esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework
### Feature Description
#### 4.1.2 Pattern Features  
We apply the pattern definitions given in [47] for automated pattern extraction. The words are divided into three categories: high-frequency words (HFWs), content words (CWs) and regular words (RWs). Assume a word \(f\) and its corpus frequency \(fr_{f}\); if \(fr_{f}>F_{H}\), then \(f\) is considered to be a HFW. On the other hand, if \(fr_{f}<F_{C}\), then \(f\) is considered to be a CW. The rest of the words are characterized as RWs. The word frequency is estimated from the training set rather than from an external corpus. In addition, we treat as HFWs all consecutive sequences of punctuation characters as well as URL, REF, TAG and RT meta-words for pattern extraction, since they play an important role in pattern detection. We define a pattern as an ordered sequence of HFWs and slots for content words. The upper bound for \(F_{C}\) is set to 1000 words per million and the lower bound for \(F_{H}\) is set to 10 words per million. In contrary to [47], where \(F_{H}\) is set to 100 words per million, we provide a smaller lower bound since the experimental evaluation produced better results. Observe that the \(F_{H}\) and \(F_{C}\) bounds allow overlap between some HFWs and CWs. To address this issue, we follow a simple strategy as described next: if \(fr_{f}\in\left(F_{H},\frac{F_{H}+F_{C}}{2}\right)\) the word is classified as HFW; otherwise, if \(fr_{f}\in\left[\frac{F_{H}+F_{C}}{2},F_{C}\right)\), the word is classified as CW. More strategies can be explored, but this is out of the scope of this paper and is left for future work.  
We seek for patterns containing 2-6 HFWs and 1-5 slots for CWs. Moreover, we require patterns to start and to end with an HFW, thus a minimal pattern is of the form [HFW][CW slot][HFW]. Additionally, we allow approximate pattern matching in order to enhance the classification performance. Approximate pattern matching resembles exact matching, with the difference that an arbitrary number of RWs can be inserted between the pattern components. Since the patterns can be quite long and diverse, exact matches are not expected in a regular base. Therefore, we permit approximate matching in order to avoid large sparse feature vectors. The weight \(w_{p}\) of a pattern feature \(p\) is defined as in Equation (1) in case of exact pattern matching and as  
\[w_{p}=\frac{\alpha\cdot N_{p}}{count(p)} \tag{2}\]  
in the case of approximate pattern matching, where \(\alpha=0.1\) in all experiments.
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 3 Engine Performance
### Brake Specific Energy Consumption  
Brake specific energy consumption (BSEC) indicates the performance of fuel BSFC and calorific value, which can represent the energy input required to generate unit power. BSEC is more ideal than BSFC because it is not related to fuel type and takes into account the mass flow and heating value [48; 49]. The formula for BSEC is as follows [50]:  
\[\text{BSEC}=\frac{q\dot{m}_{f}}{2\pi NT_{e}} \tag{2}\]  
were, \(q\) is the calorific value, \(\dot{m}_{f}\) is the fuel consumption flow rate, \(N\) is the operating speed, and \(T_{e}\) is the brake torque.  
In contrast to brake thermal efficiency (BTE), most biodiesel has a higher BSEC compared with conventional diesel. As shown in Figure 2, [PERSON] et al. mixed water hyacinth biodiesel with diesel at 0, 10, 20, 30, 40, and 100 vol%, and found that under different load conditions, the BSEC of the fuel increased significantly with the biodiesel content [30]. However, [PERSON] et al. found that under all load conditions, pure apricot seed kernel oil methyl ester biodiesel has the highest BSEC, while the BSEC of the B5 and B20 are less than diesel under all load conditions [48]. This shows that the single increase, decrease or a combination of both may occur for biodiesel. The change mechanism of BSEC is similar to BTE. There is more available oxygen in the biodiesel blend, which helps to improve combustion performance [7]. However, the lower calorific value and higher viscosity of biodiesel will have an adverse effect on atomization and combustion, resulting in an increase in BSEC [48].