esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed GA and DPSO Solutions
### GA
#### 4.1.2 Fitness Evaluation  
The proposed chromosome representation is evaluated to find an energy-efficient movement repertoire with a fitness function. To maximize the network lifetime, the evolutionary process of our proposed GA should achieve two objectives: (1) minimizing the total traveled distance of all sensors and (2) optimizing the balance between the residual energy of each sensor. In other words, the first objective involves reducing the total energy consumption, whereas the second objective centers on maximizing the remaining energy of each sensor or controlling the energy consumption of each sensor. These two objectives correspond to the following reasonable considerations. When two movements or chromosomes have the same total distance traveled, a chromosome with a high similarity of residual energy is conducive to network lifetime extension because the remaining energy of each sensor is sufficient to provide full coverage for other moving objects. For the two objectives, we use a fitness function, and the proposed evolutionary algorithm becomes a search for a trajectory that minimizes the fitness function. The fitness value of a trajectory decreases according to the degree to which the objectives are fulfilled. In other words, a trajectory of mobile sensors that fulfills all the objectives well would correspond to a low fitness value. We developed our fitness function to evaluate the fitness of each individual as follows:  
\[F=D_{c}+E_{c},\]  
where \(D_{c}\) is the cost of the average traveled distance of each sensor and \(E_{c}\) is the cost of the imbalanced energy consumption of each sensor. \(D_{c}\) and \(E_{c}\) can be calculated through the following equations:  
\[D_{c}=\frac{\sum_{k=1}^{m}\sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{k}}{m},\]  
\[E_{c}=\frac{\sigma}{\mu}\cdot T_{d},\]  
where \(\sigma\) and \(\mu\) are the standard deviation and the mean value of the residual energy of all mobile sensors, respectively, and \(T_{d}\) denotes the total distance of the predetermined trajectory.
# Machine Learning Based Color Classification by Means of Visually Evoked Potentials
## 2 Materials and Methods  
Many different methods, such as the onset/offset, flash, and pattern reversal methods were proposed for the measurement of VEPs, resulting in different characteristic VEP signal shapes. For our study, we used the pattern reversal stimulus because of its high validity in inducing human VEPs, simplicity of implementation, lower intra- and inter-subject variability, and its major effects on the magnitude of the VEP [1].  
Nine healthy subjects with normal or corrected to normal vision, and no known color deficiencies, were tested. The participants with a median age of \(27.6\pm 12.2\) years were all white, six of them were male and three were female. Subjects were informed about the experiment and consented. Care was taken to have the subject in a comfortable well-supported position to minimize eye movements and other artifacts. The checkerboard stimuli were generated on a DELL U2412 Mc monitor. According to [19], the visual angle was set to \(\alpha=1^{\circ}\) (60 min of arc) leading together with the check size \(C=1.5\,\mathrm{cm}\) to a visual distance of \(D=\arctan\left(\frac{D}{\alpha}\right)=88\,\mathrm{cm}\) (Figure 1b). Two colors (red and green) were tested in addition to the achromatic stimulus, which was recorded in beginning of the experiment for ensuring the general functionality of the setup. For the experiment itself the checkerboard was designed as black/colored checks and the reverse frequency was \(2\,\mathrm{Hz}\). Intensities of the checkerboard colors were adjusted to have both the same value; that is \(15\) cd/m\({}^{2}\), calibrated by using a light-meter (Gossen Mavolux 5032C) in a darkened room.  
VEPs were recorded using a biomedical signal amplifier (gtec USBAMP research, 16 channel EEG amplifier) at a sampling rate of \(fs=1200\) Hz. Gold cup electrodes were fixed following the 10-20 international system along the mid-line at the occipital (OZ), central (CZ), and frontal lobe (FZ), measuring the voltage difference between OZ and FZ and setting CZ to ground [19]. Electrode placement was modified, until the electrode impedance was constantly below \(5\) k\(\Omega\) for any electrode.
## 3 Technological Platform for the Wireless Sensor Node
### Lightweight PaRTiKle Operating System Design  
In this section, we describe the architecture of the PaRTiKle operating system, which adheres to a classical layered multi threaded design, as shown in Figure 2.  
