibration algorithms: passive calibration algorithm [24] and active calibration algorithm [25; 26]. In WIOBSS-AA, we focus on the reverse application of the term multiple signal classification (MUSIC) [27] and develop a new engineering calibration algorithm based on eigenvalue decomposition.  
Assume that a single signal is picked up by a multiplex parallel array in one incident direction. There is the following relation between the amplitude, phase error vector of each channel \(\Gamma\) and received data \(X(t)\):  
\[X(t)=\Gamma a(\theta)s(t)+n(t) \tag{3}\]  
Figure 8: Azimuthal total radiating field plots for the five-beam antenna array phased at 0\({}^{\circ}\) (red line), 18\({}^{\circ}\) (green line), and 36\({}^{\circ}\) (blue line) from the boresight. The operating frequency is 10 MHz and the elevation angle is 32\({}^{\circ}\). The boresight is to the right of the Figure. The circular grid labels are with reference to the outer ring value, where dBi values are with reference to an isotropic gain pattern.  
Figure 7: Comparison of the antenna array radiation patterns at 8 MHz (red line), 10 MHz (green line), 12 MHz (blue line) (main lobe vector alignment), where dBi values are with reference to an isotropic gain pattern.  
where \(a(\theta)\) stands for the array, \(s(t)\) stands for the signal space, and \(n(t)\) stands for the noise space. The principle of MUSIC is to estimate the signal space \(s(t)\) using the known \(\Gamma\), \(a(\theta)\)and \(X(t)\). However, when we know the signal space \(s(t)\), the joint estimation to \(\Gamma\) could be carried out using \(s(t)\), \(a(\theta)\), and \(X(t)\)
## 2 Materials and Methods
### Questionnaire  
Two questionnaires were created for the evaluation of consumer preferences and their perception of fish freshness; one with regard to the whole fish and a second one regarding the fillets. Whole fish were evaluated as raw samples; fillets were evaluated both as raw and cooked samples. Both questionnaires consisted of questions about the perception of fish freshness and consumer preference, in the first part. The last part of the questionnaire consisted of demographic questions and questions regarding attitudes towards fish consumption and packaging. Table 1 presents in detail the questions asked in the two questionnaires and the corresponding scales used. Scales were converted to numeric data from 1 to 5 or 1 to 3 according to the points of the respective scale. The more positive the rating was, the higher the score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Category** & **Question** & **Questionnaire Anchors for Whole Fish** & **Questionnaire Anchors for Fish Filleds** & **Scale** \\ \hline \multirow{6}{*}{Sensory perception of raw samples} & Odor & Very fresh to unpleasant & Very fresh to unpleasant & 5-point \\  & Skin & Very bright, bright, drill & Very bright, bright, drill & 3-point \\  & Eyes & Convex and shiny, less convex to flat and & N/A\({}^{*}\) & 3-point \\  & Liguid in the packaging & Presence or absence & Presence or absence & Binary \\  & Macus & Thin and transparent or no mucus/dry & N/A & Binary \\  & Fresh & Very firm, elastic, fingerprint with pressure & Bright white and firm, white to dull and less & 3-point \\  & & firm, yellowish and soft & & \\ \hline \multirow{3}{*}{Overall perception of raw samples} & Overall freshness & Very fresh to not fresh at all & Very fresh to not fresh at all & 5-point \\  & Purchase intention & Very likely to not likely at all & Very likely to not likely at all & 5-point \\  & Proximity to the ideal & Very close to not close at all & Very close to not close at all & 5-point \\ \hline \multicolumn{5}{c}{Which of the two samples do you prefer?} \\ \cline{2-6}  & Odor & N/A & Very fresh to unpleasant & 5-point \\  & Flavor & N/A & Pleasant/fresh, relatively fresh, neutral & 3-point \\  & Julticanes & N/A & Julicy, slightly dry, dry & 3-point \\  & Texture & N/A & Elastic/frm, soft & Binary \\ \hline \multirow{2}{*}{Overall perception of cooked samples} & Liking & N/A & \({}^{*}\)I like it very much\({}^{*}\) to \({}^{*}\)I don’t like it at all\({}^{*}\) & 7-point \\  & Overall freshness & N/A & Very fresh to not fresh at all & 5-point \\ \hline \multicolumn{6}{c}{\({}^{*}\)N/A stands for not applicable.} \\ \end{tabular}
\end{table}
Table 1: Questions asked in the consumer study for the whole fish and the fish filts. Anchors and type of scale are also indicated.
