esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 3 Overall Framework
### Adaptive Spatial Feature Fusion (ASFF) Module  
After the downsampling operation, the objects of different sizes on the images can have a large semantic generation gap, resulting in low segmentation accuracy of small objects. The feature pyramid can generate features of different sizes at different scales. Based on this, various objects can find suitable feature representations at different scales. Meanwhile, the fusion of multiscale features can make full use of the deep semantic and shallow detail features of the network, thus enhancing the segmentation accuracy of the network. Currently, many feature pyramid structures use the FPN [25] method, and concat or element-wise methods are employed to fuse deep semantic and shallow detail features. However, these two fusion methods cannot fully utilize the features of different scales. Therefore, [PERSON] et al. [26] proposed a new feature pyramid method called adaptive spatial feature fusion (ASFF). It multiplies the weights trained by the network and the corresponding features to obtain features of different sizes that fuse multiple scales. ASFF is used in the target detection network YOLOV3, and good results are obtained. The main thought of ASFF is to fuse information of multiple scales. Its main steps include adjusting the features to the same size and calculating the weight map of each feature.  
The feature pyramid structure is mostly used in object detection to address the issue of scale change. In the semantic segmentation branch of the Panopic FPN network, [PERSON] [27] fused features of multiple scales and obtained good results. Inspired by this,this study uses AFF in AdvSemiSeg-MA semantic segmentation network and fuses the multiscale output of ASFF into one output. As shown in Figure 2, the output of ASFF is upsampled two times, and it is spliced with the features of the corresponding size. Then, \(1\times 1\) convolution is used for channel adjustment. The adjusted features are input to the next ASFF module to fuse high-level semantic features and low-level fine-grained features.  
Specifically, the features obtained after the 7 \(\times\) 7 convolution through RestNet101 (7 \(\times\) 7 convolution output), the features obtained after the first layer (Layer 1 output), and the features obtained through the FRASPP module (FRASPP output) are input to ASFF0. After upsampling twice, the output of ASFF0 is spliced with the output of the first layer. Meanwhile, a 1 \(\times\) 1 convolution is used to adjust the channel. Together with the output of the FRASPP module and the output of the 7 \(\times\) 7 convolution, they are input to ASFF1. After upsampling twice, the output of ASFF1 is spliced with the output of 7 \(\times\) 7 convolution. In addition, a 1 \(\times\) 1 convolution is used to adjust the channel. Together with the Layer 1 output and the FRASPP output, they are input into ASFF2 to obtain a class probability map.
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 2 Background
### Policy Distillation
The student is trained using the Kullback-Leibler divergence (KL) between the teacher (\(q^{T}\)) and the student (\(q^{S}\)) outputs, with \(\theta_{S}\) the trainable student parameters and \(\tau\) a temperature used to sharpen or smoothen the teacher outputs:  
\[L_{KL}(D,\theta_{S})=\sum_{i=1}^{|D|}\text{softmax}(\frac{q_{i}^{T}}{\tau}) \ln(\frac{\text{softmax}(\frac{q_{i}^{T}}{\tau})}{\text{softmax}(q_{i}^{S})}) \tag{1}\]  
In this original definition of the policy distillation [12] loss for DQN teachers, the network outputs (\(q\)) correspond to a list of Q-values, one for each possible action. The softmax function is used to transform these Q-values into a probability vector over the actions, to be used as input for the KL-divergence. This results in Algorithm 1 and Figure 3.  
