esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Videos Data Augmentation for Deep Learning Models
## 4 Future Directions and Conclusions
### From Static Image to Video Data Augmentation  
Data augmentation for static images is a well developed research field. Several models able to augment static image datasets are already available, and it is worth analysing more in detail the possibility of their use on video data augmentation. In order to apply standard image data augmentation methods to video sequences, the time domain needs to be taken in consideration. The changes applied to each frame must be coherent through time.  
In video analysis, time series of images are usually organised in mini batches representing short clips. To guarantee a time coherence, geometric and color transformation must remain constant through the entire mini batch, and they can be randomised over different mini batches. In other scenarios, 2D motion models can be used to extend static image data augmentation methods to videos. Data augmentation systems based on random erasing, for example, generate the augmented images removing the pixel values from random patches of the original ones. For video data augmentation, instead of randomly selecting a patch for each image, the patch size and position can change based on a predefined 2D motion on the image plane. This approach simulates the occlusions created by a dynamic moving object in the scene.  
DL based data augmentation models can perform better on videos if they are able to keep a memory of the previous frames to generate the new ones. The past frames contain information about temporal variations in the scene, like object motions, dynamic light changes, and weather evolution, among others. RNN are widely used for the analysis of text and time series due to their ability to retain a memory of the past inputs through their internal loops. Recently, 1D RNN models (i.e., LSTM and GRU) have been integrated to CNN (ConvLSTM [64], ConvGRU [65]) to perform video analysis and generation. Another approach used to analyse image temporal sequences is the use of 3D convolutions. In this case, the third dimension is used to stack several contiguous frames to obtain temporal information. Extending generator networks with a time series specific model like 3D convolutions or RNN is a promising solution.  
Several simulator tools able to generate synthetic images for object detection dataset augmentation (e.g., the Unreal Engine 4 plugin \"NVIDIA Deep learning Dataset Synthesizer (NDDS)\" [66]) already exists, but their randomisation routines do not usually take into consideration time dependency for the creation of simulated video sequences. Some of the reviewed papers are starting to move in that direction (e.g., ElderSim, a synthetic data generation platform for human action recognition [49]).
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using a statistical software program (SPSS version 29, IBM Corp., New York, NY, USA). Differences in L*, a*, and b* values between groups were evaluated using one-way ANOVA, followed by Bonferroni post hoc tests for multiple pairwise comparisons. Mean color differences (\(\Delta\)E00) between the MC-DSG and SC-DSG in comparison to the spectrophotometer were analyzed using an independent \(t\)-test. Furthermore, the magnitude of \(\Delta\)E00 values between the MC-DSG and SC-DSG in comparison to the spectrophotometer was compared against PT of 0.8 and AT of 1.8 as reference standards.  
For reliability, the photographs of all shade tabs were retaken using both mirrorless and smartphone cameras under identical parameters for consecutive weeks. The reliability of the MC-DSG, SC-DSG, and spectrophotometer was assessed using the Intraclass Correlation Coefficient (ICC), based on a two-way mixed-effect model with absolute agreement and single measurements. The interpretation of ICC values followed the thresholds: <0.50 = poor, 0.50-0.75 = moderate, 0.75-0.90 = good, and >0.90 = excellent [41]. The results were then compared using one-way ANOVA with Bonferroni post hoc tests. The agreement between both MC-DSG and SC-DSG with spectrophotometer was further analyzed using the Bland-Altman test. The significance value was set at \(\alpha=0.05\).  
Figure 3: Color measurements of L*, a*, and b* values at the middle of the shade tabs using Adobe Photoshop.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 4 Result and Analysis  
### Experimental Environment  
The deployment scheme of Apache + OpenSSL was adopted in the experiment. The versions used were httpd-2.4.37 and openssl-1.1.0K, corresponding to TLSv1.2. There were three comparison platforms used. The first was the XiLimx's high-performance board card XiLinx Zynq UltraScale+ MPSoC ZCU104, which adopts the RISC instruction set and ARM architecture inside the processor. The second was the FT-2000+ processors on the prototype Tianhe-3 cluster [24], which integrates 64 cores and has a homogeneous many-core ARM architecture with a multilevel internal structure. Its operating system is YHKylin 4.0-1D. The processor is divided into eight panels that access memory individually. The third was Intel(R) Core(TM) I7-6700 [EMAIL_ADDRESS] GHz with the CISC instruction set and x86 architecture, whose performance is generally better than that of the ARM architecture processor, but it consumes more energy; different architectures are the main reason for the differences in energy consumption and performance. The compiler optimization instruction uses -O2.
