esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussion
### Numerical Simulation Analysis of Single Vehicle
#### 5.1.1 Safety Distance Threshold Simulation of Avoiding a Rear-End Crash  
Rear-end crashes may occur between the \(LV\) and \(MV\), the \(MV\) and \(MV\), and the \(MV\) and \(RV\), as shown in Figure 2f. The same as in Section 2, this study expressed the \(MV\) or \(RV\) of the vehicle that intends to change lanes as the \"ego vehicle,\" and the \(LV\) or \(MV\) of the vehicle in front of the target lane was expressed as the \"front vehicle\" for clarity. Figure 7 shows the critical distances \(D_{max}\) and \(D_{min}\) between the ego vehicle and the front vehicle. The velocity difference between the two vehicles was represented using the horizontal axis and the distance was represented using the vertical axis. It can be seen from each diagram that with the increase in velocity difference \(\Delta v\), the maximum distance \(D_{max}\) and the minimum distance \(D_{min}\) gradually increased and the feasible gap between \(D_{max}\) and \(D_{min}\) gradually increased too. In the same \(v_{f}\), \(a_{f}\), and \(v_{e}\) conditions, the maximum distance \(D_{max}\) and the minimum distance \(D_{min}\) gradually decreased with the acceleration \(a_{e}\) of the ego vehicle. Meanwhile, we found that the critical distances increased when the velocity \(v_{f}\) of the front vehicle increased or when the acceleration \(a_{f}\) of the front vehicle decreased. This illustrates the relationship between the required critical distances and different motion states of the two vehicles.
# An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
## 1 Introduction
[PERSON] and co-workers have also used three lidar beams intersected in a single point to derive turbulence statistics [19], but no adequate reference was available to assess the accuracy of their measurements. This paper presents measurements performed during the _Kassel 2014 Experiment_. The aim of the _Kassel 2014 Experiment_ was to experimentally explore and demonstrate the advantages of ML measurements under conditions which are realistic for wind-energy applications in complex terrain. The experiment also served as a test bed for the complex terrain experiments in the New European Wind Atlas which will heavily rely on ML measurements [20]. Here, we present measurements and comparisons of four long-range WindScanners, which intersected at one point, a DBS lidar, and a sonic anemometer at a reference mast at a complex forested site. An analysis of the radial velocity statistics is used to examine the measurement accuracy of the individual instruments as well as the accuracy of the setup process. It is also used to provide insight into the path-averaging effect on turbulence measurements with the WindScanners. An analysis of the first- and second-order statistics of the horizontal wind speed contrasts error sources for the different lidar techniques. The ML-DBS-mast inter-comparison is especially valuable to analyse and quantify the benefits of the ML technique over the standard DBS lidar and experimentally compare their measurement behaviour in complex terrain. The WindScanners were located at distances ranging from directly next to the mast to 3740 m from the reference mast; they reflect a measurement scenario which is \"realistic\" in windfarm development scenarios in complex terrain.
## 3 Results and Discussion
### Electromagnetic Field on Different European Roads
#### 3.1.2 Bulgaria  
In Bulgaria, EMF measurements were carried out in the car cabin during driving on a motorway and a main road (between points P1 BG-P2 BG, P2 BG-P3 BG, and P3 BG-P4 BG), as shown in Figures 4-6. The road sections between points P1 BG-P2 BG and P2 BG-P3 G cover part of the motorway A2. The third road section (between points P3 BG-P4 BG) is a main road that is part of the E83 and E85.  
Figure 4: Bulgaria: (**a**) Boxplot showing total electric field (_Etotal_) and electric field on each axis (_Ex_, _Ey_, and _Ez_). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
Figures 4-6 and Table 3 show that the data do not have a normal distribution. From the boxplots, we can see that the distributions of the E-field in the car's cabin during traveling on the different road sectors in Bulgaria are skewed: \(E_{total}\) median is 0.25 V/m (P1 BG-P2 BG) and 0.22 V/m (P2 BG-P3 BG and P3 BG-P4 BG) and mean is 0.36 V/m (P1 BG-P2 BG) and 0.22 V/m (P2 BG-P3 BG and P3 BG-P4 BG).  
