esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech
## 4 Experimental Results
### Proficiency Evaluation Test
#### 4.2.6 Proficiency Evaluation Results  
In order to validate that the proposed automatic proficiency evaluation system measured the proficiency scores effectively and meaningfully, we computed and compared a [PERSON]'s correlation coefficient between the proficiency scores of the proposed system and those of human raters. The [PERSON]'s correlation coefficient is a commonly used metric for evaluating the performance of proficiency assessment methods [52; 53; 54]. Tables 7 and 8 show the proficiency evaluation results obtained by the proposed method with and without data augmentation. For comparison, the range of correlation coefficients of the inter-rater scores (\"Human\" in Tables 7 and 8) were used as a reference. As shown in Tables 7 and 8, we confirmed that the proposed automatic proficiency evaluation method measures proficiency scores efficiently for all proficiency area scores. In addition, we confirmed that data augmentation for AM training with the proposed speech conversion method improves the averaged correlation performance for all proficiency area scores compared to the method employing conventional AM trained without data augmentation. By automatically evaluating the proficiency of the L2 speaker's utterance, the proposed proficiency scoring system is able to perform fast and consistent evaluation in various environments.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Feature Name**} & \multicolumn{1}{c}{**Description**} \\ \hline Tpsec & Types (unique words) per second \\ Tpsecutt & Types normalized by speech duration \\ Wdstress/Ultstress & Word/sentence stress score \\ Rhymean/Rhystddev & Mean/standard deviation of rhythm score \\ FlucMean/Flucstddev & Mean/standard deviation of the range of fluctuation \\ propV & Vocalic segment duration ratio in sentence \\ deltaV/deltaC & Standard deviation of vocalic/consonantal segment duration \\ varcoV/varcoC & Standard deviation of vocalic/consonantal segment duration normalized by speech rate \\ Genie\_amscoreK0/Genie\_amscoreK1 & AM score/anti-model AM score reflecting Korean pronunciation characteristics of English \\ Numdf & Number of disfluencies \\ Dpsec & Disfluencies per second \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rater** & **Human** & **MLR** & **Neural Network** \\ \hline Holistic & 0.68–0.79 & 0.78 & 0.82 \\ Intonation & 0.64–0.72 & 0.73 & 0.77 \\ Stress and rhythm & 0.71–0.74 & 0.75 & 0.78 \\ Speech rate and pause & 0.71–0.75 & 0.75 & 0.77 \\ Segmental features & 0.59–0.67 & 0.69 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Correlation between human rater and proposed proficiency scoring system without data augmentation.
# Auditory Device Voice Activity Detection Based on Statistical Likelihood-Ratio Order Statistics
## 5 Experiments and Results
Table 2 illustrates that the proposed method increased the RDER in all noise environments at all SNRs. In the table, the proposed method relatively reduced the average detection error rate by 15.8% compared to a conventional VAD, with only minimal change in the false acceptance probability for three different noise conditions whose signal-to-noise ratio ranged from 0 to 20 dB. This finding implies that an LRT-based VAD employing the proposed LR order statistics can be an effective solution for attenuating the detection error by improving the false reject robustness in noise environments. Figure 3: False rejection probability (FRP) histograms for speech in white noise (**left**) and babble noise (**right**) at 5 dB SNR at different \(M\): (**a**) \(M=100\%\), (**b**) \(M=50\%\), and (**c**) \(M=25\%\).
## 5 Experimental Results
### Training and Data Augmentation  
Dental scans of the Poseidon3D dataset are divided into two groups: those used in the training procedure and those used for the evaluation. From the 200 3D shapes, 160 models are used for the network training, and 40 models are used to evaluate the proposed method. Furthermore, 160 meshes of training phase are split in the ratio of 4:1 into a training set and validation set, respectively.  
To improve the generalization ability, we augment the training and validation data samples. We apply different transformations on the 3D shapes to generate augmented meshes. For each original surface already pre-aligned to occlusion via \(M_{ICP}\), we generate 10 random rigid transformations, each represented by a matrix \(M_{AUG}=[R|t|S]\), where \(R\in SO(3)\), \(t\in\mathbb{R}^{3}\) and \(S\) denote the scaling operation. We rotate the meshes with a random angle uniformly sampled within \([-\frac{\pi}{4},\frac{\pi}{4}]\) around an arbitrary axis. The vector \(t\) displaces the mesh by translating it by a value uniformly sampled between \([-5,5]\). The dental scan is resized by an isotropic scaling value uniformly sampled between \([0.8,1.2]\). Then, the sequence is generated without additional augmentations on the 2D maps. No augmentation is applied in the testing phase.  
