esa-sceva/satcom-chunk-collection huggingface dataset:
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 3 System Design
### Processes for Decentralized Live Streaming in VidBlock
* **Selecting content and entering the live room:** The process begins with the user selecting the desired content and entering the corresponding live streaming room. This action initializes the environment and prepares the system for establishing a P2P connection with other participants (viewers or other nodes in the streaming session). * **Setting up the renderer and starting P2P live streaming:** Once the user is in the live room, the system triggers the initialization of the P2P connection using the session description protocol (SDP). This step is carried out through the function call initialization(dest ViB ID*, orig ViB ID*, SDP*), which exchanges the unique identifiers (ViB IDs) of the destination (viewer) and origin (broadcaster) nodes, along with SDP data. The SDP information includes critical connection parameters such as media format and codec details, which are essential for configuring a compatible streaming session between the two nodes. Figure 5: The process of starting live streaming in VidBlock. Following the initial setup, the nodes exchange interactive connectivity establishment (ICE) candidates. This phase involves the transmission of ICE details through sendICEs (dest ViB ID*, orig ViB ID*, and ICEs*) messages, allowing each node to share network-specific information such as IP addresses and ports. The exchange of ICE candidates is a vital part, as it facilitates the discovery of the most optimal network path for direct P2P streaming, bypassing potential firewalls or network restrictions. By negotiating the best connection path, the system minimizes latency and ensures a smooth streaming experience. After the successful negotiation of ICE candidates and verification of SDP parameters, the P2P live streaming session is officially initiated. At this stage, the nodes establish a direct connection, allowing for a seamless, low-latency transfer of live streaming content from the broadcaster to the viewer. This direct P2P connection eliminates the need for intermediary servers, ensuring a decentralized and user-controlled streaming experience. VidBlock leverages Web3.0 technologies, incorporating blockchain and smart contracts to enhance the security, efficiency, and autonomy of its streaming service. By decentralizing content distribution and only storing metadata on the blockchain, VidBlock minimizes dependency on centralized servers and enhances user privacy. This architecture empowers content creators to independently manage their streams, while viewers can access content directly, embodying the Web3.0 model of user control and transparency. Through this P2P, decentralized approach, VidBlock not only addresses limitations in traditional streaming but also pioneers a new, user-centric standard in digital media distribution.
## 3 Simulation Results
### Estimation of Sparse Channels  
In this experiment, we showed the sparse system estimation results. The simulation was performed under the same experimental conditions in [6]. The true system parameter \(\mathbf{w}_{o}\) had an order of M = 64. Out of the 64 coefficients, there were S nonzero coefficients. The nonzero coefficients were placed randomly, and the values of the coefficients were drawn from a \(N(0,1/\text{S})\) distribution. The impulsive noise is generated according to the Gaussian mixture model [26]  
\[p_{\sigma}\ =\ (1-p_{r})N\big{(}0,\sigma_{1}^{2}\big{)}+p_{r}N\big{(}0,\sigma_{2 }^{2}\big{)} \tag{15}\]  
where \(N\big{(}0,\sigma_{i}^{2}\big{)}(i\ =\ 1,2)\) denote the Gaussian distribution with zero-mean and variance \(\sigma_{i}^{2}\). The \(p_{r}\) denotes the occurrence probability of the Gaussian distribution with variance \(\sigma_{2}^{2}\), which usually is much larger than \(\sigma_{1}^{2}\) so as to generate the impulsive noises. The zero-mean Gaussian distribution with variance \(\sigma_{1}^{2}\) generated the background noise, and the zero-mean Gaussian distribution with variance \(\sigma_{2}^{2}\) (usually \(\sigma_{2}^{2}\gg\sigma_{1}^{2}\)) generated the impulsive noise with the probability \(p_{r}\). In this experiment, we set the variance of \(\sigma_{1}^{2}\) to 0.01 and generate the input signal so that SNR keeps 20 dB. The other parameters were set as \(\sigma_{2}^{2}\ =\ 500\) and \(p_{r}\ =\ 0.01\).  
We compare CR-RMC [20] using the true system response information and the proposed algorithm using a regularization factor that did not use the true system response. It also included the results of  
\begin{table}
\begin{tabular}{l} \hline Initialization: \(\mathbf{\Phi}(0)\),\(\mathbf{r}(0)\),\(\hat{\mathbf{w}}(0)\), \(\sigma^{2}\). \\ \hline For n = 1... \\ \hline \(\mathbf{\Phi}(n)\ =\ \lambda\mathbf{\Phi}(n-1)+\exp\Big{(}\frac{-\mathbf{\varepsilon}(n)^ {2}}{2\sigma^{2}}\Big{)}\mathbf{x}(n)\mathbf{x}(n)^{T}\) \\ \(\mathbf{r}(n)\  
the MCC-RLS [27] and the conventional RLS without considering impulsive noise and sparsity. For the performance evaluation, we simulated the algorithms in the sparse impulse response for S = 4, 8, 16, 32.  
