esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Performance Testing on Other Datasets and Comparison with Previous Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**Imbalanced Training Set**} & \multicolumn{3}{c}{**Balanced Training Set**} \\ \hline
**Metrics** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** \\ \hline T(U)F(D)(F(cls) for imb set) & 87.30 & 85.78 & 87.84 & 85.81 & 84.90 & 86.13 \\ \hline T(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D)(F(cls) for imb set) & 90.56 & 89.28 & 91.01 & 87.87 & 85.78 & 88.61 \\ \hline T(U)F(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D)(F(cls) for imb set) & 90.68 & 86.0 & 92.33 & 88.90 & 85.78 & 90.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results obtained on Messidor-2 test set (imb set, the imbalanced training set). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**EyePaCS**} & \multicolumn{3}{c}{**DDR**} & \multicolumn{3}{c}{**Messidor-2**} \\ \hline
**Metrics** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** & **ACC (\%)** & **TPR** & **TNR** & AUC (\%) & **TPR** & **TNR** (\%)** \\ \hline Customized CNN model in [15] & 75.0 & 30.0 & 95.0 & - & - & - & - & - \\ \hline CABNet in [13] & 86.18 & - & - & 78.98 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed method with other previous methods: ‘-’ indicates that no value was provided for the item. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**Imbalanced Training Set**} & \multicolumn{3}{c}{**Balanced Training Set**} \\ \hline
**Metrics** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** \\ \hline T(U)F(D)(F(cls) for imb set) & 86.15 & 83.43 & 88.88 & 88.31 & 83.20 & 93.43 \\ \hline T(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D)(F(cls) for imb set) & 89.2 & 88.64 & 89.73 & 88.76 & 84.97 & 92.54 \\ \hline T(U)F(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D)(F(cls) for imb set) & 89.50 & 87.81 & 91.18 & 89.62 & 86.39 & 92.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results obtained on DDR test set (imb set, the imbalanced training set).
###### Abstract  
The primary objective of this case study is to evaluate the effectiveness of drone-based ground penetrating radar (GPR) in detecting and mapping underground water levels and soil-to-rock boundaries. This knowledge is crucial for accurate structural engineering analyses, including foundation engineering and site-specific response evaluations. The paper also considers drone-based GPR to overcome common urban obstacles, topographic variations, and environmental factors by simply flying over them, offering a promising solution to these challenges. The research utilized drone-based GPR equipped with an unshielded 150 MHz dipole antenna and employed filtering procedures to diminish the effect of above-ground obstacles on the interpretation of our results. The study unequivocally demonstrated the feasibility and effectiveness of drone-based GPR in these applications, reassuring the civil engineering community. The findings of this study significantly advance our understanding of drone-based GPR technology for mapping disturbed soil boundaries and water table levels in foundation engineering and site response applications and provide valuable recommendations for optimizing its performance in complicated terrains, thereby inspiring and guiding future research and practice in this field.  
ground penetrating radar; drone technology; geophysical prospecting; civil engineering
## 4 Characteristics of Different Time Distribution Systems
### White Rabbit
#### 4.3.2 Accuracy  
There are two key elements that distinguish the implementation of WR PTP from other PTP implementations: (1) tight cooperation between the PTP synchronization and the Layer 1 (L1) syntonization, L1 syntonization means a matching frequency of physical clocks at the hardware layer; (2) enhanced timestamping precision through phase detection. The implementation of L1 syntonization is evaluated using SyncE metrics and phase noise transfer analysis [93]. These techniques enable WR to achieve sub-nanosecond accuracy, provided the network employs only fiber interconnections and dedicated telecom switches.  
The frequency distribution can achieve a precision better than 50 ps [95].  
The National Time Laboratories in different countries including NIST are studying WR with different types and lengths of fiber links. The time error is within 8 ns. The stability (at \(\tau=1\) s) is \(1\times 10^{-11}\) and can be improved to \(1\times 10^{-12}\) without any modifications to the WR-PTP Protocol [92].
