esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theoretical Parameters of FE-SEA Method
### Power Flow Balance Equation  
The transfer relationship between the input power and output power of the hybrid FE-SEA model system is defined as follows [12; 13; 14; 15]:  
\[p_{in,dir}^{(m)}=p_{out,rev}^{(m)}+P_{diss,m} \tag{26}\]where \(P_{in,dir}^{(m)}\) is defined as the power of the direct field received by the subsystem, \(P_{out,rep}^{(m)}\) is defined as the power lost by the boundary constraint of the subsystem in the direct field, and \(P_{diss,m}\) is the power lost inside the subsystem. The direct field power \(P_{in,dir}^{(m)}\) received by the subsystem is expressed as [13; 14; 15]:  
\[P_{in,dir}^{(m)}=P_{in,0}^{(m)}+\sum_{n}h_{nm}\frac{E_{n}}{n_{n}} \tag{27}\]  
where, \(P_{in,0}^{(m)}\) is defined as the power obtained by the subsystem under external excitation. \(\sum_{n}h_{nm}\frac{E_{n}}{n_{n}}\) represents the sum of the power output by the other subsystems to the current subsystem in the reverberation field and the power of the subsystem in its reverberation field. \(h_{nm}\) is the power transfer coefficient, namely the coupling loss factor between systems. \(\frac{E_{n}}{n_{n}}\) is the ratio of statistical subsystem energy to statistical subsystem modal density, which is called modal energy density. Therefore, the output power \(P_{out,rep}^{(m)}\) of the system can be expressed as:  
\[P_{out,rep}^{(m)}=\frac{E_{m}}{n_{m}}h_{tot,m}=\frac{E_{m}}{n_{m}}\left(h_{m} ^{\alpha}+\sum_{n}h_{nm}\right) \tag{28}\]  
where \(h_{tot,m}\) is the sum of the power output of the subsystem to the other subsystems. \(h_{m}^{\alpha}\) is the power lost through the connection between subsystem and deterministic subsystem. According to Equations (27) and (28), the dissipated power \(P_{diss,m}\) of the system can be expressed as:  
\[P_{diss,m}=M_{m}\frac{E_{m}}{n_{m}} \tag{29}\]  
where \(M_{m}\) is the modal overlap factor of the statistical subsystem.
## 1 Introduction  
The modern cello bridge's design has remained pretty much intact since its introduction in the late 1700s [1]. The upper shape of the bridge is determined by playability--the musician needs to be able to bow in each of the strings--whereas the lower part needs to perfectly match the belly of the instrument. The middle part, however, has seen the most variation in design during its history. One wants a bridge that is light enough to transfer as much energy as possible to the body of the instrument, yet strong enough to support the load of the strings without breaking or deforming [2, 3].  
Bridges commonly used nowadays fall into the \"French\" or the \"Belgian\" model, the former being shorter and wider, while the latter is more slender and has longer legs. It is known by makers that different designs favor different sounds, yet there is no clear scientific understanding of this phenomenon. Recently, the Cremonese luthier [PERSON] started designing and using a revised version of the bridge in which the legs, rather than being curved, are carved straight from the center of the bridge towards the instrument. Pictures of a traditional bridge and [PERSON]'s cello bridge are shown in Figure 1.  
Only a small portion of the literature in musical acoustics focus specifically on the cello bridge. The most relevant and complete review of the behavior of bridges is given by [PERSON] and [PERSON] in [2] and by [PERSON] in [3]. Most of their discussion considers violin bridges and is extended to cello bridges only partially. In all instruments of the violin family, the bridge serves two main purposes. It statically sustains the strings, holding them in place at the correct height and at the correct distance from the fingerboard, effectively determining their vibrating length and guaranteeing the possibility for the player to bow each string individually. Moreover, the bridge also acts as very necessary impedance adapter between the strings and the body, and transforms the mainly lateral motion of the firsts into mainly vertical forces on the top. This filter can be controlled by the addition of mass or the stiffening of the bridge [4].  
