esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a low-noise multi-path operational amplifier for high-precision sensors. A chopper stabilization technique is applied to the amplifier to remove offset and flicker noise. A ripple reduction loop (RRL) is designed to remove the ripple generated in the process of up-modulating the flicker noise and offset. To cancel the notch in the overall transfer function due to the RRL operation, a multi-path architecture using both a low-frequency path (LFP) and high-frequency path (HFP) is implemented. The low frequency path amplifier is implemented using the chopper technique and the RRL. In the high-frequency path amplifier, a class-AB output stage is implemented to improve the power efficiency. The transfer functions of the LFP and HFP induce a first-order frequency response in the system through nested Miller compensation. The low-noise multi-path amplifier was fabricated using a 0.18 \(\upmu\)m 1P6M complementary metal-oxide-semiconductor (CMOS) process. The power consumption of the proposed low-noise operational amplifier is 0.174 mW with a 1.8 V supply and an active area of 1.18 mm\({}^{2}\). The proposed low-noise amplifier has a unit gain bandwidth (UGBW) of 3.16 MHz, an input referred noise of 11.8 nV/\(\surd\)Hz, and a noise efficiency factor (NEF) of 4.46.  
low-noise amplifier; chopper stabilization technique; ripple reduction loop (RRL); notch; multi-path amplifier; class AB output; noise efficiency factor (NEF)
## 4 Proposed HE-DEPSO Algorithm
### Adaptive Hybrid Mutation Operator
#### 4.1.1 DE/Current-to-EHE/1 Mutation Strategy
Consequently, the search diversity of the HE-DEPSO algorithm is considerably improved, which is beneficial for escaping from possible local minima. At the beginning of the execution of the HE-DEPSO algorithm, the archives HE and HL are defined as empty. Through the evolutionary process, they store the elite and obsolete individuals, respectively. When analyzing the effect of the parameter \(pb\%\), which determines the percentage of individuals within the participation of \(\text{E}_{\text{t}}\), it was found that values close to 0.9 promote the participation of a more significant number of elite individuals, causing a greater diversity in the population. However, this diversity increase may affect the HE-DEPSO algorithm's convergence capability. On the other hand, values close to 0.1 may restrict the number of elite individuals to be considered, which improves the convergence of the algorithm but may cause stagnation at local minima. Due to the above, within the HE-DEPSO algorithm we choose to update in each iteration the value of \(pb\%\) following a linear decrement, given as follows:  
\[pb\%=p_{max}-\frac{t\cdot(p_{max}-p_{min})}{t_{max}} \tag{38}\]  
where \(t\) refers to the current iteration, \(t_{max}\) is the maximum number of iterations, and \(p_{min}\) and \(p_{max}\) are the minimum and maximum values of the interval assigned for the percentage of individuals within the partition \(\text{E}_{\text{t}}\). In this work, we have chosen to use the values \(p_{max}=0.4\) and \(p_{min}=0.1\). In this way, the sensitivity of the \(pb\%\) parameter is reduced and, at the same time, a good balance between exploitation and exploration is maintained.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method  
The key to enhancing ship-classification accuracy lies in selection of appropriate features that characterize ship-image properties. Deep neural networks can automatically capture structural information. Compared to their low-level counterparts, features captured by deep neural networks are more abstract, robust, and discriminative when dealing with in-class differences and inter-class similarities. Thus, deep neural networks demonstrate good feature-extraction and classification abilities. At present, deep learning is considered the most advanced method, and it has tasted great success in the field of image classification. In scene classification, the purpose is to separate different kinds of objects in the same picture, and the between-class scatter is relatively large. However, there is small difference between classes for ship classification. CNN features are based on low-level features obtained in the first convolution layer, which may not fully capture all local features and result in the loss of some important information, such as edges and contours. HOG has excellent capacity to describe the contour of objects. LBP can capture spatial texture information and local structure. Thus, in this study, we proposed an effective multi-feature fusion learning framework for ship classification, which combines the handcrafted features with high-level features obtained by CNN. Figure 2 depicts the flowchart of proposed ship classification framework. Firstly, the improved CNN in this paper is used to capture high-level features. Then the HOG and LBP features are used to extract handcrafted features to supplement global information of ship images. High-level features and handcrafted features are concatenated together to obtain a more discriminating representation. Finally, the Softmax function is used to classify different types of ship images in the output layer.