PaRTiKle [28, 29] is a new embedded real-time operating system designed to be as compatible with the POSIX.51 standard as possible. The native API consists of \"C\" POSIX threads and provides support for C++, Ada and Java (tasking, synchronization, protected objects, exception handling, _etc._). PaRTiKle has been designed to support applications with real-time requirements, providing features such as full preemptability, minimal interrupt latencies, and all the necessary synchronization primitives, scheduling policies, and interrupt handling mechanisms needed for these types of applications. To meet the application requirements of sensor networks, the PaRTiKle OS implements a lightweight and energy-efficient scheduler, a user-level network stack, as well as other components such as device drivers all this in less than 12 Kbytes code lines. A layered network stack and hardware driver system is included to simplifying communication in an embedded platform. The PaRTiKle OS itself is coded mostly in C, and it presents a simplified C POSIX.51 standard programming interface. An application developer may write the application code in standard ANSI C and compile it with gcc, avoiding the need to learn a specialized language or compiler. PaRTiKle's structure provides several advantages over existing sensor network systems because it:  
* is portable, configurable and maintainable
* supports multiple execution environments. This allows it to execute the same application code (without any modification) under different environments. Presently, it can be used in a bare machine and to facilitate application development. It also runs as a Linux regular process and as a hypervisor domain, increasing the number of devices that can participate in a PaRTiKle network.  
Figure 2: PaRTiKle OS Architecture.  
* supports multiple programming languages. PaRTiKle currently supports Ada, C, C++, and Java (the current support of this last language is only supported when the 4.2 GCC compiler is used).
* supports a great deal of existing code. A significant amount of open-source code can be ported to PaRTiKle OS.
# Study of the Dynamic Adaptive Calculation Method for River Water Environmental Capacity
## 4 Results and Discussions
### Water Environmental Capacity Calculation
#### 4.1.2 Wet, Normal and Dry Periods  
The WEC of COD and NH\({}_{3}\)-N in the different functional areas of the Wei River, during different water periods, at a 90% design frequency, has been calculated. The frequency arrangement mode is based on the frequency of the water period, and the standard model is selected as the calculation model. Under a design frequency of 90%, the calculation results for COD WEC in wet, normal, and dry periods are 137,418.76 t/a, 69,795.8 t/a, and 57,546.77 t/a, respectively. Similarly, the calculation results for NH\({}_{3}\)-N WEC are 4871.63 t/a, 2733.44 t/a, and 1940.03 t/a, respectively. Comparing and analyzing the WEC of COD and NH\({}_{3}\)-N in the wet, normal, and dry periods, it can be concluded that the wet period has the highest WEC, followed by the normal period, and then the dry period. The variation  
Figure 9: Calculation results of NH\({}_{3}\)N WEC under different design frequencies.  
process of the WEC of COD and NH\({}_{3}\)-N in different water periods in the Shaanxi section of the Wei River is shown in Figures 10 and 11.  
It can be seen from Figures 10 and 11 that the WEC of each water function area is relatively large during the wet period and relatively small during the dry period. In practical management and planning, decision-makers can formulate corresponding pollutant emission control strategies and water resources protection measures based on the characteristics of WEC in different periods.
## 4 Lifetime Improvement Mechanisms in Routing
### Bio-Inspired Mechanisms
They mentioned three factors related to the routing decision as follows: the power level of each node, the distance between the node and the sink, and the pheromone value of the link from the source to the destination which is presented as Equation (10), where \(\tau_{i\text{-}j}\) is the pheromone value of the sub-path between nodes \(i\) and \(j\). It presents the strength of the link. \(N_{i\text{-}j}\) is the number of times the algorithm use the link between nodes \(i\) and \(j\). It initially is equal to 1 for  
\[\mathrm{P_{j}}=\frac{\mathrm{d_{s-j}^{w1}\times c_{j}^{w2}\times r_{s-j}^{w3}}}{ \sum_{i\in\mathrm{Neighborood(s)}\,\mathrm{d_{s-j}^{w1}\times c_{j}^{w2}\times r _{s-j}^{w3}}}} \tag{11}\]  
Based on Equation (11), the Euclidean distance between nodes \(S\) and \(j\) is shown by \(d_{s\cdot j}\). \(c_{j}\) is the battery capacity of node \(j\) which is equal to 0 when the node's battery is exhausted. Furthermore, information about the power level of nodes and preferred neighbors is exchanged on demand by request to send (RTS) and clear to send (CTS) messages. In comparison with GPSR [38], the OBGR protocol can significantly increase the network lifetime with a gain of 50% [54]. The philosophy behind this improvement is that OBGR schedules the power usage of nodes by utilizing an online-battery model and specify their recovery time in order to prolong their lifetime. Nevertheless, this protocol has two disadvantages: firstly, the RTS, CTS and RTR control messages impose some overhead. Secondly, compared to GPSR, the overall complexity of algorithm is a bit high.
