esa-sceva/satcom-chunk-collection huggingface dataset:
# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 5 Conclusions  
This paper introduces MSDA-Net, a medical image segmentation framework comprising an encoder, a decoder, an MRC, and a PDA. The encoder efficiently extracts both global and local semantic information. In the decoder, a multi-receptive field dense connection module and a parallel dual-branch attention module are added. MRD uses dense connections and convolutions with different expansion rates to extract feature information at various receptive fields, thereby enhancing the segmentation performance of the lesion area. The PDA module integrates spatial and channel attention to evaluate feature representation, mitigate the impact of irrelevant information, and enhance the precision and resilience of segmentation outcomes.  
Although our network demonstrates promising performance, it still possesses certain limitations. Firstly, its design around the encoder-decoder structure hinders its adaptability for general network integration. Secondly, MSDA-Net exhibits a complex architecture and a considerable parameter count. Our future efforts will primarily focus on addressing the limitations of our proposed module to enhance its adaptability. Additionally, we will continue to explore more efficient methods for parameter reduction and increased computational speed to streamline the network's complexity while maintaining its performance.  
The authors confirm their contribution to the paper as follows: study conception and design, C.Z. and K.C.; writing--original draft preparation, C.Z.; writing--review and editing, C.Z. and X.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The dataset is made available online at [[https://bbbc.broadinstitute.org/BBBC038](https://bbbc.broadinstitute.org/BBBC038)]([https://bbbc.broadinstitute.org/BBBC038](https://bbbc.broadinstitute.org/BBBC038)), [[https://challenge.isic-archive.com/data/#2018](https://challenge.isic-archive.com/data/#2018)]([https://challenge.isic-archive.com/data/#2018](https://challenge.isic-archive.com/data/#2018)), [[https://www.kaggle.com/datasets/balraj98/cvcclinicalb](https://www.kaggle.com/datasets/balraj98/cvcclinicalb)]([https://www.kaggle.com/datasets/balraj98/cvcclinicalb](https://www.kaggle.com/datasets/balraj98/cvcclinicalb)) (accessed on 13 March 2024).  
The authors declare no conflicts of interest.
## 1 Introduction
Using the ZYGO interferometer for real-time quantitative measurement, the automatic closed-loop adjustment of mosaic error is realized [22]. In 2019, we proposed an online compensation technique for grating profile errors, which effectively compensates for the influence of grating substrate profile errors on grating wavefront quality [23]. In 2021, by analyzing the influence of the groove displacement error and the yaw error caused by the indexing system on the grating resolution capability, the error was corrected by modifying the structure of the grating ruling machine workbench [24]. But for the grating ruling machine ruling system error and grating ruling machine itself single error for grating performance index research has not been systematically studied. To solve this problem, this paper investigates the impact of each error on the grating ruling machine and proposes a deterministic correction scheme to improve the success rate and efficiency of the grating ruling. The experimental results verify the accuracy and effectiveness of the proposed method. The main contributions of this paper are as follows:  
1. Based on the scalar diffraction theory of grating groove function, the physical phenomenon of light source splitting after grating interference and diffraction is described, and the complex amplitude distribution of light source under the action of a diffraction grating on the diffraction screen is obtained. The complex amplitude distribution can give the position and intensity distribution information of the diffraction light and then analyze the influence of the ruling error on the grating ghost line and stray light. 2. In order to eliminate the ghost line and stray light error, which affect the grating performance in the grating ruling process, the worm gear and screw are mechanically corrected. 3. The measurement's optical path of random error is established. The grating ruling error is controlled by an active control system, and the error compensation system is optimized. To improve the performance index of the ruling grating.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 3 Objective Function Formulation
### First Level  
The goal of the first level is to minimize the power loss of the DN, as well as the voltage offset. The object function is shown in Equation (20).  
