esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Analysis in Green Building via Machine Learning: A Case Study in a Hospital
## 2 Materials and Methods  
In this study, the energy data in a hospital with green building status will be analyzed by interpreting it with machine learning techniques. The data received with the Siemens PAC 4200 energy analyzer located on the main distribution panel of the hospital will be interpreted. The data set consists of 103 different parameters, including voltage, current, power, energy, THD, TPE, harmonic voltage (1 st, 3 rd, 5 th, 7 th, 9 th, 11 th), and harmonic current (1 st, 3 rd, 5 th, 7 th, 9 th, 11 th). The relevant values were taken every 10 s for approximately 2 months. The time range of the data used in the study is between 14 March 2023 00.00.00 and 11 June 2023 23.59.50. After debugging and simplifying the data, a data set consisting of 779,280 rows and 102 columns remained. The matrix dimensions of the data set are 102 \(\times\) 779,280, which means we have a data set consisting of 79,486,560 pieces of data.  
Nowadays, large data sets are used in many fields for different purposes. Manual processing of this data leads to great waste of time and perhaps errors. For this reason, machine learning has gained an important place in our lives in order to interpret large data sets quickly and accurately. There are different categories for machine learning, like supervised, unsupervised, etc. Classification and regression tasks can be applied with machine learning methods. Although these two tasks seem to be the same, they have a fundamental difference. The prediction task, in which the data set is discrete, is called classification. If the data are continuous, it is called regression. In this study, the classification process, which is a supervised learning prediction task, will be applied to the data set we have.  
Classification is a supervised machine learning method, in which an attempt is made to predict the correct label of a given input data. A model is created in classification. The created model is fully trained using the training data. The model is then evaluated on test data and used for prediction. Models, such as SVM, nearest neighbor, and neural networks, can be used in classification. There are different types of artificial neural networks. Recurrent neural network (RNN) is one of the frequently used types of artificial neural networks. LSTM network is the name of a method of this type. In this study, the data set will be trained with the LSTM model.
## 1 Introduction
A quad-band wrenched-square-shaped resonator was proposed in [24], and a triple-band square split-ring resonator (SSRR) with an inner Jerusalem cross was presented in [45]. Both designs ([45] and [24]) achieved absorption peaks above 95% and exhibited polarization-insensitive behavior at S, X, and Ku frequency bands. In [46], a combination of eight identical 7-shapes and SSRR achieved three absorption peaks at 8.5, 13.5, and 17 GHz with 99.9%, 99.5%, and 99.9% absorption, respectively. In [47], A V-shaped polarization-insensitive MMA was designed for Ku- and K-band frequency applications, achieving absorption peaks at 15.52 and 27.24 GHz with 98.38% and 90.7% absorption, respectively. A T-shaped polyimide substrate-based polarization-insensitive MMA was presented in [48], and polarization-insensitive behavior was achieved because of the rotational symmetry of the MMA, with two absorption peaks at 16.77 GHz and 30.92 GHz with 98.7% and 99.3% absorption, respectively. In [49], a diagonally slotted patch MMA was designed for Ku-band applications, producing two absorption peaks at 12.45 and 14.18 GHz with 99.73% and 99.87% absorption, respectively. The orientation of the diagonal slot was 45\({}^{\circ}\); due to this design, the MMA exhibited polarization-sensitive behavior. Most MMAs achieve single or dual absorption peaks at Ku- or K-band frequencies; some are polarization-sensitive and show lower absorption peaks or larger unit-cell sizes. In this paper, we present an MMA for Ku- and K-band sensing applications. The geometry of the proposed MMA was chosen to provide quad-band polarization-insensitiveabsorption behavior. The designed MMA simulated for TM and TE modes, and all simulation setups resulted in similar absorption curves due to the symmetrical rotational design. We evaluated the proposed MMA in order to understand the effect of structural design on absorption behavior. In the following sections, we discuss the metamaterial properties, normalized input impedance, polarization conversion ratio (PCR), and H-field and E-fields with respect to absorption behavior. The advantages of the designed MMA include its quad-band absorption peaks with near-unity and polarization insensitivity.
