esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 2 Materials and Methods
### Computation Offloading Decision Process
The entire system state is composed of the Markov decision process set. A single Markov decision process includes the system state set, action set, state transition probability, cost function and strategy space. At each decision moment of task offloading, the control parameters of the system correspond to the actions in the Markov decision process. The state transition probability of the system controls the dynamic changes of the state and control parameters at each stage. Further, the evaluation metrics for the final task computation offloading are presented by the cost function. In the three-layer network architecture of Space-Air-Ground Integrated Network, select the offloading sites that meets \(f_{maxgive}\geq w_{in}\) to constitute the target state set \(X_{target}=\{0,1,2,\cdots,n,n+1\}\) in the Markov decision process, and \(w_{in}\) represents the computing power required for the offloading task. Among them, the newly added state is the initial state \(x_{0,0}\) of task computation offloading and the state \(x_{n+1,0}\) of task computation offloading completed. The specific Markov decision process of the model in Figure 2 is \(M=(S,A,P_{sa},R)\). During the decision period \(t\), the offloading terminal selects the action \(a\) (\(a\subset A\)) according to the system state information \(x_{i,i}=(t,i,Y_{t})\), and transitions from the current state \(s\) to the next state \(s^{\prime}\). \(P_{sa}\) represents the state transition probability and the reward function \(R\) represented as \(r(s^{\prime}|s,a)\), where \(s\) (\(s\subset S\)) represents the set of all states in a single Markov process. In the system status information, \(Y_{t}\) represents the channel status between the current offloading terminal and the offloading site; \(i\in[0,k+6+5]\) represents the set of task offloading locations, where \(k\) represents the optional offloading sites in the edge access network, and 6 represents the offloading sites of the space-based backbone nodes, 5 represents the offloading sites of the ground-based backbone nodes. The state \(s\) is determined by the channel state and the location of the task offloading site. Assuming that the initial channel state is good, the initial state of the system is expressed as \(x_{0,k}=(0,k,g)\), and the final state is expressed as \(x_{n+1,k}=(n+1,k,Y_{n+1})\). When the target state of the offloading task transitions to the state \(x_{n+1,0}\in X_{target}\), the task computation offloading is completed.
## 2 Materials and Methods
### Missing Tooth Region Detection Model  
Missing tooth region detection is required for implant placement in the panoramic radiographic image. Through this model, missing tooth regions, except the third molars, are detected simultaneously. Missing tooth regions are detected in the form of bounding boxes and assigned numbers (#11\(\sim\)#47). For missing tooth region detection, we used Faster R-CNN, which exhibits high performance in object detection. The detection model's backbone is ResNet-101. The model was trained via the synthetic data, and real data were used for evaluation. To improve the training efficiency, the input image of the model was resized to 600 \(\times\) 300. Figure 4 shows the entire process for detecting missing tooth regions from the panoramic radiographic image. The detection model was trained using an SGD optimizer with a learning rate of \(1\times 10^{-2}\), a batch size of 32, and the Smooth L1 loss function. The detection model was trained for 100,000 iterations and evaluated every 500 iterations, and the number of parameters of model is 63.3 M.  
Figure 4: Entire process for missing tooth region detection. Tooth instance segmentation model segments 28 teeth, excluding the third molars, in the (**a**) panoramic radiographic image. (**b**) shows result of the tooth instance segmentation model. Furthermore, then, (**c**) is generated from results of the segmentation model. The missing tooth region detection model detects the regions of missing teeth from the generated teeth mask. Furthermore, the result of the detection model is shown in (**d**).  