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Engine** & **Test Condition** & **Test Fuels** & \begin{tabular}{c} **Blend** & \begin{tabular}{c} **Increase Rate** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Ref.** \\ **of BSFC (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Single cylinder, direct \\ injection diesel engine, \\ 454 cc \\ \end{tabular} } & Over the entire speed & \begin{tabular}{c} \\ range under full load \\ \end{tabular} & \
\begin{tabular}{c} **Diesel blend with** \\ soybean biodiesel \\ \end{tabular} &
\begin{tabular}{c} **B20** & 4 \\ **B50** & 7 \\ \end{tabular} & [4] \\ \cline{3-6}  & & B100 & 9 & \\ \cline{3-6}  & & B10 & 1 & \\ \cline{3-6}  & & & B20 & 2.1 & [44] \\ \cline{3-6}  & & open throttle & & B30 & 3 \\ \cline{3-6}  & & & & PB30 & 8.58 & \\ \cline{3-6}  & & & & & CB30 & 9.03 & [45] \\ \cline{3-6}  & & & & PB15 CB15 & 8.55 & \\ \cline{3-6}  & & & & & B5 & 2.5 \\ \cline{3-6}  & & & & & B20 & 3 \\ \cline{3-6}  & & at full-load condition & & & B70 & 5.5 & \\ \cline{3-6}  & & & & & B100 & 7.5 & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & \\
# Numerical Study on Dynamic-Stall Characteristics of Finite Wing and Rotor
## 2 Numerical Methods
### Flowfield Solver Method  
The Navier-Stokes (N-S) equations employed in this research can be expressed as:  
\[\frac{\partial}{\partial t}\iiint\limits_{\Omega}Wd\Omega+\iint\limits_{ \partial\Omega}(F_{c}-\mathbf{F}_{v})d\mathbf{S}=0 \tag{1}\]  
where, \(\mathbf{W}\) denotes conserved variables, \(\mathbf{F}_{c}\) represents convective fluxes, and \(\mathbf{F}_{v}\) represents viscous fluxes, i.e.,  
\[\mathbf{W}=\left[\begin{array}{c}\rho\\ \rho u\\ \rho v\\ \rho v\\ \rho E\end{array}\right],\mathbf{F}_{c}=\left[\begin{array}{c}\rho\mathbf{V }_{r}\\ \rho u\mathbf{V}_{r}+n_{x}p\\ \rho v\mathbf{V}_{r}+n_{y}p\\ \rho v\mathbf{V}_{r}+n_{z}p\\ pH\mathbf{V}_{r}+\mathbf{V}_{t}p\end{array}\right],\mathbf{F}_{v}=\left[ \begin{array}{c}0\\ n_{x}\tau_{xx}+n_{y}\tau_{xy}+n_{z}\tau_{xz}\\ n_{x}\tau_{yx}+n_{y}\tau_{yy}+n_{z}\tau_{yz}\\ n_{x}\tau_{zx}+n_{y}\tau_{zy}+n_{z}\tau_{zz}\\ n_{x}\Theta_{x}+n_{y}\Theta_{y}+n_{z}\Theta_{z}\end{array}\right] \tag{2}\]  
where,\(\mathbf{V}_{r}=\mathbf{V}-\mathbf{V}_{c}\) (\(\mathbf{V}_{c}\) represents contravariant velocity). Symbols \(\rho\),\(p\), and \(E\) represent the density, static pressure, and total airflow energy, respectively. \(\tau_{ij}\) represents viscous stresses, \(n_{i}\) represents the unit normal vector, and \(\Theta_{i}\) describes viscous airflow stresses.  
Figure 1: Schematic of moving-embedded grids and geometric shape of the finite wing.  
Aiming at predicting the unsteady flowfield of the finite wing and rotor, temporal discretization of the N-S equations is accomplished by employing the dual time-stepping approach, which can be written as:  
\[\frac{3\mathbf{W}^{n+1}-4\mathbf{W}^{n}+\mathbf{W}^{n-1}}{2\Delta t}+\mathbf{R}(\mathbf{W}^{n+1})=0 \tag{3}\]  
where \(\Delta t\) represents the global physical time step, and \(\mathbf{R}\) represents the residual one.  
In order to improve computational efficiency, the implicit LU-SGS scheme was employed in this study. The LU-SGS scheme is based on the factorization of the implicit operator into the following three parts:  
\[(\mathbf{D}+\mathbf{L})\mathbf{D}^{-1}(\mathbf{D}+\mathbf{U})\Delta\mathbf{W}^{n}=-\mathbf{R}^{n} \tag{4}\]  
where, \(\mathbf{L}\) is a strictly lower triangular matrix, \(\mathbf{U}\) is a strictly upper triangular matrix, and \(\mathbf{D}\) is a diagonal term. Based on this discretization, the LU-SGS scheme is solved by two steps, a forward and a backward sweep, i.e.,  
\[\begin{split}&(\mathbf{D}+\mathbf{L})\Delta\mathbf{W}^{(1)}=-\mathbf{R}^{n}\\ &(\mathbf{D}+\mathbf{U})\Delta\mathbf{W}^{(n)}=\mathbf{D}\Delta\mathbf{W}^{(1)}\end{split} \tag{5}\]  
where, \(\mathbf{W}^{n+1}=\mathbf{W}^{n}+\Delta\mathbf{W}^{n}\).  
