esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Kalman Filter  
Kalman filtering (KF) is an optimal estimation tool that provides a sequential recursive algorithm for the optimal least mean-variance (LMV) estimation of the system states [31; 32]. The theory of KF is well established, and details can be found in [32; 33; 34]. KF is the optimal estimator if the system and measurement models are linear. However, the INS/GNSS integration problem has non-linear models. Thus, the linearization of these models is needed, and the filter works with linearized error-state models rather than the total-state non-linear model. Moreover, KF is an algorithm for predicting the error states of a system after knowing the initial measurement states and noise. Its theory of operation is based on the least mean-variance estimation theory. After assuming that linear models express the system, the measured noise and the noise corrupting the system are white noise, and the initial state vector is expressed as a random vector [17; 18; 21; 35].  
The KF process is divided into two states. The first state is prediction (time update) which is the current state of the system and its covariance estimates. The second state is a correction (measurement update) in which the KF obtains the measurements from the prediction state and measurements from an aided source (GNSS). Moreover, the KF calculates the Kalman gain \((K)\) which minimizes the mean square error of the estimates and updates its covariance estimates to utilize these updates to update the last estimates to eliminate the errors, as shown in Figure 7[20; 36].  
The extended KF (EKF) is considered as a closed-loop configuration of the KF, where the KF obtains the error states and feeds back them to the INS algorithm to predict a more accurate INS solution and keep the system model in the linearity region [37; 38; 39].  
The EKF deals with non-linear systems and utilizes Taylor expansion as a linearizing technique by taking only the first order of the expansion. As long as the higher-order terms of the expansion are very small and undervalued, the EKF obtains a more accurate estimation than the KF. The EKF has the disadvantages of its complexity and the requirements of the Jacobean calculations, which are very hard to obtain [35]. Both loosely coupled and tightly coupled integration are utilized in the paper. First, the KF used for this paper operates in a loosely coupled fashion to fuse the GNSS positions and velocities with the 2D RISS computed position and velocity components. A block diagram of the 2D RISS and GNSS integration is shown in Figure 8.  
The KF fuses the RISS computed position and velocity components with the corresponding GNSS positions and velocities when the RISS-based loosely coupled integration approach is used. This enables the computation of the positions, velocities, and altitude errors as well as sensor errors. When a GNSS outage occurs (i.e., less than four satellites are visible to the receiver with clear line-of-sight), the KF will only run the prediction stage of the filter and relies mainly on the error model.
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 4 Improved Inertia Control of MMC-HVDC
### Synchronous Generator  
The AC system considered herein is composed of an SG and a load. In the previous section, it was mentioned that when the type-4 offshore wind turbine penetrates the system, the back-to-back converter relationship cannot provide an effective inertia constant for the system, thus making it difficult for the AC system to maintain its frequency stability. In this case, the conventional SG of the onshore AC system has only an inertia constant, which can supply inertia to the entire system. The formula for the moment of inertia is derived from the kinetic energy theorem as follows:  
\[\textit{Kinetic Energy}_{SG}=\frac{1}{2}J\omega_{r}^{2} \tag{9}\]where \(J\) is the inertia moment of the SG, and \(\omega_{r}\) is the synchronous speed of the SG. The formula for the inertia time constant of the SG can be expressed as follows:  
\[H_{SG}=\frac{K.E._{\text{-SG}}}{P_{SG_{BASE}}}=\frac{J\omega_{ir}^{2}}{2P_{SG_{ BASE}}} \tag{10}\]  
where \(\omega_{ir}\) is the initial rotor speed of the SG, \(P_{SG_{BASE}}\) is the rated active power of the SG, and \(H_{WT}\) is the inertia constant of the SG. In this case, the relationship equation is calculated using the torque of the SG and the inertia moment as follows:  
