esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain-Based Supervision Data Security Sharing Framework
## 3 Blockchain-Based Framework for Secure Sharing of Engineering Supervision Data
### Secure Sharing Process of Engineering Supervision Data Based on Blockchain  
This article constructs an engineering supervision data-sharing framework based on consortium chain through rapid retrieval of engineering supervision data, and designs corresponding smart contracts. The specific data flow of this framework model is as follows:  
1. After categorizing and organizing the files, the participating unit users encrypt the original files using the project participant's public key and store them in the IPFS cluster.
2. After the upload is completed, IPFS returns the data access address to the client of the participating unit.
3. The participating unit users package the attribute information such as private key signatures, file IDs, and file hash addresses of all parties related to the file, and construct the authentication information. Upload information to the blockchain through deployed smart contracts and stamp it with a timestamp.
4. After completing the on-chain process, send back the on-chain file ID to the client of the participating unit.
5. Ordinary users on the chain send query requests and perform query operations through smart contracts based on the on-chain file ID.
6. After completing the query, return the operation status and query results.
7. Project participants send file data retrieval requests to IPFS and send the hash address of the file to the IPFS cluster for querying.
8. IPFS retrieves the encrypted file based on the index query and sends it to the client. The searcher decrypts the complete original file using the private key off chain.
9. During the data circulation process of this framework, a hash table will be maintained for the on-chain node data to record the position of the engineering supervision data summary in the consortium chain, and fast retrieval will be achieved by locating the block where the data are located.
## 1 Introduction
Traditional GNSS positioning error estimation methods are mostly based on linear assumptions, and cannot effectively capture complex nonlinear relationships, which limits the accuracy of error estimation. GNSS positioning error estimation methods based on deep learning can comprehensively consider multiple error factors, and have powerful nonlinear modeling capabilities, which can effectively improve the accuracy of error estimation. Therefore, a GNSS positioning error estimation method is studied based on an improved multi-layer perceptron (MLP) model. The multi-head attention (MHA) and gating operation are exploited to selectively weight and transform input features, enabling adaptive feature selection and information filtering. Unlike traditional GNSS positioning error estimation methods that rely on precise mathematical models and assumptions, the proposed method is a data-driven deep learning model. The relationship between error factors is learned through historical GNSS data. The proposed method does not require assumptions about error factors, and can adaptively extract features from GNSS data, thereby providing more accurate error estimates. The main contributions of the paper are as follows:  
1. A MLP model integrating MHA and gating operation is proposed for GNSS positioning error estimation. To our knowledge, no scholars have utilized this method to address the problem of GNSS positioning estimation. 2. Traditional GNSS positioning error estimation methods are usually based on linear models or empirical rules. The presented method can model nonlinear relationships more flexibly, thereby achieving higher estimation accuracy. 3. Two types of comparative experiments are performed. The experimental results show that the presented method has higher estimation accuracy than other GNSS positioning error estimation methods. The remainder of the article is structured as follows. In Section 2, the GNSS positioning error estimation problem is formulated. Section 3 focuses on the presented improved MLP model. In Section 4, the performance of the improved MLP model is evaluated, and the experimental results are analyzed in detail. In Section 5, the proposed method is discussed. Finally, a brief summary is presented in Section 6.
## 4 Protocol Description
### Protocol Format  
There are three possible types of packets that are used in the protocol:* BEACON packets that are broadcast to neighboring nodes at periodic intervals of 30 s.
* DATA packets that are generated by the sensor nodes at periodic intervals of 120 s.
* ACK packets that are generated by the gateway or intermediate forwarding node (_i.e._, the node that receives the data from another node with a higher metric).  
