esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To balance the search speed and the performance of the generated MTV, this paper sets the threshold and the number of threshold searches to improve the AO* model and adjusted the threshold in an adaptive way. At the same time, this paper establishes a heuristic function model combining the characteristics of the boundary scan test and the characteristics of the circuit under test and used a genetic algorithm to generate the parameters of the heuristic function that describe the characteristics of the circuit under test. When using the improved AO* model to generate MTV, this paper determines the generation direction according to the results of the heuristic function so that generation efficiency can be improved. This paper has the following innovations:  
* A probability model of typical fault for the boundary scan test is established, and a new heuristic function is proposed based on this model to balance the test accuracy and the search efficiency of the algorithm. * A method is designed to describe the characteristics of the system under test by using several undetermined parameters, which effectively improves the test precision of the complex circuit system. * An improved AO* model with adaptive strategy is proposed to improve the MTV generation efficiency of complex circuit system. This paper is organized as follows: In Section 2, this paper establishes and analyzes the mathematical model of interconnect short-circuit fault, then this paper identifies the most common types of faults. In Section 3, based on these fault characteristics, this paper gives the mathematical form of the heuristic function in AO* mode. Then, this paper uses the genetic algorithm to determine the parameters in the heuristic function, which are used to describe the characteristics of the actual circuit under test. After obtaining the heuristic function and the parameters, this paper uses the improved AO* model to generate the MTV. In Section 4, this paper uses different algorithms to generate MTV and compare them with the MTV generated by our method from the perspective of priori index and actual test results. Finally, the conclusion is wrapped up in Section 5.
# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 3 Self-Training SwinIR Network Structure  
### Overall Network Structure  
Considering the inverse problem of recovering vectorized image \(a\) from measurements:  
\[b=S\{a\}+\varepsilon \tag{16}\]  
Among them, \(S\) is \(a\) forward measurement operator, and \(\varepsilon\) represents the noise generated by imaging. For image reconstruction applications suitable for deep learning methods such as image denoising and super-resolution direction, the forward model \(S\) is unknown [3]. At present, the deep learning method for such problems is to train a DNN as a reverse model. The training is performed in a supervised manner, so the data sets need to have (\(a\), \(b\)) pairs of images.  
For many applications of computational imaging, such as FPM, the forward model S is partially or completely known. Therefore, the forward imaging process is implemented using open source machine learning libraries such as TensorFlow and PyTorch. A method known as physics-based learning allows the measurement \(b\) to estimate the image \(a\) by means of unsupervised back propagation. Since the results of each image reconstruction can be estimated independently, the training set is not required for training. Based on the method of physics learning, [PERSON] et al. [41] introduced FPM and [PERSON] et al. [43] improved it. The physics-based learning method combines the interpretability of the model and the automatic differentiation of the network. In addition, it does not require a separate reconstruction model to reconstruct the output image.  
This paper introduces the SwinIR network that has performed well in the field of super-resolution recently and uses SwinIR as a physical model to reconstruct our data. Figure 4 shows the structure diagram of the SwinIR physical model. The SwinIR physical model includes a shallow feature extraction module, a deep feature extraction module, and a reconstruction module [2]. It is worth mentioning that the feature map of the image after the deep feature extraction module and the feature map obtained by the shallow feature extraction module are cascaded. With feature fusion, the final output to the reconstruction module, the reconstruction result is obtained [2].
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
## 2 Basic Model  
### 5G Uplink Channel Transceiver Process  
After LDPC coding and rate matching, the processing of the baseband transmitter chain at the physical layer level includes scrambling code, modulation symbol mapping, DMRS generation, precoding, etc. These processes are described in detail in the section 6.3.1 of the 3 GPP TS 38.211 [16] standard document on the 5G uplink physical layer. The subcarrier mapping modulation symbol data are OFDMA modulated by an IFFT operation. The signal modulated by OFDMA is transmitted by the transmitter and received after transmission in the wireless channel. The receiver redivides the OFDMA data into data of multiple subcarriers through FFT operation [17], and estimates the channel according to the known data (pilot) inserted at the transmitting end. Finally, all the transmitted data are inversely derived according to the results of channel estimation. Combined with the process of channel estimation at the receiver, the basic flow of 5G uplink can be summarized as shown in Figure 1[16]. The main work of this paper is in the channel estimation module. A detailed flowchart can be found in Section 3.