\[\left\{\begin{array}{l}\min(\sum\limits_{t=1}^{T}P_{\text{loss},t}+\omega V_{ off})\\ \\ P_{\text{loss},t}=\sum\limits_{i=1}^{N_{\text{{}_{I}}}}s_{i,t}r_{i}\frac{P_{i,t }^{2}+Q_{i,t}^{2}}{\left(U_{i}^{t}\right)^{2}},V_{off}=\sum\limits_{t=1}^{T} \sqrt{\frac{\sum\limits_{i=1}^{N_{\text{{}_{I}}}}\left[([|U_{i}^{t}-1|/0.05] \times 10+|U_{i}^{t}-1|/0.05)\times 0.05\right]^{2}}{N_{\text{{}_{I}}}}}\\ \\ \end{array}\right. \tag{20}\]  
\[SW=\left[s_{1,t},s_{2,t},s_{3,t}\ldots s_{N_{\text{{}_{I}}},t}\right] \tag{21}\]  
\[\Phi=\left[P_{MG,1}^{t},P_{MG,2}^{t},P_{MG,3}^{t}\ldots P_{MG,N_{\text{{}_{MG} }}}^{t}\right] \tag{22}\]  
\[N_{f}=N_{Bus}-1 \tag{23}\]  
The first term in Equation (20) represents the total network power loss of the DN. \(P_{loss,t}\) is the power loss of the DN at time slot \(t\). The second term in Equation (20) represents the node voltage offset, which can be regarded as a penalty function. \(V_{off}\) is voltage offset in a day [26]. \(\omega\) is a coefficient indicating the degree of importance. \(r_{i}\) is the resistance of the branch \(i\). \(P_{i,t}\), \(Q_{i,t}\) are the active power and reactive power of the end node of the branch i.  
The controlled variables of the reconfiguration of the DN are shown in Equation (21), \(SW\) represents the set that controls the open/closed state of the switch on each branch, \"0\" represents the switch is open while \"1\" represents the switch is closed, the inequality constraint of \(SW\) is shown in Equation (15). \(\Phi\) represents the set that controls power interaction between the MGs and the DN. The relevant constraints of \(\Phi\) are shown in Equations (13) and (14). \(N_{\text{{}_{MG}}}\) is the number of the MGs connected to the DN. The operating status of the DN can be adjusted by changing the values or states of the controlled variables above when the DN is running.  
In order to facilitate the maintenance of the distribution network and reduce the short-circuit current when the distribution network fails, the topology of the distribution network is usually radial. The constraint is shown in Equation (22). \(N_{f}\) is the number of branches in the DN.  
Figure 3: Diagram of the proposed model.
# A Reconfigurable Radio-Frequency Converter IC in 0.18 \(\upmu\)m CMOS
## 4 System Implementation and Validation
### Mixers  
Figure 9 shows the measured operation of the RF mixer in terms of noise figure and gain for a supply of \(3\,\mathrm{V}\), both for the high-linearity and the low-power modes. In particular, Figure 9a shows the dependency of these two magnitudes with respect to the RF input frequency in a range from 400 MHz to 1600 MHz, whereas Figure 9b plots the variation of noise figure and gain with respect to temperature in the desired operation range from \(0\,\mathrm{\SIUnitSymbolC}\) to \(100\,\mathrm{\SIUnitSymbolC}\). Figure 9c shows the values of the 1 dB compression point (P1 dB) and the third-order intercept (IP3) of the mixers with respect to the RF input frequency, also for the high-linearity and low-power modes.  
Finally, Table 1 provides a summary of the performance of the main building blocks of the frequency synthesizer, which have been characterized as stand-alone cells: the voltage-controlled oscillators and the mixers. It has to be noted that the figures corresponding to VCO\({}_{1}\) have been obtained by extracted post-layout simulations, since only VCO\({}_{2}\) has been fabricated for testing; nevertheless, the synthesizer, which incorporates VCO\({}_{1}\) has a correct operation over its whole frequency range.  