Other work has extended this approach for use in combination with actor-critic teacher algorithms for discrete action spaces by applying a similar KL-divergence loss between the two policies directly [15] and optionally including an additional term to learn the critic values [14]. In this paper, we look at how to adapt this loss for the distillation of continuous actions. ```
Input: Fully trained teacher \(M_{t}\), RL environment env Output: Fully trained student \(M_{s}\) Randomly initialize \(\theta_{s}\) of \(M_{s}\) ;  Create empty replay memory \(D\) with size \(S_{D}\); \(D\leftarrow\) update_memory(\(M_{t}\), \(M_{s}\), \(S_{D}\)) ; Functionupdate_memory(\(M_{t}\), \(M_{c}\), steps); for\(i\gets 0\)to stepsdo ifenv.done()then \(o\leftarrow\) env.reset()// \(o=\) state observation  end if \(q^{C}=M_{c}(o)\)// Control policy for action selection: can be either the teacher (\(M_{t}\)) or student (\(M_{s}\)) network. \(q^{T}=M_{t}(o)\)// Teacher outputs are recorded in the replay memory \(D\leftarrow\) replace oldest \(D_{0}\in D\) with (o, \(q^{T}\)) ; \(a\leftarrow\) argmax(\(q^{c}\)) ; \(o\leftarrow\) env.step(\(a\)) ;  end whileM_s has not convergeddo for\(i\gets 0\)to batch_sizedo  Sample \(D_{i}\subset D\), with size (\(S_{D}/batch\_size\)) ; \(q^{S}=M_{s}(D_{i})\) ; \(\theta_{s}\leftarrow\) gradient descent using \(L_{KL}(D_{i},\theta_{s})\) and \(q^{S}\) (from Equation (1));  end for ifteacher-driventhen // Generate trajectories according to the teacher policy. \(D\leftarrow\) update_memory(\(M_{t}\), \(M_{t}\), \(refresh\_steps\)) ; elseifstudent-driventhen // Follow the student policy to generate transitions. \(D\leftarrow\) update_memory(\(M_{t}\), \(M_{s}\), \(refresh\_steps\)) ;  end if  end for
```  
**Algorithm 1**Policy Distillation
## 2 Materials and Methods
### Gabor Filters-Based Feature Extraction Method
```
Input: images after resizing Output: feature regions and features in the image (length, width, orientations, frequency, bandwidth) Initialize:\(R\)-sinusoid frequency, spatial aspect ratio \(\gamma\), gaussian envelope \(\sigma\) and offset phase \(\phi\), Gabor function normal orientations \(\theta\) 1: Read half-resized images with input values (\(f\), \(\pi\), \(\gamma\), \(\varphi\), and \(\phi\)) 2: Estimate Gaussian function using: \(G(x,y)=\frac{f^{2}}{\pi\gamma\eta}\exp(-\frac{x^{2}+\gamma^{2}y^{2}}{2\sigma^ {2}})\exp(|2\pi f\gamma^{\prime}+\phi)\) 3: Compute the Gaussian function values by using Equations (1) and (2): 4: Compute the image features according to the orientation and width values by using Equations (6) and (7). ```  
**Algorithm 1**An algorithm for the Gabor filter for feature extraction  
The experiments are conducted on OLR database (56 \(\times\) 46 image pixel) and the Extended Yale-B database (96 \(\times\) 84 image pixel). During the analysis, 40 Gabor filters were applied in five different scales and eight different orientations. The description of these images are illustrated in Figure 8. The dimension of the feature vector for the OLR database using 40 Gabor filters is 56 \(\times\) 46 \(\times\) 40 = 103,040, while the size of the feature vector for the Extended Yale-B database is 96 \(\times\) 84 \(\times\) 40 = 322,560, because adjacent pixels in an image are frequently highly correlated. Figure 6: Representation of Gabor filtering width and Spatial Sampling. Figure 7: Length and width described in the above figure: (**a**) Gabor filter outputs changed concerning the width and orientations (**b**,**c**), implying Gabor filter selectivity of line width and orientation [17]. Furthermore, Gabor filter feature images can reduce information redundancy [12; 19]. Downsampling feature images by a factor of sixteen yields a vector of 1680 in size for the OLR database and 5280 for the Extended Yale-B database. These vectors were also normalized to have a unit zero mean and variance. The derived Gabor filter facial features were then fed into the deep neural network model of stacked sparse autoencoders (SSAE).
## 2 Materials and Methods
### Numerical Simulation  
As mentioned in the first paragraph of Section 2.3, it is only necessary to verify the situation that a circuit experiences a short circuit when there is no load. Assuming that a short circuit occurs at point S in the circuit shown in Figure 2, Figure 5 shows the equivalent model of the fault circuit for simulation.  
Assuming the DC-side includes a 6 m-long bus-bar (50 \(\times\) 5 rectangular copper bar) and a 30 m-long cable (2 \(\times\) 50 mm\({}^{2}\) copper core). By consulting their resistance and inductance values per unit length, the total resistance R and inductance L can be calculated. For the bus-bar: R = 2 \(\times\) 0.087 m\(\Omega\)/m, L = 2 \(\times\) 0.64 u\(\Omega\)/m. For the cable: R = 2 \(\times\) 0.435 m\(\Omega\)/m,L = 2 \(\times\) 0.256 \(\mu\)H/m. C is the voltage regulator capacitor of the VSC, and the initial voltage of the DC-bus is V\({}_{0}\) = 750 V. It can be calculated that R \(<\) 2\(\sqrt{\frac{\text{L}}{\text{c}}}\).  