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 5 Results
### Relative Selfish Revenue
#### 5.1.1 Network Delay  
Note that we refer to high block latency, slow block propagation times or slow block diffusion times synonymously. When we refer to latency we mean block latency, i.e., the waiting time for a block to diffuse from one miner to a peer.  
Figure 2 shows the impact of network latency, or block propagation time, on the relative reward of selfish mining with different hashing power levels. The control parameter \(\lambda_{nd}\) is the rate of block propagation. A high rate of block propagation implies short block diffusion times. In Figure 2a, selfish mining is a more profitable strategy, if the lines are above their respective \"fair share\" thresholds (i.e., their hashing power share), as indicated by the respectively coloured, dotted lines. For example, for \(\alpha=0.4\), we can see that relative selfish revenue never falls below 0.4, meaning that selfish mining is more profitable no matter how fast block propagation is.  
There are two important observations to be made. First, relative selfish revenue, as defined in Section 4.2, is decreasing as the block propagation rate increases. Second, for a lower rate of block propagation (higher network delay), selfish mining is always a more profitable strategy than honest mining. The selfish miner benefits relatively more from slow block diffusion because they are more likely to extend their chain than honest miners. The reason is that the selfish miner does not suffer from slow block diffusion time, since they do not need to broadcast it in the first place. Notably, for higher values of \(\alpha\) selfish mining is more robust against fast block diffusion. This is intuitive, as selfish mining becomes more profitable with increasing mining power.  
Figure 2b shows for some values of \(\lambda_{nd}\) the relationship between the selfish miner's share of the network's hashing power, \(\alpha\), and relative selfish revenue, \(R_{selfish}\). The values of \(\lambda_{nd}\) have been chosen to expose the dynamics of different latency regimes. We use these values for most following results. Importantly, the figure confirms some above findings, namely that \(R_{selfish}\) is increasing in \(\alpha\). The faster block diffusion is, the more mining power is required to surpass the fair share threshold for the reasons stated above. Noteworthy is the fact that our model can confirm [2]'s main finding: selfish mining is always relatively more profitable for mining power levels exceeding one-third of the network's total mining power. In terms of latency, this means that no matter how high the block propagation rate is--and therefore unfavorable to selfish miners (see graph of \(\lambda_{nd}=79.43\))--selfish mining is always relatively more profitable for \(\forall\alpha\gtrapprox\frac{1}{3}\).
## Materials and Methods
### Data and Study Areas
#### 2.1.2 Historical Maps
Nevertheless, most of the maps are relatively similar with respect to their color properties (e.g., 2-3 color print), except the Atlanta maps (5-color print), which are also the most recent maps, covering the largest spatial extent. Regarding the cartographic styles used for depicting urban, built-up areas, most of the maps use either street blocks (Sao Paulo, London) or building blocks (Boston, Birmingham), while in the Atlanta maps, urban areas are depicted in a red color tone, comprising both roads and buildings, and the Lahore map uses individual (symbolic) building outlines. Figure 3 highlights these different visualization styles in detail, and Table 1 summarizes the properties of the historical maps used in this study. Moreover, Figure 11 shows selected original map sheets for four out of the six study areas. Figure 2: Input data for the study areas in South America and Asia: (**a**) GHSL built-up areas in 2014 and 1975 for the greater Sao Paulo area (Brazil), including the coastal city of Santos, (**b**) historical topographic map from 1906 (scale: 1:100,000) covering the same area, (**c**) GHSL built-up areas in 2014 and 1975 for the Lahore (Pakistan) and Amritsar (India) region, and (**d**) historical topographic map from 1943 (approximate scale: 1:250,000). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**City** & **Country** & **Map Type** & \begin{tabular}{c} **Map** \\ **Resolution [m]** \\ \end{tabular} & **Map Scale** & \begin{tabular}{c} **Reference** \\ **Year** \\ \end{tabular} & **Print Colors** & \begin{tabular}{c} **Urban Built-Up** \\ **Area Depiction** \\ \end{tabular} & **Data Source** & \
\begin{tabular}{c} **Download \&** \\ **Compositing** \\ \end{tabular} &
\begin{tabular}{c} **Geo-** \\ **Referencing** \\ \end{tabular} \\ \hline Atlanta & USA & Composite & 2 & 1:24,000 & 1954-1969 & 5 & Red color tone & [81] & Automated & By provider \\ Boston & USA & Composite & 5.3 & 162,500 & 1885-1918 & 3 & Building block & [81] & Automated & By provider \\ Birmingham & UK & Composite & 2.4 & 1:10,560 & 1888-1913 & 2 & Building block & [82] & Automated & By provider \\ London & UK & Composite & 12 & 1:63,360 & 1896 & 3 & Street block & [83] & Manual & Manual \\ Soo Paulo & Brazil & Single sheet & 9.3 & 1:100,000 & 1906 & 2 & Street block & [84] & Manual & Manual \\ Lahore & Pakistan & Single sheet & 36.7 & 1:254,440 & 1943 & 2 & Building outlines & [85] & Manual & Manual \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the six historical maps/map composites used in this study and their properties.
