esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 2 Materials and Methods
### Extreme Gradient Boosting  
The XGB model is a high-performance implementation of gradient boosting for supervised learning tasks [35]. In particular, the latter leverages an ensemble of decision trees, where each employed tree progressively refines the predictions of the prior trees by focusing on the errors of previous models [36].  
Formally, consider a dataset with \(m\) features and \(n\) samples denoted as follows: \((\mathbf{x}_{1},y_{1}),\ldots,(\mathbf{x}_{n},y_{n})\), where \(\mathbf{x}_{i}\in\mathbb{R}^{m}\) represents the feature vector for the \(i\)-th sample and \(y_{i}\in\mathbb{R}\) represents the corresponding label. The ensemble learning approach in XGB aims to minimize a selected loss function \(L(\phi)\) defined over the entire dataset. In particular, the core principle of XGB lies in iteratively building an ensemble model \(\hat{y}_{i}=\phi_{M}(\mathbf{x}_{i})\) through a sequence of decision trees \(f_{k}(\mathbf{x}_{i})\), where \(M\) represents the number of trees in the ensemble. Each of the latter trees learns from the residual errors of the previous trees, aiming to improve the overall predictive performance. Mathematically, the latter additive strategy can be expressed according to the following Equation (6):  
\[\phi_{M}(\mathbf{x}_{i})=\phi_{M-1}(\mathbf{x}_{i})+\eta f_{n}(\mathbf{x}_{i}) \tag{6}\]  
where \(\eta\) represents the learning rate that controls the impact of each new tree on the ensemble. It must be noted that the decision trees themselves are usually constrained to a maximum depth of \(m_{depth}\) to prevent the occurrence of overfitting.  
The loss function \(L(\phi)\) typically includes the following two key components:  
1. Data Loss: This term measures the discrepancy between the predicted outputs, \(\hat{y}_{i}\), and the actual labels, \(y_{i}\). A common choice for the latter one is the absolute loss function, defined as \(l(y_{i},\hat{y}_{i})=|\hat{y}_{i}-y_{i}|\).
2. Model Complexity Penalty: This term, often referred to as regularization, discourages overly complex models that might lead to overfitting. In the context of XGB, an \(L_{2}\) regularization term, expressed as \(\lambda||\mathbf{w}||^{2}\), is frequently employed, where \(\lambda\) controls the strength of the penalty and \(\mathbf{w}\) represents the weights associated with each leaf node in the employed decision trees.  
Figure 4: Illustration of the architecture of an MLP with two hidden layers composed of 5 neurons. The activation function is represented by \(\varphi\). The diagram shows the flow of information from the input layer through the first and second hidden layers to the output layer, highlighting the fully connected structure of the network.  
Through the minimization of the loss \(L(\phi)\) with the iterative construction of the ensemble, XGB achieves enhanced prediction accuracy within supervised learning tasks.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Testing Scripts and Procedures
#### 8.3.3 Test Scenarios and Their Justifications  
1. Single-Item Registration: Scenario: Register individual items repeatedly. Justification: This scenario simulates the most common operation and serves as a baseline for performance comparisons;
2. Item Retrieval: Scenario: Retrieve details of registered items. Justification: It assesses the efficiency of data retrieval, which is critical for user interfaces and third-party integrations;
3. Ownership Transfer: Scenario: Transfer ownership of registered items between accounts. Justification: Evaluates the performance of ownership management, a key feature of NFT-based systems;
4. Metadata Update: Scenario: Update the metadata of existing items. Justification: Tests the system's ability to maintain up-to-date information, crucial for dynamic digital registries;
5. Batch Registration: Scenario: Register multiple items in a single transaction, varying the batch size. Justification: Assesses the system's capability to handle bulk operations efficiently, which is important for large-scale data migrations or high-volume registration periods;
6. Concurrent Operations: Scenario: Simulate multiple users interacting with the contract simultaneously. Justification: Evaluates the system's performance under concurrent load, mimicking real-world usage patterns;
7. Gas Limit Stress Test: Scenario: Gradually increase transaction complexity until reaching block gas limits. Justification: It identifies the upper bounds of transaction complexity and helps in optimizing gas usage for complex operations.  
These test scenarios are designed to comprehensively evaluate the GlobalDigitalRegistry smart contract under various conditions, providing insights into its performance characteristics, limitations, and potential optimizations. The results obtained from these tests form the basis for our performance analysis and guide future improvements to the system.
