esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Studies, such as [37], focusing on a similar geographic region, suggest that for wind speed variables, the 3D-Var method might still be superior, despite the 4D-Var analysis focusing on a similar region. While significant enhancements, ranging from a 5 to 10% reduction in mean absolute bias, were observed for temperature fields, the improvement for wind speed remained slightly positive, with reductions ranging from 0.4 to 2% in mean absolute bias, albeit inconsistently across the forecast horizon. Their investigation, conducted during the occurrence of Medicare Janos in Greece, introduces additional complexity due to the integration of IMERG precipitation data across Europe, which poses further challenges. Nevertheless, these findings highlight the potential benefits of the WRF-DA for operational forecasting systems and contribute to ongoing efforts to optimize and parameterize models for diverse test cases and scenarios. The validation results of the ocean circulation and wave models (Figure 6) that are based on the data assimilated WRF downscaled model are particularly promising, especially given that the CMEMS model solutions are already corrected with observations through 3D-Var. In contrast, the regional ocean circulation and wave forecasting models operate on an operational cycle, using the aforementioned WRF forcing, which is corrected only with atmospheric data--data that are far more abundant and readily available compared to the limited ocean observations in the area. Below, two thematic applications are presented that showcase the usefulness of the introduced models on solving real-life problems and offer solutions as containerized applications, with a focus on the ease of use by prospective end-users. The containerization of these tools also aids in the replicability and transferability of the applications, since prospective users can alter input variables and run different what-if scenarios with alternative weather and oceanic conditions, without the need of advanced research knowledge, which contributes to the wider adoption of the tools, which aim at lowering CO2 emissions and adhering to Green Deal policies, as well as mitigating pollution and enabling safer maritime practices.
## 3 Methodologies
### Model Evaluation Method  
To evaluate the predictive performance of the MPM training model, traditional evaluation metrics such as recall, precision, and \(F1\) score [73] are used; these metrics are composed of true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs). A higher \(F1\) score indicates better performance. The formulas for recall, precision, and \(F1\) score are as follows:  
\[recall=\frac{True\ Positive}{True\ Positive+False\ Negative} \tag{6}\]  
\[precision=\frac{True\ Positive}{True\ Positive+False\ Positive} \tag{7}\]  
\[F1\ \text{score}=2\cdot\frac{precision\times recall}{precision+recall} \tag{8}\]  
Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC) are also commonly used to evaluate the performance of predictive models in Mineral Prospective Mapping (MPM) [74]. The ROC curve plots the false positive rate on the X-axis and the true positive rate on the Y-axis to describe the model's performance. The AUC value ranges between 0 and 1, with higher values indicating better model performance [75].  
In addition, Prediction-Area (P-A) plots are widely used to evaluate model performance This plot features two curves representing the prediction rate curve and the occupied area curve--that is, the ratio of the known mineralized area within the target area to the total known mineralized area, and the ratio of the target area to the total area, respectively [76]. These curves illustrate the relationships among the study area, the target area, and the known mineral deposits. As the prediction probability increases from 0 to 1, the two curves intersect at a point, indicating that the maximum number of known mineral deposits is concentrated within the smallest target area. The higher the Y-value at the intersection point, the better the model's performance [77]. Therefore, the higher the intersection point on the P-A plot, the better the predictive model performs, enabling the discovery of as many known mineral deposits as possible within a limited target area and facilitating the delineation of new mineralization targets.
# A Global Review of PWR Nuclear Power Plants
## 2 International Development of the PWR Design
### Russian PWR Evolution  
In parallel with North American nuclear development, the former Soviet Union was also taking its first steps towards generating electric power from nuclear power. In 1954 the Soviet Union was able to start the operation of the world's first nuclear-powered nuclear reactor. It was the _Obninsk_ nuclear power plant, based on the LWGR design, water-cooled, and moderated with graphite. This design, operational until 2002, had a generation capacity of 5 MWe. Soviet scientists and technicians went on to develop various designs of nuclear reactors with some success: LWGR, PWR and BWR. Finally, the communist regime opted for the development of the first two. As for the PWR design, the Soviet version was named _Water-Water Energetic Reactor_ (WWER) [36; 37], from the Russian _Vodo-Vodyanoi Energetichesky Reaktor_ (VVER), which was developed by the company currently known as Rosatom. The main differences between the Soviet VVER design and the western PWR are [38]: (i) a hexagonal geometry of the fuel assemblies with the arrangement of the fuel rods in a triangular grid; (ii) the use of a zirconium-niobium alloy as a fuel rod clad material; (iii) the possibility of transporting all large equipment by rail, resulting in a limitation of the outside diameter of the reactor pressure vessel; and (iv) an original design of horizontal steam generators.  
