esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Fault Diagnosis Scheme
### Hybrid Deep Recognition Scheme
#### 3.3.2 Multiclass Classification  
After step (1), the integrated features related to all the samples are produced. Then, the Adaboost tree-based ensemble classifier is constructed for multiclass classification using the obtained enhanced features. The number of trees is determined by iteration. After the classification, a confusion matrix is obtained which presents the classification details in terms of each fault condition. As mentioned above, the AdaBoost tree-based ensemble classifier avoids the problem of overfitting. Furthermore, after integration the feature vector gives consideration of all three domains of the raw signal. For the above reasons, an improved performance can be obtained over traditional single DCNN-based method.
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## 6 Conclusions  
In this paper, we introduce lattice-based cryptosystems with strong security properties to solve the problem that the ACPS scheme is inefficient when sampling from discrete gaussian distribution with sufficiently large standard deviation and generating extra \"malformed\" distributions. The public-key and symmetric-key cryptosystems provide security for key-dependent messages. Compared with the previous scheme, our scheme is compact and has a stable set of challenge functions. Both the size of public key and ciphertext are \(\widetilde{O}(n)\), and the cost of encryption and decryption is only \(\mathrm{polylog}(n)\) bit operations per message symbol. Therefore, our scheme satisfies KDM-CPA security under the RLWE\({}^{*}\) assumption, and carries the advantages of having simple operation, parallelization and improved asymptotic efficiency.  
However, there are still some problems to be explored and improved in this scheme, such as using an additional noise distribution in the hybrid game, and future work is still required to construct a fully homomorphic encryption scheme with circular security.  
Conceptualization: B.Y. and R.H.; methodology, B.Y.; validation, B.Y.; R.H. and J.Z.; formal analysis, J.Z.; writing--original draft preparation, B.Y.; writing--review and editing, B.Y.; funding acquisition, R.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation Project of China under Grant No. 62062009 and the Guangxi Innovation-driven Development Project under Grant Nos. AA17204058-17 and AA18118047-7.  
The authors declare no conflict of interest.
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 5 5G Open Challenges
### Vehicular Applications
#### 5.3.3 V2I Cooperation for Traffic Management  
A mixed-use traffic management use case is proposed in [103], which includes various transportation modes (e.g., automobile, train, bicycle, and pedestrian), several traffic densities, and different environmental conditions. It is shown that a V2X system would need the flexibility to adapt to changing attributes such as vehicular traffic density, rates of speed, angles of approach, and weather conditions which all may impact the optimal range and transmission rate in a specific situation. However, the description of such a mixed-use case is not sufficiently detailed in [103; 104]. Looking into the related research works, we have identified one recent contribution on traffic management [107]. It deals with urban traffic management to mitigate traffic congestion. A new architecture is defined, based on different layers, such as: environmental sensing, communication, mobile edge computing and remote core cloud server. 5G SDN and mobile edge computing are considered key enabling technologies. SDN offers high-bandwidth communication service with flexibility and programmability, thus providing agility to sensing operations. Other technologies are also listed for future research such as vehicle localization, data pre-fetching strategy, traffic prediction and traffic lights control.
###### Abstract  
A hybrid double cladding erbium-ytterbium co-doped fiber (EYDF) amplifier with a single-mode-multimode-single-mode (SMS) active fiber is demonstrated in this study. The hybrid gain fiber with an SMS structure is composed of two kinds of EYDFs with 6 and 12 \(\upmu\)m core diameters. The transmission spectra of the SMS fiber structure were theoretically analyzed and the simulation results indicated that the maximum loss in the 1-1.1 \(\upmu\)m band where the Yb-band amplified spontaneous emission (Yb-ASE) located, was much larger than that of the 1.5-\(\upmu\)m band because of the wavelength difference. The power performance and spectra properties of the hybrid fiber amplifier were theoretically and experimentally analyzed and compared with a typical uniform fiber amplifier under the same conditions. The experimental results demonstrated that this hybrid fiber amplifier can suppress the Yb-ASE by over 12 dB and increase the slope efficiency by more than 2%, but the ASE in the 1.5-\(\upmu\)m band increases by 2-3 dB. This work provides a possible method to enable EYDF amplifiers to suppress the Yb-ASE and overcome the pump bottleneck effect.  