## 6 Verification and Results
### Episode 3: Security Integrity (req: ii, viii)
#### 6.3.2 Route and Packet Injection  
The main focus of our contribution was to protect the SFC from being eavesdropped when the data traffic is traversing ISP-B. However, another attack method of gaining access to unauthorised traffic is to manipulate the packet forwarding services in order to redirect the traffic path and implicitly gain access to non-encrypted traffic. Hence, an important feature of the architecture is that it is resistant to packet injection or route injection (req: viii).  
We did not find any method to inject flow rules into the P4 switch without compromising the network controller. However, for test purposes, we turned off the authentication and SSL on the RESTconf interface towards to P4 switch. This allowed us to manipulate the flow rules and consequently also manipulate the SFC.  
For packet injection, we simulated that SF2 is compromised in ISP-B. We used an NSH extension in the Python based scapy tool [38] in order to inject packets into the data plane. We successfully managed to inject packets from an SF to another SF that was belonging to another SFC. Due to a lack of security features in the SFF, the SFF was not able to detect the VM source of the injected packets and simply forwarded the packet according to the packet headers. However, this was only possible for non-encrypted data flows.  
This packet injection problem is similar to spoofing the source address field in IP packets. A possible solution to this problem is to introduce an integrity attribute in the NSH headers. Extending the NSH integrity header RFC [39] to support layered NSH can potentially ensure that the NSH packet originates from a valid VM source.
# Explore Long-Range Context Features for Speaker Verification
## 3 Hierarchical-Split Block  
Local-based approaches enlarge the local receptive field through pooling, dilated, split-stack and other operations usually. A hierarchical-Split block is one of the approaches by split-stack operations.  
The structure of the HS-block is depicted in Figure 3b. The \(3\times 3\) block in the typical ResNet is modified. After the \(1\times 1\) convolution, the feature maps are split equally into \(s\) groups, denoted by \(x_{i}\), and the \(3\times 3\) convolution filters are also replaced by several groups, denoted as \(\mathcal{F}_{i}()\). Each \(x_{i}\) will be fed into \(\mathcal{F}_{i}()\), and the output feature maps are denoted by \(y_{i}\). Here, each \(y_{i}\) is equally split into two sub-groups, denoted \(y_{i,1}\) and \(y_{i,2}\). Then, \(y_{i,2}\) is concatenated with the following group \(x_{i+1}\), and then sent into \(\mathcal{F}_{i+1}()\). All \(y_{i,1}\) are concatenated in the channel dimension as the output of the \(3\times 3\) convolution filters, denoted as \(y_{i}\). Especially, each \(y_{i,1}\) has different channels and RFs, and the more channels \(y_{i,1}\) contains, the larger RFs are gained. In this manner, the feature maps could  
Figure 2: ResNet34 and ResNet50 architecture.  
contain detailed information and larger-range dependencies. \(\bigoplus\) means two feature maps are concatenated in the channel dimension.  
\[y_{i}=\left\{\begin{array}{cc}x_{i},&i=1\\ \mathcal{F}_{i}(x_{i}\bigoplus y_{i-1,2}),&1<i<=s\end{array}\right. \tag{1}\]  
Moreover, the hyperparameters \(s\) and \(t\) are used to control the HS-ResNet's parameters and complexity. \(s\) is the group the feature maps are divided into, and \(t\) means how many times the number of channels will be expanded. \(k\) means the size of convolution kernel, \(w\) means the number of channels. The computational complexity of the HS-block can be calculated as follows.  
\[c_{0} = t\times w=t\times\frac{C}{s}\quad c_{i}=c_{0}+\frac{c_{i-1}}{2} \tag{2}\] \[PARAM = k^{2}\times(\sum_{i=0}^{s-2}c_{i}^{2}+c_{0}^{2})\] \[= c_{0}^{2}\times(4s-\frac{29}{3}+16\times 2^{-s}-\frac{16}{3} \times 2^{-2s})\] \[= C^{2}\times(\frac{t}{s})^{2}\times(4s-\frac{29}{3}+16\times 2^{-s }-\frac{16}{3}\times 2^{-2s})\]  
Here, we denoted:  
\[\beta=(\frac{t}{s})^{2}\times(4s-\frac{29}{3}+16\times 2^{-s}-\frac{16}{3} \times 2^{-2s}) \tag{4}\]  
The parameters of the traditional convolutional layer are \(C^{2}\), and the parameters of the HS-block are \(\beta\times C^{2}\). By controlling the parameters \(t\) and \(s\), \(\beta\), that is, the parameters of the HS-block can be precisely designed.