Inspired by the design modifications of the [PERSON] family, we study the relationship between the shape of the legs in a cello bridge and its mechanical properties: dynamic as well as static. By parameterizing the shape variation, we can easily study how the design determines the vibrational response of the bridge. This relationship between shape and vibrational behavior has been greatly studied in the case of the violin and the guitar [5; 6; 7] but has not yet been applied to the cello. In this article, we aim to set that record straight.
## 5 Conclusions  
The results of this research prove that the ML methodology used for discriminating tectonic environments can also be used to discriminate volcanic regions belonging to the same tectonic setting. In the case of the present study, four volcanic regions of the HVA producing IABs in a converging tectonic environment were successfully discriminated using the XGB classifier with an accuracy of 93.07% during training and 90% during validation.  
The final model compiled was used for the implementation of a CBA [48] written in Python code and deployed in the flask framework. The CBA implemented in the framework of this study can be used as a valuable tool in the field of geology to classify volcanic rocks found in the Southern Aegean Sea in one of the four active volcanic regions of the HVA using 10 major elements. This will also be a useful tool for mapping of the HVA in the future. Furthermore, in geoarchaeology, the CBA can be used as a valuable tool for tracking the origin of artifacts found all over Greece, such as millstones and anchors made from volcanic rock dating from prehistory to antiquity, a procedure known as geochemical fingerprinting. The CBA is publicly accessible at www.geology.gr (accessed on 6 September 2021).  
The authors' future plans include applying this methodology for the two active volcanic arcs in the Mediterranean, namely the Hellenic Volcanic Arc in the Aegean Sea and the Calabrian Volcanic Arc in the Tyrrhenian Sea, as similar datasets are available in the GEOROC database. This would produce a tool for applying geochemical fingerprinting of ancient artifacts to a broader geographic region.  
Conceptualization, A.G.O. and G.A.P.; methodology, A.G.O. and G.A.P.; software, A.G.O.; validation, A.G.O. and G.A.P.; investigation, A.G.O.; writing--original draft preparation, A.G.O.; writing--review and editing, A.G.O. and G.A.P.; visualization, A.G.O.; supervision, G.A.P.; project administration, G.A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The Geochemical Rock Database used in this study was retrieved from the Max Planck Institute for Chemistry, [[http://georoc.mpch-mainz.gwdg.de/georoc/](http://georoc.mpch-mainz.gwdg.de/georoc/)]([http://georoc.mpch-mainz.gwdg.de/georoc/](http://georoc.mpch-mainz.gwdg.de/georoc/)) accessed on 30 June 2021.  
We would like to pay our respects to all the individuals involved in collecting rock samples in the field, those in the laboratory producing the geochemical data, and, last but not least, the people responsible for GEOROC preparing the datasets and distributing them for free. This research would not have been possible without them. Moreover, we wish to express our gratitude to the geologists [PERSON] and [PERSON] for reviewing the geological context of this paper. Finally, we would like to thank [PERSON] for linguistic revision of the manuscript.  
The authors declare no conflict of interest.
# A Survey of Ledger Technology-Based Databases
## 4 Permissioned Blockchain Based Databases
### Corresponding Studies to Hyperledger Fabric
#### 4.7.2 FastFabric  
Architectural changes were made to the 1.2 version of Hyperledger Fabric [52], all of which have a great impact on transaction processing, thus achieving major improvements in performance. The research claims to have increased transactions per second from 3000 to 20,000. Two primary changes have been made to the ordering service, and a number of improvements have been implemented in the peers.  
**Ordering Improvements:** The first change in the ordering service is that it is able to decide on the transactions' order based on the transaction's ID. Thus, the communication overhead is decreased, with which throughput capability is increased. The second change implements the parallel validation of transactions. Therefore, instead of by default sequentially validating transactions, transactions are able to be validated simultaneously.  
