esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### The Investigated Buildings
#### 2.1.1 Detailed Description of the Buildings and Climatic Conditions
The meteorological data for each location were retrieved from the DesignBuilder weather library [23]. Moreover, the building's thermal transmittance value of structural constructions, namely, external walls, windows, and floor and ground slabs were determined by the national guidelines of each respective country. Table 4 includes the thermal transmittance values (U-value) per building construction and city. Additionally, the average thermal transmittance value of the entire thermal cell was calculated for both buildings B1 and B2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**U-Value [W/m\({}^{2}\) K]**} \\ \hline
**Structural Construction** & **Athens** & **Barcelona** & **Munich** & **Stockholm** \\ \hline External Walls & 0.45 & 0.49 & 0.21 & 0.18 \\  Root Slab & 0.4 & 0.4 & 0.15 & 0.13 \\ Ground Slab & 0.8 & 0.75 & 0.263 & 0.15 \\ Windows & 2.6 & 2.1 & 1.05 & 1.2 \\ \hline \multicolumn{1}{c}{Mean U-value of entire thermal envelope [W/m\({}^{2}\) K]} \\ \hline Building B1 & 0.726 & 0.568 & 0.676 & 0.536 \\ Building B2 & 0.568 & 0.676 & 0.536 & 0.302 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thermal transmittance (U-value) of structural constructions per city. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Winter temperature setpoint & 20 \({}^{\circ}\)C \\ Summer temperature setpoint & 26 \({}^{\circ}\)C \\ Occupants & 30 \\ Thermal load per occupants & 80 W/occupant \\ Specific lighting electrical load & 4 W/m\({}^{2}\) \\ Specific appliances’ electrical load & 2 W/m\({}^{2}\) \\ Average operating fraction of the occupants & 0.75 \\ Average operating fraction of the lighting & 0.75 \\ Average operating fraction of the appliances & 0.75 \\ Infiltration rate & 0.4 ACH \\ Natural ventilation rate & 0.8 ACH \\ DHW temperature setpoint & 45 \({}^{\circ}\)C \\ DHW demand & 50 L/day/person \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Operational data of each examined building. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Athens** & **Barcelona** & **Munich** & **Stockholm** \\ \hline Mean yearly air temperature [\({}^{\circ}\)C] & 18.0 & 14.6 & 9.0 & 7.5 \\ Mean yearly water temperature [\({}^{\circ}\)C] & 17.0 & 14.0 & 8.0 & 6.5 \\ Total horizontal irradiation [kWh/m\({}^{2}\)] & 1832 & 1630 & 1194 & 997 \\ Heating degree days & 1041 & 1658 & 3358 & 3812 \\ Cooling degree days & 588 & 108 & 11 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic climatic parameters of each examined location.
## 6 Experimental Evaluation
### Experiment II: Service Allocation
#### 6.2.5 Service Allocation Results
Summary  
Figure 21 reflects the difference in overall performance between the algorithms. We calculated the average of each performance metric and used the best metric as a benchmark. In the graph, A represents mean user delay, B represents total user delay, C represents % CPU, D represents % memory, E represents mean bandwidth, and F represents user success rate. As seen in Figure 21, the Ubunye BW and latency algorithms achieved the best results in five out of six evaluation metrics. The classic algorithms, first-fit and round robin, also performed well, securing second and third places in five of the six metrics, with the best results in terms of user request success. However, the DPDA algorithm performed poorly in the simulated scenarios, although it showed a positive outcome in the user requests metric. During our simulations of the five algorithms in EdgeSimPy, we identified several factors that impact their performance. User mobility, including movement patterns and speed, plays a key role in algorithm success. As mobility increases, achieving significant improvements becomes more challenging, as rapid user movement complicates efficient resource allocation. Additionally, increasing the number of users extends the simulation  
Figure 11:3 Memory usage: the percentage of memory used by the five algorithms varies by the number of users in each faulty scenario. Figure 11:4 User request success rate: percentage ratio between successfully answered user requests and the total number of user requests during the simulation in each faulty scenario. time (up to 30 min for 150 users) and significantly increases the size of log files, potentially reaching 0.5 GB. Other factors influencing algorithm performance include the distance between users and edge nodes, the network topology, and the number of edge nodes involved in the auction. Although the classic auction algorithm typically reaches a solution, the time required can be excessive, especially in scenarios with high mobility and many users. Auction efficiency is critical for resource allocation, as delays can negatively affect the users' quality of experience (QoE).