Figure 3: Schematic of the network architecture of ResNet-101. The network is implemented by repeating the block of three convolutional layers 3, 4, 23, and 3 times, respectively. 3 \(\times\) 3 pool means pooling layer with 3 \(\times\) 3 filters. Furthermore, K and C are the mean size of kernel and the number of channels of convolutional layer, respectively.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 2 The Density Evolution and Gaussian Approximation of the BP Algorithm  
### The Density Evolution of the BP Algorithm  
A regular LDPC code can be represented by \((dv,dc)\), where \(dv\) and \(dc\) is the degree of variable and check nodes, respectively. The iteration expression of the check messages and the variable messages are as follows [11]  
\[\tanh\frac{u_{j}^{(l)}}{2}=\prod_{i=1}^{dc-1}\tanh\frac{v_{i}^{(l-1)}}{2}, \tag{1}\]\[v_{i}^{(l)}=v_{i}^{(0)}+\sum\limits_{j=1}^{dv-1}u_{j}^{(l)}, \tag{2}\]  
\(v_{i}^{(0)}\) represents the initial message of channel received by \(i\)-th variable node. Assuming that \(P^{(l)}(v)\) and \(Q^{(l)}(u)\) are the probability densities of \(v_{i}^{(l)}\) and \(u_{j}^{(l)}\), respectively. We can obtain the result of density evolution from the check messages to the variable messages as [11]  
\[P^{(l)}(v)=P^{(0)}\otimes Q^{(l)\otimes(dv-1)}, \tag{3}\]  
\(\otimes\) represents the convolution operation. We can transform the convolution operation in Equation (3) to the multiplication operation based on the Fourier transform [11]  
\[P^{(l)}(v)=F^{-1}\!\left(F\!\left(P^{(0)}\right)\cdot F\!\left(Q^{(l)}\right)^ {dv-1}\right) \tag{4}\]  
The process of density evolution from the variable messages to the check messages was introduced by [PERSON] in detail in Reference [11]. Thus, we can calculate the bit error rate (BER) as  
\[P_{e}^{(l)}=\int_{-\infty}^{0}P^{(l)}(v)dv, \tag{5}\]  
For irregular LDPC codes, the degree distributions of the variable nodes and the check nodes are \(\lambda(x)=\sum\limits_{i=2}^{dv}\lambda_{i}x^{i-1}\) and \(\rho(x)=\sum\limits_{i=2}^{dc}\rho_{i}x^{i-1}\), respectively. We must consider the degree distribution to update probability density of the variable messages and the check messages [11]. The BER of irregular LDPC codes is also calculated by Equation (5).
## 3 Results and Discussion
### Nutritional Quality Index of Oils  
The content of fatty acids significantly impacted the dietary factors of the fats and plant oils (Table 3). Dietary fat quality is determined by the amount of SFA, MUFA, and PUFA, as well as the ratio of n-6 to n-3 fatty acids. Three nutritional quality indexes were created to assess the nutritional value of oils: the atherogenicity index (AI), the thrombogenicity index (TI), and the hypocholesterolemomic: hypercholesterolemia FA ratio (HH); these were calculated based on the fatty acid compositions of the studied oils. The concentrations of lauric acid (C12:0), myristic acid (C14:0), arachidonic acid (C20:4 n-6), docosapentaenoic acid (C22:5 n-3), and docosahexaenoic acid (C22:6 n-3) were considered as zero for the calculations of AI and HH because they were not identified in the total lipids of the oils.  
The atherogenicity (AI) and thrombogenicity (TI) indices indicate whether or not the studied oil has the potential to stimulate platelet aggregation. The lower the AI and TI values, the more the anti-atherogenic fatty acids present in a particular oil/fat, and thus the greater the potential for preventing the development of coronary heart disease. The HH index considers the specific effects of fatty acids on cholesterol metabolism, and high HH values are desired from a nutritional standpoint [52]. In the present work, the highest HH ratio value (28.34) was found in coriander seed oil among the tested oils. [PERSON] et al. [53] reported that the HH ratio values ranged from 11.2 to 15.0 in camelina oils. The AI values of the oils ranged between 0.04 and 0.14 for the analysed oils (Table 3). [PERSON] and [PERSON] [13] showed that the AI of oils from coconut, palm, and olive are 13.63, 0.88, and 0.14, respectively. The studied oils were also characterised by different TI values, which ranged from 0.05 (BO) to 0.35 (TO). Significantly higher TI values for coconut (6.81) and palm (2.07) oils were demonstrated by [PERSON] and [PERSON]. These indices suggest that incorporating coriander, basil, parsley, and dill seed oils into the human diet may help prevent coronary heart disease development.