For the AC side, R\({}_{\text{g}}\) and L\({}_{\text{g}}\) denote the total resistance and inductance of the AC side, including the resistance and inductance of the grid and transformer (4.2 m\(\Omega\) and 48 \(\mu\)H), as well as the filter inductance of the VSC (1 mH). The parameters of the transformer are 10/0.4 kV, 400 kVA, and U\({}_{\text{K}}\) = 4%. Obviously, these parameters are very consistent with the actual situation.  
The rated power of the converter is 250 kW, and proportional-integral control is adopted. The frequency of pulse width modulation (PWM) is 5 kHz. The IGBTs will be blocked after a fault occurs.  
MATLAB/Simulink was used to simulate this faulty circuit, and the results are listed in Table 1. The waveforms of the short-circuit current is and DC bus voltage V\({}_{\text{c}}\) are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline
**Initial Value (When t = 0)** & **Simulation Results** \\ \hline V\({}_{\text{g}}\) = 380/220 V & & i\({}_{\text{p}}\) = 4345 A \\ \hline V\({}_{0}\) = 750 V & & t\({}_{\text{p}}\) = 0.23 ms \\ \hline i\({}_{\text{s}}\) = 0 A & steady-state current & i\({}_{\text{s}}\) = 895 A \\ \hline \end{tabular}
\end{table}
Table 1: Simulation result.  
Figure 5: Equivalent model of the short circuit for simulation.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 6 Sensor Fusion for Detection
### Input Fusion  
Input fusion is applied to the radar point cloud. It projects radar points into a pseudo-image with the range, velocity, and RCS as channels [171; 172]. Then, similar to an RGB-depth image, the radar pseudo-image and the visual image are concatenated as a whole. Finally, a visual detector can be applied to this multi-channel image for detection. Input fusion does not make independent use of the detection capability of radar. In other words, the radar and vision modalities are tightly coupled. Assuming good alignment between modalities, it makes it easier for the network to learn joint feature embeddings. However, an obvious disadvantage is that the architecture is not robust to sensor failures.  
The fusion performance depends on the alignment of radar detections with visual pixels. As mentioned in Section 4.1, the difficulties lie in three aspects: Firstly, the radar  
Figure 11: Overview of radar and camera fusion frameworks. We classify the fusion frameworks into input fusion, ROI fusion, feature map fusion, and decision fusion. For ROI fusion, we further investigate two architectures: cascade fusion, which projects radar proposals to image view, and parallel fusion, which fuses radar ROIs and visual ROIs.  
point cloud is highly sparse. Many reflections from the surface are bounced away due to specular reflections. As a result, the detected points are sparsely distributed over the object. In addition to the sparsity, the lateral imprecision of radar measurements leads to further difficulties. The radar points can be out of the visual bounding box. The imprecision comes from different aspects, e.g., imprecise extrinsic calibration, multi-path effects, and low angular resolution. The third limitation is that low-resolution radar does not provide height information. To address these difficulties, some association techniques are required. Relying on the network to implicitly learn association is a hard task, because the network tends to simply ignore the weak modality, such as radar. The expansion methods described in Section 4.1 can be applied as a preprocessing stage for input fusion. However, object detection does not require such a strict association as depth completion, so some of the expansion methods are too costly for real-time processing. [PERSON] et al. [171] utilised the lightweight height extension as preprocessing. Both [PERSON] et al. [172] and [PERSON] et al. [173] added a one-layer convolution to radar input before concatenation. This convolutional layer can be considered as a lightweight version of the association network. Radar detections at different ranges require different sizes of the receptive field for association. Therefore, [PERSON] et al. [171] concatenated the radar pseudo-image with image feature maps at multiple scales.
## 4 Impacts of the Proposed FED-XAI Approach on V2X Applications in B5G/6G Networks
#### Standardization Impact of an Interoperable FED-XAI Implementation  
An interoperable implementation of the FED-XAI concept with a focus on an automotive scenario is expected to stimulate discussion within Standards Development Organizations (SDOs) on specifying the involved architectural entities (e.g., FED-XAI CE and FMs), communication interfaces and service protocols including the exchanged data  
Figure 6: Reference V2X service scenarios, with MNO perspective and customer/car OEM perspective.  
structures. For instance, the International Telecommunication Union (ITU) Focus Group on AI for Autonomous and Assisted Driving (FG-AI4 AD) aims to internationally harmonize the definition of a minimal performance threshold for AI \"on the road\". Additionally, the ETSI Industry Specification Group on Securing AI (ISG SAI) has introduced a new work item on explainability and transparency of AI processing (June 2021). AI platform design issues aiming to provide assurance of explainability and transparency of decisions and allowing independent determination of biases are in scope. ETSI ISG MEC is also of relevance, as one of its specifications (GS MEC 030) focuses on defining the V2X Information Service (VIS) for MEC systems: one of VIS functionalities is to facilitate issuing journey-aware QoS predictions.
