esa-sceva/satcom-chunk-collection huggingface dataset:
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 2 Zero-Copy DMA Technology Overview
### Dpdk  
DPDK [13] perfectly supports multi-queue and multi-core scaling and is the best known open-source zero-copy I/O technology in terms of performance [14]. Its high-performance implementation by (1) PMD (Poll Mode Driver). I/O threads periodically poll for packet arrival marker bits, thus avoiding the overhead of context switching introduced by interrupts. (2) The UIO [24] (Userspace I/O) driver, which intercepts NIC interrupts and resets the callback function corresponding to the interrupt, maps the operation on the hardware device to an operation on the file in userspace, circumventing the process of user state and kernel state switching and CPU copying. (3) In HugePage access, the larger page size means the lower number of page table levels and the lower number of entries in the TLB (Translation Lookaside Buffer). Therefore, reducing the number of memory accesses during the CPU address translation and increasing the probability of TLB hits when accessing memory. (4) Affinity exclusivity binds threads to a CPU core with high affinity to realize RTC (run to complete) mode, which avoids frequent switching of processes or threads between different cores and reduces the overhead of memory accesses.  
Typical applications of DPDK include DPDK vSwitch (accelerated ovs) [25] and xDPd (high-performance software switching method) [26], among others. The feature comparison in Table 1 shows that DPDK offers advantages that other DMA zero-copy technologies cannot match from the deployment, development, and performance perspectives.
# Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework
## 5 Results and Discussion
### Comparison with Other Trace Clustering Techniques
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Avg.\(N\)** & **Avg.\(A\)** & _Avg\({}_{CD}\)_ & \(D\) & **Avg.\(CN\)** & **Avg.\(CNC\)** & **Avg.\(CNC_{k}\)** \\ \hline \multirow{6}{*}{**CDP**} & **NoHiC** & 580 & **915.67** & 3.06 & 0.003 & **336.67** & **1.58** & 0.05 \\ \cline{2-8}  & **ActiTracC** & **571.18** & 993.36 & 2.85 & **0.01** & 423.18 & 1.74 & 0.06 \\ \cline{2-8}  & **K-Gram\_AHC** & 707.25 & 1311.75 & **2.93** & 0.004 & 425.5 & 1.6 & 0.05 \\ \cline{2-8}  & **TR\_AHC** & 816.25 & 1311.25 & 3.22 & 0.001 & 496 & 1.61 & 0.04 \\ \cline{2-8}  & **MR\_AHC** & 789.25 & 1255.75 & 3.19 & 0.002 & 467.5 & 1.59 & 0.04 \\ \cline{2-8}  & **SMR\_AHC** & 814.75 & 1293 & 3.14 & 0.001 & 479.25 & 1.59 & 0.04 \\ \cline{2-8}  & **NSMR\_AHC** & 697 & 1121.5 & 3.11 & 0.003 & 425.5 & 1.61 & 0.05 \\ \hline \multirow{6}{*}{**BPI**} & **NoHiC** & **24** & **41.75** & **3.47** & 0.06 & **18.75** & **1.74** & **0.27** \\ \cline{2-8}  & **ActiTracC** & 32.73 & 59.91 & 3.6 & **0.05** & 28.18 & 1.83 & 0.24 \\ \cline{2-8}  & **K-Gram\_AHC** & 26.75 & 50.75 & 3.47 & 0.06 & 25 & 1.9 & 0.27 \\ \cline{2-8}  & **TR\_AHC** & 29.5 & 57.75 & 3.74 & 0.06 & 29.25 & 1.96 & 0.26 \\ \cline{2-8}  & **MR\_AHC** & 28.5 & 50.75 & 3.47 & 0.06 & 25 & 1.9 & 0.27 \\ \cline{2-8}  & **SMR\_AHC** & 26.25 & 47 & 3.59 & 0.06 & 23.5 & 1.92 & 0.28 \\ \cline{2-8}  & **NSMR\_AHC** & 35 & 71.25 & 4.05 & 0.05 & 37.25 & 2.04 & 0.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the complexity metrics for discovered clusters through NoHiC, ActiTracC and AHC trace clustering techniques. Figure 18: Comparison of lowest, average and highest fitness of the clusters generated by three trace clustering techniques. NoHiC generated the clusters which are meaningful in the data and understandable to the business users because these clusters correspond to the business related features. The NoHiC technique also produced clusters with the best average number of classes per cluster, presented in Figure 19. K-gram feature selection using AHC and ActiTraC produced clusters with the highest number of classes, thus reinforcing the speghetti-type nature of the model generated. In contract, NSMR_AHC and NoHiC produced clusters with lowest variations in the number of classes per cluster, with averages of 580 and 697 respectively. The above analysis suggests that our NoHiC technique is broadly consistent with the results produced by other algorithms in terms of the intrinsic characteristics of the CDP and BPI datasets such as Cyclomatic number(CN) and Coefficient of connectivity(CNC). However, NoHiC generates an overall lower number of clusters which are more aligned with the features of the business processes represented by each dataset. Also, the NoHiC technique is equally suitable for extremely large process logs exhibiting Contrail-like process.