## 4 The Test Bench
### Experimental Setup  
The test bench shown in Figure 12a contains an isolated oscillator, an excitation, and measuring instruments. This single-mass oscillator was originally designed to represent a vehicle suspension leg, whose parameters, such as stiffness, are adjustable, in order to build a basis for further study of passive and active vibration isolation [47, 48]. The moving mass and two Carbon Fiber Reinforced Polymer (CFRP) beams of the oscillator shown in Figure 12b are mounted on a rigid base that is suspended by four elastic ropes with low stiffness and a low damping coefficient, so that the natural frequency of the oscillator can be distinguished from the vibration of the suspension. The signal of the excitation is derived from a signal generator. Then, the signal is amplified via an amplifier and led to the shaker. The excitation force is brought to the rigid base of the oscillator by a coupling between the shaker and the base. The accelerometers in Figures 12c,d measure the accelerations at the moving mass and the base. The excitation force is measured using a force sensor between the shaker and the base. All signals are recorded simultaneously by a data acquisition system.  
Figure 12: (**a**) Components of the test bench, (**b**) details of the oscillator, (**c**) accelerometers on the moving mass and (**d**) on the base.
# PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems
## 4 The Space-Variant Point Spread Function  
Wide-field optics is usually affected by various aberrations, such as distortion which makes the PSF of the system space-variant, i.e., the PSF changes across the field of view. It is, therefore, necessary to use a complicated model of the system's PSF. Let us begin with a linear space-invariant optical imaging system, which can be expressed as  
\[h(u,\,v)=|FT\{P(x,\,y)\}|^{2}=\left|FT\{p(x,\,y)\exp\!\left(-i\frac{2\pi}{ \lambda}W(x,\,y)\right)\right\}\right|^{2}, \tag{11}\]  
where \(p(x,\,y)\) defines the shape, the size, and the transmission of the exit pupil, and \(W(x,\,y)\) is the phase deviation of the wavefront from a reference sphere. The generalized exit pupil function is described in [16] as  
\[P(x,\,y)=p(x,\,y)\exp\!\left(-i\frac{2\pi}{\lambda}W(x,\,y)\right). \tag{12}\]  
An imaging system is space-invariant if the image of a point source object changes only in position, not in the functional form [41]. However, wide-field optical systems give images where the point source object changes both in position and in functional form. Thus, wide-field systems and their impulse responses lead to space-variant optical systems. If we consider a space-variant system, we cannot use the convolution for expressing the relation between object and image. When computing SVPSF, we have to use the diffraction integral [6; 42]. SVPSF can then be expressed as  
\[\begin{array}{ll}h(u,v,\xi,\eta)=&\frac{1}{\lambda^{2}z_{1}z_{2}}\int\limits \limits_{-\infty}^{\infty}\int\limits_{-\infty}^{\infty}p(x,y)\exp\!\left(-i \frac{2\pi}{\lambda}W(x,y)\right)\cdots\\ &\cdots\exp\!\left\{-j\frac{2\pi}{\lambda z_{2}}[(u-M\xi)x+(v-M\eta)y]\right\} dxdy\end{array}, \tag{13}\]  
with a defined magnification of the system  
\[M=-\frac{z_{2}}{z_{1}}, \tag{14}\]  
where \(z_{1}\) is the distance from the object plane to the principal plane and \(z_{2}\) is the distance from the principal plane to the image plane.
# Advances in Piezoelectric Jet and Atomization Devices
###### Abstract  
In recent years, the piezoelectric jet and atomization devices have exhibited tremendous advantages including their simple construction, and the fact that they are discreet and portable as well as low cost. They have been widely used in cell printing, spray cooling, drug delivery, and other industry fields. First, in this paper, two different concepts of jet and atomization are defined, respectively. Secondly, based on these two concepts, the piezoelectric jet and atomization devices can be divided into two different categories: piezoelectric micro jet device and piezoelectric atomization device. According to the organizational structure, piezoelectric micro jet devices can be classified into four different models: bend mode, push mode, squeeze mode, and shear mode. In addition, their development history and structural characteristics are summarized, respectively. According to the location of applied energy, there are two kinds of piezoelectric atomization devices, i.e., the static mesh atomization device and the vibration mesh atomization device, and both their advantages and drawbacks are discussed. The research achievements are summarized in three aspects of cell printing, spray cooling, and drug delivery. Finally, the future development trends of piezoelectric jet and atomization devices are prospected and forecasted.  
piezoelectric; micro jet; atomization; cell printing; cooling; drug delivery +
Footnote †: journal: Applied Sciences
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Other Works Analyzing Different Aspects of LoRaWAN
#### 3.3.1 Geolocation  
Geolocation is a very useful tool that has been used in recent years, from delivery services to social networks. In this area, LoRaWAN devices have also been used to monitor the health of a person in a given location, as well as for livestock tracking applications. For example, ref. [43] describes the implementation of a livestock monitoring system using GPS, temperature, and humidity sensors.
# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 2 Proposed Methodology
### Generation of Saliency Maps
#### 2.3.2 Bottom-Up Saliency Maps (\(S_{g}\))  
Bottom-up visual saliency maps rely on image features, such as color, edges, orientation, and textures. They mostly resemble the visual pattern of a physician. In this method, a bottom-up saliency map is derived from the cues of edges, orientation, and textures [28]. Edge cues are used to locate sudden changes in pixel intensity in MRI images, which portray discontinuities in white matter and gray matter tissues. Sobel and Canny edge detection are leveraged in the proposed method.
# Real-Time Cloth Simulation Using Compute Shader in Unity3D for AR/VR Contents
## 3 Implementation of Cloth Simulation in Unity3D
### Mass-Spring System on the GPU  
In Figure 1, there are 12 directions of force acting on a single node. So, we use a node-centric algorithm to accumulate the force of each node using the 1 thread per node technique. Since the node in a cloth model can be represented as 2D, we can define the compute space using the 2D dimension of the workgroup that each local workgroup determined as (\(x=32\), \(y=32\), \(z=1\)) in the kernel. Consequently, the dimension of the workgroup is calculated as (\(x=\left\lceil\frac{\text{Row}}{32}\right\rceil,y=\left\lceil\frac{\text{Column}}{32} \right\rceil,z=1\)), where Row and Column are the numbers of nodes in the row and column of a cloth model, respectively.  
To invoke each element of the buffer by the index of buffer properly, the index of the 2D thread is calculated following the scheme demonstrated in Figure 3. _ThreadID_ is used to indicate a thread throughout the entire distribution in the thread group in the form of 3D format, and can be calculated by _GroupID_\(\times\)_ThreadGroupSize_\(+\)_GroupThreadID_, where _GroupID_ refers to the index of a specific workgroup, _ThreadGroupSize_ is the total number of how many workgroups there are in the compute space, and _GroupThreadID_ is a unique index of the thread in each local workgroup (local index).  
Figure 2: The compute space dimension of the kernel in Unity.  
We then calculate the index of the buffer as 1D by \(j\)th \(\times\) Column + \(i\)th, where \(i\)th and \(j\)th are the indices of the thread in the X and Y-axis of ThreadID, respectively. Algorithm 1 indicates the pseudo-code of the kernel to accumulate the spring force. Note that the ComputeSpringForce() function follows Equation (1), which requires the position and velocity of 2 nodes to manipulate the force as a result.  
```
\(\mathbf{Spring force}\) accumulation kernel Input: Buffer Position, Velocity, Force Output: Buffer Force Begin \(\mathbf{i}\leftarrow\) ThreadID.x \(\mathbf{j}\leftarrow\) ThreadID.y \(\mathbf{index}\leftarrow\) j\(\times\)column+i \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i, j+1]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i, j-1]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i+1, j]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i+1, j+1]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i-1, j-1]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i-1, j+1]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i, j+2]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i, j-2]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i+2, j]) \(\mathbf{Force}[\text{index}]\leftarrow\) ComputeSpringForce(node[i, j], node[i-2, j]) End
```  
**Algorithm 1** The node's centric algorithm to accumulate spring force.
## 4 Discussions, Conclusions, and Future Directions
### Discussions
#### 4.1.2 Discussion: Comparison between Proposed Approach and Previous Work  
This section shows the similarities and differences between our proposed approach and the previous work, including the signal model, communication channel, interference cancellation technique, performance metric, and the limitation of the joint design system. These issues are described in detail in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Proposed Approach** & **Previous Work** \\ \hline Channel & Downlink AWGN with & Uplink AWGN with \\  & realistic channels; & Rayleigh channel. \\  & Multiple base stations, multiple & Multiple base stations, multiple \\ Scenario & devices with downlink & devices with \\  & communication. & uplink communication. \\ Communication Signal & Includes communication and & Only communication signal at UE. \\  & & Includes transmit/receive \\ Sensing Signal & steering vectors, reflection & Similar sensing \\  & coefficient, and noise. & signal components. \\ Joint Signal & Combines communication and & Separate communication and \\  & sensing signals at UE. & sensing signals at receiver. \\  & Uses Maximum Likelihood & Utilizes Maximum Likelihood \\ Interference Cancellation & detector for & detector and MMSE estimator. \\  & communication symbols. & BER for uplink communication, \\  & & MSE for target estimation at BS. \\  & Explores alternative interference & Achieves optimal performance \\ Limitation Compensation & cancellation for optimal & with Maximum Likelihood \\  & sensing performance. & detector and MMSE estimator. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between proposed approach and previous work.