fiber amplifier; erbium-ytterbium co-doped fiber; amplified spontaneous emission; SMS structure
## Abbreviations  
In the study, some abbreviations and symbols are used, which are explained in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Full Term or Meaning** & **Unit/Description** \\ \hline MGWR & \begin{tabular}{c} Multiscale Geographically \\ Weighted Regression \\ \end{tabular} & \begin{tabular}{c} A regression model for \\ multiscale analysis \\ \end{tabular} \\ \hline GIS & Geographic Information System & \begin{tabular}{c} A system for geographic \\ data analysis \\ \end{tabular} \\ \hline SPSS & Statistical Package for the Social Sciences & Statistical analysis software \\ \hline SSE & Sum of Squared Errors & Used in clustering and regression \\ \hline AIC & Akaike Information Criterion & A model selection criterion \\ \hline GTWR & \begin{tabular}{c} Geographically and Temporally \\ Weighted Regression \\ \end{tabular} & \begin{tabular}{c} A regression model considering \\ spatial and temporal effects \\ \end{tabular} \\ \hline \(D\) & Fractal dimension & \begin{tabular}{c} Indicates spatial filling \\ and complexity \\ \end{tabular} \\ \hline N(_r_) & Number of non-empty boxes & \begin{tabular}{c} A variable in fractal \\ dimension calculations \\ \end{tabular} \\ \hline \(r\) & Box side length & Unit length \\ \hline \(\text{X}_{1}\) & Ratio of flat terrain & \begin{tabular}{c} Proportion of areas with \\ 0-5\({}^{\circ}\) slope \\ \end{tabular} \\ \hline \(\text{X}_{2}\) & Average elevation & Unit meters \\ \hline \(\text{X}_{3}\) & Ratio of south-facing slopes & \
\begin{tabular}{c} Proportion of south-facing \\ slope areas \\ \end{tabular} \\ \hline \(\beta_{0}\) & Constant term in regression equation & - \\ \hline \(\beta_{k}\) &
\begin{tabular}{c} Regression coefficient for the \\ k-th factor \\ \end{tabular} & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviations and Symbols.
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 5 Experimental Results
### Ablation Study  
In this research, a comprehensive ablation study was conducted on the C2B hybrid model to evaluate the contributions of its key components. By systematically removing the CodeT5 and attention mechanism we analyze their impact on the model's performance using precision, recall, F1-score, and accuracy metrics. This ablation study provides valuable insights into the model's architecture, guiding future improvements and optimizations for enhanced performance and robustness.
