esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 4 Current Distribution Analysis  
To realize the creation of notch bands, the current density distribution at three notched frequency bands for AWS, C band, and X band was studied. Figure 9 illustrates the respective elements responsible for the band-notch characteristics. Strong current concentrations around slot, stub, and resonator inferred that there existed high mismatch in impedance, which indicated the creation of notch resonance at corresponding notch frequencies. Figure 9a shows that the maximum current was concentrated within the  
Figure 8: Parametric variation of the independently controllable C band notch by varying \(C_{2}\).  
E-shaped stub intended to notch the AWS band. Figure 9b depicts the surface currents at 6 GHz; the current was largely distributed around the elliptical-shaped slot compared to the other part of the antenna. Figure 9c gives the surface current distribution in the antenna at 10 GHz. A strong current dispersion was examined at the C-shaped resonator for the 10 GHz notched band, which further elucidated that this notch was produced due of the adverse effect of current caused by the C-shaped resonator.  
Figure 9: Simulated surface current distribution at the notch frequency (**a**) at 2 GHz with E-shaped stub, (**b**) at 6 GHz with split elliptical slot, and (**c**) at 10 GHz with C-shaped resonator.
## 3 Experimental Investigation and Results
### Refinement and Enhancement
Figure 13: The maximum deformation of the C-light lip channel structural frame. The simulation results indicate that altering the size of the flat steel impacts the structure's susceptibility to damage, and it was concluded that a flat steel thickness of no less than 3 mm is necessary to prevent damage to the structure. However, increasing the thickness of the flat steel to more than 3.0 mm does not help to make the structure stronger, because the structure will still tear at the joints with the C-light lip channel due to the thinness and increased deformation of the C-light lip channel. During model simulations 01 and 02, the total mass of the CBF main structure and side-swing door was 325 kg. But when the C-light lip channel was modified in model simulations 03 and 04, the total mass decreased to 319 kg, a reduction of 1.8%. Although this modification caused the FOS value to decrease by approximately 53% from the previous value, the FOS value remained within the acceptable range of the safety standards. In most cases, structural designs include a higher factor of safety, with the exception of the aerospace and automobile industries, where minimizing weight is critical to performance and cost. To achieve this, safety factors are deliberately kept low, typically around 1.25 to 2.0, for the design of structures that must withstand static loads with high confidence, and all available design data are used to ensure their integrity.
###### Abstract  
The aim of this paper was to delve deeper into the nuances of incident solar irradiance on the photovoltaic field of a fixed tilt angle system versus a horizontal single-axis tracker. The fixed tilt angle system was used as a baseline for comparison. Three assessment indicators were analysed (annual energy gain (\(AEG\)), monthly energy gain (\(MEG\)), daily energy gain (\(DEG\)). The procedure used comprised the following steps: (i) choice of solar irradiance estimation model; (ii) theoretical study; (iii) study under real operating conditions--for this purpose, an experimental setup was used; and (iv) comparison of these studies. The experimental setup was installed at the Department of Electrical Engineering of the University of Oviedo (Gijon, Spain) (latitude \(43^{\circ}31^{\prime}22^{\prime\prime}\) N, longitude \(05^{\circ}43^{\prime}07^{\prime\prime}\) W, elevation \(28\) (m) above sea level). Gijon is characterised by a temperate oceanic climate typical of Spain's Atlantic coast, with cool summers and wet and mostly mild winters. The code assigned to Gijon under the Koppen climate classification is \(C_{fb}\). The horizontal single-axis trackers that comprise photovoltaic power plants have three operating modes (Scenario 1). Some studies consider a unique mode of operation from sunrise to sunset (Scenario 2). The following conclusions can be drawn from the results obtained: (i) although the results obtained in the theoretical study and in the study under real operating conditions were different, a trend can be seen in the results; for example, the \(AEG\) obtained was approximately \(13\%\) and \(8.5\%\) in the theoretical study and in the real study, respectively, in Scenario 1 and approximately \(18\%\) and \(10.5\%\), respectively, in Scenario 2; Scenario 2 obtained higher results than Scenario 1 in all the assessment indicators; but it must be considered that Scenario 1 is the real mode of operation; (ii) from March to September, the horizontal single-axis tracker generates more electrical energy; as this period contains the months of greatest solar irradiance, the horizontal single-axis tracker performs better annually; considering the theoretical study and Scenario 1, the highest value of \(MEG\) was in June (\(43\%\)) and the lowest was in December (\(-29\%\)); when the study was considered under real operating conditions, the highest result was in July (\(30\%\)) and the lowest was in December (\(-24\%\)); (iii) on the days between \(70\) and \(277\) in Scenario 1, the horizontal single-axis tracker generated more electrical energy; on the other days the opposite occurred; taking into account the theoretical study, the highest and lowest \(DEG\) values were \(43\%\) and \(-30\%\), respectively; when the study was considered under real operating conditions, the highest and lowest \(DEG\) values were \(58\%\) and \(-47\%\), respectively.  