The corresponding format of each packet is as follows:  
* BEACON: [sequenceNum], [UNIXTime], [gatewayAddress], [gatewaySequenceNum], [metricToGateway]
* DATA: [portNumber], [dataLength], [metricToGateway], [dataSequenceNumber], [routeIncluded]
* ACK: [dataSource], [dataSequenceNumber]  
**Example 1**: _Transmitted message (BEACON): 105, 65535, 0, 3, 9, 1404388288, 101, 1404388247, 30._  
In Example 1, we illustrate a BEACON packet of sequence number nine, that is broadcast by node 105 to its neighboring nodes at time 1404388288. The BEACON contains the information that the metric from node 105 to the gateway 101 is 30 and that the last known gateway sequence number from 101 is 1404388247. The gateway sequence number is used to expire stale routes and prevent routing loops in the network. We present in the following the testing results we obtained while testing this new protocol from four properties.
## 3 Control Approach
### A Method for Generating the Reference Trajectory Based on Cubic Spline and Virtual Structure
#### 3.2.1 Calculating the Formation Reference Information Based on Cubic Splines  
For convenience of description, this section takes a two-dimensional format as an example to introduce the formation. Discrete formation path points provided by the path planning layer are denoted as \(\mathbf{p}_{dis}=[\mathbf{p}_{1},\mathbf{p}_{2},\cdots,\mathbf{p}_{N}]\). \(\mathbf{p}_{i}\) is the position of the \(i\)-th discrete path point. \(N\) is the number of the discrete path points. An interpolation method based on cubic splines was adopted to compute the other information of the robot. The cubic spline refers to the curve between each cell being a cubic equation, and the cubic spline equation must meet the following conditions.  
1. In the section between every two adjacent points \([\mathbf{p}_{i},\mathbf{p}_{i+1}]\), \(S(\mathbf{p})=S_{i}(\mathbf{p})\) is a cubic equation.
2. Satisfy the interpolation condition that is \(S(\mathbf{p}_{i})=z_{i}\).
3. The curve is smooth, which means that \(S(\mathbf{p}),\dot{S}(\mathbf{p}),\dot{S}(\mathbf{p})\) is continuous.  
The function of the interpolated curve is denoted as \(f(\dot{)}\), which is a piecewise function represented by multiple cubic equations. The interpolated curve is resampled at equal intervals, and the distance of the \(k\)-th resampling point is \(d(k)=kT\times u_{d}\). \(u\) represents the surge speed. \(T\) is the sampling time. The cubic equation corresponding to the interval segment where the k-th sampling point is located is represented by a parameter equation as Equation (12).  
\[\begin{cases}x=f_{x}(s)\\ y=f_{y}(s)^{\prime}\end{cases} \tag{12}\]  
where \(s\) is the distance. The position, attitude angle, and velocity information of the \(k\)-th sampling point are represented in Equation (13).  
\[\begin{cases}x_{r}=f_{x}(d(k))\\ y_{r}=f_{y}(d(k))\\ \phi_{r}=\arctan(\frac{\dot{f}_{y}}{\dot{f}_{x}})\\ u_{r}=u_{d}\\ r_{r}=\frac{(\ddot{f}_{y}f_{x}-\ddot{f}_{x}\ddot{f}_{y})u_{d}}{(\ddot{f}_{x}^{2 }+\ddot{f}_{y}^{2})^{15}}\end{cases} \tag{13}\]  
The above obtains the state information of the \(k\)-th reference point in the two-dimensional plane. The state information of the heave degree of freedom can be expressed as \(z_{r}=\omega_{d}T,\omega_{r}=\omega_{d}\). The state vector of the formation reference point based on cubic spline resampling is denoted as \(\mathbf{q}^{\prime}=[x_{r},y_{r},z_{r},\phi_{r},u_{r},\omega_{r},r_{r}]^{T}\).
