esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The SASW Method  
In the 1960s, [PERSON] [33] and [PERSON] [34] proposed frequency-domain methods for analyzing the characteristics of layered structures, where the stiffness of each layer decreased with increasing depth. The dispersion characteristics of surface waves, as a function of frequency or wavelength, were gradually used to evaluate the properties of layered structures. By the 1980s, based on the research of [PERSON] and [PERSON], several researchers from the University of Texas at Austin introduced the SASW method, based on the principles of surface wave dispersion, to accurately measure the modulus and thickness of each layer in a pavement system.  
The dispersion phenomenon makes the velocity of the surface wave highly dependent on the frequency. The surface wave with different velocities has different frequencies, penetrating the different depths of the layered structure. Moreover, the shear wave velocity can be obtained from the surface wave velocity, and the shear wave velocity represents the properties of the medium itself. Thus, based on the dispersion phenomenon, the inherent characteristics of the material in the pavement can be evaluated according to the surface wave at various velocities.
# On Ad Hoc Communication in Industrial Environments
## 3 Comparing Methods for the Extraction of NEPs  
It is hypothesized that the necessary base assumption for the NEP extraction from network traces is not applicable to the industrial scenario. In this section differences between the indirect generation of NEPs by means of network traces and the direct recording of NEPs by means of a custom protocol are shown. Two primary metrics are examined.  
The average number of simultaneous encountered per node \(N_{\varnothing}\) and the average duration of these encounters \(D_{\varnothing}\) are examined. Both metrics can be directly calculated from a NEP. Both metrics are important when analyzing the characteristics of the ad hoc communication channels. The first is an indicator for the number of reachable destinations from any node, while the second indicates the duration for which these destinations are reachable.  
The first NEP acquisition method is the acquisition by means of a custom protocol, further denoted as trace protocol. The protocol is described in Section 4. The second is the acquisition by means of network traces [28]. This second method is based on the assumption that any two nodes encounter each other if they are registered at the same access point. Both approaches have different strengths and weaknesses.  
In this section the fundamental behavioral differences of encounters acquired by both methods are observed. The goal is to analyze the behavior of the average number of simultaneous encounters in regards to the number of nodes \(N\) and number of access points \(N_{AP}\). Additionally, the average encounter duration in regards to the communication range \(r\) of the nodes and their speed \(v\) is analyzed. The communication range \(r\) results in a node coverage area \(A_{r}=\pi r^{2}\).  
In the following subsections the behavior of the average number of simultaneous encountered per node \(N_{\varnothing}^{XX}(N,N_{AP})\) and the average duration of these encounters \(D_{\varnothing}^{XX}(r,v)\) are explored. \(XX\) describes the type of examined acquisition method. \(TP\) is the recording of encounters by means of the tracing protocol, and \(NT\) is the acquisition by means of network traces. Models are proposed to emulate the behavior of the different metrics, when observed by the different methods. The goal of these models is to predict the network performance, as best as possible, given the impact of certain network parameters on the network behavior.
## 5 Conclusions  
The main aim of the research was to analyze and compare microstructure and properties of the Ti-6 Al-4V alloy obtained in three different processes: SLM, EBM and the conventional wrought material. As it turned out in the experiment, the fabrication process conditions strongly influence the features of the material, especially in terms of microstructure and mechanical properties. The main conclusions that can be drawn from the conducted research are as follows:  
1. The manufacturing method influences the equilibrium conditions of the crystallization process. As a result, the microstructure changes can be seen. In the SLM method, only the \(\alpha^{\prime}\) martensitic phase can be detected, whereas the EBM processed material had a very fine needle-like \(\alpha+\beta\) Widmanstten microstructure. Additionally, due to non-equilibrium cooling, pores can be seen in the SLM samples that drastically reduce ductility, preventing its use in medical applications. In contrast, the EBM vacuum processed material met the requirements of the ASTM F136-13 standards. For the SLM, additional heat treatment is necessary; however, the issue of pores may be still an obstacle.
