esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 3 Optimal Configuration Analysis
### Static Emitter Scenario
Since \(\mathbf{J}\) is a two-dimensional symmetric positive definite matrix, according to the Courant-Fischer-Weyl principle, the equation holds when \(\mathbf{J}\) is diagonal and has equal eigenvalues. Hence it implies that  
\[\mathbf{J}=\lambda\mathbf{I}. \tag{34}\]  
As for the \(\mbox{\it Tr}(\mathbf{J})\), we can obtain  
\[\mbox{\it Tr}(\mathbf{J})=\eta_{1}-\eta_{2}\left[\left(\sum_{i=1}^{M}\cos( \theta_{i})\right)^{2}+\left(\sum_{i=1}^{M}\sin(\theta_{i})\right)^{2}\right] \leq\eta_{1}. \tag{35}\]  
Combining Equations (34) and (35), we get  
\[ \begin{array}{l}\sum\limits_{i=1}^{M}\cos(\theta_{i})=0\;,\;\sum\limits_{i= 1}^{M}\sin(\theta_{i})=0\\ \sum\limits_{i=1}^{M}\cos(2\theta_{i})=0\;,\;\sum\limits_{i=1}^{M}\sin(2\theta _{i})=0\end{array} . \tag{36}\]  
As is known from the formulas above, when the distance between each receiver is identical, the measurement accuracy depends on the included angle \(\theta_{i}\) between each receiver and the emitter. Therefore, it can be called the angle rule for the optimal configuration. \(\square\)  
Figure 1 shows \(\mbox{\it Tr}(\mathbf{J})\) when \(M=3\) and \(\eta_{1}=7\), where \(A=\theta_{2}-\theta_{1}\), \(B=\theta_{3}-\theta_{1}\) and \(A+B\leq 2\pi\). At this time, when \(A=2\pi/3\) and \(B=2\pi/3\), \(\mbox{\it Tr}(\mathbf{J})\) has the only maximum value. Figure 1: 3D plot of the information function \(\mbox{\it Tr}(\mathbf{J})\) for three sensors. (**a**) The value of \(\mbox{\it Tr}(\mathbf{J})\); (**b**) The contour plot of \(\mbox{\it Tr}(\mathbf{J})\). When \(M\geq 3\), it is proven that the receiver distribution with uniform angular arrays (UAAs) can meet the above conditions [9]:  
\[\theta_{i}=\theta_{0}+\frac{2\pi}{M}(i-1),\ i=1,2,\ldots,M, \tag{37}\]  
where \(\theta_{i}\) is any constant given on \([0,2\pi M/(M-1))\). Figure 2 shows the optimal receiver geometries for \(M=3\), \(M=4\), and \(M=5\). When \(M=4,5\), UAAs distribution method is the unique solution of Equation (38). For \(M\geq 6\), even though the optimal deployment is still given by partitions of appropriate angle each with UAA distribution, the UAAs distribution method is an optimal solution. **Remark 1**. : _The Cramer-Rao bound \(\mathbf{J}^{-1}\) under different distribution methods is a function of the receiver-emitter distance and angle. The optimal distribution method is to approach the distance lower bound \(r_{0}\), according to the distance rule and select a good angular separation according to the angle rule._  
For the case of arbitrary distances and angles, getting an analytic solution for the receiver-emitter geometry problem may be impossible. Some optimization algorithms can be applied to acquire a local solution.
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 3 Experimental Results  
### Experimental Setup  
In this section, we compare the proposed ITFDS scheduling algorithm with some related methods, namely PSS [35] and QuAS [39], for real-time and non-real-time applications. Every user equipment packet could be scheduled to the queue for transmission through the proposed LTE downlink scheduling technique. The frequency domain is used for the utilization of bandwidth in an efficient manner. Table 4 shows parameters for simulation.  
\begin{table}
\begin{tabular}{l l} \hline \hline Available Bandwidth & 20.0 MHz \\ Amount of RB’s & 100 (12.0 sub-carrier per PRB) \\ Cell assortment & 2 Km \\ Number of UE’s & 30.40,50, 70, 90,110,130,150 \\ Model & Random \\ Speed & 3 km/hr,30 Km/hr,120 km/hr \\ GBR Flows & 8.4 kbrps for voice and H.264 for videos \\ Non GBR Flows & 12 Kbrps \\ Simulation time & 180 s \\ Simulation round & 100 \\ Head of Line Delay (HOL) & 0.1 s \\ UE speed limit & 100 km/h \\ Bandwidth & 25 MHz \\ Total amount of eNodeB & 3 \\ Duration & 55 TTI \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for the simulation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Modulation Type**} & & \\
**CQI** &
\begin{tabular}{c} **(00- QPSK,** \\ **01-16 QAM,** \\ **02-64 QAM)** \\ \end{tabular} & **Actual Coding Rate** & **Required SINR** \\ \hline
1 & 00 & 0.07618324 & \(-\)4.46 \\
2 & 00 & 0.11795332 & \(-\)3.75 \\
3 & 00 & 0.18848214 & \(-\)2.55 \\
4 & 00 & 0.30078125 & \(-\)1.15 \\
5 & 00 & 0.48730469 & 1.