Figure 8: Measured response of the stand-alone fabricated VCO, which is the one covering the higher frequency range. (**a**) Tuning range and coarse frequency step and (**b**) phase noise at the highest frequency of 2.7 GHz.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 2: Polynya Monitoring in Polar Areas
Multispectral measurements, therefore, provide important characteristics of the polar oceans, e.g., the position of the marginal ice zone, presence and dynamics of icebergs, and eddies. Small satellites (eventually in a constellation configuration) represent the most convenient solution to study polynyas and improve the knowledge of their dynamics, allowing for a continuous monitoring of specific polar ocean areas at a very high resolution [135].
# Deep Learning-Based Low-Frequency Passive Acoustic Source Localization
## 3 Test Case Setups
### Case 2: Two-Dimensional ASL on the Horizon  
This case used a classification approach to determine the direction of arrival (DoA) of the acoustic signal from a source located on the horizon. The four-microphone array was used for this case and the azimuth angle (\(\theta\)) had to be determined. The range of possible values of \(\theta\) (0-180\({}^{\circ}\)) was discretized into \(N\) classes. The output of the network is the probability distribution of all the classes, and the class with the highest probability is the estimated DoA. The setup is shown in Figure 8.  
Figure 8: Case 2: Two-dimensional ASL on the horizon. Two possible source configurations \(S_{1}\) and \(S_{2}\) are shown. \(\theta<90^{\circ}\) for \(S_{1}\) while \(\theta>90^{\circ}\) for \(S_{2}\). \(r=10\). The source coordinates for these two positions are \((r\cos\theta,r\sin\theta)\) and \((-r\cos\theta,r\sin\theta)\), respectively.  
Since this case uses a classification approach, the final dense layer of the network had the softmax activation function, which normalized the output of the network to give the probability distribution over \(N\) classes. ReLU activation was used everywhere else. _ADAM_ was used as the optimizer while the loss function was cross-entropy, which is the standard loss function for classification problems [34]. We used both types of sources in this case, the monopole and the sinusoidal plane wave signal, and they have been discussed separately.
# A g\({}_{m}\)/I\({}_{\rm D}\)-Based Low-Power LNA for Ka-Band Applications
## 3 MOSFET Characterization and Simulation Results
It achieves a notable gain of 19.5 dB with an NF of 4.7 dB, but at the cost of high power consumption (59 mW). A single-ended-input, differential-output tunable K/Ka-band LNA operating at 28 and 39 GHz is demonstrated in a 65 nm CMOS in [42], maintaining a very low NF of 2.8 dB with a gain of 17.2 dB, yet with a very high power consumption of 28.5 mW. In [43], a 22 nm CMOS fully depleted-SOI low-power LNA with a single-stage cascode configuration is presented. It achieves a remarkable power consumption of only 4.6 mW, a gain of 7 dB, and an NF of 5 dB. A dual-band LNA in 22 nm CMOS FDSOI for 5G wireless systems is demonstrated in [44]; the authors report a high gain (19.3 dB) and a minimum NF of 5.2 dB with a power consumption of 11.4 mW in  
Figure 7: 3D view of the two MOSFETs used to design the cascode LNA. 0.27 mm\({}^{2}\). The LNA achieves simultaneous dual-band operation by employing a two-stage single-ended cascode topology with carefully optimized transmission lines and capacitor-based matching networks. In [29], a wideband (14 and 31 GHz) LNA in 54 nm CMOS SOI is demonstrated, providing a low NF of only 1.4 dB with 12.8 dB gain. However, this LNA draws as much as 15 mW and occupies an area of 0.3 mm\({}^{2}\). In contrast, we propose a low-power LNA that achieves a gain of 11.4 dB with only 1.98 mW power consumption, while maintaining an NF within the average range of the other state-of-the-art designs. The developed work demonstrates the \(g_{m}/I_{D}\) methodology can effectively be used to obtain an LNA with a gain above 10 dB with a low NF. We report a very low power consumption with remarkably high performance at Ka-band frequencies. Figure 8: Simplified frontal view of the cascode LNA final layout. Figure 9: Simulation results of the proposed LNA with the EM characterization of each passive component (**a**) and two-tone linearity simulation for the obtainment of the IIP3 (**b**).
