esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Materials
### Data Collection and Performance Monitoring  
#### 2.5.1 Performance Monitoring  
A 27.7 kW inverter is deployed to convert solar-generated DC power into AC for mining operations. An energy management system (EMS) is integrated to monitor energy production and consumption in real-time, optimizing the use of stored energy during peak demand periods. Additionally, real-time monitoring sensors are installed to track energy generation, battery charge/discharge cycles, generator usage, and fuel consumption. The collected data is analyzed to optimize system performance using smart energy management system and identify areas for improvement. The analysis is performed with the assumption3% p.a. escalation in power cost [31] and 0.8% p.a. degradation [32] in panel efficiency.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Module Type** & **SRP-425-BMZ-HV** & **SRP-430-BMZ-HV** & **SRP-435-BMZ-HV** & **SRP-440-BMZ-HV** \\ \hline  & STC & STC & STC & STC \\ \(P_{\text{max}}\) at STC & 425 & 430 & 435 & 440 \\ \(V_{\text{OC}}\) & 52.1 & 52.2 & 52.4 & 52.6 \\ \hline \(l_{SC}\) & 10.31 & 10.40 & 10.47 & 10.53 \\ \(V_{\text{max}}\) & 43.8 & 43.9 & 44.1 & 44.3 \\ \hline \(l_{\text{max}}\) & 9.71 & 9.80 & 9.87 & 9.93 \\ Efficiency (\(\mu\)m) & 19.46 & 19.69 & 19.91 & 20.14 \\ \hline Tolerance Power & & & (0, +4.99) & \\ System Voltage (max) & & & 1500 VDC & \\ Series Fuse Rating (max) & & & 20 A & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solar Panel Properties [30].  
Figure 4: Components and interconnections of the system in block diagram.  
Figure 3: Overview of Solar Design Merredin WA [27].
###### Abstract
As such, the solution used in Moldova Noua to afforest the waste heaps' perimeter as well as add forest belts inside them is optimum in regard to hindering wind wrecking of the sterile located inside the waste heaps (this being the most concentrated in heavy metals). Afforestation works conducted on waste heaps from Moldova Noua had the purpose of reducing pollution from waste heap dust crumbled by the wind. Different pioneering species were used, such as black locust (_Robinia pseudoaccia_ L.), oleaster (_Eleaagusus angustifolia_ L.), sea buckthorn (_Hippphaa' rhamnoides_ L.), black cherry (_Prunus serotina_ Ehrh. ), shaky poplar (_Populus tremula_ L.), etc. Besides reducing dust pollution, these methods can also improve the soil's microbiological diversity [18], the diversity of the planted species, and the different degrees of decomposition of the dead wood. At the same time, they influence the soil's respiration rate and diversify the environmental conditions of the soil [19]. The purpose of the investigations conducted in the present article was to emphasize the influence of forest vegetation on improving the content of potentially toxic elements from the soil, to increase the content of micro and macro elements, and to correlate these variations with the characteristics of the forest vegetation from the studied areas. The context indicates a decrease in the percentage of these heavy metals, as well as other soil compounds that were levigated or absorbed at the tree's radicular level, followed by a decrease in specific soil chemical processes. The purpose of the investigation was obtained by fulfilling the following specific objectives:  
* Comparing the chemical composition of the sterile before planting with the chemical composition of the soil from forest plantations;
* Analyzing the variation of the soil's chemical composition from forest plantations located on waste heaps;
* Establishing the influence of forest vegetation on the variation of the soil's chemical composition in forest plantations located on waste heaps. This study's hypothesis is based on previously conducted investigations on waste heap plantations from Moldova Noua, which led to the observation of sustained growth of the forest vegetation planted there. Taking into account this aspect, as well as the accumulation of wood mass, this study's hypothesis is that the content of potentially toxic elements from the soil of waste heaps is improved under the influence of forest vegetation. Our research involved comparing the content of micro, macro, and potentially toxic elements from the soils of waste heaps present in Moldova Noua, from areas with forest vegetation, with their content determined by analyzing the sterile 31 years ago during afforestation projection works. The observed differences were correlated with the different characteristics of stands planted on waste heaps.
