esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Performance Evaluation Metrics  
The metrics used for the performance evaluation of the proposed SFPCR scheme and the existing schemes [21; 33; 37] were: packet success probability, energy consumption, and the number of packets. In simple terms:  
* Packet success probability A frame transmission is considered successful when no collisions occur and all the bits in the frame are accurately decoded despite interference [54]. The harsh environment and capture effects are two factors that can have an impact on the success of data transmissions between LDs and GWs. It is critical to consider both the likelihood of the GW receiving an uplink from the source LD and the likelihood of the device successfully receiving a downlink.
* Energy consumption We considered the energy consumption among LoRa devices, which refers to the number of delivered data bits per unit of energy consumed by a LoRa device [39]. The SFPCR scheme aimed to balance the HSFZ's energy efficiency by shortening the ToA of the LDs.
* Number of Packets When analysing traffic behaviour in a LoRa network, the number of packets passing through the network to the GW is critical. In the LoRaWAN network, packets are sent infrequently and are affected by a variety of factors. As a result, the primary objective here was to ensure an effective transmission that limited duplicate packets, collisions, and retransmissions, all of which produce large quantities of network congestion and thus impede overall network performance.
# Research and Evaluation Method for Echelon Usage of Double-Life Equipment Considering Calendar Life
## 3 Analysis and Solution
### When the Number of Units Used Each Time Is Less Than Total: \(M<n\)
#### 3.2.2 Minimum Working Life Controllable Range  
From the number of units used, \(M<N\), it can be seen that the maximum consumption of the working life of a single item of equipment is \(Ty\div M\) each month, so the working life of the equipment is not completely effective and only part of the working life can be consumed. If condition 3.2.1 is met, the effective working life of the equipment \(i\) in the \(k_{i}\) month of use is \(VTwl_{i}=Min(k_{i}\times\frac{Ty}{M},Twl_{i})\). Since the starting time of the equipment is the same, the effective working life of the equipment in the \(J^{1}\) month is \(AVTwl_{J^{1}}\):  
\[AVTwl_{J^{1}}=\sum\limits_{i=1}^{N}VTwl_{i}=\sum\limits_{i=1}^{N}Min(L\times \frac{Ty}{M},Twl_{i})\]  
\[L=Min(J^{1},Tcl_{i})\]  
Since there is a supplement of new equipment during the service period, that is, the new equipment can also be used to undertake the consumption of tasks, \(N^{\prime}\) new equipment is added, the equipment No.\(i\) is added to the unit at \(j_{i}\) month (\(j_{i}<J^{1}\)), and the equipment \(i\) is put into use in \(k_{i}=J^{1}-j_{i}\) month, and then the formula of the effective working life provided by the new equipment is:  
\[NVTwl_{J^{1}}=\sum\limits_{i=N+1}^{N+N^{\prime}}Min(k_{i}\times\frac{Ty}{M}, Twl_{i})\]  
In order to have enough equipment for use in the \(J^{1}\) month, the following conditions must be met:  
\[AVTwl_{J^{1}}+NVTwl_{J^{1}}\geq J^{1}\times Ty \tag{2}\]  
Assumption 2 in this paper shows that new equipment is supplemented every month. When enough equipment is used in the \(J^{1}=M\) month, the supplemented \(M-1\) new equipment and the newly supplemented equipment in the next month can meet the task requirements in the \(J^{1}=M+1\) month.  
The fixed formula of effective working life provided by the new equipment is:  
\[NVTwl_{J^{1}}=\sum\limits_{i=1}^{M-1}i\times\frac{Ty}{M}\]  
The minimum working life sum of the first \(N\) equipment can be calculated as:  
\[Min(\sum\limits_{i=1}^{N}Twl_{i})=M\times Ty-\sum\limits_{i=1}^{M-1}i\times \frac{Ty}{M}\]  
Draw a straight line through the point \((Tcl_{N},Ty)\) with the slope of \(\frac{Ty}{M}\). The area of the right triangle formed with the X axis is equal to the minimum value of the working life sum of the first \(N\) set of equipment. This straight line is defined as the minimum life curve, as shown in Figure 3.
