esa-sceva/satcom-chunk-collection huggingface dataset:
# Bounce: A High Performance Satellite-Based Blockchain System
## 5 Implementation
### Signature Schemes  
For the core _Bounce_ protocol, we use the Boneh Lynn Shacham (BLS) signature [49; 50; 53], as it provides a fast and efficient way to aggregate and verify signatures. BLS signatures can be aggregated into a single signature. Given a set of distinct messages \(m_{1},\ldots,m_{n}\), a set of distinct secret keys \(sk_{1},\ldots,sk_{n}\), and signatures \(s_{1},\ldots,s_{n}\) on the messages signed by respective private keys, (i.e., \(s_{i}=Sign(sk_{i},m_{i})\), the aggregate signature \(s=Aggregate(s_{1},\ldots,s_{n})\) is a valid signature, i.e., \(Verify(s,[m_{1},\ldots,m_{n}],pk)=true\), where \(pk=Aggregate(pk_{1},\ldots pk_{n})\) is the aggregate public key of the secret keys.  
To defend against rogue key attacks, BLS IRTF Draft V5 [50] provides three different variants of the scheme: Basic, Message Augmentation, and Proof of Possession. We use the Proof of Possession variant of the scheme. In this variant, the public keys need to be validated before they can be used to verify signatures. Each component in our implementation verifies the public key when the reset message distributes public keys of other components.  
As we're using the Proof of Possession scheme, we make use of fast verification of aggregate signatures on the same message. In our protocol, we have a common pattern where a single component (e.g., Sending Station) sends a request to other components(e.g., Broadcast Ground Stations or Satellite) to sign a Merkle root or Commit Record. The requesting component then aggregates the returned signatures into a single signature, and broadcasts it.  
We're aware that BLS IRTF draft [50] and the Rust library we're using [53] are vulnerable to splitting zero attacks [54] if the non-fast variant of aggregate verify in BLS is used. For that reason, we don't use the non-fast variant but rather the fast variant of aggregate verify to avoid the vulnerability.
## 3 Case Study of a Damaged Simply Supported Beam
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & **Experimental** & \multicolumn{3}{c}{**Updated Frequency/Hz**} & \multicolumn{3}{c}{**Difference/\%**} \\ \cline{3-7}  & **Frequency/Hz** & **Direct GMPSO** & **Hybrid Method** & **Direct GMPSO** & **Hybrid Method** \\ \hline
1 & 15.39069 & 15.43708 & 15.31826 & 0.30 & 0.47 \\
2 & 37.14974 & 37.25102 & 37.14082 & 0.27 & 0.02 \\
3 & 52.36193 & 52.31448 & 52.41372 & 0.09 & 0.10 \\
4 & 83.96296 & 83.96269 & 83.58468 & 0.00 & 0.45 \\
5 & 96.36669 & 96.97276 & 96.11117 & 0.45 & 0.27 \\
6 & 116.64350 & 116.43540 & 116.98770 & 0.18 & 0.30 \\
7 & 144.77070 & 145.07480 & 144.88010 & 0.21 & 0.08 \\
8 & 184.79670 & 186.12250 & 184.99380 & 0.72 & 0.11 \\
9 & 223.79820 & 223.20420 & 223.93680 & 0.27 & 0.06 \\
10 & 254.07320 & 255.31840 & 254.94600 & 0.49 & 0.34 \\ \hline \hline \end{tabular} \({}^{1}\) Difference = \(\frac{\text{Updated Frequency}-\text{Experimental Frequency}}{\text{Experimental Frequency}}\times 100\%\).  
\end{table} 
Table 2: The comparison between updated and experimental frequencies \({}^{1}\). Figure 3: A 10 m-span beam structure (unit: cm). In this case study, it can be seen that both direct GMPSO and the proposed hybrid method can get an approximate model of this damaged bridge, which means the damage in elements 3 and 8 are both identified. In Table 2, the differences of all the first ten mode frequencies between the updated and experimental model are below 1% which indicates the updated model reflects the damaged one very well. However, from the view of efficiency, the proposed hybrid method is better than direct GMPSO, being five times faster. It is evident that the results of direct GMPSO and hybrid method are highly consistent, which demonstrates that the proposed hybrid method is applicable to model updating of structures with multi-parameters. More importantly, the updating process can be performed more efficiently without losing accuracy.
# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 3 SDN-Driven Traffic Migration Engineering (TME) Model
### Adaptive Traffic Migration Control Model
then
7: The original model of the first traffic prediction:
8:\(\overset{T}{t_{u,v}^{T}}=\tau_{u,v}^{t}+\rho\times(R_{u,v}^{t}-\tau_{u,v}^{t})\)
9: Calculate the peak rate at time \(t\):
10:\(\overset{T}{t_{u,v}^{T}}=(1-\rho)\times\tau_{u,v}^{t}+\rho\times B_{u,v}\times \log_{2}(1+SNR_{u,v}^{t})\)
11: The second modification of the model:
12:\(\overset{\sim}{t_{u,v}^{T}}=\rho\times\tau_{u,v}^{T-1}+\tau_{u,v}^{T}\)
13:\(\overset{\sim}{t_{u,v}^{T}}=\tau_{u,v}^{t}+\rho\times[\tau_{u,v}^{t-1}-\tau_{u,v} ^{t}\)
13:\(\quad\quad\quad+B_{u,v}\times\log_{2}(1+SNR_{u,v}^{t-1})]\)
14:\(\quad\quad\quad+\rho_{\sim}^{2}\times[B_{u,v}\times\log_{2}(1+SNR_{u,v}^{t})- \tau_{u,v}^{t-1}]\)
15:while\(\tau_{u,v}^{t}\geq R_{u,v}^{t}\)do
16: Use the TIV principle for traffic migration
17:endwhile
18:endif
19:endfor
```  
**Algorithm 1** The TME algorithm.
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 4 Multi-Hop Transmission and Synchronization
### Fitness Function  
In the case of the particle swarm optimization algorithm applied to the problem domain, the set of fitness functions is directly determined by the selection and removal of the transmission path. In order to minimize the energy consumption of the whole network, the design of the fitness function should satisfy the following two conditions: (1) The distance between the nodes of any hop should be as small as possible; (2) The maximum number of hops in the monitored area should be as small as possible. Therefore, the fitness function can be constructed as:  
\[\left\{\begin{array}{l}fitness=w_{1}*MaxDist+w_{2}*MaxHops+w_{3}*Direc\\ 0\leq w_{i}\leq 1,\ \sum w_{i}=1\end{array}\right. \tag{14}\]  
where \(w_{1}\), \(w_{2}\) and \(w_{3}\) are the distance factor, the hop factor and the direction factor, respectively. \(MaxDist\) is the maximum communication distance between any adjacent two-hop nodes in the monitoring area, \(MaxHops\) is the maximum number of hops in all transmission paths, and \(Direc\) denotes the deviation angle between the source node and the relay node.
## 3 Results and Discussion
### Interactive Application Prototyping
#### 3.3.2 Interaction Function Design  
To improve the usefulness of the visualization platform and optimize user experience, three main interaction functions are designed in this study, namely, text manipulation, visual sentiment analysis graphs, and an emotional feedback adjustment method based on an intelligent assistant.  
1. Text operation  
The application is designed with a simple text operation interface, as shown in Figure 9. After the user writes text in the input box and clicks the \"Finish\" key, the application will generate sentiment data through sentiment analysis and store the sentiment data in the database. At the same time, the application provides the functions of viewing history, deleting and modifying, etc. The home page only shows the text records of the last seven entries. If the user needs to view historical data, they can query by month or day. Only the first 100 words of each record are displayed, hiding the rest of the text. After the user clicks the \"View Details\" key, the application will pop up a modal box to display the text details, and the user can delete or modify the content in this modal box.  
Figure 8: System architecture.  
