esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modelling and Methods
### ANN Development Protocol
#### 2.5.3 Data Splitting  
Splitting the dataset into training and testing sets is a fundamental part of the ANN algorithm development process. To avoid introducing figure selection bias, the use of automatic random selection was opted for: the \"train_test split\" function from the \"sklearn.model_selection\" library, under the Scikit Learn API [35]. Random selection is currently the most-used unsupervised splitting technique [9]. Following the example of several other research studies, a ratio of 80-20% of the available observations was allocated to training and testing the algorithm, respectively [14; 36].
# Infrared Thermography to Evaluate Thermal Comfort under Controlled Ambient Conditions
## 4 Results of Phase II: Evaluation of Thermal Comfort  
### Temperature Variation within the Face  
The preliminary step to evaluate thermal comfort using IRT is to evaluate the heterogeneity of the temperature on the face. For this purpose, the methodology presented in Section 2.4.2 and Figure 3 was implemented. Figure 10 presents the box plots of the maximum (Figure 10a), minimum (Figure 10b), and average (Figure 10c) temperature distributions within the six regions of the face, throughout the range of TSP values. As expected, all of the different regions display an increase in temperature with the increase in \(\mathrm{T_{SP}}\). However, among them, some relevant differences can be noticed, reinforcing the findings discussed in Section 3.1.  
Considering the maximum and minimum values, one can conclude that the former occur somewhere outside the five subregions considered here (box plots 2 to 6 in Figure 10a), while the latter are on the nose (box plots 1 and 6 in Figure 10b). In fact, the previous analysis already showed that the maximum temperature occurs in the inner eye corner or in the mouth (Figure 8).  
Regarding the average temperatures (Figure 10c), with the lower \(\mathrm{T_{SP}}\) values, the heterogeneity is more obvious, with the nose having the most extreme discrepancy in comparison with the other regions, always presenting lower temperatures (box plot 5). Both cheekbones exhibit similar temperatures (box plots 3 and 4), which are higher than the nose but lower than the other regions. Low temperatures in the nose were already reported in the literature [15; 17]. However, according to the same authors, the values should be closer to the ones on the cheekbones. One possible explanation for this may be the limits  
Figure 9: Box plot representations of the maximum temperatures for the entire range: (**a**) \(\mathrm{T_{SP}}\); (**b**) \(\mathrm{RH_{SP}}\).  
defined for the area that represents the cheekbones, which were small in this case study and might not have included the cooler zones. The forehead and chin (box plots 2 and 6) are the regions with the highest values, with the chin exhibiting slightly higher temperatures.  
Figure 10: Box-plot representations: (**a**) maximum temperature; (**b**) minimum temperature; (**c**) average temperature for each region (1—face; 2—forehead; 3—cheekbone right; 4—cheekbone left; 5—nose; 6—chin).  
At intermediate T\({}_{\text{SP}}\) values, the dispersion of temperatures between regions becomes less marked. The nose remains the region with the lower temperature, in clear contrast with the homogeneity presented by the other regions. In fact, all regions exhibit closer temperature values and the relative differences between them remain unchanged. At higher temperature values, there is a clear increase in the homogeneity of the temperatures of the regions. This phenomenon starts occurring when the T\({}_{\text{SP}}\) reaches 26 \({}^{\circ}\)C.
