esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN-NER +
## 1 Introduction  
In the last decade, the evolution of information and communication technologies (ICTs), in particular cloud-related technologies and solutions, has enabled the possibility of defining standard methods to find, access, reuse, and integrate community-related data, especially via a web-based platform.  
At the same time, in particular within the scientific community, the need for a rationalised organisation of digitalised data (and metadata) via standard methods and tools [1; 2], has emerged.  
In particular, the definition of the so-called findable, accessible, interoperable, reusable (FAIR) data principles [3], and their adoption in the European Open Science Cloud initiative [4], became an important activity devoted to identifying proven methods for making digital research objects more FAIR.  
In this context, the Competence Centre for the Conservation of Cultural Heritage (4 CH) European project [5] (the ancestor of this platform was devised and developed in the context of the European projects ARIADNEplus [6; 7] and EOSC-Pillar [8]), and has the aim of setting up the methodological, procedural, and organizational framework of acompetence centre--an infrastructure dedicated to knowledge organization and transfer through means such as training, standardization, and interdisciplinary collaboration.  
In this respect, part of the activities are devoted to define and improve the tools for heritage science processing, integration, and analysis (THESPIAN) suite, a set of web services aimed at offering multiple microservices to the researchers of the Cultural Heritage Network (CHNet) of Istituto Nazionale di Fisica Nucleare (INFN), for storing their raw data by following the FAIR principles for establishing integration and interoperability among shared information.  
In this paper, we describe the design, development, and deployment of a cloud-native web service, offered on the aforementioned THESPIAN platform, called THESPIAN-NER. Based on a deep neural network for natural language processing (NLP), it has been integrated within the 4 CH cloud infrastructure. The goal of such research activity was focused on helping researchers to compile, archive, and query their research dataset items in the INFN-CHNet database via a web application offered as a software-as-a-service (SaaS) cloud suite.  
The structure of the paper is as follows. In Section 2, the previous and related works on the THESPIAN suite and its related services are described. In Section 3, the design, development, and training of the NLP models adopted for the semantic classification is reported. In Section 4, the THESPIAN-NER web application design and related user experience is presented. In Section 5, the organisation of the 4 CH infrastructure and the THESPIAN integration as a platform-as-a-service (PaaS) is highlighted. Final comments and a discussion are reported in Section 6.
## 2 Materials and Methods
### Basic Electrochemical Electrical Equivalent Circuit (EEC) Models
[24] propose that it is possible to obtain a reasonable approximation fit by selecting the appropriate number of Voigt-type elements; using synthetic data, the authors evaluated the modelling of different processes (CPE, coated materials, diffusion and inductive systems) using the sum of squared errors as a parameter to determine the appropriate number of Voigt-type elements. Thus, it is common to find that CPE and Warburg elements are modelled as combinations on Kelvin-Voigt arrays. For instance, in [20], the authors discuss a simplified model, which considers R\({}_{\text{B}}\), R\({}_{\text{P}}\), R\({}_{\text{CT}}\), C\({}_{\text{D}}\) and the coating capacitance, C\({}_{\text{C}}\) (Figure 2C). Tests are also conducted on the bulk solution to determine an appropriate frequency range, where the measured impedance values are best represented. However, different compounds may exhibit great differences over the measurement frequency spectra, and thus, it is necessary to start the modelling process. Software solutions are proposed to facilitate the modelling process [25]. In addition, the measurement range is often constrained up to a few hundred kHz. Nevertheless, it is important to extend the frequency measurement range to capture the frequency response of all processes involved. For instance, in electrochemical systems that exhibit Warburg impedance for a diffusion flux with non-vanishing relaxation, [PERSON] et al. argued that it is necessary to modify the phenomenon-logical description of the electrochemical system in a non-equilibrium state, based on Fickean Diffusion [26]. They further proposed a hyperbolic equation for diffusion flux to explain the prevalence of smaller imaginary values at high frequencies. [PERSON] recently proposed a new impedance expression, which includes three diffusion coefficients (diffusion coefficient of cations, the diffusion coefficient of anions, and the ambipolar diffusion coefficient) to describe the mass transport in electrolytic solutions [27]. Another approach to elucidating system properties is frequency-domain system identification, where a model is estimated from frequency data [28]. Here, we use frequency-domain system identification methods to estimate electrochemical models to differentiate drug compounds. Figure 2: Basic electrochemical electrical equivalent circuit models. (**A**) Modified Kelvin–Voigt (also known as simplified Randles Model) (**B**) Randles model and (**C**) linear approximation of the Randles Model using passive components [20]. Reproduced with permission. © Copyright 1990–2016 Gamry Instruments, Inc.  