**Keywords:** photovoltaic power plants; horizontal single-axis trackers; fixed tilt angle systems; energy gain
# Vibro-Acoustic Numerical Simulation for Analyzing Floor Noise of a Multi-Unit Residential Structure
## 3 An Illustrative Example
### Test Specim and Field Measurements  
The 17-story reinforced concrete shear wall residential structure under construction, which is located at Uijeongbu-si Gyeonggi-do South Korea, was selected as a test structure, as shown in Figure 3. The overall structure has 17 stories with a roughly 50 m height, and the total area of the unit floor is about 650 m\({}^{2}\). The main structural system of this building is a bearing wall-slab system, and the thickness of the floor slab is 210 mm without any floor finishing materials. An 11 th story of the structure was considered as a target floor. The test structure consists of four individual apartment units and the floor plan is shown in Figure 3. The overall dimensions of the apartment building were \(45.6\times 11.2\times 50.2\) m (b x w x h).  
For the field measurements, the floor impact sound pressure was measured. The bang machine, as mentioned earlier, was used as a standard heavy impact source, which is certificated in KS F 2810 [19]. For storage of the measurement data, a laptop, sensor signal acquisition device (Front-End SIEMENS SCADAS Mobile), microphones (Bruel and Kjaer 4188), and preamplifier (Bruel and Kjaer 2671) were used. The method of installation and location of the five impact points and receiving points are specified in KS F 2810 [19], shown in Figure 4.  
Figure 4: _Cont._  
Figure 3: Overview of a 17-story test structure and the unit floor plan.  
Figure 5 shows the five microphones' field measurement results of the bang machine, applied to the center point, with respect to a 1/3 octave band frequency. As shown in Figure 5, a peak sound pressure level of the average of five microphones is 88.46 dB at 24.8 Hz for a 1/3 octave band frequency. Table 1 shows the sound pressure level (SPL) and a single number quantity (SNQ) for the field measurement results at a 1/1 octave band frequency.  
It should be noted that the SNQ, regulated by KS F 2863 [35], is adopted as a rating method for floor noise in South Korea. The method described in KS F 2863 [35] is the same as that of ISO 717 [36], that is for the standard light weight impact source, such as a tapping machine, except for the difference in the considered frequency range for calculation. Detailed information related to the computation can be found in KS F 2863 [35] and ISO 717 [36]. Additionally, the narrow band of frequency ranges of up to 800 Hz was determined for the analysis because the floor noise in multi-unit residential buildings is evaluated by a single number quantity (SNQ) that considers the 1/1 octave band frequency range (i.e., 63, 125, 250, and 500 Hz).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**1/1 Octave Band Frequency (Hz)**} & \multicolumn{6}{c}{**Field Measurements (dB)**} \\ \cline{2-7}  & **MC@1** & **MC@2** & **MC@3** & **MC@4** & **MC@5** & **Average** \\ \hline
63 & 78.29 & 82.91 & 78.45 & 81.09 & 79.72 & 80.27 \\
1
## 5 Biomarkers of Response and/or Resistance to Immune Checkpoint Inhibitors
### Markers of Response and/or Resistance in Tumor Genome
#### 5.1.2 Somatic Mutations  
The somatic mutations of specific genes may confer to tumor cells the ability to evade immune surveillance or, conversely, make malignant cells more susceptible to immune system aggression [16]. In a mouse model of HCC, _CTNNB1_ mutations and/or activation of the WNT--\(\beta\)-catenin signaling pathway interfere with dendritic cells (DCs) chemotaxis, promote immune escape, and favor resistance to ICIs through the downregulation of CCL5 expression [64]. Mutations causing _MYC_ overexpression, present in approximately 50-70% of HCCs, result in PD-L1 translational upregulation [87]. _TP53_ mutations, occurring in about 40% of HCCs, lead to p53 loss of function and promote the recruitment of immunosuppressive cells in the TME [34]. _ARID1A_ mutations, a common driver event in HCC tumorigenesis, undermine mismatch repair thus leading to a higher TMB and an elevated number of tumor-infiltrating