esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Algorithms and Implementation  
The Kalman filter (KF) is the broad name given to a class of stochastic estimation algorithms. However, in the strict sense, a KF refers to an estimation scheme for linear systems under the assumption of additive white noise, which follows a Gaussian distribution. Assuming that the system is stochastic in nature is truer to reality than assuming a purely deterministic process. This means that the computation is performed in a KF is accounting for variability and uncertainty within the system and attempts to supply an estimate of the optimal value, given this extra information. This stands in contrast to the deterministic observer, which uses a constant observation error gain, and does not dynamically evolve [36; 37; 38; 39; 40; 41].  
The assumptions made within the formulation of the KF are linear dynamics and additive Gaussian noises. Under these assumptions, properties of linear stochastic systems could be applied and neatly manipulated into an exact closed-form algorithm. As with many variants of Bayesian estimators, the algorithm for the KF is divided into two stages. The first stage is termed the model prediction/time update, where it uses a linear model with additive Gaussian process and measurement statistics to predict the next state in the time evolution of the system. Meanwhile, the second one is the measurement update that utilizes sensor measurements of the system and statistically updated \"Kalman\" gain to correct the model prediction estimate of the next state to the most likely value. The algorithm for the KF is shown below [42; 43].  
Figure 1: Electro-Thermal-Aging model coupling.  
Stochastic State-Space Model:  
\[x_{k+1}=A_{k}x_{k}+B_{k}u_{k}+W_{k}\]  
\[y_{k}=C_{k}x_{k}+D_{k}u_{k}+v_{k}\]  
Model Prediction:  
\[x_{k}=Ax_{k-1}+Bu_{k}\]  
\[P_{k}=AP_{k-1}A^{T}+Q\]  
Measurement Update:  
\[K_{k}=P_{k}H^{T}\Big{(}HP_{k}H^{T}+R\Big{)}^{-1}\]  
\[x_{k}=x_{k}+K_{k}(z_{k}-Hx_{k})\]  
\[P_{k}=(I-K_{k}H)P_{k}\]
# Mechanical Performance of Concrete Made with the Addition of Recycled Macro Plastic Fibres
## 2 Materials and Methods
### Fibre-Reinforced Concrete (FRC) Specimens Made with RPF  
Specimens of PFRC were prepared by adding recycled plastic fibres (RPFs) to the concrete mix at 2, 4, and 6 kg per m\({}^{3}\) of concrete. The prepared mixtures were assigned the following codes: PFRC-2, PFRC-4, and PFRC-6, respectively. Two control specimens were also prepared: a control specimen of concrete without reinforcement (C-REF), and fibre-reinforced concrete made with a commercial polypropylene fibre (PFRC-REF; dosage of 2 kg per m\({}^{3}\) of concrete).  
Figure 1: Plastic fibres: plastic waste rolls from food packaging (**a**); the prepared fibres (**b**); commercial polypropylene fibre (**c**).  
The aggregates used for manufacturing the concrete mixes in the laboratory were coarse gravel (7-25 mm) at 960 kg per m\({}^{3}\) and sand (0-7 mm) at 970 kg per m\({}^{3}\), both with a siliceous nature. A total of 280 kg of CEM II 42.5 AV-R cement per m\({}^{3}\) of concrete produced was used, reaching a water-to-cement ratio of 0.54. In order to improve the workability and avoid segregation of the mixture, along with having an S3 slump class according to UNE-EN 206:2013 + A1:2018, two different types of additives were used: the plasticizer Complast MR260 at 5 mL per kg of cement, and the superplasticizer Structure 357 at 7.7 mL per kg of cement. The slump ranged between 15 cm for C-REF and 10 cm for PFRC-6 mix. Slump values decreased as macro-fibre content increased, in agreement with the findings of other authors [18]. The concrete produced for each single mixture was casted, resulting in nine cubic specimens with a length of 100 mm, three prismatic beams measuring 100 mm \(\times\) 100 mm \(\times\) 400 mm, and six cylindrical specimens with a diameter of 150 mm and a height of 300 mm, which were then cured in a climatic chamber (at 20 \({}^{\circ}\)C and 95% \(\pm\) 5% relative humidity) and subjected to testing at various ages.