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 2 Preliminary of Near-Crash Event Identification
### Related Work
[20] introduced a feature extraction framework based on XGBoost and the Fuzzy C-Means algorithms, achieving 89% accuracy in identifying near-crash events within the NGSIM dataset. Further, [PERSON] et al. [21] applied discrete Fourier transforms and K-means clustering to the longitudinal acceleration data from naturalistically driven vehicles, marking distinctive patterns in the acceleration time series that indicate imminent or near crashes, and reducing the false-positive rate to 22%. [PERSON] et al. [22] proposed a classification model based on Extreme Gradient Boosting (XGBoost), utilizing Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for feature engineering, achieving an accuracy of 84.7% and a recall rate of 71.3% in accident identification. However, these models did not account for the temporal characteristics of natural driving data, resulting in suboptimal performance. Upon reviewing the literature, several limitations are identified. Firstly, statistical methods are often used to study the impact of specific factors on the occurrence of near-crash events but lack the ability to effectively identify such events. Secondly, threshold methods require significant human and time resources, and the threshold settings vary across different datasets, making it difficult to establish a unified, standardized framework. Lastly, previous studies have overlooked the time-series characteristics of natural driving datasets using simple machine learning or neural network models. Considering the characteristics of natural driving datasets, this paper proposes a near-crash event identification method based on the CNN-LSTM-Attention model.
## 2 Strength Theory of Unsaturated Soil
### Linear Shear Strength  
In the linear cases, two forms of matric suction distribution are considered: uniform distribution and linear increase with burial depth. [PERSON] et al. [34] introduced a formula for calculating soil strength under uniform matric suction based on the M-C failure criterion:  
\[\tau=c^{\prime}+(\sigma_{n}-u_{a})\tan\varphi^{\prime}+(u_{a}-u_{w})\tan\varphi ^{b} \tag{1}\]  
where \(\tau\) denotes the shear strength of unsaturated soil, while \(\varphi^{\prime}\) and \(c^{\prime}\) represent to the soil's friction angle and cohesion, respectively. \((\sigma_{n}-u_{a})\) represents the net normal pressure, and \((u_{a}-u_{w})\) represents the matric suction, with \(u_{w}\) representing the pore water pressure and \(u_{a}\) representing the pore gas pressure. \(\varphi^{b}\) is commonly referred to as the matric suction angle, which reflects the influence of matric suction on shear strength. According to previous studies [43], \(\varphi^{b}\) is often assumed to have a constant value of \(15^{\circ}\).  
Equation (1) can also be reformulated in the M-C criterion form as follows:  
\[\tau=c+(\sigma-u_{a})\tan\varphi^{\prime} \tag{2}\]  
where the cohesive force, accounting for unsaturated strength components, is redefined as follows:  
\[c=c^{\prime}+(u_{a}-u_{w})\tan\varphi^{b} \tag{3}\]  
Furthermore, apart from the uniformly distributed matric suction, [PERSON] et al. [40] introduced an alternative linear distribution form, depicted in Figure 1. In this model, matric suction linearly increases with the buried depth, commencing from 0 at the groundwater level. This relationship can be described as follows:  
\[\left(u_{a}-u_{w}\right)_{z}=\rho z \tag{4}\]  
In which \(\rho\) represents the constant coefficient for matric suction linearly increasing with depth.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 4 Discussion  
In this section, we first discuss the impact of increasing the satellite number of GACO, PSOCO, and the proposed SSPCO. Then, we discuss the limitations of the proposed method.
