esa-sceva/satcom-chunk-collection huggingface dataset:
# Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form
## 4 Conclusions  
In this work, we described an Information Fusion architecture using the Factor Graph in Reduced Normal Form paradigm.  
The proposed approach permits learning a sort of measure's context that, in presence of high uncertainty, helps to detect disagreement between the injected evidence and the system knowledge, giving to overall system great flexibility and robustness in the handling missing and wrong values. The proposed architecture, in fact, also in presence of missing values and errors, continues to have a good classification performance.  
We also demonstrated how it is possible to condition the system in the presence of information sources with different reliability or in presence of single unreliable detection. This is another demonstration of the flexibility of the paradigm that can manage several information sources taking into account their peculiarities.  
Even though the approach is completely general and applicable to several contexts where it is required to fuse information from several sources, the framework has been applied to a classification problem of identity documents, where different detectors are fused into a unique classifier.  
Conceptualization, A.B., G.D.G., F.A.N.P.; methodology, A.B., G.D.G., F.A.N.P.; software, A.B., A.N., G.C.; validation, A.B., G.D.G., F.A.N.P.; investigation, A.B., G.D.G., F.A.N.P.; data curation, A.N.; writing--original draft preparation, A.B.; writing--review and editing, A.B., A.N., G.C., G.D.G., F.A.N.P., M.V., G.G.; visualization, A.B., A.N.; supervision, A.B., F.A.N.P., M.V., G.G.; funding acquisition, F.A.N.P. All authors have read and agreed to the published version of the manuscript.  
Figure 8: (**a**) Classification accuracy, **(b)** Jensen-Shannon divergence on Class Variable, (**c**) Conditional Entropy of Class Variable varying the number of wrong values and setting different combination of \(\
u\).  
**Funding:** Work partially supported by FOR CAMPANIA FESR 2014/2020, ITS for Logistics, awarded to CNIT (Consorzio Nazionale Interuniversitario per le Telecomunicazioni)  
**Data Availability Statement:** The data are not publicly available due to privacy. The dataset of extracted features used in this study is available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Separation Methodology
### Method for Identifying Abnormal Conditions for Hourly Data  
According to the above analysis, only the clustering algorithm is suitable for this situation, of which the steps are similar to those shown in Figure 8(b). The steps of this method for hourly data are not exactly the same as the steps for daily data.  
1. Using the clustering algorithm to generate the critical eigenvalues, \(T_{III,\ Min}\) and \(\overline{E}_{II,Min}\), when the power consumption and the outdoor temperature are less than \(T_{III,Min}\) and \(\overline{E}_{II,Min}\), respectively, the state of the air-conditioning is closed. For the contrary state, the air-conditioning is open.  
However, the state of the air-conditioning does not completely depend on the outdoor climate, and human behavior is also a type of complex influencing factor. For example, when the power consumption is higher than \(\overline{E}_{II,Min}\) and the outdoor temperature is less than \(T_{III,Min}\), it is possible that air-conditioning is closed and the lighting socket power consumption is higher, as shown in Figure 12 (the fuzzy area in the upper left corner). When the outdoor temperature is higher than \(T_{III,Min}\) and the power consumption is less than \(\overline{E}_{II,Min}\), it is possible that people do not use air-conditioning due to their habits, even if the outdoor temperature reaches the degree of using air-conditioning, as shown in Figure 12 (the fuzzy area in the bottom right corner). Therefore, after the end of step (1), the next step should be identified.
2. The theoretical maximum power consumption \(e_{i,Max}\) of the lighting socket in this special hour is calculated and compared with the actual power consumption \(e_{i}\). If \(e_{i}\) is higher than \(e_{i,Max}\), it is considered that the power consumption of the lighting socket is mixed with that of the air conditioner; otherwise, it is not mixed.  
Figure 11: Comparison of identification results using different methods.