## 8 Conclusions and Future Remarks  
A unique design and characteristics of underwater sensor networks provide advantages in the development of scientific, industrial, defense, and disaster prevention applications, which are dissimilar with terrestrial sensor networks. However, the features of UWSNs and application requirements restrict their use and reveal the gap between applications and technologies. This study shows the essence of acoustic communication as an elementary principal for designing and executing the algorithms, protocols, and services in UWSNs to manage such limitations. First, we investigated the state-of-the-arts in the literature related to UWSNs. We then determined the requirements of UWSN, and provided a thematic taxonomy. This paper analyses the literature from trusted and well-known article databases. Investigation of these classifications assist us to identify the challenges for future improvement and open more opportunities for long-term success in the field of underwater sensor networks.  
Even though UWSNs have received a great number of improvements in the previous few years, there is still substantial room for improvement, especially in implementing systems on a large scale. As future work, the researchers can offer better solutions on node mobility with high monitoring area (with high neighbourhood range) scenarios to investigate the effect on network connectivity, coverage, energy consumption and network lifetime. To increase efficiencies of the UWSNs and improve its performance, the studies should direct the focus of the prospective research towards implementing cooperative control among a few underwater vehicles. The future studies should enhance the cooperative communication of the vehicles in terms of channel bandwidth and autonomy level (such as covering a re-planning decision). Simultaneously, the subsequent studies could analyze the environment and underwater vehicles models to enable the algorithm to have a wider range of applications. Next, the researchers can plan to develop the significant high-level planning layer to designate the desired configurations or strategic region of interest that the vehicle ought to explore towards. The works could also cover more complex network scenarios including mobility, shadowing, multi-path fading and evaluate how it influences the results. Finally, a hybrid harvesting energystrategy for ocean environment monitoring needs to be taken into consideration, to harvest and apply more reliable renewable energies in the hostile marine environment.  
This work is supported by Ministry of Higher Education Malaysia Fundamental Research Grant Scheme (FRGS) FP055-2019A.  
The authors declare no conflict of interest.