## 2 Results and Discussion
### Plant Growth Promotion Potential  
#### 2.4.1 Nutrient (N, P, S, Fe) Acquisition  
Rhizospheric bacteria use various mechanisms of action to stimulate plant growth. First, due to their direct vicinity of plant roots, they enhance the bioavailability and uptake of beneficial nutrients, such nitrogen (N), phosphorus (P), and sulfur (S). N is an essential nutrient for plant growth, development, and reproduction; however, plants are unable to utilize atmospheric nitrogen (N\({}_{2}\)). Fortunately, some rhizosphere bacteria, called diazotrophs, are able to convert N\({}_{2}\) to ammonia (NH\({}_{3}\)) in a process called biological nitrogen fixation (BNF) using a complex system known as nitrogenase [5]. Among diazotrophs, the _Azotobacter_, _Azospirillum_, _Rhizobium_, and _Bradyrhizobium_ genera are the best known [29]; however, species of _Pseudomonas_ (e.g., _Pseudomonas stutteri_) have also been reported [30,31]. The genome of the ZCR6 strain lacks a full set of genes necessary for nitrogen fixation. Nevertheless, the presence of _iscS_ and _iscU_ genes encoding cysteine desulfurase and nitrogen fixation protein, respectively, with a primary sequence similar to those of NifS and NifU proteins, was reported (Table 4). The role of NifS and NifU is the formation of iron-sulfur (Fe-S) clusters required for the maturation of nitrogenase in nitrogen-fixing microorganisms [32]. Because it was found that functional cross talk between the Nif and ISC systems is possible [33], NifS- and NifU-like proteins from the ZCR6 strain may have been engaged in the formation of Fe-S clusters by other nitrogen-fixing rhizobacteria when the loss of neither NifS nor NifU function occurred. The ZCR6 strain was not able to fix atmospheric nitrogen but showed the ability to produce ammonia (probably via ACCD activity) (Table 1). This mechanism of PGP may also support plant growth by facilitating better supplementation of plants with assimilable forms of N.  
Figure 4: KEGG pathway classification of predicted genes in the ZCR6 strain using KEGG BlastKoala (**A**). Protein-coding sequences grouped into the functional subsystems in the ZCR6 genome using RASTtk (**B**).
## 3 Materials and Methods
### Experimental Dataset: The PROSTATEx17 Dataset  
This work considers the MRI dataset provided by the PROSTATEx Challenge 2017 [43] as part of the 2017 SPIE Medical Imaging Symposium [56], organized by the Society of Photographic Instrumentation Engineers (SPIE) and supported by the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI). The aim of the PROSTATEx Challenge 2017 is to develop a quantitative diagnostic classification method of prostate lesions. This dataset was previously collected and curated by the Radboud University Medical Centre (Nijmegen, the Netherlands) in the Prostate MR Reference Center under the supervision of Prof. [PERSON] [18].  
The dataset contains mpMRI studies of 344 subjects. All studies include T2w, PDw, DCE, and DWI MRI sequences, acquired using the MAGNETOM Trio and Skyra 3T MRI scanner models from Siemens (Siemens Healthineers, Erlangen, Germany), without employing an endorectal coil. For more details on image acquisition, please refer to [43]. Although DCE imaging conveys relevant functional information, it carries the drawback of needing an external agent, typically via the injection of a Gadolinium-basedcontrast [32; 57]. The contrast may cause discomfort to the patient, with an increase in the risk of residual deposition in the human body [58] and without proof of an improvement in cancer detection quality [32]. In this study, we used non-contrast-enhanced MRI sequences only, since DWI--and especially ADC maps [59]--showed promising applications in the clinic. In a very recent study [60], similar cancer detection rates from biparametric MRI (bpMRI)--focusing on T2w and DWI--and contrast-enhanced mpMRI, particularly for Clinically Significant (CS) cases of PCa, were achieved. Examples of input T2w, PDw and ADC MR images are shown in Figures 1a, 1b and 1c, respectively.  
Each MRI study was evaluated under the supervision of an expert radiologist that identified areas of suspicion. If an area was marked as likely for cancer, a biopsy was performed and then graded by a pathologist. If the biopsy results had a GS higher than 7, it was considered CS [18]. The ultimate goal of the PROSTATEx Challenge 2017 is to predict the clinical significance of a patient's lesion based on his MRI studies.  
The whole cohort was divided into two sub-sets; each lesion's CS information was available in the training set (204 subjects) but not in the test set (140 subjects). Therefore, only the training set was considered in this study.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Modelling Optimisation Analysis
#### 2.4.2 Optimization Method  
This study identifies a non-coplanar trajectory that meets specific constraints by optimizing the distance-averaged objective function and completing the scaling parameter optimization using the presented nonlinear planning method.  
