esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Geometric Design Standards for Freeways under a Fully Autonomous Driving Environment
## 4 Geometric Design Standard
### Vertical Alignment
#### 4.3.2 Width of Shoulder  
According to Section 3.3.1, in an autonomous driving environment, the lane widths of freeways at different design speeds are not exactly the same. On the freeways with high design speed, the separation of passenger cars and trucks will be strictly implemented. Since the speed of passenger cars is much higher than trucks, if a broken-down truck stays in the truck lane at low speed for a long time, the vehicles in the truck lane will not be able to change lanes to the adjacent passenger lane. This will seriously affect the capacity of autonomous freeways. Therefore, the width of the hard shoulder should meet the need for large vehicles to pull over, so as to ensure traffic capacity and smoothness.  
When the design speed is small, freeways set up mixed driving lanes. The driving speed of trucks and passenger cars in their lanes is not much different, which can provide favorable conditions for trucks to join. When there is a troubled vehicle in front of the lane, the truck behind can receive the relevant information in time and then decelerate and brake in advance or carry out local route planning, change lanes, and merge into mixed lanes to improve road traffic efficiency. The lane's capacity will be restored when the faulty vehicle enters the emergency parking strip. Obviously, when the design speed is small, the faulty vehicle has little effect on the road capacity. To reduce road construction costs, a combination of a narrow hard shoulder and emergency stop strip can be considered.  
In this paper, based on traffic characteristics of autonomous vehicle roads, considering the value of hard shoulder width in China, as well as the economy of highway construction, the suggested width of the right shoulder of automatic driving freeways is put forward, as shown in Table 18. When the width of shoulder is 1.5 m, it is necessary to set an emergency parking strip.
# Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
## 6 Conclusions  
We showed through simulations that the inter-polarization time skew, polarization rotation, and PMD can be compensated in optical systems employing single-mode fibers and, therefore, will not affect the performance of clock synchronization. Particularly, it would need more than 10,000 km of a modern fiber with DGD of 0.1 ps/\(\sqrt{\text{km}}\) in order to be able to see some impact of DGD on the clock recovery performance. However, in multi-mode systems the mode coupling and delay spread could have a impact much worse in the performance of timing synchronization. It was demonstrated although strong coupling reduces the coupled group delay spread [11] and nonlinearities [19], the clock tone will completely vanish even for very low group delays, making timing synchronization unfeasible. In order to have a detectable clock tone higher than 50% of the maximum value, the multi-mode optical fiber would need to have an impractical uncoupled group delay of less than 0.03 ps/km. Also, we showed that increasing the transmission distance will reduce the group delay tolerance of MDM systems even more under strong coupling regime. Possible solutions could be use of pilot tones for timing synchronization or operation in weak coupling regime with sparsity managed MIMO equalizers [17; 18]. Other transmission impairments such as the equalization enhanced phase noise [31] and fiber nonlinearities [32] could possibly impact the clock recovery performance and are planned to be analyzed in future works.  
**Author Contributions:** [PERSON] conceived, designed and performed the simulations; [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the paper.  
**Funding:** This work was supported by Villum Foundation, Soborg, Denmark, under Villum Foundation Young Investigator program.  
Figure 9: Normalized clock tone amplitude as a function of uncoupled group delay between mode groups in a 3-mode FMF for distinct transmission distances.  
**Acknowledgments:** The authors would like to thank [PERSON] for valuable discussions. The authors alone are responsible for the content.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 4 Threat Model for a Blockchain-Based Supply Chain System
### Sources of Attacks  
Importantly, we investigate the attacks that mainly arise due to the weak design of smart contracts and applications, blockchain execution environment, and untrusted-environment among interconnected supply chain services.  
Design of smart contract and application (DSC) is key source of various attacks. Current smart contracts are limited, only offering transaction related functionality, and they fail to handle any serious security requirement of the application. This is due to the fact that smart contracts were design without considering security and privacy of the applications interacting with them.  
Blockchain execution environment (BEE) is an important source of various attacks because typical deployment of blockchain-based solutions involve public ledgers and contracts (i.e., they are visible to anyone), therefore, adversaries can easily determine their vulnerabilities and weaknesses and can exploit them to launch attacks.  