Within Generation I, the first two units of the _NovoVoronezh_ nuclear power plant, named V-210 and V-365 based on their power generation capacity, served as prototypes of the VVER design. After the construction of these first two units, the construction of two units began at the _NovoVoronezh_ nuclear power plant with the VVER-440 variant (Figure 10a). With minor changes from its predecessors, the VVER-440 design became the base design for the construction of more than twenty nuclear power plants around Europe: Slovakia, Hungary, the Czech Republic, Bulgaria, and Finland. The third stage of development of the VVER-440 design was the V-213 variant, which included as a design accident the severe breakage of the lines with the maximum diameter of the primary.  
The continuous evolution of the VVER-440 design resulted in the VVER-1000 design (Figure 10b), first implemented in 1980 in Unit 5 of the _NovoVoronezh_ nuclear power plant. While the VVER-440 reactor has six loops, the VVER-1000 design reduces the number of these to four [39]. This design was developed within Generation II and Generation III nuclear reactors (Figure 1). In this design, the reactor is located within a concrete containment. The number of fuel elements increased, and the number of control bars was reduced.  
In the mid-2000s, began the development of a yet further modernized design: the VVER-1200. The new Russian reactor Generation III+ VVER-1200 represents the latest model currently offered for construction by Rosatom. It is an evolutionary development of the well-proven VVER-1000 with an increasing thermal power to about 3200 MWt and providing additional passive safety features [40].
## 3 Materials and Methods
### Classification Algorithms
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Parameter** & **Value** \\ \hline LSTM models & filters & 64 \\  & kernel size & (1,2) \\ \cline{2-3}  & epochs & 40 \\  & activation function (units) & ReLU \\  & dropout & 0.2 \\  & optimizer & Adam \\  & learning rate & \(10^{-3}\) \\  & batch size & 8 \\  & hidden units & 128 \\  & activation function (class) & Softmax \\  & loss function & Categorical crossentropy \\ SVM & kernel & rbf \\  & C & 1.0 \\  & \(\gamma\) & escale \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\) Multiplicative inverse between the number of features and the variance of the data.  
\[\delta(x_{i})=\operatorname*{argmax}_{j\in[1,\ldots,|L|]}\frac{e^{z_{j}}}{ \sum_{k=1}^{l}e^{z_{k}}}, \tag{3}\]  
where \(z=\{z_{1},\ldots,z_{l}\}\) is the intermediate output of the softmax layer for the test data point \(x_{i}\). With regard to SVM, the decision function depends on \(n_{s}\) vectors that help to form the class separation hyperplane (support vectors) with their respective weights \(\alpha_{j}\) and classes \(y_{j}\) and a kernel function that makes it possible to transform the input data \(K(\cdot)\) to another dimension, where \(b\) is a scalar value and \(\operatorname{sign}(\cdot)\) is the sign function [54; 55; 56]:  
\[\delta(x_{i})=\operatorname{sign}(\sum_{j=1}^{n_{s}}\alpha_{j}y_{j}K(\vec{x}_ {i},\vec{x}_{j})+b), \tag{4}\]  
In SVM classifiers, we apply kernel rbf to capture the nonlinear patterns generally present in temporal data such as COVID-19. The selected kernel is defined as \(K(\cdot)=\exp(-\gamma||\vec{x}_{i}-\vec{x}_{j}||)\) with \(\gamma\) a hyperparameter that controls the tradeoff between error due to bias and variance in the model, keeping the other parameters as default (refer to Table 1) [33; 57]. Finally, in both classifiers we considered \(s=4\) time steps for each of the \(i\)-th sample features [58; 59]. In the case of SVM, an average of the four time steps was considered to form an \(f\)-dimensional feature vector at each time instant.  
\end{table} 
Table 1: Parameters of the classification algorithms.
# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
## 2 Methods
### Convolutional Neural Networks and the Use of Transfer Learning  
Currently, the modern deep learning models in computer vision use convolutional neural networks (CNNs). These layers make the explicit assumption that any input to them is an image. Early convolutional layers in a network process an image and come up with detecting low-level features in images like edges. These layers are successful in capturing the spatial and temporal dependencies in an image. This is done with the help of filters. Unlike normal feed-forward layers, these layers have a much lower number of parameters and use a weight-sharing technique, thus reducing computation efforts. The learnable parameters of each layer consist of filters (or kernels), extended through the full depth of the input volume but these have a small receptive field. When an input is subjected to forward pass, each kernel is convolved across the height and width of the input volume, creating a 2-D activation map of that filter. If 'N' filters are used, then stacking those 'N' activation maps along the depth forms the full output of the convolutional layer [48] (see Figure 2).  
The activation layer is very useful as it helps to approximate almost any nonlinear function [49]. The feature map from the convolutional layer is taken as input to the activation layer.  
Pooling layers are used to reduce the spatial size of representation generated by previous kernels after convolution. This helps in reducing the number of parameters, thus reducing the computation work. These layers are used to extract dominant features that are positional and rotational invariant. It is common practice to include a pooling layer in between two convolutional layers. The most common pooling layer is the max pooling layer; it separates input into squares of a given size, and outputs the maximum value of each square. On the other hand, an average pooling layer finds the average of each square, both the methods reduce the dimensionality and computation efforts [50].  
When working on a similar computer vision problem, we can use those pre-trained models, instead of going through the long process of training models from scratch. This method of transferring  
Figure 2: Filter operation over convolutional layers.  
learning from one predefined and trained model to some new domain by reusing the network layer weights is called transfer learning (see Figure 3). Transfer learning is a very useful technique and has achieved significant results in computer vision and other areas also [51; 52; 53; 54].
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 4 Future Research Prospects
### Edge Computing and Scalability in NB-IoT Network  
Scalability in an NB-IoT network requires the introduction of new devices into the network without any reduction in the network performance and minimum human intervention. To achieve any level of scalability in the network, some challenges have to be resolved. The interoperability among devices deployed, memory space needed for data storage, the processing power of the system, and bandwidth for smooth and effective data transmission are all parts of the key challenges that need to be resolved for efficient scalability [114].  
In order to mitigate some of these challenges, the edge computing technology can be deployed to provide a more robust and cost effective computing power in terms of memory, speed, and enhanced scalability to the NB-IoT network. Edge computing, however, is an  
Figure 14: Application of SON in NB-IoT networks.  
intermediary between the NB-IoT network and cloud computing. Therefore, this proposed system will require a stable and reliable cloud computing platform that supports NB-IoT for industrial cases [115]. Resolving these challenges will go a long way to build the confidence of industrial managers in the implementation of NB-IoT networks.
## 7 Testing
Both ETX-NH and ETX present an improvement over ETT with regard to energy efficiency. High correlation between scale and each metric performance can be observed from Figure 12. This presents scale as a good indicator of how well a solution might perform in the network. It remains difficult to distinguish between solutions at small scales however higher contrast emerges as scale increases. A particular nuance with reliability is noticed for ETT over increasing scale. ETT shows improved reliability over both competitors for scale less than 4.5 hops. Over this threshold however ETT performs poorly in comparison. ETX and ETX-NH handles increasing scale more gracefully with regard to reliability. Figure 13 presents each solutions performance under light and heavy channel interference. As expected, degraded channel quality has a significant effect on network reliability. Stability and latency are also heavily effected. Figure 12: Network response from each solution as scale varies. Figure 13: Network response from each solution as quality varies. Figure 14 shows the performance of each of the solutions over a range of standard deviation of both density and scale in the network. The standard deviation are regarded as secondary features as they do not have as great an effect the performance as the primary features. The secondary features can however be used effectively for modelling and can be useful for characterising particular types of deployments which the primary features cannot distinguish. In these simulations the density (sd) can be used to distinguish between solutions with regard to stability and perhaps efficiency. Scale (sd) is effective at distinguishing between ETT and the other solutions with regard to latency. It is clear that there are many insights to be garnered from the data presented. Certainly, the physical features chosen present a strong case for network performance characterisation visually. It is important that the performance can be modelled efficiently using the physical features for the purpose of solution comparison.