## 4 Experimental Results  
### Datasets and Evaluation Criteria  
We utilized the CAIL2021 case label prediction dataset [30] as the primary experimental resource to validate the effectiveness of the proposed hierarchical prototype neural network. The labels in the CAIL2021 case label prediction task dataset are curated by legal experts and represent factual labels for private lending cases. These labels are applied to publicly available judgment documents, constructing a case label prediction dataset. The CAIL2021 dataset comprises 2496 legal text samples, with labels categorized into three hierarchical levels, classified as evidence, private lending relationships, contract parties, and contract performance. The number of label types at each level is 11, 75, and 234. The dataset is split into training, validation, and test sets in an 8:1:1 ratio, respectively, with an average of 7.6 labels per sentence.  
Considering the limited sample size in the CAIL2021 dataset and the current unavailability of other legal multi-label recognition datasets, we also selected the WOS [31] and AAPD [32] datasets to supplement our validation of the effectiveness of the proposed model for general-level multi-label classification tasks. Despite the relatively shallow depth of the label hierarchy in these two datasets (comprising only two levels), the abundance of labels and samples within them provides an effective supplementary means to assess the capabilities of the model in multi-label classification tasks, particularly under conditions characterized by a high number of labels and uneven sample distribution. The detailed distribution of the three benchmark datasets is presented in Table 1, while the distribution of sample counts across different labels is illustrated in Figure 3. The sample distributions in these datasets vary significantly, enabling an effective evaluation of the performance of the model under conditions of uneven sample distribution.  
The study assessed model performance using micro-F1 and macro-F1, which are widely used in multi-label classification tasks [33]. Micro-F1 is a micro-averaging algorithm that focuses more on the overall classification performance, while macro-F1 is a macro-averaging method that emphasizes the classification performance of each individual class.
## 3 Results and Discussion
#### 3.2.4 The Establishment of the Settlement Performance Evaluation Model
Based on the evaluation of flow performance and settlement performance, it is recommended to select CSFMs with fractal dimensions between 2.50 and 2.59 to achieve the best balance between flow performance and settlement performance and ensure the reliability of long-distance transportation and construction quality. Although the model based on fractal dimensions performs well on coal gangue slurry, it should be pointed out that the application of the model still has limitations. The material composition of the coal gangue slurry discussed  
Figure 15: Relationship between the fractal dimension and settlement performance. in this paper is only coal gangue and water and does not contain cementitious materials. However, for other types of slurry or filling materials, it still needs to be verified by further experimental research.
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Applications
#### 4.3.3 Wearable FSSs
The average heart beat rate is sent periodically by concurrent modulation by two FSS, singly at every arm. Numerous sensors and checksum bit can send data and easily be incorporated by utilizing communication protocols.