## 3 Results and Discussion
### Evaluation of Basic Characteristics of Tight Reservoir  
#### 3.2.1 Reservoir Space Characteristics  
The observation of drilling cores and thin sections shows that the lithology of Chang 9 oil layer formation consists mainly of sandy dolomite, oolitic dolomite, algal debris dolomite and powder fine-grained dolomite. The types of reservoir space mainly include intergranular pores, intercrystalline pores and dissolution pores. Intergranular pores are mainly developed in sand debris dolomite and oolitic dolomite, which are formed by the diffusion of intergranular pores through atmospheric freshwater dissolution (Figure 2a,b). Intercrystalline pores are mainly distributed between relatively coarse powder and fine-grained dolomite crystals, and the pore size is generally 100 \(\upmu\)m. It is formed by further dissolution and expansion on the basis of crystals (Figure 2c,d). Dissolution pores are the most developed pore types in the gypsum-salt carbonate symbiotic system on the eastern side of the paleo-uplift in the Ordos Basin. They are common in fine-grained dolomite, dolomite and microbialite. Secondary filling minerals such as quartz, medium and coarse-grained dolomite and calcite are common inside (Figure 2e,f).  
Figure 1: Geological overview (**a**) and representative stratigraphic histogram (**b**) of the study area.  
Figure 2: Rock structure and pore characteristics of Chang 9 reservoir in Ordos Basin. (**a,b**) are Intergranular pores; (**c,d**) are Intercrystalline pores; (**e,f**) are Dissolution pores.
## 4 Outline and Methodology
### Evaluation of Consistency
#### 4.4.1 Area Consistency  
The Pearson's correlation coefficient (\(R\)) [50] and the percentage disagreement (\(PD\)) [51] are used to evaluate the area consistency. Specifically, \(R\) (Equation (1)) represents the overall correlation degree and \(PD\) (Equation (2)) represents the correlation degree on each type. It is a method of measuring the correlation degree between two datasets and used to identify a linear correlation for a number of features between the validated MCD12Q1 data, based on the five different classification schemes and GlobeLand30 reference data. Similarly, the \(PD\) is used to depict the ratio of the different classification results of the same classification type shared by the verified five data layers of MCD12Q1 and the GlobeLand30 reference data, _i.e.,_ the degree of consistency between the two datasets. Thus, the smaller the value of \(\left|PD\right|\), the closer the results and the better the consistency.  
\[R=\frac{\sum_{k=1}^{n}(x_{k}-\overline{x})(y_{k}-\overline{y})}{\sqrt{\sum_{k =1}^{n}(x_{k}-\overline{x})^{2}\sum_{k=1}^{n}(y_{k}-\overline{y})^{2}}}\times 1 00\% \tag{1}\]  
\[PD=\frac{x_{k}-y_{k}}{x_{k}+y_{k}}\times 100\% \tag{2}\]  
where \(n\) is the classification number; \(x_{k}\) and \(y_{k}\) are, respectively, the total area of type \(k\) in MCD12Q1 and GlobeLand30; and \(\bar{x}\) and \(\overline{y}\) are, respectively, the average area of all land cover categories in MCD12Q1 and GlobeLand30.
## 3 Results
### Space State: Blood Spot Coordinates in the Camera View
#### 3.2.1 State and Action Space  
* The discrete world to explore is an \(N\times M\) grid where the state is defined by the position of the blood sample in one of these \(N\times M\) locations that corresponds to the pixels of the camera view. These positions are encoded in the OpenAI environment into a sequence of consecutive states.
* Blood samples can be represented by more than 1 pixel as shown in the different camera views in Figure 3. The final state is given after computing the center of mass of the blood sample.
* Two level zooms with the same pixel resolution (\(30\times 20\) pixels) were considered to simulate the real 3D problem. Zoom Out images with \(60\times 30\) pixels are down-sampled to reduce the dimensionality using nearest-neighbor interpolation.
* The number of possible states raises up to \(2\times N\times M+1\). This number takes into account both levels of zooms and the null case (no blood appears in the image).
* Big [b] and small [s] steps of 4 and 1 pixel, respectively, in Move Left (ML), Move Right (MR), Move Up (MU) and Move Down (MD) actions are considered, as well as Zoom Out (ZO) and Zoom In (ZI)) movements. The set of possible actions \(A\) varies for each state \(s\in\mathbf{S}\), \(\mathbf{A}(s)=\{\)\(ML[b]\), \(ML[s]\), \(MD[b]\), \(MD[s]\), \(MR[b]\), \(MR[s]\), \(MU[b]\), \(MU[s]\), \(ZO\), \(ZI\)}.