The network is optimized via batches of size 4, using AdamW optimizer with a learning rate and a weight decay of 0.001. Cosine annealing restarts are applied every 10,000 iterations. Models are trained by minimizing the Dice loss \(\mathcal{L}_{Dice}\)[22]:  
\[\mathcal{L}_{Dice}=1-2\frac{\sum_{n=1}^{N}p_{n}r_{n}+\epsilon}{\sum_{n=1}^{N} p_{n}+r_{n}+\epsilon}, \tag{2}\]where \(r_{n}\) are values from reference foreground segmentation image of \(N\) pixels, \(p_{n}\) are image elements of the predicted probabilistic map for the foreground labels, and \(e\) is a term to ensure the loss function stability.  
To provide shorter paths for gradients to flow during backpropagation, deep supervision [23] is employed, so the final loss value is computed as follows:  
\[\mathcal{L}_{Dice}^{\textit{total}}=\sum_{i}^{S-1}\mathcal{L}_{Dice}^{i}+ \mathcal{L}_{Dice}^{\textit{final}}, \tag{3}\]  
where \(\mathcal{L}_{Dice}^{i}\) is the loss value computed at stage \(i\) of \(S\)-stage decoder and \(\mathcal{L}_{Dice}^{\textit{final}}\) corresponds to the loss value computed from the output layer.  
For the detection module, we follow the setup as presented in [16].
# A Neural-Network-Based Watermarking Method Approximating JPEG Quantization
## 5 Computer Simulations  
### Evaluation of the Qaf  
First, the ability that the QAF function can approximate the quantization process of JPEG compression was assessed, using computer simulations. Because the DCT and quantization were applied to \(8\times 8\)-pixel blocks in the JPEG compression, the QAF was also applied to \(8\times 8\)-pixel blocks. The evaluation images were taken from a dataset provided by the University of Granada [20]. They consist of 49 images of \(512\times 512\) pixels. Each image was normalized by (7). The \(512\times 512\)-pixel image is divided into blocks of \(8\times 8\) pixels, resulting in \(64\times 64\) blocks. These blocks were indexed in raster scan order as \(\mu=1,2,3,\cdots,4096\). The DCT was performed on each block. Let \(I_{b}^{\mu}(u,v),\mu=1,2,3,\cdots,4096\) be the DCT coefficients of the \(\mu\)-th block. The QAF was applied to the \(\mu\)-th block as  
\[I_{\text{QAF}}^{\mu}(i,j)=\text{QAF}\bigg{(}I_{b}^{\mu}(i,j)\bigg{|}\frac{T_{ 70}(i,j)}{255}\bigg{)}, \tag{21}\]  
where the quantization level of the JPEG compression was set to \(Q=70\). The parameters of the QAF in (13) were set as gradient \(\beta=1000\), and the number of the hyperbolic tangent functions \(n=500\). For all the QAF-applied blocks, an IDCT was performed, and the luminance values were inversely normalized using (18). Next, all blocks were combined. The combined image was converted back to the original image size. Then, the QAF-applied image was given by  
\[\mathbf{I}_{\text{QAF}}=\Big{(}I_{\text{QAF}}^{1},I_{\text{QAF}}^{2},\cdots,I_{ \text{QAF}}^{406}\Big{)}. \tag{22}\]  
In general, the peak signal-to-noise ratio (PSNR) of an evaluated image \(\mathbf{I}^{\prime}\) against a reference image \(\mathbf{I}\) is defined by  
\[\text{PSNR}\big{(}\mathbf{I}^{\prime}|\mathbf{I}\big{)}=10\log_{10}\bigg{(}\frac{255^ {2}}{\text{MSE}(\mathbf{I}^{\prime},\mathbf{I})}\bigg{)}[\text{dB}], \tag{23}\]  
where  
\[\text{MSE}\big{(}\mathbf{I}^{\prime},\mathbf{I}\big{)}=\frac{1}{HW}\sum_{i=0}^{H-1} \sum_{j=0}^{W-1}\{I^{\prime}(i,j)-I(i,j)\}^{2}. \tag{24}\]  
To see the difference between the QAF and JPEG-compressed images shown in Figure 6, the difference could be evaluated by PSNR rather than MSE. The accuracy of the QAF could be measured by the PSNR of a QAF-applied image against a JPEG-compressed image, that is, \(\text{PSNR}\big{(}\mathbf{I}_{\text{QAF}}|\mathbf{I}_{\text{peg}}\big{)}\), where \(\mathbf{I}_{\text{peg}}\) is the JPEG-compressed image. Note that the PSNR was measured against the JPEG-compressed image, not the original image. Similarly, we evaluated the approximation ability of JT-Net using PSNR and compared it with QAF. Figure 9 shows a histogram of PSNRs for JT-Net-applied images by using (9) and QAF-applied images given by (21) and (22), where the quantization level is \(Q=70\). The PSNRs for QAF-applied images are clearly greater than those for JT-Net-applied images. Figure 10 shows three examples of QAF-applied images and their PSNRs. Thus, we found that the QAF more adequately represents the quantization of the JPEG compression.