\[J\frac{d\omega_{r}}{dt}=T_{SG_{Mac}} \tag{11}\]  
By recombining (10) and (11), we obtain an improved equation for the acceleration torque of the SG as follows:  
\[T_{SG_{Mac}}=\frac{2H_{SG}VA_{BASE}}{\omega_{ir}^{2}}\frac{d\omega_{r}}{dt}= \frac{2H_{SG}T_{SG_{BASE}}}{\omega_{ir}}\frac{d\omega_{r}}{dt} \tag{12}\]  
where \(T_{SG_{Mac}}\) is the acceleration torque of the SG, and \(T_{SG_{BASE}}\) is the rated torque of the SG. As it is an SG, the active power changes of the synchronous motor result in corresponding changes in the rotor speed, and \(\omega_{r}\) (the active power changes of the synchronous motor) are the same as the changes in the rotor speed \(\omega_{r}\). The formula for the active power change is as follows:  
\[\Delta P_{SG}=\omega_{i}\Delta T+T_{i}\Delta\omega_{r} \tag{13}\]  
In this case, we assume that \(\omega_{i}=1\) and \(\Delta\omega_{r}\) are small and can be neglected. The improved formula of the inertia time constant of the SG can be calculated based on the rotor speed of the SG as follows:  
\[\Delta P_{1}=P_{SG_{in}}-P_{SG_{out}}=\frac{2H_{SG}}{\omega_{i}}\frac{d\omega _{r}}{dt} \tag{14}\]  
As this study only takes the SG into consideration, the frequency of the SG is calculated based on the rotor speed, which is divided by \(2\pi\). This is obtained via multiple experimental runs. In this case, the change in frequency is the same as the change in the rotor speed per unit. However, the new formula is as follows:  
\[\Delta P_{1}=P_{SG_{in}}-P_{SG_{out}}=\frac{2H}{f_{iAC}}\frac{d\Delta f_{AC}}{dt} \tag{15}\]  
where \(f_{iAC}\) is the rated frequency of the onshore AC system, and \(\Delta f_{AC}\) is the change in frequency that corresponds to a small system perturbation or load tripping in the AC system.
# A Blockchain-Based Protocol for Fair Delivery for Receips
## 7 Proof of Concept
### Protocol Execution Example

Table 2: Blockchain addresses for \(A\) and \(B\), along with the corresponding pair of public (\(Pu\)) and private (\(Pr\)) keys. Once the delivery was accepted by \(B\), \(A\) called the _publish_ function to provide the necessary data for \(B\) to obtain the delivered document. Similar to the execution of the _accept_ function, the execution of the _publish_ function returned the identifier of the transaction (\(Tx\)), where _publish_ was included in the blockchain (see Table 4). Figures 6 and 7 show the input data included in the function call (see Figure 6) and the log associated with the transaction (see Figure 7). The transaction log contains the data in the event notification (see Section 6.2.1), that is, the delivery _id_ (_identifier_), the delivery status (\(info=2\) corresponds to delivered), and the proof (_evidence_), which in this case is the value of \(k\), the ephemeral public key that was obtained from the ECIES process and was needed to decrypt the document (see Table 3). \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline \(M\) & Ethereum Yellow Paper in PDF format \\ \(id\) & 0 xa4d656f44b2b91812 bf2 ffdd0f61c3c581a487c37 ce284c6b3f5f98c9964 ed238 \\ \(h\) & 0x62d296b37a4 ff30 ze79 bebe54065 db50896 fb70891 ccc2f8013f7 cb08a0d7e56d \\ \(t_{m}\) & 1721282400 (Thu Jul 18 2024 06:00:00 GMT+0000) \\ \(t_{d}\) & 1721887200 (Thu Jul 25 2024 06:00:00 GMT+0000) \\ \(f(C)\) & 0x394b7a6 ef945456 bd455 bb63c6107c30 cf6b2 ac2c75183038a194f0711377b3 bd \\ \(I\)bbf321e2e056a919d4 fa4d93 aaaa23a98d7d68 cc6473646124281b357c4b861c \\ \(IV\) & 0x1d8b0f92c6919097 cefb5367 de1a740d \\ \(k\) & 0x035821728754 dc3b2f7 cb5632 fb3543270b9b61585b92610e662173a9f5b801 ce \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values of the parameters that are required for the execution of blockchain-related transactions. Figure 4: _Accept_ function input data. Figure 5: Event logs for the _Accept_ transaction receipt. Although we used the transaction hash to show the content published on the blockchain, for analysis purposes, \(A\) and \(B\) can directly receive the information on the events by configuring the receipt of the events associated with a specific _id_ issued by the contract. Thus, \(A\) and \(B\) receive the information published in the transaction logs and learn the status of the delivery in real time. In addition, all the data associated with the transaction, including the input data and logs, are published on the blockchain, making the process auditable.
