esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 1 Introduction  
Oxygen is a key element in many industrial branches. The most common way of using oxygen is to use it to improve the combustion process in metallurgy, power generation, and welding technologies. Due to its properties, oxygen is also used in the chemical industry, for post-production wastewater treatment and disinfection. Also, oxygen, being a gas necessary for life, is widely used in all kinds of medical procedures. In recent years, the oxygen demand has been increasing in branches showing relatively moderate daily consumption of this gas, such as biotechnological processes, water treatment, the construction industry, and medicine [1].  
The primary source of oxygen in the industry is its separation from the atmospheric air. The most important technology used on an industrial scale is low-temperature air rectification [2]. The capacity of cryogenic installations reaches several thousand tons of oxygen per day in a single process line [3]. The alternatives to cryogenic methods include adsorption, membrane, and chemical looping [4, 5]. With the oxygen demand lower than approximately 100 tons per day, cryogenic methods of oxygen separation are no longer viable. This effect has been analyzed both energetically and economically [6]. In the case of such low oxygen demands (up to the laboratory scale), technologies based on the adsorption process can be perceived as the only mature, economically justified technology for separating oxygen from the air [4]. The use of adsorption technology is also justified in oxy-fuel power blocks with an electrical capacity of up to 25 MW [7].  
The literature review shows that there are no data on energy consumption and optimization methods for small adsorption-based oxygen generators, though such work is carried out on adsorption chillers [8, 9]. Considerations regarding energy optimization seem to be crucial for low-capacity oxygen generators, in which auxiliary devices have a significant impact on the energy consumption of the entire oxygen separation process.  
This paper presents a method of optimizing the process of oxygen separation from the air using the PSA method to obtain the lowest energy consumption while maintaining the nominal oxygen purity. As a result of the conducted experiments, the authors show the oxygen generator efficiency optimization by choosing an appropriate adsorption pressure and oxygen transfer time. Additionally, the authors indicate the influence of the pressure difference between the adsorption bed and the oxygen tank on the purity of separated oxygen.  
The possibility of reducing the energy consumption of a standard small-scale oxygen generator by about 40% with an increase in maximum efficiency by over 80% was experimentally demonstrated by using the presented methods.
###### Abstract  
Active and passive techniques are two different techniques with which to detect buried explosives. In practice, the most preferred active method works by broadcasting a signal underground. This signal may stimulate the buried explosive and cause it to explode. It is important to eliminate or minimize this drawback to ensure the safety of the detector operator. In this respect, it is important to increase the studies on the passive detection technique which is not currently used in practice. The aim of this study was to passively detect improvised explosive devices without stimulating them, and to classify underground objects as explosive or non-explosive. A fluxgate sensor array having 33 components was used for passive magnetic field measurements, and the nearest neighborhood algorithm was preferred for classifying the resulting data. In experimental studies, 33 different samples having different amounts of ferromagnetic properties were used. Successful imaging and classification were achieved for the measurements up to 20 cm below the surface of soil. Data were recorded as 32 \(\times\) 25 matrices, and then they were reduced to 32 \(\times\) 2 matrices having the same features. Samples having explosive properties were distinguished from other underground objects with success rates of 86% and 95% for 32 \(\times\) 25 and 32 \(\times\) 2 data matrices, respectively. Classification times for 32 \(\times\) 25 and 32 \(\times\) 2 data matrices were 42 ms and 3.62 ms, respectively. For data groups where the best results were obtained for the data matrices, frame numbers classified in one second were calculated as 23.80 and 276.2, respectively. False alarm rate achieved was 5.31%. The experimental results proved the successes of the matrices reduction and classification approach. One of the most common problems encountered in passive detecting techniques is that the sensor position affects the measurements negatively. In this paper, a solution has been proposed for this important problem.  
