esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Instrument  
The GC-MS/MS experiments were performed using the Thermo Scientific TSQ 8000 EVO triple quadrupole mass spectrometer coupled with a Trace 1310 gas chromatograph and a TriPlus RSH autosampler (Thermo Fisher Scientific, Austin, TX, USA). An Agilent J&W Scientific DB-5 MS (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m) capillary column was used for GC separation. The temperatures of the injector, transfer line, and ion source were maintained at 250 \({}^{\circ}\)C, 280 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. The column oven temperature was programmed with an initial temperature of 60 \({}^{\circ}\)C for 1 min, raised to 230 \({}^{\circ}\)C with a rate of 20 \({}^{\circ}\)C min\({}^{-1}\) (hold for 5 min), and finally ascended to 290 \({}^{\circ}\)C at 30 \({}^{\circ}\)C min\({}^{-1}\) (hold for 6 min). Ultra-high-purity helium (99.999%) was used as the carrier gas at a constant flow rate of 1.0 mL min\({}^{-1}\). All the samples were analyzed in electron ionization (EI) and multiple reaction monitoring modes (MRM). The detailed information of the characteristic ions and the retention time of all insecticides are listed in Table S1.  
The morphology and size of the sorbent materials were characterized using scanning electron microscopy (SEM, JSM-6330 FT, JEOL, Tokyo, Japan) and transmission electron microscopy (TEM, JEM-2010, JEOL, Tokyo, Japan). Infrared absorption spectra were obtained using a NICOLET AVATAR 330 Fourier-transform infrared (FT-IR, Thermo Fisher Scientific, Waltham, MA, USA) spectrometer. X-ray diffractometry (XRD) experiments were carried out using a Shimadzu XRD-6000 (Shimadzu, Kyoto, Japan) with Cu K\(\alpha\) radiation (40 kV, 30 mA, \(\lambda\) = 1.5418 A), recorded with 2\(\theta\) ranging from  
Figure 1: The schematic illustration of the synthetic process of the 3D-rGOPFH.  
5\({}^{\circ}\) to 90\({}^{\circ}\). The nitrogen adsorption/desorption measurements were conducted by a Quantachrome AUTOSORB-SI instrument. The magnetic property was examined using a SQUID-based magnetometer (Quantum Design, San Diego, CA, USA).
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
## Materials and Methods
### Patient's Spine 3D Modelling  
The 3D CAD models of the vertebral bodies were obtained starting with a CT scan that gave a DICOM output. This output was imported in Materialize Mimics software (Materialize NV--Leuven, Belgium) for human structures segmentation, which allows for reconstructions of the lumbar regions with both automatic and manual thresholding techniques, based on the Hounsfield Grayscale.  
Segmentation generates 3D Standard-Triangulation-Language (STL) models of the vertebrae that were imported into Geomagic software (3D Systems-- Rock Hill (SC), USA) for conversions in poli-surfaces format (IGES). This step is particularly useful because the IGES model is composed by closed surfaces that are suitable for processing with CAD modelling software. After the conversion into IGES format, the vertebral model was imported using the Rhinoceros Software (Robert McNeel & Associates--Seattle, WA, USA). Figure 1 shows a 3D reconstruction.
# Compact Photonic-Crystals Based Isolator Using Ni-Zn Gyromagnetic Ferrite Posts
## 2 Materials and Methods
### PBG of the SLPCs  
The PBG of the SLPCs determines the frequency of the electromagnetic waves which can transmit through the SLPCs or be reflected by them. When the lattice constant \(A\) is 4 mm, expressed as the distance of the two adjacent Al\({}_{2}\)O\({}_{3}\) rods, the PBGs of the SLPCs mentioned above are calculated by PWEM.  
In Figure 2a, there are three band gaps for TE modes with the different radius of the Al\({}_{2}\)O\({}_{3}\) rods. The corresponding gaps' radios with increasing rods' radius are shown in Figure 2b. The interested frequency region is coincidentally contained in the first PBG (Gap 1). When the radius of Al\({}_{2}\)O\({}_{3}\) rods \(r_{0}\) takes 0.207\(A\), the optimal gap ratio achieves 32.7%, and the corresponding frequency range of the PBG is from 23.36 to 32.45 GHz. Theoretically, the SLPCs forbid the transmission of signals within the frequency range and reflect them back. If certain transmission line surrounded by the SLPCs, the electromagnetic waves with the frequencies in the PBG can transmit stably in the PCW just as mentioned above.  