## 2 Oil and Gas Geological Characteristics of the Basin
### Oil and Gas Distribution Characteristics  
As of October 2024, a total of 72 oil and gas fields were discovered in the Mangeshlak Basin, including 44 oil fields and 28 gas fields. The total proven recoverable reserves of oil and gas are 1.19 billion tons (8.7 billion barrels) of oil equivalent, of which oil is the main source, accounting for 60.3%. From the perspective of planar distribution, the main oil and gas fields discovered in the basin and most of the reserves are distributed on the Zhetybay step, which is a gentle south-dipping structural terrace located between the Bekbashkuduk anticline and the deep depression in the south of the Mangeshlak fold belt. Oil and gas were discovered in the basin from the Upper Paleozoic to the Upper Cretaceous. Nine reservoirs can be identified, namely, the Upper Paleozoic, Lower Triassic, Middle Triassic, Upper Triassic, Lower Jurassic, Middle Jurassic, Upper Jurassic, Lower Cretaceous, and Upper Cretaceous, which are divided into three sets of reservoir combinations: Triassic, Jurassic, and Cretaceous. The discovered oil and gas reserves of the Jurassic reservoir assemblage account for 92.2% of the entire basin, mainly distributed in the southern part of the Zhetybay-Uzen terrace. A total of 28 oil and gas fields have been discovered in the Middle Jurassic reservoirs, accounting for 89.8% of the discovered oil reserves in the entire basin, and the discovered natural gas reserves account for 34.7% of the entire basin (Figure 4). Although the Triassic reservoir assemblage has relatively few discovered reserves, it has a wide distribution, mainly located in the Karaki saddle in the northwest of the Zhetybay-Uzen terrace and the eastern part of the Shegendek sag. No large-scale oil and gas discoveries have been made, and the exploration prospects are good (Figure 5). Strengthening the understanding of the Triassic sedimentary development characteristics is of great significance for guiding oil and gas exploration.  
Figure 4: Distribution map of discovered reserves in different layers of the Mangeshlak Basin (according to IHS, 2024).
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Test in Hardware Platform  
In order to make the test results more accurate, we built a hardware platform to test the functions of protocol stack.  
As shown in Figure 17, the hardware platform is composed of components such as CPU(central processing unit), FPGA(field programmable gate array), computer, and interface, the functions of which are defined as follows:  
1. CPU: a place where the protocol stack runs. It implements the functions of the control plane and processing of the management message.
2. FPGA: for control and management, this should support the functions of DCN (Figure 5). For service, this is divided into two types: 1. Adapter FPGA: used only in source/destination node of a path. It can transform different data (bit stream or IP packet) into AOS frames and plug the label into the label field according to the FIB at the source node. Additionally, it can also transform AOS frames into corresponding services at the destination node. 2. Switch FPGA: used to switch AOS frames by their labels according to local LFIBs.
3. Computer: composed of three computers. Two of them are used as sources to create and process services. The other is used as the network manager to manage the platform through Ethernet interface of node 1.
4. Interface: divided into three kinds: 1. Ethernet Interface: used between network manager and node 1 (CPU) to transport the management messages. 2. Serial Interface: used between the CPU and FPGA in each node, transmitting control and management messages. 3. LVDS Interface: used between different FPGAs to transport AOS frames by using LVDS (low-voltage differential signaling).  
Based on the architecture designed above, we used a combination of adapter FPGA, switch FPGA and CPU as the source/destination node. Additionally, a pair of CPU and switch FPGA was used as one intermediate node (node 2). Then, we built the hardware platform as follows:  
Based on the hardware platform shown in Figure 18, we downloaded the protocol stack to the system installed in the hardware by using the simulation software. Then, we ran the protocol stack, tested the functions of protocol stack and analyzed the results.  
Figure 16: Label distribution of the signal protocol in simulation network.
## 3 Test Result Analysis
### Correlation Analysis of Vehicle Acceleration and Pavement Surface Deflection  
Table 2 shows that the surface layer of the five test sections has the same structure, namely 4 cm SMA-13 and 6 cm AC-20C. Moreover, the surface condition is quite similar according to the video recorded by the motion camera. Therefore, it is reasonable to compare the vehicle acceleration and pavement surface deflection.  
Pearson's correlation coefficient is commonly used to measure the correlation between two variables (the peak probability density of acceleration and central deflection). In statistics [26], [PERSON]'s correlation coefficient (\(r\)) can measure the correlation between two variables, \(-1\leq r\leq 1\). If \(r\) is positive, there is a positive correlation between the two variables, and the larger the value of \(r\) is, the stronger the correlation is. A negative \(r\) value means the two variables have a negative correlation; the smaller the value, the stronger the correlation. There is no correlation between the two variables if \(r\) is 0.  
