esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Statistical Analysis
### Surface Roughness  
The arithmetic mean height (\(Sa\)) parameter, which is an areal surface roughness parameter, expresses, as an absolute value, the difference in height of each point measured compared to the arithmetical mean of the surface. The \(Sa\) parameter has become one of the more popular methods for characterizing surface roughness of AM manufacturing parts because of the tendency of AM parts to have highly irregular and rough surfaces. Therefore, this parameter was chosen as the first quality mark for further process analysis.  
The results presented herein in Figures 6 and 7 are for test pieces with an overhang angle of 45\({}^{\circ}\) and 35\({}^{\circ}\), respectively.  
Figure 5: Visual observation of the down-facing surfaces shows significant irregularities of the surface in the 35\({}^{\circ}\) test piece (**right**) when compared to the 45\({}^{\circ}\) test piece (**left**).  
Figure 6: Replicate plot for 45\({}^{\circ}\) down-facing surfaces: Surface roughness (\(Sa\)) for each trial number.  
The replicate plot shown in Figures 6 and 7 depicts the process as a stable process, thereby maximizing the possibility for developing a process model using these experimental results. By plotting the interaction effects of the various parameters on the obtained \(Sa\) as seen in Figures 8 and 9, clear trends can be seen thereby allowing us to draw conclusions.
## 3 Nanoparticles in EOR  
Due to the continuous growth in the global energy demand, new technology for finding hydrocarbon sources or for enhancing oil recovery needs to be developed [1]. The fact that finding new sources of hydrocarbon is difficult and that most oil fields have 60% to 70% of non-producible hydrocarbon in place drives the development of novel technologies for EOR [4]. The use of NP in EOR has several advantages such as (1) good stability, because the surface force is more dominant than gravity; (2) the size and shape of NPs can be facilely modified during the manufacturing process with high flexibility; (3) surface chemical properties of NPs can be easily tailored to be hydrophilic or hydrophobic; and (4) the most often applied EOR NP is silica, of which 99.8% is silicon dioxide, which is in turn a dominant substance in sandstone and thus environmentally friendly [79]. So far, the three most common forms of NPs used in EOR can be grouped into nanofluid, nanoemulsion, and nano-foam categories. By definition, nanofluid is a fluid containing dispersed NPs, forming a colloidal suspension. Nanoemulsion is a biphasic dispersion of two immiscible liquids: either water in oil (W/O) or oil in water (O/W) droplets stabilized by NPs, also known as Pickering emulsion. For the case of NP-stabilized gas bubbles in a liquid, it is called nano-foam. In contrast to the nanofluid flooding, nanoemulsion and nano-foam often combine the use of existing EOR methods and NPs. The three major types of NPs for nanoemulsion and nano-foam are functionalized NPs synergized with surfactants, in-situ surface-activated NPs, and ex-situ surface-activated NPs. The most prevailing such NPs are silica-based, of which the surface hydroxyl content can be tuned to make them either hydrophilic or hydrophobic, leading to O/W and W/O emulsions, respectively. Numerous laboratory experiments on nano-EOR have been carried out in the past decade [46]. Most research interests have been aroused by nanofluid flooding. Therefore, Table 2 summarizes the representative experimental research on nanofluid flooding that has been reported. Various types of NPs such as fullerenes, graphene, CNTs, polymers, and metallic and metal oxides have been studied, all presenting promising results in improved oil recovery. In addition, Table 3 details reported case studies using nanoemulsion and nano-foam, including the surface-coating, influencing factor, and resulting emulsion/foam type. Different types of nanomaterials have been proven to have unique properties that give various effects towards the porous media and the fluids [80]. Therefore, the following section will elaborate upon the specific nanomaterials that have been explored for EOR.
# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 3 Materials and Methods
### Language Sequence Processing  
Bidirectional Encoder Representations from Transformers (BERT) [26] is a pre-trained language model proposed by Google based on the Transformer architecture. The bidirectional encoder learns contextual information, which is widely used for natural language processing tasks. The structure of BERT is shown in Figure 3.  
Figure 3: BERT structure diagram.  
Figure 2: Text parsing method implementation framework.  