# Detection of Surface Water and Floods with Multispectral Satellites
## 3 Multispectral Indices for Water Segmentation
[105]** \\ \cline{2-7}  & \(4\cdot(GREEN-SWIR1)-0.25\cdot(NIE+275\cdot SWIR2)\) & \(\mathit{BLUE+25\cdot GREEN-1.5\cdot(NIR+SWIR1)-0.25\cdot SWIR2}\) \\ \cline{2-7}  & **Landsat** & **5-TM** & **4-(_B_2 - 85) - 0.25\cdot(B4+2.75\cdot B7) & \(B1+2.5\cdot B2-1.5\cdot(B4+85)-0.25\cdot B7\) \\  & **7-ETM+** & & & & & \\ \cline{2-7}  & **Landsat** & **5-01** & **4-(_B_3 - 86) - 0.25\cdot(B5+2.75\cdot B7) & \(B2+2.5\cdot B3-1.5\cdot(B5+B6)-0.25\cdot B7\) \\  & **Sentinel-2** & & & & & \\ \cline{2-7}  & **MSI** & **4-(_B_3 - 811) - 0.25\cdot(B8+2.75\cdot B12) & \(B2+2.5\cdot B3-1.5\cdot(B8+B11)-0.25\cdot B12\) \\ \cline{2-7}  & **Terza-Aqua** & **MODIS** & **4-(_B_4 - 86) - 0.25\cdot(B2+2.75\cdot B7) & \(B3+2.5\cdot B4-1.5\cdot(B2+B6)-0.25\cdot B7\) \\ \cline{2-7}  & **Landsat** & **4-(_B_3 - 86) - 0.25\cdot(B5+2.75\cdot B7) & \(B2+2.5\cdot B3-1.5\cdot(B5+B6)-0.25\cdot B7\) \\  & **Sentinel-2** & & & & & \\ \cline{2-7}  & **MSI** & **4-(_B_3 - 811) - 0.25\cdot(B8+2.75\cdot B12) & \(B2+2.5\cdot B3-1.5\cdot(B8+B11)-0.25\cdot B12\) \\ \cline{2-7}  & **Terza-Aqua** & **MODIS** & **4-(_B_4 - 86) - 0.25\cdot(B2+2.75\cdot B7) & \(B3+2.5\cdot B4-1.5\cdot(B2+B6)-0.25\cdot B7\) \\ \cline{2-7}  & **Landsat** & **4-(_B_3 - 86) - 0.25\cdot(B2+2.75\cdot B7) & \(B3+2.5\cdot B4-1.5\cdot(B2+B6)-0.25\cdot B7\) \\  & **Sentinel-2** & & & & & \\ \cline{2-7}  & **MSI** & **4-(_B_3 - 811) - 0.25\cdot(B8+2.75\cdot B12) & \(B2+2.5\cdot B3-1.5\cdot(B8+B11)-0.25\cdot B12\) \\ \cline{2-7}  & **Terza-Aqua** & **MODIS** & **4-(_B_4 - 86) - 0.25\cdot(B2+2.75\cdot B7) & \(B3+2.5\cdot B4-1.5\cdot(B2+B6)-0.25\cdot B7\) \\ \cline{2-7}  & **Landsat** & **4-(_B_3 - 86) - 0.25\cdot(B2+2.75\cdot B7) & \(B3+2.5\cdot B4-1.5\cdot(B2+B6)-0.25\cdot B7\) \\  & **Sentinel-2** & & & & & \\ \cline{2-7}  & **MSI** & **4-(_B_3 - 811) - 0.25\cdot(B8+2.75\cdot B12) & \(B2+2.5\cdot B3-1.5\cdot(B8+B11)-0.25\cdot B12\) \\ \cline{2-7}  & **Terza-Aqua** & **MODIS** & **4-(_B_4 - 86) - 0.25\cdot(B2+2.75\cdot B7) & \(B3+2.5\cdot B4-1.5\cdot(B2+B6)-0.25\cdot B7\) \\ \cline{2-7}  & **Sentinel-2** & & & & & \\ \cline{2-7}  & **MSI** & **4-(_B_3 - 86) - 0.25\cdot(B2+2.75\cdot B7) & \(B3+2.5\cdot B4-1.5\cdot(B2+B6)-0.25\cdot B7\) \\ \cline{2-7}  & **Terza-Aqua** & **MODIS** & **4-(_B_3 - 86) - 0.25\cdot(B2+2.75\cdot B7) & \(B3+2.5\cdot B4-1.5\cdot(B2+B6)-0.25\cdot B7\) \\ \cline{2-7}  & **Sentinel-2** & & & & & \\ \cline{2-7}  & **MSI** & **4-(_B_3 - 811) - 0.25\cdot(B8+2.75\cdot B12) & \(B2+2.5\cdot B3-1.5\cdot(B8+B11)-0.25\cdot B12\) \\ \cline{2-7}  & **Terza-Aqua** & **MODIS** & **4-(_B_4 - 86) - 0.25\cdot(B2+2. In the following sections, a summary of the performances of each selected index according to evidence derived from the literature review, as well as some indications about their applicability in different land cover conditions, is reported. To provide an overview of spectral index performances in quantitative terms, common error metrics used in image classification accuracy assessment are briefly reviewed in the following.