## 5 Biomarkers of Response and/or Resistance to Immune Checkpoint Inhibitors
### Markers of Response and/or Resistance Associated to the Host
#### 5.3.2 The Modulatory Role of Microbiota  
Several studies demonstrated the existence of dysbiosis (an altered composition of gut microbiota) in different stages of chronic liver disease and in HCC patients [114; 115]. Moreover, strong pre-clinical evidence demonstrate that HCC development in the setting of chronic liver diseases is promoted by the gut microbiota, probably through microbic metabolites and pathogens-associated molecular patterns (PAMPs) [116].  
The gut microbiota has a role in the conversion of primary to secondary luminal bile acids, and alterations in the composition of normal commensal gut bacteria lead to the accumulation of primary bile acids and, in turn, an immunosuppressive suppression. Indeed, high levels of primary bile acids causes the secretion of CXCL16 from liver sinusoidal endothelial cells and the consequent recruitment of antitumor CXCR+ NKT cells [117]. In addition, secondary bile acids (in particular deoxycholic acid) have been demonstrated to induce senescence of hepatic stellate cell, resulting in the release of cytokines that promote HCC development [118]. The role of gut microbiota in modulating systemic immunity, as well as influencing immunotherapy response and immune effects of chemotherapy, is now broadly accepted. In particular, microbial diversity is a host trait that can modulate the clinical response to immunotherapy-treated HCC patients [71]. Moreover, the dynamic variation of the gut microbiome characteristics may provide an early prediction of the outcomes of immunotherapy. These insights into the role of microbiota in modulating the response to ICIs, together with the evidence coming from pre-clinical studies on HCC prevention through the modulation of gut microbiota with antibiotic treatment [116], provide the rationale for evaluating the combination of immunotherapies and antibiotics or probiotics in clinical trials. Only preliminary experiences with this combination in the treatment of solid cancers are currently available. In a phase I trial, 30 patients with metastatic renal cell carcinoma were randomize to receive nivolumab + ipilimumab with or without the bifidogenic live bacterial product CBM588 [119]. PFS was longer (12.7 vs. 2.5 months; \(p\) = 0.001) and ORR was higher (58% vs. 20%; \(p\) = 0.06) in patients receiving nivolumab+ipilimumab with CMB588 compared to patients treated without the probiotic [119]. These preliminary data are extremely intriguing, but further studies better characterizing the effects of gut microbiome on hepatic immunity and specifically addressing the potential role of microbiota as biomarker of response in ICIs-treated patients are awaited [115].
## 3 Edge Decomposition of Bipartite Circulant Graphs \(C_{2 nm,nm}\) Based on Cartesian Products
Let \(\lambda_{i}=f_{i}-e_{i}\), which is unique for every pair \((e_{i},f_{i});i\in\mathbb{Z}_{2 nm}\). For \(u\
eq v\in\mathbb{Z}_{2 nm}\), let \((e_{i}+u,f_{i}+u)\in E(\mathbb{C}_{u})\) and \((e_{i}+v,f_{i}+v)\in E(\mathbb{C}_{v})\). Suppose that \(|E(\mathbb{C}_{u})\cap\ E(\mathbb{C}_{v})|\
eq 0\) such that there is at least one edge \((e_{i},f_{i})\in\{E(\mathbb{C}_{u})\cap\ E(\mathbb{C}_{v})\}\). From the translation definition, we have \((e_{i}+u-u,f_{i}+u-u)=(e_{i},f_{i}),(e_{i}+v-v,f_{i}+v-v)=(e_{i},f_{i});\lambda _{i}=f_{i}-e_{i}\) is unique for every edge in \(\mathbb{C}_{0}\). Hence, \(|E(\mathbb{C}_{u})\cap\ E(\mathbb{C}_{v})|\