The experimental results analyzed that the GO-SVM meta-heuristic classifier is capable of diagnosing bearing failures in rotating electrical machines. Already in [PERSON] et al. [8], the outputs of the BBO and DE optimization techniques were used as the initial weights in the training process of the radial basis function (RBF) neural classifier. This DE/BBO algorithm was implemented to the RBF to overcome the drawback of this classifier's slow convergence rate. The experimental results demonstrated the superior performance of the optimized RBF classifier compared to the conventional RBF classifier in the diagnosis of bearing defects in electrical machines. On the other hand, [PERSON] and [PERSON] [27] have already used the ABC algorithm to identify computer programs failures. The results showed that the proposed approach to fault diagnosis is promising, considering that the ABC methodology can efficiently avoid the optimal location and ensure the validity of fault finding for all software. Moreover, [PERSON] et al. [28] used the ABC algorithm to investigate faults in photovoltaic systems. This tool allowed to reduce the number of samples for efficient diagnosis of defects in these systems. In the present work, these optimization tools can collaborate to select the most relevant attributes of the failure patterns to reduce the input matrix of the pattern classifiers. Thus, the contribution of this work consists of developing an alternative methodology for the diagnosis of bearing failures in three-phase induction motors based on the optimization of the input matrix of the pattern classifier. First, this approach selects the most relevant attributes of mutual information measurements between the signal processing stage's current signals using the ABC algorithm. Finally, the pattern classifiers, the C4.5 decision tree (C4.5 DT) and the multi-layer artificial neural network (MLP ANN), better identify the bearing fault signatures extracted in the previous stage, are evaluated. To validate the proposed approach presented in this work, experimental tests were conducted with TIMs running in steady-state, powered by frequency inverters. Several operating conditions found in the industry were emulated, such as the variation of the supply frequency from 20% to 100% concerning the nominal value and the load torque variation (10% \(\leq T_{n}\leq\) 120%, where \(T_{n}\) is the nominal torque). The analyzed severity levels are based on 15 to 90 min of wear of the bearings of two TIMs of 0.74 and 1.48 kW. This paper is divided into five sections. Section 2 describes the theoretical background of the proposed methodology, which is fully explained in Section 3. Section 4 shows and discusses the experimental results, and Section 5 brings a conclusion and final remarks.
## 3 Results
### Analysis Part
#### 3.2.1 Comparative PDI, Particle Size and Stability Analyses of DFMACs at Different pH Values
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**pH** & **AMP** & **CEF** & \begin{tabular}{c} **PMVEMA-** \\ **AMP** \\ \end{tabular} & \begin{tabular}{c} **PIBMA-** \\ **AMP** \\ \end{tabular} & \begin{tabular}{c} **PEMA-** \\ **AMP** \\ \end{tabular} & \begin{tabular}{c} **PMVEMA-** \\ **CCF** \\ \end{tabular} & \
\begin{tabular}{c} **PIBMA-** \\ **CEF** \\ \end{tabular} &
\begin{tabular}{c} **PEMA-** \\ **CCF** \\ \end{tabular} \\ \hline
2 & 0.32 & 0.34 & 0.39 & 0.11 & 0.35 & 0.33 & 0.31 & 0.30 \\
3 & 0.34 & 0.35 & 0.38 & 0.30 & 0.32 & 0.33 & 0.33 & 0.34 \\
4 & 0.30 & 0.32 & 0.29 & 0.28 & 0.33 & 0.33 & 0.33 & 0.30 \\
5 & 0.31 & 0.33 & 0.34 & 0.26 & 0.33 & 0.35 & 0.36 & 0.30 \\
6 & 0.67 & 0.37 & 0.35 & 0.29 & 0.32 & 0.37 & 0.35 & 0.37 \\
7 & 0.33 & 0.41 & 0.33 & 0.30 & 0.30 & 0.35 & 0.33 & 0.32 \\
8 & 0.31 & 0.34 & 0.36 & 0.24 & 0.44 & 0.45 & 0.32 & 0.36 \\
9 & 0.33 & 0.60 & 0.37 & 0.31 & 0.36 & 0.42 & 0.34 & 0.32 \\
10 & 0.31 & 0.36 & 0.39 & 0.31 & 0.38 & 0.36 & 0.33 & 0.36 \\
11 & 0.31 & 0.36 & 0.34 & 0.31 & 0.34 & 0.33 & 0.35 & 0.37 \\
12 & 0.33 & 0.32 & 0.33 & 0.32 & 0.42 & 0.35 & 0.31 & 0.67 \\
13 & 0.34 & 0.32 & 0.34 & 0.36 & 0.36 & 0.39 & 0.34 & 0.46 \\ \hline Mean, SD & 0.35 \(\pm\) 0.10 & 0.37 \(\pm\) 0.08 & 0.35 \(\pm\) 0.03 & 0.28 \(\pm\) 0.06 & 0.65 \(\pm\) 0.04 & 0.36 \(\pm\) 0.04 & 0.33 \(\pm\) 0.02 & 0.37 \(\pm\) 0.10 \\ \hline U & 0.03 & 0.02 & 0.01 & 0.02 & 0.01 & 0.01 & 0.00 & 0.03 \\ \hline \end{tabular}
\end{table} 
Table 2: The PDI values of AMP, CEF and DFMACs particles at different pH values. In Table 4, the zeta potential values of DFMACs increased regardless of charge and showed greater stability in the middle of the pH range. However, drugs were significantly more stable in basic conditions. Absolute zeta potential values of all the particles of drugs shrank in the very low pH values and showed no stability. However, at the same pH values, the DFMACs had greater zeta potential values than the values of incipient stability. It is understood that absolute zeta potential values of AMP and CEF increased in acidic conditions by chemical conjugation with maleic anhydride copolymers and, as a result, drugs became stable in DFMAC form, especially with the PMVEMA conjugation.