# 3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning
## 5 Discussion  
In this paper, we propose a fusion based action recognition framework, which mainly consists of three components: 3D space-time convolutional neural network, human skeleton manifold representation and classifier fusion. Our fusion based framework combines global structure information, motion information and details information of the action together. It automatically learns advanced features from the input data sources with the help of space-time convolutional neural networks so that it can easily adapt to different working scenarios. A simple multiply-score fusion technique is proposed in order to fully utilize the decision results from different aspects. Experimental results on three popular public benchmark datasets proved that our proposed method is the best among the current state-of-the-art methods. Although our method introduced an additional fusion step, this involves a simple SVM inference step and scores multiplications. The computational cost overhead of the additional fusion step is in the order of tens of milliseconds, which is relatively low. Furthermore, three streams in our proposed method could run in parallel so that it is still quite efficient to be executed on modern multi-core CPUs. The parallel processing time of feature learning step for three streams is about 200 ms on a PC equipped with Intel 3.4 GHz i7-4770 CPU.  
Our method is suitable for human-computer interaction applications. For example, our proposed method could be used in controlling TV or video games in the living room. The drawbacks of our proposed method is that our method could only work in batch mode, which is to say the depth video  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Average Classification Accuracy** \\ \hline Ref. [44] & 90.92\% \\ Ref. [35] & 90.53\% \\ Ref. [45] & 91.50\% \\ Ref. [19] & 90.91\% \\ Ref. [48] & 96.00\% \\ Our proposed method & **97.29\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of the average classification accuracy on UTKinect-Action3D dataset.  
Figure 7: Confusion matrix of our proposed method in UTKinect-Action3D dataset.  
sequence has to be collected before sending it to the network. In the future, we plan to extend our work for online action recognition applications.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
This research was partially funded by National Natural Science Foundation of China under contract number 61501451 and 61572020.  
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, and in the decision to publish the results.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Merkle-Transformer  
#### 3.3.1 Overall  
In this paper, we propose an innovative method that combines the advantages of Merkle trees and Transformer models to enhance the security and efficiency of financial computations. The core concept of the Merkle-Transformer model is to ensure the security and integrity of data through Merkle trees while leveraging the efficient computational capability of the Transformer model. This combination not only improves the accuracy of data processing but also ensures that security is not compromised in the pursuit of efficiency.  
Figure 3 illustrates a schematic of the working principle of the Merkle-Transformer model. The figure elucidates how the outputs of the intermediate layers of a deep learning model are combined with the Merkle tree structure to ensure the security and verifiability of the model's processing. Each layer (L1 to L8) are first considered as leaf nodes in the Merkle tree. Subsequently, adjacent outputs are paired and combined through a hash function Hash, progressively building the Merkle tree until forming the top hash, representing a unique fingerprint of the entire model's output.  
Specifically, the raw data passes through a series of attention modules, each generating a feature representation (L1 to L8). This paper introduces a shallow convolutional neural network at the model's entrance for initial feature extraction, and the extracted features are then fed into the Transformer model. These feature representations are further combined through the hash function to form intermediate nodes (Hash 0-0, Hash 0-1, etc.), culminating in the top hash. At the end of the model, after passing through the flattened and dense layers, the output is obtained. Throughout this process, every step of the model's output is linked to the Merkle tree structure via the hash function, implying that any minor change in output will cause a variation in the root hash, thereby verifying the integrity and unaltered state of data throughout the transmission and processing. This design enables the Merkle-Transformer model not only to process complex financial data but also to ensure security during data processing, as any unauthorized data modification will be detected. This is particularly important for financial data, often involving sensitive information and critical decision-making. Additionally, the Merkle-Transformer model provides a verifiable computation process, allowing every step of the model's operation to be traceable and verifiable, greatly increasing the transparency and trustworthiness of financial computation models.  
