esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Piercing Riveted Joint of Vibration-Damping Steel and Aluminum Alloy
## 2 Experimental Procedure
### SPR Equipment, Rivet, and Die  
The SPR joints were made from a hydraulic-type riveting machine (Rivset Gen2, BOLLHOFF) that had a maximum setting force of 78 kN. The rivet (boron steel with Almac(r) coating (combination of aluminum and zinc), 480 \(\pm\) 30 HV in hardness, supplied by BOLLHOFF) with a flat countersunk was used in the SPR process. The engineering drawing of the rivet is illustrated in Figure 2. The rivet shank diameter and rivet length were 5.3 mm and 4.5 mm, respectively. The rivet length of 4.5 mm was selected to be approximately 2 mm thicker than the total thickness of base materials (2.6-2.7 mm). In this study, three die types (supplied by BOLLHOFF) were tested to investigate the effect of the die geometry on the joining performance. Geometrical information of these dies is presented in Figure 3. Type A1 (diameter: 8.8 mm, depth: 1.8 mm) die was a benchmark die that had a basic flat-bottom shape, and both type B (diameter: 9.5 mm, depth: 1.8 mm) and type C (diameter: 9.0 mm, depth: 2.0 mm) had a hump at the center of the die cavity. The hump of type B was conical and the height of the hump was smaller than the depth of the die cavity. However, the hump height of type C was larger than the depth of the die cavity, resulting in more extrusion. In addition, for the benchmark die, the influence of cavity diameter and cavity taper angle on the joint characteristic was examined. In this examination, two modified benchmark dies (type A2 and A3) were used. Type A2 die had a cavity taper angle of 5\({}^{\circ}\) (which was 5\({}^{\circ}\) more than that of type A1), and type A3 die had a diameter of 9.2 mm (which was 0.2 mm more than that of type A1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sheet Material** & **Ultimate Tensile Strength (MPa)** & **Elongation (\%)** & **Thickness (mm)** \\ \hline Vibration-damping steel & 618 & 17 & 1.5 \\ SPFC590 DP & 609 & 25 & 1.4 \\ Al5052-H32 & 233 & 12 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tensile strengths and thicknesses of the materials used in this study.  
Figure 1: Cross-sectional view of the vibration-damping steel plate.
# Knowledge Graph Recommendation Model Based on Feature Space Fusion
## 3 Our Model
### Problem Analysis and Solution
To solve the abovementioned noise problem, a knowledge graph recommendation model based on feature space fusion (KGRFSF) is proposed in this paper. Next, the feature modeling process of the user is taken as an example to explain the thinking of the feature space fusion method in the model, as shown in Figure 2, considering the semantic differences between behavioral features and content features. First, the behavioral and content features of users are modeled in the behavioral and content feature spaces. In the UIG, the behavioral feature vector \(u_{\text{cf}}\) of the user is extracted in the behavioral feature space. According to the interaction history of the user, the content entities associated with the interactive items of the user are screened out from KG. In the content feature space, this entity information is combined to form the user's content feature vector \(u_{\text{kg}}\); then, before the two are fused to output a complete user vector, the behavioral feature and content feature are projected into the preference feature space by the behavioral feature projection matrix and content feature projection matrix \(M_{\text{cf}}\) and \(M_{\text{kg}}\), respectively, which simplifies the semantic information behind the behavioral feature and content feature so that the feature vector after the projection only expresses the user's preference information, which is  
Figure 1: Message propagation on CKG. convenient for subsequent fusion of the two. Finally, the two features are fused in the same feature space by splicing and a multilayer perceptron to obtain the user's complete feature vector \(u_{final}\), which avoids the noise problem caused by the simple fusion of behavioral features and content features in the same space.
## 4 Communication Architecture
### Modeling the Architecture of Inter-Level Communication  
Overall, the different types of sensors in architecture must be organized to collect the big data generated in a smart city. To do this, the three levels of architecture interact by sending a permanent message. As described above, some sensors cannot start their activities before their predecessor completes its task (except for level 1 sensors that have no predecessor in the transmission chain and those initiate communications in our scenario). At the beginning of the deployment, the sensors are activated simultaneously and begin their initialization phase with mutual recognition in order to have a comprehensive view of the network. The level 1 sensors, CHs, level 2 sensors, and the Sink switch off their components except for the transceiver and MCU which remain active (the sensor unit remains inactive) waiting for wake-up messages to be initiated by the level 1 sensor nodes (periodic sending of data/alert messages). The level 1 sensor monitor people and their related activities and react if specific  
Figure 7: Diagram of activity sequences of second level architecture sensors.  
events occur (high or low temperature, abnormal pulse, drop, etc.) and transmit the data via the CHs to the level 2 network.  