## 4 Experimental Results
### Experimental Setting
#### 4.1.2 Effect of Integrating Different Stages Progressively  
Two experiments were made to correctly choose the right organization of the network's components. The first suggestion was to apply rain accumulation removal as an initial pre-processing step; however, as shown in Figure 7, this has negatively affected the whole deraining process. This is because the rain accumulation removal step alters the quality of rain streaks, it boosts their contrast and appearance, this makes rain streaks look different compared with their original appearance in both real-world and synthetic datasets. Therefore, the second proposed suggestion was to apply rain accumulation removal progressively in combination with rain streak removal, shown in Figure 8. Additionally, the corresponding PSNR and SSIM values for both suggestions are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **PSNR** & **SSIM** \\ \hline Rain accumulation removal as an initial stage & 15.683 & 0.683 \\ Rain accumulation removal performed progressively with rain streak removal & 33.01 & 0.901 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of applying rain accumulation removal as pre-processing vs. applying it progressively.  
Figure 5: Effect of increasing number of progressive stages in rain streak removal on PSNR and SSIM evaluation metrics.  
Figure 6: Effect of increasing number of progressive stages in rain streak removal network on running time (in seconds).
## 5 Experiment Results and Analysis
### Allan Variance  
Continuous measurement of methane within a duration of 6000 s was carried out to evaluate the stability of the PAS system. Through two MFCs, the flow rates of the pure nitrogen and methane were set at a ratio of 9:1. The methane concentration is constant at \(\sim\)10 ppm. The time resolution of measurement was 10 s, and a total of 600 data points were obtained during a measurement time of 6000 s. The measurement results are shown in Figure 10. The system achieved a detection sensitivity of 0.116 ppm with an average time of 10 s. A linear black line is indicated in Figure 10, which shows the minimization of white  
Figure 8: 2f signals of different concentrations of methane.  
Figure 9: Linear regression results of methane concentration and signal intensity.  
noise at a low integration time and provides a minimum of Allan deviation 5.7 ppb at an optimal integration time of 300 s.
## 2 Fault Identification Method for Deep Integration
### Fault Identification Method  
If only the pseudo-range errors in \(\mathbf{Z}_{k}^{\text{Nav}}\) are considered, i.e., \(\mathbf{Z}_{k}=[\Delta\rho_{1},\Delta\rho_{2},\ldots\ldots,\Delta\rho_{N}]^{ \text{T}}\), the expressions of innovation \(V_{k}\) and its covariance matrix \(A_{k}\) defined in [34] are  
\[V_{k}=\ \mathbf{Z}_{k}-\mathbf{H}_{k}\Delta\mathbf{X}_{ijk-1}, \tag{13}\]  
\[A_{k}=E\Big{[}V_{k}V_{k}^{\text{T}}\Big{]}=\mathbf{H}_{k}P_{ijk-1}\mathbf{H}_{ k}^{\text{T}}+\mathbf{R}, \tag{14}\]  
where the dimension of \(\mathbf{H}_{k}\) needs to be modified to \(N\times 17\).  
The innovation \(V_{k}\) is assumed to be Gaussian white noise with a zero mean in the fault free case [35]. However, if a fault occurs, a large deviation will arise between the measurements and the estimates of \(\mathbf{Z}_{k}\). At this moment, the mean value of \(V_{k}\) is greater than zero, so it can be used as the rule for fault detection.  
According to the w-test method, the fault identification statistic is constructed as:  
\[w_{i,k}=\left|\frac{-e_{i}^{\text{T}}V_{k}}{\sqrt{e_{i}^{\text{T}}A_{k}}e_{i}} \right|, \tag{15}\]  
where \(e_{i}=[0,\ldots\ldots,0,1,0,\ldots\ldots,0]^{\text{T}}\). It represents that the value of the \(i\)th component is 1, and the rest are 0.  
When there is no fault, \(w_{ik}\) should follow the standard normal distribution, i.e., \(w_{i,k}\sim N(0,1)\).  
Given the false alarm probability \(P_{\text{fa}}\), if  
\[w_{i,k}>N_{1-P_{\text{fa}}/2}(0,1), \tag{16}\]  
then the \(i\)th channel is considered to be faulty; otherwise, it is normal.  