# Laser Vibration Characteristics of Marble Specimens and Failure Criterion
## 5 Conclusions  
The laser vibration waveform characteristics of marble specimens were investigated with LDV in uniaxial compression tests and Brazilian tests. The parameter-based and waveform-based AE analysis methods were taken as comparisons. A failure criterion of the loaded marble specimen was proposed to forecast the oncoming failure of marble.  
1. The unimodal low frequency, bimodal low frequency spectra, and wide frequency spectra were predominant for the laser vibration waveform. In the elastic stage, the low (second) dominant frequency was distributed around approximately 7.5 Hz and 32.5 Hz.  
Figure 6: Schematic diagram of the distribution of dominant frequency and cumulative AE counts of marble specimens in (**a**) uniaxial compression test and (**b**) Brazilian test.  
Wide frequency spectra always occurred in the plastic stage, while the high (second) dominant frequency spectra were observed in the failure stage.  
2. The LDV whole vibration waveform could reflect plastic deformation of marble specimens. It was proposed that the appearance of LDV wide frequency spectra could be regarded as an early warning for rock failure, and the high velocity amplitude in the elastic stage acted as a pioneer before the appearance of wide frequency.  
3. The rock failure time indicated by the AE dominant frequency, cumulative AE counts, and laser frequency spectra characteristics were compared. It was found that the results are affected by stress conditions. Under uniaxial compression stress, forecasting with AE high dominant frequency or laser wide frequency is preferable to cumulative AE counts. Under indirect tensile stress, forecasting with laser-wide frequency is preferable to the AE high dominant frequency and cumulative AE counts.  
Conceptualization, Q.X. and X.F.; Data curation, Y.B.; Funding acquisition, Q.X.; Methodology, J.D.; Writing--original draft, S.L.; Writing--review & editing, Y.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project (KJQN201902504) from Scientific and Technological Research Program of Chongqing Municipal Education Commission and the open project (LNTCCMA-20200103) from the Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### \(SpO_{2}\) Estimation  
For the estimated \(SpO_{2}\) values obtained through the ratio calibration, the scatter plot and Bland-Altman analysis results are plotted in Figure 8. The Bland-Altman analysis provided a bias of \(-0.0894\pm 3.293\), and the limits of agreement ranged from \(-3.38\) to \(3.20\). For the estimated %\(SpO_{2}\) values, statistical analysis using MSE, ME, MAD, and RMSE yielded \(2.831\), \(-0.089\), \(1.392\), and \(1.683\), respectively. The reference closeness factor (RCF), defined as Equation (43), was found to be \(0.986\), as shown below:  
\[RCF=\frac{1}{N}\sum_{i=1}^{N}\left(1-\frac{\left|SpO_{2}^{Ref}(i)-SpO_{2}^{Est }(i)\right|}{100}\right)\,. \tag{43}\]
# Research Progress on Conducting Polymer-Based Biomedical Applications
## 3 Biomedical Applications
### Drug Delivery  
The CPs in drug-delivery applications utilize their electrical stimulation to release several therapeutic proteins and drugs. PPy is one of the most widely studied CPs for drug delivery due to their controllable and reversible redox reactions that enable the delivery of drugs [41]. The rate of drug release in the CPs can be precisely controlled via alteration of their redox state, resulting in changes in polymer charge, conductivity, and volume. Specifically, as illustrated in Figure 9, the mechanism of drug release by the CPs is similar to that of bioactuators. Anionic drugs that are incorporated  
Figure 8: Bending of a trilayer actuator is caused by one CP film that swells by anion drive and the other CP film that shrinks by cation drive. Republished with permission of Royal Society of Chemistry, from Biomimetic polypyrrole based all three-in-one triple layer sensing actuators exchanging cations, [PERSON] et al., [PERSON]. Chem., 21, 2011 [132]; permission conveyed through Copyright Clearance Center, Inc.  