The relationship between the correlation degree of the variable and \(\mid r\mid\) is shown in Table 4.  
For the peak probability density (PPD) and the center deflection (D1), their scatter diagram is shown in Figure 10. The greater the acceleration probability density is, the smaller the center deflection is. There is a linear correlation between the two variables so that the Pearson correlation coefficient can be used for evaluation.  
The Pearson correlation coefficient (\(r\)) between PPD and D1 is calculated to be \(-0.722\). According to Table 4, there is a strong negative correlation between them. D1 is negatively correlated with pavement structural stiffness. Therefore, it can be concluded that a strong positive correlation exists between PPD and the pavement structural stiffness. The greater  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mid r\mid\) & **0.8–1.0** & **0.6–0.8** & **0.4–0.6** & **0.2–0.4** & **0–0.2** \\ \hline Correlation degree & Very strong & Strong & Middle & Weak & Very weak or no correlation \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation degree of the variable.  
Figure 10: Relationship between PPD and D1.  
the PPD is, the higher the stiffness of the pavement structure is. The reason may be that the stronger pavement causes the vehicle to vibrate more frequently. Frequent vibrations will cause the vehicle acceleration to cross zero more frequently, which means that the probability of a vehicle acceleration value being zero is greater. When the abscissas are equally spaced, greater probability means higher probability density. This result preliminarily proves that the vehicle acceleration can reflect the pavement's structural stiffness.
# Dual-Mic Speech Enhancement Based on TF-GSC with Leakage Suppression and Signal Recovery
###### Abstract  
The transfer function-generalized sidelobe canceller (TF-GSC) is one of the most popular structures for the adaptive beamformer used in multi-channel speech enhancement. Although the TF-GSC has shown decent performance, a certain amount of steering error is inevitable, which causes leakage of speech components through the blocking matrix (BM) and distortion in the fixed beamformer (FBF) output. In this paper, we propose to suppress the leaked signal in the output of the BM and restore the desired signal in the FBF output of the TF-GSC. To reduce the risk of attenuating speech in the adaptive noise canceller (ANC), the speech component in the output of the BM is suppressed by applying a gain function similar to the square-root Wiener filter, assuming that a certain portion of the desired speech should be leaked into the BM output. Additionally, we propose to restore the attenuated desired signal in the FBF output by adding some of the microphone signal components back, depending on how microphone signals are related to the FBF and BM outputs. The experimental results showed that the proposed TF-GSC outperformed conventional TF-GSC in terms of the perceptual evaluation of speech quality (PESQ) scores under various noise conditions and the direction of arrivals for the desired and interfering sources.  
dual-mic speech enhancement; transfer function-generalized sidelobe canceller; steering error; leakage suppression; signal recovery
## 2 Broken Rotor Bar in IMs
### Effect of Broken Rotor Bar Fault on Rotor Magneto-Motive Force  
The effect of broken rotor bar on the rotor magneto-motive force (MMF) and consequently its impact on the stator current waveform will be discussed in this section. The MMF of the rotor can be resolved into the forward component corresponding to the healthy case, which is rotating at synchronous speed, \(\omega_{syn}\), with respect to the stator (or \(s\omega_{syn}\) with respect to the rotor). However, when a rotor bar cracks, no current can flow through the bar, and hence no magnetic flux is generated around that bar. In the case that there is no magnetic flux around a bar, a non-zero backward rotating field and thus an asymmetry in the rotor MMF are produced. It has to be noted that for a symmetrical rotor with no broken bar, the resultant of backward rotating field is zero. The non-zero backward rotating component, which is generated due to the virtual presence of a bar carrying an equal opposite current to the original bar, is subject to breakage in the healthy rotor.  
This non-zero backward MMF due to broken bar rotates at slip frequency corresponding to the slip speed, \(s\omega_{syn}\), with respect to the rotor and induces harmonic currents in the stator windings, which are superimposed on the stator winding currents. Accordingly, the speed of the non-zero backward MMF with respect to the stator can be calculated as follow:  
\[\omega_{bMMF}=-s\omega_{syn}+\omega_{m}=-s\omega_{syn}+(1-s)\omega_{syn}=(1-2 s)\omega_{syn} \tag{1}\]  
These superimposed features are used as signatures for the detection of broken rotor bar in MCSA techniques [33].