eq 0\). Then, the addition of \(2 nm\) to the second component of the ordered pairs \((e_{0}+\sigma,f_{0}+\sigma),(e_{1}+\sigma,f_{1}+\sigma),\ldots,\)\((e_{2 nm-1}+\sigma,f_{2 nm-1}+\sigma),\sigma\in\mathbb{Z}_{2 nm}\) constructs the edge decomposition of \(C_{4 nm,2 nm}\). Hence, \(\cup_{\sigma\in\mathbb{Z}_{2 nm}}E(\mathbb{C}_{\sigma})=E(\mathbb{C}_{4 nm,2 nm})\). \(\square\)  
Finally, as we stated in Proposition 1, if there is an edge decomposition of \(C_{2n,n}\) by graph \(G\) and an edge decomposition of \(C_{2m,m}\) by graph \(H\), then there is an edge decomposition of \(C_{2 nm,m}\) by graph \(\mathbb{C}\). Hence, we can show the classes of graph \(G\), graph \(H\), and graph \(\mathbb{C}\) to summarize the constructed results in Section 3. Table 1 exhibits these results. **Example 1**. : _The labeling of the circulant graphs \(C_{8,4}\) and \(C_{10,5}\) is exhibited in Figure 2. Figure 3 shows an edge decomposition of \(C_{8,4}\) by \(K_{1,4}\). In addition, an edge decomposition of \(C_{12,6}\) by \(K_{1,6}\), based on the Cartesian product, is exhibited in Figure 4._ \begin{table}
\begin{tabular}{c c c} \hline \(G\) & \(H\) & \(\mathbb{C}\) \\ \hline \(nK_{2,2}\) & \(K_{1,m}\) & \(nK_{2,2m}\) \\ \(K_{2,2}\) & \(mK_{2,2}\) & \(mK_{4,4}\) \\ \(2K_{2,2}\) & \(mK_{2,2}\) & \(2 mK_{4,4}\) \\ \(K_{1,n}\) & \(K_{1,2}\cup\ K_{1,2(m-1)}\) & \(K_{1,2m}\cup\ K_{1,2n(m-1)}\) \\  & & \(K_{1,4}\cup\ K_{1,4(n-1)}\cup\) \\ \(K_{1,2}\cup\ K_{1,2(n-1)}\) & \(mK_{2,2}\) & \(mK_{2,4}\cup\ mK_{2,4(n-1)}\) \\ \(K_{1,n}\) & \(K_{1,2}\cup\ K_{2,m-1}\) & \(K_{2,n}\cup\ K_{2,n(m-1)}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of results of Section 3. Figure 2: The labeling of the circulant graphs \(C_{8,4}\) and \(C_{10,5}\). We provide the general tensor product method for constructing the decomposition of the circulant graphs in the section that follows.
## 3 The Results of Experiments
### Design of Digital Control  
The initial response of the signal to the plasma torch movement control pulse is shown in Figure 15. The blue curve is the step response of moving the servo 1 mm from the PLC encoder. Since the second-order delay system approximates the response, the dynamic behavior of a welding robot's tracking of the weld line can be approximated using a second-order system. In this control process, the gain is increased when matched with the step response of the servo. Considering the time delay, such as image processing, the secondary order indicates delayed response, with the orange curve slightly delayed in step response. The second-order system is shown as follows in Equation (2):  
\[G_{t}(s)=\frac{X_{t}(s)}{U_{t}(s)}=\frac{\omega^{2}}{s^{2}+2\delta\omega s+ \omega^{2}}\times\frac{1}{s} \tag{2}\]  
\(\delta\) and \(\omega\) are 0.7, 148 rad/s separately, and Xt(s) and Ut(s) are the position with respect to the seam line and value of the weld line from weld pool images. Deviation et (i) from the weld line reference can be calculated by Equation (3):  
\[e_{t}[i]=R_{t}-X_{t}[i] \tag{3}\]  
To determine the transfer function for the desired responses and X[z] to the reference value, the system is analyzed in discrete time with a sampling interval of 10, as shown in Equation (4):  
\[\begin{array}{ll}X[z]&=0.05Z^{-1}+0.15Z^{-3}+0.25Z^{-3}+\cdots+0.95Z^{-10}+ 1.0Z^{-11}+Z^{-12}+\cdots\\ &=p_{1}Z^{-1}+\cdots p_{10}Z^{-10}+\frac{Z^{-11}}{1-Z^{-1}}\end{array} \tag{4}\]  
The characteristic of the controller was found in Equation (5):  
\[D_{t}[z]=\frac{1}{G_{t}[z]}\frac{X_{t}[z]}{R[z]-X_{t}[z]}=\frac{a_{0}+a_{1}Z^ {-1}+a_{2}Z^{-2}\cdots+a_{12}Z^{-12}}{1+b_{1}Z^{-1}+b_{2}Z^{-2}\cdots+b_{12}Z^ {-12}} \tag{5}\]  
Figure 14: Image processing sequence. (**a**) Detect the boundary line area; (**b**) obtain the center of gravity coordinate; (**c**) detect the weld line.  
