esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 3 Methodology
### Out-of-Corpus Command Handling  
To build a robust command recognition system, one of the main problems that occurs is the unknown command, which may happen because the user does not know the command covered or when the predicted command is not predicted with high confidence. To handle this problem, we set a prediction probability threshold.  
We chose this method because our model is designed to predict one of 20 specific voice commands. Due to this relatively limited command set, it is crucial that the model not only correctly identifies the spoken command but does so with a high degree of certainty. To achieve this, we have set the threshold at 75%. If the model predicts a command with a probability lower than 75%, it suggests that the model is uncertain, which could lead to incorrect or unintended actions being triggered. By implementing this threshold, we aim to filter out low-confidence predictions, thereby reducing the likelihood of errors in command recognition.  
The decision to set the threshold at 75% rather than a higher value, like 90%, is informed by the conditions in which our model is expected to operate. Specifically, our system needs to function effectively in both noisy and clean environments. In noisy conditions, even correctly predicted commands might have slightly lower confidence levels due to background interference. Setting the threshold too high, such as at 90%, could result in the system rejecting too many valid commands, causing unnecessary frustration for the user. By choosing a 75% threshold, we strike a balance that allows the model to perform reliably across varying conditions while still maintaining a reasonable level of confidence in its predictions. This approach enhances the system's robustness, ensuring that it can handle a wide range of scenarios without compromising on accuracy or user experience.
# Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?
## 4 Case Study
### Apply Our Method to MC
#### 4.3.2 The Second Phase  
After obtaining the framwork of _MC_, we carry out the second phase to predict the defect situation for Alternatives-layer. In the paper, we use the same 10 historical defect datasets from the other projects, as in paper [38]. The basic information about these data is listed in Table 5. For the target project _MC_, we use LDRA TESTBED, as mentioned in Section 4.2, to gain the metrics. Moreover, the linear regression model (LRM), which is applied in paper [38], is also used as the basic learner for predicting the result.  
Finally, we obtain the predict result of Alternatives-layer, which is listed in Table 6. Therefore, the probability vector of the layer is as Formula (9).  
\[P_{alter}=\left[\begin{array}{cccccccc}0.045&0.027&0.040&0.027&0.053&0.022&0. 018&0.020&0.012&0.162&0.064&0.057\end{array}\right]^{T} \tag{9}\]  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**ID** & A1 & A2 & A3 & A4 & A5 & A6 & A7 \\
**Probability** & 0.045 & 0.027 & 0.040 & 0.027 & 0.053 & 0.022 & 0.018 \\ \hline
**ID** & A8 & A9 & A10 & A11 & A12 & A13 & \\
**Probability** & 0.020 & 0.012 & 0.012 & 0.162 & 0.064 & 0.057 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The Defect Probability of Alternatives-layer by Linear Regression Model (LRM) for _MC_.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Name** & **\#Examples** & **\#Metrics** & **Defect \%** & **Source** & **Language** & **Description** \\ \hline ar1 & 147 & 29 & 6.12 & Softlab & C & Embedded controller \\ ar4 & 117 & 29 & 17.01 & Softlab & C & Embedded controller \\ cm1 & 352 & 21 & 11.93 & NASA & C++ & Spacecraft instrument \\ kc1 & 2096 & 21 & 15.51 & NASA & C++ & Storage management for ground data \\ kc3 & 194 & 39 & 18.56 & NASA & Java & Storage management ground data \\ mc2 & 125 & 21 & 35.20 & NASA & C++ & Video guidance system \\ mw1 & 253 & 37 & 11.49 & NASA & C++ & A zero gravity experiment \\ pc1 & 705 & 21 & 8.65 & NASA & C++ & Flight software \\ pc3 & 1125 & 21 & 12.44 & NASA & C++ & Flight software \\ pc4 & 1399 & 21 & 12.72 & NASA & C++ & Flight software \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Basic Information about Historical Cross-project Defect Data.  
Figure 2: The Association Hierarchy Structure of Software MC (Note: “A \(\rightarrow\) B” represents that B calls A to implement the task).
