esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Step Enhancer: Deblurring and Denoising of OCT Images
###### Abstract  
Optical coherence tomography (OCT) is a rapidly evolving imaging technology that combines a broadband and low-coherence light source with interferometry and signal processing to produce high-resolution images of living tissues. However, the speckle noise introduced by the low-coherence interferometry and the blur from device motions significantly degrade the quality of OCT images. Convolutional neural networks (CNNs) are a potential solution to deal with these issues and enhance OCT image quality. However, training such networks based on traditional supervised learning methods is impractical due to the lack of clean ground truth images. Consequently, this research proposes an unsupervised learning method for OCT image enhancement, termed one-step enhancer (OSE). Specifically, OSE performs denoising and deblurring based on a single step process. A generative adversarial network (GAN) is used for this. Encoders disentangle the raw images into a content domain, blur domain and noise domain to extract features. Then, the generator can generate clean images from the extracted features. To regularize the distribution range of retrieved blur characteristics, KL divergence loss is employed. Meanwhile, noise patches are enforced to promote more accurate disentanglement. These strategies considerably increase the effectiveness of GAN training for OCT image enhancement when used jointly. Both quantitative and qualitative visual findings demonstrate that the proposed method is effective for OCT image denoising and deblurring. These results are significant not only to provide an enhanced visual experience for clinicians but also to supply good quality data for OCT-guide operations. The enhanced images are needed, e.g., for the development of robust, reliable and accurate autonomous OCT-guided surgical robotic systems.  
Keywords:optical coherence tomography; image enhancement; generative adversarial network; unsupervised learning +
Footnote †: journal: Applied
# Heat-Only-Driven Vuilleumier Refrigeration
## 3 Heat-Only Operation and Refrigerator Designs  
The heat-only operation mode is particularly desirable for applications in which waste heat can be used and a permanent auxiliary power supply is costly. As already discussed, the displacements \(D_{1}\), \(D_{2}\), \(D_{31}\), and \(D_{32}\) cannot have all the same size in heat-only operation. More specifically, for the hot displacer \(D_{1}\) and \(D_{31}\) must be different and/or \(D_{2}\) and \(D_{32}\) for the cold displacer.  
In the following, we will compare three different design cases: In the first design, only the hot displacer piston has to provide the needed mechanical power; we will refer to this case as the D1 design. In the second case--the D2 design--all mechanical power comes from the cold displacer piston. Finally we will look at the DA design, in which each of the two pistons provides the mechanical power for its own operation.  
On the technical level, heat-only operation of the three design cases is characterized as follows: The DA design requires each cycle average of the two bookkeeping reservoirs WTH and WTC in Figure 2 to be zero separately, as in this design no power exchange \(P_{\text{PE}}\) between the two reservoirs is possible. Thus, for both displacer pistons, mechanical power is neither gained nor lost during operation. For the D1 and D2 designs, this is different: Due to the active power exchange \(P_{\text{PE}}\), only the combined bookkeeping reservoirs WTH and WTC need to balance, and power produced on the hot side can be utilized at the cold side and vice versa.  
The obvious question is now which of the three designs is optimal with respect to providing maximal cooling power. In order to prevent an unintended increase or decrease in the overall working volume of the VR during the cooling power optimization, in all three designs the sum of the displacements on the hot and cold side of each displacer piston must remain fixed and equal:  
\[D_{1}+D_{31}=D_{2}+D_{32}=D_{\text{tot}} \tag{8}\]  
Given this requirement, the volumetric influence of the two pistons are then described by their displacement ratios \(d_{1}\) and \(d_{2}\):  
\[d_{1} =D_{1}/D_{31}, \tag{9}\] \[d_{2} =D_{2}/D_{32}, \tag{10}\]  
which will be the variables of interest. Below we will investigate how these displacement ratios have to be chosen to ensure a heat-only operation of the Vuilleumier refrigerators. Of particular interest in this context is to what extent the displacement ratios need to be adjusted if the thermodynamic transport parameters \(\alpha\) and \(\kappa\) of the model are varied.