2. Higher temperature gradients in the SLM method in comparison to EBM and wrought material led to high residual stress at the atomic level. The material after the SLM process had the highest UTS (ca. 1300 MPa); however, the elongation was very small (ca. 2%). Furthermore, this changed with the building direction of the sample, which is evidence of significant anisotropy. In the case of EBM and the wrought material, the mechanical properties were homogenous and independent of sample orientation. This factor is especially important for larger sizes of the fabricated elements. Therefore, the size effect should be accounted for and energy dissipation should be considered when planning an AM process.  
This work was supported by the European Regional Development Fund within the Innovative Operational Programme in the framework of the BIO-IMPLANT project (Grant No. POIG.01.01.02-00-022/09), National Center for Research and Development Applied Research Programme project LasIMP (Grant No. PBS3/A5/53/2015) and COST Action MP1301 New Generation Biomimetic and Customized Implants for Bone Engineering (NEWGEN) in the framework of Short Term Scientific Mission. The authors would like also to thank [PERSON] from Warsaw University of Technology for TEM investigations.  
[PERSON], [PERSON] and [PERSON] conceived of the experiments. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON], [PERSON] and [PERSON] contributed materials and analysis tools. [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 2 Methodology
### WST Comparison  
For the comparison study of the WSTs, the probability of wave occurrence is summed with respect to wave height to plot the probability density function (pdf) vs. wave height for each WST. In order to carry out a quick comparison of WSTs regarding the regional and seasonal WSTs, each WST's pdf corresponding to the Rayleigh pdf function is mapped, and the Rayleigh shape parameter (\(\alpha\)) is compared to identify the variation in WSTs. To carry out the mapping of the Rayleigh pdf to match the hindcast WST's pdf, the following three techniques were used: (i) least square error method (LSE), (ii) least area error method (LAE) and (iii) least square error method for the peak of the curve (LSEP). The peak of the curve is the region above the mean probability of exceedance (MPoE) calculated using Equation (3). The smaller Rayleigh shape parameter means the spread is narrow with a sharp peak located closer to the lower wave height, while the larger shape parameter means the pdf is broad and flat and the peak of the pdf is towards the higher wave height.  
\[\text{MPoE}=\frac{1}{\text{Maximum wave height with non zero probability of occurance}} \tag{3}\]
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 4 Model of Corrosion-Fatigue
### Service Life of Corrosion-Fatigue Crack Propagation  
As aforementioned, once the critical flaw size is achieved, the fatigue effects as well as the corrosion effects are the two major driving forces in crack propagation. Accordingly, the fatigue process competes with the corrosion process in terms of the crack growth rate, and the propagation is dominated by the one with a higher growth rate. It can be inferred from Equations (7) and (9) that, with the development of the crack, the growth rate is decreasing for the corrosion process while increasing for the fatigue process. Once the growth rate induced by the fatigue process exceeds that caused by the corrosion process, the fatigue effect becomes the dominated driving force and the influence of corrosion could be ignored. Therefore, it is crucial to determine the crack growth rate induced by the fatigue effect. Studies [48] show that the fatigue crack can be divided into small (short) cracks and long cracks during the propagation. For small cracks, the fatigue limit is significantly lower than that of long cracks, while the propagation rate is much faster [48]. Obviously, the behavior of small cracks cannot be reflected by the crude Paris law since the difference is not considered.  
Thus, the advanced NASGRO equation has been applied, in which the small crack is considered, as shown in Equation (11):  
\[\frac{da}{dN}=D\Big{[}(\Delta K-\Delta K_{thr})/\sqrt{1-K_{max}/A}\Big{]}^{m}, \tag{11}\]  
where \(\Delta K_{thr}\) is the decreased fatigue limit in terms of the SIF range, which will be discussed later; \(K_{max}\) is the maximal SIF; \(R\) is the stress ratio; \(A\) is the fracture toughness and \(D\) is a material-related constant.  
