esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 2 Study Area  
Taeyoung EMC's Samdo Mine, located in Dogye-eup, Samcheok-si, Gangwon-do, was selected as the study area (Figure 1). The Samdo Mine was established in April 1993 by registering six mining rights. It is an underground mine that produces high-quality limestone for iron and steel production, and the board-and-pillar method with modernized facilities was applied. At the Samdo Mine, the main mining target is the Pungchon Formation limestone, where white to grayish-white microcrystalline calcite-type high-grade limestone deposits form along the homogeneous limestone zone. It was formed by the alteration action of tidal water (100-200 \({}^{\circ}\)C) during the Jurassic period (189-205 Ma) [22]. About 1.5 million tons of limestone annually are continuously mined at the Samdo Mine. Particularly, because the equipment operation is repeated and blasting operations are performed several times daily, we must monitor the amount of vibration and dust in the mine. Currently, the Samdo Mine operates a ventilation system that uses both a method using fans and a natural ventilation shaft. This study applied the developed technology to the area around the blasting site, which is approximately 2 km from the mine adit.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wireless Technical** & \multirow{2}{*}{**ZigBee/802.15.4**} & **Bluetooth** & **Wi-Fi** & **GSM 1/GPRS 2** \\ \hline System resource requirements & 4 KB–32 KB & \textgreater{}250 KB & \textgreater{}1 MB & \textgreater{}16 MB \\ Power waste & Low & Common & Common & High \\ Battery life (days) & 100–1000+ & 1–7 & 1–5 & 1–7 \\ Number of nodes in local network & 65,000+ & 7 & 30 & 1000+ \\ Data rate (Kbps) & 20–250 & 1000 & 11,000+ & 64–128 \\ Communication distance & 1–3200 & 1–10+ & 1–100 & 1000+ \\  & Reliability, low cost, low power consumption & Low cost and easy to operate & High speed and adaptability & Good transmission quality and large coverage range \\  & Wireless detection and control & Short-distance & transmissions such as Web access and video. & Voice and data transmission \\ \hline \hline \end{tabular} \({}^{1}\) GSM: global system for mobile communications. 2 GPRS: general packet radio service.  
\end{table}
Table 1: Comparison of wireless communication technologies (modified from [PERSON] and [PERSON] [21]).
## 2 Dataset and Methods  
### GNSS-R Airborne Data  
#### 2.1.1 Instrument  
The airborne GNSS-R data presented in this study were recorded with the GLORI instrument during the GLORI'2015 Campaign [30]. The GNSS-R GLORI instrument, developed by the CESBIO, is a polarimetric device with four channels. Two antennas are connected to the receiver: the first of these is pointed towards the zenith, to receive the direct RHCP (right-hand circular polarization) signals transmitted by GNSS satellites; the second 'nadir' antenna is pointed vertically downwards, to receive the signals reflected from the ground in two polarizations (RHCP and left-hand circular polarization (LHCP)). A transfer switch is used to perform the relative calibration of these polarimetric measurements. The nadir antenna is qualified in terms of its cross-polarization isolation between the LHCP and RHCP signals. The cross-polarization isolation is better than 15 dB up to 45\({}^{\circ}\) from boresight (45\({}^{\circ}\) elevation) for both LHCP and RHCP ports. Below 15 dB of cross-polarization, the performance is considered insufficient to perform polarimetric measurements [30].