Figure 1 illustrates the mean standard deviation (MSD) curves. The results show that the estimation performance of the proposed algorithm is similar to that of CR-RMC using the regularization factor referring to the true system impulse response. As expected, the conventional RLS produced the worst MSD in all cases.  
Figure 1 confirms that, without _a priori_ information about the true system impulse response, the proposed regularization factor works similarly to that of the regularization factor in CR-RMC using the true system impulse response information.
## 2 Background and Related Work
### Overview of SOA Standards  
Based on the communication patterns of DDS, SOME/IP and SDC, the commonalities can be identified, although the protocols realize the patterns differently. These commonalities are summarized in Table 1 for patterns for the usage of services. Furthermore, the presented communication patterns represent a selection which is supported by several SOA standards. Additionally, each standard provides patterns for the binding of services such as find, offer, subscribe, stop offer or stop subscribe, as these are the basis for an SOA standard.  
In our comparison, we differentiate between the _publish/subscribe, request/response_ and the _stream_ pattern. In particular, the term publish/subscribe is used in different standards with different meanings. We use it to refer to communication in a 1-N channel where a particular service provider is selected by any number of clients. There can also be multiple providers, but only one server can be selected by each client at any given time. In most cases, this pattern is used to implement event-based communication, in which a notification is sent in response to status changes. The request/response pattern, on the other hand, is typically used for a method call on the server. This also includes get and set functions that retrieve or change parameters from servers. The response can also be empty or non-existent (as in the case of SOME/IP fire/forget methods). Stream services are used to consecutively push information to 0 or more service consumers provided by 1 to N publishers (see SDC Section 2.2).  
Since in an SOA, theoretically, any participant can consume and also provide a service based on the communication patterns presented, securing these mechanisms is central to the security of a SOA. By analyzing abnormal behavior in the relations of the services in a network depending on their communication patterns, an intrusion by an attacker could be the source of this behavior.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Pattern** & **SDC** & **SOME/IP** & **DDS** \\ \hline publish/subscribe (1-N) & \
\begin{tabular}{l} publish/subscribe (e.g., Description Event \\ Service, State Event Service) \\ \end{tabular} & event, field & / \\ \hline request/response (1-1) &
\begin{tabular}{l} request/response (e.g., Get/Set Service), \\ Context Service \\ \end{tabular} & request/response, fire/forget & 2x Topic with DDS-RPC \\ \hline stream (N-M) & streaming (e.g., Waveform Service) & / & Topic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of SOA operation communication patterns.
# Twitter Data Mining to Map Pedestrian Experience of Open Spaces
## 4 Discussion
### Precision of Sentiment Determination  
In the context of this work, it was noticed that sentiment classification of individual tweets does not always perform classification accurately. For instance, the tweet \"I wonder if London holiday guides still refer to Oxford Street as the \"ultimate shopping destination\". Cos... it's not. Just bare American candy shops, traffic, and Zara's. It's so yuck now\" was classified as positive, even though by examination it was identified that this tweet carries negative sentiment. Something similar can be seen with the tweet \"I never thought I'd love walking down a heaving Oxford Street, but it was bloody brilliant! Normality\", which was classified as negative, and it carries positive sentiment. Previous research identifies VADER classification of social media for all normalized sentiment scores between \(-1\) and \(+1\), at F1 Classification Accuracy of 0.96 [25]. A possible reason for this is due to the lack of sentiment-oriented text, often expressed by emoticons, slang, initials and abbreviations [25]. VADER does not detect irony and phrases, but will rate individual words, which may lead to the wrong classification of the sentiment [32].  
Different authors have analysed and compared VADER's precision and accuracy. Accuracy is defined as a proportion of total number of correctly classified tweets and total number of analysed tweets, while precision represents the measure of correctly classified positive tweets only. VADER's accuracy rate is 83%, while the precision is 90% (F1 score is 89%) [33]. The high accuracy and precision rate of the VADER method are not the only advantages of this method. Its computational efficiency and quickness are also an advantage. Furthermore, VADER is transparent and easily accessible, with its use and easy interpretation, making it useful for non-computer scientists. As different researchers report much lower precision and accuracy when VADER is applied to different topics and domains, future research should analyse further the precision and efficiency of other sentiment analysis approaches that may provide better performance when analysing social media on the topic of urban planning of public spaces.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 4 Results and Discussion
### Confusion Matrix  
The confusion matrix is a performance measure of classification models [48], it consists of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) measures. The accuracy (ACC) of the model in terms of aforementioned measures is:  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \times 100 \tag{1}\]  
Here, TP is the number of the alphabet that is positive (current class) and predicted correctly as positive, TN is the number of the alphabet that is negative (classes other than current class) and predicted correctly as negative, FP is the number of the negative alphabet that is negative but predicted incorrectly as positive, and FN is the number of the alphabet that is positive but predicted incorrectly as negative.