* Invalid joints space actions such as trying ZI action when the current state in already in this framework, are not included as possible movements in the action space
## 4 Conclusions  
Dispersion-adapted PhC cavities of length 10 um were fabricated on a low-temperature, CMOS compatible a-Si platform. The fabrication process was optimized to ensure a high-uniformity of hole diameters and smooth sidewalls. A low-loss polymer waveguide was formed on top of the PhC cavity to optimize optical coupling to the PhC cavity.  
Measurements of the transmission spectra of the PhC cavities show that the PhC exhibit resonant reflections in the C and L wavelength bands relevant for optical telecommunications. Data shows that it is possible to obtain PhC cavities with Q-factors as high as \(Q=\) 15,862. Data shows that is is also possible to achieve lithographic control over the positions of the resonant wavelengths by careful choice of the PhC lattice constant.  
Measurement of the optical spectrum of the hybrid external cavity photonic crystal laser shows that it is possible to operate this laser in a single-mode configuration with SMSR of 33 dB. Unfortunately,  
Figure 8: Observed ASE broadening caused by narrow longitudinal mode spacing of external laser cavity. The PhC lattice constant in this case is 386 nm.  
Figure 7: Measured optical spectra of external cavity lasers with PhC cavity lattice spacings of 386 nm, 388 nm, and 390 nm. The lasers are operating at wavelengths of 1562.76 nm, 1569.42 nm, and 1577.95 nm with SMSR of 26 dB, 24 dB, and 33 dB respectively.  
it was not possible to maintain single-mode operation due to the length of the cavity used to make the hybrid external cavity laser. To ensure single-mode operation, the length of the laser cavity would need to be reduced to the range of millimetres. This could be achieved by packaging the a-Si PhC reflector with an RSOA gain chip whose length is on the order of 500 um.  
Conceptualization, investigation and validation, S.I., C.D., A.L., R.S. and L.O.; formal analysis, S.I., C.D., R.S. and L.O.; writing-original draft preparation, S.I., R.S.; writing-review and editing, S.I., R.S. and L.O.; supervision and project administration, L.O.; funding acquisition, C.D., R.S. and L.O. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the following funding sources EU ERC-SG 337508 DANCER, EU H2020 Marie Sklodowska-Curie 713654 and SFI 18/TIMA/6128.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
a-SiAmorphous Silicon  
a-Si:HHydrogenated Amorphous Silicon  
c-SiCrystalline Silicon  
CMOSComplementary metal-oxide-semiconductor  
DADispersion Adapted  
SOGSpin-On-Glass  
PhCPhPhPhotonic Crystal  
SEMScanning Electron Microscope  
SMSRSide-Mode Supression Ratio  
SOA Semiconductor Optical Amplifier  
SOISilicon-On-Insulator
# SEA and GATOR 10 Years Later
## 4 Structural Features of the SEA and GATOR Complexes
### Seacit/Gator1  
The structural profile of the SEACIT/GATOR1 subunits is completely different (Figure 2A). Npr2/NPRL2 is a paralog of Npr3/NPRL3 [10; 41] and both proteins possess N-terminal longin domains [42; 43]. Iml1/Sea1 and its human homologue DEPDC5 contain a unique composition of domains that are not found in any other proteins. SEACIT components also have PEST motifs that often exist in rapidly degraded proteins [3]. However, PEST motifs are not well preserved in mammalian orthologues and, thus, could be a specific feature of the yeast SEA complex.  
The structure of GATOR1, resolved recently by cryo-EM, revealed the architecture of each GATOR1 component [29] (Figure 2A). DEPDC5 has the following five defined domains: N-terminal domain (NTD), followed by SABA (structural axis for binding arrangement), SHEN (steric hinderance for enhancement of nucleotidase activity), DEP (Dishevelled, Egl-10 and Plekstrin) and C-terminal (CTD) domains. Interestingly, NTD, SABA and DEP domains can be found in membrane-associated proteins. For example, a domain similar to NTD exists in the SNARE chaperone Sec18/NSF, the SABA domain--in Sec23 of COPII vesicles (again returning to the theme of coating complexes). The DEP domain, which has diverse functions in signal transduction, is involved in the interactions between the regulator of G protein signaling (RGS) proteins and their membrane-bound receptors, the GPCRs [44]. The DEP domain is also found in a DEPTOR subunit of mTOR1 [45].  