1. Optimization function.  
The FMINCON function in MATLAB is a widely utilized optimization tool designed to find the minimum of a constrained nonlinear multivariate function. To achieve faster convergence, enhanced computational efficiency, and improved boundary search capabilities, this paper employs the Sequential Quadratic Programming (SQP) algorithm in conjunction with the FMINCON function. The formulation of the FMINCON function is as follows:  
\[r=Fmincon(\,f,r,A,b,Aeq,beq,lb,ub,@(r)nonlcon(r,n_{1},n_{2}),options), \tag{11}\]  
The parameter \(\,f\,\) represents the objective function, while \(r\) denotes the initial scaling parameter, which includes two parameter values. The matrices \(A\) and \(b\) define the linear inequality constraints, expressed as \(A\times r<b\). The matrices \(Aeq\) and \(beq\) represent the equation constraints, formulated as \(Aeq\times r<beq\). The variables \(lb\) and \(ub\) indicate the lower and upper bound constraints, respectively, such that \(lb<r<ub\). Additionally, the function nonlocal is a nonlinear constraint function that can return two output values: the nonlinear inequality \(C(r)\leq 0\) and the equation \(C(r)=0\).  
2. Establishment of an objective function.  
The optimization objective is defined as the average value of the minimum distance between the Bezier curve and the data points in the point set during the spanning motion phase. Optimization of the Bezier curve is accomplished by minimizing this average value using the FMINCON function. According to the matrix of Equation (4) presented in Section 2.2, any data point within the spanning phase can be represented as \(O_{i}\). Furthermore, based on the Bezier curve Equation (9) outlined in Section 2.3, any point on the curve can be denoted as \(B(t_{i})\). Then, for each point in the given point set, the Euclidean distances  
Figure 5: The specific optimization process.
## 1 Introduction
While many clustering-based methods are inspired by ROM principles, in the strictest sense, ROMs refer to low-dimensional dynamical systems derived via projection (e.g., Galerkin or Petrov-Galerkin) from governing equations. The method proposed in this study does not involve such a projection and does not construct time-evolving models. Instead, it focuses on extracting and organizing spatial flow structures directly from data. Therefore, our approach is best categorized as a data-driven dimensionality reduction method, guided by physical insights from POD. Building on these foundations, clustering-based dimensionality reduction methods have shown increasing promise. [PERSON] et al. [18] proposed a CVT-based approach but did not address mode importance. [PERSON] et al. [19] introduced modified pole clustering for improved initialization, with emphasis on clustering rather than reduction. [PERSON] and [PERSON] [20,21] combined clustering and POD to analyze vortex dynamics, yet lacked an effective mode-ranking strategy. [PERSON] et al. [22] presented CROM, which models transitions via a Markov matrix, but its dimensionality and sparsity hinder application to complex systems. To tackle the dual challenges of unstable clustering center initialization and the absence of mode ranking, we propose a clustering-based dimensionality reduction method guided by POD structures (C-POD). This framework leverages POD to enhance initialization robustness and introduces a novel entropy-controlled Euclidean-to-probability mapping (ECEPM) to provide probabilistic, interpretable mode ranking. We validate the C-POD framework on the one-dimensional Burgers' equation and a two-dimensional cylinder wake flow. We further test its performance in an inverse reconstruction task using sparse sensor data. To enhance conceptual clarity, Figure 1 summarizes the taxonomy of the methods discussed. In summary, this study offers the following contributions:  
* A clustering-based dimensionality reduction framework guided by POD, improving stability in nonlinear mode extraction. * A novel ECEPM-based probabilistic mode-ranking method. * Empirical validation demonstrating improved interpretability and reconstruction accuracy over traditional POD methods.
## 3 Results
### Elevation-Based Performance Evaluation of GPPs
This study further revealed the significant influence of elevation on ERA-5's performance, as it exhibits a poor association with the RG data in region I, with lower correlation and higher RMSE, remarkable overestimations, and negative KGE values. In the rest of the regions, the correlation of ERA-5 with the observed data is nearly the same (CC = 0.76-78). However, the RMSE and KGE values show better performance with a decrease in elevation and depict good agreement at 0-500 m elevation (region IV). IMERG7 demonstrates a highly significant association with the RG data across all the regions (CC = 0.89-0.91) except region I (CC = 0.65). The statistics for the correlation, RMSE, and KGE elucidate the better agreement of the dataset at lower elevations than in mountainous regions. MSWEP2.8 also exhibits poor performance in region I due to the relatively low correlation, higher overestimation, and negative KGE value, while the RMSE value is better than in region  
Figure 10: Rows 1, 2, 3, and 4 present box plots of the correlation, RMSE, KGE, and PBias, respectively. Additionally, the first, second, third, and fourth columns represent region I, region II, region III, and region IV, respectively. The orange line in the plot shows the standard score for each measure. II. In the rest of the regions, the correlation is nearly the same, with the range of 0.81 to 0.83. Moreover, the RMSE and KGE values reveal that the dataset precipitation estimation skills improve with a decline in elevation and closely align with the reference data in low-elevation plain areas (Figure 10).