# Propulsion Performance of the Full-Active Flapping Foil in Time-Varying Freestream
## 3 Results and Discussions
### The Propulsion Performance of Flapping Foil at Uniform Freestream
Based on the above research, we conclude that when the reduced frequency is small, the smaller pitching amplitude can improve the propulsion performance of the full-active flapping foil. When the reduced frequency is large, the proper pitching amplitude can reach the best propulsion performance. [PERSON] et al. [7] studied the influence mechanism of the leading-edge vortex on the hydrodynamic performance of the flapping foil. They reveal that the proper pitching amplitude is beneficial to the generated of the leading-edge vortex and reverse Karman vortex, which can improve the propulsion performance of the flapping foil. Meanwhile, we also note that when the pitching amplitude is large enough, the mean propulsion power coefficient is less than zero. Thus, the flapping foil cannot generate thrust in this condition. Figure 4: The mean output power coefficient contour on the parametric space of reduced frequency and pitching amplitude.
## 4 Conclusions and Future Work  
In this article, the implementation of smart grid NANs with multi-hop wireless mesh networks was considered. Specifically, a modification of the HWMP protocol was proposed and evaluated, based on the maintenance of multiple paths between each pair of network nodes. In addition, independent frequency channels were defined for each of the paths, as well as a special channel for control messages. With the joint application of both techniques, a more efficient utilization of the available network resources was achieved.  
To evaluate the obtained benefits, the ns-3 network simulator was used, on which all the proposed modifications were implemented. The results of the simulations allowed us to verify the improvements in the network performance in terms of packet delivery ratio, throughput and network transit time. On the other hand, since the application of multipath techniques supposes an increase in the size of the routing tables stored in the nodes, the necessary amount of memory to store them was also measured. The results allow affirming that no memory problems will arise in the nodes.  
As future lines of work, our research to improve NANs performance will continue by adding network security and data privacy. In this field, new extensions for the HWMP protocol based on multiparametric optimization techniques will be proposed, in order to take advantage of the improvements obtained with the multipath proposal and at the same time guaranteeing a better network service by prioritizing the paths through the nodes with a better reputation.  
Figure 11: Control channel utilization factor. (a) Lifetime of reactive routing information: 2.05 s; (b) lifetime of reactive routing information: 5.12 s.  
**Acknowledgments:** This work was supported by the Spanish Research Council under projects INRISCO(TEC2014-54335-C4-1-R) and MAGOS(TEC2017-84197-C4-3-R), and [PERSON] is the recipient of a full scholarship from the Secretaria de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT), Ecuador.  
**Author Contributions:** [PERSON] and [PERSON] conceived of the proposal, designed the implementation and the experiments, performed the experiments, analyzed the data, and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 1 Introduction
By using the Bhattacharyya distance [28], which is a method of calculating the degree of separation between classes, a feature set was constructed by obtaining features that can discriminate well between the cough sounds of COVID-19-positive subjects and those of negative subjects. As a result, the feature set was composed of mel frequency cepstral coefficients (MFCC), \(\Delta^{2}\)-MFCC, \(\Delta\)-MFCC, and spectral contrast. With this feature set and a mel spectrogram as input, we trained a model [22] that combined ResNet-50 [29] and a deep neural network (DNN), and the model achieved a 0.95 sensitivity and a 0.96 specificity. The result showed improvement compared with previous studies. The structure of this paper is as follows. The collection of three crowdsourcing datasets is described in Section 2, along with an earlier study on models for diagnosing COVID-19 infections using each dataset. The current study's database, database preparation, method for determining the Bhattacharyya distance and creating the feature set, and model are all covered in Section 3. The experimental results compared with previous studies are presented in Section 4. How to apply the constructed model to an application is covered in Section 5. The study's findings and future directions are covered in Section 6.
## 3 Results and Discussion
### Analysis of the Effect of Helmholtz Resonators on Micro-Pressure Waves
#### 3.2.1 Analysis of Helmholtz Resonator Resonance Conditions
Among them, 15 Hz carries the highest energy and is in the resonant band, triggering the resonance of the Helmholtz resonator and causing a large amount of dissipation of the initial compressional wave energy.
