esa-sceva/satcom-chunk-collection huggingface dataset:
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.2 Standard Logic-Based Formalisms
In summary, standard logic-based formalisms excel in providing precise, and accurate, representations within their defined scope and rules. They offer excellent explainability and expandability, but may face challenges in handling uncertainty, scalability, and generalizing beyond explicitly defined logical rules.
## 1 Introduction
Following this, the influence of the introduction of noise was eliminated by an averaging operation. Based on previous research, [PERSON] et al. improved EEMD methods [38] by introducing the auxiliary noise in positive and negative forms, which aims to eliminate the residual auxiliary noise in the reconstructed signals. In addition, the number of the added noises can be lower, with the subsequent computational efficiency being higher. This method is called complementary ensemble empirical mode decomposition (CEEMD) [38]. Recently, a tensor decomposition algorithm has been applied to the field of EEG signal processing [39], which provides a potential way for solving the above SCBSS problem. However, SCBSS based on tensor decomposition still has some limitations, which mainly includes unsatisfactory convergence and obscure estimate of tensor rank. EMD and its improved editions have some positive effects on signal decomposition. However, they have a problem of large computation requirements. In addition, if the amplitude and the iteration number of the added white noise are not appropriate, some undesirable components will appear in the results of decomposition. Thus, the IMF component needs to be recombined and subsequently processed. In essence, the purpose of adding white noise is to change the distribution of the extreme points of the signal. Since the employed signal is asymptotically stable and the extreme points are distributed evenly after adding white noise for several times, there is no need to add white noise for integration and average decomposition in the whole process. Inspired by the idea of getting assistance from random noise, the sinusoid-assisted method may be another powerful tool for EMD. In this paper, a novel regenerated phase-shifted sinusoid-assistedEMD (RPSEMD) is introduced [40]. RPSEMD is intended to solve the problem of MMP by designing sinusoids and a high-performance phase-shifting scheme. The observed signal is decomposed by RPSEMD to solve the problem of mode mixing and large computation costs. The lift-dimensional signal is realized by RPSEMD, with the undetermined blind source separation problem being completely solved. Additionally, the accurate estimation of the number of source signals is achieved using the Bayesian information criterion (BIC) [41]. Moreover, the joint diagonalization method based on four-order accumulation is employed to realize the multi-fault separation [42]. The results of the numerical simulation and experiment show that the proposed method has obvious advantages compared to the blind source separation of single-channel composite fault and performs well in the extraction of the fault features of rolling-bearing.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 2 Analysis of Solenoid Valve Current Characteristics
### Magnetic Circuit Equation  
From the electromagnetic correlation theory, the self-inductance coefficient of the electromagnetic coil is:  
\[L=\frac{\mu_{0}\pi D^{2}N^{2}l_{\mathrm{v}}}{4l_{\mathrm{v}}(l_{0}-x)+rD} \tag{1}\]  
where \(L\) is the spool coil inductance, \(\mu_{0}\) is the vacuum permeability, \(D\) is the spool diameter, \(l_{\mathrm{v}}\) is the spool armature length, \(l_{0}\) is the maximum width of the working air gap, \(x\) is the spool displacement, and \(r\) is the average non-working air gap width.  
During solenoid valve operation, the air gap of the electromagnet may change. Instantaneously, the electromagnetic attraction \(F\) performs mechanical work, and the total magnetic energy of the system also changes accordingly with the mechanical work \(F\mathrm{d}x\) made by the electromagnetic attraction that interplays with the total magnetic energy of the system. The quantities \(\mathrm{d}W\) are equal, so:  
\[F\mathrm{d}x=\mathrm{d}W=\frac{1}{2}i^{2}\mathrm{d}L \tag{2}\]  
\[F=\frac{1}{2}i^{2}\frac{\mathrm{d}L}{\mathrm{d}x} \tag{3}\]  
where \(i\) is the coil current.