Figure 1: (**a**) The schematic diagram of the PC isolator; (**b**) the top view of the isolator; (**c**) the plan view of the isolator; (**d**) the detailed view of the coupler.  
Figure 2: (**a**) The TE band gaps of the SLPCs with the Al\({}_{2}\)O\({}_{3}\) rods’ radius; (**b**) the gaps’ ratios with the radius of the Al\({}_{2}\)O\({}_{3}\) rods.
# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 2 Literature Review
The main advantage of such systems is their high mobility; however, limited autonomy and the influence of external factors on the accuracy of measurements remain important challenges. Fixed monitoring stations are installed at specific points in a city or region for the long-term monitoring of air quality. They provide high accuracy of measurements and can be integrated with other monitoring systems. At the same time, the limited number of measurement points and the high cost of installation and maintenance are the disadvantages of such systems. Another type is \"smart\" sensors that are integrated into urban infrastructure, such as in streetlights, bus stops, or buildings. They provide a dense monitoring network with relatively low maintenance costs. However, the limited set of measured parameters and the dependence on urban infrastructure can be challenging for their widespread use. Components of IoT-based air quality monitoring systems include sensors that measure the level of air pollution by various harmful substances (PM\({}_{2.5}\), PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), etc. ), data transmission networks (Wi-Fi, LoRaWAN, and NB-IoT), and cloud platforms for data storage and processing, as well as user interfaces that provide access to information in a convenient format through web portals or mobile applications. The main functionalities of such systems are real-time air quality monitoring, automatic notifications when levels exceed permissible pollution standards, and data visualization in the form of maps and graphs, as well as integration with other city management systems, such as transport or energy. Despite their numerous advantages, these systems also face challenges, including the need to calibrate sensors to ensure measurement accuracy, develop energy-efficient solutions, and protect data from unauthorized access. Overcoming these challenges will contribute to the further development of effective air quality monitoring systems.
# Using Graph Neural Networks for Social Recommendations
## 4 Experimental Evaluation
### Performance Metrics
#### 4.3.3 Normalized Discounted Cumulative Gain (NDCG)  
Normalized Discounted Cumulative Gain (NDCG) evaluates the quality of the recommended items' ranking. This metric accounts for the relevance of items based on their position in the recommendation list, with items at higher positions being deemed more relevant. The metric is normalized based on the ideal DCG (Discounted Cumulative Gain). The ideal DCG represents the cumulative gain of the perfectly ordered items that are most relevant to each user.  
The formula for Normalized Discounted Cumulative Gain (NDCG) is:  
\[NDCG@n=\frac{DCG@n}{IDCG@n} \tag{19}\]  
where \(n\) represents the position in the recommendation list. The Discounted Cumulative Gain (DCG) is defined as:  
\[DCG@n=\sum_{i=1}^{n}\frac{2^{reli}-1}{\log_{2}(i+1)} \tag{20}\]  
where \(rel_{i}\) is the score that indicates how relevant the item recommended at position \(i\) is, and IDCG (Ideal Discounted Cumulative Gain) is the DCG score of the ideal (i.e., ground truth) list of recommended items. The higher the NDCG score (closer to 1), the better the recommender system is performing in terms of providing relevant and well-ranked recommendations.  
In this paper, HR and NDCG are evaluated on the test data to determine how well the models perform. By utilizing these top-\(n\) metrics, we can effectively gauge the ability of the model to make accurate recommendations and compare the effectiveness of different models.