Modelling electrochemical cells often involve the use of arrays of passive components, resistors and capacitors, where the frequency response data are used to determine the corresponding resistance and capacitance values to fit the frequency response. Similarly, frequency-domain system identification techniques are suited to model RC networks.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 4 CLOR Protocol
### Main Idea
#### 4.1.3 Coding-Based Burst Transmission Mechanism  
As the traffic rate increases, the nodes with more neighboring nodes may buffer more than one packet, increasing the collisions in the network once each node frequently sends a control packet to request to forward a data packet. To resolve this problem, we introduce a burst transmission mechanism. In CLOR, each node can send a train of data packets in the transmission phase. Based on this strategy, the CLOR protocol mitigates the congestion problem and improves the efficiency of negotiation. Considering the varied number of data packets that are buffered in the senders, we adaptively set the burst size in each transmission.  
Moreover, the channel of shallow water changes significantly in the networks. Each node should transmit more packets over the burst size to increase the transmission reliability. However, the transmission with a fixed redundancy is inefficient due to the large variations of the channel condition in the network. The sender can encode data without limitation to ensure the reliable recovery of packets at the receiver. However, this type of transmission is costly in terms of energy consumption and limited batteries for a UWSN. Thus, we introduce a packet-level coding strategy based on network coding in the burst transmission process to improve the transmission reliability [15; 48; 49; 50]. The coding redundancy is related to the channel conditions of each forward link. As considered in [43], we estimate the link quality via the feedback of each data transmission. Due to the asymmetry of underwater links, each node is essential to maintain the channel state of bidirectional links.
## 5 Conclusions  
The method for predicting the required volume of grouts for treating the karst caverns in the way of shield tunneling was proposed and verified through a case study. The following conclusions can be drawn:  
1. Since a karst cavern could be filled with groundwater, sand, or clay, this water along with other sources of groundwater may worsen the geological conditions during shield tunneling. Additionally, severe geo-hazards, such as water ingress, surface settlement, and sinkholes could easily be triggered while tunneling in the buried karst of Guangzhou.
2. Based on the fractal theory, the proposed method can be used to reasonably estimate the required volume of grouts during treatment. The procedure is: (i) to investigate the distribution of karst caverns along the tunnel alignment; (ii) to calculate the fractal parameters of the karst caverns; (iii) to calculate the total volume of karst caverns; and (iv) to predict the required volume of grouts by introducing the proposed karst grouting coefficient.
3. The two fractal characteristics--self-similarity and scaling invariance--identified in the literature are also in line with those observed in the karst geology in the Ma-Lian section of Guangzhou metro line no. 9, implying that the fractal theory can be effectively applied to investigate the distribution of karst caverns.
4. Three datasets including: (i) 98 geological drilling holes; (ii) 48 geological drilling holes; and (iii) 937 grouting holes were established via the presented case history. In the buried karst of Guangzhou, the karst grouting coefficient of 0.26 is recommended for predicting the required volume of grouts for treating karst caverns along tunnel alignment.  
**Acknowledgments:** The research work described herein was funded by the National Nature Science Foundation of China (NSFC) (Grant No. 41672259) and National Basic Research Program of China (973 Program: 2015 CB057802). This financial support is gratefully acknowledged.  
**Author Contributions:** This paper represents a result of collaborative teamwork. [PERSON], [PERSON], developed the concept and wrote the manuscript; [PERSON], [PERSON], and [PERSON] provided constructive suggestions and revised the manuscript. The five authors contributed equally to this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Theory  
### Underwater De-Bubble Algorithm  
Figure 1b shows the effect of underwater bubbles on the fringes captured by the CMOS sensor in the experiment. It is obvious that the existence of these bubbles seriously affects the contrast of the original dark and bright stripes captured by the CMOS sensor, which will undoubtedly reduce the accuracy of the signal decoding. Notice that the entire pixel line is not completely contaminated by bubbles; hence, a method based on row pixel sorting to eliminate the effect of bubbles on stripes is used. First, we sort elements of each  
row matrix of grayscale values in a raw grayscale image frame polluted by bubbles in ascending order. The shadow generated by bubbles will be moved to the left of the image, and the pixels in this line that are not disturbed by bubbles will be moved to the right of the image. It is worth mentioning that the selection of vertical column in the sorted image may lead to the elements in the column matrix coming from all parts of the original image, making the pixel values of the same data bit \"1\" fluctuate greatly. Considering this problem, a reasonable column matrix (we marked it as A) is determined by the following method.  