Trust among interconnected supply chain services (TIS) is a crucial source of very critical attacks. Several sub-processes of the supply process are not digitized, therefore their trust cannot be established easily. Importantly, the trust of digitized and automated sub-processes is also threatened, mainly because of the supply services that are not context-aware. Importantly, the trust among services also critically depends on secure exchange of information through underlying communication channel.  
The mapping between the above-mentioned attacks, security requirements of the supply chain management system, and potential causes of attacks is shown in Table 2.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attack Type** & **Attack Mechanism** & **Affected Requirements** & **Causes** \\ \hline \multirow{3}{*}{Computational} & Contract modification & Traceability, Privacy & DSC, BEE \\ \cline{2-4}  & Execution modification & Traceability, Privacy & DSC, BEE, TIS \\ \cline{2-4}  & Vulnerability exploit & Traceability, Transparency, Privacy & DSC, BEE \\ \hline \multirow{3}{*}{Communication} & Tampered input values & Traceability, Transparency, Privacy & BEE, TIS \\ \cline{2-4}  & Breach of data integrity & Traceability, Transparency, Privacy & BEE, TIS \\ \cline{2-4}  & False information injection & Traceability, Transparency, Privacy & DSC, BEE, TIS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mapping of Attacks to Requirements.
# Containerised Application Profiling and Classification Using Benchmarks
## 6 Experimentation
Next, the results were tested against different classification algorithms, concluding that Random Forest returns the most accurate predictions. The cross validation method gave strong indication for this decision, but the \"no workloads\" datasets underpins it even more, since the Random Forest algorithm achieves an accuracy rate of 89.56% when testing with the external data, with the ForestPA algorithm coming second with only 75.10%. This big difference in terms of accuracy leaves no choice in selecting Random Forest as the best fit for the profiling system, \begin{table}
\begin{tabular}{l l} \hline \hline
**Training Dataset Hardware** & **External Testing Dataset Hardware** \\ \hline Configuration A & Configuration D \\ cpu: Intel(R) Core(TM) i7-3820 CPU @ 3.60 GHz & cpu: Intel(R) Core(TM) i5-3570 CPU @ 3.40 GHz \\ RAM: 32 GB (4x Nanya NT8 GC648 BHB0N) & RAM: 16 GB (4x AMI CMZ8 GX3 MZA1600C9) \\ Disk: 256 GB (ADATA SU800) & Disk: 256 GB (Samsung SSD 850) \\ OS: Ubuntu LTS distribution (20.04) & OS: Ubuntu LTS distribution (20.04) \\ \hline Configuration B & Configuration E \\ cpu: AMD FX-8370 eight-core processor & cpu: 2 out of 4 physical cores \\ RAM: 16 GB (2x Mushkin 992125R) & RAM: 8 GB out of 16 GB RAM \\ Disk: 256 GB (Samsung SSD 850) & Disk: 100 GB out of 256 GB Disk \\ OS: Ubuntu LTS distribution (20.04) & OS: Ubuntu LTS distribution (20.04) \\  & VM created in Configuration D using VirtualBox, with constrained resources \\ \hline Configuration C & \\ cpu: 2 cores with 1 thread from an Intel(R) Xeon(R) & \\ CPU E5-2650 v3 @ 2.30 GHz & \\ RAM: 4 GB & \\ OS: Debian 10.11 & \\ VM hosted on academic cloud service & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hardware configuration of the testing and training datasets. \begin{table}
\begin{tabular}{l l} \hline \hline
**Training Dataset** & **External Testing Dataset** \\ \hline Configuration A: 70\% & Configuration D: 80\% \\ Configuration B: 15\% & Configuration E: 20\% \\ Configuration C: 15\% & Total number of data points: 1050 (63 benchmark and workload combination) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training and external dataset data point distribution. even though the error metrics were a little worse than some of the other algorithms. In general, the \"no workload\" testing improved the accuracy for all algorithms. Specifically, for the Random Forest algorithm we saw a 4.82% improvement, which reveals that many wrong predictions actually were able to decide the actual type of the benchmark, but not the specific workload. Hence, the assessment of the results showed that a model created with data collected from different hardware configurations (some of them in virtualised environments), is able to predict at least the type of the benchmark to which data from different configurations correspond to, and even deduce (with lower accuracy) the exact workload of the benchmark.