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 5 Simulation Results and Discussions
### Impact of Maintenance Strategy
#### 5.2.2 Condition-Based Maintenance  
The impact of the application and quality of the CMS on the O&M of an OWT is also investigated. Three scenarios are considered in the investigation although due to the structure of the model developed other scenarios could easily be considered. They are  
* Scenario 1--CMS not used, only use \"advanced service\" every half year;
* Scenario 2--Use a basic CMS and \"advanced service\" takes place every half year;
* Scenario 3--Only use a premier CMS that has a powerful fault detection capability as described in Table 2 without adopting \"advanced service\".  
The number of faults repaired, the number of failures recovered, the downtime, and maintenance costs are calculated. All results are listed in Table 5 for comparison.  
From Table 5, it is seen that  
* When using a CMS, a greater number of faults are detected and repaired. Moreover, the better the quality of the CMS, the more faults will be detected and repaired.
* The comparison of the data obtained in Scenario 1 and Scenario 2 suggests that the unexpected shutdown of the wind turbine can be significantly prevented by using a basic CMS, conducting a frequent inspection, and taking prompt action to repair the faults. Thanks to the use of the basic CMS, the consequent downtime and maintenance cost are significantly reduced;
* The comparison of the results in Scenario 3 and Scenario 2 shows that when the fault detection capability of the CMS is improved, the number of failures, downtime and maintenance costs can be further reduced.  
The data obtained in Scenario 3 suggests that periodic maintenance will become unnecessary when using a premier CMS. However, this is based on the assumption that the fault detection capability of the CMS is 0.997. Undoubtedly, it is impossible to achieve such  
Figure 12: The tendency of Cost for achieving turbine availability per day.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Maintenance** & **Number of** & **Number of** & **Consulting** & **Downtime** & **Maintenance** \\
**Strategy** & **Faults Repaired** & **Recovered** & **(Days)** & **Cost (\(\Omega\))** \\ \hline Scenario 1 & 19.65 & 2.49 & 208.21 & 2,406,661.02 \\ Scenario 2 & 22.13 & 0.68 & 162.75 & 1,222,719.05 \\ Scenario 3 & 23.04 & 0.06 & 87.67 & 477,866.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of the application and quality of the CMS on the operation and maintenance (O&M).  
a well-performing CMS in reality at the moment, but it does suggest that a better-quality CMS does have the potential to relieve the burden of periodic maintenance. However, CMS should not be too expensive. The maintenance cost obtained in Scenario 2 and the cost obtained in Scenario 3 indicate that the maximum capital cost of the premier CMS should not exceed \(\varepsilon\)745,000. Otherwise, the operation of the OWT will become no longer economical.
## 2 System Overview
### Sick Ladar Digital-Multilayer Range Scanner (LD-MRS) Laser Scanner  
In this project, the Sick Ladar Digital-Multilayer Range Scanner (LD-MRS) laser scanner is utilized to detect obstacles in tramline. The laser scanner is mounted near the center of the frontal area of the test vehicle, named \"Silkworm\", as seen in Figure 1. The sensor can detect all objects in front of the vehicle up to 50 m with the accuracy of \(\pm\)5 cm. The LD-MRS laser scanner has a variable scan area.  
Figure 1: Ladar Digital-Multilayer Range Scanner (LD-MRS) laser scanner mounted in front of the test vehicle named “Silkworm” which is manufactured by Durmazlar Machine Inc. and control unit of the test vehicle.  
In this study, the horizontal angle of visual field is limited to \(110^{\circ}\) from \(+50^{\circ}\) to \(-60^{\circ}\). For obtaining simultaneous records of a scene, a video camera is mounted near the laser scanner sensor.  
The LD-MRS is a four-layer laser scanner that uses Time-of-Flight (ToF) technology. It scans the surroundings radially with several rotating laser beams, receives the echoes with a photodiode receiver, and processes the data by means of a time of flight calculation. If the round trip time is \(t\), then the distance to the object is calculated as seen below:  
\[d=\frac{ct}{2}, \tag{1}\]  
where \(d\) is the distance to the object and \(c\) is the speed of light.  
Multilayer laser scanner technology provides pitch angle compensation through four scan planes with various vertical angles. Thus, the sensor can detect all of the objects reliably, even while accelerating or breaking. Moreover, the LD-MRS has multi-echo capability. So, it can gather and evaluate up to three echoes per transmitted laser pulse. While the echo of a raindrop yields a very low voltage over a short period of time, the echo of an object yields a high voltage over a longer period. Therefore, a raindrop and an object can be distinguished easily.