Figure 9: The input interface.  
The application uses ECharts to design sentiment analysis charts and provides three visual charts, as shown in Figure 10. Mood polarity graphs show the probability of emotional polarity for each text, and the range of the displayed curves can be adjusted through the timeline. Mood distribution pie charts show the distribution of the number of positive and negative diaries. The mood swing graphs show the ratio of positive diaries to the total number of diaries for a month, which better reflects the user's mood direction.
# A Framework for the Evaluation of the Cultural Heritage Information Ontology
## 3 Evaluation Framework for the Cultural Heritage Information Ontology
#### New Ontology Metrics for Interoperability
Since the original CIDOC CRM does not include external ontologies, the result of the evaluation with the three new metrics (_EXOnto_, _CPOnto_, _AGOnto_) integrated into the OQUARE framework yielded a value of 2.89. To prove the usefulness of the extended OQUARE framework, we cloned the CIDOC CRM and modified it with the reuse of the core ontologies (time, foaf, schema) from the section \"Core ontologies for reuse\". The modification included the replacement of the concepts \"E2_Temporal_Entity\", \"E52_Time-Span\", \"E74_Group\", \"E40_Legal_Body\", and the property \"P98i_was_born\":  
[[http://www.cidoc-crm.org/cidoc-crm/E2_Temporal_Entity](http://www.cidoc-crm.org/cidoc-crm/E2_Temporal_Entity)]([http://www.cidoc-crm.org/cidoc-crm/E2_Temporal_Entity](http://www.cidoc-crm.org/cidoc-crm/E2_Temporal_Entity))  
[[http://www.w3.org/2006/time#TemporalEntity](http://www.w3.org/2006/time#TemporalEntity)]([http://www.w3.org/2006/time#TemporalEntity](http://www.w3.org/2006/time#TemporalEntity))  
[[http://www.cidoc-crm.org/cidoc-crm/E52_Time-Span](http://www.cidoc-crm.org/cidoc-crm/E52_Time-Span)]([http://www.cidoc-crm.org/cidoc-crm/E52_Time-Span](http://www.cidoc-crm.org/cidoc-crm/E52_Time-Span))  
[[http://www.w3.org/2006/time#DateTimeInterval](http://www.w3.org/2006/time#DateTimeInterval)]([http://www.w3.org/2006/time#DateTimeInterval](http://www.w3.org/2006/time#DateTimeInterval))  
[[http://www.cidoc-crm.org/cidoc-crm/E74_Group](http://www.cidoc-crm.org/cidoc-crm/E74_Group)]([http://www.cidoc-crm.org/cidoc-crm/E74_Group](http://www.cidoc-crm.org/cidoc-crm/E74_Group))  
[[http://xmlns.com/foaf/0.1/Group](http://xmlns.com/foaf/0.1/Group)]([http://xmlns.com/foaf/0.1/Group](http://xmlns.com/foaf/0.1/Group))  
[[http://www.cidoc-crm.org/cidoc-crm/E40_Legal_Body](http://www.cidoc-crm.org/cidoc-crm/E40_Legal_Body)]([http://www.cidoc-crm.org/cidoc-crm/E40_Legal_Body](http://www.cidoc-crm.org/cidoc-crm/E40_Legal_Body))  
[[http://xmlns.com/foaf/0.1/Organization](http://xmlns.com/foaf/0.1/Organization)]([http://xmlns.com/foaf/0.1/Organization](http://xmlns.com/foaf/0.1/Organization))  
[[http://www.cidoc-crm.org/cidoc-crm/P98i_was_born](http://www.cidoc-crm.org/cidoc-crm/P98i_was_born)]([http://www.cidoc-crm.org/cidoc-crm/P98i_was_born](http://www.cidoc-crm.org/cidoc-crm/P98i_was_born))  
[[http://schema.org/birthDate](http://schema.org/birthDate)]([http://schema.org/birthDate](http://schema.org/birthDate))  
The evaluation of the cloned and modified CIDOC CRM presented in Figure 2 resulted in a final value of 3.15. The integration of additional external ontologies to the CIDOC CRM can increase the result for the interoperability sub-characteristic, improving the final evaluation. Figure 1: Quality of the CIDOC CRM according to the original OQuaRE evaluation framework. Figure 2 presents the evaluation results of the CIDOC CRM with the application of the new metrics for interoperability.
