esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 4 Floods  
Floods are one of the most dangerous environmental hazards, every year bearing enormous losses in terms of human lives and infrastructures. Flash floods are a particular concern because they happen quickly, intensely and without warning, thus requiring systems that allow to predict them and give time for evacuation and other security measures. Events that can generate floods are heavy rainfalls, thunderstorms and rapid snow melts. Hydro-geological instability and soil properties are also to be taken into account when assessing the risk of floods. Some existing and operational flood EW systems, such as the European Flood Awareness System, use rainfall detection (for example, from radars) or rain forecasts to generate alarms when the amount of rain detected has risen above a certain threshold [45]. IoT systems produce data that is immediately accessible for real-time warning applications. Prediction methods in EW IoT systems can rely on hydro-geological models or statistical and Machine Learning models that collect data in real time from WSNs, send them to a remote server for processing and then display results or generate alarms. Figure 2 illustrates a generic implementation of a flood Early Warning system based on the IoT which makes use of different types of sensors.
## 5 Conclusions  
The angular beam profiles of a THz emitter based on a PIN photodiode with attached bow-tie antenna were measured in both the E-plane and H-plane orientations at frequencies between 100 GHz and 1 THz using a Golay cell power detector. Beam profiles were recorded in the far-field of the antenna, and were found to be distance-invariant. The effects of angular resolution were examined, and it was concluded that the employed setup is capable of resolving features within 10\({}^{\circ}\) angular width.  
For frequencies above 600 GHz, the beam profiles in the H-plane orientation were found to be significantly asymmetrical and skewed away from the centre. This may be attributed to a slight misalignment between the antenna and the centre of the lens. Moreover, the small feeding point structure that connects the photodiode and the antenna might act as a contributing radiating element and thus distort the beam profile when the wavelength becomes sufficiently small. At frequencies below 250 GHz, side lobes in the beam profiles were observed in the E-plane, and these may be attributed to internal reflections within the silicon lens.  
Although some of the lower frequency profiles consist of a single peak centred around 0\({}^{\circ}\), none of the measured profiles are perfectly Gaussian. This indicates the importance of acquiring accurate measurements of the radiation profiles of such emitters before considering them for use in free-space transmission systems such as those used for communications. Accurate profiles will allow for better link budget estimations in communication links. Moreover, reliable methods for beam profile measurements are essential for efficient component design. It is also important to consider the beam profile in designing optics for spectroscopy systems in order to ensure artefact-free measurements with maximum sensitivity. In addition, accurate beam profile measurements are essential for the development of broadband terahertz emitters. Hence, this paper can be an important starting point for establishing beam profile measurements as a characterization tool for terahertz emitters.  
Conceptualization, J.S. and M.N.; methodology, J.S., M.N., and S.N.; formal analysis, J.S. and M.N.; investigation, J.S. and M.N.; writing--original draft preparation, J.S. and S.N.; writing--review and editing, M.N., S.N., and B.G.; visualization, J.S. and M.N. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was funded by the iCASE programme of the Engineering and Physical Sciences Research Council (EPSRC), UK. The work of [PERSON] was funded by the Horizon 2020 project \"TERAPOD\", under grant agreement No. 761579. The work of [PERSON] and [PERSON] were funded by the Horizon 2020 project \"TERAWAY\" under grant agreement No. 871668.  
Data Availability Statement:Data is contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## Materials and Methods
### Sample Collection, Control Samples and Quality Control (QC)  
Water samples were collected (in 2.5 L amber bottles) by one of three techniques depending on the source: (a) traditional grab sampling direct into the sampling container; (b) grab sampling via a bailer device, or (c) by pump (peristaltic or submersible). The sampling container was rinsed three times with the source water prior to collection. Samples were transported to the laboratory under chilled conditions, in individual, sealed, polypropylene bags and stored at 4 \({}^{\circ}\)C until analysis (within 7-10 days after collection, as determined by matrix stability studies).  
Samples found to be free of analyte, or to contain analyte levels of \(<\)30% of the lowest calibration point (in accordance with SANTE [38]), were deemed to be suitable as negative control samples for method development, matrix matched calibration and validation experiments. Negative control samples were also used to produce QC Trip (Field) blanks. A QC trip blank (500 mL negative control aliquot) was transported to and from field sites while sampling. In the field, at each sampling location,the trip blank was exposed (open capped) in the vicinity of the sampling point, for the duration of sampling, and accompanied samples back to the laboratory in the same cooler container and under the same conditions. This trip blank was subsequently analysed along with samples, to demonstrate there was no contamination of samples during collection and transport. Fortified QC samples were not used during this study as some sampling was carried out by external organisations, and fortified samples were not feasible in such cases.  
