esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Shallow Failure on a Slope Using Volumetric Water Content Gradient Characteristics
## 2 Materials and Methods
### Testbed Conditions
#### 2.3.2 VWC Gradient Analysis in the Testbeds  
We examined rainfall events in the three regions during the measurement period. The highest total rainfall was recorded in Yeonki (905 mm), followed by Inje (541 mm) and Dongrae (256 mm) (Table 3). For these three regions, the characteristics of the VWC gradient were examined at different slope angles (\(20^{\circ},30^{\circ},40^{\circ}\), and \(50^{\circ}\)) and installation depths (0.2 and 0.4 m) (Figure 7). In all of the three regions, the characteristics of the VWC  
Figure 5: VWC gradient at different measurement locations (depths): (a) crest; (b) slope part; (c) toe part.  
Figure 6: Testbed site: (a) location of testbed sites; (b) cross section of the installed sensor.  
gradient were not distinguished according to the slope angle or measurement depth. For Dongrae, the VWC gradient was less than 0.1. For Inje, the VWC gradient values that were less than 0.1 were dominant, but 0.1 was exceeded in some cases. Comparison of these two regions showed that Yeonki yielded a higher ECR range, and there were many VWC gradient values that were higher than 0.1.  
Figure 7 shows that the VWC gradient can have various ranges even at sites where no failure occurs. Yeonki, where the VWC gradient was higher than 0.1 in many cases, shows that the VWC gradient can be increased by rainfall conditions as the highest rainfall was  
Figure 7: Plots of the VWC gradients of different testbeds: (**a**) slope angle (Busan); (**b**) measurement depth (Busan); (**c**) slope angle (Inje); (**d**) measurement depth (Inje); (**e**) slope angle (Yeonki); (**f**) measurement depth (Yeonki).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Busan** & **Yeonki** & **Inje** \\ \hline Total rainfall(mm year\({}^{-1}\)) & 256 & 905 & 541 \\ Maximum rainfall (mm h\({}^{-1}\)) & 56 & 70 & 29 \\ Maximum rainfall (mm day\({}^{-1}\)) & 90 & 155 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rainfall in Testbed Area.  
observed in the region. As in the flume experiment results, the characteristics of the VWC gradient were not distinguished according to the measurement depth or slope angle. This may be because the sensor installation locations had different soil characteristics, and the increase pattern of the VWC was also different even though all of the testbed sites were granite areas. As it is practically difficult to identify changes in the VWC characteristics based on considerations of all the soil characteristics, it may not be reasonable to clearly determine the presence or absence of failure based on the specific values of the VWC gradient using only some measurement data.
## 4 Conclusions  
This study explored the identification and quantification of OC using two analytical techniques: chromatography and electroanalysis. Calibration curves obtained by both methods showed high confidence and precision, indicating that electroanalysis, under the same conditions, is a reliable approach for detecting OC in both synthetic and real water samples --particularly when using a GCS sensor-- without interference from other components in the water matrix. OC concentrations in sunscreen products were determined using the standard addition method to eliminate matrix effects. A statistical comparison of the two methods revealed no significant differences at the 95% confidence level. Additionally, OC concentrations were effectively reduced during electrochemical treatment, with minimal differences observed between the results of the two analytical techniques.  
Finally, it is important to highlight that the electroanalytical protocol described here offers both advantages and limitations. On the positive side, it eliminates the need for an extraction procedure by employing an organic/inorganic solvent-electrolyte system, facilitating OC detection with a significant limit of detection (LOD). However, further studies are needed to investigate the potential interference from other sunscreen components, OC degradation byproducts, and other inorganic substances present in swimming pool water.  
Conceptualization, E.V.d.S., M.K.S.M. and L.G.A.C.; methodology, L.R.D.B.S. and M.K.S.M.; validation, L.G.A.C., L.R.D.B.S. and M.K.S.M.; formal analysis, L.G.A.C., L.R.D.B.S. and M.K.S.M.; investigation, S.F., L.R.D.B.S. and M.K.S.M.; resources, E.V.d.S. and C.A.M.-H.; data curation, S.F., L.R.D.B.S. and M.K.S.M.; writing--original draft preparation, L.R.D.B.S. and M.K.S.M.; writing--review and editing, S.F., E.V.d.S. and C.A.M.-H.; visualization, E.V.d.S.; supervision, E.V.d.S.; funding acquisition, E.V.d.S. and C.A.M.-H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, CNPq, (Brazil): 315879/2021-1, 403008/2022-0, 402736/2022-2, 408110/2022-8, 317075/2023-3, 421313/2023-4, 408782/2024-2.  