Figure 5: Bulgaria: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
Figure 6: Bulgaria: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
and 0.26 V/m (P2 BG-P3 BG and P3 BG-P4 BG). During driving on the third road section (P3 BG-P4 BG), we obtained the highest maximal \(E_{total}\) value of 11.3 V/m. We suppose that the measured single high value of the electric field is due to the passage of the vehicle near EMF sources, such as base station antennas of telecommunication operators. From Figure 6b, it can be seen that the third road section passes through one of the largest cities in Bulgaria--Ruse. The two selected road sections of the A2 motorway do not pass through densely populated areas. However, the maximum obtained values of the electric field in the car's cabin in these two road sections were also high: 7.1 V/m (P1 BG-P2 BG) and 9.6 V/m (P2 BG-P3 BG). Comparing these values with the maximum \(E_{total}\) value on the A1 motorway in Austria (1.4 V/m, P2 AT-P3 AT), we can conclude that the maximum measured values when driving on the A2 motorway (P1 BG-P2 BG and P2 BG-P3 BG) in Bulgaria are higher. This may be due to the fact that there are base station antennas near the A2 motorway tunnels in Bulgaria, as shown in Figure 5b.  
The lowest \(E_{total}\) value in all road sections is 0.11 V/m.
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 5 Related Work  
Several features introduced by our Accelerator proposed in this paper are verified above. The Accelerator is orthogonal to these features. This zero-copy-driven programmable architecture for multi-queue stream forwarding can support 100 Gbps. In this section, we review the most closely related prior work.  
Multi-queue management based on the hardware clock. The Accelerator is similar to the Corundum NIC [28] in that it has hardware queues and supports time synchronization based on the hardware clock. Corundum NIC Implementation of Microsecond Time Division Multiple Access (TDMA) Hardware Scheduler. However, the corundum NIC uses a hardware scheduler to manage the queue. Compared with the Accelerator based on software management, it lacks considerable flexibility.  
Multistream-oriented scheduling. The Accelerator is also similar to PANIC [32], which improves fairness between competing flows. PANIC provides features such as chaining without involving a CPU. Adopting PANIC's scheduler and non-blocking cross-bar can solve these fundamental problems with many-core NICs. In addition, in terms of flexibility, the multistream scheduling based on flow rules adopted by the Accelerator is programmable, which is more advantageous than the hardware-based scheduling of PANIC.  
New transport protocol acceleration support. Some NICs have protocol acceleration support with hard-wired transport protocols [33; 34]. However, they only implement two protocols, either RoCE [33] or a vendor-selected TCP variant, and can only be modified by their vendor. The Accelerator enables programmers to implement various transport protocols (such as QUIC) with modest effort. Without a publicly available detailed description of these NIC architectures, we could not compare our design decisions with theirs.  
Accelerating network functionality. Some academic and industrial projects unload the virtual switching and network functions of the terminal host to FPGA and process the generated packet stream [35; 36; 37; 38]. On the other hand, The accelerator can directly forward packets according to the flow_table programmed in advance without the participation of the host CPU. When necessary, it can change the forwarding behavior through remote programming of the flow_table. This method is more adaptable than the above-mentioned technologies.