The maximal SIF can be calculated based on the stress ratio, as shown in Equation (12):  
\[K_{max}=\frac{\Delta K}{(1-R)}. \tag{12}\]  
Based on Equation (11), the service life of propagation could be solved, as shown in Equation (13):  
\[t_{p}=\frac{1}{f}\int_{a_{i}}^{h}da/\Big{\{}D\Big{[}(\Delta K-\Delta K_{thr}) /\sqrt{1-K_{max}/A}\Big{]}^{m}\Big{\}}, \tag{13}\]  
where \(f\) is the frequency of the applied cyclic loads and \(a_{i}\) is the crack initiation depth.
## 2 Two-Way Laser Transponders for Interplanetary Ranging and Time Transfer  
Given the weak lunar return signals and the resultant dependence of successful lunar ranging on large telescopes, the present author suggested the use of two-way laser transponders to extend both precision ranging and time transfer capabilities over interplanetary distances [6]. As with lasercom systems, the signal strength varies as R\({}^{-2}\) instead of R\({}^{-4}\) as in SLR to passive reflectors. Figure 2 illustrates the manner in which range and relative time can be transferred between Earth and the spacecraft. Subsequent to this concept, range and time offsets between an Earth-based clock, located at the 1.2 m telescope at NASA Goddard Space Flight Center, and the Mercury Laser Altimeter (MLA) mission, enroute to Mercury at a distance of roughly 22 million km, with an estimated 20 cm range accuracy [7]. A few months later, over 500 laser pulses transmitted by the same 1.2 m Earth station were observed by NASA's Mars Orbiter Laser Altimeter (MOLA) at a distance of 80 million km. MOLA had previously mapped the surface of Mars, but, unfortunately, the onboard laser was no longer operational, and a return laser signal was not possible [8]. With the exception of a wideband optical transceiver operating in the MHz to GHz range, the hardware and operational requirements are similar, and the various operations can be consolidated within a single interplanetary instrument. The addition of the transceiver also permits the ability to transfer a coded value of the time at the start of the coded sequence from Earth to the interplanetary spacecraft, and vice versa, while a series of coded signals traded between  
Figure 1: Current global distribution of SLR stations in the ILRS Network.  
the two terminals can also provide the range rate, resulting in accurate atomic clock offsets, range, and range rate between the two terminals as described in Figure 2[6].
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### System Architecture  
The architecture of the MASU framework includes two separate layers (Figure 2): the _Control Tier_ and the _Sensing Tier_, which interact among themselves and define two different categories of roles. This is a crosslayer architecture that enables interactions with other layers of the computing system, and benefits from the information that other layers may provide (i.e., hardware and network information). The particular pervasive sensing applications developed over MASU are in the upper layer, and therefore they consume the services provided by the framework.  
The _Control Tier_ provides all the services required for the management and monitoring of the overall collaborative sensing activity, coordinating the tasks within the activity and also among different activities, if required. This layer is also in charge of managing the use of resources within each sensing device (MASU unit).  
This layer includes two roles: Manager and Monitor, which interact among themselves to coordinate the operation of this tier and provide the collaborative sensing services. Therefore, the Control Tier performs role selection and activation functions, and it also has control over the Sensing Tier. This layer also monitors the state of the device's hardware components, such as battery level, processor load, memory available and quality of the sensors available (hardware monitoring). Moreover, it keeps track of events related to the underlying network infrastructure, including topological changes, network traffic, congestion and delay (network monitoring). Such monitoring allows the MASU units to be context-aware and make appropriate role selection and activation decisions, as well as to adapt to the unpredictability of dynamic environments, activating or deactivating roles accordingly.  
Figure 2: Architecture of the MASU Framework.  
The _Sensing Tier_, which is in charge of performing the specific data sensing and sharing tasks, has four distinct roles: Producer, Consumer, Storage and Relay. These roles interact among themselves enabling the provision of complex data sensing services.