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 2 Methodology
### Convolutional Equivalent Layer in Rotated Coordinates
These products can be written generically as  
\[\mathbf{H}\mathbf{v}=\mathbf{H}_{1}\mathbf{v}_{1}+\cdots+\mathbf{H}_{L} \mathbf{v}_{L}, \tag{15}\]  
and  
\[\mathbf{H}^{\top}\mathbf{v}=\mathbf{H}_{1}^{\top}\mathbf{v}_{1}+\cdots+ \mathbf{H}_{L}^{\top}\mathbf{v}_{L}, \tag{16}\]  
in terms of a generic partitioned matrix  
\[\mathbf{H}=\Big{[}\mathbf{H}_{1}^{\top},\ldots,\mathbf{H}_{L}^{\top}\ \Big{]}^{\top}, \tag{17}\]  
and a generic partitioned vector  
\[\mathbf{v}=\Big{[}\mathbf{v}_{1}^{\top},\ldots,\mathbf{v}_{L}^{\top}\ \Big{]}^{\top}. \tag{18}\]  
The matrix-vector product \(\mathbf{H}_{l}\mathbf{v}_{\ell}\) or \(\mathbf{H}_{\ell}^{\top}\mathbf{v}_{\ell}\) in Equations (15) and (16), \(\ell\in\{1,\ldots,L\}\), is computed using the fast discrete convolution approach introduced by [33]. It is important to note that \(L\) (Equations (15)-(18)) represents the number of measured gravity-gradient components. In the case of gravity-gradient data obtained from the FTG system, for example, the matrix \(\mathbf{H}\) (Equation (17)) consists of \(L=5\) sub-matrices, as defined by Equation (7). Otherwise, when the data is measured by the AGG system, the matrix is defined with \(L=2\) sub-matrices, as specified in Equation (9). Our convolutional equivalent-layer method can be schematically defined as follows:  
The convergence criterion of the algorithm (step 3) is based on the invariance of the normalized data-misfit function (Equation (5)) between successive iterations. The algorithm stops when  
\[\delta=\frac{\left|\Phi(\mathbf{m})^{(i)}-\mathbf{m})^{(i+1)}\right|}{\Phi( \mathbf{m})^{(i)}}\leq\epsilon, \tag{19}\]  
where \(\epsilon\) varies from \(10^{-2}\) to \(10^{-5}\), \(\Phi(\mathbf{m})^{(i)}\) and \(\Phi(\mathbf{m})^{(i+1)}\) are the data-misfit function at the \(i\)th and \((i+1)\)th iterations, respectively.
## 2 Radar Jamming Signal Analysis and Modeling
### Smart Noise Jamming
#### 2.2.1 Noise Convolution Jamming  
Noise Convolution Jamming (NCJ) is a form of convolution modulation jamming based on Digital Radio Frequency Memory (DRFM) technology, aiming to generate coherent jamming utilizing target signals. It involves the jamming system performing a convolution operation between the intercepted radar transmission signal and a noise signal, thereby achieving suppression jamming across the entire radar operational frequency range.  
The radar's transmitted signal is received by the jamming system, then amplified and filtered. After down-conversion to intermediate frequency, it's stored in a DRFM. After certain processing, the signal is up-converted back to the radar signal frequency band. Simultaneously, the radar signal undergoes reception and processing, and the noise unit generates noise data of appropriate length and type. The signals from both paths undergo convolution within the convolver, ultimately producing a highly effective Smart Noise jamming waveform.  
The noise considered in this paper is modeled as Gaussian white noise denoted as \(n(t)\). Let the radar signal input to the DRFM be \(s(t)\), and its output after a delay of \(\tau\)is \(s(t-\tau)\), where \(s(t-\tau)\) and \(s(t)\) are in phase. The convolver performs a convolution between \(s(t-\tau)\) and \(n(t)\), resulting in the output Smart Noise jamming signal:  
\[J(t)=s(t-\tau)\otimes n(t) \tag{4}\]
# Cycle-to-Cycle Variation of a Diesel Engine Fueled with Fischer-Tropsch Fuel Synthesized from Coal
## 3 Results and Discussion
### Combustion Parameters  
The ignition point is the crank angle that forms the first flame kernel. Ignition delay is calculated as the crank angle between fuel injection timing and the ignition point. CA10, CA50, and CA90 are defined as the crank angles where 10%, 50%, and 90% of mixture undergoes combustion, respectively [35].  