## 2 Materials and Methods
### Collection of the Required Data
the digital terrain model (DTM) provided by the Italian geoportal ([[http://www.pcn.minambiente.it/mattm/schedadatat](http://www.pcn.minambiente.it/mattm/schedadatat)]([http://www.pcn.minambiente.it/mattm/schedadatat](http://www.pcn.minambiente.it/mattm/schedadatat)), accessed on 31 May 2021) was used. A DTM is a topographic model of the Earth that can be manipulated by GIS software. The DTM geospatial data files are organized as a grid where the cells contain the value of the altitude. An example of the DTM used for this study is presented in Figure 2. The DTM used in this study covers the whole National territory and it is provided in matrix (raster) format. The model is obtained by means of different methodologies (i.e., photo-grammetric restitution of numerical data, generalization of numerical cartography, semi-automatically by stereo-restitution) from Italian Military Geographic Institute's (IMGI) orographic data of 25 and 25 DB series maps. IMGI is the Army's geographic supporting office and also the National Cartographic Authority; the cartography produced by IMGI is considered the official state cartography. The Series 25 and 25 DB maps, named the \"Topographic Map of Italy\" at scale 1:25,000, consists of a series of maps carried out by digital stereo-restitution or from regional technical numerical cartography derivation. The resulting DTM product is a matrix, whose elements (pixel) indicate elevation with a spatial resolution of 20 m in the planar dimension and 5 m for the vertical dimension at scale of 1:25,000 with a degree of accuracy of 3-4 tenths millimeter graph. The altitude value for each pixel is the mean value recorded in the pixel area. The pixel value is referred to the barycenter of the cell. The DTM creation started during the 80s and was completed on 12 February 2001 ([[http://www.pcn.minambiente.it/mattm/visualizzazione-metadata/?keyword=dem](http://www.pcn.minambiente.it/mattm/visualizzazione-metadata/?keyword=dem)]([http://www.pcn.minambiente.it/mattm/visualizzazione-metadata/?keyword=dem](http://www.pcn.minambiente.it/mattm/visualizzazione-metadata/?keyword=dem)), accessed on 31 May 2021). From the DTM geospatial data it is possible to select the cells with an altitude value greater than 1500 m.  
For the information about the areas located at a distance of less than 1500 m from the national terrestrial border, a buffer with a radius of 1500 m from the Italian boundary provided by Italian geoportal was used (data source for Italian boundary: National Geoportal--[[http://www.pcn.minambiente.it/mattm/visualizzazione-metadata/?keyword=confini](http://www.pcn.minambiente.it/mattm/visualizzazione-metadata/?keyword=confini)]([http://www.pcn.minambiente.it/mattm/visualizzazione-metadata/?keyword=confini](http://www.pcn.minambiente.it/mattm/visualizzazione-metadata/?keyword=confini)), accessed on 10 May 2021). In particular the terrestrial borderline was obtained by a cutting operation of the Italian borderline and taking into account only the borderline that ranges from Liguria to Friuli Venezia Giulia (Alpine Arch) regions.
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 2 Research Method
### Step 1. Spectral Transformation Methods for Speech Processing
#### 2.1.1 Log-Power Spectrum Features  
The log-power spectrum is a widely used method for representing and processing audio signals, derived through logarithmic transformation of the spectrum. The spectrum itself is obtained by converting the voice signal from the time domain to the frequency domain using the short-time Fourier transform (STFT), which extracts the signal's frequency components and amplitudes [31]. By applying a logarithmic transformation to the spectrum, the result is a logarithmic spectrum. This approach is extensively used in various audio analysis and processing tasks [13]. The formula is shown below (Equation (1)), where y denotes the speech signal and STFT represents the short-time Fourier transform.  
The extracted birdsong power spectrogram features are depicted in Figure 4a. In (Equation (1)), \(t\) denotes the time index of the spectrogram (in seconds), \(f\) represents the frequency index (in Hz), \(y\) is the input audio signal (typically a birdsong or speech waveform in the time domain), and \(\epsilon\) is a small positive constant, added to avoid numerical instability when computing the logarithm.  
\[log\text{-power}\;spectrum(t,f)=\log\Bigl{(}\left|STFT(y)\right|^{2}+\epsilon \Bigr{)} \tag{1}\]  
Figure 3: Framework proposed in this article.  
Figure 4: Cont.
## 5 Summary of Findings  
This research aimed to design and evaluate a database system that prioritises performance, security, and user accessibility, particularly in the face of SQL injection attacks and high-traffic demands. While numerous studies have explored the use of Docker as a framework for various applications, there remains a significant gap in understanding how Docker can be used to enhance database security.  