The original contributions presented in this study are included in the article. Further inquiries can be directed at the corresponding authors.  
M.K.S.M. acknowledges the postdoctoral fellowships awarded by the Human Resources Program of the National Agency for Petroleum, Natural Gas and Biofuels--PRH-ANP/37--along with administrative and technical support.  
[PERSON] is employed by Ecas4 Australia Pty Ltd. The company provides in-line electro-chlorination systems (www.ebooster.com.au). However, all authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 3 Results and Discussion
### Nutrient Dissolution
#### 3.2.3 Ratio of Nitrogen to Phosphorus  
The growth of seaweed is closely related to the concentration of nitrogen, phosphorus, and other nutrients in the water, but also to the nitrogen-phosphorus ratio (N/P). The most suitable N/P ratiofor seaweed growth is around 16:1 [30; 31]. The nitrogen was sustainably dissolved out of the SAR specimens during the dissolution test. Thus, it can be regarded as a form of slow-release fertilizer for seaweed. However, the phosphorus content of the SAR specimen solutions was very low. At the end of the dissolution test, the N/P ratio in the immersion solution with each proportion ranged between 193.88 and 298.52 (Table 2), which is much larger than that of tidal sludge [3].  
Shuangdao Bay is part of the Yellow Sea, and the N/P ratio of its marine aquaculture area in summer and autumn is 20.809 and 17.335, respectively. Poor phosphorus content is an inherent phenomenon in the seawater in this area, which may limit the uptake of both nitrogen and phosphorus by seaweed [3]. Moreover, the nutrient source of seabed silt may be related to the shellfish rafts and the enhancement of sea cucumber populations in the surrounding seawater. Therefore, before selecting the source of seabed silt, the N/P ratio of the dissolved nutrients from the seabed silt should first be tested. In this way, the N/P ratio could be adjusted by adding phosphorus to improve the dissolution properties of the SAR.
## 4 Results and Discussion
### Discussion
#### 4.2.6 Contributions of Vegetation Indices  
In Table 6, it was found that classifications utilizing 10, 4 + VI, and 10 + VI characteristics yielded more accurate results than those using just 4 and VI features. The increase exceeded 9.0% for OA scores and 0.15 for F1 scores. This was expected, given that the 10, 4 + VI and 10 + VI features had more spectral information. In comparison to the 4-bandfeature, the VI feature demonstrated superior classification results. This is consistent with previous studies [45; 46], demonstrating that red-edge spectral bands are sensitive to crop status. Comparing the 10-band classification to the 10 + VI classification, it was found that the contribution of VI features was minimal. This was because VI consists of non-linear combinations of the 10 spectral bands and is theoretically unnecessary. In addition, the contrastive learning framework could explore this information to improve classification. Thus, we recommend that raw spectral features be fed into the contrastive learning framework.
# On the Effect of Filling Rate on Positive Macrosegregation Patterns in Large Size Cast Steel [PERSON]
## 4 Conclusions  
The effect of filling rate on positive macrosegregation characteristics in large size steel ingots was studied based on the analysis of the hot-top and a small portion of the main ingot body. The following conclusions can be drawn:  
1. General macrosegregation patterns presented the solute dependence of segregation features, which was related to the difference in the solid-liquid partition coefficient and diffusion capability of each element in the liquid iron.
2. Frequency distribution of segregation ratios, as well as the axial and horizontal concentration evolutions, presented less severe segregation in the casting body of the ingot with the higher filling rate.
3. One source of the reduced solute segregation in the casting body in the higher filling rate case is the presence of higher liquid instabilities at the start of solidification, which was predicted by the Reynolds number calculations during the filling process, and confirmed by macroetch grain morphology observations in the first solidified zones.
4. Another source for the lower segregation intensity in the casting body of higher filling rate case is the shorter solidification time, resulting in less time available for solutes to transport, which was revealed by the SDAS measurements along the cutting section of the ingot in the regions solidified in the intermediate and final stages of the solidification process.  
Therefore, the alleviation of solute segregation in the upper section of the casting body resulting from the accelerated filling process could be due to a combination of the more intense movement of the liquid metal, caused by higher kinetic energy of the liquid metal in the initial solidification stage, and shorter solidification time that reduces the available time for the segregation to build up.  
Conceptualization, M.J. and C.Z.; Methodology, C.Z.; Software, C.Z. and A.L.; Validation, C.Z.; Formal Analysis, C.Z.; Investigation, C.Z. and R.T.; Resources, R.T., M.J. and L.-P.L.; Data Curation, C.Z.; Writing-Original Draft Preparation, C.Z; Writing-Review & Editing, M.J. and A.L.; Visualization, C.Z.; Supervision, M.J.; Project Administration, M.J.; Funding Acquisition, M.J.  