# Factors Determining Ticagrelor-Induced Dyspnea in Patients with Acute Coronary Syndrome
## 3 Results
### Associations between Clinical and Genetic Characteristics and the Development of Dyspnea
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Genes**} & \multicolumn{3}{c}{**Dyspnea**} & \multirow{2}{*}{\(\chi^{2}\)} & \multirow{2}{*}{\(p\)} \\ \cline{2-2} \cline{5-5}  & **No** & & **Yes** & \\ \hline _CYP4F2_ (rs3093135) & & & & \\ AA & 18 (13.6) & 10 (17.5) & & \\ AT & 79 (59.8) & 28 (49.1) & 1.867 & 0.393 \\ TT & 35 (26.5) & 19 (33.3) & & \\ A allele & 115 (43.6) & 48 (42.1) & 0.068 & 0.793 \\ T allele & 149 (56.4) & 66 (57.9) & & \\ \hline _CYP4F2_ (rs2074902) & & & & \\ CC & 3 (2.3) & 3 (5.3) & & \\ CT & 36 (27.3) & 17 (29.8) & 1.566 & 0.474 \\ TT & 93 (70.5) & 37 (64.9) & & \\ C allele & 42 (15.9) & 23 (20.1) & & \\ T allele & 222 (84.1) & 91 (79.8) & 1.017 & 0.313 \\ \hline _CYP2C19_ (rs4244285) (*2) & & & & \\ AA & 1 (0.8) & 1 (1.8) & & \\ AG & 27 (20.5) & 11 (19.3) & 0.777 & 0.851 \\ GG & 104 (78.8) & 45 (78.9) & & \\ A allele & 29 (11.0) & 13 (11.4) & & \\ G allele & 235 (89.0) & 101 (88.6) & 0.014 & 0.905 \\ \hline _ABCB1_ (rs1045642) & & & & \\ CC & 37 (28.0) & 14 (24.6) & & \\ CT & 70 (53.0) & 32 (56.1) & 0.252 & 0.882 \\ TT & 25 (18.9) & 11 (19.3) & & \\ C allele & 144 (54.6) & 60 (52.6) & & \\ T allele & 120 (45.4) & 54 (47.4) & 0.117 & 0.731 \\ \hline _FBG-C148T_ (rs1800787) & & & & \\ CC & 60 (45.5) 1 & 37 (64.9) 1 & & \\ CT & 59 (44.7) 2 & 12 (21.1) 2 & 9.489 & **0.009** \\ TT & 13 (9.8) & 8 (14.0) & & \\ C allele & 179 (67.8) & 86 (75.4) & & \\ T allele & 85 (32.2) & 28 (24.6) & 2.214 & 0.136 \\ \hline _COX-2_ (rs689465) & & & & \\ CC & 2 (1.5) & 0 (0.0) & & \\ CT & 22 (16.8) & 9 (15.8) & 0.532 & 1.000 \\ TT & 107 (81.7) & 48 (84.2) & & \\ C allele & 26 (9.9) & 9 (7.9) & & \\ T allele & 236 (90.1) & 105 (92.1) & 0.387 & 0.533 \\ \hline _PAI-I_ (rs5918) & & & & \\ CC & 5 (3.8) & 2 (3.5) & & \\ CT & 49 (37.1) & 18 (31.6) & 0.619 & 0.840 \\ TT & 78 (59.1) & 37 (64.9) & & \\ C allele & 59 (22.4) & 22 (19.3) & & \\ T allele & 205 (77.6) & 92 (80.7) & & \\ \hline _CYP2C19_ (rs12248560) (*17) & & & & \\ CC & 63 (47.7) & 36 (63.2) & & \\ CT & 61 (46.2) & 18 (31.6) & 3.871 & 0.138 \\ TT & 8 (6.1) & 3 (5.3) & & \\ C allele & 187 (70.8) & 90 (79.0) & & \\ T allele & 77 (29.2) & 24 (21.0) & 2.677 & 0.101 \\ \hline \end{tabular} Values are the number (percentage). 1\(p\) = 0.014; 2\(p\) = 0.002.  
\end{table} 
Table 4: _Cont._the development of dyspnea (AUC = 0.612; 95% CI = 0.52-0.70; \(p\) = 0.015; sensitivity of 43.9%; specificity of 76.8%; Youden J index = 0.317). In addition, there was a trend that in the presence of an ADP HS value of \(\leq\)19.5 U, dyspnea of varying severity persisted during the entire follow-up period.