The reference R[z] is z transfer of step function recorded as follows in Equation (6):  
\[R(z)=\frac{1}{1-Z^{-1}} \tag{6}\]  
The sampling period is 200 ms with respect to the prediction processing. The gain is 800. During the PAW welding process, the weld pool changes slowly compared to the MAG welding process. A period of 200 ms provides the time needed for image acquisition, real-time prediction and image processing. By transforming Equation (6), the authors estimated the welding robot manipulating variables u\({}_{\text{t}}\) [i] and u\({}_{\text{s}}\) [i], as shown in Equation (7) at its [i](th sampling period to the discrete time system. According to Equation (7), tracking the weld line can be carried out by using the digital controller.  
\[u_{t}[i]=a_{0}\epsilon_{t}[i]+a_{1}\epsilon_{t}[i-1]\cdots b_{1}u_{t}[i-1] \cdots b_{12}u_{t}[i-12] \tag{7}\]
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 3 _Ulva_ Cultivation Methods: Towards an Innovative Future?  
Algae can be cultivated depending on the species, location, and cultivation facilities. Initially we choose our seaweed cultivation method based on the life cycle. They can be divided into different groups according to different criteria: based on manipulating or cultivation methods, cultivation systems, and several cultures [93].  
Seaweeds can be categorized into two groups based on the manipulation of their life cycles: (1) those initiated from microscopic spores, where the entire seaweed life cycle is fully controlled during cultivation, and (2) those initiated from macroscopic seaweed fragments, where only a portion of the life cycle is controlled during cultivation [93]. _Ulva_ sp. exhibits various reproductive modes, such as asexual reproduction, sexual reproduction, parthenogenesis, and vegetative propagation, which will be explored further [93; 94].  
Cultivation systems can be carried out offshore (the open sea method, which is defined as a marine product farm located at a certain minimum distance from the coast); onshore (the land method, where the cultivator can continuously monitor growth indicators; it is devoid of environmental threats such as storms, food, and other natural disasters); or nearshore (which is the best-known and most widely used seaweed aquaculture technique, which can be developed in estuarine and near-coastal locations). Photobioreactors are devices designed for the massive cultivation of microalgae; IMTA (integrated multitrophic aquaculture) is characterized by creating species of different trophic levels close to each other; saline aquaculture is terrestrial aquaculture that uses saline groundwater; and recirculating aquaculture systems are designed to control all environmental aspects of production by continuously filtering, treating, and reusing water, thus increasing operational efficiency and reducing risks from plant protection products and climate change [6; 95; 96; 97].  
Finally, based on the number of cultivated species, the cultivation methods can be di-vided into monoculture and polyculture (or integrated culture). Open-water cultivation and land-based farming can be distinguished by cultivation locations. In general, both entire life cycle and partial life cycle approaches, as well as monoculture and polyculture, can be used in open-water and land-based production [93].
## 3 Multi-Exposure Fusion via Sparse Representation of Gradient Features
### Initial Weight Map Estimation  
After obtaining the local gradient sparse descriptor and exposure quality feature, the initial weight maps can be estimated by multiplying these two terms one-by-one:  
\[T_{i}(x,y)=A_{i}(x,y)\times B(x,y). \tag{10}\]  
Then, the weight maps, \(T_{i}(x,y)\) are normalized:  
\[W_{i}(x,y)=\frac{T_{i}(x,y)+\tau}{\sum_{i=1}^{N}\left(T_{i}(x,y)+\tau\right)}, \tag{11}\]  
where \(\tau\) denotes a small positive number (e.g., \(10^{-25}\)), which is used to avoid singularities in the weight value \(\hat{T}_{i}(x,y)\).  
Figure 5: Local gradient sparse descriptor acquisition: (**a**) Input image; (**b**) Input luminance gradient map; (**c**) Luminance gradient patch; (**d**) Local gradient sparse descriptor\(\|S\|_{1}\).