## 3 System Configuration
As a result, image segmentation is needed to obtain different bump height parameters. The wafer is placed on a stage of vacuum absorption driven by a four-axis high precision motion platform. Inspection of all micro bumps on the whole wafer is realized by back-and-forth scanning, driven by the high-precision motion platform. The camera captures raw image data when it receives the trigger signal that is generated based on the position information of the stage and the parameters of scanning step. Additionally, the wafer is moved in the direction perpendicular to the strip line, as shown in Figure 7.  
To process the huge amount of data in the detected images, a parallel computing model based on the computing platform composed of FPGA, GPU and CPU is used to achieve scanning of the whole wafer. As shown in Figure 8, the image data acquired from the imaging module are first processed in the first image-processing module, composed of  
Figure 6: Scanning path generation algorithm. Figure 7: Schematic diagram of scanning path. FPGAs in real time, in which image pre-processing algorithms such as image filtering and image segmentation are completed. Then, the image data are simplified and compressed in the second processing module, in which the analysis and statistics of the light stripe data and a rough estimate of the bump height are finished through exploiting the high-parallel capacity of the CPU+ GPU platform. The simplified data are further analyzed in the third module, and the height of the bump is accurately calculated according to the calibrated parameters. In the experiment, 100% 3D scanning of a 200 mm wafer containing approximately three million bumps can be accomplished in 30 min, which represents a throughput of nearly two wafers per hour. In order to verify the measurement accuracy of the measurement method for micro bump height proposed in this paper, the following experimental setup was designed and established, as shown in Figure 9. Microbumps with a nominal height of 50 \(\upmu\)m on a 200 mm wafer were measured.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Windowing Based Methods
#### 3.2.2 [PERSON]'s Method  
In [126], the Hamming and Gaussian windows are combined as a new window function, whose expression is  
\[w(n)=\left[0.54+0.46\cos(\frac{2\pi n}{N-1})\right]e^{-\frac{1}{2}(\frac{2\pi n }{N-1})^{2}}, \tag{43}\]  
where \(0\leq|n|\leq(N-1)/2\), and the value of the parameter \(\alpha\) determines the performance of the filter.  
Figure 3 shows the frequency responses of Hamming window, Gaussian window and the new window proposed in [126] for \(N=64\). Note that the window in [126] has the best side-lobe attenuation compare with Hamming and Gaussian windows. However, it increases the width of the main-lobe.
# Ribosomal Hiberation Factor Links Quorum-Sensing to Acid Resistance in EHEC
###### Abstract  
The mechanism by which quorum sensing (QS) enhances stress resistance in enterohernorrhagic _Escherichia coli_ (_E. coli_) O157:H7 remains unclear. We employed optimized exogenous QS signal N-acyl-homoserineilactones (AHL) (100 uM3-oxo-C6-AHL, 2 h) in EHEC O157:H7 strain EDL933, which was validated with endogenous _yent_-derived AHL, to investigate QS-mediated protection against acid stress. RNA-seq transcriptomics identified key upregulated genes (e.g., _rmf_). Functional validation using isogenic _rmf_ knockout mutants generated via \(\lambda\)-Red demonstrated abolished stress resistance and pan-stress vulnerability. Mechanistic studies employing qRT-PCR and stress survival assays established Ribosomal Hibernation Factor (RMF) as a non-redundant executor in a SdiA-RMF-RpoS axis, which activates ribosomal dormancy and SOS response to enhance EHEC survival under diverse stresses. For the first time, we define ribosomal hibernation as the core adaptive strategy linking QS to pathogen resilience, providing crucial mechanistic insights for developing EHEC control measures against foodborne threats.  