From the above analysis, the statistic \(w_{i,k}\) is related to \(\Delta\rho_{i}\). If there is an abnormality in \(\Delta\rho_{i}\), \(w_{i,k}\) will exceed the alarm threshold. As described in Section 2.1, the fault information is propagated mutually among the receiving channels in the vector tracking loop. Errors in one channel can spread to all other channels, resulting in abnormal measurements in all channels. Thus, the actual fault channel will not be distinguished for the reason that the identification statistics of all channels exceed the alarm threshold.
# Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling
## 3 Multitask Learning with Knowledge Base for Joint Slot-Filling and Intent-Detection
### Incorporate External Knowledge  
In this part, our aim is to take advantage of the knowledge base to extend the Bi-LSTM model. To effectively integrate the knowledge base with information from the text inputted by a user, our model updates the hidden vector to enhance the learning of Bi-LSTM. It is capable of leveraging a knowledge base when it processes each word in the text, and the hidden vector is updated by semantic relevance to the knowledge. At each time step, the model retrieves concepts that are related to the current word as candidate concepts from WordNet. The candidate concepts are transformed to an embedded word using BERT and fed into the model along with the text input by the user.  
Specifically, the knowledge at time-step \(t\) comprises candidate knowledge, \(V(x_{t})\), for the text that a user input, \(x_{t}\). Each candidate knowledge item \(i\in V(x_{t})\) is associated with a knowledge vector \(v_{i}\). The attention weight \(\alpha_{t,i}\) for vector \(v_{i}\) via a bilinear operator can be described as follows:  
\[\alpha_{t,i}=f(\textbf{W}_{v}v_{i}h_{t}), \tag{9}\]  
where \(\textbf{W}_{v}\) is a parameter matrix to be learned and \(h_{t}\) is the current hidden vector.  
Let \(m_{t}\) be considered as a knowledge state vector that encodes external knowledge information with respect to the input at time \(t\). The mixture model is defined below:  
\[m_{t}=\sum_{i\in V(x_{t})}\alpha_{t,i}v_{i}. \tag{10}\]We combine it with the hidden vector \(h_{t}\) of the Bi-LSTMs to obtain a knowledge state vector \(h^{\prime}_{t}\) to modify the original hidden vector \(h_{t}\):  
\[h^{\prime}_{t}=h_{t}+m_{t}. \tag{11}\]  
If \(V(x_{t})\) is null, we set \(m_{t}=0\). \(h^{\prime}_{t}\) can be used for predictions in the same manner as the original hidden vector \(h_{t}\).
# High-Pressure Sensors Based on Laser-Manufactured Sintered Silicon Carbide
## 4 Conclusions  
Pressure sensors are widely used in various areas. In addition, it is often necessary to have pressure sensors made of materials suitable for use in hostile environments for particular applications. Due to its peculiar characteristics SiC is a suitable material for the realization of pressure sensors to be used also in hostile areas. In this work, fiber optic coupled pressure sensors were manufactured and characterized using S-SiC membranes with diameter from 2.0 to 3.5 mm and thickness ranging from 100 \(\upmu\)m to 300 \(\upmu\)m, obtained through laser ablation of uncontaminated samples. Experimental results show good performance for membrane with diameter around (2.4 \(\pm\) 0.05) mm. In particular, for the membrane thickness (160 \(\pm\) 10) \(\upmu\)m the experimental results show a monotonic change in the acquired amplitudes \(\Delta l_{ph}\) in the range 0-1 MPa, with a full scale non-linearity around 4%. A further characterization of the sensor has been performed in a wider pressure range (0-10 MPa) and the acquired photocurrent trend shows the expected sinusoidal trend of an interference signal. Moreover, the sensor shows good linearity (0.163 nm/kPa, i.e., 16.3 nm/bar), with linearity errors of less than \(\pm\)20 nm, i.e., \(\pm\)0.6% on the full scale investigated. For a second sample (S2 sample) with a (260 \(\pm\) 10) \(\upmu\)m thick diaphragm, the same characterization was carried out. In this case, non-linearity of about 3% of full scale was observed in the range 0-30 MPa and the sensitivity of the sensor (0.038 nm/kPa) is lower than that of the first sample, as expected. Moreover, experimental results allowed to estimate a Young's modulus between the value measured for pristine S-SiC sample and the one reported for crystalline SiC, stating a possible phase change induced by the laser treatment used to realize sensor's membrane.  