BERT is optimized by two pre-training tasks, the Masked Language Model (MLM) and Next Sentence Prediction (NSP). MLM randomly selects 15% of the words in the input sequence, replaces them with a special [MASK] tag, and then asks the model to predict these masked words. NSP provides sentences in pairs in the input sequence and requires the model to predict whether the second sentence is the subsequent sentence of the first sentence, which can help the model to understand the relationship and logic at the sentence level.  
In order to better identify sentences in the training process, the input of BERT is divided into two parts: text sequence and position embedding. The text sequence processed by BERT is separated by a special separator [SEP] between two sentences, and each word input is embedded in each tag by Token Embeddings (meaning of each word), Segment Embeddings (embedding of the sentence to which the distinction belongs) and Position Embeddings (indicating the position of words in the sequence). Finally, the Embedding representation of BERT is shown in Figure 4:  
BERT is based on the Encoder part of Transformer, and each Encoder consists of an attention mechanism and feed-forward neural network. The final output of BERT is that each word goes through multiple layers of Transformer to generate a context-sensitive representation.  
\[Attention(Q,K,V)=softmax\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V \tag{1}\]  
\[MultiHead(Q,K,V)=Concat(head_{1},head_{2},\cdots,head_{h})W^{O} \tag{2}\]  
\[FFN(x)=ReLU(xW_{1}+b_{1})W_{2}+b_{2} \tag{3}\]  
where \(Q\) (query), \(K\) (key), and \(V\) (value) are vectors obtained from the input by different linear transformations; \(d_{k}\) is the dimension of the key vector; \(W^{O}\) is a trainable parameter matrix; \(W_{1},~{}W_{2},~{}b_{1},~{}b_{2}\) are trainable parameters.
## 3 Results  
Before the clinical study, we calculated the FRE and TRE to evaluate the accuracy of the developed point-to-point registration method at the points (divots) on the phantom. We used six of the points for registration and the other forty-one points for evaluation. As a result, the mean FRE value of 0.25 mm at the initial registration was decreased to 0.14 mm by applying the fine registration after excluding the point with the highest FRE. Then, we calculated the TRE value of 0.82 \(\pm\) 0.50 mm for the developed registration method (Table 1).  
During endoscopic sinus surgery, the intraoperative position of the pointing tool tip was tracked continuously by the electromagnetic tracking system and visualized simultaneously on the multi-planar and 3D volumetric views. Our method also provided the slab volume view to the surgeon to accurately recognize the positional relationship between the pointing tool tip and the adjacent anatomical structures of the patient during the surgery (Figure 2). As a result, the 3D position of the pointing tool tip was visualized with respect to the nasal cavity and sinuses in real time (Figure 5).  
Figure 5: Clinical comparative study using developed and commercialized image-guided endoscopic sinus surgery (IGESS) systems. Measurement of the positions of the anatomical landmarks (middle turbinate (**a,b**) and posterior ethmoid (**c,d**) in the nasal cavity with the developed (**a,c**) and the commercialized (**b,d**) systems.  
We compared the two types of navigation systems, which were the developed system and the commercialized system, by clinical study. The average registration times were 36.04 \(\pm\) 4.7 and 89.35 \(\pm\) 26.1 s for the developed and commercialized systems, respectively (\(p<0.05\)). The image loading time of the developed system was also shorter than that of the commercialized system (\(p<0.05\)) (Table 2). Although the average FRE value of the developed system was higher than that of the commercialized system (\(p<0.05\)) (Table 2), the average accuracy score of the developed system was not significantly different from that of the commercialized system (\(p>0.05\)) (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Points (Divots)**} & \multicolumn{4}{c}{**Target Registration Error (mm)**} \\  & **x** & **y** & **z** & **RMS** \\ \hline  
\hline Mean & 0.48 \(\pm\) 0.50 & 0.34 \(\pm\) 0.23 & 0.41 \(\pm\) 0.33 & 0.82 \(\pm\) 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Absolute difference in x-, y-, and z-axes, and root mean square (RMS) difference between the true and measured positions of the points on the phantom after registration.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 4 Measurement Scenarios and Data Preparation
### Feature Extraction  
In total, twelve features were extracted from the DWM1000 UWB modules manufactured by Decawave [34] using the configuration described in Table 1. The extracted features were based on the typical parameters that are necessary in the traditional NLOS identification methods as expressed in Equations (1)-(3). This meant that no extra burden was involved by using these extracted features in our ML application. For the sake of completeness, two more features namely standard and maximum noises supported by the DW1000 module were included in the feature extraction of our evaluation. Therefore, the full features extracted and saved during the experimental evaluation were:  
1. the reported measured distance
2. the compound amplitudes of multiple harmonics in the FP signal
3. the amplitude of the first harmonic in the FP signal
4. the amplitude of the second harmonic in the FP signal the amplitude of the third harmonic in the FP signal
* the amplitude of the channel impulse response (CIR)
* the preamble accumulation count reported in the DW1000 chip module
* the estimated FP power level using (1)
* the estimated RX power level using (2)
* the difference between the FP and RX power level using (3)
* the standard noise reported in the DW1000 chip module
* the maximum noise reported in the DW1000 chip module  
Regarding the above-mentioned 12 features, we would like to mention that the feature extraction was solely based on the DW1000 chip as the UWB hardware, which was manufactured by Decawave.