quorum sensing; Ribosomal Hibernation Factor; acid resistance; EHEC; pathogen resilience; SOS
# An Improved Analytical Algorithm on Main Cable System of Suspension Bridge
## 3 Improved Numerical Analysis Method
### The Main Cable System Calculation in Side Span at Finished State
#### 3.2.1 Stiffness Due to Vertical Deformation Change of Main Cable  
Both sides of Equations (1) and (2) were differentiated, when considering side-span adjustment \(dl\) = 0 (horizontal projection length of each cable segment at finished state is known) and the horizontal component of each cable segment in side span \(dH_{i}\) = 0, the following equations were obtained:  
\[dh=\left[\frac{\partial h}{\partial V_{i}}-\left(\frac{\partial h}{\partial S _{0}}/\frac{\partial l}{\partial S_{0}}\right)\cdot\frac{\partial l}{\partial V _{i}}\right]dV_{i}=\left[-\frac{S_{0}}{EA}-\frac{1}{q}\left(\frac{V_{j}}{T_{j} }+\frac{V_{i}}{T_{i}}\right)+\frac{qS_{0}-V_{i}}{q}\left(\frac{1}{T_{j}}-\frac {1}{T_{i}}\right)\right]dV_{i} \tag{17}\]Given \(dh=D_{11}dV_{i}\)  
\[D_{11}=\left[-\frac{S_{0}}{EA}-\frac{1}{q}\left(\frac{V_{j}}{T_{j}}+\frac{V_{i}}{ T_{i}}\right)+\frac{qS_{0}-V_{i}}{q}\left(\frac{1}{T_{j}}-\frac{1}{T_{i}}\right)\right] \tag{18}\]  
The reciprocal of \(D_{11}\) (\(1/D_{11}\)) is defined as stiffness due to vertical deformation change of a cable segment.  
It can be seen found that \(1/D_{11}\) represents the vertical component variation of start end (or terminal end) due to unit elevation change between two points of each segment under the conditions that the horizontal force component of cable segment was unchanged, the horizontal distance between two ends of cable segment was constant, while the unstressed cable length can be varied.  
When considering that the change of vertical component for each segment is equal, i.e., \(dV_{i}=dV\), the accumulated elevation difference of cable from support point to segment \(i\) can be obtained, as follows:  
\[dY=\sum\limits_{m=1}^{i}dh=\sum\limits_{m=1}^{i}\left(D_{11}dV_{i}\right)= dV\sum\limits_{m=1}^{i}D_{11} \tag{19}\]  
\[dV=\frac{1}{\sum\limits_{m=1}^{i}D_{11}}dY \tag{20}\]  
where, \(\frac{1}{\sum\limits_{m=1}^{i}D_{11}}\) is defined as the stiffness due to vertical deformation of main cable at side span.
## 3 Proposed Method
### SVM Adopted  
SVM is a commonly used machine learning algorithm capable of handling high-dimensional vibration signal data [31]. By constructing an optimal hyperplane for fault classification, SVM achieves high diagnosis accuracy, performing particularly well when the sample size is limited. Furthermore, SVM exhibits strong robustness, being insensitive to noise and outliers, which enhances its reliability in practical applications. Its primary concept involves finding a hyperplane in the feature space that separates data from different fault categories while maximizing the distance between the hyperplane and the closest data points from each fault class [32]. The main steps of SVM are outlined as follows:  
_Step 1_: The vibration signal data are input (\(X=[(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})]\)), where any sample (\(x_{i}\)) is an \(n\)-dimensional vector, with \(x_{i}\in\mathbb{R}^{n}\). Let \(y_{i}\) represent the fault class label, where \(i=1,2,\ldots,N\).  
_Step 2_: A penalty parameter (\(C>0\)) is selected, and a convex quadratic programming minimization problem is constructed, denoted as \(\min\frac{1}{2}\sum_{i=1}^{N}\sum_{j=1}^{N}\alpha_{i}\alpha_{j}y_{i}y_{j}(x_{i} \cdot x_{j})-\sum_{i=1}^{N}\alpha_{i}\), subject to the constraints of \(\sum_{i=1}^{N}\alpha_{i}y_{i}=0\) and \(0<\alpha_{i}<C,i=1,2,\ldots,N\). This problem is solved to obtain the optimal solution (\(\alpha^{*}=\big{(}\alpha_{1}^{*},\alpha_{2}^{*},\ldots,\alpha_{N}^{*}\big{)}^{T}\)).  