## 2 Simulation Methods  
A typical THz near-field model composed of a nanotip and a substrate under the THz plane wave radiation was built and shown in Figure 1. Since the THz near-field model has a planar symmetry and previous 3D simulation in visible and infrared regions had confirmed that the shape of the nanometer light spot generated by the nanotip was spherical [16,17], we performed 2D-FDTD simulations to investigate the enhancement effects of the THz near-field microscopy. The conical nanotip has a radius of 200 nm, a length of 15 \(\upmu\)m, and a taper angle of 25\({}^{\circ}\). The substrate has a size of \(60\times 120\)\(\upmu\)m\({}^{2}\). The radius of the nanotip apex is \(r\) and the distance between the apex and the substrate is \(D\). The \(p\)-polarized incident THz plane wave with a frequency of 1.0 THz and an amplitude of \(E_{0}\) (1 V/m) radiates along the nanotip axis with an incident angle of \(\theta=45^{\circ}\) (\(\theta\) is the angle between the wave vector \(k\) and the \(x\)-axis). A fine grid size of 2 nm is selected for simulations in a region centered at the center of the nanotip apex with an area of \(400\times 400\) nm\({}^{2}\) to guarantee convergence while conserving computer memory and processing time. Beyond the said region, the grid size is automatically generated.  
A tip-sample dipole model proposed by [PERSON] _et al_. [18,19] was widely used for near-field interaction between the sample and the metal nanotip in the visible and infrared frequency bands. The nanotip apex can be assumed to be a polarized sphere (radius \(r\), polarizability \(\alpha\)). When an electric field \(E\) is implemented, the nanotip will be polarized and a dipole moment will be generated. If the detected sample is a substrate, the polarized nanotip will induce charges on the surface of the substrate and the dipole moment generated by the induced charges can be derived from the image theory. The total electric field of the nanotip-substrate system is a superposition of the incident electric field and the image dipole electric field. Therefore, the effective polarizability of the nanotip-substrate system is derived as:  
\[\alpha^{\text{eff}} =\frac{\alpha(1+\beta)}{1-\frac{\alpha\beta}{16 nH^{3}}} \tag{1}\]  
Here, \(H\) is the distance between the center of the nanotip apex and the substrate with \(H=D+r\). \(\alpha\) is the polarizability of the nanotip with:  
\[\alpha=4\upmu r^{3}(\varepsilon_{\text{p}}-1)(\varepsilon_{\text{p}}+2) \tag{2}\]  
and \(\beta\) is the dielectric response function of the substrate with:\[\beta=(\varepsilon_{\mathrm{s}}-1)/(\varepsilon_{\mathrm{s}}+1) \tag{3}\]  
where \(\varepsilon_{\mathrm{s}}\), \(\varepsilon_{\mathrm{p}}\) are the dielectric constants of the substrate and the nanotip, respectively.  
From Equations (1)-(3), we can see that \(\alpha^{\mathrm{eff}}\) depends on the dielectric constants of the substrate and the nanotip (\(\varepsilon_{\mathrm{s}}\) and \(\varepsilon_{\mathrm{p}}\)), the radius of the apex of the nanotip, \(r\), and the distance between the apex and the substrate, \(D\).