## 3 Results and Discussion
### Void Distribution Characteristics
#### 3.3.1 Reconstructed Void Measurement  
In this paper, three-dimensional reconstruction of cement stabilized macadam images obtained by X-ray CT were carried out using VG Studio Max 2.2 software of Kunzhong Electrothermal Technology Co. LTD, Suzhou, Jiangsu, China. The surface measurement, defect detection, volume analysis, and other modules in the software were used to obtain the internal void information of the mixture, as shown in Figure 3. The void information is extracted by adjusting the gray threshold, and unnecessary information is eliminated according to the actual image. Finally, the void of three-dimensional reconstructed specimens is obtained, which is called \"reconstructed void\" in this paper. Some studies [18] have proved that the reconstructed void is close to the \"measured void\" measured by experiments, and can well reflect the size of the void inside the mixture. The extracted void results of reconstructed specimens are shown in Figure 4 and Table 10.  
As can be seen from the reconstructed void results in Table 10 and Figure 4, the size of void has the following relationship: The static pressure specimens \(>\) vibration specimens \(>\) on-site sample, and the latter two are much closer. The ratio of reconstructed void results is 1.39 and 1.44 under the static pressure specimens and on-site sample, but is 1.09 and 1.10 under the vibration compacted specimens and on-site sample. The reason can be analyzed by considering that the vibration compaction has a greater compaction work compared with static pressure, and the compressed materials can achieve rearrangement because they have a greater force and displacement under greater compaction work. The movement of large particles also provides the possibility for cement mortar to fill void effectively. Therefore, the change of the molding method from static pressure to vibration compaction also changes the arrangement mode of aggregate, thus affecting the volume parameters after molding. In addition, the aggregate particles can be more tightly packed in the rolling process of the vibration roller due to a lack of mold limitation in the on-site compaction, and the cement mortar can fully fill the skeleton void molded by coarse aggregate, so the on-site sample has a lower void ratio.  
On the other hand, the void ratio of GM gradation mixed was 0.76% higher than that of XM type under the static pressure condition, while the void between the two-gradation mixed with the vibration method was small. This demonstrates that the bulk aggregate can move more freely to form an effective embedded structure in the process of the vibration compaction, which is conducive for the second or even finer aggregate and cement to fill void.
## 4 Control Strategy and Configuration Optimization
### Performance Simulation  
The optimal fuel economy and dynamic performance for all configuration candidates are simulated by applying the control strategy formulated in Section 4.1. First, the dynamic performance of all configuration candidates is simulated, and the configuration with an acceleration time of 0-120 km/h of more than 20 s is eliminated. Second, the fuel economy simulation for all configurations with acceleration time less than 20 s is carried out. According to Environmental Protection Agency (EPA) standard [33], the urban road cycle (UDDS) and highway cycle (HWFET) are selected as fuel economy test cycles. The test cycles are shown in Figure 9. By weighted calculation for the fuel consumption of test cycles, the comprehensive fuel consumption \(Fuel\) can be obtained, as shown in Equation (17). Last, the configurations with acceleration time less than 20 s and the comprehensive  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Project** & **State Variable** & **Control Variable** \\ \hline Fuel economy & SOC & \(\omega_{e}\)\(T_{mg}\)\(Mode\)\(i_{CVT}\) \\ Dynamic performance & \(\omega_{e}\) & \(\omega_{mg}\)\(T_{mg}\)\(Mode\)\(i_{CVT}\) \\ \hline \hline \end{tabular} Where \(\omega_{e}\) is the engine speed; \(\omega_{mg}\), \(T_{mg}\) are the speed and torque of MG, respectively; \(Mode\) is the operating mode.  
\end{table}
Table 3: State variable and control variable.  
fuel consumption less than 6 L/100 km are screened out. The process of performance simulation and configuration screening is shown in Figure 10.  
\[Fuel=\frac{1}{\frac{0.55}{Fuel\_\_\text{UDDS}}+\frac{0.45}{Fuel\_\_\text{HWFET}}} \tag{17}\]  
where Fuel_UDDS and Fuel_HWFET are the fuel consumption of the UDDS and HWFET, respectively.  
Figure 10: The process of performance simulation and configuration screening.  
Figure 9: Test cycle. **(a)** Urban road cycle (UDDS); **(b)** Highway cycle (HWFET).
