esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Topology and Modelling of the Studied System
### Photovoltaic Modelling  
The proposed EV charging station is based on 400 PV panels (AIGPSMONO250W, AIGER), whose power is 100 kW under the standard condition of the solar irradiance of 1000 W/m\({}^{2}\) and cell temperature of 25 \({}^{\circ}\)C, and it is composed of 40 parallel branches of 10 serial panels by branch. The voltage is imposed by the MPPT algorithm: perturb and observe [31]. The PV modelling equations are expressed by Equation (7):  
\[\begin{array}{l}\frac{d\
u_{PV}}{dt}=\frac{i_{PV}-i_{L_{PV}}}{C_{PV}}\\ \frac{di_{L_{PV}}}{dt}=\frac{v_{PV}-v_{PV}^{\prime}}{L_{PV}}\\ \left[\begin{array}{c}v_{PV}^{\prime}\\ i_{L_{PV}}^{\prime}\end{array}\right]=f_{PV}\left[\begin{array}{c}v_{C}\\ i_{L_{PV}}^{\prime}\end{array}\right]\Rightarrow\left[\begin{array}{c}v_{PV }^{\prime}\\ i_{L_{PV}}^{\prime}\end{array}\right]=m_{PV}\left[\begin{array}{c}v_{C}\\ i_{L_{PV}}\end{array}\right]\\ m_{PV}=\frac{1}{T}\int_{0}^{T}f_{PV}dt\text{ with }m_{PV}\in\left[0;1\right]\end{array} \tag{7}\]  
where \(v_{PV}\) and \(i_{L_{PV}}\) are the voltage and current of the PV converter's input side, \(L_{PV}\) and \(C_{PV}\) are the PV inductor and capacitor, respectively, \(i_{PV}\) represents the current provided by PV sources, \(v_{PV}^{\prime}\) and \(i_{L_{PV}}^{\prime}\) are the voltage and current of the PV converter's output side, \(T\) is the period, and \(t\) is the time variable.
## 10 Soft Decision Cognitive Radio and Hybrid Overlay/Underlay Cognitive Radio Waveform Design
### Hybrid Overlay/Underlay Waveform Design for Soft Decision Cognitive Radio
The remaining waveform design variables, i.e., code (\(\mathbf{c}\)), data (\(\mathbf{d}\)), window (\(\mathbf{w}\)) and orthogonality (\(\mathbf{o}\)), remain unchanged from the original SMSE framework. Applying all these design variables, the \(m^{th}\) component of the \(k^{th}\) data symbol of the SD-SMSE can be expressed as  
\[S_{k}[m]=a_{m}c_{m}d_{m,k}w_{m}e^{j(\theta_{d_{m,k}}+\theta_{c_{m}}+\theta_{w_ {m}}\theta_{O_{m,k}})}= \begin{cases}u_{m}c_{m}d_{m,k}w_{m}e^{j(\theta_{d_{m,k} }+\theta_{c_{m}}+\theta_{w_{m}}\theta_{O_{m,k}})}&a_{m}=1\\ b_{m}c_{m}d_{m,k}w_{m}e^{j(\theta_{d_{m,k}}+\theta_{c_{m}}+\theta_{w_{m}} \theta_{O_{m,k}})}&a_{m}\
eq 1\end{cases}  \tag{95}\]  
The expression in (95) can be decomposed into _unused_ and _underused_ SMSE waveform representing the new SDCR architecture shown in Figure 51. Applying the IDFT to (95) results in the discrete time domain waveform given by  
\[s_{k}[n]=\frac{1}{N_{f}}Re\left\{\sum_{m=0}^{N_{f}-1}a_{m}c_{m}d_{m,k}w_{m}e ^{j(2\pi f_{m}t_{n}+\theta_{d_{m,k}}+\theta_{c_{m}}+\theta_{w_{m}}+\theta_{O _{m,k}})}\right\} \tag{96}\]  
\[s_{k}[n]=\  
where the first summation in (97) represents the _unused_ frequency components and the second summation accounts for _underused_ frequency components. The SMSE expression in (96) was demonstrated by applying it to a number of OFDM based multi-carrier signals [159, 168, 169]. The process of generating these waveforms can be viewed as a two step approach: (1) generating the frequency related primary variables, and (2) applying the secondary variables such as the code code, data modulation, windowing and orthogonality to the frequency vector. Since the SD-SMSE only focused on manipulating the primary variables, all of the OFDM based multi-carrier modulations expression such as NC-OFDM, NC-MC-CDMA, NC-CI/MC-CDMA and NC-TDCS are applicable to both overlay-CR and underlay-CR scenarios.