Environmental data are collected at level 2. In addition, data received from level 1 are also collected. Level 2 consolidates the data and then transfers the data to level 3. To reduce energy consumption, collisions and retransmissions, we propose to adjust the transmission power of the level 1 sensor nodes and the CH so that they only reach the neighboring level 2 sensors.
## 4 Experimental Results and Analysis
### Comparison between the Proposed Method and Other Methods  
To verify the performance of the proposed algorithm, five representative multi-threshold segmentation algorithms are selected for comparative experiments. The five comparative experimental algorithms are: (1) image threshold segmentation algorithm based on particle swarm optimization (PSO), (2) image threshold segmentation algorithm based on bacterial foraging (BF), (3) image threshold segmentation algorithm based on adaptive bacterial foraging (ABF), (4) image threshold segmentation algorithm based on Nelder-Mead simplex (NMS), (5) image multi-threshold segmentation algorithm based  
suboptimal results when \(K=5\). The difference of the objective evaluation \(U\) value is small and acceptable. Overall, our proposed algorithm achieves the best segmentation results in test images, outperforming five different contrasting algorithms. Table 3 shows optimal threshold values obtained by different segmentation algorithms on the test images. Additionally, the final thresholds selected by different algorithms are different from each other. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Images**} & \multicolumn{3}{c}{**Number of**} & \multicolumn{6}{c}{**Uniformity Measure (\(U\))**} \\ \cline{2-9}  & **Thresholds (K)** & **Proposed** & **PSO** & **BF** & **ABF** & **NMS** & **RCCA** \\ \hline \multirow{4}{*}{\#022} & 2 & **0.9870** & 0.9552 & 0.9569 & 0.9569 & 0.9569 & 0.9569 \\  & 3 & **0.9  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Test Images**} & \multicolumn{1}{c}{**Number of Thresholds (\(\mathbf{Q}\))**} & \multicolumn{1}{c}{**Poposed**} & \multicolumn{1}{c}{**PSO**} & \multicolumn{1}{c}{**BF**} & \multicolumn{1}{c}{**ABF**} & \multicolumn{1}{c}{**NMS**} & \multicolumn{1}{c}{**RCGA**} \\ \hline  & 2 & 33,92 & 67,184 & 96,184 & 95,184 & 96,184 & 96,184 & 95,184 \\ \hline \multirow{3}{*}{\#022} & 3 & 17,56,110 & 69,182,307 & 65,33,116 & 69,114,185 & 58,116,185 & 58,115 & 58,115 \\  & 4 & 13,37,74,116 & 88,116,175,207 & 52,9,148,186 & 58,113,174,208 & 48,57,132,185 & 44,67,131,186 \\  & 5 & 13,37,74,116 & 76,119,154,184 & 244,49,102,170,208 & 48,38,136,176,208 & 44,104,104,176,224 & 44,86,127,274,208 \\ \hline \multirow{3}{*}{\#032} & 2 & 32,90,706 & 107,185 & 110,185 & 110,185 & 110,185 & 109,185 \\  & 3 & 27,106,116 & 74,175,192 & 72,120,198 & 51,134,187 & 56,151,186 & 53,116,185 \\  & 4 & 13,43,67,127 & 95,125,164,194 & 63,119,123,203 & 58,124,120,919 & 96,313,128,199 & 96,314,119 \\  & 5 & 11,37,67,94,125 & 80,112,139,186,223 & 63,104,110,175,207 & 52,128,167,198 & 29,75,124,172,207 & 34,76,123,174,207 \\ \hline \multirow{3}{*}{\#042} & 2 & 46,107 & 111,183 & 114,184 & 114,184 & 113,184 & 114,183 \\  & 3 & 18,60,107 & 80,148,178 & 770,136,188 & 774,130,185 & 84,123,188 & 84,123,187 \\  & 4 & 18,86,100,140 & 14,125,164,197 & 62,112,156,194 & 50,100,143,190 & 29,76,128,157 & 30,75,128 \\  & 5 & 18,558,112,142 & 81,153,142,184 & 58,114,151,158 & 25,97,128,154 & 32,176,184,218,316 & 31,76,182,177 & 25,61,116,194 \\ \hline
## 4 Case Study
### Noise Measurement and Analysis on the IEEE 9-Bus System  
In this subsection, G2 is taken as an example to test the estimation effect of AEnSRF on generator state as well as the tracking of measurement noise when it is biased. Assuming that the actual value of the standard deviation of power angle measurement noise is 3 degrees, the value is set as 2 degrees in the filtering program to detect the tracking effect of AEnSRF on power angle measurement noise.  