## 3 Results and Discussion
### Rejection Factor  
The rejection factor characterizes the possibility of the membrane to retain the compounds presented in the feed solution. Figure 3 presents the rejection of the main components at different VRRs. The highest values were obtained for fat, followed by proteins and ash. This could be explained by the phenomenon that ultrafiltration membranes retain the high molecular weight compounds, while water and the low molecular weight compounds pass through the membrane [38]. Milk fat is the highest molecular weight substance in milk and dairy products, followed by proteins, lactose, and mineral substances. The increase in the VRR led to a rise in the rejection of different buttermilk components. The rejection of ash increased by 1.49 times when the VRR increased from 2 to 5, followed by dry matter by 1.15 times, proteins by 1.05 times, and fat by 1.02 times. The correlation between the rejection and VRR can be described by the power models shown in Table 2. Many authors found that the dry matter, total proteins, fat, and ash in different types of milk increased when the volume reduction increased during ultrafiltration [37; 39; 40]. The same trend is now observed when sweet buttermilk is subjected to ultrafiltration. The authors in [37] found that the molecular weight cut-off of the membrane affected the milk compound's change during ultrafiltration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Buttermilk** & **VRR 2** & **VRR 3** & **VRR 4** & **VRR 5** \\ \hline pH & 6.29 \(\pm\) 0.05  & 6.24 \(\pm\) 0.03  & 6.16 \(\pm\) 0.025  & 6.12 \(\pm\) 0.04  & 5.98 \(\pm\) 0.03  \\ Titatable acidity (\% lactic acid) & 0.180 \(\pm\) 0.015  & 0.270 \(\pm\) 0.02  & 0.405 \(\pm\) 0.008  & 0.495 \(\pm\) 0.011  & 0.675 \(\pm\) 0.010  \\ \hline \hline \end{tabular} Different lowercase letters (a, b, c, d, e) show a significant difference between acidity at different volume reduction ratios (\(p<\) 0.05).  
\end{table}
Table 1: Tittatable and active (pH) acidity of retentates at different volume reduction ratios.  
Figure 3: Rejection at different volume reduction ratios during ultrafiltration of sweet buttermilk.
# Scale-Adaptive Simulation of Unsteady Cavitation Around a Naca66 Hydrofoil
## 2 Description of Numerical Models
### Zwart-Gerber-Belamri Cavitation Model  
The Zwart-Gerber-Belamri Cavitation model (ZGB) was developed to be implemented into the CFX-5 software for the numerical simulation of unsteady cavitation [29]. Considering good results of previous studies [30; 31], the ZGB model has been implemented in OpenFOAM version 2.2.x to study unsteady cloud cavitation around hydrofoils [4; 14]. In the present study, the ZGB model has been updated and uploaded for OpenFOAM version 4 based on studies of [PERSON] et al. [4]. The vapor volume fraction \(\alpha\), the density \(\rho\), and the dynamic viscosity \(\mu\) of the vapor-water mixture are defined as follows:  
\[\alpha=\frac{V_{\
u}}{V}, \tag{9}\]\[\rho = \left(1-\alpha\right)\rho_{1}+\alpha\rho_{\rm v}, \tag{10}\] \[\mu = \left(1-\alpha\right)\mu_{1}+\alpha\mu_{\rm v}, \tag{11}\]  
where \(V\) is the total volume, \({}_{1}\) and \({}_{\rm v}\) are subscripts for liquid and vapor, respectively. Including \(\alpha\) in Equation (1) for the effect of the phase transformation, we have  
\[\frac{\partial\left(\alpha\rho_{\rm v}\right)}{\partial t}+\frac{\partial \left(\alpha\rho_{\rm v}\overline{u}_{i}\right)}{\partial x_{i}}=\dot{m}^{+}, \tag{12}\]  
\[\frac{\partial\left((1-\alpha)\rho_{1}\right)}{\partial t}+\frac{\partial \left((1-\alpha)\rho_{1}\overline{u}_{i}\right)}{\partial x_{i}}=\dot{m}^{-}, \tag{13}\]  
where \(\dot{m}\) is the inter-phase mass transfer rate per unit volume, and the result of \(\dot{m}^{+}+\dot{m}^{-}\) is equal to 0. Finally, the ZGB model indicated in Equation (14) was written in C++ code and implemented in OpenFOAM.  
\[\dot{m}=\left\{\begin{array}{ll}\dot{m}^{+}=&F_{\rm v}\frac{3r_{\rm nuc.} \left(1-\alpha\right)\rho_{\rm v}}{R_{\rm B}}\sqrt{\frac{2}{3}\left(\frac{P_{ \rm v}-p}{\rho_{1}}\right)}&\mbox{if}\quad p<p_{\rm v}\\ \\ \dot{m}^{-}=&-F_{\rm c}\frac{3\alpha\rho_{\rm v}}{R_{\rm B}}\sqrt{\frac{2}{3} \left(\frac{p-p_{\rm v}}{\rho_{1}}\right)}&\mbox{if}\quad p>p_{\rm v}\end{array} \right., \tag{14}\]  
\(F_{\rm v}\) and \(F_{\rm c}\) are the calibration constants for vaporization and condensation, \(r_{\rm nuc.}\) is the nucleation site fraction and \(R_{\rm B}\) is the typical bubble size in water [4]. Furthermore, the model was optimized using \(F_{\rm v}=300\), \(F_{\rm c}=0.03\) and \(r_{\rm nuc.}=5\times 10^{-6}\), which are based on [PERSON] et al. [32].