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
## 2 The Principle of Notch Equivalent Stress and the Proposal of a New Method for Life Prediction
### Notch Stress Equivalent Value Method  
Based on the theory of notch stress intensity factor, the notch stress equivalent value method defines \"notch stress equivalent value point \(x_{\text{ss}}\)\". The singular stress at the weld toe is treated equivalently, and the linear regression is carried out using math CAD. The piecewise function Equation (4) of the notch stress equivalent value point \(x_{\text{ss}}\) is obtained.  
\[x_{\text{ss}}=A+B\cdot as+C\cdot as^{2}+D\cdot as^{3}+E\cdot as^{4} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(2\pi/(\mathbf{rad}\cdot\pi^{-1})\) & \(\mu\) & \(\lambda_{1}\) & \(\chi_{1}\) \\ \hline
0 & 2.000 & 0.500 & 1.000 \\
1/6 & 1.833 & 0.501 & 1.071 \\
1/4 & 1.750 & 0.505 & 1.166 \\
1/3 & 1.667 & 0.512 & 1.312 \\
1/2 & 1.500 & 0.544 & 1.841 \\
3/4 & 1.250 & 0.674 & 4.153 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of different transition angles at welding positions [21].  
Figure 3: Corresponding \(p\) values of different transition angles at welding positions [18].  
where the units of \(x_{ss}\) and as are mm, and \(A\), \(B\), \(C\), and \(D\) are the dimensionless parameters of the piecewise function, as shown in Table 2.  
Literature [18] determined the singular strength _as_ calculation Equation (5) when the transition angle of the welding position is \(135^{\circ}\):  
\[as=g(h,t,L)=\min\left[\frac{t}{6}(\frac{H}{h})^{0.1},\frac{2h+L}{8}\right] \tag{5}\]  
By Equations (4) and (5), the position of the notch stress equivalent value point \(x_{ss}\) and the magnitude of the singular strength _as_ are obtained. Substituting into Equation (3), the notch equivalent stress at the weld toe can be calculated. Combined with the S-N fatigue life prediction model of the corresponding joint grade, the fatigue life prediction value can be obtained.
## 4 Simulations and Results
### Results  
As mentioned in Section 3.1, users with a shortage are prioritized according to single and multiple criteria, which results in two different approaches for energy sharing optimization. The results according to the two approaches are presented in the subsequent subsections.
## 4 Results and Discussions
### Channel Impulse Response  
Using LoS, NLoS, and scattering ray tracing methodology, we determine the detected power and path lengths from the LED to the PD for each ray. Then, these data are processed to produce the UM-VLC CIR curves. In the context of analyzing the effect on the complete UM-VLC channel model of the number of dust particles in the hemispherical distribution, Figure 3 shows the CIR for different values of \(N\). Figure 3a compares the UM-VLC CIRs for values of 40 and 60 dust particles with the UM-VLC reference scenario developed in [12]. We can observe that with the value of 60 dust particles considered in the hemispherical distribution, there is a decrease in the magnitude of the UM-VLC CIR and a greater temporal dispersion of its components compared to the curve for \(N=40\). If we compare both curves with the UM-VLC reference scenario, we notice a decrease in the magnitude of the CIRs due to the more precise modeling of the scattering that is achieved with the hemispherical distribution of dust particles. On the other hand, Figure 3b compares the UM-VLC CIRs for \(N\) values of 100, 150, and 200 dust particles. We can notice that for the values of \(N=150\) and \(N=200\) the maximum values of the UM-CIR are similar. Therefore, it could be considered that for values greater than 150 dust particles, the UM-VLC CIR has similar behavior. This is because the scattering component is not the only factor that is influencing the CIR. The full UM-VLC channel model is influenced by random LED and PD orientations, non-regular walls, and shadowing. As a general finding, it could be considered that when we increase the value of \(N\), the light scattering affects the LoS component to a greater extent, in terms of magnitude and time.
## 4 Results from Case Studies  
Different datasets are used to evaluate the proposed synchrophasor applications for efficient data processing and post-mortem analysis (see Sections 3.2 and 3.3) as well as enhanced situational awareness (see Sections 3.4 and 3.5). These include low- to medium-voltage field measurements as well as high-voltage PMU signals extracted from dynamic grid simulations. Table 5 gives a short overview.  
