esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 MOFs for Fuel-Cell Applications
### MOFs as Electrode Application
#### 2.1.1 H\({}_{2}\)/O\({}_{2}\) Fuel Cells
H\({}_{2}\) at 0.1 MPa partial pressure; 100% RH; 0.1 mgPt cm\({}^{-2}\) Pt/C at anode. Cell 353 K; electrode surface 25 cm\({}^{2}\)[80]. In order to gain insight from the exchanges between different metal sites in key locations of the electrocataly, [PERSON] et al. created SACs with numerous operational site locations (Fe, Co, Ni) in order to explore the specific properties of the electrocatalys [66]. However, despite the presence of multifaceted metal sites on the multiple metal catalysts, the open circuit voltage suffered severe decay, requiring improvements in both the ratio of the different metal sites as well as in the structural engineering. To avoid metal accumulation through the provision of additional hierarchical pores, liquid SiO\({}_{2}\) was supplemented in the reacting system with ZIF-8, FePc and NiPc (Pc = phthalocyanine) [67]. When FeNi-N\({}_{6}\) was prepared with four N atoms coordinated with the metal atoms, there were fewer H\({}_{2}\)O\({}_{2}\) yields and the four-electron process resulted in better catalytic performance and longer operating life. In the majority of active sites in electrocatalysis, Fe dominated while Ni enhanced the cycling stability [43]. It can be concluded that active sites are very important for electrocatalysysis [67]. With the application of the above catalysis design principles, organic fuel cells can achieve rapid ORR kinetics and can also benefit from a longer cycling life. The objective is to improve the shielding of functioning sites and increase catalysis. [PERSON] et al. designed Cu, N-incorporated carbon-based materials as catalysts. They prepared them by pyrolyzing Cu-bipy-BTC, where bipy is 2,2'-bipyridine and BTC is 1,3,5-tricarboxylate. In its present form, this catalyst consists of a copper-bispyridine-bistricarboxylate alloy that was burned at 800 \({}^{\circ}\)C at high pressure to produce a sufficiently high number active sites, in particular C-N and Cu-N\({}_{\mathrm{x}}\). This catalyst was also fabricated to be porous (MOF-800) and has a high surface exposure [72]. There is an urgent need for the further development of efficient catalysts due to the slow ORR reaction kinetics that severely slow down the performance of H\({}_{2}\)/O\({}_{2}\) fuel cells. To effectively design MOF-based catalysts, it is necessary to consider ligands that contain N, such as 2-methylimidazole, as well as metals such as Fe, Co, and Mn, all of which have remarkably high catalytic activity for ORRs. In addition to good performance, morphology control is also an important aspect, since metal-centre electrodes are able to efficiently utilize the atoms due to their well-dispersed nature. An additional way of improving catalytic capacity is to take advantage of the synergistic effects between the active sites. The problem with finding the right catalyst for H\({}_{2}\)/O\({}_{2}\) fuel cells remains a significant challenge, as all of the above conditions must be met at the same time.
# Anomalous Humidity Dependence in Photoacoustic Spectroscopy of CO Explained by Kinetic Cooling
## Materials and Methods
### Collisional Energy Transfer and Relaxation  
For transitions in the infrared spectral region, spontaneous emission is relatively slow (ms timescale). Therefore, at a pressure of tens to hundreds of mbar as typically used for PAS based sensing, vibrationally excited molecules relax almost exclusively via inelastic collisions, during which energy can be transferred inter- and intramolecularly between vibrational, rotational, and translational degrees of freedom [2], of which only translational energy contributes to photoacoustic signals. The collision frequency between a single molecule A with molecules B is given by  
\[Z_{A-B}=2N_{B}D_{A-B}\sqrt{\frac{2 kT}{\pi\mu}}=\frac{2p_{B}}{kT}D_{A-B}\sqrt{ \pi\frac{\frac{M_{A}M_{B}}{M_{A}+M_{B}}}{M_{A}+M_{B}}} \tag{1}\]Herein, \(N_{B}=p_{B}/kT\) is the number density of molecules \(B\); \(p_{B}\) is the partial pressure of B; \(D_{A-B}=\pi(r_{A}+r_{B})^{2}\) is the collision cross section; \(r_{i}\) is the kinetic radius of molecule \(i\); \(k\) is the Boltzmann constant; \(T\) temperature; \(\mu=\frac{k}{R}\frac{M_{A}M_{B}}{M_{A}+M_{B}}\) the reduced mass; \(p_{B}\) the partial pressure of B; \(R\) the gas constant; and \(M\) is the molar mass. On average, only one out of \(\hat{Z}_{A-B}\) collisions leads to a particular energy transfer. Hence, the rate for that energy transfer from a single molecule A is given by  
\[\gamma_{A-B}=\frac{Z_{A-B}}{\hat{Z}_{A-B}} \tag{2}\]  
The so called collision number \(\hat{Z}_{A-B}\) is related to the collision number \(\hat{Z}_{B-A}\) of the inverse process via  
\[\hat{Z}_{B-A}=\hat{Z}_{A-B}\exp\left(\frac{\Delta E}{kT}\right) \tag{3}\]  
where \(\Delta E\) is the energy difference between the modes between which energy is transferred (\(\Delta E>0\) for \(E_{A}>E_{B}\)).  