**Peer Improvements:** In the case of peer tasks, there is much room for improvement. One of these improvements has to do with the use of a hash table as opposed to the world state database. Thus, servers are able to reach necessary keys faster as they are stored in memory. This modification has another consequence: the blockchain log and the state base are stored using a peer cluster in various hardware. The next modification frees up resources on the peer through separating commitment and endorsement onto different hardware, as up until now, the same peer has been completing these tasks.  
The research parallelizes all things that have a positive effect on performance; thus, the validation of the block and transaction headers will be parallelised. The last improvement considers transaction validation by temporarily caching unmarshalled data until the end of validation.
## 2 Methodology
### AE: Autoencoder  
With the idea of unsupervised feature learning in DL, the autoencoder (AE) forms a general framework for learning the compressed representation in the hidden space of the input data [46; 47; 48]. An autoencoder consists of two modules: an encoder module and  
Figure 3: RNN and LSTM architecture.  
a decoder model. The encoder takes the input data and compresses it into a compressed representation in a lower-dimensional space, and the decoder then reconstructs the original inputs from the compressed representation. And the goal is to minimize the reconstruction error, as illustrated in Equation (10).  
\[\begin{array}{l}f_{Enc}:\mathcal{X}\rightarrow\mathcal{H}\\ f_{Dec}:\mathcal{H}\rightarrow\mathcal{X}\\ \theta=\underset{\theta}{\text{argmin}}\|\mathbf{x}-f_{Dec}(f_{Enc}(\mathbf{x}))\| \end{array} \tag{10}\]  
where \(\mathcal{X}\) is the space of the inputs, \(\mathcal{H}\) is the space of hidden states. In the autoencoder framework, \(f_{Enc}\) learns to map the inputs to the compressed representation \(f_{Enc}(\mathbf{x})\), and \(f_{Dec}\) learns to reconstruct the inputs \(f_{Dec}(f_{Enc}(\mathbf{x}))\) from the compressed representation, and the goal is to minimize the error between the original inputs \(\mathbf{x}\) and the reconstructed \(f_{Dec}(f_{Enc}(\mathbf{x}))\). The model is then trained by minimizing the difference between the input and the output, also known as the reconstruction error. The popularity of autoencoders can be attributed to their ability to learn meaningful representations of complex data without requiring explicit supervision.  
In this way, this compressed representation promises to capture the most important underlying structure of the input data and remove redundant information. And the ability of the autoencoder to learn meaningful representations and the underlying structure of complex data has made it a valuable model in many areas. In SHM, the learned compressed representation and the reconstructed errors are used as the damage indicator [24], given the fact that a well-trained autoencoder on the SHM big data can be viewed as a data-driven agent model of the bridge structural performance, and the reconstruction error reflects the changes of the underlying structures of the input data and the condition of the bridge structure compared to the initial condition.
## 5 Method
### Feasibility Analysis
#### 5.2.2 Technical Feasibility  
The implementation at the technical level mainly faces three difficulties.  
Difficulty 1. Edge deployment of AI large models. Current approach: 1 Deploying lightweight and large models based on server Docker environment but with limited resource utilization. 2 By using large-scale model compression techniques such as quantization, pruning, or knowledge distillation, the model size can be compressed to achieve lightweight deployment, but the accuracy of the model will inevitably decrease. Our solution utilizes cloud and edge collaborative deployment, offloading lightweight tasks such as latency sensitive ones to edge servers for processing, thus avoiding the latency and security risks associated with multi-hop backhaul. When the task exceeds the load of a single machine, it is diverted to other edge servers for processing based on heuristic algorithms. Complex tasks are transmitted back to the central cloud for processing, and the inference results are distributed to the end of the rescue site through the 5G downstream network.  