The combustion parameters mentioned above are shown in Figure 5a. For the F-T diesel, the ignition point was advanced and the ignition delay was shortened due to its higher CN. The ignition delay was advanced by 1.2, 0.9, and 0.7 deg CA, respectively, for the speeds of 1200, 2000, and 2800 rpm. Furthermore, the F-T diesel vaporized at a much faster rate because it has a lower boiling point. CA50 was basically advanced by 2 deg CA with the increase in the engine speed. The increase of engine speed caused the system to prepare more mixture, so the ignition point was retarded and ignition delay was increased. However, the air-fuel mixture uniformity and combustion processes were modified when the engine was operated at higher speed, and the impact of fuel properties on combustion was reduced.  
The combustion duration refers to the crank angle between CA90 and CA10. The BTEis one of the key parameters that indicates the fuel conversion efficiency. Figure 5b shows the variations of combustion duration and BTE at different engine speeds.  
Under different test conditions, F-T diesel showed a longer combustion duration than 0# diesel, and showed an opposite trend compared to ignition delay, as shown in Figure 5a. The combustion duration decreased with increasing engine speed using the test fuels. Moreover, the combustion duration was extended by about 5.8, 6.4, and 4.2 deg CA, respectively, for the speeds of 1200, 2000, and  
Figure 4: Variations of in-cylinder pressure increase rate.  
Figure 5: Comparison of combustion parameters: (**a**) Ignition point, ignition delay, and CA50; (**b**) combustion duration and break thermal efficiency (BTE).  
2800 rpm compared to 0# diesel. Furthermore, the combustion duration decreased with the increase in engine speed. This is attributed to the fact that the shorter ignition delay resulted in a longer air-fuel mixing process to achieve a uniform result, thus leading to a slower premixed burning rate. The air-fuel mixing process was improved with the increase in the engine speed. The combustion speed was quicker, the combustion rate was higher, and the combustion process was shorter. Compared to 0# diesel, the BTE of F-T diesel was increased by 1.5%, 1.4%, and 0.7%, respectively, for the speeds of 1200, 2000, and 2800 rpm. The BTE exhibited great relevance at CA50. The BTE was increased as CA50 was advanced for the improved constant volume combustion near TDC [34; 36; 37; 38; 39]. Figure 5a,b exhibit good agreement between BTE and CA50. More heat leaked between the cylinder wall and the cooling water as CA50 was retarded, and the corresponding BTE was lower.
# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 1 Introduction
In summary, random forest models, decision tree models, and SVMs are all viable options for predicting stroke within medical datasets. The choice of algorithm will depend on factors such as the size and complexity of the dataset, missing values, and data imbalances, as well as the specific requirements of the application. In this paper, we propose a new methodology for predicting stroke within medical datasets. The proposed methodology is based on the specific data-oriented approach to the stroke dataset, paying particular attention to data imbalances. We complete the methodology with specific algorithms for each machine learning models: random forest, decision tree, and support vector machines, where the models' parameters are adjusted to achieve high values of evaluation measures. We experimentally compare our algorithms (for different models) with results of previous investigations on this dataset [17; 18; 19]. Moreover, the algorithm for SVMs achieves the best measures' values of 98% accuracy and 97% recall score, given in Section 3.3. This paper is structured in a clear and concise manner, consisting of six distinct sections. The first section introduces the topic and sets the context for the research. In the second section, the modeling methodology is discussed in detail, including the classification methods used. The third section presents the computational findings of the various models and provides an analysis of the results. The fourth section is dedicated to the conclusion, which summarizes the main findings of the research and highlights their implications. The fifth section acknowledges the contributions of individuals and organizations that have supported the research. Finally, the sixth section lists the references used in the paper, enabling readers to further explore the topic. The well-structured organization of this paper makes it easy for readers to follow the research process and understand the key findings.