## 3 System Model
### Deployment Model and Metrics of Interest  
We assume deterministic Manhattan grid deployment with street width \(l\), see Figure 1. The widths and lengths of building blocks are assumed to be \(b_{w}\) and \(b_{l}\). The height of building blocks is assumed to be a random variable (RV), \(H_{B}\), with probability density function (pdf) \(f_{H_{B}}(x)\). We consider a certain zone of interest having \(M_{V}\) and \(M_{H}\) vertical and horizontal streets, respectively. We further assume that there are \(N\) ground-mounted mmWave BSs located on the streets leading to the spatial density of \(N/\left[M_{V}*(l+b_{l})*M_{H}(l+b_{w})\right]\) mmWave BSs per squared meter. On top of this, we assume that there are \(M\) rooftop-mounted mmWave BS randomly located on the building roofs.  
MmWave BSs are assigned to streets randomly, i.e., first a discrete uniformly distributed RV between 0 and \(M_{H}+M_{V}\) is used to determine the street index, and then the position of mmWave BS is determined by choosing \(x\) or \(y\) coordinate uniformly along the street width, excluding parts occupied by crossroads. Similarly, \(N_{C}\), \(N_{C}<N\), crossroad-installed mmWave BSs are assigned to crossroads randomly using discrete RV uniformly distributed between 0 and \(M_{V}M_{H}\). A particular location of mmWave BS on a crossroad is defined with respect to the left upper corner and is fully determined by the distances \(l_{A,1}\) and \(w_{A,1}\). Similarly, we choose a particular position for BSs installed along the street at the distance \(l_{A,1}\), from the building. Note that in practice these BSs can be installed on lampposts, for example, and distances \(l_{A,1}\), and \(w_{A,1}\) may coincide with the sidewalk width.  
The UAV attitude is assumed to be constant, \(h_{R}\). We assume that UAV is in coverage of BS if there is a LoS path between UAV and BS and this path is less than a certain \(r\). UAV is assumed to cross this region following a random line at the constant speed \(v_{U}\). We are interested in the UAV coverage probability-the probability that UAV is in coverage of at least one BS.
## 4 Selenosugars: How Selenium May Be Bound to the Carbohydrate Structure
### Selenopyranoses and Selenofuranoses  
Selenopyranoses and selenofuranoses are cyclic forms of carbohydrate derivatives, with the ring oxygen replaced by a selenium atom: a polyhydroxytetrahydroselenophene or selenane derivatives [224].  
In general, selenopyranose and selenofuranose derivatives are obtained by chemical synthesis, starting from appropriate monosaccharides [225; 226; 227]. Most of them were synthesized as potent water-soluble selenium derivatives able to act as potential antioxidants with expected good oral bioavailability [228; 229].  
Numerous selenosugars have also been investigated for their capacity as glycosidase inhibitors. Glycosidase catalyze the hydrolysis of glycosidic linkages and are suitable targets for development of anti-type 2 diabetes, anticancer, and antiviral agents [230; 231; 232]. The selenosugars bearing selenium atoms in their ring positions, synthesized by [PERSON] and [PERSON] (2019) as part of the program aiming at developing powerful water-soluble antioxidants, are particularly interesting [233]. Several of them, as expected, proved to be powerful antioxidants. 1,4-anhydro-4-seleno-D-tallitol (Figure 1c) seems to have a privileged structure: it acts not only as an efficient scavenger of multiple biologically important oxidants, but also exhibits the ability to repair damaged skin tissue, including accelerating healing of diabetic wounds, and is capable of decreasing endothelial dysfunction arising from diabetes [233].  
Se-nucleosides are a specific group of carbohydrates with selenium bound in the sugar backbone. They cannot be converted into the corresponding nucleotides due to hampered  
Figure 1: Structures of the trahydroselenophene (**a**), selenane (**b**), and some derivates: 1,4-anhydro-4-seleno-hexofuranose (1,4-anhydro-4-seleno-D-tallitol) (**c**) and 1,5-anhydro-5-seleno-hexopyranose (1,5-anhydro-5-seleno-D-mannitol) (**d**).  
capacity for enzymatic phosphorylation. Most of the Se-nucleosides are biologically not active; however, the selenonucleoside developed by [PERSON] et al. showed antitumor activity against paclitaxel-resistant prostate cancer [234; 235]. Regarding the selenopyranose derivatives of natural origin, the polysaccharides containing selenium atoms in their ring positions were investigated using the XAS (X-ray absorption spectroscopy) method [39].