underground object detection; magnetic field measurement; nearest neighbor searches; remote sensing +
Footnote †: journal: Applied Sciences
# Progressive Multiple Alignment of Graphs
## 2 Theory
### Abstract Graph Alignments
The converse is not always true; however, it is not always possible to extend a common sub-object to an alignment that contains a common sub-object such as the set of matches. Probably the most well-known example is the distinction between the editing and alignment of two rooted ordered forests, see (e.g., [25] Figure 1). In the case of graphs, however, the situation is simple. Let \(G\) and \(H\) be two graphs with a common induced subgraph \(K\) with embeddings \(\varphi_{G}:V(K)\to V(G)\) and \(\varphi_{H}:V(K)\to V(H)\). Identifying the vertices \(\varphi_{G}(x)\) and \(\varphi_{H}(x)\) for all \(x\in V(K)\) amounts to gluing together \(G\) and \(H\) at the vertices of \(K\). This results in a graph \(A\) that contains \(K\) as an induced subgraph. All other vertices belong to either \(V(G)\) or \(V(H)\). By construction, \(A\) contains only edges that are present in at least one of \(G\) and \(H\), and both \(G\) and \(H\) are contained in \(A\) as induced subgraphs. In particular, therefore, \(A\) is an alignment of \(G\) and\(H\). It is, in fact, the unique edge-minimal alignment of \(G\) and \(H\) given the common induced subgraph \(K\).
# Training Tasks vs. Match Demands: Do Football Drills Replicate Worst-Case Scenarios?
## 3 Results  
Significant differences were found between most training tasks and match play for all physical variables across all playing positions (\(p<\) 0.05), with some exceptions. These differences and their effect sizes are shown in Table 4, and the percentage changes with respect to match play are detailed in Figures 1-6. For total distance, tasks represented 33.7-91.6% of match demands for central defenders, 52.0-86.1% for wide players, 32.8-87.7% for midfielders, and 36.6-88.0% for forwards. Effect sizes ranged from trivial to very large (ES = 0.08-2.61). For HSR distance, tasks covered 14.5-56.1% of match demands in central defenders, 15.1-44.6% in wide players, 18.6-58.1% in midfielders, and 22.6-69.2% in forwards, with large to very large effect sizes (ES = 1.10-3.74). Regarding HMLD, tasks reached18.6-77.2% of match values for central defenders, 26.4-73.1% for wide players, 13.4-79.9% for midfielders, and 18.2-74.9% for forwards, showing moderate to very large effect sizes (ES = 0.97-5.20). For decelerations, most tasks showed significant differences, except for TYPE 6 (\(p\) = 0.182) and TYPE 9 (\(p\) = 0.091) in central defenders. Relative values were 64.1-90.4% for central defenders, 52.8-79.4% for wide players, 52.8-81.6% for midfielders, and 52.6-81.6% for forwards, with effect sizes ranging from trivial to large (ES = 0.08-1.78). Accelerations showed significant differences across most tasks, except for TYPE 6 (\(p\) = 0.003) in central defenders; TYPE 5 (\(p\) = 0.0697), TYPE 6 (\(p\) = 0.1432), TYPE 7 (\(p\) = 0.1608), and TYPE 9 (\(p\) = 0.0752) in midfielders; and TYPE 6 (\(p\) = 0.241), TYPE 7 (\(p\) = 0.089), and TYPE 9 (\(p\) = 0.141) in forwards. Tasks covered 52.9-74.0% of match accelerations in central defenders, 52.8-77.2% in wide players, 48.0-74.9% in midfielders, and 52.6-81.6% in forwards, with trivial to large effect sizes (ES = 0.04-1.43). Sprints showed significant differences across all tasks, except for TYPE 3 (\(p\) = 0.48), TYPE 4 (\(p\) = 0.094), and TYPE 8 (\(p\) = 0.137) in midfielders. Tasks achieved 7.9-51.1% of match sprints for central defenders, 8.4-54.5% for wide players, 8.5-57.2% for midfielders, and 15.9-55.2% for forwards, with large to very large effect sizes (ES = 0.88-4.20). No results were reported for TYPE 5 and TYPE 7 in forwards for the sprint variable due to an insufficient sample size.