Figure 5 depicts the comparison results of G2 by utilizing EnKF and AEnSRF algorithms, respectively. It can be obviously noted from Figure 5 that both of them can show good estimation results under the consideration of measurement noise deviation condition.  
The simulation results of G2 show that under AEnSRF algorithm, the RMSE of power angle estimation is 0.0516, the RMSE of electric angular velocity estimation is 0.0011. Under EnKF algorithm, the RMSE of power angle estimation is 0.0536 and the RMSE of electric angular velocity estimation is 0.0012. By comparing both algorithms, AEnSRF has higher filtering-precision.  
Figure 6 is the simulation diagram of AEnSRF tracking curve for measurement noise when there is a deviation in measurement noise. As it can be noticed from this figure, when the power angle  
Figure 5: Comparisons of G2 state filtering results between EnKF and AEnSRF in the presence of measurement noise deviation: (**a**) Power angle dynamic estimation curve of G2; (**b**) electric angular velocity dynamic estimation curve of G2.  
measurement noise is biased, the Sage-Husa noise estimator with on-line real-time estimation noise is introduced by AEnSRF, which makes the estimated standard deviation of power angle measurement noise stabilize around 3 degrees at about 1.1 s.
## 3 Results and Discussion
### Soil Water Balance  
The soil moisture content was determined by measuring the weight of the soil column between the start of the experiment (dry soil) and the end of each wet/dry cycle. The results indicate that the smaller the grain size of the fraction, the higher the water-filled pore space during wet and dry phases (Table 3). The reason for this is again the different pore size distribution of the fractions resulting in a different storage capacity of the water in the pore system. The water holding capacity by capillary forces was stronger in the fraction with the smallest grain size compared to the fractions with a larger grain size due to the higher content of fine pores with smaller diameter [32; 34]. The changes in WFPS between wet and dry phases were quite high for the bigger fractions (>17%) while the change for the smaller fractions was negligibly small (3%).  
The average residence time calculation of the daily infiltrated amounts of the synthetic solution occurred based on the estimated pore volumes. It could be observed that the fraction with smaller grain size had higher residence time (125 um to 200 um--24.4 h; 1000 um to 2000 um--20.4 h). The reason for this behaviour is the different pore size distributions of the fractions resulting in a different hydraulic conductivity and residence time.
# Binaural Rendering with Measured Room Responses: First-Order Ambisonic Microphone vs. Dummy Head
## 3 Experiment II: Dummy-Head MOBRIR vs. ARIR
### Listening Experiment: Design  
Similar to _Experiment I_, listeners were asked to rate the spatial mapping, coloration, and continuity compared to the \(1^{\circ}\) reference. The test conditions included ARIR rendering with the ASDM target orders \(N=\{1,3,5\}\) as well as the corresponding MOBRIR resolutions \(\Delta\varphi=\{60,45,30\}^{\circ}\) rendered with LISHPh and broadband linear interpolation. Note that the set of MOBRIR resolutions is derived from the number of loudspeakers used for Ambisonics reproduction [23] in practice \(\Delta\varphi_{N}\approx\frac{180^{\circ}}{N+1}\), for \(N=1\), we chose \(60^{\circ}\) instead of \(90^{\circ}\) to maintain a reasonable MOBRIR resolution.  