The _LVField_ dataset contains synchrophasor measurements of five PMUs from a public vendor, which are placed at a low-voltage distribution grid in Germany. The M-class PMUs provide three-phase voltage and current phasors (12 phasors per PMU) as well as frequency and rate of change of frequency values at 10 f.p.s reporting rate. The total record time covers 16.5 h. The _MVField_ dataset contains measurements from the Texas Synchrophasor Network. It contains a 1 h record of single-phase voltage phasors and frequency measurements from six different PMUs placed at a medium-voltage distribution grid. Further information as well as the dataset provides [62]. In contrast to the previous datasets, the _HVSim_ records are based on dynamic grid simulations from a generic transmission power grid. The synthetic PMU signals comprise of single-phase voltage phasors and frequencies from 21 PMUs (25 f.p.s. reporting rate). Detailed descriptions are given in the following Section 4.1.
# Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs
## 4 The Proposed Model
### Meta-Path Embedding
#### 4.3.1 Sampling of High-Quality Path Instances  
Different meta-paths mean different semantic and interactive relationships between different types of nodes. There are several meta-paths in Figure 1. For instance, meta-path UMTM is a sequence of a user node, a movie node, a type node, and a movie node, which indicates that people fall in love with movies of the same type. Path-based similarity measures are used in HINs to enhance the representations of nodes and links [41]. Moreover, nodes can be reached with different meanings through meta-paths. Given a meta-path UMTM in a heterogeneous information network, a new term, meta-path instance \(u_{1}m_{1}t_{1}m_{2}\), is defined as a sequence consisting of various nodes following the architecture of meta-path UMTM. Specifically, [PERSON] likes watching Roman Holiday, which is a comance, and he is very likely interested in Titanic, which is of the same type; then, \(Bob\to RomanHoliday\to Romance\to Titanic\) is a meta-path instance. Multiple meta-path instances of this form can make up a meta-path.  
The embedding of meta-path \(P\) is obtained from the embedding aggregation of path instances; thus, their high-quality sampling is necessary. However, path instance sampling approaches, such as meta-path-based random walk, are inefficient, as they affect meta-path embedding generation. In a path, a source node should be linked to a node of higher priority in each step. Traditional paths obtained with the random walk method are usually composed of nodes that may not be highly related, so they may not have rich semantic information. Therefore, in order to distinguish the different priorities of different nodes,a priority score based on node similarity is presented. In such a case, nodes are more closely related if their priority score is high. Specifically, we first utilize the singular value decomposition (SVD) feature to learn the latent vector representation of each node [42; 43]. Secondly, we calculate the priority score between connected nodes for each path instance. Then, we perform uniform averaging or summation operations. Next, we compare and sort the final priority scores of all path instances. Finally, we choose the path instances with the top-\(M\) scores to generate meta-path embeddings.
## 5 Results
The photos included in this research were obtained from the de-identified database of the Medical University in Poznan, as well as from the Multimodal Brain Tumor Image Segmentation Benchmark dataset (reference number [56]). The methods described in the cited reference [8] were utilized to introduce motion artifacts into motionless photographs from the dataset. The dataset was employed to evaluate the effectiveness of motion correction approaches as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Raw Data Sampling Scheme** & **TR** & **TE** & **FOV** & **Voxel Size (mm)** & **Data Collecting Time (s)** & _p-Value_ \\ \hline PROPELLER 3.0 & 1200 & 180 & 290 & 0.96/0.96/1.00 & 359 & 0.149 \\ SENSE-ASSET & 1200 & 180 & 290 & 0.96/0.96/1.00 & 355 & 0.215 \\ GRAPPA-ARC & 1200 & 180 & 290 & 0.96/0.96/1.00 & 397 & 0.129 \\ The proposed algorithm & 1200 & 180 & 290 & 0.96/0.96/1.00 & 112 & 0.103 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The current study investigates the variations in scanning parameters employed for the production of Figure 8. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Residual Proportion of the Initial k-Space Samples [\%]** & **PSNR [dB]** & **MAE** & **IEM** \\ \hline
10 & 14.89 & 23.67 & 1.23 \\ \hline
20 & 18.76 & 19.82 & 1.79 \\ \hline
30 & 20.11 & 19.21 & 1.82 \\ \hline
40 & 25.66 & 18.01 & 1.88 \\ \hline
50 & 26.41 & 17.66 & 2.42 \\ \hline
60 & 28.88 & 17.01 & 3.71 \\ \hline
70 & 29.01 & 16.23 & 3.77 \\ \hline
80 & 29.39 & 16.01 & 3.82 \\ \hline
90 & 30.39 & 15.66 & 3.99 \\ \hline
100 & 32.99 & 13.19 & 4.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The effectiveness of the algorithm being examined was assessed for different compression ratios of the k-space related to the figure mentioned in question Figure 8. The acronym MAE represents the term Mean Average Error. shown in references [3,4]. The procedure entails the incorporation of motion into motion-free k-space data for every blade of the PROPELLER. Following this, particular portions of the k-space data are isolated, and these segmented k-space data are merged together to create the anticipated sample patterns. The dataset denoted as [56] is relevant to the domain of images. The production of simulated k-space multishot MRI data was successfully achieved, using the methods described in [9]. The fastMRI dataset [57], which is a publicly accessible raw k-space dataset, was utilized in the second experiment. The results collected from the dataset have demonstrated the superiority of the suggested approach over the current leading competitors.