## 2 The Fabrication and Sensing Principle of OMCI
### Sensing Mechanism
Thereby, it will cause the OMCI transmission spectrum to shift. When tracking a characteristic wavelength of the OMCI transmission spectrum, with the seawater temperature changes, the characteristic wavelength shift to \(\Delta\lambda\). Thus, the sensitivity of OMCI's seawater temperature sensing response can be expressed as [16; 17]:  
\[S_{t\text{-}OMCI}=\frac{d\lambda}{dT}=\frac{\partial\lambda}{\partial\varphi} \cdot(\frac{\partial\varphi}{\partial n_{3}}\cdot\frac{dn_{3}}{dT}+\frac{ \partial\varphi}{\partial n_{2}}\cdot\frac{dn_{2}}{dT}+\frac{\partial\varphi }{\partial l}\cdot\frac{dl}{dT}+\frac{\partial\varphi}{\partial r}\cdot \frac{dr}{dT}). \tag{4}\]
## Materials and Methods
### CV-MC-ICP-MS Measurements
To check the performance of the instrument, the dependence of the measurement accuracy on the signal intensity (or sample concentration) was evaluated using SRM 8610 (NIST, Gaithersburg, MD, USA) and SRM 3133 in different concentrations dissolved in 5% inversed aqua regia. We called this the linearity of the isotope measurements. In this evaluation, the concentrations of SRM 8610 were identical to those of SRM 3133, the reference solution. The measurement results show that the offsets of the isotope ratios from the reference values (offset = measured isotope ratio - reference isotope ratio) or the measurement accuracy for \(\delta^{199}\)Hg, \(\delta^{200}\)Hg, \(\delta^{201}\)Hg, \(\delta^{202}\)Hg, and \(\delta^{204}\)Hg were less than \(\pm\) 0.1% when the concentrations were between 2.5 and 10 ng g\({}^{-1}\), and their uncertainties indicate that the differences are insignificant (Figure 2). Depending on the mass, the offset became slightly larger as the concentration decreased. For samples with a mercury concentration of 1.3 or 0.8 ng g\({}^{-1}\), the offset was as large as 0.18% \(\pm\) 0.09% or \(-\)0.33% \(\pm\) 0.12%, respectively. The offsets were insignificant as 2\(\sigma\) confidence intervals of the uncertainties were considered for the 1.3 ng g\({}^{-1}\) solution and 3\(\sigma\) intervals for the 0.8 ng g\({}^{-1}\) solution. We conclude that the high accuracy and precision can be maintained when the sample concentrations are higher than 2.5 ng g\({}^{-1}\), and that the precision decreases when the concentration falls below 2.5 ng g\({}^{-1}\).
## 5 Theory and Field Test Verification
### Field Application Studies  
#### 5.2.1 Determination of Monitoring Area and Candidate Point Scheme  
Xiashijie coal mine is located in Yaoqu town, Tongchuan City, Shaanxi Province, China, and it is 54 km away from Tongchuan city, as shown in Figure 8. The minefield is 4 km long, with a stope width of 3.3 km and a coal-bearing area of 13.2 square kilometers. The mine design is two levels, which include the 'Cross Double U' roadway layout of the mining face and the adit-inclined shaft stage mining. Longwall mining and fully mechanized top coal caving are adopted, and the total collapse method manages the roof. The general structure of the mine is an undulatory monocline with a north dip, and the deep part is a syncline of Xinmin village. The fault structure is not developed and is simple.  
In view of the randomness and multiple problems of mine rock-burst, to reveal and predict potential risk areas of the working face in advance, our research group adopted the advanced high-precision microseismic monitoring system produced by the Canadian Engineering Seismology Group (ESG) in October 2019 to carry out real-time monitoring, positioning, and analysis of coal and rock fractures in the affected mining area of the working face 2305. The system consists of an underground signal acquisition system, a ground data processing system, and a remote system. The underground signal acquisition system includes microseismic sensors and the Paladin downhole digital signal acquisition system. The remote system contains a big data processing system and 3D visualization software based on remote network transmission developed by Mechsoft (Dalian) Co., Ltd. The microseismic sensors use seismometers with a response frequency range of 15-1000 Hz and a sensitivity of 43.3 v.s/m.  
The mine was in the mining stage. The mining position is shown in Figure 9. There is a syncline at a distance of 300 m from the incision. The working face about 50 m before and after the syncline is used as the microseismic risk area, and the probability of mine earthquakes increases by 20%. The mining face within the mining stop line is taken as the key monitoring area, and the microseismic importance factor \(F_{Q}\) of the key monitoring area is 1.5. According to the general principles of microseismic sensor arrangement, on-site mining technology, and geological conditions, 82 candidate points are selected in the installation area with a grid interval of 50 m according to the size of the layout area, since the installation positions are mainly arranged in the haulage roadway and the air return roadway. Using 20 sensors, the improved particle swarm algorithm (IPSO) proposed in this paper is used to optimize the design of the network optimization.  