## 1 Introduction
They are complex computer-controlled systems, which are equipped with different chambers, pumps, connections, valves, and filtration systems to reproduce peristaltic movements, pH changes and gradual secretions [2; 18; 19; 20]. Recently, the miniaturization trend has led to growing interest in microfluidic devices (chips) and millifluidic platforms. These are mainly used as reactors for extraction or synthesis, but they can also represent very promising solutions to rapidly test compound absorption and bioactivities (therapeutic effect and toxicity), when they are used as platforms for cell cultures [21; 22; 23]. They can reproduce simil-physiological flows (as for example, the tangential one), improving the cellular permeability of traditional static Caco-2 cell models, and consequently, the studies concerning intestinal absorption and bioactivities [24; 25; 26; 27]. In the last few years, interest in millifluidic systems has grown, as they are often easier and cheaper to manufacture in comparison to chips, and can reproduce biological barriers and fluid volume/mixing similarly to what happens in in vivo compartments. In addition, these systems are very useful to study a compound behavior when interacting with cells [28; 29]. In our previous investigations, a multi-organ cell-based dynamic platform was set up by using a commercial millifluidic bioreactor, namely LiveFlow(r) (IVTech Srl., Massarosa, LU, Italy), to study gastrointestinal digestion of food molecules/by-products [30; 31]. The aim of the present research was to focus on the intestinal step using Caco-2 cells under dynamic conditions, searching for a screening platform able to clarify the absorption mechanism of different secondary metabolites. We tested different phenolic acids (caffeic, quinic, and rosmarinic acids) and flavonoids (quercetin and rutin) in order to compare their different intestinal absorptions and assess the potential advantages deriving from the use of this dynamic simulation. The results were further compared to the use of Caco-2 cells under static conditions.
###### Abstract  
Faults and fracture sets in carbonate reservoirs are key geological features that govern hydrocarbon migration, accumulation, and wellbore stability. Their accurate detection is essential for structural interpretation, reservoir modeling, and drilling risk assessment. In this study, we propose an Optimally Oriented Coherence Attribute (OOCA) method that integrates geological guidance with multi-frequency structural analysis to achieve enhanced sensitivity to faults and fractures across multiple scales. The method is guided by depositional and tectonic principles, constructing model traces along directions with maximal structural variation to amplify responses at geological boundaries. A distance-weighted computation and extended directional model trace strategy are adopted to further enhance the detection of fine-scale discontinuities, overcoming the limitations of traditional attributes in resolving subtle structural features. A Gabor-based multi-frequency fusion framework is employed to simultaneously preserve large-scale continuity and fine-scale detail. Validation using physical modeling and field seismic data confirms the method's ability to enhance weak fault imaging. Compared to traditional attributes such as C3 coherence, curvature, and instantaneous phase, OOCA delivers significantly improved spatial resolution. In zones with documented lost circulation, the identified structural features align well with drilling observations, demonstrating strong geological adaptability and engineering relevance. Overall, the OOCA method offers a geologically consistent and computationally efficient solution for high-resolution fault interpretation and drilling risk prediction in structurally complex carbonate reservoirs.  
eological guidance; structural features; multi-frequency fusion; fracture sets detection; carbonate reservoir; strike-slip fault; geological risks 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 1-10. External Links: Document Cited by: SS1.
## 4 Measured Data
### Measured Data--Link 1  
Figure 3 shows 4 boxplots (when viewed from left to right for an initial joint angle of 10\({}^{\circ}\)-40\({}^{\circ}\)), which show how the initial values of the joint angle have changed in 4 different measurement cases. Each boxplot was processed based on the measured initial joint angles in 20 measurements. It can be seen from the graphs that the mean of the 20 measured initial values (red line) approaches the required input at most in the case of an angle of 20\({}^{\circ}\) (the second graph from the left with mean value 20.016\({}^{\circ}\)), where it is not possible to see the 25 th and 75 th percentiles (blue rectangle) is present. Only one different value is also a remote value of 19.872\({}^{\circ}\) (shown in the second graph on the left by a red cross). It is possible also to observe a remote measured value of 40.464\({}^{\circ}\) in the fourth graph from the left when the initial angle was set to the reference value of 40\({}^{\circ}\). The largest difference between the minimum and maximum set input value (black horizontal line) can be observed for an initial joint angle of 10\({}^{\circ}\) (first graph from the left).  