## 4 Phase Noise Analysis  
An s-domain phase noise model is shown in Figure 11. The noise contribution of the LPF is neglected.  
According to transfer function of each part, the whole system phase noise is given as:  
\[\begin{split}\phi^{2}_{\text{{\scriptsize{OUT}}},n}=\left(\phi^ {2}_{REF,n}+\phi^{2}_{INBUF,n}\right)\cdot\left|\frac{N\cdot G_{SSPLL}(s)}{1+ G_{SSPLL}(s)}\right|^{2}+V^{2}_{MSSF,n}\cdot\left|\frac{1}{1+G_{SSPLL}(s)} \cdot\frac{K_{VCO}}{s}\right|^{2}\\ +I^{2}_{SSCP,n}\cdot\left|\frac{1}{1+G_{SSPLL}}\cdot\frac{K_{VCO}} {s^{2}C_{int1}}\right|^{2}+\phi^{2}_{VCO,n}\cdot\left|\frac{1}{1+G_{SSPLL}(s) }\right|^{2}\end{split} \tag{9}\]  
where \(\phi_{REF,n}\), \(\phi_{INBUF,n}\) and \(\phi_{VCO,n}\) are the phase noise contribution from the reference clock, the input buffer and the VCO, respectively, \(V_{MSSF,n}\) is the voltage noise from the MSSF, \(I_{SSCP,n}\) is the current noise from the SSCP. Combining with the simulated noise from each part, the fitted phase noise at the output of the SSPLL system is shown in Figure 12. We find that the noise contribution from the SSCP (I-path) is much smaller than the noise from the MSSF (P-path), since the I-path gain \(G_{IP}(s)\) is much smaller than the P-path gain \(G_{PP}(s)\). Thus, compared with traditional SSPLL, the degraded noise feature of the SSCP cannot have much influence on the noise performance of the proposed dual path SSPLL. The calculated integrated RMS jitter in Figure 12 is 180 fs.  
Figure 11: Phase noise linear model of the proposed SSPLL.  
Figure 10: Variation of transconductor of the SSCP as input common-mode voltage changes.
## 4 Results and Discussions
### Effect of Pile Inclinations
#### 4.1.4 Subgrade Reaction  
Figure 13 plots the non-dimensional subgrade reaction profiles under different pile inclinations, and the reaction always increases first and then decreases with increasing  
Figure 12: Non-dimensional earth pressure profiles under different pile inclinations: (a) \(p=34.375\) kPa; (b) \(p=84.375\) kPa.  
depth. Similar to the behavior of the pile deflection and bending moment, nearly all the subgrade reactions on the battered piles (\(\beta\
eq 0^{\circ}\)) are less than those on the vertical piles (\(\beta=0^{\circ}\)). Interestingly, the locations of maximum subgrade reaction of the positive battered piles and vertical piles would keep constant at \(z_{\rm z}=0.5\). However, the counterpart of the negative battered piles would drop down from \(z_{\rm z}=0.5\) to \(1.1\) while the pile inclination varies from \(0\) to \(-20^{\circ}\). It indicates that negative battered piles can transfer the earth pressure to greater depth, and hence, the performance of the negative battered piles in resisting the lateral soil movement is significantly superior to others. Furthermore, the negative non-dimensional subgrade reaction indicates that the negative pile deflection would occur at the lower pile segment in the case of \(\beta=-20^{\circ}\), which would be validated in the next section.  
For an intuitive illustration of the influence of pile inclination on pile performance, Table 4 summarizes the maximum responses (including the maximums of pile deflection, bending moment, earth pressure, and subgrade reaction) under different pile inclinations in the cases of \(p=34.375\) and \(84.375\) kPa.
## 4 Simulations
### The Highly Dimensional Nonlinear Estimation Problem  
The system model of nonlinear estimation is defined as:  
\[\begin{array}{l}\mathbf{x}_{k}=\mathbf{x}_{k-1}^{2}\cos(\mathbf{x}_{k-1})+\mathbf{v}_{k-1}, \\ z_{k}=\sqrt[n_{c}]{1+\mathbf{x}_{k}^{T}\mathbf{x}}+e_{k},\end{array}\]where \(\cos(x_{k-1})=\left[\cos(x_{1,k-1}),\cos(x_{2,k-1}),\cdots,\cos(x_{n_{x},k-1}) \right]^{T}\). Here we set the dimensions \(n_{x}=20\). \(\mathbf{v}_{k-1}\) and \(e_{k}\) are the independent Gaussian noise with zero means and covariance \(\mathbf{I}_{n_{x}}\) and \(1\), respectively.  