# Enhanced Efficiency and Reduced Side Lobe Level Convex Conformal Reflectarray
## 3 Feed Design Procedure
The optimization process consists in modifying the radial (\(\rho_{n}\)) and longitudinal (\(z_{n}\)) coordinates of the \(N_{p}\) internal nodes \(P_{n}\) collected in the vector \(\mathbf{P}\) in order to minimize a proper cost function defined as a weighted sum of the horn reflection coefficient, the horn cross-polarization level and the distance between the actual (\(F^{H}(\mathbf{P})\)) and desired (\(F^{H}_{ideal}\)) modal content on the horn aperture:  
\[f_{H}(\mathbf{P})=\beta_{1}err(F^{H}_{ideal}-F^{H}(\mathbf{P}))+\beta_{2}X^{ H}_{pol}(\mathbf{P})+\beta_{3}S^{H}_{11}(\mathbf{P}) \tag{3}\]  
where \(\beta_{1}\), \(\beta_{2}\) and \(\beta_{3}\) are the weights used to give more or less emphasis to the different terms in \(f_{H}\). Since the operating frequency band is of the order of 10%, \(N_{p}=6\) was shown to be sufficient to achieve the desired performance. The analysis was carried out exploiting a coupled integral equation technique (CIET) [30]. A stepwise approximation of the initial linear profile of the horn was generated (see Figure 3). In the optimization, the discretization step was chosen to be equal to \(\lambda/15\), while \(\lambda/30\) was chosen for the final analysis, where \(\lambda\) is the free-space wavelength at 30 GHz. The final horn geometry in terms of longitudinal and radial \(P_{n}\) coordinates is characterized by the following:  
* \(z_{n}=\{0,9.58,16.08,18.57,23.74,29.708,34.54,35.54\}\) mm;
* \(\rho_{n}=\{4,7.142,6.9,7.55,11.35,12.428,18,18\}\) mm. Figure 2: Iso-level curves of the antenna gain (**top**), maximum cross-polarization in the horizontal plane (**centre**) and maximum side lobe level in the horizontal plane (**bottom**) at 30 GHz as a function of the magnitude and phase of the ratio \(\gamma\) between the \(TM_{11}\) and \(TE_{11}\) modal coefficients for the circular waveguide aperture of radius 18 mm. The marker (x) is placed in correspondence of the optimized value of \(\gamma\) for the configuration used in this paper. The 3D model of the horn is shown on the left of Figure 4. It is worth to mention that, if larger bandwidth or multi-band applications are required, a larger number of \(N_{p}\) would be necessary, but this design approach can be applied anyway.
## 5 Results and Analysis
### Robustness Analysis: Subperiod Performance  
To assess the robustness of our model across different economic conditions and policy regimes, we conduct a comprehensive subperiod analysis. This evaluation is crucial for understanding how model performance varies across distinct macroeconomic environments and policy frameworks, addressing concerns about temporal stability and economic regime dependence.  