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 4 Performance Analysis of the FH-DOWR Model
### The Combinational Error of FH-DOWR
 \tag{30}\]  
Equations (29) and (30) show that the relative motion between satellites A and B can cause clock correction errors. Consider that the clock difference \(\Delta t\) is 0.1 s and \(|v(t)|\leq 3000\) m/s. Taking satellite B as an example, when the clock difference is not adjusted, the distance measurement error and clock correction error are shown in (31):  
\[ \begin{cases}\delta D_{\rm A}=|0.5\overline{v}\Delta t|\leq 150\;{\rm m}\\ \delta\Delta t_{\rm A}=\left|\frac{\overline{\sigma}\Delta t}{2\overline{\sigma }}\right|\leq 0.5\;\upmu\text{s}\end{cases} \quad(|\overline{v}|\leq 3000\;{\rm m /s},\Delta t\leq 0.1\;\upmu\text{s}). \tag{31}\]  
According to (29), due to the unadjusted clock difference between satellite B and satellite A, the relative motion can cause significant distance measurement and time synchronization errors. If clock bias adjustment is implemented after each time comparison is completed, then the distance measurement error and clock correction error are shown in (30):  
\[ \begin{cases}\delta D_{\rm A}^{1}=|0.5\overline{v}\Delta t|\leq 0.15\;{\rm mm }\\ \delta\Delta t_{\rm A}^{1}=\left|\frac{\overline{\sigma}\Delta t}{2\overline{ \sigma}}\right|\leq 0.5\times 10^{-3}\;{\rm ns}\end{cases} \quad(|\overline{v}| \leq 3000\;{\rm m/s},\Delta t\leq 0.1\;\upmu\text{s}). \tag{32}\]  
The residual clock difference is \(\delta\Delta t_{\rm A}^{1}\). From (30), after adjusting the clock difference between two satellites to within 0.1 us, the distance and time synchronization errors between satellites caused by relative motion are extremely small and can be ignored.
## 3 Results  
Three-dimensional motion analysis of selected parameters revealed that acceleration was not a reliable parameter for distinguishing senior falls from the common daily activities, especially there was no significant difference for maxima of acceleration between downstairs and fall-down (sit-fall) (\(p>0.05\)). However, speed maximum was identified as a reliable parameter. Comparing the daily activities to the fall situations (Figure 7), a highly significant difference (\(p<0.000\), the adjusted \(p\)-value using Bonferroni for multi-comparisons is \(p=0.004\) [\(p=0.05/12=0.004\)]) was observed in each marker of interest. The speed maxima reached during fall situations were 2-4 times that of daily activities.  
Figure 6: Markers of interests—possible positions under investigation for wearable-sensors: (**left**): back view of the possible positions; (**right**): the front view of the possible positions.  
Figure 7: Speed maximum (mean and StDev [standard deviation]) at each marker of interest for daily activities and fall situations.  
Further, the statistic tests indicated that LSho and RSho markers could be used to identify fall directions. For a fall left, the speed maximum of RSho was significantly high than that of LSho (\(p<0.05\)), vice versa for a fall right. For the remaining fall situations, there were no significant differences (\(p>0.05\)).  
In general, falls finished within one second. There were obviously differences among the durations across all fall types (Table 1). The shortest fall was found to be fall down (i.e., sit fall), taking about 0.7 s; while the longest fall, i.e., fall left, needed about 0.9 s to finish.  
The speed maxima occurrence in relation to elapsed time can be seen in Table 2. It was concluded that max speed happened almost at the end of a fall (\(\sim\)90% of the duration). Further analysis showed that, when the maximum (i.e., 2.08 m/s) of all speeds measured during the common daily activities as a threshold value, the percentage of time elapsed dropped to \(\sim\)60% of the duration.  
In summary, due to insignificant differences of maximal acceleration between some falls (e.g., fall-down/sit-fall) and non-falls (e.g., downstairs), acceleration could not reliably tell if there is a fall or a daily activity. When maximal speed during falls is used, the identification problem is solved; but, there is not enough time left for inflating an airbag. The issue could be solved by applying the maximal speed measured in daily activities as a threshold value. For identifying fall directions, the two reliable markers (locations of sensors) are the left and right shoulders.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 4 Simulation Results and Analysis
### Path Planning in Unknown Environments  
Nowadays, Lidar, ultrasonic and stereo cameras are widely used on USVs to sense the surrounding environment and obstacles during autonomous navigation. However, the detection range of sensors is limited from several meters to hundreds of meters. Therefore, it is difficult to obtain the full environmental information in advance to plan a global path for USVs before autonomous navigation. This means that, each time, the USV can only plan a local path based on the surrounding environment information sensed by the USV.  