Figure 8: Location of Xiashijie coal mine.
# Special Feature Vibration-Based Structural Health Monitoring
## 1 Introduction  
Structural health monitoring by vibration requires the understanding of multidisciplinary fields of engineering sciences. Since vibration behavior is influenced by structural properties, their analysis requires consideration of the excitation mechanism, vibration energy transfer and subsequent dissipation. For efficient monitoring, the relevant signal processing is crucial for cost-effective and reliable identification. Although the use of vibration for structural integrity monitoring is complex, its potential to be used for a wide variety of engineering fields is continuously increasing. The studies on the application of machine learning to the classification of vibration-based monitoring are being proposed by many research groups as summarized in the review [1]. To anticipate unexpected failure and estimate remaining life time with actual implementation, continuous vibration monitoring together with statistical feature extractions are important. The proposed Special Issue was intended to collect recent advances on the use of vibration for structural health monitoring. The studies show application fields with their potential to be expanded to a wide variety of machines and infrastructures.
## 3 Communication Delay Issues with Combined TSC and TCR Controller  
As demonstrated in Figure 2, TSC and TCR can be utilized in tandem to govern the reactive power flow from the source to the loads. The separate modules are connected together to provide maximum flexibility to the consumers' loads by concurrently changing the switching of the capacitor banks in discrete numbers in the TSC branch and constantly modifying the flow of the current in the inductor in the TCR branch. It is possible to obtain completely stepless control by coordinating the control between the continuous reactor and the capacitor stages (which are discrete).  
Dynamic control strategies of coupled TSC and TCR methods have continuous control, almost no transients, and reduced harmonic production (due to the regulated reactor rating being tiny in comparison to the overall power flow) with control and operation flexibility [35; 36]. The drawback of the TSC-TCR method compared to TCR- and TSC-type compensators is the greater installation cost; nevertheless, the cost can be repaid in a shorter period of time: in the long term, this plan is more cost-effective than paying the Electricity Authority for KVA demand charges on a regular basis. A static VAR system compensates for reactive power delivered into the line in a rapid, smooth, and stepless manner. It guarantees that bus voltages are accurately controlled throughout a wide variety of loads.
## 2 Methodology
### Case Study
#### 2.1.2 Climate Data  
Beyond the constructive features, heat transmission in buildings depends strongly on climatic variables (namely outdoor temperature, solar radiation, wind speed and relative humidity); the altitude at which the building is located and the building orientation. In Portugal, due to its geographical situation in the northern hemisphere, the South orientation is the one that receives the most solar radiation and therefore, this orientation should be preferred for maximizing solar gains. Despite being a small country, Portugal is divided into three heating climate zones (I\({}_{1}\), I\({}_{2}\), I\({}_{3}\)) and three cooling climate zones (V\({}_{1}\), V\({}_{2}\), V\({}_{3}\)). The case study dwelling is located in Barrocal, a civil parish of the municipality of Pombal, district of Leiria, central region of Portugal. Due to its location, the reference building is located on the climatic zone I\({}_{2}\) V\({}_{2}\). Data on temperature, relative humidity and solar irradiance from a local weather station belonging to the Portuguese Water Resources Information System (SNIRH--[[https://snirh.apambiente.pt/](https://snirh.apambiente.pt/)]([https://snirh.apambiente.pt/](https://snirh.apambiente.pt/)) (accessed on 18 August 2020)) was used in the simulations (Figure 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Gross Wall Area** & **Window Opening Area** & **Window-Wall Ratio** \\  & **(m\({}^{2}\))** & **(m\({}^{2}\))** & **(\%)** \\ \hline  & 49.00 & 14.30 & 29.18 \\  & 61.60 & 0.34 & 0.55 \\  & 49.00 & 9.01 & 18.40 \\  & 61.60 & 12.24 & 19.86 \\ Total & 221.20 & 35.89 & 16.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Glazing surfaces and window-to-wall ratio of the case study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Building Element** & **Constructive Solution** & _U-Value_ (W m\({}^{-2}\) K\({}^{-1}\)) \\ \hline External walls & & \\ Internal partition walls & & \\ \hline Pitched roof & & \\ Ground floor slab & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Case study constructive solutions.  