We partition our FOMC dataset into four economically meaningful subperiods: precrisis (1977-2006), financial crisis (2007-2009), post-crisis/QE era (2010-2015), and normalization period (2016-2024). Each period represents distinct monetary policy paradigms characterized by different communication strategies, policy tools, and economic contexts.  
Table 4 demonstrates that our model maintains consistent performance advantages across all economic subperiods, with improvements ranging from 5.2 to 5.9 percentage points. Notably, the model shows particularly robust performance during the financial crisis period, achieving a 5.9 pp improvement when policy communication was most critical and markets were most volatile. This consistency across diverse economic regimes validates the generalizability of our approach and suggests that the learned representations capture fundamental relationships between central bank language and policy actions that transcend specific economic conditions.  
The slight performance degradation during the financial crisis period (compared to other periods) is expected and consistent across all models, reflecting the inherently challenging nature of predicting policy actions during periods of extreme uncertainty and unconventional policy measures. However, our model's superior performance in this  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Pre-Crisis** & **Financial Crisis** & **QE Era** & **Normalization** \\  & **(1977–2006)** & **(2007–2009)** & **(2010–2015)** & **(2016–2024)** \\ \hline Longformer + LoRA & 0.798 & 0.742 & 0.811 & 0.823 \\ Ours (Full Model) & **0.857** & **0.801** & **0.868** & **0.875** \\ Performance Gain & +5.9 pp & +5.9 pp & +5.7 pp & +5.2 pp \\ \hline \hline \end{tabular}
\end{table}
Table 4: Subperiod performance analysis: macro F1 scores across different economic regimes.  
Figure 8: Model accuracy (FOMC macro F1) vs. inference latency (per 2000-token sample).  
challenging period demonstrates its capacity to extract meaningful signals, even from highly complex and unprecedented policy communications.
## 3 Results
### Test-Retest Reliability
 } & R & 291 \(\pm\) 45 & 8 & 15 & 304 \(\pm\) 53 & 14 & 17 & 0.77 & 0.69 \({}^{*}\) \\ \cline{2-9}  & L & 297 \(\pm\) 50 & 11 & 17 & 302 \(\pm\) 43 & 11 & 14 & 0.75 & 0.79 \({}^{*}\) \\ \cline{2-9}  & R & 3.68 \(\pm\) 0.41 & 0.08 & 11 & 3.48 \(\pm\) 0.59 & 0.09 & 15 & 0.82 & 0.74 \({}^{*}\) \\ \cline{2-9}  & L & 3.57 \(\pm\) 0.46 & 0.09 & 12 & 3.74 \(\pm\) 0.39 & 0.10 & 10 & 0.82 & 0.80 \({}^{*}\) \\ \cline{2-9}  & R & 0.96 \(\pm\) 0.20 & 0.03 & 21 & 1.08 \(\pm\) 0.16 & 0.04 & 15 & 0.75 & 0.66 \({}^{*}\) \\ \cline{2-9}  & L & 0.82 \(\pm\) 0.11 & 0.02 & 14 & 0.86 \(\pm\) 0.23 & 0.04 & 20 & 0.73 & 0.73 \({}^{*}\) \\ \hline \multirow{3}{*}{\ \begin{tabular}{c} Peak Force (N) \\ \end{tabular}  } & R & 587 \(\pm\) 68 & 13 & 11 & 605 \(\pm\) 74 & 20 & 12 & 0.84 & 0.72 \({}^{*}\) \\ \cline{2-9}  & L & 606 \(\pm\) 84 & 16 & 13 & 619 \(\pm\) 74 & 12 & 20 & 0.81 & 0.69 \({}^{*}\) \\ \hline \multirow{3}{*}{ \begin{tabular}{c} Peak Power (W) \\ \end{tabular}  } & R & 717 \(\pm\) 157 & 30 & 21 & 746
## 2 Literature Review
Using NADI 2023 data, the researchers converted multiclass forecasts to multi-label data using both supervised and unsupervised techniques. To extract samples from the training set, they additionally employed multilingual cross-encoders and Arabic-based sentence encoders. The best validation performance was an F-score of 48.5% using an unsupervised method [11]. The three Arabic dialects--Gulf, Levantine, and Egyptian--were identified using a new method presented in a study by [PERSON] et al. Several classification techniques, such as Gaussian Naive Bayes and Support Vector Machines, were used in four experiments. With average gains in precision, recall, and F1-score metrics, the suggested classifier based on convolutional neural networks performed better than other classifiers in all three dialects [12]. The