To accurately predict the unsteady aerodynamic characteristics of a vortical flowfield, the Roe method [28], coupled with a third-order MUSCL scheme, was employed to accomplish spatial discretization. In addition, the one-equation Spalart-Allmaras (S-A) turbulence model [29] was employed to close the N-S equations.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Peer-To-Peer
#### 4.2.1 Requirement Satisfaction  
P2P virtual worlds, firstly proposed in [PERSON] et al. [123], remove the single point of failure of C/S virtual worlds. By running applications on user devices with open protocols, application data are no longer owned or controlled by any entity. Thus, they are promising to address persistency issues. The authors of [78] provided a comprehensive survey of P2P game state persistency. Later, [89] proposed a two-tier architecture. Overlay storage employs a distributed hash table (DHT) and super-peers to provide high reliability and fault tolerance. Group storage employs a distance-based grouping approach to improve availability and responsiveness.  
For content persistency, P2P virtual worlds can also employ a P2P file storage protocol to store user content. Existing P2P file storage systems, such as BitTorrent (bittorrent.com), have shown their persistency property without central storage. [PERSON] et al. [92] designed a communication infrastructure for distributing and storing virtual objects to Kad, a global P2P storage network. Total Recall [125] provides optimized storage efficiency with high responsiveness and low bandwidth cost. The authors of [126] proposed a content retrieval approach based on Total Recall to provide an efficient content integrity check. These properties are requested by the content persistency requirement. Moreover, [127] proposed a P2P 3D object streaming scheme from nearby peers, which can be used in virtual world content sharing. The authors of [128] improved its efficiency.  
For data security, P2P virtual worlds can leverage Blockchain technology, combined with cryptographic measures, to achieve user-centric secure data access [129]. Blockchain-based privacy-preserving access control has been widely discussed in many fields, including personal identity management [130], healthcare record sharing [94], collected data access [131], etc. Blockchain technology has some advantages. First, a third-party service is not needed for stewarding user data, on which users must put their trust. Second, security policies added to Blockchain [132] are transparent to all users and data compliance can be achieved through the consensus mechanism. Moreover, a Blockchain data structure is tamper-proof, providing additional merits, including integrity and nonrepudiation, etc.  
Blockchain technology can also be used in digital rights management [98]. Its nonrepudiation property can enable the conditional tractability of license violation. Moreover, a P2P virtual world does not have a definitive platform owner, attributed to its decentralized structure. It removes ownership inconsistency issues and users really have the ownership of their virtual assets as well as the underlying data. Thus, a P2P virtual world can achieve higher credibility.
# Thin Film Encapsulation for RF MEMS in 5G and Modern Telecommunication Systems
## 5 Release as a Function of Barrel Parameters  
Power (P) and time (t) of the barrel etching, which was used for cap release, were varied in the ranges 100-600 W and 8-80 min, respectively. The aim of these tests was to identify the right trade-off between power and time, in order to achieve the complete release of the cap without any damage. The different barrel etching parameters and the results in terms of cap release are summarized in Table 3.  
It is notable that with the powers of 600 and 400 W, the complete cap release was achieved with an etching time of 8 min. By reducing the power to 300 W, 8 min were not enough to achieve the complete release that was, instead, obtained at 20 min. We also tested what happens if an etching time longer than 20 min is used with the power of 300 W and found that a process time between 20 and 35 min allowed the sacrificial to be completely removed without any cap damage. Otherwise, for a time longer than 35 min, caps partially collapsed on the CPW and were completely down under the application of a stylus tip force as low as 20 \(\upmu\)N. A time of 80 min was required for complete release when the low powers of 200 W or 100 W were used.
## 5 MIMO Diversity Analysis  
This section investigates the MIMO performance including ECC, DG, TARC, MEG, and MIMO channel capacity for analyzing the performance of the proposed wideband flexible MIMO antenna for smartphone applications.