## 2 Materials and Methods
### Characterization Methods  
Transmission electron microscopy (TEM) images were obtained on a JEOL JEM-100 CX II (JEOL, Tokyo, Japan) at 80 kV with a MegaView G3 camera (Emsis, Munster, Germany) running with a Radius 2.1 software (Emsis). A drop of water suspension of Au/ AgNPs@CDs was deposited on a carbon-coated copper grid and waited for water to evaporate, the excess water was removed by filter paper after 10 min.  
The UV-Vis absorption spectra of colloidal solutions were recorded using a T92+ UV-Vis Spectrophotometer (PG INTRUMENTS, Lutterworth, UK) and using standard quartz cells at room temperature, over a spectral range between 300-800 nm for AgNPs and 400-900 nm for AuNPs and a spectral resolution of 2 nm. Usually, the samples were diluted to have absorbance values within the measuring range of the device.  
Hydrodynamic size and zeta potential measurements of samples were determined using a Zetasizer Nano ZS90 (Malvern Instruments, Worcestershire, UK) in a 90\({}^{\circ}\) configuration, at room temperature.  
Fourier transform infrared (FT-IR) spectra were recorded on a TENSOR II instrument (Bruker Optics Inc., Billerica, MA, USA) in attenuated total reflectance mode, using the platinum attenuated total reflectance (ATR) accessory with a single reflection diamond ATR. The spectra were recorded with a resolution of 4 cm\({}^{-1}\), with 16 scans per sample between 400 and 4000 cm\({}^{-1}\).  
All SERS spectra have been acquired in back-scattering geometry in the 200-2000 cm\({}^{-1}\) range using a DeltaNu Advantage spectrometer (DeltaNu, Laramie, WY, USA) equipped with a laser diode emitting at 785 nm. The laser power was 100 mW and the spectral resolution was 5 cm\({}^{-1}\). All SERS spectra were recorded in 1 mL glass vials filled with 450 mL of colloid and 50 mL of the analyte. Each SERS spectrum is the average of five recordings with an acquisition time of 20 s.
# An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning
## 2 Principle of the Improved PKO Algorithm
### Time Complexity  
Time complexity is a crucial metric for assessing algorithm performance and computational cost [26]. In the case of the IPKO algorithm, its time complexity is analyzed from both macro and micro perspectives. From a macro perspective, assuming \(T\) as the maximum number of iterations, \(D\) as the dimension, and \(M\) as the population size. Then, according to the time complexity calculation formula for intelligent optimization algorithms, the time complexity of PKO is \(O_{1}=O(T\times D\times M)\). For the IPKO specifically, since its structure remains unchanged, its time complexity is also \(O_{2}=O(T\times D\times M)\). It is evident that there has been no increase in time complexity from a macro perspective.  