From the 20 measured time courses of the joint angle, where the initial joint angle is 10\({}^{\circ}\), the angles were set for individual measurements in the interval <9.216\({}^{\circ}\); 10.08\({}^{\circ}\)>. The  
Figure 3: Boxplots of initial values of joint angles—link 1.  
maximum value of the interval represents the statistic parameter Mode. Figure 4 represents the mean response (red curve) and standard deviation (the grey area) from measured data.  
The interval of the initial angles of rotation of the joint with a reference value of 20\({}^{\circ}\) was \(<\)19.872\({}^{\circ}\); 20.016\({}^{\circ}\)>. Such a small range was caused by the fact that out of 20 measurements, the value 20.016\({}^{\circ}\) was set 19 times. Waveforms of standard deviation and mean response of data are shown in Figure 5.  
Figure 6 shows the waveform of mean response at a joint angle of 30\({}^{\circ}\) for link 1, where the initial values of the joint angle were set in the range \(<\)29.808\({}^{\circ}\); 30.24\({}^{\circ}\)>. More than half of the measurements started from 30.096\({}^{\circ}\). Figure 6 shows a more pronounced standard deviation also (compared to the deviation for angles of 10 \({}^{\circ}\) and 20 \({}^{\circ}\)).  
Figure 4: The mean response and standard deviation for the joint angle 10\({}^{\circ}\)—link 1.  
Figure 5: The mean response and standard deviation for the joint angle 20\({}^{\circ}\)—link 1.  
Figure 6: The mean response and standard deviation for the joint angle 30\({}^{\circ}\)—link 1.  
The last group within link 1 is closed by the curve mean response and standard deviation if the initial angle was 40\({}^{\circ}\), as shown in Figure 7 (respectively, in reality, the initial angles were set in the interval \(<\)39.888\({}^{\circ}\); 40.464\({}^{\circ}\)\(>\) during the measurements). Half of the measurements were started at a joint rotation angle of 40.032\({}^{\circ}\).
## 3 Results
### Speech-Synthesis Systems
There are several other adaptation techniques that could be utilised to account for the mismatch, and these techniques include, although not exhaustively: adapting acoustic models with maximum-likelihood linear regression (MLLR), constrained structural maximum a posteriori linear regression (CSMAPLR), maximum a posteriori (MAP), speaker-adaptive training (SAT) based on constrained MLLR (CMLLR), and vocal-tract-length normalisation (VTLN) [37]. VTLN aims at reducing the interspeaker and inter-age-group acoustic variability due to vocal-tract-length (and shape) variations among speakers by warping the frequency axis of the speech-power spectrum [49]. There are two ways to account for the differences in vocal-tract length between adults and children. One could apply VTLN to adult utterances during training to make the normalised features more like children's speech. Alternatively, one could apply VTLN to the child's utterances during training and testing to make them more similar to the adults' speech [25]. [PERSON] et al. 's [55] study shows that combining VTLN with CSMAPLR resulted in an improved adaptation method for both HMM-based automatic speech recognition and text-to-speech. It was clear that when there was a limited amount of adaptation data available, even as little as one sentence, the VTLN yielded the best naturalness and intelligibility results. When only one sentence was used, the CSMAPLR transformation was not intelligible at all. This is an indicator that VTLN is useful in child-speech synthesis. Although VTLN has been recommended for children's speech, it has also been suggested that it should be conducted differently for children when compared to adult speech [63]. The results from [PERSON], [PERSON] and [PERSON]'s study [63] suggested that unlike conventional VTLN, it was better not to scale the bandwidths of the filters, but rather to scale the filter centre frequencies [63]. Interestingly, aside from creating a child voice by adapting an average adult voice or an average child voice, [PERSON] et al. 's study [44] compared two additional adaptation methods using stacked transformations: StA and StVA. In the first method, StA, an average voice trained from adult data was adapted using training data of the average child voice. The resulting synthetic voice sounded child-like with regards to pitch, pronunciation, and rhythm. This model was then further adapted to resemble a specific target child speaker. In the second method, StVA, an average voice trained from adult data was adapted using training data of the average child voice, and then VTLN occurred [44]. It was found that stacked transformation systems (StA and StVA) were preferred by listeners and resulted in better adapted voices than directly adapting the average adult voice or the child voice [44].