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 7 Discussion
### Future Research Directions  
This study establishes a foundation for several vital directions in future research. Expanding upon our findings, subsequent investigations could focus on developing machine learning models trained on cyclical treatment data to refine personalized treatment recommendations by capturing the iterative nature of patient responses more effectively. Such models could significantly improve AI-driven care, particularly for rare diseases and atypical conditions, where conventional static data structures often fail to identify nuanced response patterns.  
Future research should prioritize a thorough and systematic evaluation of the usability of cycle-based frameworks. Employing iterative user feedback throughout different stages of system design and implementation (guided by established scientific usability evaluation frameworks) will be essential. This approach will help to continuously refine the system based on real-world physician experiences, enhancing its effectiveness, efficiency, and user satisfaction.  
Moreover, a critical research direction entails the development of standardized treatment cycles customized for specific medical domains. Academic efforts aimed at defining and validating cycle templates for fields such as infertility treatment, oncology, and chronic disease management would ensure that cyclical documentation aligns with the clinical practices of these specialties while preserving system interoperability and adaptability. These initiatives would significantly enhance the practical utility and adoption potential of cycle-based EMRs across diverse healthcare settings.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 3 Our Method
### Network Architecture  
For the multi-view subspace clustering task, given a dataset with \(N\) data samples and \(V\) views, using the multi-view features \(\left\{q_{i}^{(v)}\in Q^{(v)}\right\}_{i=1}^{N}\in\mathbb{R}^{D\times N}\) and clustering these data points into \(K\) clusters, where \(Q^{(v)}\) represents the \(v\)-th view of the original input data. As illustrated in Figure 1, the proposed network mainly consists of three parts: multi-view convolutional encoders, multi-view conventional decoders and reconstructing self-expressive matrix modules.  
Figure 1: The network architecture of our proposed network DCRSM. This model contains three components: multi-view convolutional encoders, multi-view convolutional decoders and reconstruction model. The input of the network is dataset \(Q^{(v)}\) with \(V\) views and the autoencoder networks are used to learn the latent representations \(Z^{(v)}\) for each view of the input data. The reconstruction module is applied to simulate the self-expressive matrix \(C^{(v)}\), which is used as input to the spectral clustering approach to obtain the final clustering results.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.1 The 5G Millimeter Wave (mmWave)
The authors collected millimeter wave communication measurements for next-generation cellular networks in the congested urban New York City environment. The availability of high bandwidth is one of the primary advantages of mmWave communications over microwave communications. Even though increased bandwidth may not result in higher rates in the noise-limited zone, the higher propagation losses associated with mmWave frequencies must be carefully considered, particularly at distances greater than 100 m and in NLOS conditions [73]. Furthermore, because its wavelength is significantly shorter than that of sub-6 GHz transmissions, the mmWave signal can fit many more antennas to a compact array. This approach will lead to the large-scale antenna communication range expansion of next-generation mobile networks. However, the beam width is constrained when multiple antenna elements are present. Despite the numerous advantages of the mmWave band, propagation path loss, which is a common problem with line-of-sight (LoS) communications, is a key challenge. Dry walls and clear glass have relatively low penetration losses for 28 GHz transmissions indoors (when compared with microwave bands). Still, brick and tinted glass have much higher penetration losses (between 28 dB and 40 dB). At higher frequencies, penetration losses frequently increase. Because of the significant penetration loss, it is challenging to conduct indoor measurements using mmWave nodes placed outside and vice versa. In addition, communication in the mmWave band is nearly line-of-sight. Line-of-sight (LOS) obstructions, including people, buildings, and furniture, significantly and negatively impact the connections, making them sporadic. Consequently, the signal is of substantially weaker and significantly reduced quality [11].