# Forecasting Irregular Seasonal Power Consumption.
## 1 Introduction
Regularization techniques have also been applied to prevent over- and under-fitting issues, based on a Least Absolute Shrinkage and Selection Operator (LASSO) [32], and have been applied to short-term load forecasting models based on multiple linear regression [33]. Banded regularization is also used to estimate parameters without overfitting in autoregressive models [34]. Newer methods use an anti-leakage least-squares spectral analysis (ALLSSA) to simultaneously estimate the trend and seasonal components before making a regularization and make forecasts [35]. The ALLSSA method determines the statistically significance of the components preventing under- and over-fitting issues. The least-squares wavelet analysis (LSWA) is a natural extension of the least-squares spectral analysis and allows the forecaster to obtain spectrograms for equally and unequally spaced time series and identify statistically significant peaks in the time series [36]. One common feature of most demand-related time series is their strong seasonality components [37]. In some cases, the electricity demanded by a process could present an irregular seasonal component that seriously distorts the behavior of the series in a way that the models cannot deal with. The zinc hot-dip galvanizing process is a process that is widely used in the automotive sector to protect steel against corrosion [38,39]. It requires an enormous consumption of energy, and this has a direct impact on companies' income statements. However, the process also significantly affects energy distribution companies, since they must foresee the instantaneous consumption in their lines in order to ensure the distribution of energy both for the process and for the other consumers. A characteristic of the demand in this process is the presence of seasonal patterns that resemble seasonality but, because of their irregular behavior, are difficult to assimilate to seasonality. The structure shown by the series in this study means that it is more suitable to work with time series models rather than frequency or signal analysis. We have therefore considered it convenient to preferably use traditional time series models with seasonality. In this article, we present several solutions to this problem based on the use of ARIMA models, multiple seasonal Holt-Winters models with and without discrete interval moving seasonalities (DIMS), state space models, and neural network models. To verify the effectiveness of the techniques described, they are applied to the industrial process of hot-dip galvanizing. The article is organized as follows: Section 2 conducts a review of the forecasting methods as well as an explanation of the production process; Section 3 demonstrates the results and their analysis; Section 4 discusses the results; and finally, in Section 5, the conclusions are summarized.
# Age and Source of the Jinjingzui Skarn-Type Gold Deposit in the Southeastern Hubei Province, China
## 5 Discussion  
### Petrogenesis  
The samples obtained from the Jinjingzui deposit show low SiO\({}_{2}\) and high TiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), FeO\({}^{\rm T}\) and MgO concentrations, enrichment of LILE (Rb, Ba) and depletion of HFSE (U, Nb, Ti). The standard curve of chondrite and rare earth elements showed a right-leaning chondrite distribution curve with a strong differentiation of light REE (LREE) and heavy REE (HREE) with barely perceptible Eu anomalies (Figure 9a,b). All these geochemical characteristics are analogous to those of the coeval Echeng diorite within the ore cluster, and both display diagnostic signatures of arc-related magmatic rocks. This similarity can likely be attributed to the remote influence of the Mesozoic Pacific Plate, which induced lithospheric delamination and subsequently reactivated the source region containing Proterozoic arc magmatic materials, ultimately leading to the formation of ore-bearing magmas [20]. Based on the (Y + Nb)-Rb diagrams, the Jinjingzui deposit diorite samples lie in the volcanic arc-syn collision area (Figure 11a). The samples in this study showed a high concentration of Y (26.88-27.93 ppm) and low Sr/Yb ratio (38.93-44.43), with all data points falling into the normal island arc area in the Y-Sr/Y plots (Figure 11b).  