## 4 Conclusions  
PD location estimation was performed by using RSS and a novel algorithm has been proposed. Practical measurements were conducted and signals received were the voltage level recorded. Voltage levels were converted into power (d_Bm_) as input to the location algorithm. The location estimation was performed by converting the received signal into distance and \((x,y)\) coordinates of the source were estimated for nine different positions in two field-trial scenarios. The algorithm  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source Position** & **LS Error (m)** & **LS PLE** & **R\&S Error (m)** & **R\&S PLE** & **Proposed Algorithm Error (m)** & **Proposed Algorithm PLE** \\ \hline Position 1 & 1.47 & 2 & 1.32 & 1.65 & 1.06 & 1.75 \\ Position 2 & 1.19 & 2 & 1.35 & 2.15 & 1.43 & 2.20 \\ Position 3 & 0.86 & 2 & 1.15 & 3.2 & 1.28 & 2.80 \\ Position 4 & 1.72 & 2 & 2.08 & 1.50 & 2.77 & 3.55 \\ Position 5 & 3.22 & 2 & 4.13 & 1.50 & 2.85 & 1.55 \\ Position 6 & 1.52 & 2 & 1.94 & 2.50 & 1.72 & 2.50 \\ Position 7 & 3.31 & 2 & 2.60 & 3.20 & 2.08 & 2.95 \\ Position 8 & 2.28 & 2 & 2.43 & 2.80 & 1.44 & 2.70 \\ Position 9 & 3.46 & 2 & 1.25 & 2.80 & 1.24 & 2.85 \\ Mean Error & 2.11 & 2.03 & & 1.76 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Error and PLE comparison for eight measurement sensors.  
Figure 12: Error comparison with eight measurement sensors used.  
did not have any prior information about the source transmitted power and the path loss exponent. An initial path loss exponent value of 2 was chosen and then it was optimized using measured spread. The proposed algorithm was compared to least squares and ratio and search algorithms. The results show that RSS based localization is a plausible technique and the proposed algorithm offers better results in terms of localization accuracy and path-loss exponent estimation as the number of receiving nodes are increased from seven to eight for the majority of positions. The proposed algorithm optimizes the path loss exponent, however, this optimized value is the same for all node pairs. In a real environment, path loss exponent may vary. This is one of the limitations of the proposed algorithm as well as the reference algorithms.  
U.F.K., P.I.L. and I.A.G. developed the idea of the location algorithm. U.F.K. proposed the method and theoretical modelling of the algorithm. I.A.G. and P.I.L. validated the theoretical modelling. U.F.K., P.I.L. and I.A.G. implemented the algorithm. U.F.K., P.I.L., R.A., R.C.A., H.M., I.A.G., C.T. and Z.D.Z. all contributed to data analysis and review literature. All authors contributed to the editing, revision and results validation of the algorithm. All authors acknowledge and accept the responsibility of the research conducted and presented in this article. This research was funded by the Engineering and Physical Sciences Research under grant EP/J015873/1.  
The authors acknowledge the Engineering and Physical Sciences Research Council for their support of this work under grant EP/J015873/1.
## 3 Data Description and Preprocessing
### Data Preprocessing  
Data preprocessing plays an important role in improving the quality of data, which includes the elimination of irrelevant data, standardization, and addressing multicollinearity [54]. Multicollinearity was checked with the Pearson correlation coefficient [55]. A correlation threshold of 0.7 was considered to identify highly correlated features. After removing highly correlated features, 45 features remained. Then, entries with null values were removed, resulting in the removal of 1867 crashes. The final number of crashes used for the analysis was 11,329.  
ICT is classified into four classes based on 30 min equal intervals to improve modeling efficiency [56], ranging from short (0-30), medium (31-60), intermediate (61-90), and long (greater than 90). Among the crashes, 7322, 1507, 1061, and 1436 were classified as short, medium, intermediate, and long, respectively. The frequency distribution histograms of incident duration are shown in Figure 1.  
Categorical data are usually presented in words and cannot be directly loaded into the model [57]. Therefore, this categorical data must be converted to numerical data in advance. Label encoding, one-hot encoding, and target statistics are among the most frequently employed processing methods. This study used one-hot encoding and transformed data into a 0-1 variable.  
The data set used in the analysis includes 11,329 incidents. The data set was categorized into 4 levels: 7322 short, 1507 medium, 1061 intermediate, and 1436 long. There is considerable variation in the number of categories, which has resulted in category imbalance. Such imbalances lead to bias towards the majority class since modeling classifiers prioritize the class with the most observations, generating overprediction of this class [58]. This overprediction can significantly limit the model's generalization capacity, resulting in poor classification results. In this study, the synthetic minority oversampling technique (SMOTE) was adopted to address this issue by balancing class distribution across the data set, which was introduced by [PERSON] et al. in 2002 [59]. Instead of repeating existing minority cases, the SMOTE approach generates synthetic minority instances using random intervals between them.