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 2 Proposed Method: MICP
### Factors to Consider in Proposed Method
#### 2.3.3 Down Sampling  
One of the differences between volume data and surface data is their resolution. Surface data have a much higher resolution than CBCT volume data. The resolution of volume data is not as high as that of surface data even if 3D interpolated intensity values are given to all the points. A voxel in the volume data may even correspond to more than dozens of points in the surface data. Thus, using all points of the surface data is ineffective. To improve the efficiency of MICP registration, input surface data was down sampled to match the volume data. The conventional registration process, always contains a segmentation process that takes at least 20 min, so the expected time reduction is low. However, the expected elapsed time of the proposed registration method is dramatically lowered because segmentation is contained in the iteration. Generally, there are 4 down sampling algorithms that are widely used in 3D data handling; uniform sampling, random sampling, normal sampling, and covariance sampling [39]. The volume data has uniform resolution along the \(x\), \(y\), and \(z\) directions. Given this feature of the volume data, it is reasonable to use uniform sampling to adjust the resolution of the input data.  
Figure 4: Results of normal correction methods for scan data. (**a**) raw scan data, (**b**) normal smoothing, (**c**) remeshing.
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 4 Implementation and Simulation Results of the N78 Band Sub-6 GHz DPA
### The Performance of the Unequal Wilkinson Power Divider  
Figure 20 displays a photograph of a 2.5:1 Wilkinson power divider that underwent fabrication and subsequent connection to a vector network analyzer device. This connection allowed for the measurement of its circuit S-parameters, focusing on a central frequency of 3.5 GHz. Figure 21 presents a comparison between the measurement and simulation results, utilizing CST Studio Suite software version 2021, for the proposed unequal divider. The measurements revealed insertion loss values of approximately 2 dB for S21 and 7.2 dB for S31, both at 3.5 GHz. Additionally, the output port isolation (S23) exhibited performance better than 12 dB, indicating effective isolation between the two output ports. The measured return loss (S11) also surpassed 20.6 dB at 3.4 GHz. However, some inconsistencies between the simulation and measurement outcomes were observed in Figure 21. These disparities could be attributed to unforeseen variations that occurred during the fabrication and assembly processes. Furthermore, the decision to employ two 220 \(\Omega\) resistors in parallel, necessitated by the unavailability of a single 110 \(\Omega\) resistor in the Egyptian market, may have played a role in these observed discrepancies.  
Figure 19: Performance comparison of the three DPA designs versus input power: (**a**) large-signal gain, (**b**) output power, (**c**) drain efficiency, and (**d**) power-added efficiency at saturation.  
Figure 21: Simulated (dashed) and measured (solid) scattering parameters of the 2.5:1 Wilkinson-power divider. (**a**) S11, (**b**) S21, (**c**) S31, and (**d**) S23.  
Figure 20: Photograph of the fabricated 2.5:1 unequal Wilkinson power divider.
## 6 Simulation Results
### Runtime Study of the Proposed Scheme on Raspberry Pi  
In this section, we test the execution time of our scheme that was implemented on a Raspberry Pi 4 model B board. This hardware board is equipped with 2 GB RAM and a 1.5 GHz quad-core CPU. To perform the experiments in the present runtime analysis, we used all 48 ECG recordings from the MIT-BIH arrhythmia database, which is largely used in medical research. The length of the frames used to generate the feature vectors and the size of the corresponding QR code are indicated in Table 5. Then, our scheme was executed on Raspberry Pi 4 model B for the ECG recordings using two execution modes: sequential and parallel execution. Our scheme was run 10 times, and the average execution time for  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attacks** & **Proposed Scheme** & **Scheme** & **Scheme** & **Scheme** & **Scheme** & **Scheme** & **Scheme** \\  & **Scheme** & **[**18**]** & **[**10**]** & **[**23**]** & **[**19**]** & **[**61**]** & **[**17**]** & **[**62**]** \\ \hline Circular right shifting by & & & & & & & \\ factor \(k=+20\) & 0.0181 & 0.0723 & 0.0953 & 0.0192 & 0.0190 & 0.0494 & 0.0236 & 0.0401 \\ \hline Circular left shifting by factor & & & & & & & \\ \(k=-20\) & 0.0179 & 0.0719 & 0.0955 & 0.0189 & 0.0191 & 0.0556 & 0.0633 & 0.0523 \\ \hline Scaling of the amplitude by & & & & & & & \\ the factor \(\delta=1.5\) & 0 & 0.1258 & 0.2369 & 0.0201 & 0.0182 & 0.1625 & 0.1409 & 0.1368 \\ \hline Scaling of the amplitude by & & & & & & & \\ the factor \(\delta=0.5\) & 0 & 0.2067 & 0.2336 & 0.0302 & 0.0201 & 0.0955 & 0.1002 & 0.0905 \\ \hline Signal cropping in three & & & & & & & \\ arbitrary indices & 0 & 0.0523 & 0.0662 & 0.0255 & 0.0126 & 0.0892 & 0.0705 & 0.0806 \\ \hline Signal compression with & & & & & & & \\ CR = 75\% & & & & & & & \\ \hline Gaussian noise of strength & & & & & & & \\ k = 0.04 & & 0.0031 & 0.0065 & 0.0082 & 0.0042 & 0.0038 & 0.0081 & 0.0056 & 0.0092 \\ \hline Average filtering of window & & & & & & & \\ \(w=5\) & & & & & & & \\ \hline Gaussian noise with k = 0.06 & & & & & & & \\ + compression with CR = 50\% & & & & & & & \\ + amplitude scaling by & & & & & & & \\ \(\delta=1.25\) & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the average BER corresponding to the retrieved watermarks using various bio-signals zero-watermarking methods.  
the test recordings was computed and is reported in Table 4. From this table, we can notice that the parallel computing on Raspberry Pi significantly improved our scheme's global execution time by an ETIR \(>\) 65% compared to the sequential execution. This improvement allows for saving important energy in the case of large databases due to the reduction in the Raspberry Pi board's running time. Thus, our security scheme suggestion is cost-effective regarding energy consumption and can be efficiently deployed in the IoMTs and smart city projects.
# Analysis of Soil-Water Characteristic Curve and Microstructure of Undisturbed Loess
## 2 Materials and Methods
### SWCC Measurement
#### 2.2.2 Experimental Process  
The SWCCs of the five sets of undisturbed loess samples at different depths were measured using the HYPROP device along the drying path. The test procedure is illustrated in Figure 3.  
The specific steps involved in measuring the SWCC using the HYPROP system are as follows: Initially, the procedure involved preparing and saturating the undisturbed loess samples. The ring knife was pressed into the soil block to make the loess sample while ensuring the potential gaps between the side wall of the ring knife and the sample was  
Figure 3: Procedures of HYPROP for measuring SWCC. (**a**) Preparation of undisturbed loess samples; (**b**) saturated undisturbed loess sample; (**c**) evaporation process monitoring.  
Figure 2: Particle size distribution curve of the five sets of loess samples, measured using a Mastersizer 2000 (UK) laser particle size analyzer. (**a**) Cumulative particle size distribution curves of loess; (**b**) particle size density distribution.  
filled with Vaseline. A layer of filter paper and permeable stone was placed at the top and bottom of the ring knife sample, and the assembled sample was immersed in degassed water to naturally saturate. The ring knife sample was weighed at regular intervals until the weight no longer changed, indicating that the sample was fully saturated. The subsequent procedure involved saturating the tension shafts and the sensor unit, with the ceramic cup of the tension shafts being filled with degassed water. The third stage involved the compilation of the saturated soil specimen within the HYPROP system. An auger was used to drill holes in the soil sample for the tension shafts while ensuring the tension shafts' proper alignment with the sample holes and fixing the soil sample in place with clips. Subsequently, we proceeded by positioning the tension unit and sample onto the balance for automated weighing and initiated the test. The final step to measure the SWCC with HYPROP was to monitor the evaporation process of the undisturbed loess sample. The variation in the value of matric suction with the volumetric water content was monitored using HYPROP-VIEW software [[https://metergroup.com/products/hyprop-2/](https://metergroup.com/products/hyprop-2/)]([https://metergroup.com/products/hyprop-2/](https://metergroup.com/products/hyprop-2/)) (accessed on 11 April 2024). Once the tension value began to decrease, signifying the end of the test, the sample was extracted from the sensor unit, and the weight of the dry soil sample was ascertained using a drying method. In general, these procedures guarantee the precise determination of the SWCC through the utilization of the HYPROP system, thereby offering reliable information on soil moisture attributes and hydraulic behaviors.