esa-sceva/satcom-chunk-collection huggingface dataset:
# A High Full Quality CMOS Image Sensor for Space Applications
## 2 Sensor Architecture
### Radiation Damage Effects on the Proposed Pixel Structure  
The effects of energetic particle radiation on CMOS devices and circuits in space can be categorized as total ionizing dose (TID) and displacement damage dose (DDD). The TID effect causes a build-up of trapped charge in the gate oxide of a CMOS device. The DDD effect occurs when high energy particles penetrate a silicon substrate. The energetic particles collide with silicon atoms and generate the dislocation of the silicon atoms which causes Frenkel pair defects. These effects cause a threshold voltage shift in CMOS devices and result in an excessive leakage current or a variation in device operating voltage.  
In a CIS, the radiation damage effects deteriorate the optical performances, such as dark current, temporal noise, and random telegraph noise [30] or result in the generation of permanent hot pixel [31,32]. It is known that the radiation damage effect on CMOS devices is decreased as CMOS technologies shrink to deep sub-micron regimes and the oxide thickness becomes thinner. However, pixel devices do not benefit from advanced technologies because a thick gate oxide is still used in the pixel device.  
Figure 6: Readout circuit and timing diagram: (**a**) Overall readout circuit and timing diagram; (**b**) Signal sampling; (**c**) Reset value sampling; (**d**) Initializing the switched capacitor amplifier; (**e**) Differential output generation.  
In a conventional 4T pixel, the size of the transfer gate and PPD affects the dark current performance under radiation [33; 34; 35]. While small PPD size and short gate length help to reduce the radiation damage effect on the dark current, the FWC also decreases as a result of the reduced PPD capacitance. Moreover, the leakage current from the PPD to FD node can increase. In general, the radiation damage effect of a pixel device is reduced as the device size shrinks, and hence, the increment of dark current due to irradiation is alleviated [36]. Recent pixel design demands smaller pixel size in order to increase pixel resolution for a fixed chip area. Therefore, pixel design requires careful consideration of the trade-off between optical performance and radiation tolerance.  
In this work, the pixel design followed previous researches for a conventional 4T pixel except for the in-pixel MOS capacitor. It has been reported that an amount of charges that can be trapped in an interface of SiO\({}_{2}\)/Si depend on the MOS capacitor size and radiation dose [37]. The trapped charges cause a shift in threshold voltage and variation of capacitance [17; 37; 38; 39; 40; 41; 42]. However, the radiation damage effect on a MOS capacitor used inside a pixel has not been studied yet. In order to analyze the effect of radiation on an in-pixel MOS capacitor, two different pixels having different sizes of capacitors has been fabricated and tested. The pixel performance under a radiation environment has been analyzed, and the analysis results are presented in the next section.
# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 4 Simulation Experiments
### Ablation Experiments
#### 4.3.4 Comparison with Other Feature Transformation Methods
The performance comparison among these methods is summarized in Table 5. The results show that while TCA yields low performance degradation, this is primarily because it starts from a very low initial accuracy in the first session. In fact, TCA consistently achieves the lowest accuracy across all incremental sessions, making it the weakest performer overall. We believe this is due to the lack of an appropriately defined target domain. PCA achieves relatively high initial accuracy but suffers from significant degradation as the incremental learning process progresses. GRAND performs comparably to our proposed method, with slightly lower per-session accuracy and slightly higher degradation. The performance gap can be attributed to two main factors: (1) the MLP structure in GRAND acts as a more complex learnable transformation, and (2) the label propagation mechanism induces feature smoothing across samples, which can enhance overall stability but slightly compromise discriminability compared to purely feature-based transformation methods. Overall, our proposed feature transformation method demonstrates the best performance. It achieves consistently high accuracy across all incremental sessions while maintaining relatively low performance degradation.
## 3 Results and Discussion  
### Burned Area Mapping  
Figure 3 shows a burned area from Sentinel-2 imagery; (a) pre-fire image, (b) post-fire image and (c) the spatial distribution of burned area detected in the study area using NBR. The burn scars in the post-fire image are clearly noticeable. Burned areas are characterized by residues of charcoal and ash, removal of vegetation, and modification of the vegetation structure [83].