## 4 Experimental Results and Analysis  
### Data Set Introduction  
The data set used in the experiment in this paper is selected from the relevant data for Tianjin Metro Line 2 for a total of 396 days from 1 January 2023 to 31 January 2024. It covers a variety of types of data, as follows: Passenger flow data were taken from detailed records for the Tianjin Metro Line 2 passenger flow during the study period. For the weather data, weather conditions are divided into three categories, sunny, rainy, and snowy, in order to reflect the possible impact of different weather conditions on subway passenger flow. In the time dimension setting, the target prediction period is determined to be 5:30-24:00 daily. Moreover, 30 min is selected as the length unit of a time slice. According to this division, 37 time slices are generated every day (because the total duration from 5:30 to 24:00 is 18.5 h, with one time slice every 30 min; that is, 18.5 \(\times\) 2 = 37 time slices). Thus, in the entire 396-day period, there are a total of 37 time slices/day \(\times\) 396 days = 14,652 time slices. For each time slice, we further determine the corresponding multi-faceted information regarding the passenger flow from the urban area to the airport; the specific situation of the passenger flow in this direction is clarified. For the weather information, the weather conditions corresponding to each time slice are included, whether sunny, rainy, or snowy. The time period information is divided into 30 min units, such as 19:30-20:00, for specific time period information. It is clearly marked whether the date of each time slice is a working day or a non-working day, so as to analyze the impact of different date types on passenger flow. According to the usual law of subway passenger flow, it is distinguished whether each time slice belongs to the peak period or the non-peak period. It is also clear whether the date of each time slice is a holiday, allowing us to explore the impact of holiday factors on passenger flow and related conditions.
# An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0
## 3 Industry 4.0 Safety and Exposure Assessment Tools
### Occupational Safety Technologies
On the other hand, smart sensors and detection systems, as well as the wearable devices, are administrative controls offering risk awareness to the operators. Coupled with ML algorithms and being part of the IoT, they can be used as surveillance tools to monitor work patterns and give instructions [61]. Additional training tools are the VR/AR for safety coordination, induction training and providing instructions in the workplace. Smart PPE has been developed in the literature, whereas equipment, as both as a safety tool and a monitoring device for environmental, proximity, and biometrics data, enhances workers' safety and health [62]. Within the concept of transforming the manufacturing and workplace environments within the Industry 4.0 and Industry 5.0 concepts, as well as the transformation of occupational and operational routines in the COVID-19 post-pandemic era, digital transformation technologies and the analyzed safety assessment tool can be utilized in a variety of sectors [63]. [PERSON] et al. outlined the main driving influences for human-centric occupational health and safety assessments, including the virtual workplace and non-standard employment, thus minimizing potential exposure to hazards [64]. Additionally, climate change and the use of advanced technologies present new challenges in the safety assessment, while the use of intelligent systems presents a novel human-machine dynamic for further examination [64]. Summarizing the application of digital technologies in occupation health and safety in general, [PERSON] et al. categorized different risks in the occupational workspace, such as physical, chemical, mechanical, biological, ergonomic and psychologi  
Figure 3: Trend of number of publications between 2013 and 2023 referring to the keywords, “Industry 4.0”and “Industry 4.0” AND “Safety” (based on Web of Science platform). cal, explaining how the use of automated processes and tools could support minimizing their impact [63]. For example, in their analysis, the use of digital tools could aid in minimizing the potential chemical exposure, as well as providing a training platform for the optimization of an organization's ergonomics. Furthermore, accidents due to mechanical risks can be prevented by developing tailored AI algorithms and designing safety zones to improve company safety and sustainability systems. In this way, the digital transformation of safety systems in general can revolutionize multi-faceted occupational health and safety foundations and support accident and risk prevention systems [65].
## 3 System Modeling
### Evaluation Metrics
#### 3.3.3 Average Response Latency  
\(ARL\) is used to measure the average number of rounds experienced by responded access demands from enqueuing to dequeuing in each round. This metric is crucial for evaluating the system's response efficiency and timeliness assurance capability.  