## 5 Setup Description  
Figure 2 represents the test setup overview. All the selected tools that are considered to emulate the railway and road coexistence scenarios are installed on a virtual machine. The considered network topology is created using Mininet-Wifi for road and railway coexistence scenarios. To control the functionality of the network topology, an SDN application is developed, installed, and activated for the ONOS SDN controller. The SDN application is developed to support the moving of end nodes and the inter-cell handover of created nodes in a defined virtual space. It also has the ability to differentiate the data traffic based on VLAN tagging. Detailed information about this developed SDN application is elaborated in Section 8. SUMO is integrated with Mininet-WiFi to graphically represents the movement of network nodes on an open street map.
# A Dynamic Route-Planning System Based on Industry 4.0 Technology
## 3 Models and Algorithms for Route Planning
### An Optimization Model for Route Planning  
A shortest-path model is used for comparing the results from other methods. The model is summarized as follows [23]: given a directed graph \(G=(V,A)\), where \(V\) and \(A\) represent a set of nodes and a set of arcs of the graph. The shortest-path problem determines a path with the minimum distance between two nodes, \(s\) (source or starting) and \(t\) (target or destination). The cardinality of \(V\) and \(A\) are represented by parameters \(n\) and \(m\), respectively. A path is a sequence of nodes \(v_{1}\),..., \(v_{k}\), and it is considered to be elementary if no node is visited more than once.  
Let \(\delta^{+}(i)\) and \(\delta^{-}(i)\) represent sets of arcs leaving and entering node \(i\in V\), and let \(A(S)\) be a set of arcs with both ends in \(S\subseteq V\).  
Indices, parameters, and decision variables are presented as follows:  
_Indices_  
\(i,j\), and \(k\): indices representing nodes in \(G\)  
A: a set of arcs from \(G\)  
_Parameters_  
\(c_{ij}\): distance from node \(i\) to \(j\), \((i,j)\in\mathrm{A}\)  
_Z_: the total distance  
_Decision variables_  
\(x_{ij}\) is equals to 1 if (\(i\),\(j\)) is chosen and 0 otherwise.  
The objective function and constraints for the shortest-path problem are presented next.  
\[Z=\min\sum_{(i,j)\in A}c_{ij}x_{ij}. \tag{1}\]  
\[\sum_{k\in\delta^{+}(i)}x_{ik}-\sum_{k\in\delta^{-}(i)}x_{ki}=1,\ i=s. \tag{2}\]  
\[\sum_{k\in\delta^{+}(i)}x_{ik}-\sum_{k\in\delta^{-}(i)}x_{ki}=0,\ i\in V\backslash \{s,t\}. \tag{3}\]  
\[\sum_{k\in\delta^{+}(i)}x_{ik}-\sum_{k\in\delta^{-}(i)}x_{ki}=-1,\ i=t, \tag{4}\]  
\[x_{ij}\geq 0\ \mathrm{for}\ (i\),\(j)\in A,\ x\in Z^{|A|}.\]  
The objective function is represented by Equation (1), where the total distance is minimized. Equations (2) and (4) represent the net flow at nodes \(s\) and \(t\), where the net flow at node \(s\) is 1, and the net flow at node \(t\) is \(-1\). The flow conservation constraints for nodes other that \(s\) and \(t\) are represented by Equation (3). When applying a shortest-path model for determining a solution for a dynamic route-planning problem, the distance (\(c_{ij}\)) associated with each path segment (defined by (\(i\),\(j\))) that contains an obstacle and cannot be used is set to a large value that is based on the size of the transportation area. If there exists a feasible solution, the segments with large cost values are not selected in the optimal solution.
## 3 Materials and Methods
### Materials
#### 3.1.3 IOTA Stranghold  
A key management system (KMS) is specialized software designed to securely store and manage cryptographic keys, which are vital for securing transactions, authenticating users, and ensuring overall network security. These systems function like a secure vault, safeguarding keys used for tasks such as signing and validating transactions and performing other cryptographic operations. In the context of the IOTA network, IOTA Stranghold 6 is an open-source software solution built to provide secure storage and management for cryptographic keys, seeds, and other sensitive information related to IOTA's operations.  