## 1 Introduction
This feedforward method was named the disturbance observer (DOB) method [25; 26; 27]. It has been applied to several mobile devices with high-precision control systems, such as robot motion control, the swing arm actuator in the hard disk drive, the VCA in the optical disk drive, and the permanent magnet synchronous motor control [28; 29; 30; 31; 32; 33]. To improve the control performance of the original control system for the FSM system, high open-loop gain in the dynamic disturbance frequency domain is needed to sufficiently attenuate disturbances. FSM systems commonly use proportional-integral (PI) controllers with high bandwidth owing to their robustness to modeling errors and simplicity of implementation [34; 35]. In addition, the addition of a disturbance observer (DOB) to the base controller significantly improves the disturbance-attenuation performance because of its simplicity and suitability for real-time implementation [36; 37]. Consequently, DOB implementation typically imposes a very light computational burden, and it is inexpensive because additional sensors are not required. Recently, several studies have developed robust FSM control systems by using DOB-based controllers to improve tip-tilt control performance. In this study, we propose a more reliable tip-tilt control algorithm by using an IMP-based controller with DOB for the FSM system to reduce the control-error signal (CES) in the presence of dynamic disturbances. In experiments involving an analysis of periodic disturbances under operating conditions, we report the use of IMP-based control with a DOB to attenuate disturbances of a few frequencies. The remainder of this paper is organized as follows. Section 2 introduces the basic FSM control system. In Section 3, an IMP-based controller with a DOB is described, and its performance is analyzed. Section 4 describes the experiments conducted to verify the proposed method. Finally, our concluding remarks are presented in Section 5.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 2 Model Architecture
### Mixture Alignment Encoder
#### 2.2.3 Length Regulator  
Suppose there is a sequence of character hidden representations: \(\mathcal{H}_{char}=[h_{1},h_{2},\dots,h_{n}]\), which satisfies \(\sum_{i=1}^{n}d_{i}=m\), where \(m\) represents the spectrum length, \(\mathcal{LR}\) represents the length regulator, and \(\mathcal{D}\) represents the character duration frames. The hidden feature for spectrum generation, \(\mathcal{H}_{char}\) is derived from the following equation:  
\[\mathcal{H}_{mel}=\mathcal{LR}(\mathcal{H}_{char},\mathcal{D},\alpha), \tag{1}\]  
where the parameter \(\alpha\) determines the length of the extended sequence, thereby regulating the rate of speech. In the inference stage, this paper uses the duration generated by a duration predictor as the input.  
To illustrate this process more clearly, consider the example shown in Figure 4. The figure demonstrates the transformation from \(\mathcal{H}_{char}\) to \(\mathcal{H}_{mel}\) using the length regulator and the character duration frames. In this specific example, when \(\alpha=1\) and \(\mathcal{D}=[2,3,1]\), the length regulator adjusts the sequence by repeating each character frame according to the corresponding duration, resulting in the final sequence in \(\mathcal{H}_{mel}\).
# Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests
## 2 Data and Methods
### Tree Crown Silhouette Extracted and Reconstruction  
To conveniently obtain the geometric shape attributes and digital mesh model of the tree crown, the segmented individual tree point clouds are taken as the input for crown surface reconstruction. By re-sampling the contours of all layers, the surface points with uniform distribution on the crown silhouette can be obtained. Then, the crown surface geometry can be reconstructed based on a 3D \(\alpha\)-shape [34] or modified \(\alpha\)-shape method [36].  
Figure 11: The individual tree identification with the soft segmentation algorithm. (**a**) The segmentation result of Forest B; (**b**) the top view of (**a**).  
With the reconstructed tree mesh models, some attributes of the tree crown, including width, height, superficial area, and projecting ground area can be easily estimated.  
The width \(W_{ crown}\) of a tree crown is estimated according to Equation (9):  
\[W_{ crown}=\max\left\{\left|\overline{P_{i}^{\prime}P_{j}^{\prime}},\;\forall P_{ i}^{\prime},P_{j}^{\prime}\in\Gamma\right|\right\}, \tag{9}\]  
where \(\Gamma\) is the boundary of projection points set that vertical mapping \(\Lambda\) onto the \(xOy\) plane.  
The height \(H_{ crown}\) of the crown can be estimated with laser scanning point data:  
\[H_{ crown}=\ Z_{\max}-\ Z_{\min}, \tag{10}\]  
where \(Z_{\max}\) and \(Z_{\min}\) are the maximum and minimum \(z\)-coordinates of the crown.  
The superficial area of the crown is the sum of the areas of all triangles on the surface of the crown. In the traditional \(\alpha\)-shape method, the triangles in the crown perhaps are turned into boundary triangles if the radius \(r\) of the probing ball is too small. This led to a bigger superficial area than the real case. In addition, if the \(r\) is set too large, the reconstructed crown will become a convex hull, which also induces a significant error. As our method only uses boundary points to build the crown surface, the superficial area only consists of boundary triangles, making the error small.  
For calculating the projecting ground area, all points of \(\Lambda\) are projected onto the \(xOy\) plane, and a polygon \(\mathcal{P}\) is built on these projected points by employing the 2D \(\alpha\)-shape method. Note that the polygon \(\mathcal{P}\) may not always be convex, and the built \(\mathcal{P}\) based on projecting \(C\) or \(\Lambda\) onto the \(xOy\) plane is the same.
# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 2 Materials and Methods
### Rml-YOLO Network Structure  
YOLOv8, one of the most widely used object detection algorithms, is known for its fast detection speed and relatively low computational complexity. Building upon YOLOv8, this paper introduces RML-YOLO, an improved algorithm that integrates MSFENet, LMSHead, and RFCAConv to enhance its performance, particularly in detecting insulators in complex environments. The overall structure of RML-YOLO is depicted in Figure 5. In the backbone network, the original convolutional layers of YOLOv8 are replaced with RFCAConv, which aggregates features with different receptive fields. This modification enables the model to effectively capture features from various locations, thereby enhancing its capability to detect insulators in complex background environments. In the neck layer, this study employs MSFEPNet, where the TSFF module is utilized to fuse features from the P1-P5 layers extracted by the backbone network, generating three feature maps of different scales: p2, p3, and p4. Unlike the traditional FPN structure in YOLOv8, which only upsamples small-scale feature maps, MSFEPNet incorporates abundant semantic information from large-scale feature layers (P1 and P2), significantly improving the detection performance for small objects.To ensure that the feature maps fed into the detection head retain rich detailed features, the TSFF module is also applied to the P2-P5 layers of the neck, resulting in two additional feature maps of different sizes: p3 and p4. The P2 and P5 layers are directly fused and processed. Finally, the fully fused multi-scale feature maps (P2-P5) in the neck are input into the detection head for final detection. Specifically, the P2 layer, containing abundant spatial information, is particularly advantageous for small object detection. The four different scale feature maps enhance the model's multi-scale detection capabilities.
## 3 Simulation Analysis and Method Comparison
### Comparison with Existing Chaotic Detection Methods
Curve \(d\) obtained by proposed method estimates that the frequency value of the test signal is 1.02 Hz at 15 s, then fluctuates between 9.995 Hz and 1.005 Hz. The simulation results show that the new method is superior to the existing methods in detection efficiency and accuracy.