## 2 Case Study: Energy Harvesting Floor  
### Theoretical Analysis  
The external energy to charge the battery or capacitor is given by Equation (1),  
\[E_{ext}=\frac{1}{2}\cdot C_{ext}\cdot V_{ext}^{2}\left[\text{J}\right] \tag{1}\]  
where \(V_{ext}\) is the external voltage, which charges the external capacitor; and \(C_{ext}\) is the external capacity.  
The load power is obtained by Equation (2),  
\[P=\frac{E_{ext}}{t}\quad\left[\text{W}\right];\;\;P=l^{2}\cdot R_{load}\left[ \text{W}\right] \tag{2}\]  
where \(R_{load}\) is the resistance of the load, or the internal resistance of the battery; \(t\) is the time; and \(I\) is the current.  
The theoretical analysis assumes that all the force is transmitted to the transducer and that only one transducer is used. A prototype was built to test it in a real case study to address the non-linearity of the system.  
Piezoelectric devices are simple in terms of components, geometry, and conversion of mechanical energy to electricity [15]. [PERSON] [7] divided the energy harvesting process into the following three phases, showing the performance in each transformation of the energy:  
* Mechanical-mechanical energy transfer: This refers to the constructive aspects of the piezoelectric, for example, the maximum possible displacement without damaging it, supported stress, etc. [PERSON] showed that 87% of the mechanical energy could be transmitted from the source to the piezoelectric transducer.
* Mechanical-electrical energy transduction: This is related to the electromechanical coupling factor between structure and transducers. [PERSON] showed that the energy conversion rate was between 2% and 9%, depending on the electromechanical coupling factor parameter.
* Electrical-electrical energy transfer: It refers to the current conversion to transfer the energy to a load or battery. This has been divided into two phases as follows:
* Transducer to harvesting circuit: Previous research has demonstrated that the difference of impedance between the piezoelectric and the harvesting circuit has resulted in only 57% of the energy being conserved in this step.
Harvesting circuit to rechargeable battery: The reduction of energy in this phase is motivated by the energy consumption of the circuit and the mismatch between the electrical impedance of the circuit output and the battery input impedance. The study showed that 81% of the energy could be transmitted in this phase.  
The paper showed that the total current energy harvesting rate from the vibration source to the storing battery was about 3.6%.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
###### Abstract  
The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver-vehicle unit is greatly enhanced. With V2X technology, the driver-vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle's energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.  
vehicle-to-everything technology; traffic flow theory; car-following model; traffic information and control; intelligent and connected vehicle
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 4 Results
### Evaluation of Filtering Based on Staring Measurements
#### 4.1.2 Comparison of LiDAR and Anemometer Velocity Measurements
With the exception of the combined filter approach from [PERSON] et al. (2017) that was able to reduce the average velocity standard deviation error to 0.0 m/s, both dynamic data filters generated the smallest error in the comparison of three out of four error calculation categories. To give an overview of the overall performance, we distinguish between all wind directions in Table 1, wake affected situations, 110-180\({}^{\circ}\) wind direction, and free inflow, 180-210\({}^{\circ}\) wind direction, in Tables A1 and A2 in the Appendix C. In each of those data classifications, we see mostly a similar behaviour of the filters in mutual perspective as well in relation to the results in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Dyn. Data** & **Dyn. Data** & **CNR** & **Std.** & **Iter.** & & & & \\  & **Hist.** & **Gauss.** & **Threshold** & **Dev.** & **Std** & **Quartile** & **Wang** & **Newman** & **Unfiltered** \\ \hline
**Velocity** & & & & & & & & & \\   Reg. slope & 0.92 & 0.95 & 0.96 & 0.92 & 0.91 & 0.93 & 0.97 & 0.97 & 0.86 \\   Offset [m/s] & 0.31 & 0.26 & 0.22 & 0.33 & 0.32 & 0.32 & 0.22 & 0.22 & 0.44 \\   R\({}^{2}\) & 0.85 & 0.84 & 0.90 & 0.83 & 0.79 & 0.85 & 0.90 & 0.90 & 0.78 \\ \hline
**Velocity std. dev.** & & & & & & & & & \\   Reg. slope & 1.50 & 1.39 & 0.73 & 1.27 & 1.05 & 0.86 & 0.50 & 0.62 & 3.04 \\   Offset [m/s] & 0.42 & 0.33 & 0.63 & 0.45 & 0.74 & 0.49 & 0.50 & 0.58 & 0.08 \\   R\({}^{2}\) & 0.06 & 0.04 & 0.02 & 0.04 & 0.02 & 0.02 & 0.01 & 0.01 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlations and residuals of the linear regression between the ultrasonic anemometer and the LiDAR for the velocity and the standard deviation of the velocity. From 21.12.2013 15:35 h (UTC) till 19.01.2014 7:55 h (UTC) for all wind directions. Figure 14: Histogram in double logarithmic scaling with exponential increasing bin width of the **(a)** absolute average velocity error and **(b)** the absolute velocity standard deviation error. Vertical dashed lines indicate the centre of a fitted Gaussian curve.