NPRL2 and NPRL3 have a similar structure with N-terminal longin domains that heterodimerize (Figure 2A). C-terminal domains of NPRL2 and NPRL3 also form a large contact surface. The SABA domain in DEPDC5 interacts with the NPRL2 TINI domain (tiny intermediary of NPRL2 that interacts (with DEPDC5)). By the way, the domain nomenclature within the GATOR1 complex created a doubtful precedent, where protein domains are named after the first (SHEN) and the last (SABA-TINI) authors of the article that reported the structure [29].
## 2 Role of AI in Dentistry
### Identifying Periodontal Disease  
Identifying periodontal disease presents significant challenges due to the complex nature of its development. Periodontal disease, which includes conditions like gingivitis and periodontitis, is a chronic inflammatory disease that affects the supporting structures of the teeth. Its progression is influenced by a combination of factors, including microbial, immunological, and genetic elements. However, the distinction between aggressive periodontitis and chronic periodontitis has been a persistent challenge in clinical practice. This is because traditional diagnostic methods, such as clinical examinations, histological assessments, and genetic tests, have not been able to reliably differentiate between these two forms of the disease. At this juncture, computer-aided diagnostic systems that incorporate advanced computer vision techniques offer a promising solution. These systems can analyze a vast array of imaging data to detect subtle differences in periodontal tissue, which might indicate the presence of more aggressive forms of the disease. By doing so, they assist clinicians in making more accurate diagnoses, leading to more targeted therapeutic interventions. For instance, computer vision can be used to analyze the depth and pattern of periodontal pockets, the amount of bone loss, and the overall condition of the gingiva. This level of precision is crucial in preventing the progression of periodontal disease, which, if left untreated, can lead to tooth loss and other serious health complications. The integration of AI into periodontal disease diagnosis is a significant step forward, offering the potential to revolutionize how these conditions are identified and managed in dental practice.
## 4 Extensions of RUMVRHF Smoother
### Extension to Receding-Horizon Rauch-Tung-Striebel Fixed-Lag Smoother  
Because the proposed RUMVRHF smoother is established based on two local optimal estimators, three steps are required to obtain the overall estimate and its error covariance matrix. Using the Rauch-Tung-Striebel (RTS) smoothing algorithm [25; 26], the proposed recursive smoothing process can be implemented in two steps: filtering for the current state and smoothing for the fixed-lag state estimations. Although the estimation structure of RTS smoothing is different from that of the proposed smoother, it can provide computational efficiency and reliable results [27]. Thus, in this subsection, we propose a receding-horizon Rauch-Tung-Striebel fixed-lag (RHRTSF) smoother as an extension and application of the proposed recursive method.  
First, the backward recursion of the overall posteriori covariance matrix \(P^{+}_{k-l}\) on the time-interval \([k-h\;\;k]\) can be represented in terms of posteriori local covariance matrices \(\bar{P}^{+}_{1,k-l}\) and \(P^{+}_{2,k-l}\) as follows:  
\[P^{+}_{k-l} = \bar{P}^{+}_{1,k-l}-\bar{P}^{+}_{1,k-l}\Big{(}\bar{P}^{+}_{1,k-l} +P^{+}_{2,k-l}\Big{)}^{-1}\bar{P}^{+}_{1,k-l} \tag{95}\] \[= P^{+}_{1,k-l}-\bar{P}^{+}_{1,k-l}\bar{A}^{T}_{k-l+1}\Big{(}\bar{ P}_{1,k-l+1}+P_{2,k-l+1}\Big{)}^{-1}A_{k-l+1}\bar{P}^{+}_{1,k-l}\] \[= \bar{P}^{+}_{1,k-l}-\bar{P}^{+}_{1,k-l}A^{T}_{k-l+1}\Big{[}\bar{ P}_{1,k-l+1}+\Big{\{}\Big{(}P^{+}_{2,k-l+1}\Big{)}^{-1}+\Big{(}\bar{P}^{+}_{1,k-l+1} \Big{)}^{-1}\] \[-\bar{P}^{-}_{1,k-l+1}\Big{\}}^{-1}\Big{]}^{-1}A_{k-l+1}\bar{P}^{+} _{1,k-l}\] \[= P^{+}_{1,k-l}-P^{+}_{1,k-l}A^{T}_{k-l+1}\Big{[}\bar{P}_{1,k-l+1}+ \Big{\{}\Big{(}P^{+}_{k-l+1}\Big{)}^{-1}-P^{-}_{1,k-l+1}\Big{\}}^{-1}\Big{]}^{-1}\] \[\times A_{k-l+1}\bar{P}^{+}_{1,k-l}\] \[= P^{+}_{1,k-l}-\bar{P}^{+}_{1,k-l}A^{T}_{k-l+1}P^{-}_{1,k-l+1} \Big{[}\bar{P}^{-}_{1,k-l+1}+\bar{P}^{-}_{1,k-l+1}\Big{\{}\Big{(}P^{+}_{k-l+1} \Big{)}^{-1}\] \[-\bar{P}^{-}_{1,k-l+1}\Big{\}}^{-1}\bar{P}^{-}_{1,k-l+1}\Big{]}^{- 1}A_{k-l+1}\bar{P}^{+}_{1,k-l}\] \[= P^{+}_{1,k-l}-K_{k-l}\Big{(}P_{1,k-l+1}-P^{+}_{k-l+1}\Big{)}K^{T} _{k-l},\]for \(1\leq l\leq h\), where \(K_{k-l}=\bar{P}^{+}_{1,k-l}A^{T}_{k-l+1}\bar{P}^{-1}_{1,k-l+1}\).  