Figure 7: A bilayer actuator bends upon oxidation of the CP: anion-driven swelling results in the CP layer on the concave side (**a**), whereas cation-driven shrinking results in the CP layer on the convex side when the CP is reduced (**b**).  
using small anions leach out as the volume of the CP decreases upon reduction, whereas cationic drugs prepared with immobile anions are released by cation-driven actuation when the CPs are oxidized [137].  
Drugs of interest can be incorporated during the polymerization of the CPs. For anionic drugs, the incorporation into the CPs occurs by anions that balance out the positive charges induced by oxidation. In contrast, cationic drugs are incorporated into the CPs with the help of cations [138]. The incorporation of drug molecules into CP matrices during polymerization can be hindered due to the unfavorable interaction among drug molecules and polymers. Moreover, the drug molecules weaken the adherence of the CPs to the electrode, which is crucial to provide electrical stimuli for drug release [137]. In order to resolve such challenge, drug molecules can be incorporated after synthesizing the CPs. A buffer layer of pristine CPs between the electrode and CP/bioactive drug mixture can improve adherence preventing detachment of the CP/bioactive drug layer [138].  
A drug molecule for the use in the CP-based drug delivery system needs to meet the following requirements; (1) it should not be electroactive at the voltages applied to the systems in order to maintain its biological activity, (2) the pKa needs to be moderated to precisely control the loading and releasing of the drug, (3) short half-lives are preferred to reduce the risk of accumulation, and (4) it should not be toxic to local tissues that may be subject to higher concentrations of the drug [137].
## 3 Results and Discussion
### Synthesis and Characterization of HB-AuNPs
#### 3.1.4 In Vitro Stability of HB-AuNPs  
Colloidal stability of NPs in relevant media is very crucial for in vivo downstream applications [24,43]. The biologically stable NPs do not aggregate when incubated in various media that simulate biological conditions [44]. In this study, the stability of HB-AuNPs was examined in water, DMEM, RPMI-1640, and PBS at various time points between 0 and 420 h at 37 \({}^{\circ}\)C. As shown in Figure 5, the HB-AuNPs incubated in water,  
Figure 4: TEM micrograph (**A**), core size (**B**), EDX (**C**), and SAED patterns (**D**) of the 2 mg/mL_HB-AuNPs. Red arrows in the EDX show Au peaks.  
RPMI-1640, and PBS demonstrated excellent stability for the entire period, as shown by the retention of their UV-Vis spectra. However, a shift in their SPR band was observed over time in DMEM (Figure 5B). The shift could be due to their interaction with the media components such inorganic salts, vitamins, amino acids, proteins, glucose, and supplements such as FBS and antibiotics [45, 46, 47]. This behavior was reported for both the chemically (citrate) and green synthesized AuNPs, where pronounced changes in the absorption peak and hydrodynamic size were observed in the AuNPs suspended in DMEM compared with those in the RPMI-1640 [48]. The broad curves in 4 mg/mL HB-AuNPs in PBS (Figure 5D) demonstrated a formation of larger-sized NPs [47] or NP aggregation. Aggregation of AuNPs can be immediate and visually detected by the color change of the medium to blue [47, 48]. The alteration in the SPR of the HB-AuNPs was not accompanied by aggregation in the test solutions, except for 4 mg/mL_HB-AuNPs in PBS. The HB-AuNPs remained stable in these solutions for 420 h and did not change color or aggregate. Aggregation of NPs in biological media can often compromise their biological functions by modifying their cellular uptake and toxicity and can produce misleading results [49, 50]. So, it became evident that 1 and 2 mg/mL HB-AuNPs can be used in these test solutions for various biomedical applications [48].
# Business Models for the Internet of Services: State of the Art and Research Agenda
## 2 Theoretical Foundation  
### Internet of Services  
While humans first delivered services to humans, technological advances over the years allowed computers to deliver services to humans.  
The term IoS-based service is used to identify services discovered, ordered, and provided through the Internet [10]. When there is a service request, the requester informs specifications to the provider, that customize the feedback, returning value-added result. Both requester and provider can operate automatically or with humans through a human-machine interface. Furthermore, automated systems can also invoke the services to access functions provided remotely by business providers [10].  