_Step 3_: \(\omega^{*}=\sum_{i=1}^{N}\alpha_{i}^{*}y_{i}x_{i}\) is computed; then, a component (\(\alpha^{*}\)) that satisfies the condition of \(0<\alpha_{j}<C\) is selected, and \(b^{*}=y_{j}-\sum_{i=1}^{N}\alpha_{i}^{*}y_{i}\big{(}x_{i}\cdot x_{j}\big{)}\) is calculated.  
_Step 4_: The separating hyperplane defined by \(\omega^{*}\cdot x+b^{*}=0\) is obtained, and the classification decision function is set as \(f(x)=sign(\omega^{*}\cdot x+b^{*})\).
## 4 Monitoring System Framework
### Components of the Multi-Source Monitoring System
#### 4.1.3 Roof Separation Monitoring  
An FBG layer-separation sensor as shown in Figure 8 is used to detect roof separation and its specific parameter models are shown in the Table 4.  
The FBG separation layer sensor is similar to the FBG top displacement sensor in principle. An FBG is bonded to both sides of two strain beams to detect displacement changes. The two ends of the sensor tension spring are respectively fixed on the upper part of the spring pull rod and the precision screw rail and is used to tighten the precision screw rail and cause the precision screw to slide reciprocally inside the precision guide rail; the precision screw rail is fixed with the winding coil and when the displacement of the wire rope connected with the external anchor claw changes, the wire rope pulls the winding coil to cause the precision screw rail to slide, resulting in deformation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Type** & **Parameter Values** & **Parameter Type** & **Parameter Values** \\ \hline Range (mm) & 100 & Grating central wavelength (nm) & 1510–1590 \\ Installation mode & Surface mount & Reflectivity (\%) & 90 \\ Resolving power & 0.1\% F.S. & Connection mode & Welding or FC/APC splicing \\ \hline \hline \end{tabular}
\end{table}
Table 4: FBG separation sensor parameters.  
Figure 8: FBG separation sensor.  
Figure 7: Structural sketch of an FBG displacement sensor.  
of the strain beam. This changes the central wavelength of the FBG. The layer separation sensor will measure the data once a day and the measured data will be demodulated by the fiber Bragg grating demodulator to obtain the layer separation change.
## 3 Results and Discussion
### Inter-Individual Differences
A comparison with the data obtained from the listening part of the experiment reveals that the subjects were considerably more consistent when it comes to reading at a \"normal\" speech level, than when it comes to setting the \"normal\" level by themselves when listening to reproduced speech. As shown in Figure 4, the overall spread of the speech levels obtained for all subjects while reading is considerably smaller than the one obtained for listening. As described above, only the visual stimulus was varied in this part of the experiment, thus yielding three observations per listeners, i.e., three measured levels of read speech. Table 8 shows the average level and the standard deviation for the entire group for each of the three conditions of the visual stimulus. In comparison with the standard deviations obtained in the listening part of the experiment, the ones obtained here are considerably smaller, i.e., in the range of 3.1 to 4.2 dB. The differences between subjects were also examined with regard to the demographic and other data they provided during the experiment. No plausible connection was found between the subjects' age, gender, possible hearing impairment, and their listening habits, and the observations they provided both as listeners and as readers. When it comes to the self-assessment regarding the way they talk (quiet, normal or loud talkers), the basic analysis does show that there is a 2-dB average increase in the average A-weighted level of read speech between these three categories, i.e., 54, 56, and 58 dBA, respectively. The analysis of the data reveals that most of the subjects have made an accurate enough self-assessment regarding how loud they talk. No serious misjudgments were observed, i.e., no self-proclaimed loud talker read the text sample in a quiet voice, and vice versa. Occasional slight misjudgments were observed, most of which refer to self-proclaimed loud and quiet talkers who actually ended up reading the text at normal level. Only one subject self-assessed their way of talking as normal and then read the text as a loud talker would. The term \"normal\" level denotes the average A-weighted level at which the self-proclaimed normal talkers read the text, i.e., 55 dBA.