For internal (within batch) QC, a system suitability check to monitor analyte response and retention was injected prior to each instrumental run, to ensure the instrument was performing as expected. Negative control samples (\(n=2\)) were included to confirm no cross contamination during the extraction process. Post-extraction spiked recovery samples were included to ensure the performance of the method for each analyte. Solvent blank injections were incorporated following calibration samples, prior to injection of unknown samples, to demonstrate no carryover of analytes. Retention checks involved re-injection of a matrix calibrant several times throughout the analytical run to check for accuracy. A minimum of four retention check injections were used to ensure no drift in retention during the analytical run, and to ensure no variation in detector response.
# Seismic Analysis of Steel Solid Web Girder-RC Tubular Column Hybrid Structure
## 2 Case Study  
A typical steel solid web girder-RC tubular column hybrid structure was designed for the case study. The total height was 61.4 m. According to the China seismic design code [17], the seismic precautionary intensity is 8-degree with a peak ground acceleration (PGA) of 0.20 g. This means that when an earthquake with 10% probability of exceedance in 50 years occurs in this region, the seismic intensity is 8 degrees and the corresponding PGA is 0.20 g. The site soil class was II. The structural plan layouts and elevation views are shown in Figure 2. Table 1 lists the strength grades of the materials and cross sections of main components.  
Figure 2: Structural layouts (unit: mm): (**a**) Plan layouts of RC tubular columns; (**b**) elevation view along the A-shaped frame direction; (**c**) elevation view perpendicular to A-shaped frame direction.  
All the structures should satisfy the seismic demands under three hazard levels according to the China seismic design code [17]. The three hazard levels mean frequent, basic and rare earthquakes, respectively. The corresponding probabilities of exceedance in 50 years for the three hazard levels are 63.3%, 10% and 2%. All the structures should keep elastic under frequent earthquakes (Level 1), be in use after repair under basic earthquakes (Level 2), and not collapse under rare earthquakes (Level 3). Especially, according to the seismic ground motion parameters zonation map of China [18], the air-cooled condenser supporting structure presented in this paper should also not collapse under very rare earthquakes with \(10^{-4}\) probability of exceedance in one year (Level 4) except for the three hazard levels introduced above, since it belongs to lifeline engineering, which should have much higher reliability compared to ordinary buildings.
## 4 Risk Analysis for Current Protocols
### TCP/IPv6
#### 4.1.3 Packet Sniffing  
Packet sniffing is possible at the physical layer with a passive inline tap [26]. Additionally, data fields are fully vulnerable to sniffing. To avoid packet sniffing, IPSec can be implemented for encryption [25].
## 5 Algorithm Design
### Predictive Models  
The prediction model is used to predict server and link occupancy when the SFC request arrives, and it is the core part of the whole method. It consists of four parts, namely the input, the encoder, the decoder, and the output layer, and its structure is shown in Figure 5.  
The input layer processes and normalizes the computational and bandwidth resource data required to extract the SFC into a \(2\times n\) feature matrix \(x=[c,bw]\), where n is the chain length of the longest SFC, \(c\) is the computational resource normalized data, and \(bw\) is the bandwidth resource normalized data. The normalized formula is as follows:  
\[c_{i}=\frac{C_{i}-min(C_{i})}{max(C_{i})-min(C_{i})}, \tag{15}\]  
\[bw_{i}=\frac{BW_{i}-min(BW_{i})}{max(BW_{i})-min(BW_{i})}, \tag{16}\]  
where \(C\) and \(B\) are the amount of computing and bandwidth resources required by VNF, respectively.  
The core of the neural network model employs encoders and decoders for sequence-to-sequence models. Its most important feature is that the length of the input and output sequences is variable, which makes it ideal for use in situations where the SFC chain length is uncertain.  
Figure 4: Workflow of proposed DRL-G.  
Figure 5: SFC deployment prediction model based on sequence\(-\)to\(-\)sequence model.  
The encoder and the decoder consist of several Long Short-Term Memory Networks (LSTMs). LSTM is a special kind of Recurrent Neural Network (RNN). Each LSTM input consists of the previous LSTM output, cell states \(h_{t-1}\), \(c_{t-1}\), and current input \(x_{t}\), where \(x_{t}\) is the data of the \(t\)th row of feature matrix \(x\). The formulae for the output of the LSTMs, \(h\), and the cell state, \(c_{t}\), are as follows:  
\[c_{t}=\sigma(\theta_{f}\cdot[h_{t-1},x_{t}]+b_{f})\cdot c_{t-1}+\sigma(\theta_{ i}\cdot[h_{t-1},x_{t}]+b_{i})\cdot tanh(\theta_{c}\cdot[h_{t-1},x_{t}]+b_{c}), \tag{17}\]  
\[h_{t}=\sigma(\theta_{o}\cdot[h_{t-1},x_{t}]+b_{o})\cdot tanh(c_{t}), \tag{18}\]  
\[f_{t}=\sigma(\theta_{f}\cdot[h_{t-1},x_{t}]+b_{f}), \tag{19}\]  
where \(\theta_{f}\), \(\theta_{i}\), \(\theta_{c}\), \(\theta_{o}\), \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) are the parameters on which the neural network needs to be trained.  