In summary, the Merkle-Transformer model integrates deep learning and Merkle trees to provide a secure and efficient new framework for financial computations. This combination effectively utilizes the strengths of both technologies--the powerful data processing ability of deep learning and the unique security advantage of Merkle trees--offering a novel solution for financial data processing and analysis.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Accuracy Evaluation
The _SSIM_ map can be used to indicate the local similarity of the images, and a mean _SSIM_ (MSSIM) index can be used to evaluate the overall similarity [53]. We calculated the indexes for each pixel with the FPARMCD15A2 as the reference image and the other FPAR data as processed images in this study to evaluate the similarity at a regional scale.
## 2 Materials and Methods
### Host Strain and Bacteriophage
#### 2.1.4 Acidic Inactivation of Bacteriophage K5  
Analogous to Section 2.1.3, bacteriophage K5 was characterized with respect to its survival behavior in an acidic environment. Therefore, 0.1 mL pure bacteriophage K5 lysate (Section 2.1.2) with an initial bacteriophage titer of \(N^{*}\) = 2.3 \(\times\) 10\({}^{7}\) pfu mL\({}^{-1}\) was incubated in 9.9 mL of an acidic solution. Acidic solutions were composed of 2 g L\({}^{-1}\) NaCl (Carl Roth GmbH and Co. KG, Karlsruhe, Germany) adjusted with HCl (Th. Geyer GmbH and Co. KG, Renningen, Germany) to pH values of 2.0, 3.0, 3.5, 4.0, and 5.0 (inoLab\({}^{\circledR}\) 7110, Xylem Analytics Germany Sales GmbH and Co. KG, WTW, Weilheim, Germany). A pure NaCl solution served as a reference (native pH 6.6). The first sample was taken directly after mixing (\(t\) = 20 s). After that, incubation times ranged from \(t\) = 1-120 min. Samples were neutralized immediately in a 0.1 M sodium phosphate buffer (pH 6.8) (Na\({}_{2}\)HPO\({}_{4}\), Carl Roth GmbH and Co. KG, Karlsruhe, Germany; NaH\({}_{2}\)PO\({}_{4}\), AppliChem GmbH, Darmstadt, Germany) using a 1:1 ratio of sample and buffer. Bacteriophage titers were determined after 20 s (\(N_{0}\)) and after respective incubation times (\(N_{t}\)) using the plaque assay (Section 2.1.2).
## 3 Results and Discussion
### Antiviral Properties  
Figure 2 shows the plaque-forming units per mL measured in the antiviral tests after 10 min, and 18 and 48 h of contact with bacteriophage T4.  
These results showed no statistically significant antiviral activity in the samples at 10 min of contact, indicating that there is not enough time for viral inactivation. In addition, these results at a short time demonstrate that no viral inactivation is produced by the sonication-vortex treatment (see Figures 2 and 3).  
However, after 18 h of contact with the non-enveloped double-stranded DNA T4 virus, the viable phage counts were partly significantly (\(p>0.05\)) reduced in calcium alginate (see Figures 2 and 3) as reported for the enveloped double-stranded DNA HSV-1 virus [19].  
Calcium alginate's antiviral action did not improve after a longer exposure period (48 h). However, the calcium alginate films with a 0.1% carbon nanofiber content achieved approximately a log of inactivation after 48 h (see Figures 2 and 3), showing that CNFs provide additional antiviral capacity.  
Figure 2: Antiviral activity results of control, alginate films, alginate/carbon nanofibers composite films (CNFs0.1%) after 10 min, and 18 and 48 h of contact with the bacteriophage T4 viral model. ***\(p>0.001\); ***\(p>0.01\); *\(p>0.05\); ns: not significant.
# A Local Feature Engineering Strategy to Improve Network Anomaly Detection +
## 3 Background
### Intrusion Detection Systems  
The main task of an IDS is the analysis and classification of the network activity, which is aimed to discriminate the normal network events from the intrusion ones. The concept of intrusion can be related both to a software activity, such as malware [53; 54] (e.g., spyware, virus, rootkit, etc.) and to a human activity [55] addressed to the illegitimate exploitation of network resources.