# Study and Investigation on 5G Technology: A Systematic Review
## 4 5G Technologies
### 5G IoT Based Approaches
State-of-the-Art of Approaches  
Plenty of approaches is devised to address the issues of IoT [14; 65; 66]. In [65], the paper focuses on 5G mobile systems due to the emerging trends and developing technologies, which results in the exponential traffic growth in IoT. The author surveyed the challenges and demands during deployment of the massive IoT applications with the main focus on mobile networking. The author reviewed the features of standard IoT infrastructure, along with the cellular-based, low-power wide-area technologies (LPWA) such as eMTC, extended coverage (EC)-GSM-IoT, as well as noncellular, low-power wide-area (LPWA) technologies such as SigFox, LoRa etc. In [14], the authors presented how 5G technology copes with the various issues of IoT today. It provides a brief review of existing and forming 5G architectures. The survey indicates the role of 5G in the foundation of the IoT ecosystem. IoT and 5G can easily combine with improved wireless technologies to set up the same ecosystem that can fulfill the current requirement for IoT devices. 5G can alter nature and will help to expand the development of IoT devices. As the process of 5G unfolds, global associations will find essentials for setting up a cross-industry engagement in determining and enlarging the 5G system. In [66], the author introduced an IoT authentication scheme in a 5G network, with more excellent reliability and dynamic. The scheme proposed a privacy-protected procedure for selecting slices; it provided an additional fog node for proper data transmission and service types of the subscribers, along with service-oriented authentication and key understanding to maintain the secrecy, precision of users, and confidentiality of service factors. Users anonymously identify the IoT servers and develop a vital channel for service accessibility and data cached on local fog nodes and remote IoT servers. The author performed a simulation to manifest the security and privacy preservation of the user over the network. This section covered various works done on 5G IoT by multiple authors. Table 8 shows how different author's worked on the improvement of numerous parameters, i.e., data rate, security requirement, and performance with 5G IoT.
## 3 Results  
In terms of shear bond strength (SBS), the milled materials (LU and GR) demonstrated significantly higher mean SBS values across all treatments compared to the 3D-printed materials (CT and CB), with ranges of 14.8 MPa to 20.4 MPa and 13.9 MPa to 20.0 MPa, respectively. On the other hand, CT displayed the lowest mean SBS values, ranging from 5.7 MPa to 16.1 MPa across all treatments, as shown in Table 2. Regarding surface treatment methods, specimens treated with sandblasting (SB) and hydrofluoric acid (HF) exhibited significantly higher SBS values compared to the control (C) and diamond bur (DB) treatments, with the control group showing the lowest SBS values, as presented in Table 2.  
The adhesive remnant index (ARI) results varied based on the composite resin type and surface treatment (see Figure 4). Most specimens showed ARI scores of 0, followed by scores of 1. For Lava Ultimate (LU), the majority of surface treatments resulted in complete or near-complete adhesive failure with the restoration (ARI 0), except for Group HF, where 90% of specimens retained less than 50% adhesive (ARI 1). In Grandio (GR), Groups C and DB exhibited 80% complete adhesive failure (ARI 0), while Group SB displayed a mixed distribution of ARI scores. For Crownttec (CT), Groups C, HF, and SB had 60%, 80%, and 90% complete adhesive failure (ARI 0), respectively, while Group DB showed greater variability in adhesive retention (ARI 1 and 2). In C&B Permanent (CB), all groups, including the control, demonstrated mixed adhesive retention outcomes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{4}{c}{**Surface Treatment**} \\ \cline{2-5}  & **C** & **HF** & **DB** & **SB** \\ \hline \hline
