esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Tooth Surface Integrity of Non-Circular Gear WEDM Based on HPSO Optimization SVR
## 5 Conclusions  
In this study, an experiment with a WEDM non-circular gear was carried out based on the Taguchi method. The surface roughness, surface residual stress, and surface microhardness were measured at different process parameter levels. Prediction modelling of surface roughness, surface residual stress, and surface microhardness was carried out using HPSO optimized SVR. Further analysis and comparison of various SVR kernel functions and prediction results was performed. The results of this study are summarized as follows:  
1. By comparison, the innovative HPSO in this paper was superior to the traditional particle swarm optimization algorithm with concave function inertia weight, in terms of the convergence accuracy and convergence speed.
2. The results of the ANOVA analysis of surface roughness, surface residual stress, and surface microhardness showed that pulse-on time and peak current were the main process parameters affecting the surface roughness, surface residual stress, and surface microhardnessness.
3. To build the SVR model, three different kernel functions were utilized. The results demonstrated that the rbf kernel function had better performance in the prediction model of surface roughness, surface residual stress, and surface microhardnessness (surface roughness: \(R^{2}=0.996671\), \(MAPE=1.276123\), surface residual stress: \(R^{2}=0.999188\), \(MAPE=0.415134\), surface microhardnessness: \(R^{2}=0.99411\), \(MAPE=0.301652\)).
4. Comparing the actual value and the predicted value, \(R^{2}\) was greater than 0.9, thus using the HPSO optimized SVR model could achieve high-performance prediction of surface roughness, surface residual stress, and surface microhardnessness.  
In addition, in future research, the authors plan to explore the use of larger data sets to improve the accuracy of prediction.  
Conceptualization, J.Z., Q.W. and Y.W.; methodology, J.Z., Q.W. and Y.W.; software, Q.W.; validation, Q.W.; formal analysis, J.Z., Q.W., Y.W., D.W., L.Z. and B.S.; investigation, Q.W., D.W., L.Z. and B.S.; resources, J.Z. and Y.W.; data curation, Q.W., D.W., L.Z. and B.S.; writing--original draft preparation, Q.W.; writing--review and editing, J.Z. and Y.W.; visualization, J.Z. and Q.W.; supervision, J.Z. and Y.W.; project administration, J.Z. and Y.W.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Defense Basic Scientific Research program of China, grant number JCKY2019427D002.  
Figure 11: Comparison of prediction results ((**a**) surface roughness comparison, (**b**) surface residual stress comparison, (**c**) surface microhardness comparison).
## 4 Methods
### The Spatial Durbin Model for Hypothesis 1  
The Spatial Durbin Model is set as follows:  
\[Y_{it}=\rho_{1}WY_{it}+\rho_{2}WX_{it}+\beta_{1}X_{it}+\varepsilon_{it} \tag{1}\]  
where \(W\) is the spatial weight matrix, \(WY\) is the spatial autocorrelation term of the dependent variable, \(WX\) is the spatial autocorrelation term of the independent variable, \(\rho_{1}\) is the spatial lag coefficient of the dependent variable, and \(\rho_{2}\) is the spatial spillover effect of the independent variable, which refers to the influence of independent variables on dependent variables in neighboring areas.  
The selected variables in this study are as follows:  
1. Explained variable. The main indicators to measure the level of industrial agglomeration include location entropy, Herfindahl index, and Gini coefficient, etc. Consider the availability and computability of statistics data, industrial location entropy (IE) is adopted in this study to reflect the degree of urban industrial agglomeration. The IE calculation formula is \(IE_{it}=\frac{q_{it}/Q_{it}}{q_{t}/Q_{it}}\), where \(q_{it}\) is the industrial GDP of city \(i\) in year \(t\), \(Q_{it}\) is the GDP of city \(i\) in year \(t\), \(q_{t}\) is the national industrial GDP in year \(t\), and \(Q_{t}\) is the national GDP in year \(t\).
2. Core explanatory variable. Urban agglomeration transportation integration level (TI) is the core explanatory variable, and its related definition and calculation formula are detailed in Section 4.1.
3. Control variables. Control variables include government investment intensity (Fin), level of opening-up (FDI), level of urbanization (Ur), and level of foreign trade (Ex). In this study, these control variables are respectively measured by the ratio of the general public financial expenditure to GDP of each city, the ratio of foreign direct investment to GDP of each city, the urbanization rate of each city, and the ratio of total export to GDP of each city [23].
4. Spatial weight matrix. In this study, the geographical adjacency spatial weight matrix is constructed as follows: \(\varpi_{\bar{\eta}}=\left\{\begin{aligned} \varpi_{\bar{\eta}}=1,i \text{ is adjacent to }j\\ \varpi_{\bar{\eta}}=0,i\text{ is }\text{n}\text{r}\text{t}\text{ adjacent to }j \text{ or }i=j\end{aligned}\right.\)[32]. Then we can obtain the Spatial Durbin Model [33] for Hypothesis 1  
\[IE_{it}=\lambda\sum_{j=1}^{n}W_{ij}IE_{jt}+\beta_{1}TI_{it}+\beta_{2}X_{it}+ \theta_{1}\sum_{j=1}^{n}W_{ij}TI_{it}+\theta_{2}\sum_{j=1}^{n}W_{ij}X_{it}+ \varepsilon_{it} \tag{2}\]  
where \(X_{it}\) is measured by the above control variables.
# Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control
## 4 Conflict Resolution Algorithm Based on DNMPC
### Algorithm Description for Conflict Resolution  
According to the idea of distributed MPC and receding horizon optimization, for the multi-UAV conflict resolution problem, the specific collision avoidance and trajectory coordination algorithm for any given UAV can be described as follows:  
Figure 7: Solution framework for DNMPC.  
* Set the sampling time \(k\), the status of UAV \(i\) is \(\mathbf{x}_{i}(k)\). The current input control vector is \(\mathbf{u}_{i}(k)\), in the period of prediction \([k,k+N]\), based on the model of UAV, the predicted flight status can be given as: \[\mathbf{\tilde{X}}_{i}(k+N)=[\mathbf{x}_{i}(k+1|k).\mathbf{x}_{i}(k+2|k),\mathbf{x}_{i}(k+3|k), \ldots,\mathbf{x}_{i}(k+N|k)]^{T};\]
* If conflict group for UAV \(i\)\(Cf=\phi\), UAV \(i\) need no trajectory coordination; else, set \(j\in Cf\), denote the predicted flight status of UAV \(j\) as: \[\mathbf{\overline{X}}_{j}(k+N)=\Big{[}\mathbf{x}_{j}(k+1|k),\mathbf{x}_{j}(k+2|k),\mathbf{x}_{ j}(k+3|k),\ldots,\mathbf{x}_{j}(k+N|k)\Big{]}^{T};\]
* According to \(\mathbf{\overline{X}}_{j}\) and the resolution rule in Section 3.3, combined with Constraints (4) and (5), plan a conflict-free reference path for UAV \(i\), then generate the predicted reference status: \[\mathbf{\widetilde{X}}_{ir}(k+N)=[\mathbf{x}_{ir}(k+1|k),\mathbf{x}_{ir}(k+2|k),\mathbf{x}_{ir }(k+3|k),\ldots,\mathbf{x}_{ir}(k+N|k)]^{T}\]
* According to \(\mathbf{\widetilde{X}}_{ir}\) and \(\mathbf{\widetilde{X}}_{i}\), combined with constraints (4) (5) and performance indicators (10), solve the optimal control vector \(\mathbf{U}_{i}=[\mathbf{u}_{i}(k|k),\mathbf{u}_{i}(k+1|k),\ldots,\mathbf{u}_{i}(k+N-1|k)]\). Then feedback optimal control vector \(\mathbf{U}_{i}\) to UAV \(i\).
* Execute the first item \(\mathbf{u}_{i}(k|k)\) in \(\mathbf{U}_{i}\), then get the status of UAV \(i\) in \(k\)+\(1^{\text{th}}\) time; and again sends the updated predicted flight status \(\mathbf{\widetilde{X}}_{i}^{\prime}\) to other associated UAVs.
* If UAV \(i\) has reached the specified state, i.e., it reaches the specified target point, the calculation is ended; otherwise, let \(k=k+1\), and repeat Steps 1-5.  
In the resolution process, if some UAVs reach the designated target point earlier than other UAVs, then these UAVs are removed from the optimization list, that is, the next round of process will not consider these UAVs. The flow chart of the process for the conflict resolution algorithm is constructed as Figure 8:
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 3 Test-Bed and Field Trial Environment
### Indoor Wireless Channel Environment Set-Up  
Line-of-sight (LoS) and non-line-of-sight (NLoS) environments were both implemented to measure the BER performance in typical indoor situations. The BER measurement was performed in the various spots on the 3 rd floor in the IT building at Yeungnam University, Korea.  
Figures 5 and 6 shows the transmitter (TX) (or user) and receiver (RX) (or base station (BS)) location set-up for LoS and NLoS environments, respectively, in the floor map of the building. The BS was tested at each of the three different positions specified with BS\(i\) (\(i=1,2,3\)). In the uplink system, the inter-user interference (IUI) from the very next (left and right) sub-band (SB) is dominant over the those from the other SBs. Hence, for an efficiently down-scaled field test, three consecutive subbands are allocated to three uplink users (See Figure 7), and, then, we measured the performance of only the middle user who undergoes the IUIs from the left and right SBs.  
The maximum straight distance between the TX and BS is 24 m and 12 m for LoS and NLoS environments, respectively. In the NLoS environment, the TXs were placed inside the rooms (shaded area in Figure 6), and the BS was placed in the corridor. Thus, there are more than one wall between TX and BS. Figure 8 is a sample shot of the NLoS environment set-up. The locations for the three equipments labelled by BS1, BS2, and BS3 in Figure 8 correspond to the points that are specified by BS1, BS2, and BS3 in Figures 5 and 6, respectively. At each of three BS positions, the average uplink BER was separately measured and then it was averaged. Note that, by the channel reciprocity of either direction between two positions, i.e., TX \(\rightarrow\) BS, and TX \(\leftarrow\) BS, the field test results that are obtained with the TX and BS location combinations in Figures 5 and 6 inherently include the case when the locations of TX and BS are switched.  
Figure 6: User area (shaded) and BS location in non-line-of-sight (NLoS) environment.  
Figure 7: Power spectra of the SC-FDMA, FBMC, and LP-FBMC, \(K=3\), \(N=64\), LoS environment, \(\Delta f=15\) kHz.  
To synchronize the transmission(access) timings of the uplink users, or to apply the intended (To realize the effect of the practical inter-user synchronization process, we intentionally apply timing offsets at the transmission stage. The field test results for this case are coverer in Section 4.3.) timing offsets among the users, the SDR devices of the users' TXs are wired to a clock distribution device. Figure 9 shows the configuration of host PCs for users, SDR devices for users' TXs, and the clock distribution device.
## 1 Introduction
Moreover, imbalanced amounts of training samples belonging to different categories generally lead to imbalanced recognition performance, and categories with significantly fewer training samples tend to have lower recognition accuracy. Data augmentation, which can overcome this imbalance problem, is therefore critical for the training process. In most algorithms [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 18; 19; 20; 21; 22], ground truths (bounding boxes and the points inside them) may randomly be inserted into existing samples to augment the training dataset [9]. However, this method may insert ground truths into inappropriate areas. This may create incorrect contextual information for the training of the model [24]. But semantic information of the current scene could help to resolve this problem. So, a random sampling data augmentation algorithm (RS-Aug), based on scene semantic information, is proposed. RS-Aug can identify reasonable areas for placing ground truths. It initially acquires semantic information regarding obstacles and road surface areas in point cloud scenes. Based on this semantic information, it then removes the areas on the road surface occupied by obstacles. The remaining road surface areas are therefore suitable spaces for placement. By identifying these reasonable areas, RS-Aug can strategically position the ground truths of less frequently occurring categories in appropriate areas, such as the road surface, effectively balancing the number of categories within the dataset. The contributions of this paper are summarized as follows:  
1. This study proposes an object recognition algorithm called ASCA-PointPillars. In the algorithm, we design an ASP module that innovatively utilizes multi-scale pillars to encode point clouds, effectively reducing the loss of spatial information. Subsequently, a CPA module is employed to establish contextual associations within a pillar's point cloud, thereby enhancing the point cloud's features. 2. A data augmentation algorithm called RS-Aug is proposed, leveraging semantic information to identify reasonable areas for placing ground truths, addressing the issue of imbalanced categories in datasets. The first section of this paper is an introduction, indicating the contributions of this article. The second section presents a literature review of related works. The third section is the methodology, which introduces the algorithm of this paper in detail. The fourth section presents the experiment, evaluating the algorithm of this paper and analyzing the experimental results. The fifth section is the conclusion, summarizing the method of this paper and looking forward to the future.