## 2 Related Work  
### Image Retrieval  
Vision-based topological localization is generally cast as image retrieval that searches the most similar keyframe in the geo-tagged topological map to the query image via feature descriptions matching [14; 15]. To this end, the quality of the topological mapping and localization directly depends on the performance of image retrieval method.  
As a long-standing research topic in the computer vision field, image retrieval has been extensively investigated [16; 17]. Conventional approaches to image retrieval can be divided into two categories: text-based methods and content-based methods [18; 19; 20]. In text-based image retrieval, text annotation is employed to represent the images, forming keywords for each image, such as objects and scenes. Manual intervention is required in the labeling process, so text-based method is only suitable for small-scale image data. Afterwards, content-based was proposed, indexing the images by visual cues, such as color, shape, and texture. Early works for content-based image retrieval are based on global descriptors [21]. However, a well-known problem is that global features are susceptible to the interference of occlusion, translation, and illumination, which restricts its application scope. This issue prompted extensive studies on local feature based image retrieval to emerge, such as SIFT and SURF. Concurrently, encoding techniques that aggregate the features to construct an image descriptor has been extensively studied, such as vector of locally aggregated descriptors (VLAD) [22] and Fisher vector (FV) [23].  
Recently, features generated by convolutional neural networks has exhibited superior ability for image representation in various tasks. Extensive works have proved that features extracted from CNN can achieve state of the art performance in image retrieval [24; 25]. The work [26] studied the applicability of using the activation from full connectivity layer of CNN to describe the image for image retrieval. In the work [27], off-the-shelf features extracted from the OverFeat network was adopted as the image representation, which exhibited powerful ability for image retrieval. In addition, there exists many works focus on the region descriptors for image retrieval. The work [28] constructed a regional maximum activation of convolution (R-MAC) by aggregating compact features in a fixed layout spatial region which is robust to scale and translation. In the work [29], off-the-shelf features extracted from a pre-trained object detection CNN are used for initial filtering and region features constructed by region-wise pooling of activations are adopted for spatial re-ranking. Our image retrieval method required in topological localization is inspired by the work [29]. However, complementing previous work, we address the efficiency problem in real-time image retrieval by introducing the hash index technology.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 3 Methods
### Cluster Routing Implementation
#### 3.3.3 Cluster Routing with Single Channel  
In cluster routing, optimal performance is typically achieved through utilization of the single-channel version, as illustrated in Table 1. Technically, this approach can be summarized as creating multiple versions of the original capsule channel, increasing its dimension by one. Next, the.view() function is utilized to divide the additional dimension into two dimensions, \(\mathbf{K}\) and \(\mathbf{N}\), as depicted in Figure 1. Subsequently, the mean and standard deviation are computed along the \(\mathbf{K}\) dimension to determine the centroid and agreement vector, reducing the dimension by one. Following this, the mean tensor is multiplied with the weight and then summed to derive the output capsule channel, further reducing the dimension by one. This process involves expanding the dimension and subsequently reducing it to preserve the original dimensionality of the input capsule channel. Further details and the code implementation of this version can be found in our GitHub repository.  
The averaging and summing components of this technique can be conceptualized as a type of vector dimension reduction, in which the dimensionality is reduced while preserving essential information. However, additional experiments (which are not addressed in this study) are required to validate this hypothesis.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**smallNORB**} & \multicolumn{2}{c}{**Fashion-MNIST**} & \multicolumn{2}{c}{**SVHN**} & \multicolumn{2}{c}{**CIFAR-10**} \\  & **Error (\%)** & **Param** & **Error (\%)** & **Param** & **Error (\%)** & **Param** & **Error (\%)** & **Param** \\ \hline Baseline CNN & 3.76 & 3.30M & 5.21 & 3.38M & 3.30 & 3.38M & 7.90 & 3.38M \\ Dynamic [1] & 2.7 & 8.2M & - & - & 4.3 & \(\simeq\)1.8M & 10.6 & 8.2M \\ EM-Routing [4] & 1.8 & 310K & - & - & - & - & 11.9 & \(\simeq\)460K \\ RS-CapsNet [8] & - & - & 6.49 & 5.00M & 3.5 & 5.01M & 10.19 & 5.01M \\ Attention Routing [21] & - & - & - & - & - & - & 11.39 & 9.6M \\ DA-CapsNet [22] & 1.74 & - & 6.02 & - & 5.18 & - & 14.53 & - \\ Efficient-CapsNet [23] & 2.54 & 151K & - & - & - & - & - & - \\ RVC [25] & - & - & 5.87 & - & 2.97 & - & 8.3 & - \\ Inverted dot-product & & - & - & - & - & - & 14.83 & 560K \\ attention routing [26] & & 1.6 & 169K & 5.2 & 172K & 3.9 & 323K & 11.2 & \(\simeq\)323k \\ MoCapsNet [28] & - & - & - & - & 7.00 & - & 27.83 & - \\ SRM-CapsNet [29] & 8.54 & - & 8.35 & - & 6.90 & - & 28.26 & - \\ ER-CapsNet [30] & - & - & - & - & 4.33 & - & 11.46 & 9.4M \\ Metric Learning [31] & - & - & 9.59 & - & 7.24 & - & 26.76 & 9.4M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of test error rates in the capsule networks literature [7].