Convolutional Long Short-Term Memory (CLSTM) model, a deep learning and machine learning technique, was used in this study to identify cyberbullying in Arabic YouTube comments. With an accuracy rate of 90%, the model revealed the effectiveness of deep learning in safeguarding users who speak Arabic and offered a framework for dealing with comparable linguistic difficulties in other languages. A study by [PERSON] et al. investigated how to enhance speaker recognition in autonomous speaker identification using synthetic data. By comparing data from synthetic and real speakers, the researchers trained a character-level recurrent neural network on five spoken sentences. To determine whether the synthetic data could offer any useful rules, the network weights were applied to the original dataset. The findings demonstrated that improved classification accuracy,F1-score, precision, and recall were achieved through fine-tuned learning from synthetic speech data. Class imbalance problems were also resolved by this method [13]. A deep learning model was used to create an autonomous dialect detection system. The system uses multi-scale product analysis to extract glottal occurrences, fundamental frequency, and cepstral properties from speech signals. Multi-dimensional characteristics and relationships can be encoded using the Hamilton neural network classifier. The system is reliable in terms of recognizing Arabic dialects, and experimental results demonstrate notable performance improvements over existing HNN-based methods [14]. Researchers are creating automated SLR frameworks to handle the approximately 7000 distinct sign languages. According to an analysis of 988 research papers, transformer-based models and ensemble learning techniques perform better in terms of accuracy and resilience compared with conventional methods [15].
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 3 Results and Discussion
### Qualitative and Semi-Quantification Analysis of Volatile Compounds in the Fish Sauces
#### 3.3.3 Aldehydes  
Previous studies have shown that aldehydes primarily arise from lipid oxidation or are produced through Maillard-induced amino acid degradation and the interaction between lipids and the Maillard reaction [16]. Benzaldehyde imparts almond, nutty, and fruity odors [26]. Nonanal imparts rose, fresh, and fatty flavors [16]. Benzene acetaldehyde imparts a rosy-like aroma [27], while decanal contributes to a pleasant fatty, floral, and green aroma [10]. In this study, the concentrations of benzaldehyde, benzene acetaldehyde, nonanal, decanal, and 4-propylbenzamidealdehyde were highest in F10, F10, B10, F60, and F10, respectively, with values 2.13, 1.58, 3.09, 13.24, and 3.32 times higher than those in U. Furthermore, the aldehyde content in all other cooked samples was higher than in sample U, except for benzene acetaldehyde in F30. This suggests that the cooking process resulted in changes in aldehyde levels, thereby enhancing the flavor of fish sauce.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 3 Energy-Efficient Communication  
Once the pecan crop irrigation model was appropriately identified and validated, we implemented the WSN communication strategy. Energy-efficient communication was achieved by reducing the number of communication messages by the sensor node. This node included a triggered mechanism to estimate the next sampling instance \(h+1\) based on soil moisture dynamics \(\theta\) and weather conditions \(eto\). The control system implemented a feedback/feedforward loop where soil moisture was the process variable, while weather conditions disturbed the process dynamics. The control node sent an on-off signal \(ir\) to the actuator node to manipulate the irrigation valve, based on the difference between the reference level \(r\) and the measured variable \(\theta\), as depicted in Figure 5. The irrigation was activated when the error was below \(-2\%\) and deactivated when it was above \(+2\%\).