## 2 Theoretical Model of a System in the Presence of Gaussian Noise
### N-Correlator Receiver
 \tag{25}\]  
The mean value of this random function over all \(w_{1}\) values is the probability of that the first symbol is correctly transmitted. It can be expressed as  
\[ \begin{split}\mathrm{P}(c|Sym1)=\int_{-\infty}^{\infty}\mathrm{P }(c|w_{1}=w)f_{W1}(w_{1})dw_{1}=\\ \int_{-\infty}^{\infty}\left[1-\sum_{i=1}^{N-1}\binom{N-1}{i} \left(-1\right)^{i+1}[\frac{1}{2}erfc\frac{w_{1}}{\sqrt{2\sigma_{wn}^{2}}}]^{i }\right]f_{W1}(w_{1})dw_{1}.\end{split}  \tag{26}\]  
The probability of symbol error is  
\[ \begin{split} Pesym=1-\mathrm{P}(c|Sym1)\\ =\sum\
olimits_{i=1}^{N-1}\binom{N-1}{i}\left(-1\right)^{i+1} \int_{-\infty}^{\infty}[\frac{1}{2}erfc\frac{w_{1}}{\sqrt{2\sigma_{wn}^{2}}}]^ {i}f_{W1}(w_{1})dw_{1}.\end{split}  \tag{27}\]  
If one symbol is represented by \(K\) bits then the number of spreading sentences is \(N=2^{K}\). By inserting Equation (21) the bit error probability can be calculated as  
\[ \begin{split} p_{e}=\frac{N/2}{N-1}\frac{1}{\sqrt{2\pi\sigma_{w1 }^{2}}}\sum_{i=1}^{N-1}\binom{N-1}{i}\left(-1\right)^{i+1}\int_{-\infty}^{ \infty}[\frac{1}{2}erfc\frac{w_{1}}{\sqrt{2\sigma_{wn}^{2}}}]^{i}\cdot e^{ \left(-w_{1}^{2}/2\sigma_{w1}^{2}\right)}dw_{1}.\end{split}  \tag{28}\]
## 2 System Model
### Signal Model  
Consider a MIMO communication scenario in which transmitter node \(a\) is equipped with \(N_{a}\) antennas and the receiver node \(b\) possesses \(N_{b}\) antennas. The signal transmitted over a Rayleigh fading channel from node \(a\) to node \(b\) can be modeled as  
\[\mathbf{\bar{r}}_{ab}=\sqrt{\frac{\gamma_{ab}}{N_{a}}}\mathbf{\bar{G}}_{ab} \mathbf{\bar{x}}_{a}+\mathbf{\bar{n}}_{ab}, \tag{1}\]where \(\gamma_{ab}\) represents the average received SNR. The channel matrix \(\mathbf{\tilde{G}}_{ab}\in\mathbb{C}^{N_{b}\times N_{a}}\) is assumed to follow a Rayleigh fading distribution, where each element is independently drawn from a circularly symmetric complex Gaussian distribution with zero mean and unit variance. The transmitted signal vector \(\mathbf{\bar{x}}_{a}\in\mathbb{C}^{N_{a}\times 1}\) is generated from encoded data symbols drawn from the modulation alphabet \(\mathcal{\bar{A}}^{N_{a}}\), where \(\mathcal{\bar{A}}\) represents an \(M\)-ary quadrature amplitude modulation (QAM) constellation. This signal is normalized such that its average power satisfies \(\frac{1}{N_{a}}\mathbb{E}\{|\mathbf{\bar{x}}_{a}|^{2}\}=1\). The received signal is corrupted using additive white Gaussian noise \(\mathbf{\bar{n}}_{ab}\in\mathbb{C}^{N_{ab}\times 1}\), which has independent components with zero mean and unit variance.  