## 5 Discussion  
### Model Application  
After completing the systematic evaluation and comparative analysis of the model performance, the research team finally selected Model 3 as the deployment model. This model showed better performance in multiple test dimensions, especially in terms of the intensity of heatmap responses, spatial focusing ability, and stability of damage identification, which were better than Model 1 and Model 2. Figure 14 shows the recognition effect of Model 3 on typical damage types, including seven categories: stain, plant, moss, missing, damage, crack, and vandalism. Through the triple comparison of images, heatmaps, and box selection results, the model's recognition ability for different types of damage can be more intuitively observed.  
From the results, Model 3 has a high accuracy in identifying stains, plants, damage, cracks, and human damage. These categories often have obvious texture changes, morphological features, or edge breaks, such as stains with dark and mottled colors, damage with material peeling, cracks with linear structures, and human damage with tool penetration. These are easy to form feature focus in convolutional neural networks, so the model can stably identify them. In addition, the model performs well in identifying \"plant\" damage and can effectively distinguish the biological erosion features attached to the brick surface. However, the model still has some confusion when distinguishing between \"moss\" and \"plant\", two categories with similar semantics. The heatmap and annotation results reveal that the model mistakenly identifies some moss areas as plants. This misdetection is mainly due to the high similarity between the two in image performance, especially when the shadow is heavy or the moss is attached in large pieces; it is effortless to confuse it with the wall-attached vines of the plant class. In addition, there are certain errors in the identification of missing damage, and the model often identifies it as damage. This confusion may be related to the \"complete bricks as the labeling unit\" in the label set, which causes the model to tend to make judgments based on local morphology rather than structural integrity, thus confusing the boundary between severe surface erosion and missing bricks.  
Figure 14: Comprehensive analysis of model detection capabilities. (Image source: drawn by the authors).  
In general, Model 3 has a good ability to identify damage to gray bricks, especially in damage types with clear feature expressions that show high classification accuracy. Although there are still misjudgments in individual categories with similar semantics, these results are basically consistent with the real complexity of current brick surface weathering. In future research, we can further optimize the definition system of damage categories, introduce more fine-grained image features, and enhance semantic segmentation capabilities to continuously improve the adaptability and stability of the model in tasks for identifying damage in diverse historical buildings.
## 5 Conclusions  
In conclusion, we have proposed MIPO-CSGI, which employs a slow bucket detector to image a fast periodically moving/state-changed object using compressive ghost imaging. The proposed scheme constructed a random matrix based on the speckle patterns which  
Figure 8: Experimental results of reconstructing two frames of the “_T_” under different cycle times \(T\) using F-CSGI, S-CSGI, and MIPO-CSGI.  
Figure 9: Experimental results of reconstructing four frames of the “_T_” under different cycle times \(T\) using F-CSGI, S-CSGI, and MIPO-CSGI.  
acted on the object in each cycle \(T\) and establish a one-to-one correspondence between each row of the matrix and the measurement of the slow bucket detector. The multi-frame imaging of a periodically moving/state-changed object is converted into the imaging of a static large image composed of each state of the moving object to compensate for the sampling rate of the bucket detector. Finally, compressive sensing is used to reconstruct multiple images based on the random matrix with fewer bucket detector measurements. To further prove the feasibility of MIPO-CSGI, simulations and experiments are taken to compare with traditional F-CSGI, which uses a fast bucket detector, and S-CSGI, which uses a slow bucket detector. Through the simulations, we can see the imaging quality of MIPO-CSGI is almost the same as that of F-CSGI and better than that of S-CSGI. In the experiments, the noise introduced by the long response time of the slow detector makes the imaging quality of MPIO-CSGI slightly lower than that of F-CSGI, but it is always better than that of S-CSGI. Therefore, MIPO-CSGI provides more application flexibility for GI to use a slow bucket detector to image objects with fast periodic moving/state-changes.  