## 3 Modelling and Simulation
### Mode #4 Non-Harmonic Supply and Nonlinear Load
#### 3.4.2 Non-Symmetric Network, Symmetrical Load  
The network asymmetry has been provided by a step change of voltage in phase \(a\) from 325 V by 33% at the time 0.1 sec (after settling).  
Parameters for steady states at \(t\in\langle 0.0;0.1\rangle\) and at \(t\in\langle 0.1;0.2\rangle\) are given in the Table 40:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Load** & \(R_{a}\) [\(\Omega\)] & \(L\) [mH] & \(Z\) [\(\Omega\)] & \(\tau_{a}\) [ms] & \(\varphi_{a}\) [\({}^{\circ}\)el.] & \(\cos\varphi_{a}\) [\({}^{-}\)] & \(\sin\varphi_{a}\) [\({}^{-}\)] \\ \hline before & 18.4 & 43.93 & 23 & 2.39 & 36.76 & 0.8 & 0.6 \\ after & 9.2 & 43.93 & 16.59 & 4.78 & 56.33 & 0.5545 & 0.8322 \\ \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \(U_{ms}=230\) V, \(f=50\) Hz, 3-phase system.  
\end{table}
Table 40: Load parameters for steady states before and after load change.  
Figure 27: Apparent-(**a**), active-(**b**), blind-(**c**), and distortion (**d**) power components during transient states for 3-phase system under a symmetrical network supplying non-symmetrical linear RL load.  
Simulation Results Using Matlab/Simulink, Simulation Step 200 us  
Measurable quantities, i.e., voltages and currents in \(\alpha,\beta\) (\(rms\)), and their parameters are given in Table 41.  
Power component average values, computed in Matlab/Simulink are given in Table 42.  
Compensation of reactive and distortion power in the case of non-symmetry systems needs 4-leg PAF instead of three. Calculation of reference currents for such a PAF configuration is similar to Equation (27a,b) but needs to consider zero-sequence components Equation (22), and it is done, e.g., [8, 9, 27].  
Figure 30: The waveform of zero power component \(Po\).  
Figure 29: Apparent- (**a**), active-(**b**), blind-(**c**), and distortion (**d**)power components during transient states for the 3-phase system under inverter-supplied non-symmetrical linear RL load.
## 2 Materials and Methods
### Assessment of Drug Cytotoxicity by MTS Assay  
All assays were performed in triplicate in 96-well microtiter plates with flat-bottom (Falcon), using a CellTiter 96 Aqueous One Solution Cell Proliferation Assay (Promega), which is a MTS colorimetric assay. The method is based on the ability of metabolically active cells to reduce MTS, which is a yellow tetrazolium salt to the colored formazan that is soluble in the culture medium. Briefly, 1.5 \(\times\) 10\({}^{4}\) cells/well were cultured in 100 \(\mu\)L for 24 h, culture supernatants were discarded; then, cells were treated for an additional 24 hwith increasing concentrations of Ru(III) complexes or CisPt. After the end of the incubation time, 20 uL of reagent mixture were added in each well: (a) MTS[3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxy-phenyl)-2-(4-sulfophenyl)-2H-tetrazololium, inner salt] and (b) PES (phenazine ethosulfate), which has a high chemical stability, and might be combined with MTS to form a stable solution.  
After adding the coloring mixture, plates were incubated for 4 h at 37 \({}^{\circ}\)C with mild agitation every 20 min. The color that developed during incubation was spectrophotometrically quantified at \(\lambda\) = 492 nm, using a Dynex ELISA reader (DYNEX Technologies--MRS, USA) [48; 50; 54; 57].  
The percentages of cell lysis were calculated using with the equation below, and compared to untreated cells (considered 100% viable):  
**Cell lysis (%) = 100 - (T - B)/(U - B) \(\times\) 100**  
where T = absorbance of treated cells, U = absorbance of untreated cells, and B = absorbance of culture medium (blank), for \(\lambda\) = 492 nm.  