An example of an IoS-based service would be a value-added service package of a system used for monitoring public lighting poles, connected to the city's municipality network. In addition to light intensity measurements on each light pole, this system collects other environmental parameters such as CO\({}_{2}\) level, temperature, noise, etc. Then, through the IoS, third-party service providers can use the collected data to optimize or offer new services to people who are connected, such as secure access to homes, predictive maintenance, sustainability applications, etc.  
The IoS is seen as an ecosystem that allows services vendors and consumers to collaborate across organizations via the Internet, increasing the popularity of business services in the business markets [11].  
[PERSON] and [PERSON] first coined the term in 2007 [12]. According to them, the IoS is a result from the merge of two other components: Web 2.0 and Service-Oriented Architecture (SOA). The confluence of these two areas reveals the idea of composing and reutilizing services as they were flexible modules.  
While SOA and Web services enable service procurement through different channels, their main goal is to provide the technological infrastructure for the IoS, which is a more complex ecosystem.  
The main goal of IoS is to present everything as a service on the Internet, such as the software applications, the platform for development and delivery, and the network infrastructure [13]. Nevertheless, developing solutions for the IoS is more elaborate since services are intangible, often inseparable, immersive, bi-polar, hybrid, variable, ostensible concerning ownership, have long-running interactions, and are decoupled [14].  
For some authors, IoS is a \"future internet that detect, and use contextual information for seamlessly adapt to an unpredicted scenario\" [5; 15].  
Within the IoS, service providers work collaboratively to offer and deliver services. A service-based value network may cover the entire lifecycle, from R&D to operations, passing through the design, marketing, sales, production, and delivery of a service. All the areas should interactively cooperate to add value to the service. Value is created from the collaboration among the different stakeholders: the company, its customers, intermediaries and suppliers [10].
## 3 Results of the Case Study
### In-Use Steel Products' Material Quantities  
Using GeoMaps, AutoCAD, and Revit software, the study obtained the schedule of quantities of the investigated metal wall cladding and roofing products from the assessed buildings. The main output of the mapping process was to identify the type of metal products attached to the buildings and estimate the metal products' quantities. Table 2 shows the buildings and product information from the investigated case buildings.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Area** & **Site Address** & **Building Type** & **Metal Product Type** & \begin{tabular}{c} **Width of the** \\ **Metal Sheet** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Linear** \\ **Meter of** \\ **Roof (m)** \\ \end{tabular} & \begin{tabular}{c} **Linear Meter** \\ **of Wall** \\ **Cladding (m)** \\ \end{tabular} \\ \hline \begin{tabular}{c} 201 Lincoln Drive, \\ Henderson \\ \end{tabular} & Residential & Long Run 7 Ribs & 934 & 303 & 289 \\ \hline \begin{tabular}{c} 59 Arabella Lane, \\ Snells Beach \\ \end{tabular} & Commercial & \begin{tabular}{c} Tray Roofing Profile \\ \end{tabular} & 630 & 504 & 326 \\ \hline \begin{tabular}{c} 77 Rotu Drive, \\ Massey \\ \end{tabular} & Commercial & Long Run 5 Ribs & 765 & 920 & 956 \\ \hline \begin{tabular}{c} 380 Cowes Road, \\ Waiheke Island \\ \end{tabular} & Residential & \
\begin{tabular}{c} Tray Roofing Profile \\ \end{tabular} & 630 & 142 & 169 \\ \hline
\begin{tabular}{c} Manawa View, \\ Kerikeri \\ \end{tabular} & Residential & Long Run 5 Ribs & 765 & 376 & 242 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Schedule of quantities of investigated specific metal products.