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 7 Evaluating the Scalability
### Virtual Device Scalability  
The cluster used in the experiments described in this test is composed of 50 cheap physical nodes, each one equipped with Intel Core i5 4590T CPU, 8 GB of RAM, mechanical HDD and Debian 9 as OS. To test the VD scalability, we used Megalos to implement the virtual network of a small data center consisting of about 2K VDs. The topology is the one of a Fat-Tree Network, analogous to that described in Section 6. In the network there are 2048 servers, each containing an Apache Webserver (using 20 MBytes of RAM), and 128 ToRs, 128 Leafs, 64 Spines, and 2 Exit Nodes, each equipped with a fully-fledged FRRouting [54] daemon (using 10 MBytes of RAM). Hence, we have an average of 48 VDs per worker. The test was successful, we were able to smoothly run the entire topology and we ensured that all hosts were reachable each other. Megalos was able to startup the network scenario in our low-performing cluster.
# A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
## 3 ASIC Implementation of 2D DCT/IDCT Based on B.G Lee DCT Algorithm
### Transposition Buffer Register for 2D DCT  
The decomposition row-column technique is used for the DCT computation of each 8 \(\times\) 8 image segment using the separability property of 2D DCT. The 2D DCT of an 8 \(\times\) 8 image matrix is carried out with eight 8 \(\times\) 1 1D DCTs. Figure 6 depicts the synchronization of a single pixel on an 8 \(\times\) 8 block of an image using a clock signal. The 1D DCT is performed initially on the image block's first row, represented by addresses R11 to R18. Then, the 1D DCT is carried out on the second row till it traverses to the eighth row. The 1D DCT is performed similarly for all 8 \(\times\) 8 image blocks of the 256 \(\times\) 256 image.  
The data are input in a parallel manner in the proposed method. The transpose of each image block is as depicted in Figure 6 and each pixel element is 8 bits in size, represented by Rij, where the row is denoted by i and the column by j. In the first eight clock cycles, each row is written in parallel to the corresponding memory location such that each row becomes the column in the memory. To realize the transpose of each 8 \(\times\) 8 image block, in the ninth clock, the pixels in M0 to M7 are read out simultaneously. The columns 1 to 8  
Figure 5: Block structure of the image compression process.  
of each 8 \(\times\) 8 image block are read out in parallel in the clock cycles from 10 to 17. Once the 1D DCT is performed on all 8 \(\times\) 8 image blocks, the transpose of each image block is taken to obtain the matrix, as indicated in Figure 6. The 1D DCT is applied on each 8 \(\times\) 8 image block of the resultant matrix similarly. The image matrix obtained after 1D DCT operation is further processed to execute the 1D IDCT on each of the 8 \(\times\) 8 image segments. The transpose of the resultant image matrix is obtained and the 1D IDCT is applied on the resultant 8 \(\times\) 8 image blocks.
# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### Construction of Multi-Channel LoG Filtering
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Database**} & **CIELab** & **YIQ** & **LMN** & **YUV** \\ \hline \multirow{4}{*}{TID2013} & SROCC & 0.7805 & 0.7983 & 0.8123 & 0.7983 \\  & KROCC & 0.5828 & 0.6060 & 0.6192 & 0.6061 \\  & PLCC & 0.7899 & 0.8158 & 0.8241 & 0.8159 \\  & RMSE & 0.7603 & 0.7169 & 0.7023 & 0.7168 \\ \hline \multirow{4}{*}{TID2008} & SROCC & 0.7735 & 0.8103 & 0.8138 & 0.8103 \\  & KROCC & 0.5717 & 0.6123 & 0.6172 & 0.6123 \\  & PLCC & 0.7713 & 0.8130 & 0.8161 & 0.8130 \\  & RMSE & 0.8541 & 0.7813 & 0.7755 & 0.7813 \\ \hline \multirow{4}{*}{CSIQ} & SROCC & 0.8585 & 0.9075 & 0.9093 & 0.9076 \\  & KROCC & 0.6620 & 0.7269 & 0.7298 & 0.7269 \\  & PLCC & 0.8595 & 0.9069 & 0.9086 & 0.9069 \\  & RMSE & 0.1342 & 0.1106 & 0.1097 & 0.1106 \\ \hline \multirow{4}{*}{LIVE} & SROCC & 0.9462 & 0.9656 & 0.9665 & 0.9656 \\  & KROCC & 0.8107 & 0.8586 & 0.8612 & 0.8586 \\  & PLCC & 0.9152 & 0.8772 & 0.8766 & 0.8772 \\  & RMSE & 12.6080 & 15.0241 & 15.0581 & 15.0244 \\ \hline \multirow{4}{*}{Direct average} & SROCC & 0.8397 & 0.8704 & **0.8755** & 0.8705 \\  & KROCC & 0.6568 & 0.7010 & **0.7069** & 0.7010 \\ \cline{1-1}  & PLCC & 0.8340 & 0.8532 & **0.8564** & 0.8533 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall performance of color spaces in different databases. quality scores of these groups are calculated using different algorithms (i.e., traditional LoG filtering and MLG). The corresponding similar images in this calculation process are listed, as shown in Figure 1.  
