esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
## 1 Introduction
In this work, we present a novel approach capable of generating six different facial expressions from a single input image of a neutral face. Our approach utilizes a Conditional Generative Adversarial Network (CGAN) [9] specifically designed to synthesize highly realistic facial variations while conditioning on the provided neutral face image. To evaluate the efficacy of our technique for synthetic image generation, we fine-tuned and trained several CNN models (such as VGG-Face, ResNet-50, FaceNet, and DeepFace) along with a custom CNN model on the generated images. We then evaluate their performance on the corresponding real images. The CNN models trained on our synthesized images and achieved 99% accuracy, demonstrating the realism and diversity of our generated facial expressions. Furthermore, we explored the impact of our model on the challenging single sample per person (SSPP) problem faced in face recognition tasks. Initially, by training and fine-tuning the CNN models solely on real-world single neutral face images per person, the accuracy was approximately 76%. However, by augmenting the dataset with six synthetic expression images generated by our CGAN for each person, the accuracy of the CNN models improved to 99%. To further validate the robustness of our approach, we also generated realistic facial expressions for random neutral face images obtained from the Internet, as depicted in Figures 13 and 14, confirming the generalization capability of our CGAN model beyond the training data. Our contributions are listed as follows:  
1. We propose a novel Conditional Generative Adversarial Network (CGAN) that generates six diverse facial expressions from a single input image of a neutral face. 2. We address the data scarcity problem of the single sample per person (SSPP) by generating multiple facial expressions for each individual. This enhances the training data for facial recognition systems and improves their performance. 3. We improve the accuracy of the single-sample face recognition system from 76% to 99% when trained with our generated images. We validate the generalization ability of the CGAN model by generating facial expressions from randomly selected neutral face images outside the dataset. The rest of the paper is defined as Section 2, which explains all the related work related to SSPP and the Image Generation Method. Section 3 explains the details of the dataset. Section 4 explains the proposed model architecture and how it works. Section 5 contains the results of the experimentation, and Section 6 concludes the paper.
## 2 Materials and Methods  
### Vineyard Site and Climate Conditions  
The experiment was conducted in 2019 in a commercial vineyard in the Vrgorac Valley (43\({}^{\circ}\)10\({}^{\prime}\)9.2\({}^{\prime\prime}\) N, 17\({}^{\circ}\)23\({}^{\prime}\)44.2\({}^{\prime\prime}\) E, 23 asl) in the Dalmatian Hinterland wine region (Croatia). _Vitis vinifera_ L. 'Marastina' grafted on 1103 Paulsen rootstock was planted in 2010 in fluvial soil (undeveloped hydromorphic soil) on karst at a spacing of 0.6 m (within row) \(\times\) 2.2 m (between row) in an east-west orientation. Vines were Guyot-trained to a single-curtain vertical trellis (VSP) and pruned back in winter to a single cane of 10 buds and one spur of two buds.  
The climate in the Vrgorac Valley is Mediterranean, based on climate data from the nearest meteorological station with at least one month of drought in summer. The average annual temperature in 2019 was 15.2 \({}^{\circ}\)C, and the annual precipitation was 1389.3 mm. Weather conditions during this experiment in Vrgorac (Figure 1) were very hot and dry. Most of the precipitations during the growing season occurred in May (maximum 235.8 mm). The driest month was August (12.9 mm), which was also the month with the hottest average temperature (27.6 \({}^{\circ}\)C). In August, the temperature exceeded 30 \({}^{\circ}\)C for three consecutive days. Detailed analysis of meteorological data from June to September revealed 78 days with a maximum daily temperature above 30 \({}^{\circ}\)C, and the peak of 39.5 \({}^{\circ}\)C was on 12 August. Minimum night temperatures were above 20 \({}^{\circ}\)C on 54 days, which may be crucial for the balance of photosynthesis and respiration, as well as for flavor development.
# The Role of Elasticity in the Vortex Formation in Polymeric Flow around a Sharp Bend
## 3 Results  
### Newtonian Flow  
To better understand the effects the elasticity of polymers have on the formation of vortices in a right angle bend geometry, simulations of Newtonian fluid flow were conducted first in order to obtain a baseline or reference. It is well known that in Newtonian flow vortices form downstream of a bend corner at sufficiently high Reynolds number [22; 27].  