## 3 Results  
Smooth distance curve, velocity curve, and parameter estimates according to Preece-Baines model 1 are presented in Figure 2. The estimated value for male adult stature was 177.9 cm, and the estimated age and stature at peak height velocity was 13.45 years of age and 160.5 cm respectively  
Practice experience, training load and estimated APHV did not differ significantly between the oldest and youngest groups. Nevertheless, compared with the younger players, the older players were: (a) heavier (+6.6 kg; \(t=-3.465\), \(p=0.001\), \(\eta_{P}^{2}=0.066\)), taller (+7.2 cm; \(t=-4.490\), \(p<0.001\), \(\eta_{P}^{2}=0.106\)), had more arm span (+7.2 cm; \(t=-3.832\), \(p<0.001\), \(\eta_{P}^{2}=0.080\)), more hand span (+0.9 cm; \(t=-3.006\), \(p=0.003\), \(\eta_{P}^{2}=0.050\)) and more FFM (+5.5 kg; \(t=-4.403\), \(p<0.001\), \(\eta_{P}^{2}=0.102\)); (b) were faster (\(-0.09\); \(t=2.180\), \(p=0.031\), \(\eta_{P}^{2}=0.027\)); jumped higher on CMJ (+1.9 cm; \(t=-2.001\), \(p=0.047\), \(\eta_{P}^{2}=0.023\)), had more CMJ power (+99 W; \(t=-3.729\), \(p<0.001\), \(\eta_{P}^{2}=0.075\)), jumped higher on CMJ-S (+2.3 cm; \(t=-2.228\), \(p=0.027\), \(\eta_{P}^{2}=0.028\)), CMJ-S power (+108 W; \(t=-3.856\), \(p<0.001\), \(\eta_{P}^{2}=0.080\)), and handgrip strength (+8.0 kg; \(t=-2.497\), \(p=0.016\), \(\eta_{P}^{2}=0.046\)), and threw the medicine ball longer (+60 cm; \(t=-4.305\), \(p<0.001\), \(\eta_{P}^{2}=0.098\)); and (c) had better PIR/game (+1.83; \(t=-2.927\), \(p=0.004\), \(\eta_{P}^{2}=0.048\)), PIR/min (+0.10; \(t=-2.228\), \(p=0.027\), \(\eta_{P}^{2}=0.028\)), points/game (+1.25; \(t=-3.178\), \(p=0.002\), \(\eta_{P}^{2}=0.045\)), rebounds/game (+1.1; \(t=-2.909\), \(p=0.004\), \(\eta_{P}^{2}=0.047\)) and play more minutes per game (2.6 min; \(t=-3.607\), \(p<0.001\), \(\eta_{P}^{2}=0.071\)). When the maturity groups were based on APHV = 13.81, \(\sim\)22% of the subjects were early maturers, \(\sim\)78% were on-time, and no late mature players were found (see Table 3). When APHV = 13.45 years was used to establish maturity groups, \(\sim\)7% of the subjects were early maturers, and \(\sim\)89.5% were on-time and \(\sim\)3.5% were late mature. When APHV = 13.30 years was used to establish maturity groups, \(\sim\)5% of the subjects were early maturers, \(\sim\)86% were on-time, and \(\sim\)9% of the subjects were early maturer (see Tables 4 and 5). Figure 2: Smooth distance curve (height estimates), velocity curve (first derivate of the fitted function) indicating age at peak height velocity (APHV), and parameter estimates according to Preece-Baines model I.
## 5 Experimental Outcomes, Comparative Analysis, and Discussion  
The following section presents the outcomes of the two experiments conducted using the respective testing setups described in Section 4. Initially, vessel classification is addressed. The trajectory prediction outcomes are explored in the section following the classification outcomes. The experimental results are then discussed and validated using rigorous statistical tests. The bold font indicates the best results in the tables to follow. Finally, the best attained models are analyzed and the feature importance is presented.
## 3 Results
### Power Performances According to Hydraulic Flow Connection and Direction  
All MFCs showed slightly different polarization and power curves according to the hydraulic flow connection and direction (Figure 5). Overall, S-MFC showed a lower MPD than M- and L-MFCs (Figure 5). This was attributed due to the rapid depletion of the substrate with decreasing HRT as the volume decreased under the same flow rate. In the case of MFC treated pharmaceutical sewage with different HRTs (8 and 5 h), short HRTs increased the volumetric organic loading rate, thereby reducing the MFC performance due to rapid substrate depletion [27].  