## 4 Numerical Results
### Application to a Network Design Problem  
The proposed algorithm was tested inside a procedure for solving Network Design Problem (NDP) on the rural real-scale network described in Section 4.3. In particular, we considered a network capacity expansion problem [28] where there were limited resources for improving the performances of a rural road network. In this problem, the current configuration of the road network is known, and no other roads can be added to it; instead, each existing road can be improved, increasing its capacity and free-flow speed. The decision variables of this problem are binary, \(y_{i}\) = 0/1, where \(y_{i}\) = 1 indicates that road \(i\) is improved in the solution, while \(y_{i}\) = 0 indicates that road \(i\) remains in the starting configuration. The details of NDP and of its model formulation are reported in Appendix C.  
In order to compare the results obtained with the proposed muffled algorithm and the classic MSA, we tested the Neighbourhood Search Algorithm (NSA) with steepest descent method; it was used, for instance, in References [28; 29]. A brief description of the algorithm is reported in Appendix D.  
The tests were conducted under the actual demand (1.0 \(d\)), and therefore, assuming the parameter \(\eta\) = 0.5 in the muffled proposed algorithm, since it is the best value for this demand level (see Table 9). The results of the comparison between MSA and muffled algorithm are summarised in Table 10; note that the computing time saved is about 44% (over 2.5 h). The different numbers of examined neighbourhoods and objective function values are only due to the convergence test; indeed, even if the convergence test is the same, the flows generated at each iteration of the algorithms can be slightly different and may influence the final solution. In Figure 9, the comparison among MSA and muffled algorithm is reported in terms of computing times and objective function values. These results underline the utility of the proposed algorithm inside Network Design Problem solution procedures; indeed, we have to consider also that several meta-heuristic algorithms use NSAs as subroutines (Scatter Search, Tabu Search, etc.), and then, the computing time saved should be multiplied by the number of NSAs performed for reaching the final solution.  
Figure 8: Results on the rural real-scale network.
## 5 Insertion Loss Reduction Techniques (ILRTs)
### Simplified \(T\)-Structure  
The insertion loss of a conventional T-type attenuator is related to the on-resistance of the series transistors. While the on-resistance of series switches is unavoidable in a switching T-type topology, in some cases, the series transistors can be removed without loss of functionality.  
Figures 2a and 3a show the T-type and simplified T-type structure, respectively. The simplified T-type structure removes the series transistor Ms and series resistors \(Rs\), which greatly reduces the insertion loss.  
Figure 9 shows the insertion loss of the simplified T-type and the T-type attenuators, where, the dashed line shows the simulation results for the conventional T-structure and the solid line shows  
Figure 8: Simulation results of conventional structure and SBCS. (**a**) Relative attenuation and (**b**) return loss.  
Figure 7: Schematic of 16 dB attenuator with small bit compensation structure.  
the simulation results for the simplified T-structure. The result shows that the insertion loss of the simplified T-type structure is reduced by 1.15 dB. In this paper, both 0.5 dB and 1 dB attenuation bits use the simplified T-type structure, which reduces the insertion loss of the 6-bit DSA by 2.3 dB.
## 5 Experiments
### Common Parameters of the Simulations and Experiments  
For all the experiments, the selected feedback gain matrices in (33) are \(K_{P}=10I_{3\times 3}\) and \(K_{O}=20I_{3\times 3}\), where \(I\) is an identity matrix. The initial value of \(\alpha(t)\) in (30) is set to \(\alpha_{s}=3\), and the blending time is set to \(t_{b}=0.2t_{f}\) for the variation factor \(\beta(t)\) in (24). The parameters for self-collision avoidance in (20) are \(\rho=1\times 10^{-3}\), \(c_{1}=50\) and \(c_{2}=1\). The starting configuration of the robot arm is \(\mathbf{q_{a}}=\begin{bmatrix}0&-80&110&-120&-90&0\end{bmatrix}(^{\circ})\). Furthermore, a sampling time \(t_{s}=0.02(s)\) was used for the simulations and experiments.