Cell lysis data were expressed as the mean values \(\pm\) standard deviations (SD) of three different experiments. A parallel assay for the evaluation of DMSO cytotoxicity was determined in the same experimental conditions, but no cell cytotoxicity was observed for concentrations lower than 1% (data not shown).
## 3 Case Study
### Data Processing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Parameter** & **Unit** & **Mean** & **Std.** & **Min.** & **Max.** \\ \hline \multirow{5}{*}{**Training Set** **(950)**} & \multicolumn{1}{c}{Cutterhead rotation speed (CS)} & r/min & 1.28 & 0.07 & 1.00 & 1.54 \\  & \multicolumn{1}{c}{Cutterhead torque (CT)} & KN-m & 3260.83 & 915.94 & 893.04 & 5401.23 \\  & \multicolumn{1}{c}{Thrust (T)} & KN & 11,751.07 & 2362.87 & 5522.75 & 19,048.84 \\  & \multicolumn{1}{c}{Soil pressure (SP)} & bar & 0.55 & 0.36 & 0.09 & 1.88 \\  & \multicolumn{1}{c}{Screw conveyor speed (SS)} & r/min & 4.07 & 1.84 & 0.21 & 10.12 \\  & \multicolumn{1}{c}{Advance rate (AR)} & mm/min & 27.07 & 10.47 & 9.07 & 66.79 \\ \hline \multirow{5}{*}{**Test Set (238)**} & \multicolumn{1}{c}{Cutterhead rotation speed (CS)} & r/min & 1.28 & 0.07 & 1.09 & 1.57 \\  & \multicolumn{1}{c}{Cutterhead torque (CT)} & KN-m & 3309.93 & 1020.57 & 1007.39 & 5390.16 \\ \cline{1-1}  & \multicolumn{1}{c}{Thrust (T)} & KN & 11,778.58 & 2294.79 & 6652.89 & 19,238.06 \\ \cline{1-1}  & \multicolumn{1}{c}{Soil pressure (SP)} & bar & 0.57 & 0.39 & 0.10 & 1.90 \\ \cline{1-1}  & \multicolumn{1}{c}{Screw conveyor speed (SS)} & r/min & 3.89 & 1.83 & 0.42 & 9.01 \\ \cline{1-1}  & \multicolumn{1}{c}{Advance rate (AR)} & mm/min & 26.87 & 11.54 & 9.30 & 62.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Brief description of operational parameters. Figure 7: Correlation analysis of several geological parameters.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
###### Abstract  
This study examines the issues of privacy protection, data security, and query efficiency in blockchain-based electronic medical record (EMR) sharing. It proposes a secure storage and sharing scheme for EMR based on Hyperledger Fabric and the InterPlanetary File System (IPFS). To mitigate the privacy risks of data mining that could reveal patient identities, we establish an attribution channel in Hyperledger Fabric to store EMR ownership information and a data channel to store the storage location, digest, and usage records of medical data. Encrypted medical data are stored in the IPFS. To improve query efficiency in the blockchain, we integrate queryable medical data attributes into a composite key for conditional queries, avoiding complex data filtering processes. Additionally, we use a zero-knowledge proof combined with smart contracts for decentralized identity verification, eliminating reliance on third-party centralized verification services and enhancing system security. We also integrate AES and proxy re-encryption techniques to ensure data security during sharing. This scheme provides a more secure, efficient, and privacy-preserving approach for EMR systems, with significant practical implications and broad application potential.  
blockchain; electronic medical record sharing; privacy protection; zero-knowledge proof; proxy re-encryption +
Footnote †: journal: Applied sciences  
enhance data sharing efficiency. However, there is limited research on improving the efficiency of specific data queries on blockchain.  
To address these challenges, this study proposes an efficient and secure EMR storage and sharing scheme based on Hyperledger Fabric and the InterPlanetary File System (IPFS), which works as follows:  
1. We propose a storage method that combines dual channels with the IPFS. In Hyperledger Fabric, we create an attribution channel to store EMR's attribution information and a data channel to store the storage location, summary, and usage records of medical data. The encrypted medical data are then stored in the IPFS. This ensures that patient privacy is not threatened by attacks such as data mining.