On the other hand, from a micro perspective, the time complexity of the Pied King-fisher's perching phase, improved by refracted opposite learning, is \(O_{3}=O(T\times D\times M)\). The time complexity of the development phase, improved by the variable helix sine-cosine search algorithm, is \(O_{4}=O(T\times D\times M)\). Additionally, the time complexity of the algorithm enhanced using the Cauchy mutation is \(O_{5}=O(T\times D\times M)\). Therefore, from a micro perspective, the time complexity of the improved algorithm increases by \(O3+O4+O5=O(T\times D\times M)\), but the increase in each step does not cause a change in the order of magnitude; therefore, the total time complexity remains \(O(T\times D\times M)\). In  
Figure 5: The flowchart of the IPKO algorithm.  
summary, from both macro and micro perspectives alike, there are no changes observed in terms of computational cost. Therefore, the improved algorithm does not increase computational cost, further proving the feasibility of IPKO.  
In practical applications, although the IPKO algorithm has the same complexity as PKO, it has stronger exploration and balancing capabilities under the sine-cosine strategy and refraction reverse learning mechanism.
# The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
## 1 Introduction
In a similar context as in the NEWA project, mesoscale model data were correlated with the measurement data, not just from one offshore met mast but several of those and a number of vertically profiling lidar devices installed on offshore platforms within the NORSEWInD project [10]. The activities within this project proved the usefulness of Doppler lidar technology for assessing offshore wind profiles provided a sufficient data availability is obtained. Like NWP models, the nowadays fully commercially available lidar devices can provide wind velocity data at a number of height levels more or less at the same point in time. This allows a direct assessment of the wind profile  
Figure 1: Minimum coverage of the New European Wind Atlas and sites of experiments. The minimum onshore coverage is shown in light green, the NEWA partner countries in a darker lime-green, offshore coverage in light blue, and experimental sites in red. The red line marks the route in the Ferry Lidar Experiment. (Graphic reproduced from Reference [2]). for up to several hundreds of meters, with the range depending on the explicit device specifications. With the Ferry Lidar Experiment, the concept of spatially distributed wind profile measurements is further developed. The profiling instrument moves with the ship and covers a distance that is comparable to a typical mesoscale dimension. The article pursues the following three objectives:  
* the ship lidar technology applied in the NEWA Ferry Lidar Experiment is to be introduced;
* the data produced by applying this technology is to be described in detail, such that the generated dataset can be used in future studies;
* and finally, a first comparison of the Ferry Lidar data to mesoscale model data is to be presented and discussed. Approaching these objectives in the suggested order, the article is structured as follows. Following this introductory section, the ship lidar technology is presented in Section 2. The Ferry Lidar campaign within the NEWA project and the processed dataset from this campaign are described in Sections 3 and 4, respectively. In Section 5, we show an initial comparison between the recorded measurements and simulated data using an NWP model. An outlook in Section 6 and the conclusions in Section 7 complete the article.
## 5 Conclusions  
### Acoustic Emission (AE) Signal Analysis for Rockburst Prediction  
The variation patterns of various characteristic parameters of acoustic emission signals can be used to infer the evolution of rock mass fracture activities and assess the stability status of rock masses. This, in turn, allows for the prediction of dynamic disasters such as rockbursts. Therefore, acoustic emission technology demonstrates certain effectiveness and feasibility in studying the processes of rock mass fracture instability and disaster evolution.
# Long-Lifetime Event-Driven Wireless Monitoring System for Pole-Mounted Transformers
## 6 Conclusions  
In this paper, a long-lifetime event-driven wireless monitoring system for pole-mounted transformers is proposed as a low-cost battery-powered solution. The proposed monitoring system is designed for detecting an intermittent abnormal signal in a pole-mounted transformer and transmits measurements of physical quantities on an event basis. To increase the lifetime of the battery, the system includes an EC, which reduces energy consumption by minimizing the normal operating time of each block. The proposed event-driven system increased the battery lifetime by up to two orders of magnitude compared to the conventional system. In addition, an RMS converter and peak detector maximize the SNR and reduce the required sampling frequency. The proposed system substantially reduces operation and maintenance costs and enables low-cost diagnosis of components in pole-mounted transformers.  
Conceptualization, S.S.K. and Y.S.K.; methodology, S.S.K.; formal analysis, S.S.K.; investigation, S.S.K.; data curation, Y.S.K.; writing--original draft preparation, S.S.K. and Y.S.K.; writing--review and editing, Y.C.I. and Y.S.K.; visualization, S.S.K. and Y.C.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article.  