# Deterioration Process of Cementitious Material Properties under Internal Sulphate Attack
## 3 Results and Discussion
### XRD Analysis
#### 3.1.2 Samples Curing in Different Temperatures  
Figure 6 shows the XRD patterns of mortar specimens with 4.8% SO\({}_{3}\) in the aggregate at 28 d and 360 d under different temperature maintenance. From Figure 6a, it can be seen that the gypsum-doped sand specimens cured at 28 d, 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C all have calcium alumina, gypsum and calcium hydroxide diffraction peaks, indicating that the gypsum aggregate failed to dissolve completely. From Figure 6b, it can be seen that after 360 d of curing, there are still gypsum diffraction peaks in the gypsum-doped sand specimens cured at 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C. The intensity of gypsum diffraction peaks in the specimens cured at 20 \({}^{\circ}\)C \(>\), those cured at 40 \({}^{\circ}\)C \(>\) and those cured at 60 \({}^{\circ}\)C, indicating that increasing the curing temperature accelerates the rate of gypsum dissolution. In general, the calcium alumina diffraction peak tends to be enhanced and the gypsum diffraction peak tends to be weakened as the age of maintenance increases.
# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 4 Proposed Methods
### Meta-Classification Model  
In few-shot settings, the environmental pattern recognition problem of TCSs can be considered a set of _N-way K-shot M-query_ tasks, each containing \(N\) classes with \(K\) support samples and \(M\) query samples in each class. These tasks are called _episodes_, the _N_\(\times\)_K_ labeled TCS samples are called the support set, and the \(M\) TCS samples to be recognized are called the query set. The goal is to query the \(M\) unlabeled TCS samples using only the _N_\(\times\)_K_ labeled samples for \(N\) environmental patterns. To solve this problem, a meta-classification model has been designed, containing three distinct stages.  
The first stage is training the feature extractor by training a CNN on all base categories, which will remove the final fully connected layer and obtain \(f_{\theta}\). The second stage is the meta-learning stage, which is the main component of the model; the task here is to improve generalization. Here, many episodes are extracted from the base dataset consisting of \(K\) input and output samples randomly selected from each category. This makes a total of _N_\(\times\)_K_ samples that are trained per episode, and the parameters of the classifier \(\mathcal{M}(\cdot|S_{base})\) are shared over all episodes. Given a few-shot task with support-set \(S\), let \(S_{c}\) denote the  
Figure 6: The network structure of the pre-segmentation model in our method. The encoder module encodes multi-scale contextual information, while the decoder module refines the segmentation results along object boundaries.  
few-shot samples in class \(c\) and compute a mean embedding \(w_{c}\) as the centroid of class \(c\) as follows:  
\[w_{c}=\frac{1}{|S_{c}|}\sum_{x\in S_{c}}f_{\theta}(x) \tag{1}\]  
For query sample \(x\) in the few-shot task, the probability that \(x\) belongs to class \(c\) is predicted based on the distance between the embedding of \(x\) and centroid \(w_{c}\) of class \(c\). Here, the cosine similarity is used for the metric, and a learnable scale parameter \(\tau\) is added to adjust the original range of values of cosine similarity \([-1,1]\); \(\tau\) is initialized to 10. With \(\langle\,,\,\rangle\) denoting the cosine similarity of the two vectors, the prediction can be formalized as follows:  
\[p(y=cx)=\frac{\exp(\tau\cdot\langle f_{\theta}(x),w_{c}\rangle)}{\sum_{c^{ \prime}}\exp(\tau\cdot\langle f_{\theta}(x),w_{c^{\prime}}\rangle)} \tag{2}\]  
The last stage is the meta-testing stage, where the generalization performance of \(\mathcal{M}(\cdot|S_{base})\) is evaluated by constructing \(D_{\text{novel}}\) from a sample of TCSs from different areas. In the meta-testing stage, a new set of episodes is randomly selected from \(D_{\text{novel}}\), which consists of a new support set \(S_{\text{novel}}\) and a new query set \(Q_{\text{novel}}\), and \(S_{\text{novel}}\) can be used to predict \(Q_{\text{novel}}\).