In Figure 1, it can be seen that there are fewer bright spots in Figure 1g, h compared to Figure 1e,\(f\), indicating that the algorithm in this paper has a strong characterization ability for the differences between reference and distorted images. Moreover, for the score values analysis, the higher the subjective score value, the smaller the difference between the reference and distorted images, and the higher the quality for the distorted image. The lower the objective score, the smaller the difference between the reference and distorted images, and the higher the quality, and vice versa. For example, in Figure 1, the subjective score of Figure 1d is lower than that of Figure 1c, the objective score of Figure 1e is lower than that of Figure 1f, and the objective score of Figure 1g is higher than that of Figure 1h. These results indicate that MLG outperforms traditional LoG filtering in terms of predictive performance and is more consistent with human eye observation results in characterizing image structural information. The subjective scores of Figure 1c,d show that MLG is more consistent with human visual perception and has a higher prediction accuracy for IQA. It can be seen that MLG can better express the degree of distortion.
# Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
###### Abstract  
To solve the problem that the existing planetary gear train fault diagnosis methods have, namely their low diagnostic accuracy under low signal-to-noise ratio (SNR), a fault diagnosis method based on a double channel-deep residual shrinkage network (DC-DRSN) is proposed. The short-time Fourier transform (STFT) is used to convert the original vibration signal into a two-dimensional time-frequency graph, which effectively enhances the ability to express information. A DC-DRSN model is constructed, and the optimal number of residual shrinkage modules is determined by combining the diagnostic characteristics with different noises, which effectively improves the accuracy and anti-noise ability of fault diagnosis. The results of bearing and planetary gear train crack fault diagnosis show that the diagnosis method based on DC-DRSN has higher diagnostic accuracy while realizing fault diagnosis, which is better than other deep learning diagnosis methods. At the same time, the method can adapt to fault diagnosis in different noise environments, and has good expression ability and generalization ability.  
deep residual shrinkage network; planetary gear trains; crack; fault diagnosis; deep learning
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
## 2 Electromagnetic Theory and Simulation of the Output Torque of a Spherical Motor
### Electromagnetic Theory of Output Torque  
Electromagnetic torque of the PMSM can be theoretically analyzed by using the finite element method (FEM) and virtual displacement principle [15; 16]. The current through coils is assumed to be constant and the motor is considered to be an ideal system without the loss of energy. Then, the change in electrical energy as an input into the PMSM will be converted partly into the change in magnetic  
Figure 1: Structure of the stator and rotor.  
field energy \(\Delta W_{f}\), and partly into the change in mechanical energy \(\Delta W_{\rm m}\). If the current is constant, the change in the electrical energy can be ignored and we obtain  
\[\Delta W_{f}=-\Delta W_{m} \tag{1}\]  
According to the relationship between energy and torque, when the rotor rotates at a small angle \(\Delta\theta\), its torque \(T\) can be expressed as:  
\[T=-\frac{\Delta W_{m}}{\Delta\theta} \tag{2}\]  
The magnetic field energy \(\Delta W_{f}\) of the system in the whole solution region can be obtained as follows:  
\[W_{m}=\iint\limits_{\
u}\!\!\!\iint\left(\int_{0}^{\bf B}HdB\right)\!d\
u \tag{3}\]  
where \(H\) is the magnetic field intensity and \(B\) is the magnetic flux intensity.  