# Adaptive Metrics for Adaptive Samples
## Appendix A Details on Metric Smoothing
Define \(x^{\prime}:=\operatorname*{argmin}_{x\in X}\mathrm{d}^{L}(y,x)\) and \(x^{\prime\prime}=\operatorname*{argmin}_{x\in X}\widetilde{\mathrm{d}^{L}}(y,x)\). We remind the reader that these points' existence is guaranteed by the extreme value theorem. By examining these variables with respect to the previous inequality we know that:  
\[\frac{1}{1+\alpha}\mathrm{d}^{L}(y,x^{\prime})\leq\frac{1}{1+\alpha}\mathrm{d}^ {L}(y,x^{\prime\prime})<\widetilde{\mathrm{d}^{L}}(y,x^{\prime\prime})\leq \widetilde{\mathrm{d}^{L}}(y,x^{\prime})<\frac{1}{1-\alpha}d^{L}(y,x^{\prime}).\]By applying the definitions of both adaptive distance functions to the previous expression, we obtain the desired inequality,  
\[\frac{1}{1+\alpha}f_{X}^{L}(y)<\widetilde{f_{X}^{L}}(y)<\frac{1}{1-\alpha}f_{X}^ {L}(y).\]  
Define the Riemannian adaptive offsets of \(X\) as \(\widetilde{A}_{X}^{L}(\alpha):=\{x\in\mathbb{R}^{d}\ |\ \widetilde{f_{X}^{L}}(x)\leq\alpha\}\), and denote the corresponding filtration by \(\widetilde{A}_{X}^{L}\). The following result reestablishes Lemma A2 in the language of filtrations and establishes an interleaving of the Riemannian adaptive offsets with the original adaptive offsets. **Corollary A1**. : _Let \(L\subset\mathbb{R}^{d}\) be a compact set. Given \(\alpha,\beta\in(0,1)\), for compact \(X\subset\mathbb{R}^{d}\setminus L^{\beta}\), there exists a Riemannian distance function \(\widetilde{f_{X}^{L}}:\mathbb{R}^{d}\to\mathbb{R}\), such that \((\widetilde{A}_{X}^{L},\widetilde{A}_{X}^{L})\) are \((h_{6},h_{7})\)-interleaved on \((0,\infty)\), where \(h_{6}(r)=(1+\alpha)r\) and \(h_{7}(r)=\frac{r}{1-\alpha}\)._  
Proof. : By Lemma A2, there exists a Riemannian distance function \(\widetilde{f_{X}^{L}}:\mathbb{R}^{d}\to\mathbb{R}\), such that for all \(y\in\mathbb{R}^{d}\setminus L^{\beta}\),  
\[\frac{1}{1+\alpha}f_{X}^{L}(y)<\widetilde{f_{X}^{L}}(y)<\frac{1}{1-\alpha}f_{ X}^{L}(y),\]  
so for \(r\in(0,\infty)\) and \(y\in\widetilde{A}_{X}^{L}(r)\), \(\widetilde{f_{X}^{L}}(y)\leq r\), and thus, \(f_{X}^{L}(y)\leq(1+\alpha)r\), which implies that \(y\in A_{X}^{L}((1+\alpha)r)\), so \(\widetilde{A}_{X}^{L}(r)\subseteq A_{X}^{L}((1+\alpha)r)\). On the other hand, for \(r\in(0,\infty)\) and \(y\in A_{X}^{L}(r)\), \(f_{X}^{L}(y)\leq r\), and thus, \(\widetilde{f_{X}^{L}}(r)\leq\frac{r}{1-\alpha}\), so \(A_{X}^{L}(r)\subseteq\widetilde{A}_{X}^{L}(\frac{r}{1-\alpha})\). Combining the previous corollary with Theorem 2 in Section 3.2.4, we obtain an interleaving between the smoothed adaptive offsets and the approximate offsets. This will then allow us to apply Lemma 16 and standard topological data analysis techniques to this interleaving to give a method of homology inference for arbitrary small offsets of \(X\) as we have a Riemannian distance function generating the smooth adaptive offsets' filtration.
## 2 Materials and Methods  
### Database and Population  
The E-HOL-12-0051-016 database, developed by the Telemetric and Holter ECG Warehouse (THEW) project [25], represents a valuable tool for advanced research in electrocardiography (ECG). This database includes long-term 12-lead Holter recordings obtained continuously for 48 h, with a sampling frequency of 1000 Hz, ensuring the standards required for HRV analysis from patients at different stages of hemodialysis treatment: before, during, and after. The study, conducted between February 2009 and June 2010, included a total of 51 adult patients (\(\geq\)40 years). Informed consent was obtained from all participants, ensuring ethical participation in the study. Inclusion criteria required a confirmed diagnosis of ESRD with dependence on maintenance hemodialysis while excluding individuals with non-cardiac terminal illnesses or an inability to adhere to monitoring protocols. Patients in this cohort are classified as high-risk due to their heightened susceptibility to ventricular arrhythmias, dialysis-induced electrolyte imbalances (e.g., hyperkalemia), and ANS dysfunction. These factors, compounded by the hemodynamic stress of recurrent dialysis sessions, create a pro-arrhythmic environment that significantly elevates their risk of sudden death.  