IOTA Stranghold offers a comprehensive approach to key management by using secure enclaves and trusted execution environments (TEEs) to protect data, ensuring that cryptographic keys remain safe from unauthorized access or tampering. It is designed to support high levels of security and privacy, enabling users to store keys in a way that is resistant to both physical and remote attacks. This makes it a crucial tool for maintaining the integrity of identities and transactions within the IOTA ecosystem.  
One of the key features of IOTA Stranghold is its zero-knowledge architecture, which ensures that sensitive data, including private keys, are never exposed to the system or any external party, even during their use. This enhances privacy by ensuring that only the authorized user has access to the keys, without needing to rely on a central authority or external service. In addition to key management, IOTA Stranghold also supports seed management, which is essential for creating and managing decentralized identities (DIDs) in the IOTA network. The software can securely store the initial seeds used to generate private keys and addresses for IOTA accounts, ensuring that the entire identity creation and management process remains secure.  
IOTA Stranghold's integration with IOTA Streams further extends its utility, allowing users to securely manage the cryptographic keys used for encrypted messaging and secure data transmission in decentralized IoT networks. Its flexibility allows it to be used in a variety of applications, from managing the identities of IoT devices to securing digital assets and private data across various platforms.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Advantages of C-RAN
#### 2.5.1 Adapting to Dynamic Traffic Load  
Mobile phone users perform dynamic movement throughout the day. This results in varying traffic loads in different areas at different times of the day. At certain times of the day, a few BSs are oversubscribed, whereas others are in the idle mode. Consequently, radio resources are utilized inefficiently, and a large amount of processing power is wasted. After the centralization of the BBUs into the BBU pool, the RRHs with low traffic loads can be merged and managed by one BBU, whereas the RRHs with high traffic loads can be assigned to multiple BBUs according to the user demand. This can improve the overall utilization rate and ensure the effective utilization of the computing resources, which results in a significant statistical multiplexing gain. It has been demonstrated in [33] that a flexible, reconfigurable mapping between a BBU and an RRH following different traffic profiles maximizes the statistical multiplexing gain.
## 4 Experimental Analysis and Results
### Implementation of Metaheuristic GA-Based Feature Selection
#### 4.6.4 Results on the Arabic Stance about Women's Empowerment  
This subsection explores the result based on the users' stance on women's empowerment. This class has two values: positive and negative. During the experiments of the stance scenario, we trained the adopted ML models to classify stance labels. The outcomes of trained models are presented in Table 12 for the original dataset and in Table 13 for the augmented dataset, respectively. The best F1-score results are 94.11% for the original dataset and 95.50% for the augmented data using the ETC model with feature selection. The results with bold font represent the highest F1-score for each classification model. Using a GA with different feature vectors, we optimize the learning procedure and feature selection method.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Classifier Model** & **Feature** & **Vector** & **F1-Score with Feature Selection (\%)** \\ \hline ETC & Unigram & CountVectorizer & 96.52 \\ ETC & Unigram & TfidfVectorizer & 96.22 \\ LRC & Unigram & CountVectorizer & 94.21 \\ RFC & Unigram & CountVectorizer & 96.22 \\ RFC & Unigram & TfidfVectorizer & 96.12 \\ \hline \end{tabular}
\end{table}
Table 11: The results of the five configuration settings for the augmented sarcasm task scenario with and without the feature selection and optimization method.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Classifier Model** & **Feature** & **Vector** & **F1-Score without Feature Selection (\%)** \\ \hline ETC & Unigram & CountVectorizer & 90.73 & 94.11 \\ ETC & Unigram & TfidfVectorizer & 90.41 & 90.41 \\ LRC & Unigram & CountVectorizer & 87.42 & 88.25 \\ RFC & Unigram & CountVectorizer & 89.79 & 89.79 \\ RFC & Unigram & TfidfVectorizer & 90.44 & 90.44 \\ \hline \end{tabular}
\end{table}
Table 12: The results of the five configuration settings for the stance task scenario with and without the feature selection and optimization method.  