## 2 The Six Sufficient Conditions for Identification in [1]
Condition V written above can be read in [1] (p. 445): \"Sections 3 and 4 proved that parameters of VARMA model (2.1) are identified (\(\ldots\)) for MFD if \(C_{L}(F,\,VH_{1}^{t})\) and \(O_{L}(F,\,H_{1})\) have full rank, (\(\ldots\)) for sufficiently large \(L\)\", because to identify VAR parameters with the specific procedure in [1] (pp. 441-442), rank \(D_{1}=np\) is necessary, and therefore the full rank of \(C_{L}(F,\,VH_{1}^{t})\). (We have taken into account that matrices \(D_{1}\) and \(E_{1}\) in [1] (p. 442) must be written without a subscript in the second \(\widetilde{H}\)). Moreover, to identify VMA parameters with the specific procedure in [1] (pp. 442-444), the full rank of \(O_{L}(F,\,H_{1})\) is necessary. **Condition VI:** The \(nq\times nq\) matrix \( \begin{pmatrix}-B_{1}B_{0}^{-1}&I_{n}&&0\\ \vdots&0&\ddots&\\ &\vdots&&I_{n}\\ -B_{q}B_{0}^{-1}&0&\cdots&0\end{pmatrix} \) is diagonalizable, i.e., it has a linearly independent set of eigenvectors. **Remark 1**. : _[PERSON], in [1], proves that Conditions I, II, III and IV are sufficient for identifiability with SFD. However, if Conditions I, II and III hold, Condition IV is sufficient but not necessary in the SFD case (see Counterexamples I and 2)._  
_[PERSON] et al. in [6] prove that if \(q>r\), a VARMA(\(r,\,q\)) model with MFD is not identifiable. Next, we will show that rank \(O_{L}\) (\(F,\,H_{1}\)) = np in Condition V excludes not only the case \(q>r\), but also the case \(q=r\)._  
**Lemma 1**. : _If \(q\geq r\), then rank \(O_{L}(F,\,H_{1})<np\)._  
Proof. : Considering that if \(q\geq r\) then \(p=q+1\) and \(A_{r+1}=\ldots=A_{p}=0\). On the one hand, the first \(n(p-r)\) columns in the \(n(p-r)\times np\) matrix  
On the other hand, considering the \(nr\times np\) matrix\(((HF^{p-r})^{t}\cdots\ (HF^{p-1})^{t})^{t}\), it is easy to see that the submatrix formed by its last \(nr\) columns is lower triangular with ones on the diagonal, and therefore,  
\[\text{rank}((HF^{p-r})^{t}\cdots(HF^{p-1})^{t})^{t}=nr. \tag{3}\]  
Moreover, if \(i\geq p\), \(HF^{i}=A_{1}HF^{i-1}+\ldots+A_{r}HF^{i-r}\) and, from (3), rank \(((HF^{p-r})^{t}\cdots\ (HF^{L-1})^{t})^{t}=nr\). As a consequence:  
\[\text{rank}((H_{1}F^{p-r})^{t}\cdots(H_{1}F^{L-1})^{t})^{t}\leq nr\qquad\text{ if }L>p \tag{4}\]  
From (2) and (4), rank \(O_{L}(F,H_{1})\leq n_{1}(p-r)+nr=n_{1}p-n_{1}r+(n_{1}+n_{2})r=n_{1}p+n_{2}r<np\) and Lemma 1 has been proven. \(\square\)  
Note that if we do not have the hypothesis from Lemma 1, i.e., if \(q<r\), then \(p=r\) and we do not have (2). Therefore, rank \(O_{L}(F,H_{1})\) could be equal to \(np\).
## 4 Methodology
### Feature Selection Methods
#### 4.1.2 Hierarchical Correlation Clustering  
Correlation analysis measures how strongly each feature is related to the target variable and other features. A high correlation with the target indicates that the feature may be useful for predictions. However, if two features strongly overlap, keeping both can be redundant. By calculating correlation values, we can identify pairs of features that convey nearly the same information and determine which ones are more valuable or relevant to the outcome. This method helps us maintain a balanced feature set, reducing unnecessary complexity and focusing our model on the variables that influence predictions. This study utilizes the Pearson correlation coefficient, as defined in Equation (2), to identify the weather parameters that exhibit the most similar behaviors.  
\[\begin{array}{l}Cov(X_{i},X_{j})=\frac{\sum_{s=0}^{n}{(X_{i}-\overline{X_{i}}).(X_{i,s}-\overline{X_{j}})}}{n-1}\\ \rho(X_{i},X_{j})=\frac{Cov(X_{i}\cdot X_{j})}{\sqrt{Var(X_{i})\cdot Var(X_{j}) }}\end{array} \tag{2}\]  
where \(\rho(X_{i},X_{j})\) is the Pearson correlation coefficient between features \(X_{i}\) and \(X_{j}\). This value lies between \(-1\) and \(+1\).  