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 3 Noise Modeling and Building the Training Sets
### Building the Training Sets
#### 3.2.2 Noise Modeling  
The NM-GAN is trained using the method proposed in Section 3.1 with real noise samples. During training, the constructed data is segmented frame by frame (1024 points, 50% overlap) before being synthesized. The learning rate for \(G\) and \(D\) is set to 0.0002, and within a minibatch, all training samples are padded with zeros to ensure the same number of time steps as the longest sample. The process is stopped after 50 epochs if there is no improvement in the validation set according to the loss measurement. Once this process is complete, the last-obtained model is further fine-tuned, with the batch size doubled, and the learning rate lowered to 0.00001. This is re-iterated until 50 epochs without any improvement in the validation loss. Finally, the model with the best validation loss is selected. In this way, the generator can generate countless noise samples.
###### Abstract  
This paper introduces a novel deep learning model for ECG signal classification using feature fusion. The proposed methodology transforms the ECG time series into a spectrogram image using a short-time Fourier transform (STFT). This spectrogram is further processed to generate a histogram of oriented gradients (HOG) and local binary pattern (LBP) features. Three separate 2D convolutional neural networks (CNNs) then analyze these three image representations in parallel. To enhance performance, the extracted features are concatenated before feeding them into a gated recurrent unit (GRU) model. The proposed approach is extensively evaluated on two ECG datasets (MIT-BIH + BIDMC and MIT-BIH) with three and five classes, respectively. The experimental results demonstrate that the proposed approach achieves superior classification accuracy compared to existing algorithms in the literature. This suggests that the model has the potential to be a valuable tool for accurate ECG signal classification, aiding in the diagnosis and treatment of various cardiovascular disorders.  
CNN; GRU; ECG; arrhythmia; heartbeat; deep learning; feature fusion Article
## 3 The RIS-Aided UAV Secure Transmission
### UAV's Trajectory Optimization  
For the given \(w[n]\) and \(\Phi\), the trajectory is need to be optimized. In the following, we adopt the successive convex approximation (SCA) algorithm to transform the non-convex items into convex items for the trajectory optimization [36].  
First, we introduce several relaxation variables as follows  
\[\begin{split} t[n]&\leq H^{2}+\left\|s_{\mathbb{I} }[n]-s_{\mathbb{C}}\right\|^{2},\\ \xi[n]&\leq H^{2}+\left\|s_{\mathbb{I}}[n]-s_{ \mathbb{C}}\right\|^{2},\\ \eta[n]&\leq H^{2}_{2}+\left\|s_{\mathbb{I}}[n]-s_{ \mathbb{C}}\right\|^{2}.\end{split} \tag{20}\]  
Since the above inequality and maximum rate constitute problem are non-convex, we use the first order expansion of Taylor series to transform the trajectory problem as\[t[n] \leq H^{2}+2\big{(}\mathbf{s}_{U}^{m}[n]-s_{G}\big{)}\big{(}\mathbf{s }_{U}[n]-\mathbf{s}_{U}^{m}[n]\big{)}^{T} \tag{21}\] \[\qquad\qquad+\big{\|}\mathbf{s}_{U}^{m}[n]-s_{G}\big{\|}^{2},\] \[\xi[n] \leq H^{2}+2\big{(}\mathbf{s}_{U}^{m}[n]-s_{E}\big{)}\big{(}\mathbf{ s}_{U}[n]-\mathbf{s}_{U}^{m}[n]\big{)}^{T}\] \[\qquad\qquad+\big{\|}\mathbf{s}_{U}^{m}[n]-s_{E}\big{\|}^{2},\] \[\eta[n] \leq H^{2}+2\big{(}\mathbf{s}_{U}^{m}[n]-s_{B}\big{)}\big{(}\mathbf{ s}_{U}[n]-\mathbf{s}_{U}^{m}[n]\big{)}^{T}\] \[\qquad\qquad+\big{\|}\mathbf{s}_{U}^{m}[n]-s_{B}\big{\|}^{2},\]  