Involving the same derivation as (95), the backward recursion of the overall smoothed estimates can be written as follows:  
\[\hat{x}^{+}_{k-l|k} = \hat{x}^{+}_{1,k-l|k}+K_{k-l}\Big{(}\hat{x}^{+}_{k-l+1|k}-\hat{x}_ {1,k-l|k}\Big{)}, \tag{96}\]  
for \(1\leq l\leq h\).  
Thus, the RHRTSF smoother can be obtained by combining the receding horizon optimal filter on the overall horizon \([k-N\;\;k]\) and backward recursions (95) and (96) by the following algorithm:  
The concept of the proposed RHRTSF smoother is depicted in Figure 2.
###### Abstract  
Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny-Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software labelling and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny-Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision (\(mAP\)) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny-Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection.  
machine vision; industrial inspection; defect detection; size measurement +
Footnote †: journal: Applied Sciences
# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
## 2 Materials and Methods  
Our system pipeline is an extension of ORB-SLAM3. The proposed algorithm improves the tracking thread, local mapping thread, and loop closing thread. We added the map restoration function and introduced the grayscale histogram as a tool to speed up loop closure detection.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 6 Simulation Results
### Failure Ratio of Solution  
The simulation shows the relationship between failure ratio (%) and task size. By changing the size of the task, we studied the failure ratio of the five scenarios. Failure ratio means that the residual energy is less than 0 or the task is not complete within time \(T\). Assuming the task size follows from 60 KB to 150 KB, as the task size increases, the result of the failure ratio is shown in Figure 6.  
It can be seen in Figure 6 that the failure ratio grows with the increase in tasks. First, the failure ratio is very high when the task is very large, but the proposed MMM has a relatively low failure ratio. MMM can find the best combination of task-offloading size, energy harvesting time and task-offloading time. Second, the curves of FEH and OFF are very tortuous. When the task is small, the failure ratio is the highest. When the task is large, the failure ratio is almost equal and relatively high, and it is higher than the failure ratio of MMM only. Then, the growth rate of the failure ratio of the FTO curve increases rapidly as the task increases, especially when the task is relatively large, the failure rate approaches 1. The fixed offload task size makes the number of local executed tasks too large to be completed within \(T\). MMM can reasonably divide the task offload size, thereby allowing users to complete tasks within time \(T\). Finally, when the task is small, the failure rate of the LOC can almost reach zero. The local parallel execution meets the demand, but the task offload of the MMM scheme is  
Figure 5: Time block vs. residual energy.  
serial execution, which will cause relatively more energy consumption. When the task is large enough, the failure rate of LOC is as high as 100%. When the task is small, the harvesting energy can fully satisfy the task to execute locally. As the task increases, the harvesting energy cannot meet the large amount of energy consumed by the local execution.