## 4 Results
### Material Characterization  
The result of applying the heat treatment in Section 2.1 to the material is shown in the SEM micrographs of Figure 2, where the microstructures of the LT and HT steels can be observed. MA islands are found in a bainitic matrix with different amounts of carbides. It is clearly seen in the pictures that the HT sample exhibit a larger amount of carbides, as well as a slightly larger size of MA islands.  
Most microstructural properties were quantified using EBSD measurements. The total fraction of martensite measured by this method is almost the same for both materials: 8.2% for the HT steel and 8.6% for the LT one. On the other hand, the MA fractions measured from BSE images in the LT and HT samples are, respectively, 3.4% and 2.4%. The remaining martensite is assumed to be LCM. Since the amount of silicon in this steel is quite low (only 0.10%), the measured amount of austenite is negligible in both samples, with a volume fraction lower than 0.4%. Figure 3 shows the distribution of grain sizes (equivalent diameter), aspect ratio of equivalent ellipsoid axes and angle of the principal axis with respect to RD. Phase fractions, average shape parameters and other microstructural magnitudes are summarized in Table 3. The plots (\(\varphi_{2}=45^{\circ}\) sections) of the ODFs calculated for bainite and martensite are displayed in Figure 4.  
Figure 2: SEM micrographs of the two bainitic steels obtained after the heat treatments in Figure 1: (**a**) HT; and (**b**) LT. The images show the presence MA islands (larger bright areas) and carbides in the bainitic matrix (small bright particles).
## 4 Discussion
Finally, small changes in the shape of the patient circuit and endotracheal tube between measurements could also account for some of the inaccuracies in the calculation of \(R_{\rm rs}\). The presented method of measuring \(R_{\rm rs}\) during HFOV is suitable for bedside patient monitoring because only a pressure and flow orifice is added to the patient circuit. In our study, a custom-made system consisting of an orifice, sensors, digitizing hardware, and a laptop with evaluation software was used [14]. In a real clinical scenario, any monitoring device capable of measuring proximal pressure and flow during HFOV and transmitting data in real time could be used. The disadvantage of the presented method may be the increased flow resistance and dead space caused by the addition of an orifice to the patient's circuit. Assessment of respiratory mechanics using FOT in mechanical ventilators is now available to physicians with the Fabian neonatal ventilator [22; 23]. However, the Fabian currently only determines the respiratory system reactance measured at an oscillation frequency of \(f=10\) Hz, which is typical for neonates. Besides the fact that in our study both parameters were measured at a frequency of \(f=5\) Hz, we believe that there is no significant difference between the method investigated in our study and the FOT method used by the Fabian ventilator. Therefore, no additional hardware would be required to simultaneously measure reactance and \(R_{\rm rs}\) at the patient's bedside. Based on our study, we propose that not only reactance [24] but also \(R_{\rm rs}\) could be assessed during HFOV.