To establish the baseline, the Reynolds number value at which the vortices first appear was sought. This value, called Re\({}_{\text{crit}}\) for the duration of this paper, was found to be approximately 30. For Reynolds number values larger than 30, a clear vortex forms downstream of the bend corner. This critical Reynolds number value is in line with other numerical work [1; 2].  
For Newtonian fluids, the Reynolds number is the single parameter required to describe the flow. It is expected that higher Reynolds numbers should lead to larger vortices downstream of the bend corner. We refer to these vortices forming downstream as inertial vortices from now on. Figure 4 presents two inertial vortices. The vortex in Figure 4a corresponds to a flow with a Reynolds number of 50, while Figure 4b corresponds to Re = 100.  
Comparing both figures, it can be seen that the second vortex at \(\text{Re}=100\) is predicted to be significantly larger than the vortex that forms at \(\text{Re}=50\). This comparison shows that a higher Reynolds number value leads to a larger inertial vortex in Newtonian flow. It should be noted that the size (length) of the vortices in these and all figures that follow was determined numerically by the change in direction of the tangential velocity near the wall, equivalent to a change in sign of the shear stress (skin friction), see Appendix B.  
To validate both, the statement that the inertial vortex size increases with Reynolds number and that 'RheoTool' is a valid numerical open source tool for the solution of both Newtonian and non-Newtonian flow, we modeled the bend Newtonian flow using the commercial tool ANSYS Fluent v19.1. Figure 5 compares the non-dimensional vortex length (vortex length to channel height ratio) predicted with both Fluent and Rheotool up to a Reynolds number of 250. The values predicted by RheoTool are within 1% of those predicted by Fluent. The figure clearly shows that the vortex length (l) increases with Reynolds number. Additionally, the comparison also indicates that RheoTool is as good as Fluent in solving the Navier-Stokes equations for Newtonian flow. It should be noted that this Newtonian inertial vortex would become unstable at a sufficiently high Reynolds number value [1].  
Figure 4: Downstream inertial vortices, Newtonian Flow: (**a**) Reynolds number \(\text{Re}=50\); (**b**) Reynolds number \(\text{Re}=100\).
## 4 Results and Discussion
### Effect of Vibration on the Apparent Viscosity
#### 4.3.3 0.35 g Vibration at Different Frequency  
The comparison of the apparent viscosity within ME obtained respectively by DMMFB and FEA is shown in Figure 14. No matter without or with vibration applied (the amplitude is 0.35 g and the frequency is, separately, 100, 200, 300, 400 and 500 Hz), the analytical and simulated apparent viscosity have a good agreement with each other.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Effective Value**} \\ \cline{2-5}  & **DMMFB (Pa-s)** & **Drop (\%)** & **FEA (Pa-s)** & **Drop (\%)** \\ \hline
0 (No vibration) & 3601 & - & 3438 & - \\
100 & 2852 & 20.8 & 2673 & 22.3 \\
200 & 2220 & 38.4 & 2061 & 40.1 \\
300 & 1760 & 51.1 & 1619 & 52.9 \\
400 & 1368 & 62.0 & 1302 & 62.1 \\
500 & 1134 & 68.5 & 1090 & 68.3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Effective value of the apparent viscosity without and with 0.25 g vibration applied at different frequencies.  
Figure 13: The apparent viscosity without and with 0.3 g vibration applied at different frequencies.  
Table 11 provides the effective value of the above apparent viscosity for each condition. Again, the value decreases with vibration applied, and further decreases with the increase of vibration frequency.  