## 3 Description of the Tool
### Algorithm of the Coolant State  
The thrust chamber is discretized into a certain number of sections as shown in Figure 4. The coolant is assumed to be entering the channels at the nozzle exit; therefore, the nozzle exit is the first section. In the solution algorithm, channel volumes in each discretized section, depicted in Figure 2, is analyzed as an independent control volume. Energy flows into each control volume by means of the heat transferred from the hot gas and through the enthalpy of the entering coolant fluid, and flows out of the control volume through the enthalpy of the exiting coolant fluid. Therefore, in a steady state, the energy balance in a section \(j\) gives the following approximation for the coolant temperature \(T_{c}\).  
\[T_{c}^{j+1}=T_{c}^{j}+\frac{\pi d_{i}^{j}L^{j}q^{j}}{Nm_{c}c_{p,c}} \tag{13}\]  
where \(N\) is the number of channels, \(L\) is the length of the particular section, and the superscript \(j\) denotes the section index. At each section, the heat flux is calculated by  
Figure 3: Flowchart of operations in ODREC.  
Equation (12) using the temperature of the coolant calculated at the previous section by Equation (13). Furthermore, the inner wall temperature is calculated as follows:  
\[T_{w}^{j}=T_{zw}^{j}-\frac{q^{j}}{u_{g}^{j}} \tag{14}\]  
Here, it should be noted that there is an implicit relation between the gas convection coefficient and the wall temperature, since Equation (5) includes the wall temperature. Therefore, at each section, the wall temperature is iterated until a convergence criterion, namely that the temperature difference between the respective iterations should be under 0.1 K, is satisfied.  
Furthermore, the coolant pressure \(P_{c}\) in each section can be found as follows:  
\[P_{c}^{j+1}=P_{c}^{j}-\frac{q_{c}^{j}v^{\beta}}{2}\Bigg{(}K_{L}^{j}+f^{j}\frac {L^{j}}{d_{h}^{j}}\Bigg{)} \tag{15}\]  
where \(\rho\) is the density, and \(v\) is the velocity. The first term inside the parenthesis relates to the minor pressure loss, and the second term relates to the major pressure loss. The minor loss coefficient \(K_{L}\) is determined by the expansion and contraction of the diameter using the formulae given in [9], and the friction coefficient \(f\) is found by the Haaland equation given as follows [28]:  
\[\frac{1}{\sqrt{f}}=-1.8\log\Bigg{[}\Bigg{(}\frac{\epsilon/d_{h}}{3.7}+\frac{6.9}{\text{Re}}\Bigg{)}\Bigg{]} \tag{16}\]  
where \(\epsilon\) is the roughness of the channel wall.
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 2 Target Floating Wind Turbine  
### Numerical Modeling  
The target FOWT is a model in which the IEA 15 MW wind turbine, proposed by NREL for research purposes, is combined with a semi-submersible floating platform developed by Korea Research Institute of Ships and Ocean Engineering (KRISO). Figure 1 shows the numerical model and schematic depictions of the target FOWT. The mooring lines consist of three chains and six wires. Table 1 provides the specifications of the target FOWT. The target FOWT features a rotor diameter of 240 m, a hub height of 150 m, and a rated rotor speed of 7.55 rpm. The numerical modeling of the target FOWT was conducted using the open-source wind turbine simulation tool OpenFAST (Version 3.5.2, 2024).  
\begin{table}
\begin{tabular}{l c r} \hline \hline
**Specifications** & **Units** & **Values** \\ \hline Rated Power & MW & 15 \\ Rated Rotor Speed & rpm & 7.55 \\ Rated Generator Torque & MNm & 19.63 \\ Gear Ratio & - & 1 \\ Rotor Diameter & m & 240 \\ Hub Height & m & 150 \\ Cut-in/Rated/Cut-out Wind Speed & m/s & 3, 10.59, 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the target FOWT.