Difficulty 2. The fine-tuning and alignment of large models under low resource conditions. When computing resources are insufficient, large models often struggle to make effective inferences and cannot serve users properly. This article proposes a resource optimization strategy based on heuristic algorithms for a three-layer computing resource network architecture, which can effectively integrate idle resources in the network and enable large models to effectively utilize parallel training strategies such as data parallelism or model parallelism to achieve fine-tuning and alignment.  
Difficulty 3. Heterogeneous data fusion. The transition strategy of using protocol converters for heterogeneous networks is based on 5G slicing to separate different business networks. In the future, with the development of semiconductors, more intelligent terminal chips will support heterogeneous network access and have certain computing power, making the network more concise and efficient. The above-mentioned difficult issues have accumulated relevant research and field testing experience in the early stage of the research group.
# Data Protection and Multi-Database Data-Driven Models
## 3 Methodology
### Parameters  
The data sets were partitioned into test and training sets. Our partition randomly assigns 80% of the records for training and the remaining 20% of the records for testing.  
Next, we vertically partitioned training data into two databases. This corresponds to building the two databases \(DB_{1}\) and \(DB_{2}\), which will share a set of attributes. The selection of the attributes depends on the database. They are as follows:  
* CASC: The number of common attributes for \(DB_{1}\) and \(DB_{2}\) considered is \(nC=1,2,3,4,5,6\). That is, we considered six different pairs of databases. These databases were built as follows. For the first pair, \(DB_{1}\) includes attributes 0-5, and \(DB_{2}\) includes attributes 5-11 (0-5 and 5-11 correspond to columns in the database, with the first column denoted by zero). Databases \(DB_{1}\) for \(nC=2,\ldots,6\) are defined in terms of attributes 0-6, 0-6, 0-7, 0-7, and 0-7. Databases \(DB_{2}\) for \(nC=2,\ldots,6\) are defined with attributes 5-11, 4-11, 4-11, 3-11, and 2-11.
* Tarragona: The number of common attributes is the same as for the CASC data set, and the databases were also constructed following the same pattern.
* Concrete: In this case, we also have \(nC=1,\ldots,6\). For \(nC=1\), we have \(DB_{1}\) with attributes 0-5 and \(DB_{2}\) with attributes 5-7. For \(nC=2\), we have \(DB_{1}\) with attributes 0-6 and \(DB_{2}\) with attributes 5-7. For larger \(nC\), we have \(DB_{1}\) with attributes 0-6, 0-7, 0-7, and 0-7, and \(DB_{2}\) with attributes 4-7, 4-7, 3-7, and 2-7.  
Masking methods were applied considering different parameterizations. The following parameterizations have been considered.  
* Microaggregation (MDAV and Mondrian). The following values of \(k\) were considered: \(k=\{2,3,4,5,6,7,8,9,10,12,14,15,16,18,20\}\). For some experiments, we used larger values. In that case, we used \(k=\{2,4,6,8,10,15,20,25,30,40,50,60,70,100,200,300,400,500\}\).
* Noise addition (Gaussian and Laplacian noise). We used noise with parameters in the set \(\{0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.2,1.4,1.5\}\).
* SVD and NMF. In this case, we used parameters equal to \(\{2,3,4,5,6,7,8\}\).
* PCA. We used one, two, and three principal components.  
Machine learning algorithms were used with their standard parameters. No tuning was applied. As explained above, we used the sklearn Python package. Regression models were used because all data were numerical. The dependent attribute for each database is discussed above.