Jinjingzui diorite has a uniform distribution of \(\epsilon_{\rm Nd(t)}\) and \(\epsilon_{\rm Hf(t)}\), ranging from \(-5.24\) to \(-4.76\) and \(-12.2\) to \(-6.5\)[14]. These results are similar to Cu-Au skarn deposit ore-related intrusions [1, 7, 28]. These isotope characteristics indicate that these intrusions likely originated from the partial melting of an enriched mantle source, followed by crustal assimilation during the magmatic evolution. However, the zircon CL image demonstrates a clear ring of magmatic zircon oscillations, without core-mantle structure and ancient inherited zircons, suggesting a slight mixing of continental crust [14]. In addition, the Mg\({}^{\rm\#}\) value of the Jinjingzui diorite (30.18-39.17) is significantly lower than that of mantle peridotite (70-80) [29]. The composition does not represent a melt in equilibrium with the original peridotite mineral assemblages. Therefore, it can be assumed that the diorite from the Jinjingzui deposit was not formed by the partial melting of mantle peridotite at low temperatures but originated from partial melting within an enriched mantle source region, resulting in the production of basaltic magma, which subsequently underwent magmatic evolution and differentiation.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 6 Discussion
### Cosine Method  
The Cosine method is penalized when there are few or no biomedical words in the text. This happens for the MIMIC-IV dataset, where it is found that many codes and their biomedical words do not appear in the EHR, making it hard to reach high similarity with the Cosine method. It is also very sensitive to words, such as \"patient\", which are semantically empty for the purpose. These drawbacks were mitigated using strategies such as a blacklist of words, multiple runs and bucket category selection, leading to a novel algorithm called the Improved Cosine method.
Sensors_**2015**, _15_, 10221-10254; doi:10.3390/s150510221  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Data Acquisition Protocol for a Reactive Wireless Sensor Network Monitoring Application**  
**[PERSON] 1,2,*, [PERSON] 3, [PERSON] 1 and [PERSON] 1**  
\({}^{1}\) Information Science Department, University of Otago, Dunedin 9016, New Zealand;  
E-Mails: [EMAIL_ADDRESS] (J.D.D.); [EMAIL_ADDRESS] (M.K.P.)  
\({}^{2}\) National Inter-University Consortium for Telecommunications (CNIT), Pisa 56124, Italy  
\({}^{3}\) Department of Industrial Engineering (DII), University of Trento, Povo I-38123, Italy;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-342-8253-933.  
Academic Editor: [PERSON]  
_Received: 12 January 2015 / Accepted: 21 April 2015 / Published: 30 April 2015_  
**Abstract:** Limiting energy consumption is one of the primary aims for most real-world deployments of wireless sensor networks. Unfortunately, attempts to optimize energy efficiency are often in conflict with the demand for network reactiveness to transmit urgent messages. In this article, we propose SWIFTNET: a reactive data acquisition scheme. It is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes. Particularly, it combines compressed sensing, data prediction and adaptive sampling strategies. We show how this approach dramatically reduces the amount of unnecessary data transmission in the deployment for environmental monitoring and surveillance networks. SWIFTNET targets any monitoring applications that require high reactiveness with aggressive data collection and transmission. To test the performance of this method, we present a real-world testbed for a wildfire monitoring as a use-case. The results from our in-house deployment testbed of 15 nodes have proven to be favorable. On average, over 50% communication reduction when compared with a default adaptive prediction method is achieved without any loss in accuracy. In addition, SWIFTNET is able to guarantee reactiveness by adjusting the sampling interval from 5 min up to 15 s in our application domain.  
keywords: wireless sensor network; communication reduction; compressive sensing; data acquisition protocol; environmental monitoring networks +
Footnote †: journal: Journal of the Acoustical Society of America
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 6 Evaluation  
This section reports on how our system is performed in real-world environments. We demonstrate the ranging capabilities of LoRaWAPS in Section 6.1 and the positioning capabilities of LoRaWAPS in Section 6.2. During actual experiments, we use smartphones of Honor 30 Pro+ or OPPO Ace, with operating systems of Harmony OS or Android 12, and processors of Huawei Kirin 990 or Snapdragon 865. The mobile devices are mounted on the surface of buildings or on tripods. The height of them being mounted is about 1.7 m.  
Figure 9: Anchor deployment location selection and area coverage capability: (**a**) Alternative deployment locations; (**b**) Optimal placement of anchors; (**c**) Location capability coverage.