To enable symbol detection under one-bit quantization, the complex-valued received signal in (1) is reformulated in an equivalent real-valued representation \(\mathbf{r}_{ab}=[\mathrm{Re}\{\mathbf{\bar{r}}_{ab}\},\mathrm{Im}\{\mathbf{ \bar{r}}_{ab}\}]^{T}\) as  
\[\mathbf{r}_{ab}=\sqrt{\frac{\gamma_{ab}}{N_{a}}}\mathbf{G}_{ab}\mathbf{x}_{a} +\mathbf{n}_{ab}=\mathbf{H}_{ab}\mathbf{x}_{a}+\mathbf{n}_{ab}, \tag{2}\]  
where  
\[\mathbf{G}_{ab}=\begin{bmatrix}\mathrm{Re}\{\mathbf{\tilde{G}}_{ab}\}&- \mathrm{Im}\{\mathbf{\tilde{G}}_{ab}\}\\ \mathrm{Im}\{\mathbf{\tilde{G}}_{ab}\}&\mathrm{Re}\{\mathbf{\tilde{G}}_{ab}\} \end{bmatrix},\;\mathbf{x}_{a}=\begin{bmatrix}\mathrm{Re}\{\mathbf{\bar{x}}_{ a}\}\\ \mathrm{Im}\{\mathbf{\tilde{x}}_{a}\}\end{bmatrix}\mathbf{n}_{ab}=\begin{bmatrix} \mathrm{Re}\{\mathbf{\bar{n}}_{ab}\}\\ \mathrm{Im}\{\mathbf{\bar{n}}_{ab}\}\end{bmatrix}, \tag{3}\]  
where the effective channel matrix is defined as \(\mathbf{H}_{ab}\triangleq\sqrt{\frac{\gamma_{ab}}{N_{a}}}\mathbf{G}_{ab}\in \mathbb{R}^{2N_{b}\times 2N_{a}}\). The real-valued input vector \(\mathbf{x}_{a}\in\mathcal{A}^{2N_{a}}\) is drawn from the underlying modulation alphabet \(\mathcal{A}\), which corresponds to an \(\sqrt{M}\)-ary pulse-amplitude modulation constellation. The ADC applied at the receiver imposes a hard-limiting operation, which converts each entry of \(\mathbf{r}_{ab}\) to its sign. The quantized observation corresponding to the \(i\)-th dimension is given by  
\[q_{ab,i}=\mathrm{sign}(r_{ab.i})=\mathrm{sign}(\mathbf{h}_{ab,i}^{T}\mathbf{x} _{a}+n_{ab,i}), \tag{4}\]  
where \(i\in\mathcal{I}=\{1,\ldots,2N_{b}\}\) and \(\mathbf{h}_{ab,i}^{T}\) represents the \(i\)-th row of the matrix \(\mathbf{H}_{ab}\).
# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 4 Experiments
### Evaluation Indicators  
In order to effectively evaluate the performance of the model, we introduced some commonly-used indicators [34] in semantic segmentation as the performance indicators of the model. These indicators included _PA_ (Pixel Accuracy), _MPA_ (Mean Pixel Accuracy), _MIoU_ (Mean Intersection over Union), and _FWloU_ (Frequency Weighted Intersection over Union). We assumed that there were a total of k + 1 classes, from L0 to Lk, which contained the background. \(p_{ij}\) represents the number of pixels that belong to class \(i\) but are predicted to be class \(j\). _PA_ is the proportion of correctly marked pixels to total pixels:  
\[PA=\frac{\sum_{i=0}^{k}p_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}p_{ij}} \tag{12}\]  
_MAP_ is a simple promotion of _PA_, which calculates the proportion of correctly classified pixels in each class, and then computes the average of all categories:  
\[MPA=\frac{1}{k+1}{\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}}}. \tag{13}\]  
_MIoU_, as a standard measure of semantic segmentation, calculates the ratio of intersection and union of two sets, which can be transformed into the sum of positive true number ratio, false negative number ratio and false positive number ratio. _IoU_ is calculated on each category and then averaged:  
\[MIoU=\frac{1}{k+1}{\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ii}+\sum_{j=0} ^{k}p_{ji}-p_{ii}}}. \tag{14}\]_FWIoU_ is an improvement on _MloU_, which weights the importance of each class according to its frequency of occurrence.  
\[\textit{FWIoU}=\frac{1}{\sum\limits_{i=0}^{k}\sum\limits_{j=0}^{k}p_{ij}}\sum \limits_{i=0}^{k}\frac{\sum\limits_{j=0}^{k}p_{ij}p_{ii}}{\sum\limits_{j=0}^{k}p _{ij}+\sum\limits_{j=0}^{k}p_{ji}-p_{ii}}. \tag{15}\]  
Figure 9: Semantic label dataset of concrete crack. (**a**) Shows some samples from training set used in the experiment. (**b**) Shows some samples from verification set used in the experiment. (**c**) Shows some samples from test set used in the experiment.