The energy difference \(\Delta E\) is released as (exothermic direction) or consumed from (endothermic direction) kinetic energy (i.e., heat.) The thermal power deposited per unit volume \(H\) represents the source of photoacoustic pressure \(p\)[17,18].  
\[\frac{\partial^{2}}{\partial t^{2}}p-c_{ac}\
abla^{2}p=(\gamma-1)\frac{ \partial}{\partial t}H \tag{4}\]  
Herein, \(c_{ac}\) is the speed of sound and \(\gamma\) is the heat capacity ratio. As shown below, \(H\) can be simulated by calculating the rate at which energy is transferred from an excited molecule in a given process and subsequent multiplication of this rate with \(\Delta E\) for that process. The sum over all possible energy transfers yields the net thermal power \(H\) and the photoacoustic signal.
# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 2 Related Work
### Radio Technologies and 5G NR
A representation of the Received Signal Strength (RSS) of a radio transmission can be obtained with a robot carrying a receiver, while assessing a set of communication parameters, also known as CSI, which provide signal quality and power metrics during the reception and transmission of radio signals [11]. For the mmWave, the authors of [44] use 5G signals at 28 GHz with a bandwidth of 400 MHz to attain high-accuracy ranging resolution, performing mapping with a ray tracing approach [27]. The UE acts as a joint communication and radar system that scans the indoor environment while performing beam-forming [45] across different directions and observing the reflections from the environment. A static grid-based approach and an advanced dynamic tracking approach were tested. The resulting map consists of the reflection points on walls and other objects during navigation inside a corridor and draws similarities to LiDAR mapping approaches, with high-frequency radio waves instead of occupancy. Similarly, [PERSON] et al. [46] developed a mapping framework in an environment consisting of indoor and outdoor transitions, where the 5G mmWave signal propagation and channel characteristics of the 28, 30, and 60 GHz frequencies were studied. The propagation mapping was analyzed in terms of the distribution appropriateness and based on a deterministic multi-module three-dimensional ray tracing code. They concluded that the (i) beam width, (ii) frequency band allocation, (iii) Modulation Carrier Scheme (MCS) election, (iv) antenna orientation and (v) node location affect the 5G mmWave signal. Furthermore, the alteration of the interference distributions can occur within a given application scenario not only during transitions from indoor to outdoor. In indoor and outdoor environments, it is shown that signal blockages from walls and shadowing effects can also be introduced by the human body. It is foreseen that 5G systems will bring attractive features for _robot localization_, as they can provide centimeter accuracy, which is considerably more attractive than the standard state of sensing based on GNSS data or even other available radio-based solutions. In localization applications with 5G NR signals, applicability, latency, reliability, accuracy, and cost need to be taken into account. Autonomous vehicles are a clear use case that started to benefit already from 5G networks for platooning and safety, which share many similarities with mobile robots [47; 48]. Another prominent use case are Unmanned Aerial Vehicles (UAVs), which have attracted attention for surveillance and asset monitoring tasks, as well as IoT applications [49].