## 5 Outage Probability (OP) Performance Prediction Based on BP Neural Network  
### Input and Output Selection  
By the derived closed-form OP expressions, we can see that OP performance is affected significantly by \(m\), \(N\), \(G\) and \(K\). We use \(m\), \(N\), \(G\), \(K\) and other parameters as indicators. The input \(X\) includes 17 indicators, the output \(y\) is the corresponding OP performance obtained by Equation (13). The 17 indicators are\(m_{\text{SR1}}\), \(m_{\text{RD1}}\), \(m_{\text{SD1}}\), \(m_{\text{SR2}}\), \(m_{\text{RD2}}\), \(m_{\text{SD2}}\), \(G_{\text{SR}}\), \(G_{\text{RD}}\), \(N_{\text{SR1}}\), \(N_{\text{RD1}}\), \(N_{\text{SD1}}\), \(N_{\text{SR2}}\), \(N_{\text{RD2}}\), \(N_{\text{SD2}}\), \(K\), \(\gammaFor the hidden layer, input is given as:  
\[s_{j}=\sum_{i=1}^{17}w_{ij}x_{i}+b_{j},j=1,2,\ldots,q \tag{19}\]  
The output is given as:  
\[c_{j}=f\Big{(}s_{j}\Big{)} \tag{20}\]  
where \(f(\text{x})\) is the activation function.  
For the output layer, input is given as:  
\[\beta=\sum_{j=1}^{q}v_{j}c_{j}+\theta \tag{21}\]  
The output is given as:  
\[y=f(\beta) \tag{22}\]  
The output error \(EE\) is given as:  
\[EE=\sum_{h=1}^{p}\Big{(}d^{h}-y^{h}\Big{)}^{2} \tag{23}\]  
where \(y^{h}\) is the output for \(h\)-th data, and \(d^{h}\) is the desired output.
## 5 Experimental Evaluation
### Impact of Hyperparameters on MAE and RMSE
#### 5.6.3 Experimental Results and Analysis  
Figure 6 illustrates the impact of varying GCN layer depths and hidden layer dimensions on the model's MAE and RMSE performance for the garlic and scallion datasets.  
The experimental analysis reveals distinct optimal hyperparameter configurations across different datasets. For the garlic dataset, as shown in Figure 6a,b, the model exhibits optimal performance with three GCN layers and 64-dimensional hidden representations, achieving minimal MAE and RMSE values. This configuration leverages the dataset's high interaction density to construct comprehensive user-product relationship graphs. The three-layer architecture effectively captures high-order interaction patterns and uncovers latent semantic relationships, while the 64-dimensional embeddings provide adequate capacity for encoding complex feature interactions. However, deeper architectures (beyond three layers) lead to performance degradation due to redundant information and signal dilution, consistent with [PERSON] and [PERSON]'s findings [21] regarding the homogenization of node features in deeper networks.  
In contrast, the scallion dataset demonstrates optimal performance with shallower architectures (two GCN layers) and reduced dimensionality (32 or 64 dimensions), as evidenced by Figure 6c,d. This configuration addresses the dataset's inherent sparsity, where user-product connections are more limited. The shallower architecture effectively captures local interaction patterns while avoiding over-smoothing issues characteristic of deeper networks. This observation aligns with [PERSON] et al.'s findings in their LightGCN model [5], where simplified architectures demonstrated superior performance in sparse scenarios. The reduced dimensionality proves particularly effective by constraining the embedding space, thereby enhancing model generalization and robustness. The graph convolutional mechanism maintains recommendation reliability through effective neighborhood information aggregation, even under sparse conditions.  
These findings underscore the critical role of GCN depth and hidden layer dimensionality in determining recommendation performance. When GCN layers are set to two  
Figure 6: MAE and RMSE of APGCN-CF under different hyperparameter settings.  