## 3 Biofilm and Its Formation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indwelling Medical Device** & **Organisms** \\ \hline Central venous catheter & \begin{tabular}{c} Coagulase-negative staphylococci, _Staphylococcus aureus_, \\ _Enterococcus faecalis_, _Klebsiella pneumoniae_, _Pseudomonas aeruginosa_, _Candidata albicans_ \\ \end{tabular} \\ \hline Prosthetic heart valve & Viridans _Streptococcus_, coagulase-negative staphylococci, enterococci, _Staphylococcus aureus_ \\ \hline Urinary catheter & \begin{tabular}{c} _Staphylococcus epidermidis, Escherichia coli, Klebsiella pneumoniae_, _Enterococcus faecalis_, _Proteus mirabilis_ \\ \end{tabular} \\ \hline Artificial hip prosthesis & \begin{tabular}{c} Coagulase-negative staphylococci, b-hemolytic streptococci, \\ enterococci, _Proteus mirabilis_, _Bacteroides_ species, _Staphylococcus aureus_, _Viridans Streptococcus_, _Escherichia coli_, _Pseudomonas aeruginosa_ \\ \end{tabular} \\ \hline Artificial voice prosthesis & \
\begin{tabular}{c} _Candida albicans_, _Streptococcus utils_, _Streptococcus salivarius_, \\ _Rothia dentrocariosis_, _Candidia tropicalis_, _Streptococcus sobritus_, _Staphylococcus epidermidis_, _Stomatococcus mucilaginous_ \\ \end{tabular} \\ \hline Intrauterine device &
\begin{tabular}{c} _Staphylococcus epidermidis, _Corynebacterium_ species, \\ _Staphylococcus aureus_, _Microoccus_ species, _Lactobacillus plantarum_, \\ Group B streptococci, _Enterococcus species_, _Candida albicans_ \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Biofilm-associated microorganisms commonly isolated from selected indwelling medical devices [23]. Figure 1: The life cycle of biofilms beginning with the initial cell attachment and ending with biofilm dispersal. Quorum sensing (QS) is a communicative system for biofilm which allows the bacterial cells inside the biofilm to act as a community. This facilitates the growth, survival, and colonization of bacterial cells inside the biofilm [21]. The concentration of other bacteria present within a limited microenvironment is sensed through QS [27]. The bacteria then respond by activating specific genes which then produce virulence factors such as enzymes or toxins [27]. The QS molecules are N-acyl-L-homoserine lactones (AHL) in gram-negative bacteria and peptides in gram-positive bacteria [27]. Many investigations of the mechanism of biofilm resistance to antimicrobial agents have been carried out. A primary reason is the biofilm matrix preventing the access of antimicrobial agents, such as antibiotics, to the embedded bacterial cells [28]. Other reasons include slow penetration, resistant phenotypes, and altered microenvironments [29]. Because of slow penetration, antibiotics may fail to penetrate deeply into the biofilm. Some of the bacteria in the biofilm may develop into a protected phenotype resistant to antibiotics. Similarly, the accumulation of waste and nutrient depletion in the deeper biofilm layers may contribute to antibiotic resistance.
# Nanoscale Topographies for Corneal Endothelial Regeneration
## 2 Nanofabrication Techniques to Design Instructive Cell Substrates
Recently, two-photon polymerization (2 PP) lithography, the process of crosslinking photosensitive resins with a focused femtosecond laser beam, showed promising results for mimicking biologically relevant architecture with a possible feature size of <100 nm. In contrast to other fabrication techniques where substrate surfaces can be modified to obtain patterns consisting of simple grooves and ridges (also referred as to 2\(\frac{1}{2}\)D nanotexture-type structures), 2 PP (laser) lithography allows us to create real 3D nanostructure, which can include, for instance, hollow spaces. The recent deployment of 2 PP lithography for an increasing number of biological applications has in turn motivated further improvements of this technique and the development of novel biocompatible materials for new applications [42; 43]. The recent advances achieved with this technique allowed the generation of tissue-like structures, such as [PERSON]'s membrane [44], luminal walls of blood vessels [45], limbal stem cell niche [46], cardiac ECM [47] and other 3D biologically relevant models [48; 49; 50]. Apart from lithography techniques, electrospinning has had an enormous impact on regenerative medicine by producing structures that more closely mimic nanofibrous ECMs present in in vivo tissues [51; 52]. Electrospun fibers have been investigated as promising scaffolds particularly to promote cell growth by mimicking native collagen fibrils in ECM [53; 54] as well as mimicking interface tissues [55]. The combination of different patterns and structures can be of particular relevance for hCECs regeneration. For instance, overlaying nanofibrous topographies to hexagonal patterns represents a potential approach to mimic the Descemet's membrane/corneal endothelium interface. More recently, the combination of diverse manufacturing techniques for complex, hierarchical tissue constructs on multiple scales is gaining momentum, and these have been reviewed elsewhere [56]. For corneal applications, applying innovative post-processing techniques, such as microthermoforming [57; 58; 59], also opens new opportunities for such hierarchical cell-material constructs, which will help to construct tissue-mimicking grafts with even superior resolution and reliability.