lymphocytes and PD-L1 expression [88]. Moreover, an _ARID1A_-deficient mice treatment with anti-PD-L1 antibodies reduced tumor burden and prolonged survival [88]. Nevertheless, _ARID1A_ mutations are also able to downregulate IFN\(\gamma\) signaling by limiting chromatin accessibility [89]. The activation of CDK20 in HCCs promotes the recruitment of myeloid-derived suppressor cells (MDSCs) which potently inhibit autologous CD8\({}^{+}\) T cells proliferation and activity [90]. Focal gains at chromosome 6p21, harboring the gene encoding for VEGF-A, result in an increased neoangiogenic activity and thereby facilitate an immunosuppressive TME [44; 91].  
The analysis of somatic mutations in cancer cells could be used as a marker of response/resistance to ICIs therapy. In an exploratory study including 31 HCC patients receiving ICIs, activating mutations affecting genes involved in the WNT-\(\beta\)-cateninsignaling pathway were associated with lower disease control rates (0% vs. 53%), a shorter median PFS (2.0 vs. 7.4 months), and unfavorable survival outcomes (median OS 9.1 vs. 15.2 months) [65]. These results confirm the findings obtained in mice models, where \(\beta\)-catenin activation was associated with immune escape and resistance to anti-PD-1 therapy [64]. However, other studies have questioned the predictive potential of such alterations [84; 92], making it necessary to await the results of future studies based on larger prospective cohorts.
# Approximate Triangulations of Grassmann Manifolds
## 2 Materials and Methods
### Vietoris-Rips and Witness Complexes
Examples of witness complexes are shown in Figure 2b alongside the associated Delaunay triangulation. Four landmark points were chosen using the maxmin procedure described below. The complex on the left has \(R=0.0329\), and the complex on the right has \(R=0.1317\). Note that the larger value of \(R\) yields a complex with more simplices. In addition, note that the witness complex is a coarse approximation of the Delaunay triangulation. We make some observations about this definition. Let \(D\) be the \(n\times N\) matrix of distances from points in \(L\) to points in \(X\):  
* If \(R=0\), then \(\ell,\ell^{\prime}\in L\) form an edge if there is an \(x_{i}\in X\) such that \(d(x_{i},\ell)\) and \(d(x_{i},\ell^{\prime})\) are the two smallest entries in the \(i\)-th column of \(D\). This is analogous to the existence of an edge in the Delaunay triangulation \(\operatorname{Del}(L)\). * For \(R>0\), one may think of relaxing the boundaries of the Voronoi diagram of \(L\) and taking the nerve of the resulting covering of \(X\). * If \(0\leq R<R^{\prime}\), then there is an inclusion of simplicial complexes \(W(X,L,R)\subseteq W(X,L,R^{\prime})\). By a theorem of [PERSON] and [PERSON] [11], this complex is a natural analogue of the Delaunay triangulation for a space represented by point cloud data. Suppose that \(X\) is a sample of points from some object \(M\subset\mathbb{R}^{m}\). There is no guarantee that \(W(X,L,R)\) recovers the topology of \(M\), but experiments on familiar geometric objects [11] (spheres, for example) suggest that, for a suitable range of values of \(R\) and good choices of landmarks \(L\), the topology of \(W(X,L,R)\) is the same as that of \(M\). This begs the question:  
1. How should the landmark set \(L\) be chosen? 2. What is the correct value of \(R\)? The second question is best handled via the use of persistent homology, which we discussed in Section 2.3 above. As for the choice of landmarks, there are three standard options:  
1. Select landmarks at random. 2. Use the _maxmin_ procedure: Choose a seed \(\ell_{1}\) at random. Then, if \(\ell_{1},\ldots,\ell_{n}\) have been chosen, let \(\ell_{n+1}\in X-\{\ell_{1},\ldots,\ell_{n}\}\) be the point which maximizes the function \[z\mapsto\min\{d(z,\ell_{1}),d(z,\ell_{2}),\ldots,d(z,\ell_{n})\}.\]
3. Use a density-based strategy. The maxmin procedure yields more evenly-spaced landmarks, but tends to emphasize extremal points. It is generally more reliable than a random selection [11]. Another useful resource is [12]. In our experiments in Section 3.5 below, we use the maxmin process to generate landmarks.
