esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 5 Experimental Results
### Integrated Circuit Implementation  
In this section, we use the digital integrated circuit design flow to implement the circuit. The design flow includes register transfer level (RTL) hardware design, logic synthesis, testability circuit design, automated placement and routing, design rule check (DRC), layout versus schematic (LVS) and post-layout simulation. Then we use the TSMC 0.18 um 1P6M process and implement the chip.  
The value of the input signal can be observed from the waveform and the value of the output waveform is in accordance with the design expectation. In Figure 25, we can see the input data, the three-dimensional coordinates (out_X, out_Y, and out_Z), and reflectance values decoded by the chip. After calculation of the values of XYZ through the algorithm, the Golden Pattern is produced and the result is verified by Testbench.  
After the RTL code is verified, the next step is logic synthesis, which will convert the RTL code into a logic gate using the design compiler, as shown in Figure 26. In the logic synthesis, it is necessary to adjust the relevant environment settings, such as the timing, speed and related processes. Since the digital signal is not ideal, the problems of setup time and hold time need to be considered when the signal is changed.  
Figure 24: System operation time.  
Figure 25: Register transfer level (RTL) timing diagram.  
The next step is the design of the testable circuit, mainly to facilitate the test of the main function of the chip after the chip is packaged. We verify the test coverage and the fault coverage of the circuit. The test coverage rate of this circuit is 99.88%, and the fault coverage rate is 99.44% (Figure 27).  
After backend design of the automatic placement and routing, DRC, LVS, and post-layout simulation, we adopt the TSMC 0.18 um 1P6M process to develop the chip. After manufacturing, the chip is packaged by CQFP 100 package. The total chip area is 1.93 mm \(\times\) 1.93 mm, the core area is 1.32 mm \(\times\) 1.32 mm, and the number of logic gates is 129,688. The maximum operating frequency is 100 MHz, and the average power consumption is 237.34 mW. The chip specifications are shown in Table 8, and a microphotograph of the chip is shown in Figure 28.  
Figure 27: Test coverage and the fault coverage of the circuit.  
Figure 26: Gate-level circuit.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 4 Materials and Methods
### Problem Description  
We assume as input a unitary matrix \(U_{in}\) which needs to be synthesized for a specific target device. We also provide as input the native basis gate set \(B\) and qubit topology \(T\) of the target. Our compiler is then required to synthesize a circuit \(C_{syn}\), with an equivalent unitary \(U_{syn}\), using gates \(g\in B\) and adhering to topology \(T\) for multi-qubit gate placement. Let \(\mathbf{E}(U_{in},C_{syn})\) be the approximation error between \(U_{in}\) and \(U_{syn}\), \(\mathbf{D}(C_{syn})\) be the depth of \(C_{syn}\), which is used as a metric for execution time, and \(\mathbf{G_{n}}(C_{syn})\) be the number of \(n\)-qubit gates, used as a metric to estimate gate error. In our work \(n=2\), since we only consider CNOT gates. The goal of the GA-based compiler is to simultaneously minimize \(\mathbf{E}\), \(\mathbf{D}\) and \(\mathbf{G_{2}}\), possibly with a target threshold \(\epsilon\) for \(\mathbf{E}\).  
We can, therefore, formally define our optimization problem as:  
\[\min_{C_{syn}} \mathbf{F}(C_{syn})=[\mathbf{E}(U_{in},C_{syn}),\mathbf{D}(C_{ syn}),\mathbf{G_{2}}(C_{syn})]\] subject to \[g\in B,\quad\forall g\in C_{syn}\] \[g\text{ is valid in }T,\quad\forall g\in C_{syn}\text{ where }g \text{ is multi-qubit}\] \[\mathbf{E}(U_{in},C_{syn})\leq\epsilon\quad\text{ (optional constraint)}\]  
At this point we note that this optimization problem, which includes optimizing both a combinatorial problem (placement of gates in the circuit) and a continuous one (tuning gate parameters), is _non-convex_ by nature [47]. It is characterized by a complex solution landscape with multiple local minima. This problem is also non-linear, since it involves non-linear components such as the optimization of parametric gates.
## 3 Results
### Postoperative Complications and Predictive Value of Indices  
Of the group studied, two patients (3.7%) required reoperation due to bleeding (Clavien-Dindo classification IIIb), one (1.9%) developed empyema, and another (1.9%) developed chlorothorax (Clavien-Dindo classification IIIa) requiring the placement of chest drainage without intubation. The most frequent complication of the entire cohort studied (20.4%) was persistent air leak (Clavien-Dindo Classification I) [26], slightly increasing the length of stay without generating any major complication. The complications are shown in Table 2.  