2. We integrate medical data attributes that can be used for a conditional query into a composite key. This composite key, along with the medical record number, is then stored as a key-value pair in the blockchain. This approach aims to enhance the efficiency of data querying in the blockchain.
3. We introduce a zero-knowledge proof and combine it with smart contracts to achieve decentralized identity verification for system users. This eliminates the reliance on central third-party verification services, thereby enhancing system security.
4. We combine AES encryption with proxy re-encryption to ensure data security during the sharing process. We provide the principles behind implementing this technique.
## Results and Discussion  
### Total Phenolic Content and Antioxidant Capacity  
#### 3.1.1 In Pineapple Shell and Core  
As shown in Table 1, pineapple shell samples (65.75 mg GAE.g\({}^{-1}\) dry weight) presented significantly higher levels (\(p<0.05\)) of phenolic compounds than core (59.89 mg GAE.g\({}^{-1}\) dry weight). Antioxidant capacity, determined by the three tested methods (DPPH, FRAP, and ABTS) was also higher in the shell samples.  
**Legend: Cr.Y--**Control sample; **TX.Y--**Heat treated sample (X = treatment temperature: 30, 40, or 50 C); **Y--**storage time at 4 \(\lx@math@degree\) C after heat treatment (Y = 8 or 24 h). Different letters represent statistically significant differences in the same column (comparison of different samples for the same analytical method; [PERSON]'s test \(p<0.05\)). Small letters compare shell pineapple samples and capital letters compare core pineapple samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Raw Material**} & \multirow{2}{*}{**Sample**} & **TPC (mgGEa-1)** & \multicolumn{2}{c}{**Antioxidant Capacity (umol Troloxg-1 day weight)**} \\ \cline{3-5}  & & **dry weight** & **DPPH** & **FRAP** & **ABTS** \\ \hline \multirow{8}{*}{**Shell pineapple**} & Cr\_0 & 65.75 \(\pm\) 1.40 b  & 21.56 \(\pm\) 0.78 c  & 41.37 \(\pm\) 1.73 c  & 26.10 \(\pm\) 1.54 d  \\  & Cr\_8 & 79.72 \(\pm\) 2.87 e  & 33.59 \(\pm\) 3.81 d  & 52.72 \(\pm\) 3.05 d  & 33.54 \(\pm\) 2.27 c  \\  & Cr\_24 & 84.83 \(\pm\) 2.01 f  & 38.70 \(\pm\) 3.38 f  & 63.71 \(\pm\) 2.96 f  & 38.64 \(\pm\) 2.24 f  \\  & T30.8 & 69.08 \(\pm\) 1.70 b  & 26.08 \(\pm\) 1.25 d  & 46.45 \(\pm\) 1.55 b  & 30.99 \(\pm\) 1.65 b  \\  & T40.8 & 75.76 \(\pm\) 1.75 d  & 25.02 \(\pm\) 3.47 e  & 48.13 \(\pm\) 3.05 d  & 29.87 \(\pm\) 2.54 b  \\  & T50.8 & 57.05 \(\pm\) 2.39 a  & 16.85 \(\pm\) 1.33 b  & 38.15 \(\pm\) 2.03 a  & 24.06 \(\pm\) 1.12 a  \\  & T30.24 & 78.80 \(\pm\) 2.28 d  & 31.22 \(\pm\) 3.81 d  & 55.26 \(\pm\) 3.51 d  & 34.72 \(\pm\) 2.56 e  \\  & T40.24 & 70.28 \(\pm\) 1.89  & 22.75 \(\pm\) 1.98 d  & 45.65 \(\pm\) 1.89 b  & 28.25 \(\pm\) 1.73 d  \\  & T50.24 & 57.01 \(\pm\) 1.57 d  & 14.19 \(\pm\) 2.78 b  & 39.65 \(\pm\) 1.54 a  & 24.25 \(\pm\) 1.58 a  \\ \hline \multirow{8}{*}{**Core pineapple**} & Cr\_0 & 59.89 \(\pm\) 1.86 b  & 24.06 \(\pm\) 1.24 a  & 31.18 \(\pm\) 1.98 c  & 22.34 \(\pm\) 1.19 e  \\  & Cr\_8 & 67.14 \(\pm\) 2.58 a  & 25.01 \(\pm\) 1.48 b  & 38.06 \(\pm\) 1.17 b  & 26.64 \(\pm\) 0.66 c  \\  & Cr\_24 & 65.07 \(\pm\) 1.83 b  & 22.84 \(\pm\) 1.40 f  & 30.83 \(\pm\) 1.12 b  & 25.67 \(\pm\) 0.96 b  \\  & T30.8 & 70.92 \(\pm\) 2.09 c  & 28.60 \(\pm\) 1.20 c  & 42.62 \(\pm\) 1.90 a  & 31.94 \(\pm\) 1.78 f  \\  & T40.8 & 73.49 \(\pm\) 1.91 C  & 26.99 \(\pm\) 1.60 b  & 45.85 \(\pm\) 1.63 d  & 27.05 \(\pm\) 1.11 b  \\  & T50.8 & 71.94 \(\pm\) 1.62 c  & 24.50 \(\pm\) 0.99 a  & 41.14 \(\pm\) 1.54 a
## 2 Background Study
### Pca  
PCA is a prominent dimensionality reduction technique. It distinctly merges the input variable such that only the important or most valuable set of variables is preserved. The new set of variables obtained after performing PCA are bound to be linearly independent of each other and will be the rank order of the variance in each variable (the most important principal axis comes first).  
The simplified overall procedure for performing PCA is as follows:  
1. Calculate the covariance matrix \(\Sigma\) of the data points. \[\Sigma=\frac{1}{m}\sum_{i=1}^{n}\left(x^{(i)}\right)\left(x^{(i)}\right)^{T}\] (4) where \(m\) is the total number of samples and \(n\) is the number of dimensions.
2. Compute the eigenvectors of covariance matrix \(\Sigma\).
3. Choose the first k eigenvectors, which are the new k-dimensions.
4. Transform the original n-dimensional data points into k-dimensions. \[\left[\textit{new data}\right]_{k\times 1}=\left[\textit{top k eigenvectors}\right]_{k \times n}\left[\textit{original data}\right]_{n\times 1}\] (5)  
In this work, the Choudhury stiction model is simulated to obtain the open loop response of the stiction behavior. In this paper, two preprocessing methods for training an ANN are compared. Preprocessing the training data enhances the results of a neural network [52]. To emphasize the influence of the preprocessing method on stiction detection, two methods, one with reduction in neural network points without reduction in the features, namely, the D-value method [53], and a feature reduction PCA method of the data, are calculated. These methods are compared for their performance in further sections, and the results emphasize that the feature reduction technique leads to a lowered stiction detection rate.