We asked to rate the _timbre differences_, and _consistency of spatial mapping_ for the five static listener orientations \(\varphi=\{0,-35,12,-15,22.5\}^{\circ}\) which were switched automatically in 900 ms intervals and are restarted at the beginning of every audio loop. The orientations where chosen such that \(0.25<\alpha<0.83\) for all resolutions in order to test for high interpolation depth; \(\varphi=0^{\circ}\) is included as reference orientation, and it marks the start of each loop. As source positions, we used a frontal and lateral virtual loudspeaker, cf. loudspeakers 3 and 7 in Figure 1a. The _continuity_ test to compare dynamic rendering was similar as in _Experiment I_; see Section 2.2.  
Figure 5: Energy decay relief (EDR) in a third-octave band with center frequency of 2 kHz. Solid and dashed lines indicate the order partitioned EDR before and after equalization as defined in Equations (6) and (7), respectively.
## Sparse Signal Recovery from Fixed Low-Rank Subspace via Compressive Measurement  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 3**  
+++ ==WARNING: Truncated because of repetitions==  
++  
medical imaging [6], sensor network [7; 8], machine learning [9; 10], _etc_. Regarding the algorithmic aspect of CS, the main problem is to solve the underdetermined linear equation system \(v=\mathcal{A}x\,+\,\epsilon\) efficiently with the constraint that \(x\) is sparse. There are several well-known approaches to solve the sparse recovery problem, such as the greedy solver CoSaMP [11], the classic convex optimization approach [12], _etc_.  
In this paper, we consider a special noisy sparse recovery problem:  
\[v=\mathcal{A}\left(Uw+s+\xi\right)+\epsilon \tag{1}\]  
Here \(\xi\) is the Gaussian noise with small variance added into data itself, and \(\epsilon\) is the small Gaussian measurement noise. The sparse signal \(s\) is buried in structured dense signal \(l=Uw\)[13] generated by a fixed low dimensional subspace spanned by the column space of \(U\). In the CS field, this special problem represents a large body of applications in which the measured object can be regarded as the superposition of a sparse part and a dense but structured part. For example, in video surveillance, each frame contains a slowly-changing background or the background is static, which can be faithfully modeled by a low-dimensional subspace; and the remaining part--foreground, which is always of interest, is sparse.  
From the highly compressive measurement can we exactly recover the sparse signal \(s\)? In statistics and optimization, this problem can be regarded as a compressive sensing version of the classic least absolute deviations (LAD) problem [14] or \(\ell_{{}_{1}}\) regression problem. Instead of firstly recovering the full vector then separating the sparse part from the structured dense part, we propose a variant of CoSaMP approach named _CoSaMP_subspace_, which directly works on the compressive measurement to separate the sparse signal from low-rank background signal.  
This paper is organized as follows. Section 2 describes the model of the special sparse recovery problem, introduces the proposed _CoSaMP_subspace_ algorithm, and discusses its relation to ordinary CS technique. In Section 3, we conduct extensive experiments on both simulated data and the real surveillance video. Section 4 concludes this paper and points out future work.
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
## 2 Theory Analysis
### Detection Probability of Signal Photon Events  
As described in [28; 35], supposing the object point \((x,y)\) is illuminated by a pulse \(S(t)\) and the echo pulse reflected the SPD is \(S\left(t-\frac{2z_{x,y}}{c}\right)\). Then the theoretical number of photons detected by SPD is  
\[r_{x,y}(t)=\alpha_{x,y}S\left(t-\frac{2z_{x,y}}{c}\right)+b, \tag{1}\]  
Figure 1: The schematic diagram of TCSPC: (**a**) TCSPC module records the start-stop-time between the pulse emitted by the laser and the photon detected by SPD; (**b**) histogram of the number of photons vs. the arrival time.  
where \(z_{x,y}\) represents the flight distance of the laser from the target point, \(c\) represents the speed of light, \(\alpha_{x,y}\) represents the reflectivity of target and \(b\) represents the number of background photons. Considering that the quantum efficiency of SPD is \(\eta\) and the dark counts are \(d\), the actual number of photons \(\lambda_{x,y}\) detected by the SPD is  
\[\lambda_{x,y}(t)=\eta r_{x,y}(t)+d=\eta a_{x,y}S\left(t-\frac{2z_{x,y}}{c} \right)+(\eta b+d). \tag{2}\]  
Let the pulse laser period be \(T_{r}\), then the average number of photons received by the SPD in a period is  
\[\lambda_{x,y}=\lambda_{x,y}(t)\cdot T_{r}=\eta a_{x,y}\int_{0}^{T_{r}}S(t)dt+( \eta b+d)T_{r}=\eta a_{x,y}S+B. \tag{3}\]  
According to the Poisson probability model [36], for each pulse emitted by the laser, the number of photons \(k\) received by the detector satisfies the following distribution:  
\[P_{x,y}(n=k)=\frac{\lambda_{x,y}^{k}}{k!}e^{-\lambda_{x,y}}. \tag{4}\]  
When the SPD does not record any photon, that is \(k=0\), the probability can be denoted by \(P_{x,y}(n=0)=e^{-\lambda_{x,y}}\).