The output layer is a sampling function that changes continuous actions into discrete actions. This is achieved by predicting the occupied servers by sampling according to the output probabilities in the decoder. The higher the probability, the greater the chance that a server will be drawn.
# Natural Convection of Dusty Hybrid Nanofluids in an Enclosure Including Two Oriented Heated Fins
## 1 Introduction
[27] analyzed the natural convection and entropy generation in a three-dimensional cavity with an inclined fin. They observed that the inclination angle significantly affect the heat transfer and the fluid flow. [PERSON] et al. [28] studied the laminar natural convection in a square cavity attached with a horizontal fin. They investigated the effect of different aspects of the fins such as length, thickness, conductivity ratio, and position. [PERSON] and [PERSON] [29] performed a numerical study on the unsteady natural convection of a differentially heated finned cavity. It was shown that the heat transfer is a function of the Rayleigh number and the fin positions. [PERSON] and [PERSON] [30] analyzed the natural convection in a finned cavity heated from one side. They found that applying optimal thin fins reduces the heat transfer at the cavity wall up to 8%. [PERSON] et al. [31] analyzed the natural convection inside a partially differentially heated cavity with a thin fin. [PERSON] [32] studied a three-dimensional, steady and transient natural convection in a finned cubical cavity. [PERSON] [33] reviewed the natural convection of nanofluid flow in a rectangular cavity with two heated fins. The results indicated that the long fins give higher values of heat transfer. [PERSON] et al. [34] used a lattice Boltzmann model to simulate melting of the phase change materials in the porous media with a conducting fin. [PERSON] et al. [35] investigated the natural convection heat transfer and the fluid flow inside a square cavity affected by an oscillating fin on the hot wall. [PERSON] et al. [27] investigated the effects of the radiation and magnetic field on the heat transfer rate and the nanofluid entropy generation in a diagonal square cavity with a conductor fin. [PERSON] et al. [36] investigated the effects of the vertical fins on the heat transfer rate and energy density of a latent heat thermal energy storage system. They used the vertical fins on the bottom surface to enhance the charging rate. The main objective of this study is to investigate the heat transfer and fluid flow of dusty hybrid nanofluids inside an enclosure that includes two inclined heated fins using one phase model. The governing equations are presented, separately, for the hybrid nanofluid phase and the dusty phase. Comparing the heat transfer rate in cases of pure dusty fluid and hybrid nanofluid is considered one of the objectives. In addition, this study aims to show the best length and best orientation of the fins to enhance the heat transfer rate inside the enclosure.
## 3 Results
### Comparative Analysis of Modeling Results
#### 3.4.2 PCA-CNN Modeling Results Analysis  
In Table 4, Figures 7 and 8, the results of the PCA-CNN model using the ASD spectra data show R\({}^{2}\) is above 0.8, RMSE in the range 26.51-81.55 g/kg and RPD in the range 1.51-3.33, and the values of R\({}^{2}\) and RMSE are slightly higher than those of GF5 AHSI hyperspectral images. In terms of sand, silt, and clay content, the prediction values of clay using ASD and GF5 AHSI data are also higher than those of sand or silt, with ASD having a predicted value of R\({}^{2}\) of 0.948, RMSE of 26.51 g/kg, and RPD of 3.33 for clay and GF5 AHSI having a predicted value of R\({}^{2}\) of 0.908, RMSE of 31.24 g/kg, and RPD of 3.13 for clay, which are similar to the results of the PCA-PLSR algorithm. The high viscosity and plasticity of clay make it easier for PCA-CNN to capture and predict its properties, while its fine particles help algorithms analyze and model more accurately. The low-performance prediction of silt may be due to its ability to mix with sand and clay, with higher spectral mixing, making it more difficult to distinguish in the VNIR-SWIR region.  
Figure 5: Prediction of soil texture content based on PCA-PLSR algorithm and ASD data ((a) sand, (b) silt, (c) clay). The black and red lines show the 1:1 relationship and the model trend.  
Figure 6: Prediction of soil texture content based on PCA-PLSR algorithm and GF5 spectral data ((a) sand, (b) silt, (c) clay). The black and red lines show the 1:1 relationship and the model trend.  
From the results of the ASD and GF5 AHSI data, the overall predicted precision of the GF5 AHSI data inversion is similar to that of the laboratory data. However, for sand and silt with higher reflectivity, the prediction results of GF5 AHSI data are higher than those of laboratory data, while for clay with lower reflectivity, the opposite is true. Compared to clay, the higher reflectivity of sand and silt exhibits strong contrast or tendency in remote sensing images. This makes it easier to be recognized by the PCA-CNN algorithm of images with a high classification accuracy. At the same time, for the soil texture of large particles, remote sensing images can better capture scale features, such as small slopes, surface texture, etc., providing more information and details in the recognition and classification process, which is conducive to improving the prediction accuracy [46].