## 4 Experiment
### Result of Defect Classification Prediction
shown in Figure 13, in terms of response speed, the IIPSO_ISmote_Stacking model exhibits a distinct advantage, with a response time of 0.256 s, which is superior to the 0.284 s and 0.323 s observed in the IPSO_ISmote_Stacking model and the stacking model. The optimisation process results in a reduction in the response time of the model when processing tasks. Concurrently, although the stacking model incorporates multiple prediction models internally, it maintains a high response speed, indicating that the model can still provide feedback on prediction results in a timely manner despite increasing integration complexity. Additionally, due to the complexity of the inherent structure of the stacking model, the CPU usage and memory usage during operation are slightly higher than those of the single model. However, these values remain within a reasonable range. From the perspective of evaluation metrics, it can be observed that the stacking model demonstrates a significantly enhanced prediction performance in comparison to a single model. Following the implementation of data balancing through the ISmote algorithm and hyperparameter optimisation through the IIPSO algorithm, the performance of the stacking model was significantly enhanced. The value of Macro_Precision, Macro_Recall, and Macro_F1 scores reached 0.943, 0.936, and 0.932, respectively, with each indicator value attaining the optimal level. These results demonstrate that the optimised stacking ensemble model exhibits enhanced prediction accuracy. Secondly, the results demonstrate that the ISmote algorithm is capable of effectively enhancing the model's classification performance, as well as the IIPSO algorithm's ability to identify optimal hyperparameters. Furthermore, the four single models, GBDT, XGBoost, CatBoost, and LightGBM, which serve as the fundamental components of the stacking integrated model, also demonstrate high predictive capabilities. The performance indicators of these models remain at a high level. This indicates that they are suitable as base models in the stacking ensemble framework. In order to verify the prediction performance of the IIPSO_ISmote_Stacking model for seven strip defects, the ROC curves for each defect were plotted. As shown in Figure 14, all ROC curves are situated above the diagonal reference line, and the value of Macro_AUC is 0.928, which signifies an exemplary predictive performance of the model. In particular, the values of AUC for Defect 1, Defect 3, and Defect 5 in Figure 15 are close to 0.930, indicating that the model has high performance in these categories. Although the values of AUC for the remaining strip steel defects are relatively low, they nevertheless remain at a high level. Consequently, the IIPSO_ISmote_Stacking model demonstrates excellent performance in the application of strip steel defect prediction. Figure 13: Comparison of the prediction: (**a**) response time and system resource usage; (**b**) prediction performance.
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 4 Performance Evaluation  
### Simulation Setup  
To evaluate the performance of the algorithms presented in Section 3, we have significantly extended the system-level simulator NS-3 [38] by implementing the new multicast mechanisms introduced in 3 GPP specifications, M-MIMO features, and multicast traffic.  
Unless otherwise explicitly stated, we consider an Urban Macro scenario with \(N\) UEs randomly distributed in the gNB coverage area. Both LOS and NLOS channels are modeled. UEs move with a 3 kmph speed and send SRS with a 20 ms period. The gNB uses the FS EDF scheduler described in Section 3.5. Table 3 lists the main simulation parameters.  
In the experiments, we measure (i) the average PLR, and (ii) the average DL and UL resource consumption. The lower the resource consumption, the higher the mURLLC capacity. The DL resource consumption is determined as the average number of RBs used in a DL slot for data transmission divided by the total number of RBs. The UL resource consumption consists of two parts. First, some UL resource is used for UEs' feedback transmission. For the ACK/NACK scheme, the resource consumption can be found as \(\eta_{f}=\frac{NB_{f}}{B(K_{d}+k_{ul})}\), where \(B_{f}\) is the number of RBs allocated for transmission of a single UE feedback (by default, \(B_{f}=4\) RBs). For the NACK-only scheme, the resource consumption is \(N\) times lower because a single resource is allocated for all UEs. Second, UL resource is used for SRS transmission: \(\eta_{s}=\frac{NT_{slot}}{k_{0}N_{0}T_{sgg}}\), where \(k_{0}=14\) is the number of OFDM symbols in a slot, and \(N_{0}=2\) is the number of SRSs that is multiplexed in an OFDM symbol.  
In the following sections, we analyze the performance of the various transmission parameter selection algorithms described in Section 3. To simplify the evaluation, we changed the order compared with Section 3. Specifically, since the literature provides dozens of precoder selection algorithms that significantly affect performance, we start our analysis with their comparison. Based on the analysis, we determine the best precoder selection algorithms for mURLLC. After that, we evaluate other short-term transmission parameters selection algorithms (i.e., RB and MCS selection). Since the implementation of the MCS selection algorithm depends on the maximum number of TX attempts, we evaluate the joint effect of MCS and the number of TX attempts selection. Finally, we study the influence of long-term parameters (the feedback scheme and the SRS period) on the overall system performance.