# Oblique Wide-Angle Multi-Sector Metamaterial Absorber for Space Applications
###### Abstract  
This article presents the design, realization and measurement of lightweight absorbing material for space applications. The electromagnetic absorber, operating on the [2 GHz, 2.3 GHz] frequency band, is designed for oblique incidence ranging from 35\({}^{\circ}\) to 65\({}^{\circ}\). Wide-angle designs are demonstrated to be particularly challenging at oblique incidence and an approach consisting in dividing the surface in two different sectors with respect to the incoming angle is proposed. A specific measurement setup is presented in order to characterize this new kind of evolutive absorber. The measurement results show that the sectorial absorber achieves a reflection coefficient inferior to \(-11.5\) dB, corresponding to an absorptivity above 0.965 on the frequency band [2 GHz, 2.3 GHz] for both TE and TM polarizations for angles of incidence varying from 35\({}^{\circ}\) to 65\({}^{\circ}\).  
radar absorbing material; metamaterial; oblique incidence; RCS measurement Article Oblique Wide-Angle Multi-Sector Metamaterial Absorber for Space Applications
# State Merging and Splitting Strategies for Finite State Machines Implemented in FPGA
## 2 Materials and Methods
### General FSM Synthesis Method  
The general synthesis method uses two equivalent transformations of FSMs: a splitting, and a merging. For this purpose, two sets are created: \(D\)--a set of states that can be split; and \(G\)--a set of state pairs that can be joined. Next, for each equivalent machine, the power consumption \(P_{i}\), the maximum critical delay path (speed) \(S_{i}\), and the cost of implementation (area) \(C_{i}\), are calculated. The area parameter is not considered in this paper, as it was mentioned before. From the obtained results, a state (for splitting) or a couple of states (for merging) is selected for which the considered parameter is lowest (in case of speed or power) or highest (when using the balanced method) after the modification of the FSM. In the merge-then-split (MS) strategy, there is always a merging that is performed first and after all possible merges, the splitting of states should be done. This strategy for the speed minimization is described using Algorithm 1. If we want to consider another criterion of optimization (e.g., power), we should replace the \(S_{i}\) parameter with the \(P_{i}\) parameter. In the case of using a balanced variant, we should use the transformation quality ratio \(Q_{i}\) and replace all \"lower than\" operators with \"greater than\" operators in Algorithm 1. At the start of Algorithm 1, an initial FSM form is saved as the best one (line 1). Next, the subroutine for seeking couples for merging (building the set \(G\)) is executed (line 3). If there is no possibility to merge the states, the algorithm moves to the splitting phase, otherwise the trial merging is performed in the following way: first, the present FSM is saved, merging is executed, then the states are encoded, and the critical path ratio for current FSM is determined (lines 6-15). Among all solutions, the one is selected for which the critical path ratio \(S_{i}\) is minimal. After that, the real merging is performed, and a selection of states for the next merging is executed once more (lines 16-21). After the merging phase, the subroutine for seeking states for splitting (building the set \(D\)) is performed (line 23). If there is no possibility to split any states, the algorithm stops, otherwise, the trial splitting is executed as follows. First, the present FSM is saved, splitting is executed, then the states are encoded, and the critical path ratio of FSM is determined (lines 25-34). Among all solutions, the one is selected for which the critical path ratio \(S_{i}\) is minimal. Then the real splitting is performed, and a selection of states for the next splitting is performed.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 3 Session Key Recovery with Ternary PUFs
### Light Search Engine Implementation  
A light version of the RBC was developed for the ReRAMs because the BERs were low enough and because the fuzzy positions of the PUFs were masked by the ternary states. In our model, the search was restricted to Hamming distances of 0 and 1. When no message digest matches **H(K')**, rather than exploring higher Hamming distances, the process iterates through a new query of the PUF with the same challenges (**T**, _IDAccess_) to generate new responses. Using the numerical example of a case with average BERs of \(2\;10^{-3}\) following a normal distribution, statistically, such a distribution creates an average of 0.5 bad bits on a 256-bit long stream. During on-demand response generation, such a PUF has approximately a 60% chance of experiencing zero bad bit, a 30% chance of experiencing one bad bit, an 8% chance of experiencing two bad bits, and a 2% of experiencing at least three bad bits. The RBC-light typically takes \(10^{-4}\) s to check the Hamming distance of zero, 30 ms to check all configurations at a Hamming distance of one, 2 s to test all configurations at a Hamming distance of two, and 200 s to test all configurations at a Hamming distance of three. The latency to generate a fresh response from the PUF is only 10 ms, therefore re-setting the process after the search at a Hamming distance of one does cut the average latency. Statistically, 90% of the searches are positive after one query, and only 1% require a second query. A variation of the protocol was evaluated in which the client device has a database with a fuzzy state position that is able to find the full challenge (**T**, _IDAccess_) on its own, without assistance from the server. The security of such a scheme is slightly degraded as a third party could have access to the database containing these positions.