In summary, applying vibration onto the extrusion liquefier can reduce the apparent viscosity inside the liquefier, which will be further reduced with the increasing vibration frequency. This could improve the continuity and uniformity of the extrudate, and thus enhance the forming quality of ME products.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Effective Value**} \\ \cline{2-5}  & **DMMFB (Pa-s)** & **Drop (\%)** & **FEA (Pa-s)** & **Drop (\%)** \\ \hline
0 (No vibration) & 3601 & - & 3438 & - \\
100 & 2431 & 32.5 & 2241 & 34.8 \\
200 & 1712 & 52.5 & 1578 & 54.1 \\
300 & 1306 & 63.7 & 1216 & 64.6 \\
400 & 1090 & 69.7 & 1015 & 70.5 \\
500 & 962 & 73.3 & 895 & 74.0 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Effective value of the apparent viscosity without and with 0.3 g vibration applied at different frequencies.  
Figure 14: The apparent viscosity without and with 0.35 g vibration applied at different frequencies. (**a**) No vibration applied, (**b**) 100 Hz, (**c**) 200 Hz, (**d**) 300 Hz, (**e**) 400 Hz, (**f**) 500 Hz.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 1 Introduction  
Recently, significant advancements have been made in image-based object recognition, detection, and segmentation due to the rapid progress in computer processing power and deepening research on artificial intelligence algorithms. The wealth of information embedded within images enables real-time identification and positioning of specific objects such as pedestrians and vehicles through vision. Vision-based positioning technology can be categorized into two main types: active positioning technology, exemplified by structure from motion (SFM) or simultaneous localization and mapping (SLAM), which is utilized for self-positioning calculation in autonomous driving, robotics, drones, VR/AR applications [1; 2]; passive positioning technology, represented by binocular or depth camera positioning systems, is employed for target posture calculation and commonly applied in industrial detection, automatic assembly processes, spacecraft docking operations, fruit collection tasks among others. The vision-based positioning method offers high accuracy at a low cost [3].  
SFM is a 3D reconstruction strategy that leverages unordered images, showcasing significant advancements in both robustness and accuracy. However, its time complexity remains high, rendering real-time positioning unattainable [4; 5]. SLAM serves as a dead reckoning method based on visual sensor measurement data, commonly employing feature matching and optical flow technologies in monocular and binocular SLMA systems like ORB-SLAM, DSO, and VINS [6; 7; 8; 9]. Nevertheless, the stability of positioning results is suboptimal and susceptible to environmental interference. Furthermore, the positioning error exhibits an upward trend with the increase of paths. Although notable progress inrelated algorithms and sensor hardware, vision-based SLAM technology still encounters substantial challenges when operating in complex real-world environments.  
By utilizing frame regression, the object detection algorithm accurately determines the coordinate position information of the target object and locates it. However, in real-world scenarios where multiple instances containing various types of objects or similar objects coexist, mere coordinate information is often insufficient. In such cases, semantic information plays a more crucial role in scene understanding. Traditional semantic segmentation methods can interpret the pixel-level semantics of the target object, but cannot determine its relative position information. In this study, we propose a passive location method that can achieve instance segmentation and synchronous localization using low-cost binocular cameras. Besides, it offers high accuracy and stability with static positioning accuracy at the centimeter level and dynamic positioning accuracy at the decimeter level [10; 11; 12].
## 2 Materials and Methods
### Lightweight and High-Precision Target Detection Network  
#### 2.2.1 Depthwise Separable Convolution  
Compared with traditional convolution, the most significant advantage of depthwise separable convolution [15] is the reduction of the parameters. For a feature map \(H\times w\times c_{1}\), after a standard convolution (assuming the size of the convolution kernel is \(h\times w\times c_{1}\) and that the convolution has the padding operation), the output size is \(H\times w\times c_{2}\); then the parameter of standard convolution is shown in (1).  
\[P_{conv}=(h\times w\times c_{1})\times c_{2} \tag{1}\]  
Depthwise separable convolution performs a spatial convolution while keeping the channel independent, and then performs the depth convolution operation. Depthwise separable convolution is a combination of depthwise convolution and pointwise convolution. Depthwise convolution is responsible for filtering. Its convolution kernel size is \(h\times w\times 1\) and it has a total of \(c_{1}\), which acts on each channel. Pointwise convolution is accountable for the conversion channel, and the convolution kernel size is \(1\times 1\times c_{1}\). There are \(c_{2}\) in total, which act on the output feature map of depthwise convolution. The parameter of depthwise convolution is shown in (2), and the parameter of pointwise convolution is shown in (3).  