Additionally, this database includes annotation files by experts, which specify the type of heartbeat detected and highlight events such as PVCs, facilitating detailed studies on arrhythmias. To access this information, generally intended for academic and scientific research, it is necessary to register on the project portal and accept the terms of use. After the approval of the application, the data are available for download following the guidelines established on the platform. This process ensures ethical and responsible handling of the information contained in the database.
## 5 Research Results
### Publications by Year  
Using the search string presented in Section 4.1, the results are distributed according to the elaborated Figure 3, having something related to BCP or disaster recovery plans (DRPs).  
The number of publications by year does not reveal the research subject areas. Nonetheless, the number of publications returned using the search string has been increasing since 2001, mainly due to terrorism and natural-disaster-related publications. From 2000 to 2010, the average was 42 publications, doubling in the years after 2010.
## 4 Results
### Numerical Results  
The results regarding the performance of the models in the regression task are detailed in the following. Table 3 presents the summary statistics of the Root Mean Squared Error (RMSE) for the prediction of E. The results show that BAY, RDG, and LAS achieved similar performances, as evidenced by the slight differences in the summary statistics and the boxplots depicted in Figure 8. Among them, the LAS had the best performance since it achieved the lowest mean value of the RMSE. However, such a result is still unsatisfactory since the mean RMSE value was higher than 10% of the dataset's maximum E value. The DTR and SVR performed a little better than the linear models but still not in a way to be considered satisfactory. Alternatively, the RFR, KNN, and CAT stood out from the linear models, achieving mean RMSE values of close to half of the values achieved by the linear models. Despite the similar performances, the statistics show that the latter achieved more consistent results with a narrower RMSE distribution, as shown in Figure 8. Therefore, the boosting model had the best performance in the elastic modulus prediction task. On the other hand, the results for the \(\sigma\) prediction are summarized in Table 4. Similarly to what happened for E, the linear models were the ones with the worst performance. The mean RMSE values achieved by BAY, RDG, and LAS are almost equal to 20% of the maximum \(\sigma\) value in the dataset. In this situation, CAT and RFR performed considerably better than the linear models, DTR, SVR, and KNN. Despite the lower mean RMSE for RFR, CAT outperformed the former with a narrower distribution; see Figure 8.  
As previously discussed, building a model for predicting the mechanical properties of additively manufactured composites from printing properties is challenging. Therefore, it could be expected that linear models (BAY, RDG, and LAS) would not achieve adequate per  
Figure 7: Target variable versus fiber volume fraction: (**a**) depicts the correlation between \(\sigma\) and \(V_{f}\), while (**b**) shows the correlation between E and \(V_{f}\).  
formances since they cannot capture the nonlinear relationships between the input variables (printing properties) and the mechanical properties. On the other hand, models such as SVR and DTR can better deal with nonlinearities between input and output variables, thus performing better than linear regression models, as shown by the metrics reported. The RFR and CAT models performed even better because, in addition to being able to capture nonlinear relationships, they are an ensemble of decision tree models. Ensembles generally reduce the chance of overfitting and generate more robust predictions than single models. KNN also performed well, given its ability to capture nonlinearities and the fact that it is an algorithm that tends to produce good models when trained with small databases.
# Progress on Single-Feed Quality Wideband Linear Wire Array
## 6 Series-Fed Array Arrangement  
To further enhance the gain, the QWL antenna can also be configured as a series-fed array. As shown in Figure 10, [37] proposed a series-fed QWL antenna array based on the WHEMS antenna. This series-fed QWL antenna array includes a reflector, driven elements and directors. The antenna uses a series-fed arrangement in the driven element section, connecting the feed points of two WHEMS antennas in series. The series-feeding method proposed in Figure 10 cancels out the phase difference of \(\pi/2\) introduced by the feed, achieving equal amplitude and in-phase currents in the two array elements, thereby realizing high gain.  