or three, recommendation performance is superior, which closely relates to the working mechanism of GCN. With excessive GCN layers, the over-smoothing problem emerges, wherein node representations become increasingly homogeneous as layer count increases, causing the model to lose discriminative capacity. This aligns with findings from numerous graph-related research studies indicating that two to three layers of graph convolutional networks represent an optimal balance point. Hidden layer dimensionality performs best at 32 or 64 dimensions, as excessive dimensionality leads to overfitting issues, particularly when agricultural product datasets are not exceptionally large, while insufficient dimensionality fails to adequately express the feature relationships between users and products.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
###### Abstract  
Structural components with curved edges are common in many engineering designs. Fatigue cracks, corrosion and other types of defects and mechanical damage often initiate from (or are located close to) edges. Damage and defect detection in the presence of complex geometry represents a significant challenge for non-destructive testing (NDT). To address this challenge, this paper investigates the fundamental mode of the quasi-symmetric edge-guided wave (\(QES_{0}\)) propagating along a curved edge, as well as its scattering characteristics in the presence of different types of edge defects. The finite element (FE) approach is used to investigate the propagation and mode shapes of the \(QES_{0}\). It was found that the wave attenuation dramatically increases when the radius-to-thickness ratio is less than 20. Moreover, the mode shapes are significantly affected by the waveguide curvature as well as the excitation frequency. Additionally, to evaluate the sensitivity of \(QES_{0}\) to edge defects, different sizes of edge defects were investigated with the FE model, which validated against experimental results. The validated FE model was further employed to quantify the dependence of the amplitude of scattered waves for different types of edge defects. These studies indicate that the amplitude of scattered wave is very sensitive to the presence of edge defects. The main outcome of this work is the demonstrated ability of the \(QES_{0}\) wave mode to propagate over long distances and a high sensitivity of this mode to different types of edge defects, which manifest its great potential for detecting and characterising damage near the curved edges of structural components.  
edge-guided wave; curved structures; defect detection; scattering; non-destructive testing +
Footnote †: journal: Applied sciences  
1
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 5 Challenges
### Background Clutter  
Background clutter is also a challenge in 6D pose estimation. Because the target is surrounded by much useless information, it is difficult to measure the 6D pose directly. However, due to the complexity of practical scenarios, there are many conditions under which it is necessary to measure the 6D pose of objects in clutter.  
[PERSON] et al. [41] proposed Mask R-CNN, which efficiently detected objects in an image while simultaneously generating a high-quality segmentation mask for each instance. It extended Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. [PERSON] et al. proposed a method for measuring the 6D pose of objects in clutter. A global optimization process was employed to improve candidate poses by taking into account scene-level physical interactions between objects. Then, the combinations of candidate object poses were searched using a Monte Carlo Tree Search (MCTS) process that used the similarity between the observed depth image of the scene and the rendering of the scene given the hypothesized pose as a score, guiding the search procedure. [PERSON] et al. [90] proposed a two-step method for measuring the 6D pose. The first step was few-shot instance segmentation to segment the known objects from RGB images. [PERSON] et al. [91] proposed a method based on point clouds that detected objects in an end-to-end manner. They introduced a point cloud-based 6D target object detection method that used segmented object point cloud patches to predict object 6D poses and identity. It used a point cloud segmentation procedure that was easier to visualize and tune in order to overcome the problem caused by background clutter.
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
## 2 Methodology  
To address the objectives of this study, a comprehensive methodology was developed to identify, characterize, and control the peristaltic pump in a hemodialysis machine. The methodological process, as shown in Figure 1, highlights six stages: setting up communication between the pump, microcontroller, and LabVIEW; identifying the system; validating the transfer function; selecting the controller; and characterizing the system. These stages ensure the accuracy and repeatability of the pump's performance under varying conditions.  
This methodology aims not only to identify and control the system but also to characterize it to establish the precise relationship between revolutions per minute (RPM) and volumetric flow rate (mL/min). It is expected that the P.I. controller will minimize flow fluctuations and achieve low RMSE values, demonstrating stability and accuracy under realistic conditions. Additionally, repeated measurements at different setpoints are anticipated to provide consistent data, highlighting the system's ability to maintain uniform flow. The results will be compared to a commercial hemodialysis machine configured under identical conditions, allowing the developed system's effectiveness to be evaluated against a commercial standard.