Firstly, if the resolution of image frame is \(R\times C\), in order to determine the pixel value reference of the same bit, the column center of the light source needs to be found. A total of 100 received frames are averaged to form the average frame, which represents the grayscale distribution of the received light pattern. Each column of the average frame is summed to get \(g_{i}\) (\(i=1,2,\ \cdots R\)), and the maximum value of \(g_{i}\) is acquired. The index corresponding to the maximum value is the approximate column center index of the light source; we marked it as \(\alpha\). Secondly, in the sorted image, all the elements in columns \(R\)-220 are taken to \(R\) as candidate values of \(A\). Next, each row of the sorted image is traversed to check the column subscript of the candidate elements in the original image, and the element with the closest index to \(\alpha\) as the element of \(A\) (the red curve in Figure 1a) is found. This is equivalent to selecting pixels that bypass the bubble area on each row of the grayscale image to form a column matrix. Nevertheless, the complexity of the method based on sorting is much lower. Figure 1c shows the selected valid column matrix where the bubbles are removed. It can be clearly seen that the header information lost due to the bubble effect in Figure 1b is recovered, thus ensuring the reliability of subsequent decoding.  
Figure 1: (**a**) Diagram of de-bubble algorithm. (**b**) Raw column pixels with bubbles, (**c**) column pixels after de-bubbling, and (**d**) the column pixels after de-bubbling and gamma correction
## 3 The Time Source Techniques
### Low Earth Orbit
#### 3.2.5 Application  
As of the time of writing, no literature has documented the application of LEO-based timing in the electrical grid. However, cases in other sectors are provided here to offer insights into its potential applications.  
For nearly three decades, satellite operators have pursued the goal of deploying LEO constellations for global broadband services. The initial LEO satellites were designed primarily for communications, and recently, several companies have submitted petitions to the Federal Communications Commission (FCC) to deploy extensive constellations in both LEO and MEO [56]. The global reach, low latency, and broad bandwidth of these services position them to revolutionize broadband communications [49].  
In addition to communications, Positioning, Navigation, and Timing (PNT) has emerged as a viable service offered by LEO satellites, with applications across sectors like 5G networks, data centers, and financial services.  
Given that LEO offers timing accuracy, performance, and receiver characteristics comparable to GPS, it could potentially be used for time synchronization in all the applications listed in Section 2 that currently rely on GPS.
## 2 Physical and Mathematical Models
### Mathematical Model  
Assumptions for the model and energy balance during the operation, switching time and heat recovery were discussed in Part I of this study. The governing equations during the operation are briefly mentioned hereafter.  
For G1 and G4: Adsorption  
The energy balance equation is:  
\[\begin{array}{l}\frac{d}{dt}\{(M_{ac}*Cp_{ac}+M_{ac}*Cp_{m}*x_{ads}+M_{Al}*Cp_{ Al}+M_{cu}*Cp_{cu})T_{ads}\}=\\ M_{ac}*Q_{st}*\frac{d_{ads}}{dt}-M_{ac}*Cp_{m}*(T_{ads}-T_{eva})*\frac{d_{ads} }{dt}+Mc_{w}*Cp_{w}*(T_{ci}-T_{co})\end{array} \tag{1}\]  
The outlet temperature of the cooling water is calculated by log mean temperature difference (LMTD):  
\[T_{co}=T_{ads}+(T_{ci}-T_{ads})*exp\{-(U_{b}*A_{b})/(M_{cw}*Cp_{w})\} \tag{2}\]  
For G2 and G3: desorption  
The energy balance equation is:  