\[P_{Depthwise}=(h\times w\times 1)\times c_{1} \tag{2}\]  
\[P_{Pointwise}=(1\times 1\times c_{1})\times c_{2} \tag{3}\]  
The parameters of depthwise separable convolution are shown in (4).  
\[P_{Depthwiseseparable}=P_{Depthwise}+P_{Pointwise}=h\times w\times c_{1}+c_{1} \times c_{2} \tag{4}\]  
The comparison between depthwise separable convolution and standard convolution is shown in (5).  
\[\frac{P_{Depthwiseseparable}}{P_{conv}}=\frac{h\times w\times c_{1}+c_{1} \times c_{2}}{h\times w\times 1\times c_{1}\times c_{2}}=\frac{1}{c_{2}}+ \frac{1}{(h\times w)^{2}} \tag{5}\]  
Depthwise separable convolution effectively decomposes the traditional convolution by separating the spatial filtering from the feature generation mechanism and reducing the amount of calculation by eight to nine times, compared with the traditional convolution [20]. The structure of traditional convolution (a) and depthwise separable convolution is shown in the Figure 2.  
Figure 2: Traditional convolution (**a**) and depthwise separable convolution (**b**).
## 4 Experiments and Results
### Segmentation Accuracy Evaluation  
A comparison of the proposed segmentation model with other brain tumor segmentation methods is presented in order to obtain an assessment of segmentation accuracy. The pre-trained encoder serves as the initialization for the model, as it selects CNN-based methods and transformer-based methods as baselines.
## 1 Introduction
However, these two kinds of problems often exist in the actual system synchronously, such as a highly dynamical navigation system or high maneuvering target tracking system, typically. To overcome this deficiency, we propose a novel, robust, adaptive CKF compromising the robust M estimation and the adaptive adjustment factor. The contribution of the paper can be summarized as follows:  
1. The proposed algorithm could solve the problems of the model uncertainty and the measurement noise statistics inaccuracy. To handle abnormal measurement noise, we adopt robust M-estimation to automatically adjust the measurement noise covariance and gaining adaptivity. Compared with the literature [PERSON] (2018); [PERSON] _et al._ (2018); [PERSON] _et al._ (2018); [PERSON] _et al._ (2018), we directly use innovation to calculate the equivalent weight matrix, no-demand partial derivative operation, and iterative operation. To overcome model uncertainty, we derive an adaptive adjustment factor to modify the model as a whole. Compared with STF in the literature [PERSON] _et al._ (2018); [PERSON] _et al._ (2018); [PERSON] _et al._ (2018), the derived adaptive adjustment factor is simpler. 2. The proposed algorithm has lower computational complexity, and so it is suitable for dynamic systems with high real-time requirements. Our algorithm is not simply a cumulative combination of existing methods, but a derivation method considering the mathematical complexity and real-time, which ensures the computational efficiency of the algorithm. 3. In the application of the algorithm, we designed a stable and reliable ultra-tightly coupled structure based on a double-modulating loop. The effectiveness of the proposed method and the designed structure is verified by a highly dynamical scene experiment. The rest of the paper is organized as follows: Section 2 gives a brief introduction of the Bayesian filter theory in the Gaussian domain and the cubature Kalman filter. Then, the improved robust adaptive CKF, using robust estimation theory and adaptive filter techniques, is also derived in Section 3. In Section 4, the hypersonic vehicle state estimation problem is studied using a SINS-aided GPS ultra-tightly coupled system, and a SINS/GPS federated ultra-tightly coupled structure is designed based on a double-modulating loop. In Section 4, we also give the nonlinear pre-filter tracking loop model of the satellite signal. In Section 5, the proposed robust adaptive CKF is applied in the highly dynamical nonlinear state estimation experiment. The conclusion of the paper is given in Section 6.
# Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
## 4 Experiment
### Experimental Process  
The experimental process consists of the following four main steps, and we describe the experimental setup in detail.  