Investigation, H.G.; methodology, H.G.; validation, Y.C.; writing--original draft, H.G.; writing--review and editing, S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China, grant number 61871234 and 62001249; the University Excellent Young Talents Support Program Project of Anhui Province, grant number gxyq2020102; the Innovation and Entrepreneurship Training Program for College Students of Anhui Province, grant number s20211361909; the Scientific Research Project of College of Information Engineering, Fuyang Normal University, grant number 2019 FXGZK02 and FXG2021 ZZZ02.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results  
### Batch Experiments  
The batch adsorption data (Figure 3) were analyzed through the Langmuir and Freundlich adsorption models [26]. The corresponding model constants and correlation coefficients are summarized in Table 1. The Langmuir adsorption model fitted well to the data for the nutrients in all three materials (R\({}^{2}\) = 0.962-0.997), demonstrating that the adsorption sites on these materials were homogeneous with monolayer adsorption coverage. The Freundlich model fitted satisfactorily to the data only for zeolite and limestone bedding materials. However, the model fitting to the data was generally better through the Langmuir model compared with that using the Freundlich model, especially for basalt bedding material and MZP-PM. The deviation of NH\({}_{4}\)+-N adsorption data for MZP-PM was relatively large upon fitting through the Freundlich model, which agrees well with previously reported results when using modified zeolite adsorbent [19; 27].  
The maximum of Langmuir adsorption was obtained from the linear plot slope of the Ce/Qe vs. Ce for the pollutants followed the order of NH4+-N > TP for all of the bedding materials. The order of adsorption capacities of the three bedding materials were MZP-PM > zeolite > limestone for NH4+-N and MZP-PM > limestone > zeolite for TP. This result demonstrates that the MZP-PM was the best among the three kinds of materials. The maximum adsorbed capacities by Langmuir adsorption model were 9.72 mg/g and 1.23 mg/g for NH4+-N and TP, respectively; for MZP-PM, exhibiting the enhancement of 7.4 and 1.7 times by limestone (1.31 mg/g for NH4+-N and 0.55 mg/g for TP) respectively, which was superior to other adsorption systems in dealing with stormwater runoff [28]. Previous studies also showed the lanthanum-modified bentonite adsorbent [29] had greater phosphate adsorption capacity than MZP-PM, while nitrogen and phosphate could not be removed simultaneously. Thus, it probably provides the potential to be a capable adsorbent in the PAP system to stormwater for NH4+-N and TP removal.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 5 OpenFOAM FVTM Comparison
### Results  
From the results of the FVTM it is clear that the PSU it the hottest board inside the payload as reported in Figure 24a. Furthermore, it is possible to observe how heat flows into the upper region of the payload Figure 24b from the BEE-PCB through the FEE-PCB towards the upper detector region.  
The visualization of the heat flow path from the electronic boards towards the SDD tiles provides useful information about how to deviate such heat flux to keep the SDD temperature below the maximum value, i.e., \(-5\,\mathrm{\SIUnitSymbolCelsius}\). The solution implemented by INAF is to provide exposed areas of the ground layers on the BEE-PCB and PSU-PCB, on which conductive spacers dissipators are placed as shown in Figure 25. The spacers will be linked with the lateral panels, acting as external radiators, through the SIL-PD TSP 3500 thermal conductive pad, thus creating a thermal interface. Moreover, the PL designers also decided to expose part of the ground layer on the FEE lateral wings, and to thermally connect it to the lateral panels by means of a thermal pad.  
Figure 24: FVTM temperature field results. Crystal box and other elements are not included to ease the visibility of interested regions. (**a**) FVTM temperature field. (**b**) Heat flow path visualization (normal vectors to isothermal lines).  
Figure 25: New thermal links regions with lateral panels. Modified spacers named as Dissipators in the picture and exposed ground layer on FEE.  
The validation of the FVTM model is limited by the fact that the comparison is performed for a single steady state case, nonetheless, it is an important step to check the validity of the model. As stated in ECSS-E-ST-31C-4.5.3.3, the test data must be correlated both in steady state modes and transient modes, so the presented work satisfies only a part of the standards, the team is now validating the transient case. Once the model is validated, the team will be able to quantify the effects on the temperature field of the newly designed spacers used as thermal links with the payload panels without performing test campaigns. This will enable the team to check if other modifications to the spacers and their connections are necessary to lower the detector temperature.