The average response latency \(ARL_{i}\) is calculated independently for each queue. In each round, both the number of responded demands \(N_{i}\) and the accumulated response latency \(L_{i}\) in queue \(Que_{i}\) are updated. The average response latency for queue \(Que_{i}\) in the current round is obtained by computing the ratio of these two parameters.  
\[ARL_{i}=\frac{L_{i}}{N_{i}},\quad i\in\{low,medium,high\} \tag{12}\]  
The system's average response latency \(ARL\) is defined as the average of all \(ARL_{i}\) values:  
\[ARL=\frac{1}{3}\sum ARL_{i},\quad i\in\{low,medium,high\} \tag{13}\]  
\(ARL\), by accumulating the response delay of completed tasks in each queue, reflects the system's dynamic response capability and the effectiveness of response strategies under varying loads. The differences in \(ARL_{i}\) across queues provide a perspective to evaluate in situ data management strategies from the viewpoint of cumulative latency, which facilitates strategy adjustment based on feedback from completed demands. A smaller \(ARL\) indicates higher efficiency in task response and slower delay accumulation, thereby enhancing overall response rate and system performance.  
Under the static consideration of server performance limitations per round, the above three evaluation metrics quantitatively analyze the performance of data management strategies from the two core dimensions of system latency and demand response rate. This provides a comprehensive reflection of the optimization capabilities of different algorithms in ensuring real-time performance and load control.
## 2 Principle and Setup
The final IQ curve expression (Equation (7)) can be obtained by inserting Equation (8) (single resonance), Equation (9) (overlap and weak couple), or Equation (13) (strongly couple) into Equations (1) and (2), and then inserting Equations (1) and (2) into Equation (7). The resonance expression can be obtained by calculating the square modulus of Equations (8) (single resonance), (9) (overlap and weak couple), or (13) (strong couple). The coefficient of determination (COD, R-square) was used to evaluate the fitting results. The data \((x,y)\) = \((x_{1},y_{1})\), \((x_{2},y_{2})\),..., \((x_{m},y_{m})\) were obtained in measurement. The COD is defined as:  
\[Q_{0}(\omega) =kP_{0}J_{0}(\beta)I_{1}(\beta)\mathrm{Im}[R(\omega)R^{*}(\omega+ \omega_{\mathrm{m}})-R^{*}(\omega)R(\omega-\omega_{\mathrm{m}})] \tag{2}\]3  
where \(y_{m}\), \(\hat{y}_{m}\), and \(\overline{y}\) represent the experimental data, fitting data, and average of the experimental data, respectively. The COD ranges from 0 to 1. A larger COD indicates better fitting results. We define [14]\(|\kappa_{1}-\kappa_{2}|\)/2 as the threshold (\(\kappa_{\text{T}}\)) and the regimes where \(g<\kappa_{\text{T}}\) and \(g>\kappa_{\text{T}}\) as the weak- and strong-driving regimes, respectively; \(\kappa_{q}\) (\(q\) = 1, 2) is the total decay rate of the two resonances. In the experiment, the MBR was fabricated by heating pressurized silica capillaries [7]. Figure 1 provides a schematic of the orthogonal demodulation PDH technique. A tunable laser (1550 nm, Anritsu Tunics Plus CL; 850 nm, New Focus TLB 6716) was used to excite the MBR resonances. After passing through a polarization controller and a PM (850 nm, iXBlue NIR-MPX800-LN-P-P-FA-FA; 1550 nm, Thorlabs LN53-10-S-F-F-BNL), the laser beam was coupled into and out of the MBR via a taper fiber. A photoelectric detector (PD; 850 nm, Thorlabs PDA10A2; 1550 nm, Thorlabs PDA10 CF-CE) connected to a data acquisition (DAQ, NI PCIe6351) card and IQ demodulation circuits (ADI LT5584) was used to detect the transmission light. A syringe pump was used to inject air into the MBR, and a pressure sensor (0.25%; full scale, 2 bar) monitored the MBR's inner pressure. A high frequency function signal generator (Keysight 33622A) generated the sine wave signal to drive the PM and the signal was also used as the local oscillation signal to demodulate the detected signal from the PD. The DAQ card recorded the signal from the PD (transmitted spectrum), IQ demodulation circuits (the I-term and Q-term signal), and pressure sensor (pressure), and generated a triangular wave signal to sweep the laser frequency simultaneously. In the experiment, the laser was used to sweep the laser frequency linearly as a triangular wave signal. The PD transformed the light signal to a voltage signal. The DAQ card recorded the signal as the laser frequency changed. Then, we obtained the transmitted spectrum. The IQ signal was obtained after the signal was processed by the IQ-demodulation circuits.