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 3 System Design
### Sub-Clustering Algorithms  
#### 3.4.1 Support Vector Machine (SVM)  
SVM is a supervised learning discriminant technique that solves the convex optimization problem analytically, and unlike generative ML approaches, it does not suffer from the multi-local minima [33]; in other words, it always returns the same solution. Owing to a better data generalization performance, the regularization of non-linear datasets, theoretical guarantees regarding overfitting, relatively easier implementation, and a higher transparency in operation than neural networks [34], we choose SVM as a classifier. In multi-class support vector machine (MCSVM) problems, mainly two approaches, one against-one (OAO) and one-against-all (OAA), are used to classify data. The latter approach is faster than the former [34], and hence is why we adopt this approach. It constructs \"K\" SVM models for k classes. The nth SVM is trained with nth class samples, treating them as positive samples, and the rest as negative. The dataset is not linearly separable, so we use the Gaussian RBF kernel function for its better performance than the linear and polynomial kernel functions [35].
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 3 Results
### Data Product Analysis and Evaluation
#### 3.2.2 Continuity of Ocean Color Information
In image algorithm assessment and quality evaluation, information entropy can be used to evaluate the information content of remote sensing images. As the information entropy becomes higher the information content of an image becomes richer, and the image quality becomes better [63]. However, we are not interested in the amount of information in the constructed spatiotemporal images. Our focus is to quantify the degree of the dispersion of the data-free area, as this method can also reflect the spatial and temporal dispersion of the product information. Here, we use another characteristic of entropy: the degree of confusion. As the information entropy of an image becomes greater, the degree of confusion and uncertainty becomes greater. In other words, as the dispersion of a constructed spatiotemporal image becomes stronger, the data quality becomes worse. As shown in Table 7, the results obtained using information entropy as a measure of spatiotemporal dispersion are the same as those obtained by the standard deviation.
## 4 Model Testing of Actual Signal Denoising  
### Project Overview  
The BDa Hydropower Station is a large-scale hydraulic project located in southwestern China. This paper studies the spillway radial gate used in the ecological spillway section of the dam. This section features a roller-compacted concrete gravity dam, and the spillway radial gate employs a straight cantilever and main longitudinal beam frame structure. Due to ecological flow requirements, the gate needs to operate for extended periods at small openings. To assess the vibration behavior of the gate under these conditions, a feasibility analysis of flow-induced vibrations is necessary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(SNR_{time}\) & \(SNR_{freq}\) & \(MSE\) \\ \hline WTD & 8.113 & 8.4234 & 1.5107 \\ VMD-MPE-WTD (\(K=5\)) & 13.9798 & 14.0333 & 0.76906 \\ VMD-MPE-WTD (\(K=6\)) & 14.2041 & 14.0708 & 0.74944 \\ VMD-MPE-WTD (\(K=7\)) & 14.2582 & 14.5762 & 0.7448 \\ VMD-MPE-WTD (\(K=8\)) & 15.3158 & 14.8874 & 0.65941 \\ VMD-MPE-WTD (\(K=9\)) & 14.7305 & 14.818 & 0.70538 \\ VMD-MPE-WTD (\(K=10\)) & 15.0113 & 16.1797 & 0.68294 \\ VMD-MPE-WTD (\(K=11\)) & 16.05 & 15.4067 & 0.60596 \\ MVO-VMD-MPE-WTD & 16.3866 & 17.4829 & 0.58293 \\ VMD-MPE-WTD (\(K=13\)) & 15.5514 & 16.7632 & 0.64176 \\ VMD-MPE-WTD (\(K=14\)) & 16.1047 & 16.9452 & 0.60216 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative Metrics of Denoising Methods.  
Flow-induced vibrations of the gate fall under the category of hydroelastic vibrations. The operation under flowing water is governed by the following dynamic equation,  
\[[M]\Big{\{}\bar{D}\Big{\}}+[C]\Big{\{}\dot{D}\Big{\}}+[K]=\{F_{1}\}+\{F_{2}\} \tag{16}\]  
In the equation, \([M]\) represents the mass matrix, \([C]\) represents the damping matrix, and \([K]\) represents the stiffness matrix. \(\Big{\{}\dot{D}\Big{\}}\) denotes the acceleration vector, \(\Big{\{}\dot{D}\Big{\}}\) denotes the velocity vector, and \(\{D\}\) denotes the displacement vector of the nodes. \(\{F\}\) represents the dynamic water pressure vector acting on the fluid-structure interaction interface nodes.\(\{F_{1}\}\) represents the water flow load vectoracting on the gate when it is ina'stationarystate and the additionalload. \(\{F_{2}\}\) is the disturbed flow field resulting from gate vibrations.