## 4 Encoding and Decoding Design
### Decoding Rules  
To narrow the solution space while maximizing the discovery of near-optimal solutions to the problem, the heuristic decoding rules for allocation in the processing stage and subsequent transportation and assembly stages are as follows:  
(1) Machine assignment in the processing stage  
The earliest completion time (ECT) rule is used to assign operations to the machine that can complete them the earliest. If multiple machines can complete the operation at the same time, the minimum energy consumption first (MECF) rule is applied to select the machine with the lowest energy consumption.  
Figure 2: Schematic diagram of encoding.  
(2) Job transportation sequence and vehicle allocation in the transportation stage  
Under the constraint of vehicle load capacity, the first completed first transported (FCFT) rule is used to organize the batch transportation of jobs. This means that jobs are loaded into available vehicles in the order of their completion times, ensuring that no single vehicle is overloaded. Subsequent jobs are transported by following vehicles according to the same rule.  
(3) Product assembly sequence and machine assignment in the assembly stage  
When the jobs transported to the assembly shop are sufficient for the complete assembly of the product, the assembly is carried out according to the product assembly process. If multiple products simultaneously meet the assembly conditions, the minimum jobs first (MJF) rule is used to determine the assembly sequence. During product assembly, the machine assignment for assembly operations follows the same rules as in the processing stage to determine the assembly machine for each operation.
# Research on Pupil Center Localization Detection Algorithm with Improved YOLOv8
## 3 YOLOv8n Model Improvement
### Dual Attention Mechanism
#### 3.1.1 Position Attention Module  
The upper part of Figure 4 is a compact position attention module, as shown in Figure 5. By utilizing the correlation between two feature points, broader contextual information is encoded into local features, thereby mutually enhancing their respective feature representation capabilities. This module is used to extract spatial information. The relationship between each pixel and several aggregation centers was constructed through pyramid pooling (pooling of different sizes), and these pooling features were concatenated to form a self-attention inner product, which to some extent reduced the computational complexity and memory consumption.  
Firstly, the original features \(A\in\mathbb{R}^{C\times H\times W}\) are dimensionally reduced through a convolutional layer with BN and ReLU layers, obtaining the features \(B\in\mathbb{R}^{C\times H\times W},C\in\mathbb{R}^{C\times H\times W}\), and \(D\in\mathbb{R}^{C\times H\times W}\), and changing the dimensions of features \(B\), \(C\), and \(D\) to \(\mathbb{R}^{N\times C}\), \(\mathbb{R}^{C\times N}\), and \(\mathbb{R}^{C\times N}\), respectively, where \(N=H\times W\). Secondly, by multiplying the transpose matrix of feature \(B\) and the \(C\) matrix to obtain the correlation strength matrix between any two point features, the softmax layer is used to obtain the spatial attention mapping of each position to other unknown features.  
\[s_{ji}=\frac{exp(B_{i}\bullet C_{i})}{\sum_{i=1}^{N}exp\big{(}B_{i}\bullet C_{ j}\big{)}} \tag{1}\]  
where \(s_{ji}\) represents the similarity between position i and position j, and the larger the value of \(s_{ji}\), the more similar the two features are. Finally, transpose \(S\) and multiply it with the  
Figure 4: DA module.  
Figure 5: Structural diagram of compact position attention module.  
matrix, and then add it element by element with the original feature \(A\) to obtain the final output \(E\in\mathbb{R}^{C\times H\times W}\), which is  
\[E_{j}=\alpha{\sum_{i=1}^{N}}\big{(}s_{ji}D_{i}\big{)}+A_{j} \tag{2}\]  
where \(\alpha\) represents the scale coefficient, with an initial value of 0. By gradually learning, larger weights can be obtained.