###### Abstract  
Radioembolization (RE) with glass microspheres (MS) loaded with Yttrium-90 (\({}^{90}\)Y) has been used to treat tumors in the liver with some reported success. However, assessing absorbed doses (AD) in the planning tumor volume (PTV) and normal liver volume (NLV) is a key problem to address in RE. In clinical practice, the computation of \({}^{90}\)Y activity to be administered follows the manufacturer's recommendations, which do not consider the specific characteristics of MS deposition in each patient's liver. Our main aim is to develop a methodology to estimate the optimal activity for each patient treatment. It uses the absorbed dose distribution (ADD) derived from the Technetium-99m (\({}^{99}\)Tc)-labeled macroaggregated albumin (MAA) obtained from pre-treatment planning single-photon emission computed tomography (SPECT) images. Post-treatment positron emission tomography (PET) images of the \({}^{90}\)Y-MS distribution were used to estimate the ADD for treatment verification. Sixteen RE treatments were retrospectively selected. The agreement between the estimated mean AD based on the planning imaging and real post-treatment mean AD was good in PTV with an intraclass correlation coefficient (ICC) of 0.79 and excellent in NLV (ICC = 0.97). The optimization of \({}^{90}\)Y activity using pre-defined clinical AD thresholds (\(<\)70 Gy in NLV and \(>\)80 Gy in PTV) imposed on the PTV and NLV voxels showed remarkably high agreement (ICC = 0.96, \(p\) \(<\) 0.001) in eleven out of the sixteen RE treatments between SPECT-MAA-based and PET-MS-based optimal activity estimates. In conclusion, under well-controlled conditions, pre-treatment SPECT-MAA imaging predicts well the treatment of ADD. In addition, SPECT-MAA imaging can be used to optimize the \({}^{90}\)Y-MS activity to be administered to the liver.  
radioembolization; glass microspheres; Yttrium-90; Technetium-99m; macroaggregated albumin; voxelized absorbed dose distribution; \(\gamma\)-index; optimization 2022
## 5 Performance Analysis and Simulation
### Analysis of Communication Overhead
#### 5.2.1 Communication Overhead of the Phantom Node Determination Phase  
In the existing source node location privacy protection schemes, the PN is determined by the SoN via flooding, which will consume a lot of communication overheads. We show the reason for the following mathematical analysis.  
In the EPUSBRF scheme, the PN is determined by the SoN using \(h\)-hop flooding and the \(h\)-hop directed routing. According to the network environment described in Reference [18], the average neighbor numbers of each node is 8.64, so the communication overhead of determining PN in EPUSBRF scheme is \(\text{Hop}_{\text{PEPIUSBRF}}=8.64+8.64^{2}+\cdots+8.64^{h}+h\) where the communication overhead for \(h\)-hop flooding is \(8.64+8.64^{2}+\cdots+8.64^{h}\), and the communication overhead for \(h\)-hop directed routing is \(h\).  
According to Reference [16], the communication overhead of determining PN in RPBMP scheme is \(\text{Hop}_{\text{PRPRBMP}}=3*h\). The shortest path algorithm does not need to determine PN, so the communication overhead of determining PN in it is \(\text{Hop}_{\text{PBNortes}}=0\).  
However, in our proposed schemes, we use the sink node to help the source node to determine the PN. As the energy of the sink node is unlimited, its communication overhead is not considered in related researches. Therefore, the communication overhead of determining PN in our proposed schemes is \(\text{Hop}_{\text{PSPS}}=\text{Hop}_{\text{PDPS}}=0\).  
In summary, the following relationships are obtained:  
\[0=\text{Hop}_{\text{PShortest}}=\text{Hop}_{\text{PSPS}}=\text{Hop}_{\text{ PDPS}}<\text{Hop}_{\text{PRPRBMP}}<\text{Hop}_{\text{PEPIUSBRF}}. \tag{28}\]  
Therefore, in the phase of determining PN, the communication overhead of our proposed schemes is the same as the shortest path algorithm, and both are lower than that of the RPBMP and EPUSBRF schemes.