We first computed the correlation matrix for the remaining features to address highly correlated features. After obtaining these values, hierarchical clustering was performed using [PERSON]'s method, which merges features into clusters based on their similarity (or distance), as in Equation (3). We obtained distance-based clusters by applying a specific distance threshold to the resulting linkage matrix. The feature with the highest absolute correlation value was selected within each cluster, while the others, typically offering redundant or nearly duplicated information, were removed. This step ensured that only the most representative features from each correlation group remained in the dataset.  
A grid search systematically examined distances ranging from 0 to the maximum possible distance in increments of 0.1 to determine the optimal distance threshold for [PERSON]'s linkage. Finding an intersection between the chosen Ward's linkage distance and the desired correlation threshold removed duplicated features whose absolute correlation exceeded 0.7. Assigning the significance level to flag correlations less than \(-0.7\) or greater than 0.7 guaranteed that only truly redundant features were excluded. This procedure not only streamlined the feature set but also safeguarded the model from issues arising from multicollinearity.  
\[\Delta(C_{a},C_{b})=\sum_{x\in C_{a,b}}||x-\mu_{a,b}||^{2}-\sum_{x\in C_{a}}|| x-\mu_{a}||^{2}-\sum_{xcb}||x-\mu_{b}||^{2} \tag{3}\]  
Here, \(\mu_{a}\) and \(\mu_{b}\) are the centroids of clusters \(C_{a}\) and \(C_{b}\), respectively, and \(\mu_{a,b}\) is the centroid of the merged cluster. This step is repeated until a stopping (linkage distance) is reached.
# Architectural Design for Digital Twin Networks
## 5 Design and Implementation Strategies
### Modeling
#### 5.2.1 Simulators and Emulators  
These are crucial in developing and testing DTNs, especially for smaller network environments. These tools harness the packet processing capabilities of virtual network elements to rapidly verify the functions of both the control and data planes. Studies, such as [26], have utilized Cooja to simulate Internet of Things (IoT)-oriented networks in smaller settings. Lastly, the approach described by [24] utilizes an emulator to model the network. Employing these tools can significantly expedite the development process, particularly when existing simulators are available for the network protocols being developed.  
However, the use of simulators and emulators is not without limitations. These tools often demand substantial resources and have limited capabilities for a complete performance analysis in real-time, making them less suitable for extensive, complex network simulations. Additionally, they struggle with scalability, meaning their effectiveness decreases as network size and complexity increase.  
Figure 4: Overview of modeling tools for Digital Twin Networks.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 3 Cascading Failure Model for DGCN
### Overload Failure Model  
Apart from the dependent failure, it is necessary to consider other problems caused by the internal operating mechanism of DGCN. For the communication net, the communication node has its inherent capacity limit. When the load exceeds the tolerable range, it will lead to cascading failure. The same situation occurs on the functional net when the service  
Figure 4: Conditional group dependency failure.  
processing is overloaded. [PERSON] and [PERSON] proposed the \"M-L\" model [4; 30] to describe this phenomenon. Based on this, [PERSON] et al. [31] presented a cascading failure model considering the load and combined it with the dependent failure model. [PERSON] et al. [32] proposed a cascading failure model considering the overload state when facing a traffic jam. Inspired by these, we establish a cascading failure model considering the overload state for the communication net and functional net, respectively, and then integrate it with the dependent failure model. The specific process of overload cascading failure is as follows: Each node has an initial load at the beginning; when a node fails due to an attack or other reasons, the load of this node will be redistributed according to certain rules. The load-obtained node will fail with a certain probability. If the node fails, a new round of load redistribution and node failure will be triggered. For the communication net, the load is redistributed among the nodes in the entire network, while the load redistribution is limited to nodes of the same type as to the functional net.
###### Abstract  
This paper describes the prototype and tests carried out to demonstrate the feasibility of implementing 4G/5G systems based on mobile network operators (MNOs) to transmit signalling critical data for railway systems as part of a possible solution for the Future Railway Mobile Communication System (FRMCS). This communication system design is performed from a protocol-stack perspective, introducing the KPIs for the physical layer (RSRP, RSRQ and SINR), network layer (latency and jitter) and application layer (signalling communication timeout). The analysis focuses on the characterisation of signalling critical railway data to assess the data traffic behaviour in a telecommunications laboratory. Finally, the study validates the approach in a realistic railway environment.  
Keywords:4G; 5G; critical services; communications; FRMCS; ITS; KPI; LTE; MNO; MTC; railway; signalling services +
Footnote †: journal: Applied