Interestingly, some MFCs produced similar MPDs at each phase. The S- and L-MFCs showed similar MPDs (0.41-0.42 W/m\({}^{2}\)) at phase I, and the M- and L-MFCs showed similar MPDs at phases II (0.61-0.64 W/m\({}^{2}\)) and III (0.43-0.46 W/m\({}^{2}\)) (Figure 5). In the case of a serial flow connection and direction, the carbon source type and COD removal rate would be different at each MFC because of the different reactor volumes, which can affect the power density. In general, glucose is converted to lactate, propionate, butyrate, and acetate [28]. The glucose-fed MFC showed a higher MPD of 1.5 W/m\({}^{2}\) than the acetate-fed (1.3 W/m\({}^{2}\)), butyrate-fed (0.8 W/m\({}^{2}\)), and propionate-fed (0.7 W/m\({}^{2}\)) MFCs when using the same influent COD concentration [29]. Therefore, MFCs could produce the same power density due to a range of factors, including the COD concentrations, COD loading rate, and internal resistance [26; 29]. In this study, the Pearson correlation showed that a range of factors influenced the power density, and there was a strong relationship with the internal resistance (Table 2). On the other hand, the MFCs do not produce the same MPD even if the MFCs produce the same voltage at the same external resistance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Voltage** & **Power Density** \\ \hline Reactor volume & 0.6385 & 0.3673 \\ HRT & 0.6385 & 0.3673 \\ Influent COD concentration & 0.6337 & 0.3008 \\ Influent COD loading rate & \(-\)0.2373 & \(-\)0.0640 \\ COD removal rate & 0.1168 & 0.1448 \\ Removed COD & 0.9445 & 0.3943 \\ Internal resistance & \(-\)0.7460 & \(-\)0.8313 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pearson correlations of voltage and power density with the coulombic efficiency, energy recovery, reactor volume, hydraulic retention time (HRT), influent chemical oxygen demand (COD) concentration, influent COD loading rate, COD removal rate, removed COD concentration, the ratio of anode area for reactor volume, and internal resistance.
## 4 Results and Discussion
### Vibration Response under a Particular Exciting Force  
The theoretical analysis of the compressor stator blade with an outer ring structure is discussed above. The influence of normal contact load and air excitation force on nonlinear vibration was studied. In this paper, the fluid-structure coupling analysis method was used to give the vibration response calculation results of compressor stator blades under a specific operating condition.  
Firstly, the transient airflow excitation force analysis was carried out under a compressor design condition to obtain the unsteady airflow load on the stator blade surface. The following Figure 15 shows the time domain diagram of the flow force on the blade, including the axial and circumferential flow force. Furthermore, FFT is applied to the airflow force of the blade. The airflow force frequency domain results of the blade are shown in Figure 16. The airflow force corresponding to each order can be obtained. It can be seen that periodic airflow loads on the surface of the stator blades occur due to the wake action of the upstream blades during the normal operation of the compressor. After spectrum analysis, the relationship between the first-order frequency 1250 Hz and the number of rotor blades accords with the theory.  
Figure 15: Compressor stator blade airflow excitation force. (**a**) Circumferential force. (**b**) Axial force.  
The first-order flow force was used as the boundary of finite element calculation to simulate the vibration response characteristics of compressor stator blades under these working conditions. The normal load of the contact surface was set at 30 N. The vibration response of the 50% high of the stationary blade at the outlet side position is given here. The following Figure 17 shows the axial and circumferential vibration response curves of the air outlet node at the free end of the blade. It can be seen that the maximum circumferential vibration response value is 1.45 mm and the maximum axial response amplitude is 0.61 mm. Therefore, it is necessary to pay attention to the vibration safety of stator blades during the operation of equipment.  
In general, there may be a certain range of changes in the working conditions of a compressor during its operation. The change of working condition is closely related to the airflow force on the compressor stator blade. This means that the aerodynamic load of the blade varies in a certain range during operation. However, the normal load of the blade contact surface is roughly at a constant level, which is determined by the mechanical structure of the equipment. According to the research method used in this paper, a better normal load of stator blade contact surface can be identified for design and installation. In brief, we aim to find a normal load which can not only meet the requirements of vibration  
Figure 16: Exciting force FFT results. (**a**) Circumferential force. (**b**) Axial force.  
safety but also reduce the amplitude by analyzing the working conditions of a variety of different input parameters.