\[\begin{array}{l}\frac{d}{dt}\{(M_{ac}*Cp_{ac}+M_{ac}*Cp_{m}*x_{des}+M_{Al}*Cp _{Al}+M_{cu}*Cp_{cu})T_{des}\}=\\ M_{ac}*Q_{st}*\frac{dx_{des}}{dt}+Mc_{w}*Cp_{w}*(T_{hi}-T_{ho})\end{array} \tag{3}\]  
The outlet temperature of the heating water is calculated by LMTD:  
\[T_{ho}=T_{des}+(T_{hi}-T_{des})*exp\{-(U_{b}*A_{b})/(M_{inv}*Cp_{w})\} \tag{4}\]  
For the evaporator:  
\[\begin{array}{l}\frac{d}{dt}\{(M_{em}(t)*Cp_{m}+M_{eva}*Cp_{eva})T_{eva}\} =-M_{ac}*h_{fg}*\frac{dx_{di}}{dt}\\ -M_{ac}*Cp_{m}*(T_{con}-T_{eva})*\frac{dx_{des}}{dt}+M_{chw}*Cp_{w}*(T_{chi}-T _{cho})\end{array} \tag{5}\]  
The outlet temperature of the chilled water is calculated by LMTD:  
\[T_{cho}=T_{eva}+(T_{chi}-T_{eva})*exp\{-(U_{eva}*A_{eva})/(M_{chw}*Cp_{w})\} \tag{6}\]  
For the condenser:  
\[\begin{array}{l}\frac{d}{dt}\{(M_{cm}*Cp_{m}&+M_{con}*Cp_{con})\,T_{con}\} \\ &=-M_{ac}*h_{fg}*\frac{dx_{dz}}{dt}-M_{ac}*Cp_{m}*(T_{des}-T_{con})*\frac{dx_{ con}}{dt}+M_{con}*Cp_{pw}\\ &*(T_{con}-T_{con})\end{array} \tag{7}\]  
Figure 1: Schematic for two-stage adsorption chiller.  
The outlet temperature of the condenser water is calculated by LMTD:  
\[T_{como}=T_{con}+(T_{coni}-T_{con})*exp\{-(U_{con}*A_{con})/(M_{con}*Cp_{w})\} \tag{8}\]  
For cooling capacity and COP:  
\[CC=M_{chw}*C_{pw}*\int_{0}^{T}(T_{chi}-T_{cho})dt \tag{9}\]  
\[COP=M_{chw}*C_{pw}*\frac{\int_{0}^{T_{C}}(T_{chi}-T_{cho})dt}{M_{hw}*C_{pw}\int _{0}^{T_{C}}(T_{hi}-T_{ho})dt} \tag{10}\]  
\[COP_{carnot}=\left[\left[\overline{T}_{des}-\overline{T}_{con}\right]/\overline {T}_{des}\right]*\left[\overline{T}_{eu}/\left[\overline{T}_{ds}-\overline{T}_ {eu}\right]\right] \tag{11}\]
## 5 Conclusions  
This study employed a comprehensive approach integrating remote sensing, GIS analysis, and artificial intelligence to assess flood and rockfall hazards in the Jiului Gorge, Romania, and their impacts on traffic infrastructure. The key findings are as follows:  
Flood and Rockfall Mapping: Sentinel-1 radar imagery proved valuable for detecting and mapping both flood and rockfall events. Interferometric processing effectively distinguished flooded areas from permanent water bodies, while co-registered images aided in identifying surface changes indicative of rockfalls.  
Susceptibility Assessments: Weighted overlay analysis of factors like DEM and land cover yielded susceptibility maps, crucial for pinpointing areas prone to these hazards and informing risk mitigation strategies.  
AI-based Traffic Detection: YOLO models demonstrated potential in real-time traffic detection for road and rail networks. However, further development is necessary to address limitations associated with visibility and image quality.  
This study highlights the effectiveness of integrating remote sensing, GIS, and AI for hazard assessment and infrastructure management. The susceptibility maps and validated AI models provide valuable tools for stakeholders involved in transportation planning, disaster preparedness, and risk mitigation efforts in the Jiului Gorge region.  
Future research directions include the following:  
Enhanced Data Integration: This involves exploring the fusion of various data sources, including historical records, real-time monitoring systems, and meteorological data, to improve hazard prediction and risk assessment.  
AI Model Optimization: This involves refining YOLO models through techniques like transfer learning and hyperparameter tuning to enhance accuracy and robustness in diverse weather and lighting conditions.  
Long-Term Monitoring Systems: Implementing long-term monitoring systems for continuous data collection and analysis facilitates a more comprehensive understanding of hazard dynamics and enables proactive risk mitigation strategies.  