## 1 Introduction
[33] studied first-order parameters to identify correlations between input parameters and geometrical outputs of Ti-6 Al-4V beads manufactured with the laser metal powder deposition process. Finally, [PERSON] and [PERSON] [34] discussed the use of an artificial neural network (ANN) to predict the bead parameters based on given process parameters for the WAM system. From the above literature review, one can conclude that the machine learning approach and, more particularly, NNs, have not been applied to LWAM bead geometry prediction considering deposition parameters that influence both the bead width and the height of the produced part. This is due to the complexity of layer deposition stability and the high feed rate of the LWAM process in comparison with other techniques, such as WAAM [36]. Therefore, this paper aims to provide a machine learning approach using a regression algorithm to predict the layer geometry in the LWAM process. Different experiments are performed to deposit sample products and, with the developed prediction model, the height, width, and ratios for upcoming layers are proposed. Furthermore, the influence of the deposition parameters (laser power, wire feed rate and travel speed) on the final produced geometry is discussed based on a neural network prediction model. This case study could provide inspiration and guidance for improving surface roughness in LWAM using a proper feedback control system. For proper implementation of the prediction algorithm, we employ a technique used by [PERSON] et al. [37] for collecting the data. The method uses scripting languages to propose a method of slicing layers and generating printing paths. The use of the parametric method is adopted to generate printing paths using Rhino's Grasshopper environment (see Figure 2). In [PERSON] et al. 's approach, the prospect of flexibility in model design provides more opportunities for the designer to decide on the size and shape of the layer (flat or curved), path patterns, and other parameters affecting the surface's geometrical quality and printing time. However, this proposal could be improved by taking into consideration the height variation mentioned in [PERSON] et al. 's work and, especially, by finding a way to avoid many of the issues found with G-Code (see e.g., [38]), as well as communicating directly to the printing machine in terms of AM using an industrial robot. The paper is structured as follows, Section 2 introduces the global approach of the methodology, while Section 3 provides a detailed theoretical background for the proposed prediction model. Section 4 presents the experimental results and discussion. The paper finishes in Section 5 by a conclusion and a proposal of future work.
## 6 Conclusions  
In this study, a method for optimizing the number and locations of strain-measurement points required for predicting displacements was newly proposed. The ANN's ability for data mapping was used to define the relation between strain and displacements. Additionally, the GA's ability for global search was used to optimize the strain-measurement points because the displacement prediction accuracy of the ANN varies according to the number and locations of strain-measurement points. The proposed method was verified for two cases by using a simple beam under a sinusoidal load and a bridge under a vehicle load. Also, the proposed method was applied to a vibrating cantilever beam. From the results, the following conclusions can be drawn:  
1. The ANN prediction accuracy can be improved by optimizing the ANN input values using GA. In this study, the number and locations of strains were optimized to accurately predict displacements. The optimization results show that at least two strains at the optimized locations are sufficient to predict the displacements;
2. By comparing the ANN displacement prediction results from the strain at the optimized locations and the strain at the arbitrary locations, the difference between the MSE and the R value was found. In particular, the difference was prominent in the graph. Therefore, if the number and locations of strain-measurement points are optimized by using the proposed method, the displacements can be predicted very economically and accurately;
3. When the proposed method was applied to a vibrating cantilever beam, the feasibility of the proposed method was confirmed. As with the two cases using the numerical model, the predicted displacements from the measured strains showed good agreements with the measure displacements for the real cantilever beam. From laboratory experiment results, the applicability to actual bridges in the field can be confirmed.  
In future work, experiments applying the proposed method to the actual bridge will be described. For the verification of the proposed method in this paper, a real test was carried out for the actual bridge. From the experiment, actual strains at the optimized locations and displacements were measured. By comparing the measured displacements with the displacements predicted by the ANN from the measured strain, the real feasibility of the proposed method in this paper will be reviewed.  
Conceptualization, H.S.M., M.K.K. and Y.M.L.; formal analysis, P.-J.C.; methodology and validation, H.S.M.; writing--original draft preparation, H.S.M.; review and editing, P.-J.C., M.K.K. and Y.M.L.; supervision, Y.M.L. All authors have read and agreed to the published version of the manuscript.  
Figure 21: Overall displacements shape at time = 1.5 s.  
**Acknowledgments:** This research was supported by a grant (19 CTAP-C152286-01) from the Technology Advancement Research Program (TARP) funded by the Ministry of Land, Infrastructure and Transport of the Korean government.  
**Conflicts of Interest:** The authors declare no conflict of interest.