In order to simplify the electromagnetic analysis of the PMSM, which has multiple permanent magnets in the stator and coils in the rotor, the electromagnetic torque generated by the interaction of a single rotor permanent magnet and a single stator coil is firstly analysed. For a single pair of the permanent magnet and coil, the electromagnetic torque can be obtained as:  
\[T_{ij}=f(\theta_{ij})\frac{\mathbf{s}_{ri}\times\mathbf{s}_{si}}{|\mathbf{s}_{ ri}\times\mathbf{s}_{si}|}Ni \tag{4}\]  
where \(\theta_{ij}\) represents the deflection angle of the \(i\)-th coil relative to the \(j\)-th stator permanent magnet, and \(T_{ij}\) represents the electromagnetic torque generated by a single pair of the permanent magnet and coil. \(f(\theta_{ij})\) is a characteristic function achieved by the FEM and describes the relationship between the electromagnetic torque and the deflection angle when the coil is energized by the unit current. \(Ni\) represents the ampere-turns of the coil, \(\mathbf{s}_{si}\) and \(\mathbf{s}_{ri}\) represent the position vector of the \(i\)-th stator coil and the \(i\)-th rotor permanent magnet, respectively. According to the principle of superposition, the total torque vector \(T\) is obtained as follows:  
\[T=\sum_{i=1}^{40}\sum_{j=1}^{24}f(\theta_{ij})\frac{\mathbf{s}_{ri}\times \mathbf{s}_{si}}{|\mathbf{s}_{ri}\times\mathbf{s}_{si}|}Ni \tag{5}\]
# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 2 Method
### Deep Learning for Tool Wear Monitoring
#### 2.3.4 Loss Function  
The tool wear prediction is a regression problem. The target of network training is to minimize the error between the predicted and measured wear values. In this study, the mean squared error (_MSE_) is used to evaluate the prediction error, which can be expressed as  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\|y_{i}-\hat{y}_{i}\|^{2} \tag{13}\]  
where \(\mathbf{y}=[y_{1},y_{2},\cdots,y_{n}]^{T}\) is the measured output vector which refers to the tool wear value, \(\overset{\wedge}{\mathbf{y}}=[\hat{y}_{1},\hat{y}_{2},\cdots,\hat{y}_{n}]^{T}\) is the predicted output vector, and \(n\) is the number of samples.
## 4 Results
### Comparison with Lithology  
As previously stated, Bolvadin is located in the central part of the Afyon-Aksehir Graben system, and mainly characterized by the existence of alluvial sediments. The lithological units in the north and the north-east area of Bolvadin are composed of slope debris/talus and conglomerates. The outer regions of the town also contain limestones and schists. Mean deformation velocity maps and geological boundaries provided in Figure 5 show a clear correlation with alluvial sediments and high deformation rates that exist in the southern and western part of the city. The LOS cumulative displacement time series of points P1 and P2 are selected from these regions and results are shown in Figure 6. Slope debris/and conglomerate are made of coarse grains and blocks and thus are less deformable sediments where a decrease in deformation rate could be observed as in the case of the point P3. Displacement time series along the profile line between points P2 and P3 (Figure 6c,d) shows a clear distinction in which the rate of deformation changes between alluvial sediments and slope debris/talus. The most stable area with almost zero deformation velocity is in the north-western part of the region where schist formation is observed. The reference point is selected from that stable region  
Figure 6: LOS displacement time series at selected points are shown together with linear fit for Asc-160 (**a**) P1 (**b**) P2 (**c**) P3 and Desc-65 (**d**) P1 (**e**) P2 (**f**) P3. Linear fit of LOS displacement time series on the profile between points P2 and P3 are displayed-for (**g**) Asc-160 and (**h**) Des.-65, tracks. Points and profile locations are shown in Figure 4c,d.  
and all other measurement points are updated accordingly. Therefore, the velocity fields are relative to the reference point in this region.  
The correlation between geological units and vertical and horizontal (E-W) deformation can be seen in Figure 7. The mean, standard deviation, and minimum and maximum deformation rates of each geological unit are calculated. As expected, while most of the deformation occurs in soft alluvium deposits, the old geological formations are much less affected. The maximum subsidence rate is observed to be around 35 mm/year in the alluvial deposits in the southern part of Bolvadin. While average subsidence rate is about 15 mm/year in alluvium deposits, other geological units exhibit very little or no deformation. Although mean subsidence rate of slope debris/talus is about 7 mm/year, the maximum subsidence rate reaches up to 25 mm/year. On the other hand, mean horizontal (E-W) deformation velocity is almost zero for all geological units. However, especially in alluvial sediments, both east and west movements are observed in some regions with the maximum rate about 12 mm/year.