To achieve high gain in the series-fed QWL antenna array, it is also necessary to address the array gain loss caused by mutual coupling between adjacent elements. The red dashed line in Figure 11 represents the capacitive coupling between the two WHEMS elements due to the voltage distribution. The inverse current distribution on the adjacent edges generates differential currents, likening this structure to two transmission lines carrying opposing currents. Therefore, the adjacent side currents of the WHEMS array will cancel each other, resulting in a gain decrease of 2-3 dB compared with the ideal case calculated via array theory. The sides of the two WHEMS antennas that are close to each other are bent in Figure 11, increasing the isolation between the two WHEMS antennas and further improving the overall gain of the QWL antenna array. Ultimately, this single-feed-point, two-element series-fed antenna can achieve a directional gain of over 14 dBi.  
A photo of the series-fed QWL antenna is shown in Figure 12, and its radiation patterns are shown in Figure 13. According to the comparison between the proposed antenna and other related studies in Table 1, the advantages of this QWL antenna array include its high gain and low wind load. Additionally, it does not include a dielectric plate power divider, resulting in low loss and all-weather environmental adaptability.  
Figure 10: The series-fed WHEMS array: (**a**) exploded view and (**b**) overall isometric view.  
Figure 11: (a) Voltage and coupling analysis of series-fed QWL. (b) Simulation results regarding isolation.  
Figure 12: The photo of the series-fed QWL antenna.
# Primitive Shape Fitting in Point Clouds Using the Bees Algorithm
## 5 Results  
The five-number summary [35] of the primitive fitting tests is shown for each algorithm in Table 3 for the clean, error, and double error data sets. The five-number summary is a popular set of robust descriptive statistics that provide the central tendency (median), spread (first and third quartile), and range (minimum and maximum) of the distribution of the results.  
In terms of accuracy (median value), the bees algorithm performed particularly well on spheres and boxes, where it obtained errors that were smaller than or comparable to the errors obtained by the EA and RANSAC. On cylinders, although the performances of the bees algorithm and RANSAC were close, the latter obtained the best results.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{4}{c}{**Evolutionary algorithm**} \\ \hline
**Parameter** & **Sphere** & **Box** & **Cylinder** \\ \hline \# Individuals & 10 & 10 & 25 \\ \# Parents & 3 & 3 & 8 \\ Mutation Rate (\(p^{\prime}\)) & 1 & 1 & 1 \\ \# Iterations & 390 & 900 & 672 \\ Sampling Coverage & 5\% & 25\% & 25\% \\ \hline \multicolumn{4}{c}{**Bees algorithm**} \\ \hline
**Parameter** & **Sphere** & **Box** & **Cylinder** \\ \hline Scout bees (\(ns\)) & 2 & 3 & 4 \\ Elite sites (\(ne\)) & 1 & 1 & 1 \\ Best sites (\(nb\)) & 2 & 3 & 4 \\ Recruited elite (\(ure\)) & 9 & 10 & 10 \\ Recruited best (\(urb\)) & 4 & 4 & 6 \\ Stagnation limit (\(stilim\)) & 20 & 30 & 25 \\ Initial patch neighbourhood (\(ngh\)) & 0.15 & 0.5 & 1 \\ \# Iterations & 300 & 500 & 600 \\ Sampling Coverage & 5\% & 25\% & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameterization of the bees algorithm and evolutionary algorithm.  
In terms of consistency (first and third quartiles), RANSAC and the bees algorithm performed comparably on boxes and spheres, whilst RANSAC was clearly superior in the fitting of cylinders.  
Overall, the EA was the least accurate and consistent of the three algorithms on all data sets and shapes. All the three algorithms proved robust to error, as their performances on the clean data set are indistinguishable from those obtained on the error and double error data sets.