# Temperature Condition Analysis for Freezing on Tunnel Lining Back Side According to Tunnel Length
## 5 Conclusions  
The geothermal heat of mountain tunnels is transferred to the air inside the tunnel because the surrounding ground depth is high. However, the inside air temperature changes due to the outside air temperature under a low inlet wind speed. Therefore, the  
Figure 14: Temperature variation on the back of the waterproofing membrane by each tunnel length. **(a)** Air temperature variation inside the tunnel (Extraction duration: 168 h); **(b)** Temperature variation on the back of the waterproofing membrane—Tunnel length: 500 m (DTR: 5.0 \({}^{\circ}\)C); **(c)** Temperature variation on the back of the waterproofing membrane—Tunnel length: 2500 m (DTR: 2.2 \({}^{\circ}\)C); **(d)** Temperature variation on the back of the waterproofing membrane—Tunnel length: 5000 m (DTR: 1.6 \({}^{\circ}\)C).  
Figure 15: Freezing conditions between the minimum air temperature and tunnel length.  
minimum air temperatures causing the freezing of the groundwater behind the waterproof membrane are presented along the tunnel length.  
Field measurements were performed in the mountain railway tunnel to analyze the air temperature inside the tunnel according to the outside air temperature. The results show that the air temperature increased nonlinearly far from the tunnel entrance. Furthermore, the change in the diurnal temperature range (DTR) inside the tunnel rapidly decreased to 60% from the entrance to 90 m, and the graph decreased gradually from 90 m to 3270 m. The daily cycle equation of the air temperature variation was derived using the cosine function, and air temperature variation by each tunnel length can be predicted using the outside temperature change.  
Thermal conduction analysis inside the tunnel was performed using a FEM program, and the freezing on the back of the waterproofing membrane within the typical drainage tunnel was investigated. Ice formation on the back of the waterproof membrane occurred below the minimum inside air temperature of \(-\)10.2 \({}^{\circ}\)C at the tunnel entrance. The minimum air temperature increased as the tunnel length increased, and freezing occurred at the minimum tunnel inside air temperature of \(-\)4.9 \({}^{\circ}\)C for a tunnel length of 4500 m or more.  
The freezing inside a tunnel can be predicted by measuring the inside air using these results, and the freezing damage section can be determined.  
Conceptualization and methodology, S.L. and M.S.; software, S.L. and J.K.; validation, S.P. and J.K.; investigation, S.L., S.P. and J.K.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, S.P. and K.-H.C.; supervision, M.S. and K.-H.C.; project administration, K.-H.C. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by Seoul National University of Science and Technology.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 2 Experimental Setup  
### LIBS Experimental Setup  
The LIBS experimental system consisted of a Q-switched Nd: YAG laser, a spectrometer, a laser attenuator, optical components, and a computer, and the system schematic is shown in Figure 1. Among them, the Q-switched Nd: YAG laser was a parallel laser with an exit wavelength of 1064 nm, an energy of 300 mJ, and a frequency of 5 Hz. The laser was reduced to 90 mJ (energy fluctuation below 1%) by a laser attenuator, then through an ultraviolet fused silica plano-convex lens with a focal length of 75.3 mm. Thus, the laser was focused on the sample surface, and the coal plasma was generated and then collected by a spectrometer through two plano-convex lenses with a focal length of 40.1 mm. The experimental parameters obtained by parallel experiment optimization mainly include laser energy of 90 mJ, focus depth of 2 mm, delay time of 1.2 us, and the sample pressure is 30 MPa. At this time, the average relative standard deviation (RSD) of the main peak of the sample is within 10%.  
Figure 1: Diagram of the LIBS system.
## 7 Results and Discussion
As mentioned earlier, the mean slant range bias was already incorporated into the IPF on 5 May 2015. It can be expected that product geolocation accuracy will continue to improve, with possible changes to the operational IPF taking atmospheric PD even the bistatic residual error into account. Figure 8: Comparison of StripMap SLC ALE estimates for different orbital state vector sources; path delay and bistatic residual correction applied in all cases; (**a**) predicted; (**b**) restituted; (**c**) precise (repeated from Figure 6c). (a) predicted OSVs; (b) restituted OSVs; (c) precise OSVs. A comment on the main operational IW mode needs to be made here. Because of the more complex acquisition geometry and product configuration of the TOPS mode products (_i.e.,_ IW and EW), the handling of these products requires extra processing steps in comparison with the SM mode products. These include spectral \"deramping\" to compensate for the Doppler centroid variations caused by azimuth beam steering, and \"debursting\" _i.e.,_ merging of the azimuth bursts. At UZH, these steps have until now been nearly completed. At this point, the following can be said:  
* Ground range detected (GRD) products can be handled in the same way as SM products, but do not contain as much detail as the SLC products from the same acquisitions. Therefore, detailed analyses of these products were not performed. * The wider coverage offered by these two modes comes at the cost of resolution loss, and the deployed \(\sim\)1 m corner reflectors were only clearly visible in IW products (not EW). * Preliminary geolocation estimates were made by UZH for IW SLC products. However, the measurements were based on detected versions of these products rather than on the native complex SLC samples. As stated above, the use of the complex IW/EW data requires additional processing that has yet to be fully implemented at UZH. Since recent tests revealed differences between ALE estimates made using detected _vs._ complex rasters, we are confident that our preliminary IW SLC results do not yet fully represent the SLC product quality. Therefore, we chose not to show them here. With the above considerations in mind, we may nonetheless state that the preliminary IW SLC ALE estimates tentatively confirmed our expectations. The mean ALE corresponded quite well with the SM mode estimates. Improved IW SLC results may be published in the near future.
# Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks
## 1 Introduction
In particular, we address the fundamental role of influencers and bots in the spreading of fake news online: this helps understand how fake news spreads over a social network and provides insight into how to limit its impact. Moreover, we consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur to make agent-based simulations more realistic, in the perspective that they should become a key tool to study the most effective fake news spreading strategies, and thus inform the most appropriate corrective actions. In more detail, the contributions of this paper are the following:  
* We propose an agent-based simulation model, where different agents act independently to share news over a social network. We consider two adversary spreading processes, one for the fake news and a second one for debunking. * We differentiate among agents' roles and characteristics, by distinguishing among common users, influencers, and bots. Every agent is characterized by its own set of parameters, chosen according to statistical distributions in order to create heterogeneity;
* We propose a network construction algorithm that favors the emergence both of geographical user clusters and of clusters of users with common interests; this improves the degree of realism of the final simulated social network connections; moreover, we account for the presence of influencers and bots and for unequal trust among agents;
* We propose ways to add time dynamical effects in fake news epidemic models, by modeling the connection time of each agent to the social network and the decay of the population's interest on a news item over time. The remainder of this paper is organized as follows. In Section 2, we survey related work. In Section 3, we introduce the model of the agents and the dynamics of our epidemic model, as well as our network construction algorithm. In Section 4, we present the results of our simulations. In Section 5, we discuss our findings and contextualize them in light of the current literature, before drawing concluding remarks, discussing the limitations of our work, and presenting future directions in Section 6.
# An Interoperable UMLS Terminology Service Using FHIR
## 3 Background  
### UMLS Knowledge Structure  
In the UMLS Metathesaurus, terms (e.g., malaria, fever, etc.) from diverse standards (e.g., SNOMED, MeSH, etc.), referred to as atoms in UMLS, are mapped to a single concept--a central entity used to structure the knowledge in Metathesaurus. An atom has an AUI (Atom Unique Identifier, a unique identifier given by UMLS), a code (identifier from the source), and a name. The source standard has a fully qualified name, version, and abbreviation. A concept has a CUI (Concept Unique Identifier, a unique and permanent identifier given by UMLS), a name (a preferred name chosen by the NLM), one or more semantic type, and other attributes. In practice, the concepts and atoms are in the MRCONSO table (or RRF file), a concept's semantic type(s) are in the MRSTY table (or RRF file), a concept's relationships are in the MRREL table, and version information is in the MRSAB table. The CUI is used to join multiple tables to obtain the required knowledge [33]. Figure 2 shows atoms from diverse sources mapped to a single concept named Atrial Fibrillation with CUI C0004238.  
The Metathesaurus also encapsulates relationships, both hierarchical and associative, between concepts. The attribute's type and label allow users to interpret the relationship. The type attribute categorizes a relationship, wherein the categories such as PAR (parent), CHD (child), RB (broader relationshipships), etc., are defined in a set by NLM, which will be referred to as the REL set in this article. The REL set has 15 values. The label attribute captures the name or label of the relationship. Figure 3 shows the UMLS knowledge structure as a UML class diagram, summarizing the above explanation. There are other attributes, not described here, that can also determine the status of a concept, atom, or relationship in UMLS. Refer to the UMLS reference manual for more details [5].
## 2 Preliminaries to the C-HARQ Protocol
### Cooperative ARQ  
CARQ is basically a stop-and-wait mode of error control that exploits a form of spatial diversity provided by the nodes in the neighborhood of the source and destination. This spatial diversity is achieved by employing the neighbor nodes to work in cooperation with the source node during the retransmission process. The possibility of cooperation arises from the fact that transmission over wireless channels is a broadcast in nature and thus allows the neighbor nodes to overhear the data transmitted by the source. This phenomenon enables the neighbor nodes to act as alternate sources for the current data. In case of failure in data transmission from the main source node, the destination calls for cooperation from these alternate sources to transmit their overheard data. The main advantage of this method lies with the possibility of finding multiple, and relatively better, communication channels of the neighbor nodes which significantly increases the chance of successful retransmission. Thus, this cooperation based operation boosts the throughput performance of an ordinary S&W ARQ to a muchhigher level [3]. The performance of the simple CARQ can be further improved if the error-correcting codes could come into play. To realize that, C-HARQ protocol is designed in the following section.