**Lava\({}^{\text{TM}}\) Ultimate (LU)** & 14.8 \(\pm\) 1.2 \({}^{\text{Ac}}\) & 19.6 \(\pm\) 1.8 \({}^{\text{Cc}}\) & 17.2 \(\pm\) 1.4 \({}^{\text{Bc}}\) & 20.4 \(\pm\) 1.5 \({}^{\text{Cc}}\) \\ \hline
**Grandio\({}^{\text{TM}}\) (GR)** & 13.9 \(\pm\) 0.7 \({}^{\text{Ac}}\) & 19.7 \(\pm\) 1.6 \({}^{\text{Cc}}\) & 17.5 \(\pm\) 1.6 \({}^{\text{Bc}}\) & 20.0 \(\pm\) 1.0 \({}^{\text{Cc}}\) \\ \hline
**Crowntec\({}^{\text{TM}}\) (CT)** & 5.7 \(\pm\) 0.7 \({}^{\text{Aa}}\) & 16.1 \(\pm\) 1.5 \({}^{\text{Ca}}\) & 9.7 \(\pm\) 0.7 \({}^{\text{Ba}}\) & 15.5 \(\pm\) 0.6 \({}^{\text{Ca}}\) \\ \hline
**C\&B Permanent\({}^{\text{TM}}\) (CB)** & 11.5 \(\pm\) 1.4 \({}^{\text{Ab}}\) & 17.4 \(\pm\) 1.3 \({}^{\text{Cb}}\) & 14.8 \(\pm\) 1.2 \({}^{\text{Bb}}\) & 18.2 \(\pm\) 0.9 \({}^{\text{Cb}}\) \\ \hline \hline \end{tabular} Abbreviations: Group C (control, no surface treatment), Group HF (treated with 9.6% hydrofluoric acid), Group DB (mechanical roughening by a diamond bur), and Group SB (sandblasting using aluminum oxide). Different superscript uppercase letters in each row for each material indicate significant differences (\(p\) < 0.05). Different superscript lowercase letters in each column for each surface treatment method indicate significant differences (\(p\) < 0.05).  
\end{table}
Table 2: Mean \(\pm\) standard deviation of shear bond strength (SBS) values in MPa for each material and surface treatment method.
# Improving User Experience with Recommender Systems by
## 4 Analysis and Results
In contrast, the use of a problem-to-solution information sequence did not have a significant effect on decision-making time for all messages (b = \(-0.03805\), \(p=0.1667\)); however, when looking at the time-to-decision by type of decision, it was found that information sequence significantly reduced decision-making time for accepted recommendations (HB4: b = 0.06418, \(p<0.05\)) but not for those recommendations for which users requested more details (b = 0.01624, \(p=0.7185\). On the other hand, text styling and situational complexity did not impact decision-making time (HB3: b = 0.04046, \(p=0.4545\) and HB5: b = 0.02226, \(p=0.6831\)). In addition to the observed effect of information specificity on decision-making time, it was also observed that users were significantly more likely to accept the recommendation if the messages stated specific (rather than vague) problems (HB6: b = 0.3006, \(p<0.0001\)) and specific (rather than vague) solutions (HB7: b = 0.4073; \(p<0.0001\)). However, no effect on user behavior with respect to recommendation acceptance was observed for information sequence (HB8: b = 0.1392, \(p=0.2674\)), text styling (HB8: b = 0.1392, \(p=0.2674\)), and situational complexity (HB10: b = 0.03508, \(p=0.7815\)). All behavioral results are shown in Figure 5b and reported in Table 3. Lastly, a post hoc analysis further reinforced the favorable effect of information specificity: specific recommendation messages were significantly longer (\(p<0.05\) all cases) in character count, for both problem- and solution-specificity, whether counted with spaces or without spaces, yet the decision-making time on a per character basis was significantly lower (\(p<0.001\) in all cases).
# Time Evolution of CO2 Diffusivity of Carbonated Concrete
## 2 Modeling of CO\({}_{2}\) Diffusivity
### Porosity Reduction Due to Carbonation  
Carbonation of concrete greatly affects changes of pore structure system, which is inevitably connected to the rate of CO\({}_{2}\) diffusion through carbonated concrete. The change of pore structure system in concrete due to carbonation was limited to OPC concrete, in this study. [PERSON] et al. proposed the following equation, based on the ratio of the volume of Ca(OH)\({}_{2}\) and CSH in concrete before carbonation to calcium carbonate after carbonation [23]:  
\[V_{c}=V_{p}-\Delta V_{c} \tag{16}\]  
where,  
\(V_{c}\): porosity of concrete after carbonation,  
\(V_{p}\): porosity of concrete before carbonation.  