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 5 Discussion  
Real life studies reflect how treatments/medications administered to PD subjects during a clinical visit are effective. In order to provide continuous monitoring of the motor activity of the PD subject during daily life, we propose a system based on a single wearable device and an algorithm specifically developed for the estimation of gait analysis parameters. The existing research is not able to provide a complete analysis of the quantity and quality gait in time and frequency domains through a single inertial measurement unit [20; 22]. In fact, [PERSON] et al. [19] extracts some gait parameters related to the quality of walking only in the time domain. The performance of the proposed system was tested in an experimental protocol composed of tasks both in a laboratory scenario and in a home scenario. In consideration of the results obtained from the laboratory scenario a comparison with video recordings was performed to verify the correct functioning of the algorithm in the extraction of walking windows, in the identification of steps, and in the computation of parameters. Table 3 shows the performance of the algorithm in the identification of steps for each walking bout of a single subject. The percentage error of the algorithm was computed for each walking bout and revealed the good functioning of the algorithm. In fact, the error was 0% in short and simple walking tasks, while it ranged between 1.35% and 1.73% in longer and more complex walking bouts. The error remained low, at 2%, for all walking bouts. The sensitivity and specificity over all subjects was computed. We obtained a high sensitivity (99.13%), a small number of false negatives (93 steps out of 10,675 steps), and a high specificity (100%) with zero false positives. The proposed algorithm generated mistakes for a subject that could not walk autonomously (needed crutches support). In fact, a number of false negatives were obtained for the subject that required a medical walker so the algorithm's performance was lower. Although a small sample size group was examined, it was inclusive of 12 h of data acquisition during a task in a home scenario. As shown in Figure 7, the results of the laboratory scenario were also confirmed in the home scenario. The visual examination of the agreement using the Bland-Altman plots showed no systematic error in the measures. The distribution of the errors indicates that there are no systematic differences between the values obtained using the proposed algorithm and the video recordings, and the difference is within the 95% confidence interval.
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
###### Abstract  
This study investigates the potential application of Stateflow (SF) to design an energy management strategy (EMS) for a renewable-based hybrid energy system (HES). The SF is an extended finite state machine; it provides a platform to design, model, and execute complex event-driven systems using an interactive graphical environment. The HES comprises photovoltaics (PV), energy storage units (ESU) and a diesel generator (Gen), integrated with the power grid that experiences a regular load shedding condition (scheduled power outages). The EMS optimizes the energy production and utilization during both modes of HES operation, i.e., grid-connected mode and the islanded mode. For islanded operation mode, a resilient power delivery is ensured when the system is subjected to intermittent renewable supply and grid vulnerability. The contributions of this paper are twofold: first is to propose an integrated framework of HES to address the problem of load shedding, and second is to design and implement a resilient EMS in the SF environment. The validation of the proposed EMS demonstrates its feasibility to serve the load for various operating scenarios. The latter include operations under seasonal variation, abnormal weather conditions, and different load shedding patterns. The simulation results reveal that the proposed EMS not only ensures uninterrupted power supply during load shedding but also reduces grid burden by maximizing the use of PV energy. In addition, the SF-based adopted methodology is envisaged to be a useful alternative to the popular design method using the conventional software tools, particularly for event-driven systems.  