The financial support from the Natural Sciences and Engineering Research Council (NSERC) of Canada in the form of a Collaborative Research and Development Grant (CRDG) under number 470174 is gratefully acknowledged. Fink Steel-Sorel Co. for providing the material is greatly appreciated.  
The authors declare no conflict of interest.
# Autonomous Environment Generator for UAV-Based Simulation
## 3 Experimental Setup
### Communication Protocol  
Since the platform will handle real-time information coming from the frontend, back-end, and UAV, it was necessary to address this issue. A common technique used in many web-platforms is long-polling, where the client continually checks the server for new data. Until a decade ago, this way of getting real-time data was as effective. However, as more client requests reached the server, a problem became more apparent. The latency between client and server increased since each connection is kept open for as long as possible. When the connection times out, the client is notified, and a new request is made repeatedly. Thus the idea of a new protocol, identified as WebSocket, was born. As [PERSON] and [PERSON] described [18], WebSocket allows a long-held, bi-directional and full-duplex TCP socket connection established between client and server. This procedure starts with the client sending an HTTP request to the server asking to connect to a WebSocket. This process is known as a WebSocket handshake. After the server accepts this request, it replaces the HTTP connection by a WebSocket connection. The server maintains this connection for each client, using it to push data in real-time. Our platform utilizes this protocol to efficiently send and process the satellite image, as well as manually control the UAV.
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 3 Mechanisms-Related Approaches
### Machine Learning Based Optimization  
Machin Learning is an intelligent methodology that has emerged as promising technique in the domains of classification and prediction [34; 35]. In SDN, the flow rule setup causes delays in controllers'response time. Referring to the reactive mode in OpenFlow-based SDN, this delay introduced by the controller is important for its scalability. Some research proposals suggest to increase the controllers hardware and the memory. Yet, this solution just applies to hardware components. Recently, besides the previously mentioned approach, several proposals suggest bringing machine learning into SDN. In fact, there are many issues in SDN that can be tackles using this technique. However, focusing on the control plane scalability, machine learning can be exploited to deal with this issue using the following ways.  
Recently, the authors of Reference [28] proposed Bayesian Machine Learning algorithm for Flow Prediction in SDN Switches. The proposed algorithm enables switches to predict the traffic using machine learning, leading to reduction of the amount of unnecessary communications with the controller. Indeed, the switch infers the type of flow concerning the not matching packets within the flow table. Appropriate rules for these packets can then be applied directly without causing any delay.  
Another recent work that tackles control plane scalability using machine learning is DROM [29]. It exploits machine learning to optimize routing and table lookup processes. It applies Deep Deterministic Policy Gradient (DDPG) together with the DDPG Routing Optimization Mechanism (DROM) to achieve higher scalability. DROM uses neural networks to make flow table lookups more efficient in terms of storage usage and hence reduce delays.  
To outline the strengths of machine learning based approaches, the powerfulness of the technique has to be mentioned as the first aspect. Analyzing data in real-time condition enables new possibilities to the network management. Smart decisions reducing the network load, lead to a higher performance without causing any overhead while applying the proposed solutions within the network. The different characteristics and components of SDN represent a very good base for applying machine learning techniques to improve routing mechanisms in SDN. The simplified view of the whole network from a centralized control plane enables applications to intervene in the entire network traffic. Although applying machine learning to SDN can enable novel possibilities, it also has some challenges with an increasing complexity of the network. Occurring errors will be difficult to detect inside the process itself and can cause a longer time of unavailability.