# Seismic Performance of Frame Structure with Hysteretic Intermediate Discontinuity
## 1 Introduction
A 2-DOF model is suitable to describe systems where the isolation is placed at higher or lower levels, but cannot properly describe systems where the isolation is placed at middle-floors. In [23], a third degree of freedom has been added to the model to improve the describing capacity of the inter-storey isolation. In that paper, the disconnection devices are modelled by linear links with a non-classical Rayleigh damping model. However, the choice of a linear model to describe the behaviour of the disconnection could prevent the correct evaluation of the effectiveness of the introduction of the intermediate discontinuity. Hence, a non-linear modelling of the discontinuity, with constitutive laws closer to the real behaviour of the disconnection devices, could improve the ability of the model to capture the behaviour of real structures. In this paper, a system with intermediate discontinuity is studied by means of a 3-DOF model, which represents a medium-rise frame structure (i.e., an archetype model), and a Bouc-Wen hysteresis model representing the disconnection devices, which are hypothetically assumed to be lead-rubber-bearing isolators [24; 25]. The main purpose of the paper is to define the influence of the hysteretic characteristics of the discontinuity devices on the seismic response of the structure. Since in the paper the numerical simulations refer only to a six-storey frame structure, the results cannot be generalised, but are intended to provide preliminary information about the hysteretic discontinuity. Although the results are not generalizable, the method used to investigate the behaviour of the structure with intermediate discontinuity provides information for most of the medium-rise frame structures with hysteretic inter-storey isolation. Additionally, the paper does not provide technical specifications about the practical realization of an intermediate discontinuity, but it has to be framed as an analytical/numerical study, which precedes any practical implementation. The first part of the paper is focused on the description of the mechanical behaviour of the system. In particular, Section 2 presents the mechanical model and the equations of motion. The second part is focused on the dynamical response. Specifically, Section 3 studies the response to harmonic excitation, whereas Section 4 presents the response to seismic excitation. The results are summarized in gain maps used to find the optimal parameters for the inter-storey isolation of the archetype model.
# Experimental Characterization of RGB LED Transceiver in Low-Complexity LED-to-LED Link
## 1 Introduction
Secondly, the research focuses on increasing the data rate and system performance, increasing thereby the cost and complexity by using complex lenses and concentrators [18]. This disagrees with the nature of communication required by typical IoT devices. Finally, the energy response and noise characteristics of an off-the-shelf RGB LED as a photodetector have not been investigated. In order to fill those gaps, this paper proposes an LED-to-LED system based on off-the-shelf RGB white LEDs which are more favourable for lighting and indicators in IoT than single-coloured LEDs. It also offers reduced cost and complexity by stripping the system of all complicated circuitry, optics and processing-hungry techniques. This paper implements and experimentally verifies the proposed system with off-the-shelf components costing as low as USD 2.60 per communicating device. Finally, this paper studies the noise characteristics of the LED photodetector and verifies that it is additive white Gaussian noise (AWGN). For that reason, a matched filter is proposed to achieve error-free transmission over a 7 cm distance with a speed up to 200 kbps, reaching thereby better performance compared to a PIN photodiode employed in the same zero bias scenario and meeting the requirements for IoT devices. The proposed low-complexity LED-to-LED link can be applied to any application that requires low data rate and short-range line-of-sight communication. It is especially useful in applications where small chunks of secure data are transmitted, such as the exchange of biometric information, user identification, contactless payment as well as for communication between smart devices in a personal or body area network or for infotainment systems [20]. Other applications that could benefit from the LED-to-LED communication include sensing applications such as high-accuracy distance sensors (as replacement for ultrasonic distance sensors) and biomedical sensors [12]. Last but not least, LED-to-LED has a high potential for being used in bidirectional opto-coupling and opto-insulating applications.
## 1 Introduction
In addition, to improve the effectiveness of data fusion with different data sources, a two-step data fusion strategy is proposed: (a) disease entity standardization; (b) disease entity-centric structuring and acquisition of triple-structured data. Then, the data, information, knowledge, and wisdom (DIKW) system was used as the basis for modeling a DIK three-layer knowledge graph, and it was abstracted into a generic method model so that it can be implemented as a perfect knowledge discovery strategy for massive medical resources. To better adapt to the knowledge iteration update, a set of scientific methods for extending and updating knowledge was developed for each layer of the DIK knowledge graph. Finally, a preliminary implementation of a question-and-answer system based on the DIK knowledge graph was undertaken in conjunction with Flask and Python, and the differences between the two approaches to knowledge graphs were compared and validated. The question and answer system was capable of answering \"what/why/how/who\"-type questions in natural language. The complete framework structure is shown in Figure 1.  
Figure 1: The construction process of a multi-data source medical knowledge graph based on the DIK system. The innovative points of the proposed method in this paper are as follows:  
1. The characteristics of different medical data sources are used to improve the generalization of medical knowledge graphs;
2. According to the commonality of medical data, a two-step data fusion strategy was proposed, which is beneficial to improve the effective data fusion between different data sources;
3. According to the DIKW system, a construction method of the DIK medical knowledge graph applicable to the medical field was proposed.
# RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks
## 2 Related Works
### Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks  
The image super-resolution algorithm is based on dual-channel convolutional neural networks, such as EEDS [21], and is also a learning-based SISR algorithm. The EEDS algorithm works to improve SRCNN and FSRCNN: its structure is deeper than those of SRCNN and FSRCNN, and the residual block with jump layer, the residual network because of the existence of the fast connection. Data transmission between the network is smoother, and the gradient is improved, resulting in the loss of fitting and making it easier for the network to converge. The network structure of EEDS is divided into two parts: the deep layer and the shallow layer. The deep network contains 13 layers, including a feature extraction layer, an up-sampling layer, and q multi-scale reconstruction layer. The shallow network contains three layers. The design idea comes from the three-layer model of SRCNN, in which the anti-ciling layer replaces the original SRCNN nonlinear mapping layer. Finally, using a deep network combined with the output of the shallow network, the final output of the HR image is obtained.  
In the training process, 91 training images are first scaled, rotated, and fragmented, and then sampled according to the required ratio. The obtained \(LR\) image blocks are input into the double-layer network. The MSE corresponds to the original image, the output HR image is constructed, and the model parameters are optimized.  
The shortcomings of the network are as follows: because the deep network cannot fully extract the features of the \(LR\) image in the feature extraction stage, the nonlinear feature mapping of the up-sampling process leads to the loss of useful information and of long-term memory content in the reconstruction process, which causes the deep network to discount the effect of super-resolution. The shallow network is mainly used to restore the main components of LR images. Too many parameters will limit the fast convergence of the network.
## 3 Semi-Physical Experiment and Analysis
### Results of Semi-Physical Experiments
#### 3.2.2 Fine Registration by J-ICP
As depicted in Table 3, the total time taken by the methods presented in this paper for a single-frame point cloud estimation was approximately 98 ms, with a tracking frequency of up to 10 Hz. In summary, compared with the classical method, this paper achieved a 56% reduction in maximum translational error in position estimation and a 65% reduction in maximum rotational error, with an 18.1% increase in computational consumption. These results suggests that the proposed method has the huge application potential in practical control. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ICP** & **GICP** & **J-ICP** \\ \hline Mean Translation & 2.06 & 1.36 & 1.07 \\ Error—X (mm) & (\(\pm\)1.50) & (\(\pm\)0.94) & (\(\pm\)0.81) \\ Mean Translation & 3.59 & 2.46 & 1.57 \\ Error—Y (mm) & (\(\pm\)3.02) & (\(\pm\)2.01) & (\(\pm\)1.14) \\ Mean Translation & 0.54 & 0.35 & 0.25 \\ Error—Z (mm) & (\(\pm\)0.42) & (\(\pm\)0.27) & (\(\pm\)0.20) \\ Mean Rotation & 0.162 & 0.112 & 0.071 \\ Error–pitch (\({}^{\circ}\)) & (\(\pm\)0.135) & (\(\pm\)0.89) & (\(\pm\)0.052) \\ Mean Rotation & 0.089 & 0.056 & 0.046 \\ Error—your (\({}^{\circ}\)) & (\(\pm\)0.065) & (\(\pm\)0.041) & (\(\pm\)0.036) \\ Mean Rotation & 0.055 & 0.059 & 0.019 \\ Error—roll (\({}^{\circ}\)) & (\(\pm\)0.041) & (\(\pm\)0.043) & (\(\pm\)0.013) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of pose parameter errors among the ICP, J-ICP, and GICP algorithms. Figure 12: Comparison of errors of fine alignment results. (**a**) X-axis translation; (**b**) Y-axis translation; (**c**) Z-axis translation; (**d**) X-axis Euler angle; (**e**) Y-axis Euler angle; (**f**) X-axis Euler angle.
# Study of the Phenolic Compound Profile of _Arbutus unedo_ L.
## 2 Materials and Methods  
### Chemicals, Reagents and Standard Solutions  
Folin-Ciocalteu Reagent (FCR), 2,2-diphenyl-l-picrylhydrazyl (DPPH\(\bullet\)), and 6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox) were purchased from Sigma-Aldrich (Madrid, Spain). Potassium chloride analysis grade, hydrochloric acid (37%), ethanol (EtOH), methanol (MeOH), and acetonitrile (ACN) LC-MS grade were purchased from Scharlau (Barcelona, Spain). Ammonium acetate and formic acid LC-MS grade were purchased from Fluka (Busch, Switzerland). Sodium acetate, acetic acid, and anhydrous sodium carbonate analysis grade were obtained from Panreac Quimica (Barcelona, Spain).  
Water (resistivity 18.2 M\(\Omega\) cm) was obtained from a Millipore Milli-Q-System (Billerica, MA, USA).  