The findings of this study demonstrate the efficacy of a decentralised database approach built upon the Docker framework. By isolating database instances within Docker containers, the risk of external attacks, including SQL injection, is significantly reduced, addressing Research Question 1. Additionally, such an environment provides a robust defence against SQL injection attempts, safeguarding user data and database integrity, thus addressing Research Question 2. Furthermore, the Docker-based database showed high CPU utilisation during attack, which needs to be considered when used in high-traffic applications, fulfilling Research Question 3. A key advantage of this approach is that it does not necessitate modifications to the traditional database infrastructure. By introducing Docker as a layer of abstraction, the existing database remains unaffected. The Docker framework offers improved security. Although, this research was designed from the user's perspective as a decentralised database without altering the existing centralised database, developers or administrators of a centralised database should look at the practical benefits of being able to run Docker images with any assigned IP (Internet Protocol) address of their choice; this improves the security of a database running in a Docker container. Meanwhile, the authors of the research paper [PERSON] et al. [43] obtained a positive result  
Figure 19: DVWA with Docker before an SQLi attack.  
Figure 20: DVWA with Docker during an SQLi attack.  
when utilising Docker containers to handle data in a network environment compared to the results of the research paper by [PERSON] et al. [22] that showed Microsoft Azure, Amazon Web Services, OpenStack, IBM, VMware and Google Compute Engine, which are all cloud providers, are now being supported by Docker. Additionally, the research paper by [PERSON] et al. [47] reported that developers are not keen on using different types of tools to build their Docker-compose.yml files and Dockerfiles. Lastly, as presented in Table 3, under graphics Intel/Nvidia 2 GBRam, the more memory (Ram) a video card (Nvidia (GPU) Graphics Processing Unit) has, the more this would help in reducing the high-traffic bottle necks encountered where the CPU (Central Processing Unit) is the only processor processing information within a database. A limitation of this research was the use of convenience sampling. Positive ideas for future work/research include measuring the computer memory and investigating the potential for more data output.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 6 Requirements for De-Identification
### Partial Identities  
One of the initiatives to preserve user information is through the use of partial identities with minimal information sharing. IBM developed Identity Mixer (IDEMIX) [179], which protects the personal data of online shoppers. This concept allows internet users to use a \"partial identity\", which can be a \"persona\" issued by a reliable source, such as a bank. A \"persona\" is a type of character that differs from the real character of a person or device, acting as a representation. As such, it would be possible to have different personas depending on the context of the user and/or device, and the environment in which they are inserted, considering several characteristics.  
With the software, users can make purchases without having to reveal their real name or credit card number, for example. The system gives credibility to the buyer with the store, attesting that it is someone backed by an institution and with funds to make the acquisition. This is important in the context of human identity as, when downloading music from the internet or buying a book from a retailer, the internet user leaves data that can be tracked. This information can be tracked for other users (in the case of public information) or from a data leak (through a database disclosure or de-anonymization) or even re-identification through context data (related users' data).  
IDEMIX is a solution from PRIME [180] and its effectiveness in protecting against re-identification has been evaluated by [181].  
The solution can be adapted for IoT as it is necessary to implement new techniques that preserve privacy, along with credentials and independent of the usage scenario. Therefore, this could be an interesting future research challenge.  
Another interesting technique is the creation of fake profiles, which could leverage synthetic traces of fake users to be the target for confusion. With such a technique, it would be important for the fake profiles to be representative of real users, in order to ensure that the confusion falls on other users rather than the correct one. In this way, even if the data is de-anonymized or re-identified, it would be difficult to determine the real identity of the user, thus protecting their privacy.  
In conclusion, IDEMIX and synthetic trace techniques, as well as other privacy-preserving techniques such as encryption, are important methods to protect personal data in smart cities and IoT. However, further research is needed to develop and improve these techniques to effectively protect individual privacy in smart city data. It is also important to consider the trade-offs between the level of privacy protection and the usability of the data, in order to find the right balance and ensure the best possible solution.
## 3 Results  
### Grain Size, CaCO\({}_{3}\), TOC, and TN Contents  
The grain size composition of the surface sediments collected in the study area is shown in Figure 2. Sand, silt, and clay content ranges were 7.78-97.5%, 2.53-85.7%, and 0.00-6.55%, respectively. Grain size distribution varied irregularly, with the exception of silt, which showed the highest abundance (average = 79.9 \(\pm\)5.5%) in the NE sector (Mar Pequeno).  
The mean CaCO\({}_{3}\) content was 4.6%, ranging between 0.0% (to the west of Cananeia Island) and 11.5% (Cananeia inlet) (Figure 3a). TOC mean value was 1.7%, with maximum values close to the Cananeia and Ararapira inlets (3.7 and 3.0%, respectively) and in Mar Pequeno (2.4-2.6%) (Figure 3b). TN mean value was 0.13% (Figure 3c). As observed in the case of TOC, maximum TN values occurred close to the Cananeia and Ararapira inlets (0.31 and 0.22%, respectively) and in Mar Pequeno (0.18-0.24%) (Figure 3c). TOC/TN molar ratio varied between 21.8 and 9.6, and was found to be consistent with previous results provided by [48]. The results for each station are also reported in Table S1.  
Figure 2: Percentage distribution of the three main grain size components: (**a**) Sand; (**b**) Silt; (**c**) Clay.
## 4 Discussion of Prediction Results
### Prediction Performance of Different Scenarios
The results indicate that the prediction accuracy of most samples is also around 80%, while the prediction accuracies for Labels 2, 3, and 4 are relatively poor, especially in the \(T_{1}\) range of 0.8-1.0 s. Therefore, the distribution of the training set and prediction accuracies for samples with a \(T_{1}\) range of 0.8-1.0 s are further illustrated in Figure 11b. In Figure 11b, the prediction accuracies of Labels 2, 3, and 4 are approximately 60%, while the prediction accuracies with one level differences are all above 90%. One possible reason for these relatively poor prediction accuracies is the relatively small proportion of samples in these classes of the training set. This is also a limitation of the proposed model, and further investigation can be conducted to improve the prediction accuracy through methods such as active learning [16]. Figure 10: Confusion matrices for buildings with different PGAs: (**a**) PGA of 1–3 m/s\({}^{2}\) (prediction accuracy = 83.1%); (**b**) PGA of 4–5 m/s\({}^{2}\) (prediction accuracy = 79.2%); (**c**) PGA of 6–7 m/s\({}^{2}\) (prediction accuracy = 78.4%); (**d**) PGA of 8–10 m/s\({}^{2}\) (prediction accuracy = 77.5%). Figure 12 shows the confusion matrices for buildings with different fundamental periods. The results show that the model can achieve the prediction accuracy of approximately 80% for buildings with different fundamental periods. Figure 11: Influence of \(T_{1}\) on prediction accuracy: (a) prediction accuracies for samples with different fundamental periods; (b) distribution and prediction accuracy for samples with \(T_{1}\) of 0.8–1.0 s.  
Figure 12: _Cont._  
Figure 13 shows the confusion matrices for buildings with different fortification levels, which also exhibit approximately 80% accuracy for all scenarios. The above results show that the prediction accuracies of most classes are around 80%, and more than 90% of the predicted damage states are within one level difference to the actual damage states. Nevertheless, the prediction accuracies of some individual classes are below 70%. Note that the number of samples is small for classes with below 70% accuracy, which indicates that the proposed method can reasonably predict the seismic damages for most cases, and further studies are needed to improve the prediction accuracies of some individual classes. Figure 12: Confusion matrices for buildings with different fundamental periods: (**a**) fundamental period of 0.1–0.3 s (prediction accuracy = 79.4%); (**b**) fundamental period of 0.4–0.5 s (prediction accuracy = 78.2%); (**c**) fundamental period of 0.6–0.7 s (prediction accuracy = 80.1%); (**d**) fundamental period of 0.8–1.0 s (prediction accuracy = 80.7%). Figure 13: _Cont._