Stateflow; finite state machine; photovoltaic; energy storage; load shedding; renewable-based hybrid energy system +
Footnote †: journal: Applied Sciences
## 1 Introduction
Long-term vibrations may cause uneven soil settling, leading to irreparable damage or collapse of underground ruins or relics, which rely on the soil as their primary framework. Consequently, it is necessary to evaluate the impact of ambient vibrations on underground artifacts. This study is centered on examining the effects of vibrations caused by road traffic on nearby subsurface sites, with a specific emphasis on the Chengshang Village site. An analysis was conducted on the vibration effects of road traffic on the Chengshang Village site using field tests. The study examined different aspects of the impact of road-traffic-induced vibrations on the underground site, including the patterns of vibration attenuation, measures for vibration damping and isolation, and the factors influencing these vibrations. The peculiarity of this research is that it uses the gathered data to delineate the correlation between vibration velocity and the distance from the vibration source. Additionally, it aims to determine the optimal distance between the artifacts and the road to guarantee the safety of the artifacts. The study's findings may serve as a significant reference point for traffic planning and preserving underground cultural sites.
## 3 Results and Discussion
### Phenolics Content and Antioxidant Capacity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Ash**} & \multirow{2}{*}{**Protein**} & **Total Dietary** & \multirow{2}{*}{**Fat**} & \multirow{2}{*}{**Carbohydrates**} \\  & & & **Fiber** & & \\ \hline CP & 1.62 \(\pm\) 0.010 [B] & 6.76 \(\pm\) 0.01 [A] & 57.20 \(\pm\) 0.24 [B] & 3.72 \(\pm\) 0.02 [B] & 30.7 \(\pm\) 0.08 [A] \\ WF & 0.73 \(\pm\) 0.001 [A] & 12.74 \(\pm\) 0.02 [B] & 2.83 \(\pm\) 0.10 [A] & 1.69 \(\pm\) 0.02 [A] & 83.01 \(\pm\) 0.12 [B] \\ \hline C & 0.928 \(\pm\) 0.003 [a] & 12.98 \(\pm\) 0.01 [c] & 3.56 \(\pm\) 0.16 [a] & 1.81 \(\pm\) 0.12 [a] & 80.73 \(\pm\) 0.27 [a] \\ CP1 & 0.935 \(\pm\) 0.002 [ab] & 12.91 \(\pm\) 0.01 [bc] & 4.59 \(\pm\) 0.09 [b] & 2.06 \(\pm\) 0.05 [b] & 79.51 \(\pm\) 0.11 [b] \\ CP2 & 0.942 \(\pm\) 0.008 [bc] & 12.86 \(\pm\) 0.02 [bc] & 5.69 \(\pm\) 0.16 [c] & 2.22 \(\pm\) 0.03 [c] & 78.30 \(\pm\) 0.16 [c] \\ CP3 & 0.952 \(\pm\) 0.003 [cd] & 12.82 \(\pm\) 0.04 [bc] & 6.44 \(\pm\) 0.07 [d] & 2.29 \(\pm\) 0.03 [cd] & 77.50 \(\pm\) 0.08 [d] \\ CP4 & 0.956 \(\pm\) 0.002 [de] & 12.72 \(\pm\) 0.02 [ab] & 7.37 \(\pm\) 0.12 [e] & 2.42 \(\pm\) 0.02 [de] & 76.52 \(\pm\) 0.10 [e] \\ CP5 & 0.964 \(\pm\) 0.002 [ef] & 12.61 \(\pm\) 0.17 [a] & 8.52 \(\pm\) 0.20 [f] & 2.48 \(\pm\) 0.01 [e] & 73.43 \(\pm\) 0.13 [f] \\ CP6 & 0.974 \(\pm\) 0.003 [f] & 12.64 \(\pm\) 0.09 [a] & 9.27 \(\pm\) 0.11 [b] & 2.56 \(\pm\) 0.02 [e] & 72.56 \(\pm\) 0.16 [g] \\ \hline \hline \end{tabular}  
* CP—chokeberry pomace, WF—wheat flour, C—control bread, CP1, CP2, CP3, CP4, CP5, CP6—bread enriched with 1, 2, 3, 4, 5, and 6% of chokeberry powder, respectively; the values designated by the different letters [A], [B] or [a] are significantly different (\(p\) < 0.05).  
\end{table} 
Table 5: Basic composition [% DM] of raw materials and bread samples.