The true initial value is \(\mathbf{x}_{0}=0.1\times\mathbf{1}_{n_{x}\times 1}\), and the initial estimate \(\hat{\mathbf{x}}_{0}\) used in the filters is generated randomly from \(N(\hat{\mathbf{x}}_{0};\mathbf{x}_{0},\mathbf{P}_{0})\), where the initial covariance is \(\mathbf{P}_{0}=\mathbf{I}_{n_{x}}\). The mean squared error (MSE) for each Monte-Carlo run is defined as follows:  
\[\text{MSE}_{i}=\frac{1}{n}\sum_{k=1}^{n}\left[\frac{1}{n_{x}}(\mathbf{x}_{k}-\hat{ \mathbf{x}}_{k,i})^{T}(\mathbf{x}_{k}-\hat{\mathbf{x}}_{k,i})\right], \tag{32}\]  
Accordingly, the average MSE can be written as follows:  
\[\text{MSE}=\frac{1}{L}\sum_{i=1}^{L}\text{MSE}_{i}, \tag{33}\]  
where the Monte-Carlo runs \(L=100\). The MSEs of the algorithms are shown in Figure 1.  
As is shown in Figure 1, the MSEs of the OSCL-CKFs are smaller than those of the CKF and the SCKF, which means that OSCL points can provide more accurate performance in this highly dimensional problem. It can be also seen that the MSEs of the OSCL-CKF4 and OSCL-CKF3 are almost the same, which illustrates that the accuracy of the filter will hardly be improved if the order further increases.
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 3 Background
### Terminology and Notations  
We use the following terms throughout the paper.  
* **Drone** is an entity (known as an unmanned aerial vehicle, UAV) designed to be flown either through remote or autonomous controls using embedded software and sensors. In this paper, a drone is recognized as _authorized_ one when a ground station successfully verifies the authenticity of the drone.
* **Ground station** is an entity that allows drones to communicate with other drones using a 5G network and has the role of tracking the locations of (authorized) drones during their flight.
* **Certificate authority (CA)** is an entity which performs two roles. The first role is to issue a certificate to drones and ground stations. Another role is to confirm whether the certificate is revoked by comparing the certificate with the certificate revocation list.
* **Operator** is a person or a company that wants to control a drone. It requests a certificate for her drone to CA. After having the certificate, it has to install the certificate on the drone. We assume that each drone has a unique certificate issued by a legitimate CA.  
We use the following notations in the description of security protocols in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(\|\) & Concatenation operation \\ \(id_{d}\) & Identity of drone \(d\) \\ \(fp_{d}\) & Flight plan for drone \(d\) \\ \(n_{d}\) & None of drone \(d\) \\ \(n_{gs}\) & None of ground station \(gs\) \\ \(cert_{d}\) & Certificate of drone \(d\) \\ \(cert_{gs}\) & Certificate of ground station \(gs\) \\ \(sk_{d}\) & Private key of drone \(d\) \\ \(sk_{gs}\) & Private key of ground station \(gs\) \\ \(pk_{d}\) & Public key of drone \(d\) \\ \(pk_{gs}\) & Public key of ground station \(gs\) \\ \(pk_{ca}\) & Public key of CA \(ca\) \\ \(Sig\) & Signing function \\ \(Enc\) & Encryption function \\ \(Dec\) & Decryption function \\ \(PBKDF\) & Password-based key derivation function \\ \(pms\) & Pre-Master-Secret key \\ \(k_{d}\) & Flight session key for drone \(d\) \\ \(k_{gs}\) & Flight session key for ground station \(gs\) \\ \(HMAC\) & Hash-based message authentication code function \\ \(H_{d}\) & Hash value computed by drone \(d\) \\ \(H^{\prime}_{d}\) & Hash value computed by ground station \(gs\) \\ \(msg_{d}\) & Message of drone \(d\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations used for the protocols in SENTINEL.