From Tables 12 and 13, we can see that the ensemble classifiers improve the obtained results compared to the single LRC model. The ETC is the best model, achieving 90.73% of the F1-score for the stance scenario without feature selection, 94.11% with feature selection on the original data, and 95.50% for the augmented dataset. The F1-score of the ETC model outperforms the other models on both the original and augmented datasets by approximately 5.00%.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 4 Latent Heat Measurements of PR Data
### Remarks on Studies of Latent Heat Accompanied by Rainfall  
Important characteristics of LH vertical profiles and the diurnal variations were yielded under a wide range of studies. By comparison, the long-term climate effects of the LH are not understood enough and more studies are needed in the future, in which atmosphere circulation theories and models would be inevitably participated. On the other hand, conclusions on climate effects of the LH should be carefully examined and need to be verified because complex climate dynamics were involved and there might be uncertainty in models and computations.
# Principle and Implementation of Stokes Vector Polarization Imaging Technology
## 3 Implementation of Stokes Vector Polarization Imaging Technology  
Polarization imaging technology has been studied since the 1970s in foreign countries. After decades of development, polarization imaging technology has made great progress. The structure of polarization imaging system generally includes imaging lens, polarization analyzer and imaging detector. The polarization image is obtained by solving multiple intensity images, so the main difference between the existing polarization imaging sensors lies in the different ways of obtaining images. Polarization imaging technology can increase the information of the target by obtaining the light intensity, polarization degree and polarization Angle of the detected target. Polarization imaging technology usually uses the multi-polarization Angle image of the target to obtain the Stokes vector image of the target, so as to achieve the purpose of target detection.  
Stokes vector imaging can be divided into non-simultaneous polarization imaging and simultaneous polarization imaging according to the difference of polarization modulation. At present, the typical polarization imaging methods mainly include rotary polarization imaging, electronically controlled polarization imaging, fractional amplitude polarization imaging, fractional aperture polarization imaging, and fractional focal plane polarization imaging. Table 1 summarizes the typical characteristics of the above methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Polarization Imaging Technique** & **Type** & **The Imaging Features of** \\ \hline \multirow{4}{*}{Non-simultaneous polarization imaging} & Rotational polarization imaging & The method is more intuitive, easy to control and low difficulty and cost, medium size, simple system, suitable for static or quasi-static imaging \\ \cline{2-3}  & Electrically controlled polarization imaging & The advantages of small volume, good wavelength stability, wide scanning range and fast modulation speed \\ \hline \multirow{4}{*}{Simultaneous polarization imaging} & Fractional amplitude polarization imaging & High resolution; Good real-time performance, high energy utilization rate, suitable for dynamic scenes with large volume and weight \\ \cline{2-3}  & Split aperture polarization imaging & The optical system is stable, simple, compact and convenient for data processing, but its disadvantage is the loss of spatial resolution \\ \cline{1-1} \cline{2-3}  & Focal plane polarization imaging & Compact structure, small volume, high stability. The disadvantage is low resolution [14] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of time-sharing/simultaneous polarization imaging technology [13].
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Cargo Port Loading and Unloading
#### 4.2.6 Dynamics  
We describe the dynamic aspects of the load/unload container process in a cargo port terminal using an activity diagram for the following use cases: unload container from ship and move container to storage.  
_Activity: Unload Container from a Ship and Move to Storage_  
Summary: this activity diagram presents all the steps necessary to unload a container from a ship and move it to an appropriate location in storage. See Figure 9.  
Figure 8: Class diagram of port loading system.  
Actors: crane operator, trailer driver.  
Precondition: the location of the ship and which crane to operate have been previously entered in the system.  