No significant relation was observed between inflammatory changes and the incidence of postoperative complications after logistic regression analysis. The odds ratios (ORs) were adjusted with univariate regression, adding clinically significant variables in a second instance, but no significant ORs were found. In addition, the ROC curves obtained did not show statistical significance for any of the indices in relation to the studied variables (Figures 1 and 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Pre CX** & **Post CX** & **\(\Delta\)** & \\ \hline  & **Median (Q1, Q3)** & **Median (Q1, Q3)** & **Median (Q1, Q3)** & **_p_-Value** \\ \hline
**Neutrophils** & 4350 (3525, 5600) & 8600 (6800, 11,500) & 4150 (2025, 6675) & \textless{}0.001 \\
**Lymphocytes** & 1550 (1200, 2100) & 815 (600, 1200) & \(-615\) (\(-1000,-300\)) & \textless{}0.001 \\
**Monocytes** & 380 (300, 500) & 500 (300, 700) & 100 (7.5, 200) & \textless{}0.001 \\
**Platelets** & 218,500 (196,250, 254,750) & 199,000 (173,250, 233,750) & \textless{}14,000 (\(-39\),000, 500) & 0.001 \\
**PLR** & 139.44 (101.67, 197.34) & 225.28 (138.99, 336.25) & 82.72 (16.31, 150.00) & \textless{}0.001 \\
**NLR** & 2.56 (2.19, 4.68) & 9.94 (6.38, 15.40) & 6.70 (2.89, 11.70) & \textless{}0.001 \\
**LMR** & 4.29 (3.21, 6) & 1.63 (1.21, 2.83) & \textless{}2.20 (\(-3.70,-1.50\)) & \textless{}0.001 \\ \hline \end{tabular}  
* Pre Cx—pursurgical intervention, Post Cx—post-surgical intervention, \(\Delta\)—differences in hematological values (post-pre). A positive value implies an increase and a negative value indicates a decrease in the variable analyzed.  
\end{table}
Table 4: Pre- and post-surgical hematological variation.  
Figure 1: ROC curve between complications and preoperative values as (**A**) LMR; (**B**) PLR; (**C**) NLR and (**D**) SIL. The ROC curves, which relate complications to the results of both pre and postoperative indices, do not show good results. When the ROC curve approaches the dotted straight line on the graph, which corresponds to an AUC of 0.50, it is equivalent to random guessing by flipping a coin. In all cases, it is very close to this line, and in some cases, it crosses it downwards, indicating an even worse statistical performance.
## 2 Materials and Methods
### Interpolation Formulation  
For the CFRC laminates, Hermite interpolation functions can be used for deducing the element displacements. Four nodes of plate elements are used to model the laminates. Each node of a plate element contains seven degrees of freedom. For a plate element, a displacement vector can be denoted as  
\[\begin{bmatrix}u&v&w\end{bmatrix}_{e}^{T}=\mathbf{N}\mathbf{d}_{e}^{T}, \tag{12}\]  
A nodal displacements vector can be written as  
\[\mathbf{d}_{e}=\begin{bmatrix}\mathbf{d}_{1}^{0}&\mathbf{d}_{2}^{0}&\mathbf{d}_{3}^{0}&\mathbf{d} _{4}^{0}\end{bmatrix}^{T}, \tag{13}\]  
where each node has seven degrees of freedom, shown as  
\[\mathbf{d}_{i}^{0}=\begin{bmatrix}u_{0i}&v_{0i}&w_{0i}\ \varphi_{xi}&\varphi_{yi} &\left(\frac{\partial w}{\partial x}\right)_{i}\left(\frac{\partial w}{ \partial y}\right)_{i}\end{bmatrix}^{T}. \tag{14}\]  
The shape function matrix \(\mathbf{N}\) is a 3 \(\times\) 28 matrix. \(\mathbf{d}_{e}\) is a nodal displacement vector with 28 elements. \(\mathbf{N}\) is defined using Hermite interpolation functions [31]. Substituting Equation (12) into Equation (4), a strain vector of the element can be expressed as  
\[\mathbf{\varepsilon}=\
abla\mathbf{N}\mathbf{d}_{e}^{T}=\mathbf{B}\mathbf{d}_{e}^{T}, \tag{15}\]  
where \(\mathbf{B}\) is the strain matrix of the plate element.  