Figure 6: Weather data for Pombal region: (**a**) average monthly air temperature and relative humidity; (**b**) average monthly solar irradiance.
# Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors
## 2 Definitions and Notations
### Boolean Structural Measures
: _The backdoor size of \(\phi\) is the cardinality of \(BD_{\phi}\), denoted by \(|BD_{\phi}|\). The normalized backdoor size of a CNF formula \(\phi\) is the ratio of the backdoor size to the number of variables of \(|X|\), which is denoted \(BDsize_{\phi}\). This can be expressed as follows:_  
\[BDsize_{\phi}=\frac{|BD_{\phi}|}{|X|} \tag{5}\]  
**Definition 20** (Backbone/backdoor variable overlap [20]). : _(To ensure the correctness of computing the backbone/backdoor variable overlap set, we relax the set \(BB_{\phi}\) to be the set of variables,not literals. As such, for all backbone literals in \(BB_{\phi}\), their corresponding variables are considered.) Let \(BB_{\phi}\) and \(BD_{\phi}\) be the backbone and backdoor of a CNF formula \(\phi\), respectively. The backbone/backdoor variable overlap is referred to as \(BBD_{\phi}\), and it is given by:_  
\[BBD_{\phi}=BB_{\phi}\cap BD_{\phi} \tag{6}\]  
_The backbone/backdoor variable overlap size of a CNF formula \(\phi\) is the cardinality of \(BBD_{\phi}\), which is denoted by \(|BBD_{\phi}|\). The normalized backbone/backdoor size is the ratio of \(|BBD_{\phi}|\) to the number of variables \(|X|\), which is denoted by \(BBDoverlap_{\phi}\):_  
\[BBDoverlap_{\phi}=\frac{|BBD_{\phi}|}{|X|} \tag{7}\]
## 4 Illustrative Example
The other IFC lines either refer to the IFCWINDOW entity or they refer back to IFCWINDOW through the IFC entity codes. The properties defined in the IFCWINDOW entity include the window ID, name, location, shape, height, and width. The IFC entity represented by #295 in Figure 7 is the IFCRELCONTAINEDSPATIALSTRUCTURE entity, which contains the wall association and story association data. From this data, the story and wall on which the window is located can be determined. The material association of the window is located in IFCRELASSOCIATESMATERIAL, the renovation status is located in IFCRELDEFINESBYPROPERTIES, and the window style is located in IFCRELDEFINESBYTYPE. This figure presents how specific properties can be extracted from an IFC file to be used by facility managers for the cost estimation of window replacement. Among the 180 window cases, 175 cases were included in the CBR model with the goal of identifying a case that is similar to the new cases. Five window cases were randomly excluded from 180 cases for the CBR model verification. In this illustrative example, one window was identified from the 3D model of the building, and the properties of the window were obtained. After the model has identified the properties of the window, these properties are input into the CBR model to identify the best match. The data output from the CBR model includes the total estimation costs and total installation duration. This information is helpful to facility managers for easily determining the costs associated with replacing a window at a particular location. When considering various factors in the CBR, it is important to decide which factors are related to the estimation of a window installation. These properties include the story, position, size, material, and accessibility. Each of these properties has an influence on the cost and duration of the installation; however, the influence of each property is not equal. This must be taken into consideration when determining the costs and duration for the window installation. The next section describes the method of selecting the relevant factors using the machine learning algorithm, which is factor subset selection. The properties of the window are input into the CBR model for the case retrieval. Table 3 presents the four cases retrieved for this particular illustrative example. The similarity values are calculated based on the weights input into the model. For this illustrative example, case32 is the closest match with a similarity value of 0.96, or 96%. Similar cases are identified with the objective of estimating the cost associated with installing a new window in the building. The retrieved cases include the cost as well as the duration measured for the window installation. The duration value can also be used as an estimate for a new window replacement. The presentation of the cases retrieved can help the facility manager estimate the cost of new projects based on existing cases. The cost can be obtained from case32, which has a value of $162 and is used as an estimate for the new window installation.