Conceptualization, S.S. and S.G. ([PERSON]); investigation methodology, G.S.P., S.P., and S.G. ([PERSON]); sample fabrication and material characterization, G.S.P. and S.G. ([PERSON]); sensor fabrication, S.P. and S.S.; optoelectronic measurement set-up, S.P. and S.S.; validation, S.S. and S.G. ([PERSON]); writing--original draft preparation, S.S., S.P., and S.G.P.; writing--review and editing, S.S. and G.S.P.; supervision, S.S. and S.G. ([PERSON]); funding acquisition, S.G. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by POR-FESR LAZIO 2014-2020, grant number A0114-2017-13737 CUP F35F1800005007.  
The authors would like to thank [PERSON] for insightful discussions on the measurement set-up as well as on experimental results.  
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.
###### Abstract  
The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware-software system designed for the acquisition, processing, and analysis of surface electromyographic (sEMG) signals. The system analyzes sEMG signals to understand muscle function and neuromuscular control, employing convolutional neural networks (CNNs) for pattern recognition. The electrical signals analyzed on healthy and unhealthy subjects are acquired using a meticulously developed integrated circuit system featuring biopotential acquisition electrodes. The signals captured in the database are extracted, classified, and interpreted by the application of CNNs with the aim of identifying patterns indicative of neuromuscular problems. By leveraging advanced learning techniques, the proposed method addresses the non-stationary nature of sEMG recordings and mitigates cross-talk effects commonly observed in electrical interference patterns captured by surface sensors. The integration of an AI algorithm with the signal acquisition device enhances the qualitative outcomes by eliminating redundant information. CNNs reveals their effectiveness in accurately deciphering complex data patterns from sEMG signals, identifying subjects with neuromuscular problems with high precision. This paper contributes to the landscape of biomedical research, advocating for the integration of advanced computational techniques to unravel complex physiological phenomena and enhance the utility of sEMG signal analysis.  
sEMG; electronic system; sensor's systems; convolutional neural network +
Footnote †: journal: _s signals
## 2 Related Work  
### Structured Road Scene 3D Semantic Segmentation  
Semantic segmentation of point cloud refers to classifying each point of the input according to the corresponding class so that different types of objects can be distinguished. For the semantic segmentation of 3D point clouds in structured outdoor scenes, the input point cloud can be encoded in three ways: voxel-based, point-based, and projection-based.  
In the projection-based algorithm, the 3D point cloud is projected into the 2D space, and then the semantic segmentation network is used to calculate the \"pseudo image\" in the 2D space. After that, the segmentation result is back-projected to the coordinate space of the 3D point cloud by interpolation to realize the semantic segmentation of the original point cloud. Among them, SqueezeSeg [8], SqueezeSegv2 [9], SqueezeSegv3 [10], Salsanext [11], etc., use spherical projection, while PolarNet [12] and VD3D-FCN [13] algorithms use bird's-eye view projection for feature extraction.  
Voxel-based semantic segmentation algorithms re-encode the 3D space with voxels. For example, VoxelNet [14] is a typical algorithm that uses voxels to achieve semantic segmentation of 3D point clouds. It divides the 3D point cloud spaced at equal intervals, which are called voxels. Each voxel is converted into a unified feature representation vector by a VFE (voxel feature encoding) layer, and feature extraction is performed based on this.  
Semantic segmentation based on point cloud sequence is a method used to directly extract the feature on the original unordered point cloud sequence. Additionally, a multi-layer perceptron is used to directly perform semantic encoding and spatial position calculation on the point cloud itself. For example, PointNet [15], PointNet++ [16], RandLA-Net [17], and KPConv [18] are point-based algorithms.  
These three feature extraction methods have their own advantages and disadvantages. The method based on projection is usually faster than the method that extracts features directly in three-dimensional space. However, the precision loss caused by the forward and reverse projections cannot be ignored. Voxel-based methods are also widely used. After voxel coding, whether deep learning or traditional clustering algorithms are used, target recognition or segmentation tasks can be effectively performed. However, the 3D convolution algorithm is less efficient as the data size increases. Algorithms based on point sequences have high computational efficiency, but poor locality and easy loss of features, thus making it difficult for some small objects to be segmented from large objects. Until the PVCNN [19] algorithm was proposed, the fusion of voxel and point cloud sequences for feature extraction greatly improved the accuracy and efficiency. After that, RPVNet [20] fused three feature extraction methods and obtained excellent semantic segmentation results.