Description:  
1. Crane operator activates the crane.
2. Crane operator picks up container from the ship.
3. Crane operator deposits the container in the trailer.
4. Trailer driver receives the container.
5. Trailer driver takes the container to the stacking crane transfer point.
6. ASC picks up the container and finds its location in storage.
7. ASC deposits container in an appropriate place in storage.
8. Transaction is logged.  
Postcondition: a container has been unloaded from a ship and deposited in its corresponding place in storage. The log is updated.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 2 Modelling of Stochastic Many-Objective Optimal Power Flow Problem
### Problem Formulation  
The efficient operation of the power grid necessitates the simultaneous consideration of multiple objectives, including economic efficiency, power quality, and operational safety [27]. The specific details regarding this process are elaborated upon in a subsequent section. The stochastic OPF problem is a many-objective optimization problem that must comply with specific constraints. The subsequent formulation provides a general overview of this problem.  
\[\begin{split}\min&\mathbf{F(X,U)}=(f_{1}(\mathbf{x,u}),f_{2}(\mathbf{x,u}),\cdots,f_{M}(\mathbf{x,u}))^{T}\\ \text{subjected to}&\mathbf{g(x,u)}=0\\ &\mathbf{h(x,u)}\leq 0\\ &\mathbf{x\in D}\subseteq\mathbf{R}^{N}\end{split} \tag{1}\]  
where \((f_{1}(\mathbf{x,u}),f_{2}(\mathbf{x,u}),\cdots,f_{M}(\mathbf{x,u}))^{T}\) is the vector of optimization objectives, \(i\) is the \(i_{th}\) objective function with interval parameters, and \(g\) and \(h\) represent the equality and inequality constraints. Due to the presence of stochastic variables, the objective function can be expressed as \(f_{i}(\mathbf{x,u})\triangleq[f_{i}(\mathbf{x,u}),\overline{f_{i}(\mathbf{x,u })}]\), \(i=1,2,\cdots,M\); \(M\) is the number of objectives. The upper and lower bounds of the objective function can be obtained from the following formulations [27]:  
\[\begin{split}\underline{f_{i}(\mathbf{x,u})}&= \min_{\mathbf{u}}f_{i}(\mathbf{x,u})\\ \overline{f_{i}(\mathbf{x,u})}&=\max_{\mathbf{u}}f_{ i}(\mathbf{x,u}).\end{split} \tag{2}\]  
\(\mathbf{x}\) is the set of decision variables, including the real power of each generator \(P_{G}\), the voltages of generator bus \(V_{G}\), the tap ratios of transformers \(T_{m}\), and the reactive power generation of volt-ampere reactive sources \(Q_{C_{n}}\). Therefore, \(x\) can be presented as follows:  
\[\mathbf{x^{T}}=[P_{G_{1}},\ldots,P_{G_{N_{G}}},V_{G_{1}},\ldots,V_{G_{N_{C}}}, T_{1},\ldots,T_{N_{T}},Q_{C_{1}},\ldots,Q_{C_{N_{C}}}] \tag{3}\]  
where \(N_{G},N_{T},N_{C}\) are the numbers of controllable generators, transformer branches, and reactive power compensation sources, respectively.  
\(\mathbf{U}\) is a set of stochastic variables, that represents uncertain power outputs of the WT and PV units. An interval method is used to describe uncertainty variables by using interval endpoints or midpoint and radius [40]; its form is shown in (4).  
\[\begin{split}\mathbf{U^{T}}=[P_{WG_{1}},\ldots,P_{WG_{N_{WT}}}, P_{VG_{1}},\ldots,P_{VG_{N_{PV}}}]\\ P_{WG_{i}}=\left[\underline{P_{WG_{i}},\overline{P_{WG_{i}}}} \right],i=1,2,\cdots,N_{WT}\\ P_{VG_{i}}=\left[\underline{P_{VG_{i}},\overline{P_{VG_{i}}}} \right],i=1,2,\cdots,N_{PV}\end{split} \tag{4}\]  
where \(N_{WT},N_{PV}\) are the numbers of wind and photovoltaic farms, \(P_{WG_{i}},\overline{P_{WG_{i}}},\underline{P_{VG_{i}}},\overline{P_{VG_{i}}}\), and the upper and lower bounds of wind and photovoltaic farms, respectively.