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 6 Performance Evaluation  
The proposed methods are evaluated versus the conventional uncached approaches in terms of service delay, network cost, and energy consumption. The uncached approaches map network functions online based on the least path delays between terminal users and fog nodes in Layers II and III, i.e., without prior knowledge about content popularity as opposed to the proposed caching methods.  
The complexity of the proposed methods depends on the scale of network size in terms of nodes and links modeled as \(O(|\mathbf{N}|log|\mathbf{E}|)\). Further, the use of LSTM results in an additional complexity scaled by the number of network functions that need to be forecasted. A saliency for the LSTM algorithm here is that it features a reduced computational complexity versus other deep learning methods. It accepts random weight initializations and a variety of state information without the requirement of a prior setting of the input states [33]. The complexity per weight and time is modeled as backpropagation through time (BPTT), \(O(1)\)[34]. This is scaled by the number of network functions \(\mathbf{F}_{r}\). Along with this, the overall run-time complexity is formulated as \(O(|\mathbf{N}|log|\mathbf{E}|+|\mathbf{F}_{r}|)\).
# Study of the Ciprofloxacinacin Adsorption of Activated Carbon Prepared from Mangosteen Peel
## 3 Results and Discussion
### Isotherm Adsorption Model
#### 3.5.5 Redlich-Peterson Isotherm Adsorption Model  
The Redlich-Peterson isotherm model is a combination of the Langmuir and Freundlich isotherm models [38].  
This isotherm model is an experimental isotherm model that integrates three parameters. It combines elements from both the Langmuir and Freundlich isotherm equations; therefore, the mechanism of adsorption is mixed and excludes monolayer adsorption [39].  
Figure 13: The dependence of \(\mathrm{Ln\overline{q}}\) on \(\mathrm{Ln\overline{C_{cb}}}\) to the CIP adsorption based on the [PERSON] isotherm (**A**) adsorption model, and of \(\mathrm{Ln\overline{q}}\) on \(\varepsilon^{2}\) based on the [PERSON] isotherm (**B**) adsorption model.  
The formula of this model is shown below:  
\[\mathrm{q_{e}}=\frac{\mathrm{AC_{e}}}{1+\mathrm{BC_{e}^{\beta}}} \tag{10}\]  
where A is the Redlich-Peterson isothermal constant (\(\mathrm{Lg^{-1}}\)), B is a constant (\(\mathrm{Lmg^{-1}}\)), \(\beta\) is the exponent between 0 and 1, \(\mathrm{C_{e}}\) is the equilibrium concentration of the adsorbent (\(\mathrm{mg\ L^{-1}}\)), and \(\mathrm{q_{e}}\) is the equilibrium capacity of the adsorbent on the adsorbent (\(\mathrm{mg\ g^{-1}}\)).  
The linear form of the Redlich-Peterson isotherm is shown as:  
\[\mathrm{ln\ \frac{\mathrm{C_{e}}}{\mathrm{q_{e}}}}=\ \beta\mathrm{lnC_{e}}- \mathrm{lnA} \tag{11}\]  
We plotted a graph with \(\mathrm{ln\ (C_{e}/q_{e})}\) and with \(\mathrm{ln\ C_{e}}\) to determine the \(\beta\) and A constants. The results are shown in Figure 13B. From the results in Figure 14B, the coefficient of determination \(\mathrm{R^{2}}\) of the Redlich-Peterson isotherm adsorption model is 0.9981. Therefore, it is suitable for the CIP adsorption process of the ACMP.  
The values of constants calculated from the Langmuir, Freundlich, Tempkin, Elovich, and Redlich-Peterson isotherm adsorption models for the ACMP materials are shown in Table 6.  
As seen in the results shown in Table 6, the coefficients of determination \(\mathrm{R^{2}}\) of the models--[PERSON], [PERSON], Tempkin, and Redlich-Peterson--are 0.9986, 0.9421, 0.9653, 0.9508, and 0.9981, respectively.  