## 3 Results  
### Configurations of Hardware SNNs  
Figure 4 shows a schematic diagram of the hardware-based SNN. The silicon-based four-terminal synaptic transistors form a NOR-type synapse array. There are \(k^{l}\) wordlines (WLs) for the pre-synaptic inputs applied to G1 and the drain of the synaptic devices simultaneously. Here, \(k^{l}\) denotes for the number of neurons in \(l^{\text{th}}\) layer. Since the synaptic device does not flow current when there is no spike, the standby power can be suppressed in the hardware-based SNN. One synaptic weight was implemented by a pair of two synaptic transistors, which act as an excitatory and inhibitory synapse, respectively. There are \(2k^{l}\)\({}^{+}\)\({}^{1}\) bitlines (BLs) where \(BL^{+}_{k^{l}+1}\) and \(BL^{-}_{k^{l}+1}\) are connected to the same post-synaptic neuron \(k^{l}\); the excitatory synaptic current (\(I_{\text{exc}}\)) flows through \(BL^{+}_{k^{l}+1}\), and it is accumulated as the membrane potential of the post-synaptic neuron; the inhibitory synaptic current (\(I_{\text{inh}}\)) on \(BL^{-}_{k^{l}+1}\) withdraws the membrane potential; therefore, positive, zero, and negative weights can be implemented according to the relative weights of the two synaptic devices [29]. G2 of the synaptic device is connected in BL direction as \(G2^{+}_{k^{l}+1}\) and \(G2^{-}_{k^{l}+1}\), which are used only for the weight transfer. By connecting G2 in BL direction, all the synapses on the same column can be correlated with a spike by the same neuron, indicating the biological STDP characteristic [24].  
Figure 3: Weight distributions quantized by: (**a**) 3; (**b**) 5; (**c**) 7 and (**d**) 9-level, each of which is optimized by the interval \(\alpha\).  
For inference operation, the input image was encoded by the rate-based coding, which was applied to WL as the pre-synaptic inputs. It is assumed that the I&F neuron circuit follows the simple non-leaky I&F neuron model, and the detailed model equations are in the previous research [19]. Thus, the synaptic currents were delivered to the post-synaptic I&F neuron circuits and integrated as the membrane potential; the I&F neuron generated the spikes when the membrane potential was above its threshold; then, the spikes were transferred to the subsequent layers, and finally, the spikes occurred in the output layer. Decision making was performed by comparing the neuron index having the maximum firing rates in the output layer with the label vector in the dataset.
## 6 Case of Study: IPS at St. Olavs Hospital
### Mechanism of Performing of EBIM IPS in St. Olavs Hospital
The floor feature is connected to a unit called \"Floor\", which connects the values of \"floor Ref-ID\" from the XML files to the actual floors in the building models, and at the end the latitude and longitude create x and y coordinates for the tag (Figure 5) [18]. In following, the BIM tracker has the responsibility to read, rewrite and store the Cisco MSE data in BIM models and it leads to the creation of an IFC object from the tracker and puts it in the correct IFC model. The tracker is a service to update constantly found geo-references, the current floor and the time in the BIM models. This service also logs all positioning data in a history folder and tracks all available readable data. The tracker put its data from Cisco MSE in the BIM server, where there is a repository for all models. Based on the authorizations, the user can also make modifications and alteration to the model values. Therefore, MSM is mostly used by architects, developers, and other key stakeholders. Finally, a web browser application (St. Olavs Eiendoms portal) has been developed to present the models with tracking data in a more user-friendly ambience. The BIM server is accessible to all users, and they can view all tracking devices stored in a 2D map [26]. The accuracy created by IPS, irrespective of the technology at hand, may differ depending on the method used to estimate the positions [5]. In any IPS, precision is a key and crucial factor in determining the functional efficiency of the system [18]. Choosing an estimation method commensurate with the selected IPS enables resource optimization, since the number of readings depends on the number of integrated beacons in the network [5]. IPS abilities are made possible by various technologies, and most IPS models use a combination of technologies to perform their function [25]. The variety of different technology solutions for IPS indicates how the field of communication is deeply interdisciplinary and demonstrates that nearly any signal, technique or sensor can be used to achieve this goal [5]. Figure 5: Data sharing in IPS. Adapted from [18]. The image shows the set of components of the system and the information flow mechanism in the developed IPS from the first points to the visualization of data for end users.