# A Study on Failure Analysis and High Performance of Hydraulic Servo Actuator
## 1 Introduction
A hydraulic servo actuator, as shown in Figure 1, has a large output density, operates micro displacement very precisely at frequencies higher than 100 Hz, and can be used continuously for a long period of time, and therefore it is used as a core device for tensile compression fatigue life testing equipment. Figure 1: Tensile compression fatigue test equipment. Recently, the life expectancy of automobiles has been extended to more than 100,000 miles over 10 years, and the load conditions of accelerated life testing on auto parts have been increasing in order to secure the reliability of produced vehicles within a short period of time, and the number of tests has also increased significantly. Therefore, hydraulic servo actuators mounted on accelerated life testing equipment for automotive parts must have significantly higher performance and ten times longer life than those tested. However, compared to the high research capabilities of large automakers, small and medium-sized companies that supply fatigue life test equipment for automobile parts are stagnant or their technological development level is lagging behind due to a lack of research personnel and development budget. As a result, frequent breakdowns occur due to technical overload of test equipment. The hydraulic servo actuator, which operates the auto parts accelerated fatigue life test equipment with a maximum displacement of 50 mm and a maximum output of 2 ton at a maximum frequency of 3.3 Hz, frequently failed to burn the rod surface and cylinder surface, which are the seal contact surfaces. The analysis of this cause of failure in this study shows that the gas (air and gas) mixed in hydraulic oil accumulates inside the hydraulic cylinder, resulting in high heat (approximately 500 degrees) due to the insulation compression of the gas, and interferes with lubrication, adding friction to the seal. We changed the design to improve the root cause of the burning of the cylinder surface and the road surface, which was the seal contact surface of the hydraulic servo actuator. Therefore, static pressure bearings were designed for both rod parts, and the piston was designed with labyrinth seals. In addition, concentric tubes were inserted into the outside of the piston to secure a very high level of geometric concentricity with the piston and both rods. The newly designed hydraulic servo actuator is mixed with hydraulic oil and the gas flowing into the cylinder immediately flows out into the fine gap between the static pressure bearing and the labyrinth seal, so gas does not accumulate, and the concentricity is accurately secured without using a seal, so solid friction does not happen. Existing actuators had frequent failures even at 3.3 Hz, but in the test device equipped with the developed actuator, as shown in Figure 2, the performance was improved to control normally even at 100 Hz, and the non-contact method was improved to achieve a semi-permanent lifetime. Figure 2: Fatigue test equipment equipped with hydraulic servo actuators developed under test.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 6 Position Estimation of PDR Algorithm
### Filter Design of UKF for Position Estimation  
In this paper, the position estimation is solved based on the UKF that reference the method proposed in [38]. After the heading is updated and the step-length is estimated, these values can be used to estimate the position based on the UKF.  
According to Equation (50), a system model of nonlinear formula can be built as follows:  
\[X_{k}=\left(\begin{array}{c}N_{k}\\ E_{k}\\ SL_{k}\\ \varphi_{k}\\ \Delta\varphi_{k}\\ \end{array}\right)=\left(\begin{array}{c}N_{k-1}+SL_{k-1}\cos\varphi_{k-1} \\ E_{k-1}+SL_{k-1}\sin\varphi_{k-1}\\ SL_{k-1}\\ \varphi_{k-1}+\Delta\varphi_{k-1}\\ \Delta\varphi_{k-1}\\ \end{array}\right)+W_{k-1} \tag{51}\]  
where, \(\Delta\varphi_{k}\) is the increment of heading at time \(k\), \(W_{k-1}\) is state noise.  
The measurement equations can be obtained by  
\[Z_{k}=\left(\begin{array}{c}SL_{k}\\ \varphi_{k}\\ \Delta\varphi_{k}\\ \end{array}\right)=\left(\begin{array}{c}SL_{k}\\ \varphi_{k}\\ \Delta\varphi_{k}\\ \end{array}\right)+V_{k} \tag{52}\]  
where, \(V_{k}\) is measurement noise.  
In this paper, Equations (51) and (52) are used to estimate the position at every step. And the calculated process of UKF can be found in [25; 40].