## 2 Mathematical Model
### Constraint Equations  
The constraint equation that is proposed in this study is the definite method that restricts \(M_{m}\), \(K_{m}(\mathbf{x})\), \(Bl(\mathbf{x})\), and \(R_{m}(\mathbf{x})\). It must be noted that the relationship between these parameters can be formed by T/S parameters.  
\[\omega_{0}=\sqrt{\frac{K_{m}(\mathbf{x}(0))}{M_{m}}} \tag{20}\]  
\[Z_{T}-R_{e}=\frac{Bl(\mathbf{x}(0))^{2}}{R_{m}(\mathbf{x}(0))} \tag{21}\]  
where \(\omega_{0}\) is related to the resonant angular frequency of linear part \(K_{m}(\mathbf{x}(0))\) and the \(M_{m}\); \(Z_{T}\) is restricted to the resistance value during resonance (\(f/f_{0}=1\)); and, \(\mathbf{x}(0)\) is the linear term that does not change with the displacement.  
Equations (20) and (21) can be integrated in the Equation (3), as follows:  
\[\left(Bl(\mathbf{x})+\frac{dL_{e}(\mathbf{x})}{d\mathbf{x}}\frac{\mathbf{i}(t )}{2}\right)i(t)=\left(\frac{K_{m}^{2}(\mathbf{x}(0))}{\omega_{0}{}^{2}}+M_{ rad}S_{d}\right)\ddot{\mathbf{x}}(t)+R_{m}(\mathbf{x})\dot{\mathbf{x}}(t)+K_{m}( \mathbf{x})\mathbf{x}(t) \tag{22}\]  
and  
\[R_{m}(\mathbf{x})=\frac{Bl(\left(\mathbf{x}(0)\right)^{2}}{Z_{T}-R_{e}}\exp \left(\frac{sign(x-R_{m}(\mathbf{x}(1))^{2}}{R_{m}(\mathbf{x}(2))^{2}}\right) \tag{23}\]  
As seen from the observations above, different \(Z_{T}\), \(R_{e}\), \(Bl(\mathbf{x}(0))\), and \(K_{m}(\mathbf{x}(0))\) values can directly affect \(M_{m}\) and \(R_{m}(\mathbf{x}(0))\). In each iteration, \(Z_{T}\) can serve as the constraint for parameter changes. The \(R_{e}\) decreases at higher frequencies, whereas \(L_{e}\) increases at higher frequencies. Thus, the excitation signals must cover low and high frequencies. In this study, constraint equations were used in the BFGS-Brent method to compare the estimation results.
## 2 Related Work
### Visual Transformer  
In recent years, models based on the transformer architecture have become popular in the field of computer vision. However, transformer architecture models tend to have weak local perception. To address this problem, T2 TViT [16] can better model local features by recursively aggregating adjacent tokens into a token. Similarly, ConVit [17] made adjustments on ViT, including introducing a gated self-attention mechanism (GPSA) and imitating convolution operations to obtain inductive bias. Both CMT [18] and ResT [19] mix convolution and Swin Transformer modules in a serial manner, using transformers and CNNs to capture long-distance dependencies and local features, respectively. However, such a serial approach interferes with both local and global features. [PERSON] et al. [20] proposed a parallel conformer model, which retains both local and global features to the greatest extent, and verified the feasibility of the parallelized model.  
In the field of remote sensing image classification, the combination of CNNs and transformers is relatively rare. [PERSON] et al. [21] proposed a convolutional converter (ConvT)for SAR ATR few sample learning (FSL). This model integrates the local features of CNN and the global dependency of the transformer with the novel loss of finite SAR images and verifies the effectiveness of the proposed method on the MSTAR dataset. [PERSON] et al. [22] used the FESwin module as the backbone network, used the CNN to aggregate the contextual information perceived before and after the Swin Transformer model, and carried out scale fusion based on the visual dependency of self-attention capture, achieving good detection accuracy on SSDD and SARShip. The above multi-feature fusion methods mainly use concatenation to combine features, which may cause interference when merging features with different attributes, resulting in poor performance and weak fusion generalization.  
[PERSON] et al. [23] designed an end-to-end local-global network structure for high-resolution SAR image classification using CNN and vision transformer models to extract local and global features, respectively, and for mining complementary information through the feature fusion module and achieved superior performance. Unlike the existing studies, a three-branch parallel multi-scale feature fusion SAR classification model was proposed to allow for the local and global branches to minimize the disturbance. Moreover, the Bayesian optimization algorithm was used for the first time to search for the hyperparameters of the proposed feature fusion model.