# An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System
## 2 Methodology
If new authorized target coordinates are given, the robot follows the new target points and optimizes the path for a new target. The facial and object detection and recognition process are shown in Figure 5. The facial detection and recognition algorithm does not use the depth sensor data; rather, it works on two-dimensional images. An individual's images are studied to detect the facial area using the Haar cascading method. An individual frame captured by a Pi camera is synthesized by the processing unit and converted into the 128-bit real value numbers. A pre-training model generated based on the Microsoft Common object in the context dataset was used for object detection of a chair, table, bicycle, etc. This individual image detection classifier was applied to the resultant image, as in Figure 5 output phase. If the person was valid, then the speech recognition was enabled. This system takes input in the form of a for a new target. If the system fails to detect and recognize the face, the target point can also be input manually through a remote server. The system library function enables the microphone and registers the input until there is a delivery stoppage for 2 s. The given input is converted into a string to remove unnecessary background noise and the system finds only target inputs. The hardware setup is shown in Figure 6 with the camera setup positioned at 1 m above the ground level and embedded with the Raspberry Pi 3 module to capture an adequate vision area. The MPU 6050 is an integrated accelerometer with the compass embedded with the processing module to measure angle and displacement. The robotic action signal is sent to the H-Bridge driver module based on the decision made by the navigation algorithm processed by the embedded system. Figure 5: Face and object recognition.
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis carried out consists of the following parts:  
* We verified that there was a significant difference between the manual FRT methods (M_FRT) and the Kinect FRT (K_FRT) using the \(t\)-test of mean difference of paired samples. In order to carry out this test, we previously verified that the samples were distributed normally and that the size of the collected samples was adequate. For that reason, we applied Shapiro-Wilk normality test [41; 42] to the data to check its distribution. Moreover, we conducted power analysis to determine the number of subjects required for the mean difference between the two sets of data obtained by both methods.  
Figure 6: Protocol of experimentation.  
As the previous conclusion contradicted previous works [26], we needed to correct the measurements using a linear model to fit the data from the K_FRT sample. We saw that the linear model is adequate to carry out this adjustment. Before doing that, we run Pearson correlation test to see if there is any association between the measures obtained from the different methods (manual and Kinect). This test is also known as the \"product moment correlation coefficient\" and it measures the strength and direction of association that exists between two continuous variables [43].
* Finally, carried out the \(t\)-test again for the adjusted data, checking that there were no significant differences between the two methods.  
To analyze the reliability of the FRT, we assessed the variation of data measured by the Kinect system across the three trials of FRT using intraclass correlation coefficient (ICC, 2.1) [44]. Based on a 95% confidence interval, values of ICC less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 indicate poor, moderate, good, and excellent reliability, respectively, [45]. The level of significance for all tests was set at \(p<0.05\). The statistical analysis tool R was used.
###### Abstract
This study focuses on the details of IEC 61850-90-5 multicast communication and its cybersecurity in wide-area applications. Cybersecurity is investigated in both message level and network level. In message level, two possibilities for implementing decentralized security architectures are proposed. In network level, cybersecurity is studied for both private and public WAN, and the Defense-in-Depth security approach is discussed. Furthermore, we introduce a time synchronization setup that can be used for measuring the communication delay of R-SV/R-GOOSE messages even when sender and receiver devices are at different locations. Furthermore, the paper's contribution is to examine the applicability of novel cellular (5G and 4G) Internet in wide-area applications combined with necessary architecture solutions for secure communication of IP-multicast traffic (R-SV/R-GOOSE) over the public IP network via tunneling protocols. Two network architectures are proposed and experimented for communicating multicast R-GOOSE messages with a fresh timestamp. A software application is developed to generate R-GOOSE messages while they acquire time information from the Global Positioning System (GPS). These messages are communicated within the proposed network architectures under various configurations. The performance of each configuration is statistically analyzed in terms of communication latency (represented in 1 st centile, mean, and 99 th centile) and packet loss. The rest of this paper is structured as follows: Related Work is presented in Section 2. Section 3 discusses interoperability for wide-area applications; Section 4 explains IEC61850 multicast communication for wide-area applications; Section 5 explains communication dependability; Section 6 proposes network architectures for IEC61850 multicast communication over the public WAN; Section 7 presents the performance evaluation of multicast R-GOOSE over the public WAN; and Section 8 contains our conclusions.