For a piezoelectric beam element, a displacement interpolation formulation can be expressed as  
\[\begin{bmatrix}\mathbf{u}^{\prime}&\mathbf{v}^{\prime}&\mathbf{w}^{\prime}&\mathbf{\varphi}_ {x}^{\prime}\end{bmatrix}_{\mathbf{e}}^{T}=\mathbf{N}^{\prime}\mathbf{d}_{e}^{T}. \tag{16}\]  
A strain vector can be further denoted as  
\[\mathbf{\varepsilon}^{\prime}=\
abla^{\prime}\begin{bmatrix}\mathbf{u}^{\prime}&\mathbf{v }^{\prime}&\mathbf{w}^{\prime}&\mathbf{\varphi}_{x}^{\prime}\end{bmatrix}_{\mathbf{e}}^{ T}=\
abla^{\prime}\mathbf{N}^{\prime}\mathbf{d}_{e}^{T}=\mathbf{B}^{\prime}\mathbf{d}_{e}^{T}, \tag{17}\]  
where \(\mathbf{d}_{e}^{\prime}=\begin{bmatrix}d_{1}^{\prime}&d_{2}^{\prime}\end{bmatrix}\) and \(\mathbf{d}_{i}^{\prime}{}_{i}^{0}=\begin{bmatrix}u_{0i}^{\prime}&v_{0i}^{\prime}&w _{0i}^{\prime}&\varphi_{xi}^{\prime}&\varphi_{yi}^{\prime}&\varphi_{zi}^{ \prime}\end{bmatrix}^{T}\) are an element displacement vector and a generalized nodal degree of freedom vector, respectively.
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 4 Discussion
### Limitations  
This study recognizes several significant limitations that should be considered when interpreting the results. The lack of statistical significance in our cohort may not solely stem from the ineffectiveness of the applied vibrotactile non-somatotopical feedback but may also be attributed to limitations in the study design, including the observation period, and sample size. The small sample size of eleven subjects restricts the generalizability of the results and hinders comparisons based on varying biomechanical properties, such as residual limb length, amputation level, level of mobility (K-Level), or unilateral versus bilateral amputations. While reference values from previous studies can be useful for categorizing subjects' performance in gait tests, their applicability for evaluating our study's data is restricted. The reference values were derived from unilateral amputee cohorts and did not account for different amputation levels, despite the substantial impact that amputation level can have on walking ability and gait stability [4; 47; 48; 49; 50; 51; 52].  
Additionally, the observation period, spanning from 30 to 120 days, may not have captured the long-term effects and full impact of the device on the gait of lower limb amputees, particularly considering the average time since amputation of 6.91 (\(\pm\) 8.35) years. Although no correlation was found between the individual performance of the subjects and either the VTFS usage time or the time since amputation, a longer observation period or larger sample size may reveal potential long-term effects or correlations.  
Testing the system on a larger sample size, including both TSR and non-TSR patients, could better evaluate the differences between these groups using the VTFS. Furthermore, this study primarily focused on functional assessment and effects of the sensory feedback device on gait without addressing other important aspects of amputees' lives, such as amputation-related pain and prosthesis embodiment, which are known to significantly influence subjects' quality of life and well-being.
## 2 Construction of Assembly Multidimensional Evaluation Index
### Evaluation Index of Assembly Cost Dimension
#### 2.1.4 Positioning Cost of Key Parts  
Due to the complex assembly structure and components of an aerospace reducer, the assembly accuracy and performance of each component must be ensured before entering the small space of the box to complete the final assembly. The higher the frequency of adjusting the positioning of the key parts, the longer the assembly cycle, which will lead to a lower assembly efficiency. If the sequence planning of the key parts' assembly is not optimal, it may lead to more positioning adjustments of the key parts, which will lead to more fixtures and jigs, thereby increasing the economic cost of aero-gear assembly. The positioning cost of the key parts is defined as shown in Equation (4):  
\[D_{i}=N_{i}\times T_{i} \tag{4}\]  
\(Di\) indicates the positioning cost of the \(i\)-th key parts; the equation can calculate the positioning cost of all key parts, and the larger the value, the higher the positioning cost, that is, the higher the assembly cost.  
\(Ni\) indicates the number of positioning adjustments of the \(i\)-th key parts.  