## 2 Background
Therefore, the same ElGamal Digital Signature algorithm as the encryption algorithm is used in this method to sign privacy biometric images, which simplifies the complexity of the algorithm as a whole and solves the problem to a certain extent. After analyzing the various types of encryption and signature algorithms mentioned above, we found that these algorithms can have some minor drawbacks, such as too much computation, a small key space, inconvenient storage and transportation, and overly complex algorithms. Based on these problems, and after analyzing and summarizing the existing research, this paper proposes a complete encryption and authentication method using the Arnold Transform and ElGamal Encryption Algorithm for encryption and the ElGamal Digital Signature for authentication. The proposed method is improved by upgrading the Arnold transform from 2D to 3D while combining techniques such as the ElGamal encryption algorithm, which cleverly solves the problem of key space and improves the difficulty of the algorithm, making the whole method more complete and better at sustaining the security of privacy-preserving biometric images. This combination solves the problem of tampering during transmission in common encryption scenarios and solves problems in scenarios where privacy protection is a challenge. For example, in laboratories that use privacy-preserving biometric images for research, the experimental data needs to be protected by encryption to prevent leakage when a single encryption alone could easily be cracked and stolen, and the experiment also needs the authenticity and integrity of the data, which requires a digital signature to be added to the encryption process for verification.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 3 Proposed Method
### The Overall Fusion Framework  
As mentioned earlier, the infrared and visible image fusion is regarded as an adversarial process between G and D. The training and testing operations are not exactly the same. Only the trained G is needed when testing. The entire process is described in detail below. The training process mainly contains 4 steps. First, the infrared image I and visible image V are concatenated in the channel dimension forming a synthetic 2-channels input retaining the same height and width of the source images. Then, the input is fed to G to generate the preliminary fused image F. Meanwhile, the size and dimension of the fused image are consistent with either source image. Next, the F and V are fed to D respectively to get the discriminant probabilities belonging to (0, 1). Among them, F is the fake image whereas V is regarded as the label. Finally, the parameters of G and D are updated through backpropagation according to the loss function which will be given detail description in Section 3.3. In general, the loss of G guarantees that F contains more considerable intensity information of I and partial gradient information of V, whereas the loss of D gradually adds more visible detail to F. As to the testing process, we just need the first two steps of training, using the trained G to get the final fused image. Figure 3 shows the framework of our fusion process.
# A Survey of Graphene-to-Phoneme Conversion Methods
## 3 Strategies of G2P Conversion
### Multimodal Learning for G2P Conversion  
The majority of existing G2P systems are trained separately on spelling and pronunciation data, ignoring audio modalities. IPA character sequences used to represent pronunciation do not capture all of their subtle differences. Ref. [40] raised a multimodal model with an attention mechanism. This model can learn robust joint representations of grapheme and speech data in multiple languages and then predict phoneme sequences given only grapheme. Speech data are only used for training, and the model only utilizes grapheme input during inference. The model structure is illustrated in Figure 10.  
Figure 9: GBERT pre-trained G2P.  
The model is a Seq2 Seq model with a single encoder and two decoders; one is the MFCC decoder composed of an LSTM, which predicts MFCC coefficients from grapheme (sub-task), and the other decoder task is to predict IPA character sequences (main task). This multimodal model performs well in both accuracy and efficiency, reducing the phoneme error rate from 8.21% to 6.39% compared with the single modal. However, this study still relies on spelling and pronunciation pairs, which means that this method may not be applicable to languages without spelling and pronunciation pairs.
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 4 Barcode Image Pre-Processing  
The general processing steps of barcode recognition consist of image pre-processing and data reaction. In the pre-processing stage, the Hough transform algorithm [19] is used to correct barcode images with geometric deformations. Then, an evenly spaced grid is overlaid onto the barcode by using the method [20] of the horizontal and vertical edge projections from gradient direction images, which divides the barcode into modules. The color of each module may be black or white, and a total number of modules can be calculated by the barcode size. Figure 3 shows the process of image pre-processing.
## 2 Power System Model  
### Model System  
The power system model used in the simulation studies in this paper is depicted in Figure 1, where circuit parameters and power flow conditions are presented in the figure. It was constructed based on the Institute of Electrical and Electronics Engineers (IEEE) 9-bus standard model [16]. The system consisted of a 100 MVA ASG and two synchronous generators (SG1 and SG2). SG1 was a 200 MVA load frequency control (LFC) hydropower generator, and SG2 was a 300 MVA governor-free (GF) thermal power generator. In addition, an SCIG-based WF (60 MVA), a storage battery (15 MVA), and three loads (Loads A, B, and C) were connected to the system [17]. The ASG was operating in generating mode or pumping mode in the analysis. In addition, a simulation analysis in which the ASG was replaced with a usual fixed-rotor-speed pumping generator (FSG) of the same power rating as that of the ASG was also performed for comparing the performance between the ASG and FSG. Two power flow conditions are shown in the figure in blue and red colors, which correspond to the generating operation mode and the pumping operation mode of the ASG, respectively. Various conditions of the system and each generator, except the circuit parameters and power flow conditions, are summarized in Table 1. The total load in the generating mode of ASG was set at 480 MW, 80% of the total generating capacity of the synchronous generators and ASG, i.e., 600 MVA. The total load in the pumping mode was set at 335 MW because the ASG (or FSG) operated in pumping mode as a load and the output of SG1 and SG2 were set to values less than those in the generating mode.