Here, we assume that the sensor's detection radius is 100 pixels, and the detection angle is 360 degrees. Path planning is performed from a start node to a goal node in an unknown environment. The detection range and local path planning are shown in Figure 17.  
Figure 16: Multi-goal path planning in a dynamic environment.  
The straight blue line in Figure 17 is the initialized path assuming that the entire unknown environment is a free space. The purple circles are the detection range for each time when the USV moves to the next sub-goal node in an unknown environment. Starting from the start node, the USV acquires obstacle information from the sensors within the first detection range (prob_1). Because the USV does not detect obstacles until it reaches position \(b\), the USV follows the initial planned path and moves to position \(b\). Due to obstacles, the USV slightly turns to the right and moves to the boundary of the first detection range, which is also the center of the second detection range (position \(c\) in Figure 17(a),b). The USV detects surrounding information within the second detection range (prob_2), and moves from position \(c\) to position \(c\), which is at the boundary of the second detection range and close the obstacle (Figure 17(b)). Then, the USV continues to move from position \(d\) to position \(e\) along with the obstacle. The USV continues to collect obstacle information within the fourth detection range (prob_4) and plans a local path from position \(e\) to \(g\) (Figure 17(d)). In this way, the USV can finally reach the destination goal node. Figure 19 shows the full path planned with the improved D* Lite for the USV in the unknown environment.  
Figure 17: Detection range in unknown environments.
# Acoustic Environment and Noise Exposure in Fitness Halls
## 2 Methods
### Reverberation Time Measurements  
Reverberation time is the key factor in characterizing the acoustic environment of a space [42], and is considered a quantity that can be used to control room acoustics [43; 44]. Reverberation time values were determined according to the methodologies described by ISO 3382-2-2008 [45]. Reverberation time, \(T\), was evaluated based on the dynamic range of 20.0 dB(A) and extrapolated to a decay time of 60.0 dB(A). \(T\) values were measured while the IS & G halls were unoccupied. This was done because the presence of people may have a strong influence on measured values. The mid-frequency reverberation time (\(T_{\mathrm{\textit{inf}}}\)) across the frequency spectrum was determined for all halls.  
The measurements of \(T\) were taken using different equipment including an XL2 Acoustic Analyzer (serial no. A2A-14419-E0, FW4.03), NTi Audio M2230 microphone (serial no. 7492), omnidirectional DS3 Dodecahedron loudspeaker as a sound source, and PA3 power amplifier. The XL2 Acoustic Analyzer was calibrated before and after the measurements using a Class 1 94/114 dB(A) SPL calibrator. The calibrator was tested by the Royal Science Society in Amman, Jordan. The produced sound pressure level was sufficient to provide decay curves with the required dynamic range without contamination by background noise. A moving microphone was used, considering that the distance from the microphone position to the nearest reflecting surface, including the floor, was not less than 1.0 m. Reverberation time measurements were taken for one source position, and the average of the results of at least two source position's combinations and 2.0 m around the microphone position were applied. The source position was in the middle of the room, and if there was an irregular room shape, two sound source positions were used. The interrupted noise method was used. The frequency range covered at least 250-2000 Hz. The broadband spectrum was shaped to provide a pink spectrum of steady-state reverberant sound in the enclosure. The measurements of reverberation time were repeated three times at each IS & G hall, and the arithmetic mean was determined. An example of an IS & G hall is presented in Figure 3, illustrating the location of the measurement points of reverberation time, and locations of sound source.  
Figure 2: Layout of the measurement set-up in the fitness gym (P); red stars represent the location of measurement points of activity noise level (\(\mathrm{L_{A,eq}}\)) and blue points represent the locations of the gymnasium’s loudspeakers.
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Performance Based on Energy Consumed  
As shown in Figure 3 the energy consumed per round in the optimized path case are less than the predetermined one and NRCA. Dividing the region into virtual grids with checkpoints in each one helps in reducing the energy consumption in these grids and as a result prolongs the lifetime of cluster heads and preserves the overall energy of the whole network.