\(Ti\) indicates the time required for the positioning adjustment of the \(i\)-th key parts.
## 2 Methods
### Evaluation Metric
#### 2.3.2 Motion Feature Evaluation Metrics  
To evaluate the MDE, MVE, and OTE of the motion, we used skeleton images to avoid the influence of the aortic valve thickness on calculating opening distance and swept area velocity. The original image is segmented based on the marker-based segmentation method [36], and the segmentation image is skeletonized using the image thinning method [37] (Figure 4a). We manually extracted the skeleton images for the failed cases.  
The MDE is used to evaluate the maximum opening distance of endpoints between ground truth motion and predicted motion images. The opening distance between endpoints was calculated using the coordinates from endpoints extraction (Figure 4b). Then, the maximum opening distance is determined from the distances of all motion images. The MDE is defined as the following:  
\[\varepsilon_{\text{MDE}}=\frac{1}{N}\sum_{N}\abs{d_{o\_pre}-d_{o\_g}} \tag{6}\]  
where \(d_{o\_pre}\) is the maximum opening distance of the endpoints of the predicted motion images, \(d_{o\_g}\) is the maximum opening distance of the endpoints of ground truth motion images.  
The MVE is used to evaluate the area swept velocity by the aortic valve between ground truth and prediction. The swept area velocity between adjacent images is defined as the area swept out by the skeleton of the aortic valve per unit time (Figure 4c)  
\[v_{A}=\frac{dA}{dt} \tag{7}\]  
where \(dA\) is the area swept out by the skeleton of aortic valve during the time of \(dt\). The MVE is defined as the following:  
\[\varepsilon_{\text{MVE}}=\frac{1}{N}\sum_{N}\abs{v_{m\_pre}-v_{m\_g}} \tag{8}\]  
where \(v_{m\_pre}\) is the maximum-swept area velocity of predicted motion images, \(v_{m\_g}\) is the maximum-swept area velocity of ground truth motion images.  
The OTE is used to evaluate the opening time between the ground truth and predicted motion images. The aortic valve opening time was defined as the point at which the endpoint distance was 1 mm larger than the initial state. Since the spatial resolution of  
Figure 4: Detail of motion feature extraction. (**a**) Skeletonization process. (**b**) The process of endpoints distance extraction. The red circles in the figure represent the endpoints of the aortic valve. (**c**) The process of swept area velocity calculation.  
the CT image is 0.5 mm/pixel, 1 mm is an approximate distance change value when the endpoints of aortic valves move one pixel away from the initial location. The OTE is defined as the following:  
\[\varepsilon_{\text{OTE}}=\frac{1}{N}\sum_{N}\abs{t_{o\_pre}-t_{o\_g}} \tag{9}\]  
where \(N\) is the number of test dataset, \(N\) = 800. \(t_{o\_pre}\) is the opening time of the predicted motion images, and \(t_{o\_g}\) is the opening time of the ground truth motion images.
# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 3 Materials and Methods
### Dark and Bright Channel Prior Information  
Because of the visual resemblance between thin clouds and haze, cloud detection tasks exhibit similarities with tasks involving the dehazing of remote sensing images [37]. In the realm of image dehazing, the dark channel prior [38] and the bright channel prior [39] hold substantial importance. During the preprocessing phase of this research, alongside utilizing image information from the three RGB channels, both the dark channel and the bright channel priors are integrated to aid the detection model. For a given image, the dark channel is defined as:  
\[I^{dark}(x)=\min_{c\in\{r,g,b\}}\left(\min_{y\in\Omega(x)}\left(I^{c}(y) \right)\right) \tag{7}\]  
while the bright channel is defined as:  
\[I^{bright}(x)=\max_{c\in\{r,g,b\}}\left(\max_{y\in\Omega(x)}\left(I^{c}(y) \right)\right) \tag{8}\]  
where \(I^{c}\) represents the RGB color mode of the image \(I\), \(\Omega(x)\) denotes a local region centered at \(x\), and \(y\) represents a pixel point in the image \(I\). When extracting the dark channel and  
Figure 4: Comparison of Large Window Attention and Multi-level Large Window Attention. (**a**) Large Window Attention. (**b**) Multi-Level Large Window Attention.  
the bright channel, to retain as much original information as possible, the size of \(\Omega(x)\) is chosen as \(1\times 1\). This is because image segmentation is an end-to-end task.  