## 2 Theory And Model Building
### Model Building  
Figure 1a shows the underwater vehicle studied in this paper, which is a standard rotary body of 3.29 m in length. Figure 1b shows the computational domain of cylindrical flow field created from the model shape. The length of the front end part is taken as the length of the underwater vehicle. To accurately capture the trailing vortex region at the trailing edge of underwater vehicle, the trailing end is taken as twice the length of underwater vehicle. The diameter of computational domain is taken as 4.45 m. FLUENT MESHING was used for meshing, and the \(y^{+}\) value was taken as 1. To accurately capture the vortex region that may be generated by the flow and the region with large parameter variations, the cylindrical region and tail section that wraps the whole underwater vehicle are locally encrypted. The final total number of generated meshes is about 27 million.  
To facilitate the observation and analysis of sound field radiation characteristics and to avoid the effect of geometric features such as the sharp corners of calculation domain on sound field radiation, the acoustic field calculation domain was selected as the spherical region shown in Figure 1c. The diameter of the sphere was taken as three times the length of underwater vehicle. The noise in the low-frequency band (below 2000 Hz) was calculated using ACTRAN. According to the required calculation frequency, the accuracy of acoustic calculation results can be ensured when each wavelength contains at least six acoustic grids,so the maximum size of acoustic grid is set to 0.12 m. ICEM was used to divide the grid, and the total number of grids is about 6 million.  
To detect the flow field and acoustic field of underwater vehicle, 14 monitoring points were set up at the front and tail of underwater vehicle. The distance of each monitoring point from the wall is 1 m, as shown in Figure 2.
## 2 Materials and Methods
### Data Acquisition and Processing  
Land-use data for Ningde City in 2000, 2010, and 2020, at a resolution of 30 m, were generated based on Landsat remote-sensing images. Before land-use classification, radiometric normalization, geometric and atmospheric correction, etc., were adopted to eliminate factors affected by the sensors and atmosphere by using ArcGIS 10.8. And then, the land-use classification data of study area were acquired through the operation of human-computer interactive visual interpretation of the above remote-sensing images. Moreover, the above-mentioned land-use maps were verified by Google Earth's real-time data. The results showed that the coefficient values of Kappa test were all greater than 0.8, and the interpretation accuracies of the three stages of remote-sensing images were all more than 90%. Land-use types were classified into cultivated land (CL), forest land (FL), orchard land (OL), grassland (GL), water body (WB), built-up land (BL), and unutilized land (UL), according to the specific conditions and research objectives of the study area. Subsequently, a land-use spatial database for Ningde City was established by assigning Codes 1 through 7 to each land type, respectively.  
Additionally, the data of temperature and precipitation were provided by the National Meteorological Information Center ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)), accessed on 16 November 2023). The socio-economic data were all from \"Ningde Statistical Yearbook (2001-2021)\". Fujian Provincial Grain and Price Reserve Bureau provided us with grain prices ([[http://lsj.fujian.gov.cn/xcggk/tjxx/](http://lsj.fujian.gov.cn/xcggk/tjxx/)]([http://lsj.fujian.gov.cn/xcggk/tjxx/](http://lsj.fujian.gov.cn/xcggk/tjxx/)), accessed on 16 November 2023).
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 4 Performance Results  
The passive detection of low-altitude signal sources is evaluated in this section using the advanced method (AM) [2], and the constant false alarm rate (CFAR) [28], higher-order cumulant (HOC) [29], and proposed methods. For the CFAR algorithm, the two-dimensional (2-D) energy window slides over the entire time-frequency matrix to detect the signal source. This window consists of outer, protected, and inner windows. When the window is aligned with the signal source, the inner window corresponds to the signal source, the outer window corresponds to the noise, and the protected window is the buffer between the outer and the inner windows. Thus, when a signal source is present, the inner window to outer window energy ratio will be high. The width of the inner window is set to 9.47 MHz based on the UAV signal, and the height of this window is set to 0.7 s according to the presence of a UAV. For the HOC method, a window slides over the entire time-frequency matrix. The fourth-order cumulant of the signal covered by the window is used. When a low-altitude signal source is detected, the value of this cumulant is high. The width of the HOC window is set to 9.47 MHz.