## 3 Results
### Effect of Location on Yield Components  
The explanatory capacity of crop yield was determined by combining its three components, referencing the locations and the five selected sectors along with their 10 genotypes as columns. Table 4 presents the decomposition of the sum of squares of sugarcane yield, broken down by components of genotypes (G), year (Y), and their interaction (G \(\times\) Y) across the five cultivation sectors.  
The decomposition of the sectors increased the variance levels for each of the effects, including the interaction. However, only the independent effects of the genotypes and years had statistically significant variances with values ranging from 7% to 10% and 2% to 14% respectively. This reaffirms the dependency on a sensitive response to different genotypes and environments in sugarcane cultivation.  
In cultivation Sector 01, the explanatory power is lowest for the year of cultivation, indicating that annual variations in this sector have a lesser (but significant) impact on yield compared to other factors. This suggests that climatic conditions, farming practices, or economic factors that vary from year to year do not substantially change the yield in this location. On the other hand, Sector 03 is the location that best explains the variability of the data, covering on average 5.24% more variance in its components, which is concentrated in the year of cultivation. This infers that annual variations, in contrast, have a greater impact on yield, reflecting the dependence on or sensitivity to external factors that vary over time, such as precipitation, temperatures, or resource availability.  
Finally, sectors 01, 02, 03, and 05 did not exhibit significant G \(\times\) Y interactions. In contrast, in Sector 04, the G \(\times\) Y interaction showed moderate significance (\(p<\) 0.05) and accounted for a large proportion of the observed variance in sugarcane yield for this factor. This indicates that the performance of the different genotypes varied significantly across different periods in this location, highlighting the importance of selecting genotypes based on the specific characteristics of each location and the cultivation period.
# Parameter Optimization for an Accurate Swept-Sine Identification Procedure of Nonlinear Systems
## 4 Discussion
An analysis of the schematizations shown in these figures shows that in order for the \(G_{i}(\omega)\) functions to ensure that the frequency band covered by the correlation, for all \(K\) indices, is the necessary one, both the exponential swept-sine signal as input and the matched filter must be defined in the frequency band from \(f_{MIN}\) to OrdMax\(*f_{MAX}\). To verify that the analysis performed was correct, a specific experiment was designed. The experiment had to be able to compare the result obtained through PuC identification with the ideal result. To have such an ideal reference available, we chose to adopt a simulated experiment in which the nonlinear system was realized according to the Hammerstein model scheme. In this way, what kernel trends are expected to be estimated by the PuC identification system were known. The Results section describes the nonlinear system simulator and the results of the identification by means of the PuC procedure. The simulator was realized using a fifth-order Hammerstein system, placing on the five branches in parallel, linear filters whose parameters are given in Table 1, and whose amplitude and impulse responses are shown in Figure 9. Figure 10 shows, in terms of the amplitude responses of the five filters, the comparison between the ideal trends and the trends obtained by adopting the PuC procedure with three different choices of the frequency bands of the input signal and the matched filter. The reported result highlights the correctness of the assumptions made about the necessary bands. Only in the case in which both the exponential swept-sine signal at the input and the corresponding matched filter have frequency band ranging from \(f_{MIN}\) to OrdMax\(*f_{MAX}\), are all five amplitude response trends obtained by PuC identification reasonable estimates of the ideal trends across the whole useful band. Figures 11-13 show similar results in the time domain, comparing, in each figure, the ideal impulse responses and those obtained with the three choices of the frequency bands of the input signal and of the corresponding impulse response of the matched filter. Figures 11-13 show the comparison of the results over time for the impulse responses \(h_{1}(t)\), \(h_{2}(t)\), and \(h_{3}(t)\), respectively. The time-domain analysis also confirms all the assumptions made. The best approximation always occurs when the chosen frequency bands range from \(f_{MIN}\) to OrdMax\(*f_{MAX}\) and, in the case of different choices for frequency bands, the trends of the lowest index \(h_{k}(t)\) functions are most strongly affected by this frequency limitation.