# An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation
## 2 Data and Methodology  
### Experiment and Data  
The dataset used in this study was collected from an experiment conducted on the National Highway G318 near the Erlang Mountain Tunnel in Tianquan County, Sichuan Province, China, in February 2024. The Erlang Mountain, located along National Highway G318 in Sichuan Province, is renowned for its complex topography and challenging weather conditions. Situated in Tianquan County, which experiences a subtropical monsoon climate, the area receives an average annual precipitation of 1576.1 mm, and the average temperature is 15.3 \({}^{\circ}\)C. The region frequently encounters heavy rainfall, dense fog, storms, and mudslides, with precipitation occurring throughout much of the year. Among these conditions, dense fog is especially common, making this area a suitable location for studying fog-related phenomena and visibility challenges.  
A traffic meteorological station was set up, equipped with sensors for temperature, pressure, humidity, wind, and precipitation observation, along with a high-definition camera and a visibility meter. The GM-VTF306B visibility meter, capable of measuring visibility from 10 to 10,000 m, was used in the experiment. The dataset includes 1203 highway images along with their corresponding visibility observations. The images were classified into five grades: light fog, moderate fog, dense fog, thick fog, and very thick fog, based on their visibility measurements [3]. For normalization, the original images were resampled to 512 \(\times\) 512 pixels, as illustrated in Figure 1.
## 7 Summary, Conclusions, and Discussion
### Conclusions  
From the results presented in the previous section, we draw the following conclusions. In our study, we proposed a system composed of a 2D sensor array, a given scenario, and the pattern-matching methods considered. It was observed that for the given assumptions and the system proposed, fingerprinting-based positioning with spatial side information substantially reduces the positioning error. The reduction in the positioning error is attained at the expense of implementing a 2D sensor array and additional computing hardware on the positioning device side.
## 3 Results
### Surface Morphology and Roughness  
Figure 6 shows the surface morphology of untreated HF and HF\({}_{\text{tr}}\) samples scanned by scanning electron microscopy (SEM). The sample with untreated HF showed a microscopically smooth surface with some leftovers or impurities. In contrast, compared to HF\({}_{\text{tr}}\) samples, the samples treated with plasma exhibited significant morphological changes especially for N\({}_{2}\) and O\({}_{2}\) plasma. Due to the presence of an etching effect, the surface roughness increased and microfibrils became exposed as a result of the plasma treatment process; this can be explained by the removal of non-cellulosic components like hemicellulose and lignin from fiber surfaces [25]. The increased surface roughness is critical towards promoting better mechanical interlocking and adhesion between the fibers and matrix [26; 27].  
Figure 5: FT-IR spectra of hemp fiber untreated and treated with Ar, Ar+N\({}_{2}\), and Ar+O\({}_{2}\) plasma.  
The fracture surfaces of the untreated hep-fiber-reinforced epoxy (HFRE) and plasma-treated hep-fiber-reinforced epoxy (HF\({}_{\text{tr}}\)RE) composites were analyzed using SEM after tensile testing (Figure 7). The untreated HFRE composite exhibited weak interfacial adhesion, as evidenced by the pulling out of fibers from the matrix under external loading, as shown in Figure 7a,d,f.  
In contrast, the plasma-treated HF\({}_{\text{tr}}\)RE composites demonstrated enhanced interfacial bonding, as shown in Figure 7b,e,g, which can be attributed to the increased surface roughness of the fibers resulting from the plasma treatment. This improved fiber-matrix interface enables efficient stress transfer and delays failure under external loading conditions.
## 1 Introduction
### Twelve-Impulse Rectifiers  
Schematic diagram of a typical 12-pulse rectifier in a parallel system on the example of an ATRU unit is shown in the figure below (Figure 1). It should be noted that the independent operation of the units in the parallel connection is ensured by matching chokes. The alternating current component of individual systems does not exceed 4%. The value of this current results from the value of the magnetizing current [8; 9; 10].  