## 4 Data Source and Pre-Processing
In addition, it entails reprojecting and resampling raw data so that they may be converted from their original forms into a standard format to be compared.
## 2 Resolution of the Kinematic Problem by Traditional Methods
### Inverse Kinematics Using Traditional Methods
#### 2.2.2 Inverse Kinematics of the BH3-R Hexapod's Leg by the Analytical Method
\begin{split}(p_{x_{1}^{i}})^{2}+(p_{y_{1}^{i}})^{2}=[L_{2}\cos( q_{2})+L_{3}\sin(q_{2}-q_{3})]^{2}+[L_{2}\sin(q_{2})-L_{3}\cos(q_{2}-q_{3})]^{2} \\ \[PERSON]\\ (p_{x_{1}^{i}})^{2}+(p_{y_{1}^{i}})^{2}=(L_{2})^{2}+(L_{3})^{2}-2L_{2}L_{3} \sin(q_{3})\\ \Downarrow\\ \sin(q_{3}^{i})=\frac{(L_{2})^{2}+(L_{3})^{2}-(p_{x_{1}^{i}})^{2}-(p_{y_{1} ^{i}})^{2}}{2L_{2}L_{3}}\end{split}  \tag{14}\]  
The term \(\sin(q_{3}^{i})\) in Equation (14) can have two possible solutions, depending on whether the configuration of \(q_{1}\) is \(F\) or \(B\). Each of these solutions for \(\sin(q_{3}^{i})\) yield two possible values for \(\cos(q_{3})\) when the quadratic equation shown in Equation (15) is solved:  
\[\cos(q_{3}^{k})=\pm\sqrt{1-\left[\sin(q_{3}^{i})\right]^{2}} \tag{15}\]  
The four possible values for \(\cos(q_{3}^{k})\) in Equation (15) lead to the expression of \(q_{3}\) in Equation (16), where the superscript \(k\) may be \(FA\) (\"FRONT ABOVE\"), \(FB\) (\"FRONT BELOW\"), \(BA\) (\"BACK ABOVE\") or \(BB\) (\"BACK BELOW\"). The names of these configuration identifiers were selected in a similar way as the ones of the the PUMA 560 (see Section 2.2.1):  
\[q_{3}^{k}=\text{atan2}\Big{(}\sin(q_{3}^{i}),\cos(q_{3}^{k})\Big{)} \tag{16}\]Once \(q_{3}\) has been calculated, \(q_{2}\) may be obtained solving the equation system shown in the final part of Equation (13), as it is presented in Equation (17):  
\[ \begin{split}\sin(q_{2}^{k})=\frac{p_{y_{i}^{k}}[L_{2}-L_{3}\sin(q _{3}^{i})]+p_{x_{i}^{k}}L_{3}\cos(q_{3}^{k})}{[L_{2}-L_{3}\sin(q_{3}^{i})]^{2}+[ L_{3}\cos(q_{3}^{k})]^{2}}\\ \cos(q_{2}^{k})=\frac{p_{x_{i}^{k}}-L_{3}\cos(q_{3}^{k})\sin(q_{2 }^{k})}{L_{2}-L_{3}\sin(q_{3}^{k})}\end{split}  \tag{17}\]  
Finally, Equation (18) presents the four configurations structure that conform the solution of the Inverse Kinematic Problem of the analyzed walking hexapod:  
\[ \begin{split} q^{FA}=\begin{bmatrix}q_{1}^{F}\\ q_{2}^{FA}\\ q_{3}^{F}\end{bmatrix}\quad q^{BA}=\begin{bmatrix}q_{1}^{B}\\ q_{2}^{BA}\\ q_{3}^{B}\end{bmatrix}\\ q^{FB}=\begin{bmatrix}q_{1}^{F}\\ q_{2}^{FB}\\ q_{3}^{FB}\end{bmatrix}\quad q^{BB}=\begin{bmatrix}q_{1}^{B}\\ q_{2}^{BB}\\ q_{3}^{BB}\end{bmatrix}\end{split}  \tag{18}\]  
After analyzing the two traditional methods that are normally used to solve the Inverse Kinematics Problem, it is obvious that they are not systematic procedures because the geometric calculations done for the PUMA 560 manipulator cannot be applied to the BH3-R walking hexapod, and vice versa. Section 3 presents the procedure developed in this work to synthesize the IKM in a systematic way, using Groebner Basis theory.