Conceptualization, M.P. and B.-A.M.; methodology, M.P. and B.-A.M.; software, M.P. and B.-A.M.; validation, M.P. and B.-A.M.; formal analysis, M.P. and B.-A.M.; investigation, M.P. and B.-A.M.; resources, M.P. and B.-A.M.; data curation, M.P. and B.-A.M.; writing--original draft preparation, M.P.; writing--review and editing, B.-A.M.; visualization, M.P. and B.-A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Bucharest, Faculty of Geography, during PhD studies. This paper was supported by the Council for Doctoral Studies (CSUD), University of Bucharest.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
The authors thank SDN Deva INFO HD TV DEVA, Pro Tv, and INFO HD DEVA TELEVISION for letting us use images from the news in our detection. This paper was supported by the Council for Doctoral Studies (CSUD), University of Bucharest.  
The authors declare no conflicts of interest.
## 3 Results
### POD of GOCE Satellite Based on the RD_STP Method
This outcome indicates that the STP pseudo-observations have a stronger dynamic constraint when the quality of on-board GPS data is poor. In addition, GOCE POD solutions for DOY 318-332 of 2009 with 10 s and 30 s sampling are generated as well. The mean RMSs of the differences between the solutions (KIN-10 s, KIN-30 s, RD_STP-10 s, and RD_STP-30 s) and the RD solutions are shown Table 3. According to Table 3, for the same sampling intervals, the RD_STP solutions are smoother than the KIN solutions, and closer to the RD solutions. Moreover, when the sampling interval is smaller, the RD_STP solutions are closer to the RD solutions. This is similar to the POD results of 1 s sampling. The 15-day mean 3D RMS of the differences between the RD_STP and RD solutions is 2.77 cm for 30 s sampling and 2.53 cm for 10 s sampling. Figure 10: Radial orbit differences (compared with the RD solutions) of the KIN solutions and the RD_STP solutions based on the different stochastic models of the STP pseudo-observations at DOY 319 of 2009 (top). Zoomed view in 3 h (bottom).
## 3 Numerical Simulation of CSAMT
### Solving the Equation System  
In the three-dimensional numerical simulation of CSAMT, each node has three degrees of freedom in the x, y, and z directions. Therefore, we take  
\[\begin{array}{l}\overset{\rightarrow}{\mathbf{V}}_{j}  
\[\mathbf{A}=\int\limits_{\Omega}\left[\begin{array}{c}\sum\limits_{i=1}^{n} \left(\frac{\partial N_{i}}{\partial z}\frac{\partial N_{j}}{\partial z}+\frac{ \partial N_{i}}{\partial y}\frac{\partial N_{j}}{\partial y}+\frac{\partial N_{ i}}{\partial x}\frac{\partial N_{j}}{\partial x}+k^{2}N_{i}N_{j}\right)\\ -\sum\limits_{i=1}^{n}\frac{\partial N_{i}}{\partial y}\frac{\partial N_{j}}{ \partial x}\sum\limits_{i=1}^{n}\left(\frac{\partial N_{i}}{\partial z}\frac{ \partial N_{j}}{\partial z}+\frac{\partial N_{i}}{\partial y}\frac{\partial N_ {j}}{\partial y}+\frac{\partial N_{i}}{\partial x}\frac{\partial N_{j}}{ \partial x}+k^{2}N_{i}N_{j}\right)\\ -\sum\limits_{i=1}^{n}\frac{\partial N_{i}}{\partial z}\frac{\partial N_{j}}{ \partial x}\end{array}\right]dV \tag{19}\]  
\[\mathbf{b}=i\omega\mu_{0}\int\limits_{\Omega}\left(\sigma-\sigma^{p}\right) \left(N_{j}\overset{\rightarrow}{\mathbf{E}}_{x}^{p}\quad N_{j}\overset{ \rightarrow}{\mathbf{E}}_{y}^{p}\quad N_{j}\overset{\rightarrow}{\mathbf{E}}_ {z}^{p}\right)^{T}d\Omega\]  
Equation (18) is a large, sparse, and symmetric complex coefficient linear equation system. In this system, matrix A represents the overall stiffness matrix of the coupled finite element-infinite element method, which is a \(3\times N_{x}\times N_{y}\times N_{z}\) square matrix. Here, \(N_{x}\), \(N_{y}\), and \(N_{z}\) denote the total number of nodes in the x, y, and z directions, respectively. Each row in matrix A has a maximum of 81 non-zero elements. x represents the electric field values at each node that need to be solved; b is the right-hand side term, which includes the anomalous body and the primary field. For solving large-scale sparse linear equation systems, this study adopts Pardiso, an open-source solver with excellent performance and high parallelization. Pardiso uses LU decomposition for direct solving, making it particularly suitable for handling multi-source CSAMT problems.