Where: P1 and P2- ATRU rectifier circuits, each consisting of six diodes, V1, V2 and V3- three-phase voltage sources, I4I, I4 II- currents at the output of ATRU rectifier circuits, L, R and C- inductance, resistance and capacitance, appearing on terminal elements, I4- current on terminal devices, LOAD-terminal devices.
# On Multidimensional Congestion Games +
## Appendix A Technical Lemmas
In the second case, we get \(\theta=49a^{2}+a(62b-130d+81)+130b(d-e)+211d^{2}+d(162e-211)-162e^{2}\). Since \(\frac{\delta\theta}{\delta b}=62a+130(d-e)\), \(\theta\) is minimized at \(b=0\), which yields \(\theta=49a^{2}+a(81-130d)+211d^{2}+d(162e-211)-162e^{2}\). Since \(\frac{\delta\theta}{\delta c}=162d-324e\), \(\theta\) is minimized at either \(e=0\) and \(e=d\). In both cases \(\theta\) becomes \(49a^{2}+a(81-130d)+211d^{2}-211d\) and the claim follows by applying Lemma A10. In the third case, we get \(\theta=111a^{2}-a(422b+68d-68e-81)+422b^{2}-130 be+211d^{2}-149d-62e\). Since \(\frac{\delta\theta}{\delta b}=-422a+844b-130e\), \(\theta\) is minimized at \(b=\frac{211a+65}{422}\), which yields \(\theta=2321a^{2}+422a(3e+81-68d)+89042d^{2}-62878d-4225e^{2}-26164e\). Since \(\frac{\delta\theta}{\delta c}=1266a-8450e-26164\), \(\theta\) is minimized at either \(e=0\) or \(e=d\). For \(e=,\theta\) becomes \(11a^{2}+2a(81-68d)+422d^{2}-298d\) and the claim follows by applying Lemma A11. For \(e=d\), \(\theta\) becomes \(2321a^{2}+422a(81-65d)+84817d^{2}-89042d\) and the claim follows for any \(6\)-tuple \((a,b,c,d,e,f)\) such that \((a,d)\
eq(0,1)\) by applying Lemma A12. Hence, we are left to consider the \(6\)-tuples of the form \((0,0,0,1,e,0)\). In this case \(\theta\) becomes \(62(1-e)\) which is always non-negative since \(e\in\{0,1\}\). In the fourth case, we get \(\theta=111a^{2}-a(422b+130d-130e-81)+422b^{2}+130b(d-2e)+211d^{2}+d(162e-211)- 162e^{2}\). Since \(\frac{\delta\theta}{\delta b}=-422a+844b+130(d-2e)\), \(\theta\) is minimized at either \(b=0\) or \(b=\frac{211a-65(d-2e)}{422}\). For \(b=0\), \(\theta\) becomes \(111a^{2}-a(130d-130e-81)+211d^{2}+d(162e-211)-162e^{2}\). Since \(\frac{\delta\theta}{\delta c}=130a+162d-324e\), \(\theta\) is minimized at either \(e=0\) or \(e=d\). In these two cases, \(\theta\) becomes, respectively, \(111a^{2}+a(81-130d)+211d^{2}-211d\) and \(111a^{2}+81a+211d^{2}-211d\) which are always non-negative because of Lemma A10 and the fact the \(\theta\geq\lambda\). For \(b=\frac{211a-65(d-2e)}{422}\), \(\theta\) becomes \(2321a^{2}+422a(81-65d)+84817d^{2}+2d(42632e-44521)-85264e^{2}\). Since \(\frac{\delta\theta}{\delta e}=85264d-170528e\), \(\theta\) is minimized at either \(e=0\) or \(e=d\). In both cases, \(\theta\) becomes \(2321a^{2}+422a(81-65d)+84817d^{2}-89042d\) and the claim follows for any \(6\)-tuple \((a,b,c,d,e,f)\) such that \((a,d)\
eq(0,1)\) by applying Lemma A12. Hence, we are left to consider the \(6\)-tuples of the form \((0,0,0,1,e,1-e)\). In this case, \(\theta\) becomes \(162(1-e)\) which is always non-negative since \(e\in\{0,1\}\).