Figure 5 provides an illustration of the dark channel and the bright channel for the same scene. In remote sensing images containing cloud layers, both thin and thick clouds have pixel points \(x\) with values of \(\,l^{bright}(x)\to 255\), resulting in a more complete image structure at the edges of clouds in the bright channel compared to the dark channel. In the dark channel, the value of \(\,l^{dark}(x)\) in cloud-obscured regions is notably distinct from that in non-cloud regions, indicating that the dark channel enhances the brightness contrast between the cloud-obscured and non-cloud areas, facilitating a clearer differentiation between clouds and background information. Therefore, this study concatenates the dark channel and the bright channel for input into the model, allowing them to complement each other's strengths and weaknesses, thereby providing a more comprehensive and enriched set of image information.
## 3 Methods
### Risk Factor Extraction
#### 3.3.2 TRBERT-BiLSTM-CRF  
TRBERT transforms the input accident cause text sequence \(c=\{c_{1},c_{2},\ldots c_{m}\}\) into a word embedding \(e=\{e_{1},e_{2},\ldots e_{m}\}\), which can be further fine-tuned on the labeled text [40]. BiLSTM obtains context-aware word embeddings from the TRBERT encoder to further model the long-distance dependencies of the character sequences so as to capture more sequence features [34]. BiLSTM extends the classical LSTM by introducing additional LSTMs: one LSTM models the sequence in a forward direction \(\overset{\rightarrow}{l}=\{l_{1},\overset{\rightarrow}{l_{2}},\ldots,l_{m}\}\), and the other LSTM models the sequence in a backward direction \(\overset{\leftarrow}{l}=\{l_{1},l_{2},\ldots,l_{m}\}\). The forward and backward sequences are then combined to form a complete BiLSTM output \(l=\{l_{1},l_{2},\ldots,l_{m}\}\)[50].  
The CRF serves as the final decoding layer and is used to generate a sequence of predicted labels for named entities [51; 52]. Given the output sequence \(l=\{l_{1},l_{2},\ldots,l_{m}\}\) of the BiLSTM, the CRF produces the predicted label sequence \(y=\{y_{1},y_{2},\ldots,y_{m}\}\), the prediction score \(Score(l,y)\) is defined as:  
\[Score(l,y)=\sum_{i=1}^{m}T_{y_{i},y_{i+1}}+\sum_{i=1}^{m}P_{i,y_{i}} \tag{2}\]  
where \(T\) is the transition matrix, \(T_{y_{i},y_{i+1}}\) denotes the transition probability from label \(y_{i}\) to label \(y_{i+1}\), and \(P_{i,y_{i}}\) denotes the probability of the \(i-\)th character being assigned to the label \(y_{i}\). The conditional probability of generating the label sequence \(y\) is calculated as follows:  
\[p(y|l)=\frac{e^{Score(l,y)}}{\sum_{\widetilde{y}\in Y}e^{Score(l,\widetilde{ y})}} \tag{3}\]  
where \(Y\) represents the set of all possible label sequences and \(\widetilde{y}\) represents a specific label sequence in the set. The training objective of CRF is to iteratively optimize the model parameters to maximize the conditional probability of the true label sequence, thereby minimizing the negative log-likelihood loss. With \(y\) as the true label sequence, the CRF loss function is shown as:  
\[\mathcal{L}=-(Score(l,y)-ln(\sum_{\widetilde{y}\in Y}e^{Score(l,\widetilde{ y})})) \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Sources** & **Data Description** & **No.** & \\  & **Sentences** & **Size (MB)** \\ \hline Accident investigation & Text of case reports on traffic accidents issued by the China & & \\ reports & MEM and the relevant emergency management departments of & 584,654 & 536 \\  & various provinces and cities. & & \\  & CEC is built by the Data Semantic Laboratory at Shanghai & & \\ Chinese Emergency & University. This corpus is divided into five & & \\ Corpus (CEC) & categories—earthquake, fire, traffic accident, terrorist attack, & & \\  & and intoxication of food. We extracted the text related to traffic & & \\  & accidents from this corpus. & & \\ Laws and regulations & Text of Chinese road traffic law and regulation documents. & 10,018 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: TRBERT pretraining corpus.
###### Abstract  
A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16 QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks.  
Keywords:signal-to-noise ratio; coherent optical transmission system; deep neural network; particle swarm optimization; optical performance monitoring +
Footnote †: journal: