esa-sceva/satcom-chunk-collection huggingface dataset:
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 4 UAV's Behavior in the Approaching Area
### Entering the Intersection as Scheduled
Thus, \[t_{B,j}^{inter}=t_{B,j}^{qz}+t_{B}^{wait}+t_{B}^{qz}.\] (13)  
Note that \(t_{B}^{wait}\) can be greater than zero only when its speed at the entrance of the acceleration zone is zero, as described in Scenario 1. For \(UAV_{B}\) to enter the intersection as scheduled, \(UAV_{B}\) needs to determine \(t_{B}^{wait}\) and \(t_{B,j+1}^{qz}\) such that \(t_{B,j}^{sched}\) is equal to \(t_{B,j}^{inter}\). For this, we first determine if \(UAV_{B}\) should slow down until it stops at the entrance of the acceleration zone as described in Scenario 1 and Scenario 2. To do this, we get \(t_{B,j}^{sinter}\), the value of \(t_{B,j}^{inter}\) when \(t_{B}^{wait}=0\) and \(t_{B,j+1}^{qz}=t_{B,j+1}^{stop}\), where \(t_{B,j+1}^{stop}\) is the negative acceleration rate that will make the speed of \(UAV_{B}\) at the entrance of the acceleration zone equal to zero. In the queueing zone, \(UAV_{B}\) will change its speed from \(s_{B,j+1}\) to \(s_{B}^{qz}\), the speed of \(UAV_{B}\) when it reaches the entrance of the acceleration zone. Hence,\[t_{B,j}^{a\pi}=\Delta t+\frac{s_{B}^{a\pi}-s_{B,j+1}}{t_{B,j+1}^{a\pi}}. \tag{14}\]  
Let \(t_{B}^{acc}\) be the time it will take to accelerate from \(s_{B}^{a\pi}\) to \(s_{max}\) in the acceleration zone. Let \(d_{B}^{acc}\) be the distance it will travel while accelerating from \(s_{B}^{a\pi}\) to \(s_{max}\). Let \(t_{B}^{maintain}\) be the time it will take to reach the exit of the acceleration zone while maintaining its speed after reaching \(s_{max}\). Then,  
\[t_{B}^{acc}=\frac{s_{max}-s_{B}^{a\pi}}{r_{max}} \tag{15}\]  
\[d_{B}^{acc}=\frac{s_{max}{}^{2}-s_{B}^{a\pi}{}^{2}}{2r_{max}} \tag{16}\]  
\[t_{B}^{maintain}=\frac{l_{az}-d_{B}^{acc}}{s_{max}} \tag{17}\]  
\[t_{B}^{az}=t_{B}^{acc}+t_{B}^{maintain}. \tag{18}\]  
We now show how to determine \(r_{B,j+1}^{a\pi}\) and \(t_{B}^{main}\) based on the value of \(t_{B,j}^{*inter}\). For this we consider three cases as listed below. 1. \(t_{B,j}^{*inter}<t_{B,j}^{sched}\)  
If \(t_{B,j}^{*inter}<t_{B,j}^{sched}\), then this means \(UAAV_{B}\) will enter the intersection earlier than scheduled when it will slow down until stop and then immediately enters the acceleration zone. Thus, it must spend some time at the entrance of the acceleration zone, as described in Scenario 1. Hence, \(r_{B,j+1}^{q\pi}=r_{B,j+1}^{stop}\) and \(t_{B}^{main}\) is obtained by  
\[t_{B}^{wait}=t_{B,j}^{sched}-t_{B,j}^{a\pi}-t_{B}^{az}. \tag{19}\]
2. \(t_{B,j}^{*inter}=t_{B,j}^{sched}\)  
If \(t_{B,j}^{*inter}=t_{B,j}^{sched}\), then it will enter the intersection as scheduled when it will use rate of \(r_{B,j+1}^{stop}\) and enter the acceleration zone immediately, as described in Scenario 2. Hence, \(r_{B,j+1}^{q\pi}=r_{B,j+1}^{stop}\) and \(t_{B}^{unit}=0\). 3. \(t_{B,j}^{*inter}>t_{B,j}^{sched}\)  
If \(t_{B,j}^{*inter}>t_{B,j}^{sched}\), then this means \(UAAV_{B}\) will enter the intersection at a later time than scheduled when it will use a rate of \(r_{B,j+1}^{stop}\). Hence, \(UAV_{B}\)
## 3 Numerical Examples
### Boundary Randomness
#### 3.2.3 Effect of Permeability Coefficient on Saturated Sedimentary Valley with Random Boundary
The reason is that there is not only the focusing of seismic waves, but also the conversion of wave patterns in sediments. The critical angle corresponding to the permeability coefficient at each frequency is about \(32.2^{\circ}\). Because the oblique incident angle (\(30^{\circ}\)) is close to the critical angle, the surface response becomes complex. The average values of the variation coefficients of horizontal and vertical displacement amplitudes with permeability coefficients are listed in Tables 6 and 7, respectively. It can be seen from the tables that the coefficient of displacement variation in both directions increases with the increase in dimensionless frequency. At \(k=0.1\), the magnification of high frequency (\(\eta=\)2.0) is 6.88 times (H, \(\theta=0^{\circ}\)), and 3.42 times (V, \(\theta=0^{\circ}\)) compared with low frequency (\(\eta=0.5,\ \theta=0^{\circ}\)). The average magnification of \(k=0.5\sim\infty\) coefficient of variation is 2.27 times (H), and 2.63 times (V). From the magnification, it can be seen that compared with the permeable case, the impermeable case is more obviously magnified by
## 3 Results
### Experimental Results
#### 3.2.2 Results of the Optimisation Algorithm  
In this study, we conducted an experimental study of two heuristic optimisation algorithms. To ensure the fairness of the experiments, we uniformly set the number of populations to 5 and set the range of values for the penalty factor C and the radial basis kernel function parameter \(\gamma\) to [0.01,100].  
In the GA algorithm and SMA algorithm, fitness is an indicator to evaluate the strengths and weaknesses of an individual. During the iteration of the algorithm, the fitness value of an individual changes when the population evolves, resulting in a fitness curve. The fitness value is calculated as Equation (14).  
\[\text{Fitness value}=(100\text{-fitness})/100 \tag{14}\]  
The experimental results show that there is a significant difference in the fitness of the two algorithms. Figure 6 shows the fitness curves of the two algorithms during the experiments, and it can be found that the SMA algorithm has a faster adaptation speed than the GA algorithm.  
Combining the running times of the algorithms in Table 1, we can find that the SMA algorithm has a clear advantage in terms of computation time. Combining the results  
Figure 6: Fitness Curve.  
derived above, we can surmise that the SMA algorithm is a better choice while considering the computational efficiency.
## Abbreviations  
\(\rm{A_{e}}\) & Cross sectional area of the core in mm\({}^{2}\)  
\(\rm{B_{sat}}\) & Saturation flux density in tesla  
\(\rm{AB}\) & Maximum flux density swing in tesla in normal operation  
\(\rm{C_{O}}\) & Capacitance of the output capacitor  
\(\rm{D_{max}}\) & Maximum duty cycle ratio  
\(\rm{D_{min}}\) & Minimum duty cycle ratio  
\(\rm{E_{Hf}}\) & Estimated efficiency  
\(\rm{f_{s}}\) & Switching frequency  
\(\rm{I_{ds}}^{peak}\) & Maximum peak current of MOSFET  
\(\rm{I_{reset}}^{rms}\) & Rms current value for reset winding  
\(\rm{I_{sec(n)}}^{rms}\) & Rms current value for n transformer winding  
\(\rm{I_{lim}}\) & FPS current limit level  
\(\rm{I_{O}}\) & Output load current  
\(\rm{K_{RF}}\) & Current ripple factor  
\(\rm{L_{m}}\) & Transformer primary side inductance  
\(\rm{N_{p}}^{min}\) & The minimum number of turns for the transformer's primary side to avoid saturation  
\(\rm{N_{p}}\) & Number of turns for primary side  
\(\rm{N_{r}}\) & Number of turns for reset winding  
\(\rm{N_{sl}}\) & Number of turns for the reference output  
\(\rm{N_{L(n)}}\) & Number of turns for a output inductor  
\(\rm{N_{L1}}\) & Number of turns for output inductor 1  
\(\rm{P_{o}}\) & Maximum output power  
\(\rm{P_{im}}\) & Maximum input power  
\(\rm{R_{c}}\) & Effective series resistance (ESR) of the output capacitor  
\(\rm{V_{DC}}^{min}\) & Minimum DC link voltage  
\(\rm{V_{DC}}^{max}\) & Maximum DC line voltage  
\(\rm{V_{o(n)}}\) & Output voltage for n.st output  
\(\rm{V_{o1}}\) & First output voltage  
\(\rm{V_{F(n)}}\) & n diode forward voltage drop  
\(\rm{V_{F1*}}\) & First diode forward voltage drop  
\(\rm{V_{F1^{*}}}\) & First diode forward voltage drop  
\(\rm{V_{cc^{*}}}\) & Nominal voltage for Vcc  
\(\rm{V_{ds}}^{max}\) & Maximum voltage stress of MOSFET
## 1 Introduction
### Feasibility Analysis of Simulated Driving Experiments
#### 2.2.2 Fitting of Real Vehicle Experiments and Simulated Driving Experiments Trajectories  
The real vehicle experiments and simulated driving experiments yielded five sets of speed curves, as illustrated in Figure 4. None of the speed curves surpassed the 60 km/h speed limit. Despite differences in average speeds among the five drivers due to factors such as personality, each individual showed a similar magnitude of variation in their speed curves between real-car and simulated driving experiments. Therefore, selecting the speed standard deviation rather than the speed mean better reflects the driver's control over the vehicle during the driving process.  
Figure 5 presents the fitted curves for the lateral displacement of the five sets of real vehicle experiments and simulated driving experiments. The initial position coordinate of the vehicle was selected as 0, with positive values indicating an outward deviation from the road and negative values indicating an inward deviation. From the graph, it is evident that the lateral displacement of the driving simulation trials is greater than that of the real-car trials. However, the trend in variation is similar between the two curves. Therefore, utilizing data from simulated driving experiments to obtain the standard deviation of lateral displacement is feasible.  
Upon comparing the actual and simulated data, it was observed that the two sets of data exhibited similar trends in variation, with minimal absolute differences and similar standard deviations, as indicated in Tables 1 and 2. Consequently, simulated driving experiments can be considered a viable alternative for subsequent experimental data collection in lieu of real vehicle experiments.  
Figure 4: \(Cont.\)  
Figure 3: (a) Experimental equipment, (b) test vehicles, and (c) the experimental process.  
Figure 4: The speed curves of five groups of real vehicle experiments and simulated driving experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Min (km/h)** & **Max (km/h)** & **Mean (km/h)** & **Standard Deviation** \\ \hline Real vehicle data 1 & 43.5 & 50.8 & 47.15 & 2.8 \\ Simulated data 1 & 42.9 & 51.6 & 47.25 & 3.1 \\ Real vehicle data 2 & 41.3 & 48.6 & 44.95 & 1.6 \\ Simulated data 2 & 40.3 & 50.1 & 45.2 & 1.8 \\ Real vehicle data 3 & 41.2 & 48.4 & 44.8 & 1.8 \\ Simulated data 3 & 42.6 & 51.2 & 46.9 & 2.2 \\ Real vehicle data 4 & 46.4 & 58.3 & 52.35 & 2.3 \\ Simulated data 4 & 48.1 & 57.6 & 52.85 & 2.6 \\ Real vehicle data 5 & 47.6 & 54.3 & 50.95 & 2.2 \\ Simulated data 5 & 49.2 & 55.3 & 52.25 & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical analysis of speed for real vehicle experiments and simulated experiments.  
Figure 5: _Cont._
# PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
## 4 Problem Formulation  
In this section, our objective is to maximize the system sum-rate of PS and non-PS users connected with either MBS or mPC by considering the SINR, transmit power, and priority constraints. Thus, we propose the sum-rate maximization problem (P1) as  
\[\textbf{P1}: \text{max}\ \left(\sum_{j=1}^{\text{maxTTI}}(C_{k,j}^{\text{NPS}}+C_{m, j}^{\text{NPS}}+C_{k,j}^{\text{PS}})\right) \tag{12a}\] \[\text{s.t.}\ \gamma_{u}^{\text{rx}}(t)\geq\gamma_{u},\] (12b) \[\delta_{u}\in\{0,1\}\] (12c) \[P_{u}^{\text{PPC}}\leq P_{u}^{\text{P}}(t)\leq P_{u}^{\text{max}}. \tag{12d}\]Since we are also dealing with connection level priority instead of a service level priority model, call blocking probability (CBP) is a good performance metric to judge the quality of the system. The CBP measures the call admission rejection probability because of the limited available resources. The CBP (\(\xi\)) of a user \(u\) trying to associate with BS can be given as  
\[\xi_{u}=1-\frac{\sum_{j=1}^{\max\text{TTI}}S_{j}}{U}, \tag{13}\]  
where \(S_{j}\) is the number of user scheduled during the j-th subframe and \(U\) is the total number of user sending the admission request. In this paper, we also minimize the CBP of the users associated with the BS, by considering the user association and priority constraints. Thus, CBP minimization problem (P2) can be evaluated as  
\[\mathbf{P2}: \min\xi_{u}\] (14a) s.t. \[\gamma_{u}^{\text{rx}}(t)\geq\gamma_{u}, \tag{14b}\] \[\delta_{u}\in\{0,1\}\] (14c) \[\beta_{u}\in\{0,1\}\] (14d) \[P_{u}^{\text{fpc}}\leq P_{u}^{\text{rx}}(t)\leq P_{u}^{\max}. \tag{14e}\]  
The optimization problems discussed here are NP-hard because it has the possibility of allocating a huge number of RB combinations for cellular and mPC users. The maximum number of possible links in this scenario are \((M\times N+K\times L)^{RB}\)[38], where RB are the total number of available resource blocks to establish communication among all users.  
Hence, the solutions to these problems are not feasible and only a sub-optimal solution can be achieved. To solve these problems, we propose a PS-CARA scheme that heuristically solves the constructed optimization problem (P1) in (12) and (P2) in (14) by system-level simulations. By these simulations we found the sub-optimal solution to these problems. The proposed PS-CARA scheme's major steps are summarized in the next section.
## 5 Case Study and Discussion
### Case 1: Luggage Drifting Disturbance Propagation in an L-Shaped Corridor  
A sudden turn by a luggage-laden passenger is usually accompanied by the drifting of wheeled luggage, which is commonly seen in transportation hubs, especially in L-shaped corridors, with much more potential risk. Therefore, a typical L-shaped corridor model in Shanghai Hongqiao railway station in China, with a width of a 10 m, was established in the Pathfinder 202 software.  
As shown in Figure 7, a luggage-laden passenger made a sudden turn in an L-shaped corridor and caused luggage drifting at the position of Start A. The crowd density and the disturbing force on both sides of the luggage-laden passenger were collected, respectively. In the fifth frame of the compared scenario video, the difference between the left side and right side disturbing force was 153 N. In the sixth frame of the video, when the luggage-laden passenger started turning left and drifting motion followed, the disturbing force of surrounding passengers on the left increased and reached a peak of approximately 238N, while the disturbing force on the right side decreased.  
The crowd density of the luggage-laden passenger particle on both sides is plotted with key frames in Figure 8. Consequently, the disturbing force of the luggage-laden passenger particle on both sides with key frames is presented in Figure 9. With the completion of the passenger turning, the disturbing force on the passenger's left side dropped and the disturbing force on the right side rose, meaning that the passenger's left- and right-side disturbing forces were in a relatively balanced state. When the luggage suddenly drifted to the left, the passengers on the left side of the luggage would gather because of the obstruction of the luggage, thus increasing the density. After the passenger's turn was completed, the crowd gradually readjusted its density on both sides back to a balanced value.  
To emphasize and simplify the disturbance effect of luggage from a single luggage-laden passenger, we set the direction of all non-luggage-carrying passengers to be opposite to that of the luggage-laden passenger, as shown in Figures 6 and 7. In this assumed situation, we obtain more salient results regarding density and disturbing force. In real scenarios, both non-luggage-carrying passengers and luggage-laden passengers always mix and move in a bidirectional flow. Future research or practical applications should consider this more complex situation.  
Figure 7: A luggage-laden passenger made a sudden turn in an L-shaped corridor.
# A Survey on Hardware Accelerators for Large Language Models
## 7 Quantitative Comparison
### Technology  
There are four main computing platforms that researchers use to accelerate LLMs and Transformer networks: GPUs, FPGAs, ASICs, and in-memory computing. GPUs are the most obvious platform, as many LLMs utilize them for both training and inference. FPGAs can be customized with tailor-made architectures that speed up specific functions. The main idea is to offload the most computationally intensive tasks from the CPU to the FPGAs. This allows developers to maintain the flexibility of CPUs while simultaneously speeding up applications. The main advantage of FPGAs is that the proposed scheme can be implemented and evaluated on them to measure the real overall speedup.  
There are also several schemes that target an ASIC implementation. These schemes propose a custom architecture that can be used as a co-processor to speed up the most computationally intensive tasks, like matrix multiplication, etc. Although none of the proposed schemes have been implemented in a real ASIC, all have been modeled in a cycle-accurate design and evaluated using cycle-accurate simulators and power estimators such as CACTI [38]. Most of the schemes have been evaluated using 40 nm process technology, while some have been implemented with 45 nm or 55 nm processes.  
Finally, some accelerators target in-memory computing. In-memory computing is a promising technology that can utilize NVM and memristors to process data such as matrix multiplication. Although in-memory computing has many advantages, it is not yet widely used in the industry and it has several limitations in commercialization.  
An interesting exception on this list is MNNFast [9], where the proposed optimizations have been applied on several platforms like CPUs, GPUs, and FPGAs. For CPUs, MNNFast was implemented in C++ using the open-source BLAS library, OpenBLAS. On GPUs, the optimizations utilized cuBLAS [39] from CUDA Toolkit 10.0 for matrix-to-matrix multiplication. Finally, for FPGAs, a dedicated FPGA-based accelerator for MNNFast was designed and implemented using Vivado high-level synthesis (HLS).
# Cyclic Detectors in the Fraction-of-Time Probability Framework
###### Abstract  
The signal detection problem for cyclostostationary signals is addressed within the fraction-of-time probability framework, where statistical functions are constructed starting from a single time series, without introducing the concept of stochastic process. Single-cycle detectors and quadratic-form detectors based on measurements of the Fourier coefficients of the almost-periodically time-variant cumulative distribution and probability density functions are proposed. The adopted fraction-of-time approach provides both methodological and implementation advantages for the proposed detectors. For single-cycle detectors, the decision statistic is a function of the received signal and the threshold is derived using side data under the null hypothesis. For quadratic-form detectors, the decision statistic can be expressed as a function of the received signal without using side data, at the cost of some performance degradation. The threshold can be derived analytically. Performance analysis is carried out using Monte Carlo simulations in severe noise and interference environments, where the proposed detectors provide better performance with respect to the analogous detectors based on second- and higher-order cyclic statistic measurements.  
cyclostationarity; weak-signal detection; fraction-of-time probability +
Footnote †: journal: _inventions_  
0
Footnote 0: 0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 PB-DA Design and Fabrication
### Design of D-A Content  
Following the research objectives of this study, fly ash and mineral powder, which were the same as those used in practical applications, were used as mineral admixtures. In line with the GB/T 18736-2017 [31], I-grade fly ash and I-grade mineral powder were selected for high-grade concrete. Based on the review of Table 1, the most common concrete strength in scaled experiments is C50, with the maximum being C80; hence, this study also produced C50 and C80 PB-DA. Detailed mass information of the materials used in C50 and C80 can be found in Table 4, and the corresponding mix design information is provided in Table 5. As shown in Tables 4 and 5, for both C50 and C80 strength grades, the total amount of cementitious materials (cement, fly ash, and mineral powder) and the quantities of other components remained unchanged. Different D-A contents are achieved solely by varying the amounts of fly ash and mineral powder. This study aimed to explore the potential application of D-A in prestressed HPC bridges. Therefore, the subsequent analysis focuses on the impact of changes in D-A content on the fatigue performance of the bridge.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Serial** & **Concrete** & & & & & & \\
**Number** & **Strength Grade** & & & & & & \\
**C50** & **436.5** & & & & & & \\
**C50** & **485.  
After careful selection, the cement used in the experimental model beams was 52.5-grade ordinary Portland cement produced by Quzhai Cement Co., Ltd. in Luquan, China. The mineral powder was S95 grade from the Hebei Xingtai Huayang Mineral Powder Processing Plant, China, with a measured specific surface area of 400 m\({}^{2}\)/kg. The remaining physical performance indicators comply with the \"Ground Granulated Blast Furnace Slag Used for Cement, Mortar, and Concrete\" (GB/T 18046-2017) standard [32]. The fly ash used was F-1 grade produced by Handan Power Plant, with physical performance indicators largely meeting the requirements of the \"Fly Ash Used for Cement and Concrete\" (GB/T 1596-2017) standard [33]. Additionally, the fine aggregate was river sand with a particle diameter ranging from 0.16 mm to 5 mm, provided by Shilingqi Sand Field in Lingshou, Shijiazhuang, China. The coarse aggregate was crushed stone with a maximum particle diameter of no more than 20 mm, produced by Yinjing Stone Factory in Shijiazhuang, China. The physical performance indicators of these two materials meet the requirements of the \"Technical Specifications for Construction of Highway Bridges and Culverts\" (JTG/T 3650-2020) [5]. A polycarboxylate superplasticizer was used as the water-reducing agent, with a water reduction rate of no less than 20%.
# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 2 Measurement and Analysis of Grain Inventory Quantity
### The Selection of Correction Factors  
Through the analysis of Formula (4), it can be seen that the calculation method of grain density correction coefficient is very important. Grain depot managers have accumulated experience and suggest that the correction coefficient usually falls within the range of 1.01 to 1.08. However, it is important to consider the actual storage conditions and their impact on the average density of the grain pile. These factors also play a role in determining the appropriate correction coefficient. When selecting the correction coefficient, the grain depot management department takes into account several principles. Firstly, since grains with higher quality grades have a fuller and more tightly packed internal structure, a slightly larger correction coefficient is chosen. Secondly, as the grain storage time increases, the internal grain pile becomes denser, warranting a slightly larger correction coefficient. Thirdly, the correction coefficient is larger for higher grain loads compared to that of lower grain loads. Fourthly, manual warehousing requires a larger correction coefficient than mechanical warehousing. Fifthly, in granaries located near roads, railways, airports, and large vibration sources, the grain gradually compresses due to the impact of vibrations, resulting in a larger correction coefficient. Lastly, the more ventilation cycles in the grain depot, the greater the correction factor. Although the above rules on the selection of the grain pile correction coefficient can improve the accuracy of grain quantity detection in the grain depot to a certain extent, this method is greatly affected by human factors and cannot meet the needs of grain quantity inspection in the grain depot.