# A Survey on Hardware Accelerators for Large Language Models
## 3 FPGA-Based Accelerators
### FPGA Dfx  
In 2022, [PERSON] et al. presented DFX [14] for the acceleration of the Transformer networks used in LLMs. Similar to NPE, the DFX architecture proposed a modular architecture consisting of several computer cores for the acceleration of the Transformer networks.  
To address the sequential characteristic of text generation, the DFX compute core is optimized for single token processing. It also uses an efficient tiling scheme and dataflow architecture based on utilizing the high bandwidth of HBM memory. DFX uses model parallelism on the multi-FPGA system to increase the physical number of compute cores that work in parallel while evenly assigning a full workload to each device. DFX proposes custom instruction set architecture (ISA) at the assembly language level to support the end-to-end processing for inference. Similar to NPE, the DFX core has two processing units, namely, the matrix processing unit (MPU) and the vector processing unit (VPU).  
For the evaluation, DFX was implemented on an Intel Xeon Gold 6226R CPU with four Xilinx Alveo U280 data center acceleration cards. DFX achieved an average of 3.8\(\times\) throughput and 4\(\times\) higher energy efficiency compared to the NVIDIA V100 GPU.
## 4 Discussion
### Toxic Index  
To investigate the trend in the measured CO and CO\({}_{2}\) production in the ISO 5660-1 experiments for cellulose with varying magnesium hydroxide additions, NES 713 experiments were conducted on specimens with the same mass but differing amounts of magnesium hydroxide added. This was carried out to verify the differences in the presence and quantity of toxic gas generation. The results of NES 713 experiments indicated that, as the magnesium hydroxide addition increased, the CO production in specimens with the same mass increased, while the CO\({}_{2}\) production decreased.  
The NES 713 experiment results revealed a turning point in the Toxic Index in response to the changes in magnesium hydroxide addition, which could be attributed to a decrease in carbon dioxide production and an increase in carbon monoxide production, which are combustion by-products. It could be observed that the Toxic Index values increased up to a magnesium hydroxide addition of 20% in Sample Type C. However, beyond that, with a magnesium hydroxide addition of 30% in Sample Type D, there was a tendency for the Toxic Index to decrease. This decrease was attributed to changes in the reduction in carbon dioxide production as well as the increase in carbon monoxide production. Figure 15 represents the Toxic Index values of cellulose specimens for each magnesium hydroxide content, which are calculated based on the concentrations of toxic gases detected in the NES 713 experiment results.  
\[\text{Toxicity Index}=\ \frac{C_{\theta 1}}{C_{f1}}+\frac{C_{\theta 2}}{C_{f2}}+ \ldots+\frac{C_{\theta n}}{C_{fn}} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Type** & **Test No.** & \begin{tabular}{c} **Initial Mass** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **After Test** \\ **Mass (g)** \\ \end{tabular} & \
\begin{tabular}{c} **Mass Loss** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline A & 1 & 56 & 30.24 & 46 & \\  & 2 & 55.9 & 33.97 & 39.23 & 3.71 \\  & 3 & 56.6 & 35.45 & 37.37 & \\ \hline B & 1 & 69 & 49.48 & 28.29 & \\  & 2 & 69.8 & 48.26 & 30.86 & 1.09 \\  & 3 & 69.4 & 48.46 & 30.17 & \\ \hline C & 1 & 79.7 & 57.89 & 27.37 & \\  & 2
## 6 Discussion
### Mineral Types of Buffer Materials in Engineering Barrier Systems
* MX-80 represents montmorillonite-rich (>60%) clay, which has the lowest hydraulic conductivity among all the investigated clay types. Saponite has slightly higher permeability than the other clay types, but it is still tauter than the mixed-layer clay, palygorskite, and kaolinite. Palygorskite possesses a very high swelling pressure; thus, it can be prepared as a buffer material with a density lower than MX-80 clay. Moreover, MX-80 and saponite have the highest cation adsorption capacities. Palygorskite and very finely milled kaolinite exhibit obvious anion adsorption potentials. Kaolinite and mixed-layer clay possess the highest thermal conductivity. The creep of kaolinite and palygorskite is less than that of MX-80 and the mixed-layer clay. * Smectite-rich clays are the most suitable for preparing buffer materials, followed by the mixed-layer clay. For the backfill materials of drifts and shafts, mixed-layer clay can be considered as the main candidate material. However, artificially prepared mixtures of smectite-rich clay and ballast material may be equally good as backfill material. Bentonite has been widely studied as a buffer material for the multibarrier system of radioactive waste disposal due to its low permeability, high water retention capacity, high swelling pressure and capacity, thermal characteristics, and microstructure and contaminant transport [20,21,22,30,56,57,58,59]. In the performance analysis and evaluation of the buffer material, not only hydraulic characteristics but also thermal (T), hydraulic (H), mechanical (m), chemical (C), biological (B), and radiation (R) processes and properties need to be considered. Previous studies mainly consider T, H, and M [6,17,18]. From the C perspective of the kinetic dehydration of interlayer water, this study analyzes the kinetic dehydration and interlayer hydrous state of the smectite interlayer water for the bentonite buffer material and calculates the amount of water expelled from smectite clay minerals and porosity correction due to waste decay heat. The simulation of the migration of radionuclides through the buffer material under bentonite porosity change because of the decay heat provides the performance assessment of bentonite as the buffer material in radioactive waste disposal.