###### Abstract  
Objective and consistent assessment of locomotion recovery remains challenging in rodent spinal cord injury (SCI). We, therefore, studied the validity and relevance of the CatWalk XT(r) gait analysis as a tool for assessing functional outcome in a clinically relevant cervical SCI model in rats. In total, 20 Wistar rats were randomly assigned to either a C6 clip compression/confusion SCI or a sham laminectomy. Locomotion recovery was assessed weekly using the CatWalk XT(r) gait analysis and the BBB open field score. Six weeks after SCI, the percentage of preserved spinal cord tissue was measured by glial fibrillary acidic protein (GFAP) immunohistochemistry (IHC) staining. Statistical analyses were performed to assess the correlation of the BBB and the percentage of preserved tissue with 30 different CatWalk XT(r) parameters. SCI caused a bilateral and significant functional impairment in all studied CatWalk XT(r) parameters. Similar to the BBB, a significant spontaneous recovery could be observed in most of the CatWalk XT(r) parameters in the following weeks. Correlation between the hindlimb CatWalk XT(r) parameters and the BBB was good (53% of \(r\) values > 0.6) while the correlation between the forelimb and the hindlimb CatWalk XT(r) parameters and the percentage of preserved tissue was even stronger (83% of \(r\) values > 0.6). The CatWalk XT(r) gait analysis is closely correlated with tissue damage after cervical contusion/compression SCI and can be used as an objective and consistent tool for assessing locomotion recovery.  
CatWalk XT; automated gait analysis; spinal cord injury; cervical; behavioral test; motor function +
Footnote †: journal:
# Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System
## 5 Experimental Evaluation
### Experimental Setup  
**Comparison Algorithms:** To effectively verify the performance of our DAGAN model, five representative anomaly detection algorithms are chosen as competitors. All comparison algorithms are listed in Table 1, where the SVM is a machine learning-based anomaly detection method, GMM is a Gaussian mixture model-based method, AE is an auto-encoder neural network-based method, FenceGAN and GANomaly both are GAN-based anomaly detection methods. For all comparison algorithms, recommended values of all parameters are used to obtain the best experimental results.  
**Evaluation metrics:** In this paper, Accuracy (ACC), Recall (Rec), False positive rate (FPR), and F1-Score (F1) are selected as evaluation indicators to evaluate the quality of anomaly detection. They are defined as follows:  
\[ACC=\frac{TP+TN}{TP+FN+FP+TN} \tag{21}\]  
\[Rec=\frac{TP}{TP+FN} \tag{22}\]  
\[FPR=\frac{FP}{FP+TN} \tag{23}\]  
\[F1=2\times\frac{\left(\frac{TP}{TP+FN}\right)\times\left(\frac{TP}{TP+FP} \right)}{\left(\frac{TP}{TP+FN}+\frac{TP}{TP+FP}\right)} \tag{24}\]  
where True Positive (_TP_) is a positive sample predicted to be positive, True Negative (_TN_) is a negative sample predicted to be negative, False Positive (_FP_) is a negative sample predicted to be positive, and False Negative (_FN_) is a positive sample predicted to be negative.  
**Experimental Platform:** A normal PC server (ThinkPad L490) is used as the experimental platform. The server is equipped with an 8-core i7 CPU, 16 GB of main memory, and Windows 10 operating system. The DAGAN model is programmed with Python and runs with Spyder as an IDE. For the other algorithms, the official python implementation is downloaded from the websites of the corresponding authors.
## 3 Results and Discussion  
### Density and Porosity  
Figure 10 illustrates the physical properties of stir-cast AZ91/x-S\({}_{3}\)N\({}_{4}\) composites, specifically the density and porosity. It can be observed from the chart that theoretical (\(\rho_{\text{th}}\)) and experimental (\(\rho_{\text{ex}}\)) density are increased with the addition of Si\({}_{3}\)N\({}_{4}\)-reinforced particles. This increase in density is attributed to the higher density of Si\({}_{3}\)N\({}_{4}\) particles (3.17 g/cm\({}^{3}\)) compared to the density of the AZ91 alloy (1.8 g/cm\({}^{3}\)). The addition of Si\({}_{3}\)N\({}_{4}\) reinforcement leads to an overall increase in the density of the composite material. Indeed, it is observed that the theoretical density  
the observed increase in porosity is only 1.33%, which is relatively small and may not be of significant concern for many applications. Furthermore, this level of porosity is generally acceptable for various applications, including those in aerospace, automotive, and other industries.