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 4 Results
### SAR Preprocessing Result
Although the distribution bands of ship pixel values in the intensity difference image and the texture image show similar levels, the overall distributions of the images are different. Therefore, by using the intensity difference image and the texture image, it is possible to create the same effect as if the same ship was captured at the sea surface under different conditions. Furthermore, it could be made more insensitive to the distribution of sea surface values in ship detection by using the intensity difference image and the texture image simultaneously. Figure 7: (**a,b**) COSMO-SkyMed SAR image; (**c**) the land masking result of (**a**); (**d**) land masking result of (**b**).
# CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis
## 1 Introduction
The kernel functions of SVM can further augment classification performance in nonlinear scenarios. Among the most frequently employed kernel functions is the Radial Basis Function (RBF) [16; 17], which typically achieves favorable classification outcomes. The Hippocamp Optimization Algorithm (HO) [18] simulates the defense and evasion strategies of the hippocampus while optimizing these strategies through location updates. This algorithm demonstrates exceptional performance in enhancing accuracy, improving local search capabilities, and exhibiting strong practicality. Nevertheless, the HO algorithm still holds significant research potential for enhancing global search capabilities, strengthening local development abilities, and preventing convergence to local optimal solutions. Notably, IHO is particularly adept at addressing issues related to local optima and slow convergence rates. The contributions of this paper are outlined as follows:  
(1) To address the challenges posed by the non-stationary and nonlinear characteristics of valve leakage signals, the CEEMDAN method was proposed for signal decomposition. Subsequently, the fuzzy entropy of the decomposed Intrinsic Mode Function (IMF) signals was computed, allowing for the extraction of an initial feature set from the signals;(2) A novel intelligent search algorithm IHO was introduced to enhance the kernel parameters of SVM in order to achieve improved performance. Consequently, the IHO-SVM model was developed;  
(3) This paper presented the development of a valve fault diagnosis model referred to as CEEMDAN-IHO-SVM, which is capable of accurately extracting and diagnosing fault characteristics. The subsequent sections of this article are organized as follows. Section 2 delineates the pertinent methods that have been utilized and enhanced within this study. Section 3 presents the experimental results along with a discussion of their implications. Finally, Section 4 provides a summary of the findings and contributions of this work.
# Repair of Voids in Multi-Labeled Triangular Mesh
###### Abstract  
In this paper, we propose a novel mesh repairing method for repairing voids from several meshes to ensure a desired topological correctness. The input to our method is several closed and manifold meshes without labels. The basic idea of the method is to search for and repair voids based on a multi-labeled mesh data structure and the idea of graph theory. We propose the judgment rules of voids between the input meshes and the method of void repairing based on the specified model priorities. It consists of three steps: (a) converting the input meshes into a multi-labeled mesh; (b) searching for quasi-voids using the breadth-first searching algorithm and determining true voids via the judgment rules of voids; (c) repairing voids by modifying mesh labels. The method can repair the voids accurately and only few invalid triangular facets are removed. In general, the method can repair meshes with one hundred thousand facets in approximately one second on very modest hardware. Moreover, it can be easily extended to process large-scale polygon models with millions of polygons. The experimental results of several data sets show the reliability and performance of the void repairing method based on the multi-labeled triangular mesh.  
mesh repairing; geometry processing; 3D modeling; geological modeling; polygon mesh processing +
Footnote †: journal: Applied Sciences