## 4 Numerical Simulation and Analysis
### Establishment of the Numerical Model  
The excavation of the foundation pit affects the area outside the pit to a certain extent. Considering the influence of the boundary effect of the model on the results, the excavation depth range is expanded by 4 times in all directions for simulation [19,39]. The calculation domain of the model is 145 m \(\times\) 145 m \(\times\) 60 m. The geological exploration borehole locations of the foundation pit are shown in Figure 10a. To reflect the real situation of the soil layer, according to the complete data of 26 geological exploration boreholes, considering the heterogeneity and seepage anisotropy of layered soil, a stratum model is established. This assumption has been proven to be effective for the study of deformation and seepage [40]. The three-dimensional finite element calculation model of the foundation pit Is shown In Figure 10b, and the overall model of the supporting structure is shown in Figure 10c. The element type is a ten-node tetrahedron. The fine element distribution mode is selected for grid division, and the grid near the supporting structure is encrypted  
Figure 9: Damage situation of the accident scene: (**a**) large area cushion collapse; (**b**) sidewall damage. to intuitively reflect the evolution of foundation pit failure. A total of 93,498 mesh elements and 155,214 nodes were generated. The stratum model is simulated by the hardened soil model (HSS model) considering the small strain stiffness behavior. The calculation parameters are selected according to geological survey reports and the research of [PERSON] [4] and [PERSON] [41], as shown in Table 1. In the simulation of the foundation pit supporting structure, the equivalent plate element is used to simulate the retaining structure composed of SMW method piles (equivalent thickness \(d\) = 1.01 m, Poisson's ratio \(\
u\) = 0.15, elastic modulus \(E_{1}\) = 7.26 GPa, and \(E_{2}\) = 3.63 GPa). The good-man contact surface element in the PLAXIS\({}^{\text{3D}}\) software is used to simulate the contact interface between the retaining structure and the soil. The strength parameter of the contact surface is the strength parameter of the corresponding adjacent soil layer multiplied by the interface strength reduction factor \(R_{\text{inter}}\). The pile element and beam element are used to simulate the pile foundation structure and the ring enclosure structure in the pit, respectively (the elastic modulus \(E_{3}\) is 31.5 GPa). A point-to-point anchor rod element is used to simulate the grouting steel pipe (\(EA\) = 1.87 \(\times\) 105 kN).
## 3 Preliminaries
### Core Switching
#### 3.4.2 Node Architecture  
Figure 5 shows the node architecture of core switching [17] known as architecture on demand (AoD). It realizes not only fiber switching but also core switching in the optical fiber. AoD includes an optical backplane, which can interconnect the input, output, add, drop, single device building module (e.g., the multiplexer and spectrum selection switch (SSS)), and composed building module (such as the optical amplifier and power divider or coupler) in any way by setting an appropriate cross-connection on it, so as to realize the software-programmable switching of optical fibers. This AoD is different from the reconfigurable optical add/drop multiplexers architecture based on an optical SSS module (for switching and filtering), which cannot achieve any combination of such components because of its hardwired inputs, modules and outputs. In addition, AoD also supports direct switching of optical signals from the input port to the output port, without using any demultiplexing and multiplexing modules. Therefore, AoD has higher flexibility in that it realizes the expansion of the new features and allows more light signals to be routed to different cores, thus making full use of the existing spectrum resources. More details can be found in [17].  
Figure 4: Core switching principle.
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 4 Implementation and Experiments
### Evaluation  
To train a model, data could be split into training and testing sets. However, doing so on small datasets would cause testing scores to vary depending on which data parts were used for training and which were used for testing. Therefore, using this approach on small data in particular would not provide reliable results [39].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Values** \\ \hline Loss & Weighted binary cross entropy \\ Number of Layers & 1 and 2 \\ Hidden Layer Size & 25, 50, 75, 100, and 125 \\ Embedding Size & 32, 50, 75, and 100 \\ Batch Size & 1, 8, 16, 32, and 64 \\ Learning Algorithm & Nadam with default values \\ Early Stopping & monitor = ’val_loss’, patience = 2 \\ Dropout & None, 0.1, 0.2, and 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter Summary.  
So, to make better use of our corpus, we used 10-fold cross validation and the averages of precision, and the recall for all iterations of a single model were calculated and collected for analysis. Various research papers apply k-fold cross validation when dealing with smaller datasets [40; 41; 42; 43; 44; 45; 46].  
Additionally, we calculated and observed F\({}_{0.5}\) because, for the task of error detection, feedback accuracy (i.e., precision) has a higher priority than coverage (i.e., recall). Other evaluation measures used in the literature, such as the M\({}^{2}\)-score, were not directly valid for error detection because they require that a system suggests a correction.  
Additional code was written to be able to evaluate precision, recall, and F\({}_{0.5}\) by comparing true labels to predicted labels and computing true negatives, true positives, false negatives, and false positives.