**Step 1: Data preprocessing.**  
We transform all datasets described in Table 1 into a Mel spectrogram. The Hanning window was used, and the window size was set to 2048, the hop size to 512, and the Mel filter bank to 80 for the transform. Then, TAAP was applied with a target length of 32 according to Equations (26) and (27), and normalized to min-max normalization.  
**Step 2: Train the autoencoder model.**  
Each SSS-AE model was trained with normal data from product data _(GTC, STE, STII)_, where abnormal data exist to obtain an encoder that can represent each product well in the latent space. Then, with a trained encoder, all normal data and abnormal data, i.e., _Error 1_ and _Error 2_ of the base product of the group, were embedded in the latent space. The batch size for pre-training was set to 64, the number of epochs was set to 5000, the number of heads for multi-head self-attention was set to 8, and the Adam optimizer [27] was used.  
**Step 3: Hierarchical clustering.**  
The latent vectors, obtained by embedding the data from each product _(GTC, STE, STII)_ into the latent space using the encoder of the trained model, were hierarchically clustered using the centroid linkage mode based on Equations (19) and (20).  
**Step 4: Verification of newly collected data.**  
To validate our methodology, we verify it with the newly collected dataset. We find out how many products can be grouped and overseen without training, including the product group found through hierarchical clustering, and verify whether anomaly detection is possible with the abnormal data of the newly collected dataset.
# A Generator for Recursive Zip Files
## 1 Introduction  
The concept of self-reference has long captivated computer scientists, leading to the development of programs that can reproduce their own source code [1]. These programs are called quines, and a particularly intriguing example is the \"zip quine\". A zip quine is a program stored within a compressed zip file that contains everything required to recreate itself when extracted. To understand how this works, it is helpful to know how file compression operates. Compression algorithms like those used in zip files reduce the file size by identifying patterns or repeated sections of data, representing them with fewer bytes. This process allows large amounts of information to be stored more efficiently.  
In a zip quine, the zip file does not just hold data. It holds the exact instructions necessary to recreate the file itself. This self-replicating behavior represents an elegant intersection of compression and recursion. However, the challenge lies in preserving this property while expanding the contents of the archive. This paper investigates how zip quines can be extended by adding extra files and content, while still maintaining their self-referential nature.  
Additionally, this study explores \"looped zip files\", a novel and more complex concept where one zip file contains another zip file, and this inner zip file also contains the original zip file. This creates a recursive structure, forming an infinite loop of nested archives. Each level of the loop refers back to the previous one, creating a cycle with no clear endpoint. Looped zip files push the boundaries of compression and recursion even further, introducing new technical challenges.
## Materials and Methods
### Methods
#### 2.2.2 Sample Characterization  
Fiber diameter and pore size of the PCL membranes were determined by Scanning Electron Microscope (SEM, JSM-IT100, JEOL Ltd., Japan) at 3 separate locations and 10 measurements per location. To be specific, the values of pore size were represented by the diameter of the inscribed circle of each triangle formed by three intersecting fibers. For wettability analysis, a high-resolution camera was used to capture the image of a water drop on the membrane surface, and the contact angle was determined using Image J. Stress-strain curves were obtained (TA.XTplus, Stable Micro Systems Co., UK) to examine the physical strength of the membranes. Chemical compositions were examined by Fourier-Transform Infrared Spectroscopy (FT-IR, Spectrum GX, PerkinElmer Inc., USA).
## 4 DUV Laser Generation Based on Diamond Raman Laser
According to the proof-of-principle experiment, the conversion efficiency of 23% implied the possibility of achieving high-power at the second Stokes wavelength of the Raman laser. If the pump laser of 1078 nm could be obtained at the average power of 50 W, the output power of its 2 nd Stokes wavelength at 1512 nm could be more than 10 W. The design of the Raman laser cavity should be optimized in order to avoid the damage of the M1 and diamond surface. For instance, the current concave OC could be replaced by a plane mirror which could mitigate the power density on the surface of M1 and the diamond. Moreover, using high damage threshold coatings for M1 and diamond would also improve the output power without any damages. Then, the NIR laser at the 2 nd Stokes wavelength could be high enough, as well as its good beam quality, to generate the final DUV laser at 193 nm with a high average power.