## 5 Experiments and Results
### Pool Experiments
Although the trajectory of LADRC-TD is further away from the target trajectory than PID, this paper focuses on the heading tracking performance of different algorithms, which is different from the trajectory tracking. Figure 12: Dynamic experiment: (**a**) heading tracking with LADRC-TD; (**b**) heading tracking with PID; (**c**) trajectory when using LADRC-TD in heading control; (**d**) trajectory when using PID in heading control; (**e**) heading tracking error of PID and LADRC-TD in dynamic experiment; (**f**) depth-keeping with LADRC-TD during AUH proceeding. As observed from the tracking error (Figure 12e), the PID heading tracking error is greater than LADRC-TD, the PID adjustment time is about 23 s longer than LADRC-TD, and the fluctuation of the adjustment process is more obvious when compared to LADRC-TD. During the movement of the AUH, the steady-state accuracy of depth control remains within \(\pm\)21 cm (Figure 12f), demonstrating that the excellent anti-interference performance of LADRC-TD can meet the requirements of practical tasks.
# A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
## 3 TDR and Field Trials  
### Proposed Path Loss Estimation Method  
As mentioned in Section 2.1, the value of the complex permittivity of the soil significantly affects the attenuation of EM signals. Therefore, the accuracy of the estimation models for EM attenuation in soil is highly dependent on the accuracy of the permittivity values used for the calculations. An alternative method to the mixing models normally used for obtaining the complex values of permittivity is to extract the complex permittivity from TDR measurements [12]. This method is based on estimating the effective frequency of the TDR signal in the soil, which is the frequency that contains the majority of the energy of the signal [11], by calculating the rise time of the signal at the end of the TDR probe (Figure 2) [12]. The rise time of the signal is commonly calculated based on the 10% to 90% increase in the reflection coefficient at the end of the probe [11].  
In practice, multiple reflections at the end of the probe can make it difficult to choose the top line (horizontal asymptote) for determining the rise time [11, 28]. [PERSON] [10] proposed that the steady state value, which is commonly used for calculations of Bulk Electrical Conductivity (BEC), is used for the value of the top line of the TDR waveform. This method eliminates inaccuracies caused by the multiple reflections of the waveform at the end of the probe and provides a more robust method for calculating the rise time. Figure 2 illustrates the proposed method for calculating the rise time of a waveform when there are multiple end reflections with rise time, \(t_{r}\), and travel time, \(t_{1}\), of the signal indicated.  
As can be seen from Figure 2, the end reflection in the TDR waveform in this figure would have resulted in longer rise time based on the conventional method, which would have adversely affected the accuracy of the calculated complex permittivity.  
Following the calculation of the complex permittivity based on this alternative method (Figure 2), the permittivity is then used as an input to the Modified-Friis model to estimate the attenuation, instead of using [PERSON]'s mixing model for the permittivity values, thus eliminating potential inaccuracies associated with this mixing model. In addition, as TDR measurements can be taken in situ, it overcomes the previously mentioned limitations of the mixing models by providing an estimation of complex permittivity without the need for laboratory tests. The ability of this alternative approach for predicting RF attenuation in soil is tested by comparing the results to the standard models and, importantly, by comparing all the model results with attenuation data obtained from the field trials.