# Cyclic Tests and Numerical Analyses on Bolt-Connected Precast Reinforced Concrete Deep Beams
## 3 Test Results
### Ductility and Energy Dissipation  
In this paper, the displacement-based ductility coefficient \(\mu_{\Delta}\) is utilized to represent the ductility performance of RDBs. The \(\mu_{\Delta}\) is determined by the displacements when the specimens yield and fail, and the calculation is \(\mu_{\Delta}=\Delta_{\text{max}}/\Delta_{\text{y}}\). The equal energy method is used to obtain the value of \(\Delta_{\text{y}}\). The failure displacement is defined as 85% times the maximum value [8]. The ductility results are listed in Table 2. The RDB-B exhibited better ductility than the RDB-A, indicating that the ductility of RDBs increases if the height-to-length ratio reduces.  
Figure 6: Failure modes of the RDB-B.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test** & \multicolumn{3}{c}{**Yield Value**} & \multicolumn{3}{c}{**Maximum Value**} & \multicolumn{2}{c}{**Ductility Value**} \\
**Specimen** & \(P_{y}\) **(kN)** & \(\Delta_{y}\) **(mm)** & \(\varphi_{y}\) **(\%)** & \(P_{\text{max}}\) **(kN)** & \(\Delta_{\text{max}}\) **(mm)** & \(\varphi_{\text{max}}\) **(\%)** & \(\mu_{\Delta}=\Delta_{\text{max}}/\Delta_{\text{y}}\) \\ \hline RDB-A & 9.23 & 3.07 & 0.34 & 10.35 & 3.63 & 0.40 & 1.18 \\ RDB-B & 25.69 & 2.08 & 0.23 & 39.06 & 5.90 & 0.65 & 2.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Force, displacement and ductility results.  
Figure 7: Loading-displacement curves of the specimens.  
To evaluate the energy dissipation performance of RDB specimens, the energy dissipation coefficient \(E\) is used. The detailed calculation and the diagram are shown in Figure 7(a) [8]. The energy dissipation coefficients of the test specimens in this paper are presented in Figure 7(b). The results show that the energy dissipation coefficient decreases with the increase of the lateral displacement for the most part. The energy dissipation capacity of RDB-A is close to the RDB-B, indicating that the energy dissipation capacity of RDB-B changes little by decreasing the height-to-length ratio. Due to the limited number of specimens, it should be highlighted that the effects of the height-to-length ratio on ductility and energy dissipation coefficient are not regular.
## 6 Results
### Energy Reduction
#### 6.3.4 Energy Reduction  
Finally, we evaluate the impact of SCHC on the energy consumption of the device by analyzing the relative gain of using SCHC in terms of energy reduction. Figure 14 shows the average relative energy reduction for different message sizes and radio conditions in the case of NIDD without DTLS. SCHC yields a significant energy reduction in all the setups. The general trend is a relative energy gain due to SCHC improving with message size. Except for radio conditions 1 and 3 at message sizes of 100 and 200 bytes, the relative energy reduction monotonically increases with the message size.  
Regarding the radio conditions, there is no obvious trend. One interesting case is that of 50 bytes in radio condition 4, which exhibits the lowest gain among the results shown here. This can be explained by the small message size, where reducing the message size with compression does not compensate for the impact of retransmissions and the relatively long time needed to have a successful exchange. Nevertheless, the energy reduction increases from 2.3% to 25.4% when the message size increases from 50 bytes to 100 bytes.  
In any case, using SCHC always has a positive impact in terms of energy consumption, with a relative gain as high as 40%. Note that we noticed very similar results for transmissions over NIDD with DTLS and over thee IP with and without DTLS.  
Figure 14: Energy reduction (%) due to using SCHC over NIDD (no DTLS).  
Figure 13: Energy demand (mWh) in case the of radio condition 4 (no DTLS).