## 4 Conclusions  
The growing interest in the unique characteristics of seaweed and its bioactive compounds has led to its recognition as a valuable superfood across various industries. The findings of this study indicate the high nutritional value of seaweeds, particularly with regard to protein. Specifically, _Ulux_ sp. and _Gracilaria multipartite_ had more than 18 per cent protein. Regarding dietary fibre, the values obtained in this study were lower than those previously reported in the literature, ranging from 5.83 \(\pm\) 0.02 g/100 g dw to 6.24 \(\pm\) 1.34 g/100 g dw. Another key attribute of these macroalgae is their extremely low fat content, measuring less than 0.19 \(\pm\) 0.01 g/100 g dw, a characteristic that is highly sought after by health-conscious consumers, particularly women and the elderly. Additionally, these seaweeds exhibit rich mineral content, including potassium, sodium, calcium, magnesium, phosphorus, iron, zinc, manganese, and copper, which play significant roles in human health. For instance, potassium levels ranged from 7.05 \(\pm\) 0.53 mg/g dw in _Ulux_ sp. (Buarcos) to 86.19 \(\pm\) 9.06 mg/g dw in _Sargassum muticum_ (San Marcos), while iron levels ranged from 116.30 \(\pm\) 6.68 \(\upmu\)g/g dw in _S. muticum_ (San Marcos) to 279.52 \(\pm\) 5.65 \(\upmu\)g/g dw in _S. muticum_ (Buarcos). Spectroscopic analyses (FTIR-ATR, FT-Raman, and FT-NIR) further revealed the complex biochemical composition of these macroalgae, encompassing sugars, lipids, proteins, aromatic phenolic compounds, carotenoids, structural polysaccharides, and sul  
Figure 9: Principal component analysis for discrimination of the different samples analysed using FT-Raman spectral information (**left** side) and FT-Raman absorption spectra of data processed with Savitzky–Golay derivative (**right** side). GMB—_Gracilaria multipartite_, UB—_Ulva_ sp., SMB—_Sargassum muticum_ from Buarcos, SMSM—_S. muticum_ from San Marcos.  
phated polysaccharides. These components underscore the potential use of seaweeds for applications in various sectors, including food, feed, pharmaceuticals, cosmetics, and other industries.  
Conceptualisation T.M. and O.A.; methodology, T.M. and O.A.; validation, T.M., O.A. and A.M.R.; formal analysis, M.M.A., T.M., O.A., F.I.P. and C.V.; investigation, M.M.A., F.I.P. and C.V.; resources, T.M. and O.A.; data curation, M.M.A., T.M., O.A., O.A., F.I.P. and C.V.; writing--original draft preparation, T.M. and M.M.A.; writing--review and editing, T.M., O.A. and A.M.R.; visualisation, T.M. and O.A.; supervision, T.M., O.A. and A.M.R.; project administration, T.M. and O.A.; funding acquisition, T.M. and O.A. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 3 Architecture Overview
### Network Elements
#### 3.1.3 Gateway Stations  
Gateway stations are fixed network elements. These nodes are the endpoint of the sensed data packets, meaning that they are the final element of the data gathering plane. A fundamental characteristic of any gateway station is the capability to establish communication to a remote server (the Data Broker), so that it can forward the received information to a database. To achieve this, the gateways have connectivity with a wired network backbone.  
Due to the multiple technologies, the gateways will act as endpoints for both WiFi and LoRa communications. Regarding the WiFi technology, they are the final element of the implemented opportunistic delay-tolerant network, which means that the intermediate DTN nodes (mobile nodes) relay the information until a gateway is found in its neighborhood. On the other hand, with respect to LoRa technology, gateway stations are the destination for the dispatched information received directly from the DCUs and mobile nodes. Figure 4 shows a gateway station software architecture.