All analytical standards were of high purity grade (\(\geq\)90%). Gallic acid, protocatechuric acid, syringic acid, vanillic acid, p-coumaric acid, chlorogenic acid, caffeic acid, cafrate acid, cafrate acid, quercetin, quercetin 3-\(\beta\)-glucoside, rutin, kaempfer, (+)-catechin, (\(-\))-epigallocatechin gallate, epigallocatechin, procyanidin B2, naringin, vitexin (apigenin-\(\beta\)-C-glucoside), _trans_-piceid, and _trans_-resveratrol were from Sigma-Aldrich (St. Louis, MO, USA), whereas 4-hydroxybenzoic acid was obtained from Acros Organics (Geel, Belgium).  
Stock standard solutions (1000 mg/L) were prepared by diluting adequate amounts of each compound in MeOH and stored at \(-\)18 \({}^{\circ}\)C. Working solutions (10-100 mg/L) were prepared by appropriate dilution of the stock solutions with MeOH and stored at \(-\)18 \({}^{\circ}\)C. Mixed standard solutions (1-25 mg/L) were prepared daily by dilution of suitable volumes of working solutions with MeOH.
## 7 Discussion  
In Section 6, we put the developed platform to test; after examining the results (Section 6.2), we can conclude that CFS and MLFQ stand out as superior scheduling algorithms. They perform well overall and do not rely on prior knowledge of job running times, which is unrealistic and a drawback for SJF. While fair, the RR scheduler has inherent issues, especially when dealing with a high number of simultaneous jobs in the queue. This leads to low throughput and, in some cases, poor response time.  
Given that CFS is employed in the Linux kernel, our simulations provide insights into its efficacy, showcasing good turnaround time and exceptionally low response time. Additionally, it supports the assignment of priority to jobs, allowing for prioritization of interactive tasks.  
However, the MLPQ scheduler faces challenges, such as determining the optimal number of queues and their runtime. The crucial boosting mechanism also poses difficulty in adjustment. Despite these challenges, both CFS and MLFQ offer valuable insights into different scenarios, contributing to a comprehensive understanding of their strengths and limitations.
## 4 Design Analysis
### Attitude Determination and Control Analysis
#### 4.6.2 Attitude Pointing Analysis  
A proportional and derivative (PD) controller was simulated to control Light-1 in orbit and to estimate the time Light-1 takes to change its attitude for pointing in Vilnius and Aalborg. The worst desired attitudes for target pointing in Vilnius and Aalborg were found by running a STK scenario, where the Euler angles were found for the life of 6 months. Only three reaction wheels are simulated in the Simulink block. The magnetorquer was  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**S-Band Downlink (Eb/No) Method**} \\ \hline \multicolumn{10}{c}{Downlink margin} \\ \hline Satellite EIRP & 3.4 & 3.4 & 3.4 & 3.4 & 3.4 & 3.4 & 3.4 & 3.4 \\ \hline Isotropic Signal @ GS & \(-164.68\) & \(-162.59\) & \(-160.63\) & \(-157.33\) & \(-154.85\) & \(-153.01\) & \(-151.65\) & \(-150.68\) \\ \hline G/T & 5.07 & 6.38 & 6.62 & 6.75 & 6.81 & 6.81 & 6.81 & 6.85 \\ \hline Boltzmann’s Constant (dBi) & \(-228.6\) & \(-228.6\) & \(-228.6\) & \(-228.6\) & \(-228.6\) & \(-228.6\) & \(-228.6\) & \(-228.6\) \\ \hline Signal to noise power S/No & 68.99 & 72.38 & 74.58 & 78.02 & 80.56 & 82.40 & 83.75 & 84.77 \\ \hline System Eb/No & 17.91 & 21.31 & 23.51 & 26.95 & 29.49 & 31.33 & 32.68 & 33.69 \\ \hline Required Eb/No & 13 & 13 & 13 & 13 & 13 & 13 & 13 & 13 \\ \hline Margin & 4.910 & 8.310 & 10.51 & 13.95 & 16.49 & 18.33 & 19.68 & 20.69 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Input** & **Value** \\ \hline Moment of Inertia (kg.m\({}^{2}\)) & \([9.71\times 10^{-3},\)\(-2.80\times 10^{-5},\)\(-4.86\times 10^{-4};\) \\ \(-2.80\times 10^{-5},\)\(6.56\times 10^{-2},\)\(3.76\times 10^{-5},\) \\ \(-4.86\times 10^{-4},\)\(3.76\times 10^{-5},\)\(6.56\times 10^{-2}\)] \\ \hline Altitude (km) & 400 \\ \hline Inclination (deg) & 51.6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Input values used to run the detumbling simulation.