## 2 Materials and Methods  
The Scopus indexed articles were searched using the _Document Search_ function [8] on 4 November 2020. Four terms--\"forensic\" OR \"medicolegal\" OR \"legal medicine\" AND \"computed tomography\"--were used for document search so that any articles containing one of the three pairs of keywords (i.e., \"forensic--computed tomography\", \"medicolegal--computed tomography\", or \"legal medicine--computed tomography\") in their title, abstract, and/or keywords could be detected. Document type was limited to \"Articles and Reviews\" so other types of publications such as book chapters, letters, conference papers, and surveys were excluded. The years of publication were not specified, and [PERSON] et al.'s [9] work titled \"Diagnostic possibilities of computerized tomography in forensic examination of cerebral traumatized persons\" was marked as the earliest article. The citation information (e.g., authors, title, publication year, citation counts, and source), bibliographic information (e.g., affiliations and correspondence address), abstract and keywords, and reference information of all selected articles were exported in a comma-separated values (CSV) file format for further analyses. Informed consent was not required from the authors of the articles because the data were collected as secondary data lacking personal information.  
Microsoft Excel was used to calculate descriptive statistics and annual growth rate (AGR) and to produce related plots. AGRs were calculated as follows, which represents the percentage rate of positive or negative growth of a product in a certain year compared to the previous year [10].  
\[AGR_{i}\ =\ \frac{N_{i}-N_{i-1}}{N_{i-1}}\times 100\]  
where \(N_{i}\): number of documents in the year \(i\).  
In calculating AGRs, only the articles published between 2000 and 2019 were included, during which drastic increase in publications was observed. The publications in 2020 were excluded from the AGR calculation because the number of publications at the time of data collection for this study could not represent that of the entire year.  
VOSviewer 1.6.15 [11] was used for bibliometric analysis. VOSviewer is an open source computer program which makes it possible to perform bibliometric analyses and display two-dimensional maps based on the co-occurrences of the nodes/subjects [12]. One of the advantages of using VOSviewer for this purpose is that VOSviewer can analyze the bibliometric data exported from the major journal archives such as Scopus, Web of Science, and PubMed directly.
## 4 Simulation Results and Discussions
### Comparative Analysis of Classification Accuracy with Proposed Algorithm and Other Methods  
The simulation results in Table 8 show the classification accuracy of the proposed algorithm and other approaches using training data. From Table 8, it can be found that the proposed algorithm has a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Feature\#1 & Feature\#2 & Feature\#3 & Feature\#4 & Feature\#5 & Feature\#6 & Feature\#7 & \(M\) & \(CF\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: A representation of the solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual Predicted** & **Actual Positive** & **Active Negative** \\ \hline Predicted Positive & TP (True Positive) & FP (False Positive) \\ Predicted Negative & FN (False Negative) & TN (True Negative) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The confusion matrix.  
classification accuracy of 99.92%, which is better than other approaches such as decision tree (DT), random forest (RF), and support vector machine (SVM). As can be seen from Table 8, the classification accuracy of the only DT is 95.34%, and the classification accuracy of the proposed algorithm is 99.92%, which means that the SA algorithm has an additive effect on DT, and the parameters can be adjusted to improve its accuracy. Because SA has the advantage of jumping out of local optimum according to probability, it can effectively prevent the search process from falling into a local optimum. This paper proposes an intelligent algorithm SA added to DT. Using the advantages of SA, the \(M\) and \(CF\) in DT can be effectively determined.
## 2 Faults in a PV System
### PV Inverter Faults (PVI)  
In photovoltaic applications, one of the biggest challenges is ensuring that power electronics are reliable in order to optimize energy production. The inverter serves as the interface between the photovoltaic generator and the network and/or load. Its main function is to convert the continuous energy produced by the photovoltaic modules into alternating energy that is identical to the network. This allows the inverter to access electrical information from the generator and the electrical network, making it an intermediary between the two. Additionally, the inverter is equipped with a high level of data granularity, which enables it to detect electrical anomalies in real-time and alert the user through an audible signal or a message. However, despite its advantages, the inverter is vulnerable and subject to faults. During its operation, it is exposed to overvoltage and overcurrent constraints due to transient operating conditions, mechanical turbulence, temperatures, and humidity [68]. The IGBT (insulated gate bipolar transistor) power switch, being the main energy transfer component, is the most likely source of failure in the photovoltaic inverter [69]. The most common faults that can occur during the inverter's operation are open-circuit, insulation faults and short-circuit faults [70].