## 3 Results
### Evapotranspiration Variation Characteristics  
#### 3.2.1 Interannual Variability  
According to the actual evapotranspiration of each pixel from 2001 to 2020, the total actual evapotranspiration of the basin from 2001 to 2020 was obtained statistically (Figure 3a). The interannual fluctuation of the total evapotranspiration in the Yiluo River Basin was large; the maximum value of 656.6 mm appeared in 2003, and the minimum value of 457.2 mm appeared in 2011. The total evapotranspiration of the basin showed a decreasing trend with a multiyear average value of 521.3 mm and a decrease rate of 2.5 mm/a, which was basically consistent with the findings of [PERSON], [PERSON] [42] Gu, [PERSON] [42] Gu, [PERSON] [42].  
Figure 3: _Cont._  
Figure 2: Comparison of precipitation, runoff depth, and evapotranspiration calculated by water balance and evapotranspiration calculated by the PT-JPL model from 2001 to 2016.  
The spatial distribution of the multiyear average ETa value in the Yiluo River Basin from 2001 to 2020 is shown in Figure 3b. The spatial distribution of actual evapotranspiration in the Yiluo River Basin generally showed the characteristics of high in the mountainous area and low in the river valley, high in the south and low in the north; the annual evapotranspiration was mainly 550-700 mm in the earthy and rocky mountainous area in the upper part of the basin; the annual evapotranspiration was mainly 380-500 mm in the loess covered area in the lower part of the basin.  
The spatial distribution of the ETa coefficient of variation (Cv) of each pixel in the Yiluo River Basin is shown in Figure 3c. Based on the Cv values, the variation intensity was divided into four classes: relatively stable (Cv \(\leq\) 0.1), stable (0.1 \(<\) Cv \(\leq\) 0.2), unstable (0.2 \(<\) Cv \(\leq\) 0.3), and very unstable (Cv \(>\) 0.3) [39]. Among them, the proportion of relatively stable and stable pixels was only 0.1%, and the proportion of unstable and very unstable pixels was 99.9%. The mean value of Cv of all pixels in the basin was 0.64, and the Cv value of most of the pixels was greater than 0.2, which indicated that the interannual variation in ETa in the Yiluo River Basin had large interannual variability and strong interannual fluctuations.  
The trend of actual evapotranspiration in the basin from 2001 to 2020 is shown in Figure 3d. The areas with decreasing ETa trends were mainly located in the middle and lower reaches of the basin, and the areas with increasing ETa trends were mainly located in the upper Shaanxi section of the basin. The actual evapotranspiration of most pixels showed nonsignificant changes, and the proportions of the actual evapotranspiration that showed significantly increased, slightly increased, stable, slightly decreased, and significantly decreased in the number of pixels to the total number of pixels were 2.8%, 10.4%, 7.1%, 57.4%, and 22.3%, respectively. Overall, the basin evapotranspiration showed a decreasing trend.
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 4 Virtual Network Function as A Service, Overview
### The VNFaaS Proof of Concept  
To validate the NGPaaS workflow through the VNFaaS use case, a Proof of Concept (PoC) scenario was designed and implemented. To be considered successful, this PoC must be able to showcase both the provisioning of a Telco-grade platform and the provisioning of services related to the VNFaaS use case.  
Figure 7 illustrates an overview of the desired final status of the PoC. There, a Service Provider has been granted access to the RDCL 3D tool by the NGPaaS operator. Using this tool, the Service Provider is then able to compose and deploy service graphs of virtual routers and firewalls, which will provide the desired service to the provider's end customers. Through the NGPaaS workflow, these service graphs are then translated to platform-specific APIs and passed down to the platform orchestrator. Since a service graph request will usually comprise both compute and network resources, the platform orchestrator will translate the request to API calls targeting both the underlying VIM and SDNC components of the platform. The VIM will then provision the required virtual computing resources and the SDNC will provide the required connectivity. It should also be possible for the service provider to monitor its deployment for specific KPIs. At the same time, an alerting function should continuously check the collected data and trigger automated healing workflows, when the state of a deployed service is undesirable.  
The next couple of sections will provide more details on how this PoC was developed. More specifically, the technologies that comprise it will be detailed (e.g., Platform of choice), together with the steps required to integrate them into the NGPaaS workflow.  
Figure 6: The Virtual Network Function as a Service use-case.