where \(\mathbf{s}_{U}^{m}[n]\) is the position of UAV in the \(m\)th iteration. Therefore, the trajectory planning optimization problem can be given as  
\[\max_{s_{U}} \frac{1}{N}\sum_{n=1}^{N}R_{s}[n] \tag{22}\] \[t[n] \leq H^{2}+2\big{(}\mathbf{s}_{U}^{m}[n]-s_{G}\big{)}\big{(}s_{U}[ n]-\mathbf{s}_{U}^{m}[n]\big{)}^{T}\] \[s.t. \left\{\begin{aligned} &\mathbf{s}_{U}[1]=\mathbf{s}_{S},\\ &\mathbf{s}_{u}[N]=\mathbf{s}_{E},\\ &||\mathbf{s}_{u}[n]-\mathbf{s}_{u}[n-1]||^{2}\leq V\delta,\mathbf{ n}\in\{2,\ldots,N\},\end{aligned}\right.,\] \[t[n] \leq H^{2}+2\big{(}\mathbf{s}_{U}^{m}[n]-s_{G}\big{)}\big{(}s_{U}[ n]-\mathbf{s}_{U}^{m}[n]\big{)}^{T}\] \[+\big{\|}\mathbf{s}_{U}^{m}[n]-s_{B}\big{\|}^{2},\]  
The above optimization problem is convex, and we can effectively solve this problem through the convex optimization algorithm. In this system model, our solution is to maximize the average secure transmission rate. This scheme is composed of two rings: inside and outside. The outer ring is related to AO algorithms for reflection optimization and trajectory optimization. The internal loop is the SCA algorithm itself. Therefore, the proposed optimization problem can be solved through the proposed algorithm.
## Materials and Methods
### Procedures and Evaluations  
The investigation was completed during an in-season period, from November to December 2019. Test measurements were performed on a regular indoor handball court, at the same time of day (from 17:30 h to 19:30 h), under similar experimental and environmental conditions (temperature of \(20.5\pm 0.5\)\({}^{\circ}\)C and relative humidity of \(60\pm 5\)%), and at least 3 days after a competition. Participants were advised to maintain their normal dietary habits while refraining from drinking high calorie beverages or eating for 4 and 2 h before testing, respectively. They were also asked not to perform any vigorous physical activity for 24 h before the initial testing. Tests were performed over a period of four days in a fixed order to elicit similar fatigue effects between players. On the first day, anthropometric measurements were followed by vertical jump tests (squat jump and countermovement jump (S) and CMJ, respectively)). The second day was devoted to sprint performance. On the third day, medicine ball throws were evaluated, followed by the Yo-Yo Intermittent Recovery Test Level 1 (Yo-Yo IR1) on the fourth day.  
Two weeks after the initial testing period, fitness tests from days 1 to 4 were repeated (i.e., SJ, CMJ, sprints, Yo-Yo IR1, and medicine ball throw) to allow for the assessment of between-day test-retest reliability (Table 1).  
Based on the intraclass correlation coefficient (ICC) and coefficient of variation (CV) analyses, all tests showed an excellent test-retest reliability.  
The scores of the second set of tests were considered for analyses. Anthropometric assessments and the Yo-Yo IR1 test were only completed once at the initial testing period for convenience reasons related to time and players' schedules.
# Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Core
## 3 Application and Analysis
### Neural Network  
#### 3.3.1 Input Layer and Output Layer  
In the paper from which the above data were derived, the authors also used seawater density, gravitational acceleration, sampler density, drag coefficient, and friction coefficient to calculate the penetration depth. However, it is clear from the analysis that parameters such as seawater density, gravitational acceleration, and sampler density are essentially constant between samplers (considering that gravity piston samplers are made of steel and lead), and the friction coefficients reported in the original article were derived by extrapolation from the type of sediment. Therefore, in this paper, the six parameters of weight of sampler, internal diameter, external diameter, cutter diameter, velocity, and sediment type are selected as input parameters of the neural network (Table 1). Sediment type numbers are expressed only from smallest to largest based on particle size; other representations are also acceptable. The output layer parameter is undoubtedly the penetration depth of the gravity sampler.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Range** \\ \hline Weight of sampler (kg) & 20–2230 \\ \hline Internal diameter (m) & 0.042–0.1 \\ \hline External diameter (m) & 0.058–0.127 \\ \hline Cutter diameter (m) & 0.072–0.1411 \\ \hline Velocity (m/s) & 2.8–14.0561 \\ \hline Sediment type & Clay = 1; Silt = 2; Fine Sand = 3; Sand = 4 \\ \hline \end{tabular}
\end{table}
Table 1: Input parameters for gravity ccer penetration depth.
# Comparison of Measurement Possibilities by Non-Invasive Reflectometric Sensors and Invasive Probes
## 2 Materials and Methods
### Description of the Tested Material  
Cellular concrete was used for the tests as the building material. In the presented research, samples of 400 kg/m\({}^{3}\) and 600 kg/m\({}^{3}\) of cellular concrete were used.
## 2 Applications and Background of Uwsn
### Background of UWSN
#### 2.2.2 Deployment of Network Architecture  
Underwater network is established to perform designated tasks. The network is formed by placing the multiple number of nodes at different depths of the sea volume to perform the task collaboratively. Every sensor node will transmit data by multi-hop or clustering technique to reach surface sink. In compared to terrestrial sensor network UWSN is more sensitive and complicated due to its three dimensional (3D) nature. Two communication architectures were proposed by [PERSON] et al. [2]: (i) Two dimensional (2D) network architecture, and (ii) Three dimensional (3D) network architecture.  
When the network is 2D, sensors are remaining scattered only at the sea bed. These sensors can transmit data with each other. They can also communicate with the Underwater sinks (UW-sinks) via acoustic medium. UW-sinks send the extracted data from the sensors and send it to the sinks located at top of water surface. To perform this relaying, each UW-sink is provided with two acoustic transceivers- horizontal transceiver and vertical transceiver. Horizontal transceivers communicate with the deployed sensor nodes whereas vertical transceivers relay the data to the surface station. Vertical transceiver must be long ranged in case of deep ocean communication. These floating stations have the transceivers which can handle more than one links with the sinks at a time. These stations also have the facility to communicate with the onshore-sinks. Figure 2 shows a generalized two dimensional underwater network. Sensors are randomly spread in sea bed which collects the data and transfer it to the closest UW-sink. In 2D UWSN, the sensor nodes are deployed and placed only at the seabed. In this scenario, horizontal and vertical transceivers float at different depths of the sea to relay the data transmitted from the sensor nodes at the seabed to the UW-sinks. UW-sinks send the collected data to their respective horizontal transceiver. From horizontal transceiver data reaches to the surface sinks via long range vertical transceiver.  
Whereas, unlike the deployment of sensor nodes in 2D UWSN, in the 3D UWSN, the sensor nodes are deployed and float at different depths of the sea level. The 3D UWSN is deployed to perceive the phenomena more precisely and thoroughly. The transmission of data in 3D USWN follows similar procedure with the help of different transceiver discussed in 2D UWSN.  
But [PERSON] et al. proposed a slightly different idea in [36]. Authors suggested to tie each sensor-node to a surface buoy by a wire so that the depth of the sensor nodes can be changed by changing the wire length. By this, the sensor-nodes can be placed at different depth of the water levels with much ease. But these buoys can block the sea vehicles. Besides they are susceptible to weather tampering and pilfering.