Decreased porosity due to carbonation (\(\Delta V_{c}\)) is approximately equal to:  
\[\Delta V_{c}=\ \left[\text{Ca(OH)}_{2}\right]\ \Delta\overline{V}_{CH}+\left[ \text{CSH}\right]\ \Delta\overline{V}_{CSH} \tag{17}\]  
in which, \(\Delta\overline{V}_{CH}\) and \(\Delta\overline{V}_{CSH}\) are equivalent to \(3.85\times 10^{-6}\) m\({}^{3}\)/mol, \(15.39\times 10^{-6}\) m\({}^{3}\)/mol, respectively. Although the concrete is carbonated, the pores of the aggregate remain the same and the pores of the cement paste change. Therefore, changed pores, calculated by Equation (16), was reinputted into Equation (5) to characterize the pore system of carbonated cement paste.  
Based on the above analysis, the CO\({}_{2}\) diffusivity of concrete before and after carbonation was calculated, and the mixing conditions of concrete used in the calculation are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{_uv/c_} & \multicolumn{4}{c}{**Unit Weight (kg/m\({}^{3}\))**} \\ \cline{2-5}  & **Water** & **Cement** & **Sand** & **Gravel** \\ \hline
0.45 & 185 & 411 & 706 & 1001 \\
0.50 & 185 & 370 & 720 & 1021 \\
0.55 & 185 & 336 & 732 & 1038 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mixing condition of concrete used for calculation.
## 4 Results and Discussion: Statistical Approach
### Multiple-Regression Analysis
#### 4.3.1 Standardization
* A value between \(-0.50\) and \(0.00\) indicates that the property is below the mean, but within the 95% confidence interval. * A value below \(-0.50\) shows that the property is below the lower limit of the 95% confidence interval. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & \(X_{max}\) & \(X_{min}\) & \(X_{m}\) \\ \hline Compressive strength (MPa) & 42.23 & 26.75 & 34.49 \\ Modulus of elasticity (GPa) & 33.31 & 20.58 & 27.01 \\ Splitting tensile strength (MPa) & 3.69 & 2.70 & 3.20 \\ Flexural strength (MPa) & 4.84 & 3.49 & 4.17 \\ UPV (km/s) & 4.15 & 3.58 & 3.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Values for the standardization of the UPV readings and the mechanical properties. Figure 7 shows the standardized value of all the experimental values of the mechanical properties and the UPV. From this figure, the following aspects can be noted:  
* The compressive-behavior-related mechanical properties (compressive strength and modulus of elasticity), as well as the UPV of the mixes made with up to 50% fine RA and limestone (both types) presented values that were in general higher than the mean value (standardized values over zero). * The mixtures with limestone and fine natural aggregates (0% RA) were the ones that exceeded the upper limit of the 95% confidence interval (standardized values over 0.5). * The standardized values for those mixes made with 100% fine RA were lower than the mean value and the lower limit of the 95% confidence interval (standardized values under \(-0.5\)). * For the properties related to bending-tensile behavior (splitting tensile strength and flexural strength), only limestone powder 0/0.5 mm led to properties above the mean value in all cases (positive standardized values). * The use of RA powder together with 0% fine RA led to standardized values very close to 0.00 for all the properties that were studied, which caused the increase in fine RA content to yield values that were clearly below the mean. Furthermore, when 100% fine RA was added, they were clearly below the 95% confidence interval (standardized values under \(-0.5\)). Despite the advantages of standardization, it generally only supports the development of linear regression models. When a simple-regression analysis of the UPV as a function of each mechanical property was separately undertaken, it was found that the only conventional model that could be fitted was the linear model shown in Equation (3) (\(\mathit{UPV}_{S}\) is the standardized dimensionless value of the UPV reading; \(\mathit{MP}_{S}\) is the standardized, dimensionless value of the mechanical property; \(\mathit{a}\) is the adjustment coefficient). The adjustment coefficients and \(\mathrm{R}^{2}\) coefficients for each mechanical property are detailed in Table 5. In line with the aspects addressed in previous sections, the fit was much better than with the mechanical properties related to compressive behavior. Linear multiple-regression models were therefore developed. \[\mathit{UPV}_{S}=\mathit{a}\times\mathit{MP}_{S} \tag{3}\]