The CIP adsorption on the ACMP follows the [PERSON] > Redlich-Peterson > [PERSON] > Tempkin > Freundlich isotherm adsorption models. Therefore, it can be concluded that the CIP adsorption process of the ACMP is a monolayer and multilayer adsorption, on the conditions that the surface of the material is not uniform and that there is an interaction between the adsorbate and the adsorbent.  
Figure 14: The dependence of \(\mathrm{ln\ (\overline{q}/\overline{C_{eb}})}\) on \(\overline{q}\), based on the [PERSON] isotherm (**A**), and of \(\mathrm{ln\ (C_{e}/q_{e})}\) on \(\mathrm{ln\ C_{e}}\), based on the Redlich–Peterson isotherm (**B**) adsorption model.
# 5G Mobile Phone Network Introduction in Colombia
## 5 Radio Access Network (RAN) in 5G  
The radio access network (RAN) architectures that have been mainly deployed for 5G are the C-RAN, which stands for centralised, clean, cloud, and collaborative RAN, and the distributed RAN (D-RAN). This latter one is the classic deployment most used on mobile networks today [38; 39; 40].  
In distributed RAN architecture (D-RAN) places, all baseband units (BBUs) and remote radio heads (RRHs) at each cell site [41] communicating between them with the common public radio interface (CPRI) and its data exchange between the BBU and the core is over the backhaul, as shown in Figure 2a.  
Likewise, in C-RAN, proposed by the China Mobile Research Institute, most of the RAN functionalities are centralised in a resource pool consisting of BBUs. C-RAN split Base Station (BS) to BBUs and remote radio heads. That is, it moves the BBU to a high-performance hosting datacenter with DSP (Digital Signal Processor). The fronthaul transport network provides high-bandwidth optical fibre linking between the BBU to the RRHs [42]. The RRH, containing the local oscillator, transforms the digital signal into an analog signal for transmission [43; 44] (see Figure 2b).  
C-RAN has evolved to novel Virtual RAN (V-RAN) and Open RAN (O-RAN) [45]. On the one hand, V-RAN virtualizes BBUs in vBBUs deployed on multiple NFV platforms on standard x86 hardware [46] and grouped in centralized data centers, while RRHs continue to be located at their respective base stations [40]. As we can see in the Figure 2c) a key element of O-RAN is V-RAN [47]. O-RAN applies principles of openness through standardized and open interfaces and intelligence using artificial intelligence and machine learning to automate operational network functions [45; 48].
## 1 Introduction
### Sources of Environmental Radiofrequency EMF Exposure
\begin{table}
\begin{tabular}{c c} \hline \hline
**RF EMF Sources** & **Frequency Band, MHz** \\ \hline Radio transmitters: analogue FM & \\ (_Frequency Modulation_) and digital DAB + & 88–108 and 176–225 \\ (_Digital Audio Broadcasting_) & \\ \hline DVB-T (_Digital Video_ & 174–694 \\ _Broadcasting-Terrestrial_) TV transmitters & \\ \hline TETRA (_Terrestrial Trunked Radio_) digital & 380–400 \\ mobile communication system & \\ \hline LTE (_Long Term Evolution_)—public mobile & \\ and Internet access systems: & \\ LTE700 (5G) 1 & 694–790 \\ LTE800: DL and UL 2 & 791–821 and 832–862 \\ LTE900: UL and DL & 876–915 and 921–960 \\ LTE1800: UL and DL & 1710–1785 and 1805–1880 \\ LTE2100: UL and DL & 1920–1980 and 2110–2170 \\ LTE2600: UL and DL & 2500–2570 and 2620–2690 \\ LTE2600 1 & 2570–2620 \\ LTE3600 (5G) 1 & 3400–3800 \\ \hline WiMAX (_Worldwide Interoperability for_ & \\ _Microwave Access_) broadband Internet & 3600–3800 \\ access systems 1 & \\ \hline Wi–Fi (_Wireless Fidelity_)—local area & \\ network connectivity between devices and & \\ Internet access systems: & 2400–2483 \\ Wi–Fi 2.4 GHz (used also by Bluetooth & 5150–5350 and 5470–5725 \\ devices) 1 & \\ Wi–Fi 5 GHz 1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Radio communication systems used in Poland [6]. Due to the ubiquitous presence of RF EMF sources, it is necessary to identify the sources and characteristics of emitted EMF and to assess the consequences of human exposure, especially in new areas of their use.