# The Use of Wireless Networks and Mobile Applications in the Control of Electric Machines
## 1 Introduction
Applications that control the operation of electric motors or other industrial devices have particularly higher functional and safety requirements than ones that are typical programs that anyone can install on their smartphone by downloading them from applications stores (e.g., Google Play Store, the Apple App Store). On the other hand, applications of this type are often developed by electronic or electrical engineers who are not always proficient in programming mobile or web applications. Figure 1 shows the idea of open-loop control of a DC electric drive in a graphical form. The figure also indicates the problems to be solved when designing a wireless electric drive control system. The authors distinguish four basic problems to be rectified in this type of system. The first is the interface of the mobile application, which should be precise and ergonomic. The other two problems arise from the specifics of the chosen wireless transmission technology and are connected to the distance between the mobile device that provides the interface for the system user and the object itself. These properties, for a given transmission technology, mainly depend on the frequency of the transmitted signal and modulation. An equally important and identified problem is that open-loop control requires the creation of a means of verifying connection continuity and information coupling, which may be unnecessary in the case of closed-loop control systems. In an open-loop control system, it is a user who sets control parameters and observes the response of the object, however, it is sometimes impossible in the case of wireless control, in which the distance from the object is significant. Therefore, the facility should provide a mobile device, acting as an operator panel, with feedback information about the status of the device and the connection. The subject of this paper is the remote control of DC electric drives based on wireless communication and mobile applications. The paper comprises a description of both the most important functionalities that such a mobile application should provide, and the programming environment that can be used for the rapid prototyping of such an application by engineers with basic programming competencies. Particular stages of the conducted research include the implementation of the remote control of two wireless transmissions and the development of a mobile application in the MIT App Inventor environment. The presented remote-control system has been tested on a three-phase thyristor rectifier with a DC machine with permanent magnets (PMDC).
# Malware Classification Based on Shallow Neural Network
## 4 Experiments and Evaluations
### Experimental Results and Analysis
#### 4.2.2 The CTIB-n-gram Algorithm  
To verify the effectiveness of the proposed n-gram generation algorithm CTIB-n-gram, we conducted a malware family classification experiment with a series of n values from 1 to 6. For each n value, the standard n-gram method and CTIB-n-gram algorithm were used in the model to generate an n-gram dataset. Then, the dataset was input into our network, and the results were compared. In addition, the value of n would also have a great impact on the results. The experiments in this section also compared the classification effects of the models with different n values. Figure 8a-c show the comparison of the precision, recall and micro-f1 values.  
Figure 7: The normalized confusion matrix of the classification result (2).  
Experiments were performed on n values from 1 to 6. When n = 1, it is equivalent to no n-gram model. Since the CTIB-n-gram algorithm only retained the control transfer instruction and discarded a large number of other instructions, the classification model using the CTIB-n-gram algorithm was worse than one with the standard n-gram. At n = 2, the precision of the two methods was close, and the one with the CTIB-n-gram algorithm had a higher recall rate. At n > 2, the model using the CTIB-n-gram algorithm exceeded the model using standard n-grams in all three indicators, and the gap increased with the growth of n. It was noted that as the n value grew, the indicators did not always grow or remain stable. In the model using the standard n-gram, the performance began to decline when n > 4. When using the CTIB-n-gram model, the recall rate also began to decrease at n = 6, but the decline was not significant. As the value of n increases, the more instructions an n-gram contains, the less it discards. This is because the CTIB-n-gram model retained the control transfer instructions, which divide the assembly code into many basic blocks. The basic block, the smallest unit of assembly code analysis, is a single-entry, single-outlet instruction sequence. Therefore, the model based on the CTIB-n-gram algorithm had a higher accuracy than the standard n-gram model.  
In terms of efficiency, when the n value was 2, the 2-g dataset file generated by the standard n-gram method exceeded 2 GB. However, the model using the CTIB-n-gram algorithm took only about 140 MB for the 2-g dataset file and about 440 MB when n was 6, far less than the storage the standard n-gram method needs. Naturally, the smaller the data file, the shorter the whole time will be when the subsequent processing is the same. For the sake of time and storage cost, the classification model using n-gram generally adopts bigram or trigram, so the CTIB-n-gram method can effectively improve the effectiveness and efficiency of malware classification in practice.