## 2 System Description
### Cell Search Procedure  
In general, the initial cell search procedure consists of three steps [17]. The first step in the UE-side synchronization procedure is to obtain coarse STO, CFO, and frame timing based on the time-domain NR-PSS sequence. Once the pre-DFT synchronization processing is successfully completed, NR-PSS synchronization can be performed to obtain the CIDS in the frequency domain. Since the CSI is available only after the UE detects the ICFO, a differential detection technique is usually employed for non-coherent NR-PSS detection, thus eliminating the need for CSI estimation. This approach effectively removes the effects of channel fading and STO from the NR-SSS without requiring the CSI, consequently enhancing the synchronization process. Assuming \(G_{q}(m)\approx G_{q}(m-1)\), the differential cost function is given by [21]  
\[\Omega_{pss}(\tilde{v},\tilde{w})=\sum_{m\in\mathcal{D}}\tilde{Y}_{q}(m+\tilde {v})D_{\tilde{w}}^{*}(m), \tag{8}\]  
where the notation of the type \(\tilde{x}\) stands for trial value of \(x\), \(\tilde{v}\in\{-U,-U+1,\cdots,U\}\), \(\tilde{w}\in\{0,1,2\}\), \(\mathcal{D}=\{m|57\leq m\leq 182\}\) whose cardinality is \(M_{s}-1\), \(D_{\tilde{w}}(m)=P_{\tilde{w}}(m)P_{\tilde{w}}(m-1)\) is the differential NR-PSS, and \(\tilde{Y}_{q}(m)=Y_{q}(m)Y_{q}^{*}(m-1)\). Based on the mentioned observations, the estimated values \((\vartheta,\tilde{w})\) can be found by identifying the maximum of the cost function \(\Omega_{pss}(\vartheta,\tilde{w})\) over \((\vartheta,\tilde{w})\) as follows  
\[(\vartheta,\tilde{w})= \underset{(\vartheta,\vartheta)}{\text{arg max }}\Re\{\Omega_{pss}(\tilde{v},\tilde{w})\}, \tag{9}\]  
where \(\tilde{x}\) is the estimated value of parameter \(x\) and \(\Re\{\cdot\}\) denotes the real component of a complex number. After the completion of NR-PSS detection, the UE is prepared to estimate the CSI for the purpose of coherent detection of the subsequent NR-SSS signal. This can be achieved by performing the following task  
\[(\hat{g},\tilde{\eta})= \underset{(\hat{g},\vartheta)}{\text{arg max }}\Re\Big{\{}\Omega_{sss}(\tilde{g})e^{-|\hat{g}\pi\eta|(M+M_{g})/M}\Big{\}}, \tag{10}\]  
where  
\[\Omega_{sss}(\tilde{g})=\sum_{m\in\mathcal{S}}Y_{q}^{*}(m+\vartheta)Y_{q+2}(m +\vartheta)P_{\tilde{w}}(m)S_{\tilde{g}}^{*}(m)e^{-|\hat{g}\pi\vartheta(M+M_{ g})/M}. \tag{11}\]  
For each trial value of \(\hat{g}\), (10) carries out an exhaustive search of \(\eta\) by quantizing the potential RCFO values appropriately, resulting in a significant computational burden. To address the complexity issue, a sub-optimal solution has been proposed in [16].  
As can be seen in (6), since NR-SSS is generated through a combination of CIDS \(w\) and CIDG \(g\) in the frequency domain, NR-SSS detection is possible only after complete recognition of ICFO and CIDS. Therefore, the accurate detection of the NR-PSS is essential in the initial synchronization procedure, and this paper concentrates on the detection of ICFO and CIDS in the frequency domain.