# Synchronised TeraHertz Radiation and Soft X-rays Produced in a FEL Oscillator
###### Abstract  
We present a scheme to generate synchronised THz and soft X-ray radiation pulses by using a free-electron laser oscillator driven by a high repetition rate (of order 10-100 MHz) energy recovery linac. The backward THz radiation in the oscillator cavity interacts with a successive electron bunch, thus producing few \(10^{5}\) soft/hard X-ray photons per shot (namely \(10^{12}\)-\(10^{13}\) photons/s) via Thomson/Compton back-scattering, synchronised with the mJ-class THz pulse within the temporal jitter of electron beams accelerated in the superconducting cavities of the linac (less than 100 fs). Detailed simulations have been performed in order to assess the capability of the scheme for typical wavelengths of interest, between 10 and 50 \(\upmu\)m for the TeraHertz radiation and 0.5-3 nm for the X-rays.  
FEL oscillator; Compton source; dual source +
Footnote †: journal: Physics Letters A
# Harnessing Energy Balance and Genetic Algorithms for Efficient Building Demolition
## 2 Materials and Methods  
In this section, we offer an in-depth explanation of our method for pinpointing energy dissemination points by merging genetic optimization with potential function energy balancing. Initially, given the distinct features of various buildings, we generated multiple datasets that cover a range of potential energy dissemination scenarios and target characteristics. When identifying energy dissemination points, we utilized an iterative calculation method centered on multi-source energy balancing. Specifically, each dissemination point is viewed as a source of energy diffusion, and its energy spread is modeled using a potential function until the boundary energy of each diffusion area reaches equilibrium. This approach greatly enhances computational speed and swiftly ensures a uniform distribution of energy throughout the designated area.  
In practical scenarios, we noted that for some datasets, after multi-source energy diffusion, a significant disparity arises between the largest and smallest coverage areas. To address the challenges of energy dispersion from multiple source points, we employed an optimized genetic algorithm. Initially, after the aforementioned algorithm concludes, we can obtain the area of all current regions, identifying the largest and smallest area. Subsequently, we conduct crossover and mutation operations on the coordinates (genes) of the centers of these maximum and minimum areas, generating new coordinates. We then execute the energy balancing algorithm operations on these new coordinates once again. We consistently iterate the entire procedure until the difference between the maximum and minimum values of the current energy dissemination range meets an acceptable threshold, ensuring all boundaries comply with energy balance standards. Through empirical testing on various datasets, we have validated the effectiveness and versatility of this approach, achieving significant performance improvements. The brief overview of our algorithmic process is as shown in Figure 1.
## 2 Proposed Method
### The Revisit Time Computation Principle  
To assess the observation capacity of a future GNSS-R constellation that is compatible with the four GNSS systems, the spatial and temporal resolutions of the specular reflection points must be computed. Because the reflected points are located using the precise coordinates from the GNSS and the LEO satellites, it is difficult to find an analytical algorithm that can calculate the temporal resolution like that for monostatic remote sensing satellites. However, the reflections also form continuous ground tracks on the Earth's surface that look like the arcs and passes shown in Figure 5. Figure 5 shows the reflection point distribution in a single \(1^{\circ}\times 1^{\circ}\) bin during 48 h period for a \(1^{\circ}\times 1^{\circ}\) bin at GPS L1 frequency (left) and GPS L2 frequency (right) when eight GNSS-R LEO satellites and 119 GNSS satellites are used.  
The interval between successive passes in one bin, i.e., the revisit time, is regarded as an index of the temporal resolution. Every track on the ground stands for a pass, and the period divided by the number of tracks is mean revisit time. The mean revisit time is an important criterion that is used to evaluate the GNSS-R LEO constellation. For instance, we can find that there are 8 ground tracks and  
Figure 5: Reflection ground locations during 48 h period for a \(1^{\circ}\times 1^{\circ}\) bin at GPS L1 frequency (**left**) and GPS L2 frequency (**right**) when eight GNSS-R LEO satellites and 119 GNSS satellites are used.  
13 ground tracks during 48 h in Figure 5 (left) and Figure 5 (right), respectively. So, the values of mean revisit time in Figure 5 (left) and Figure 5 (right) are 6 h and 3.69 h, respectively. Here, the Earth's surface is divided into \(1^{\circ}\times 1^{\circ}\) bins and we have developed a program that can compute the mean revisit time for each bin.