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), Ministry of Education, under Grant NRF-2016R1D1A1B04935233 and in part by Korea Electric Power Corporation (KEPCO) under Grant R17 XH04.  
The authors declare no conflict of interest.
# Channel Modeling and Analysis for the Sensor Network Inside Tower Buildings
## 3 Channel Clustering and Performance Analysis  
### ML-Based Channel Clustering  
It is a key step to accurately cluster the propagation rays to several clusters for the cluster-based channel model. The existing k-means algorithm uses MCD instead of the traditional Euclidean distance to cluster the channel data. However, the different channel parameter has different effect weight on the MCD. In this paper, an improved k-means algorithm is used for clustering where the weight factors are introduced into the MCD as  
\[\begin{split}& MCD(r_{i},r_{j})=\left(\left\|\xi_{\text{ToA}} \cdot MCD_{\text{ToA}}(r_{i},r_{j})\right\|^{2}\right.\\ &+\left\|\xi_{\text{AAoA}}\cdot MCD_{\text{AAoA}}(r_{i},r_{j}) \right\|^{2}+\left\|\xi_{\text{EAoA}}\cdot MCD_{\text{EAoA}}(r_{i},r_{j}) \right\|^{2}\right)^{\frac{1}{2}}\end{split} \tag{3}\]  
Figure 1: Propagation rays and clusters inside the tower buildings.  
where \(\left\|MCD_{\text{ToA}}(r_{i},r_{j})\right\|^{2}\), \(\left\|MCD_{\text{AAoA}}(r_{i},r_{j})\right\|^{2}\), and \(\left\|MCD_{\text{EAoA}}(r_{i},r_{j})\right\|^{2}\) denote the distance of ToA, AAoA, and EAoA of any two rays \(r_{i}\) and \(r_{j}\), respectively, and \(\tilde{\xi}_{\text{ToA}}\), \(\tilde{\xi}_{\text{AAoA}}\), and \(\tilde{\xi}_{\text{EAoA}}\) denote the weight coefficients of ToA, AAoA, and EAoA.  
Moreover, it should be mentioned that the number of clusters \(k\) needs to be determined in advance for the traditional k-means algorithm. However, it is hard to manually determine the cluster number according to the complicated channel data. In this paper, the elbow method is used to determine the optimal number of clusters automatically [36]. The principle of elbow method is that with the increase of the number of clusters \(k\), the value of SSE will follow the decreasing tendency. Before \(k\) reaches to the optimal number of clusters, SSE changes greatly with the increase of \(k\). When SSE changes with the increase of \(k\), i.e., the slope of SSE is low enough, the first corresponding \(k\) is chose as the optimal cluster number (also called elbow point). The SSE is defined as  
\[SSE(k)=\sum\limits_{i=1}^{k}\sum\limits_{m=1}^{M(i)}MCD(r_{m},c_{i}) \tag{4}\]  
where \(M(i)\) means there are \(M\) rays in \(k\)th cluster, \(r_{m}\) and \(c_{i}\) means \(m\)th ray in \(k\)th cluster and the centroid of this cluster, respectively. The centroid of this cluster \(c_{i}\) can be calculated as  
\[c_{i}=\frac{1}{M(k)}\sum\limits_{r_{i}\in R_{i}}r_{i}. \tag{5}\]
## 3 CNQF Architecture
### Measurement and Monitoring Subsystem  
The MMS enables _closed-loop, adaptive, measurement-based control_ within the CNQF architecture. MMS consists of distributed Network Monitors (NMs) which interface with the Policy Enforcement Points to provide _active_ and _passive_ measurement capabilities. In order to mitigate potentially excessive monitoring data interchange across the network, each NM is designed to collect and process measurement data locally and can then be invoked via a central measurement and monitoring element (CMM), to provide averages/summaries or asynchronous instantaneous measurement data. CMM could provide high level summaries that may be used to gauge network-wide health status through visualization interfaces in a centralized management station. MMS basically provides measurement and monitoring services to the other CNQF subsystems.