## 4 Results  
The first step of the data processing is the sensor georeferencing, described in Section 2.3. Figure 10 shows the arrangement for one sensor using the calibration board at five different locations; the points captured by the sensor in the sensor coordinate system are shown in red, and the red lines are the surface norms of the boards calculated from these points. The blue lines are the norms of the boards at the five locations in the global coordinate system; these norms are derived from the board corners (blue dots) measured with a total station. Note that the global coordinate system is connected to the local coordinate system of which origin is the sensor position calculated with GPS. The transformation parameters between the two systems are estimated based on Equation (1). In the Figure 10, the green color depicts the boards after transforming the sensor points into the global frame. The standard deviation of the residuals of the parameter estimation is 0.3\({}^{\circ}\).  
Once all individual sensor data were transformed into the global coordinate system, the dynamic content, i.e., the aircraft body, is separated from the static background according to Section 2.3. Three algorithms listed in Table 3 are compared for Test #1. The COG algorithm is a straight-forward model-free object tracking method. In our implementation, the COG points for each scan is calculated as the average (center of gravity) of the object points. Then, the trajectory is derived with, first, smoothing the COG points with a Gaussian filter, and then, resampled with spline interpolation in order for densifying the trajectory points. The second investigated method is the model-based volume minimization introduced in Section 2.6; a 2D CA motion is assumed due to the taxiing scenario in Test #1. The third CT algorithm uses the output of a VM algorithm assuming CV motion as initial reconstruction. Then, the CT algorithm refines this solution by estimating a 2D fourth-degree polynomial motion model. Figure 11 illustrates the points of the airplane body at different times  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Test Case**} & \multicolumn{1}{c}{**Goal**} & \multicolumn{1}{c}{**Sensors**} & \
\begin{tabular}{c} **Field of** \\ **View *** \\ \end{tabular} &
\begin{tabular}{c} **Object-Sensor** \\ **Distance** \\ \end{tabular} \\ \hline
**\#1** & **Taxing** & 2D performance at taxiing &
\begin{tabular}{c} 3 vertical VLP-16s, \\ 1 horizontal HDL-32E \\ \end{tabular} & 70 m & 20 m \\ \hline
**\#2** & **Landing** & 3D performance at landing &
\begin{tabular}{c} 4 vertical VLP-16s, \\ 1 horizontal HDL-32E \\ \end{tabular} & 100 m & 33 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the field tests (* projected to the runway centerline).  
Figure 9: Test sensor configurations: (**a**) Test #1 sensor network (taxing), and (**b**) Test #2 sensor network configuration (landing).
## 5 Discussion
### Seismic Hazard Potential  
The latest surface-rupturing event revealed in the HQB trench indicates a minimum vertical displacement of approximately 1 m per single event. The activity of the JRNF haslargely shaped the boundary between the bedrock mountain range and the basin. It is inferred that the rupture length during surface-ruptturing events is consistent with its morphological extent along the mountain front, approximately 50 km. The empirical magnitude estimation formula for normal faults from [PERSON] and Coppersmith [49] is as follows:  
\[\mathrm{M_{w}}=4.86+1.32\times\mathrm{log}\mathrm{D} \tag{1}\]  
The magnitude of a surface-ruptturing earthquake on this fault is estimated to be approximately M\({}_{w}\) 7.1. Additionally, as M\({}_{w}\) relies on surface waves, it is not suitable for deeper earthquakes, and the M scale was validated specifically for Southern California, not globally. The magnitude was further estimated according to the relationship between the moment magnitude Mw and the seismic moment M\({}_{0}\), using the formula proposed by [PERSON] and [PERSON] [50], as follows:  
\[\mathrm{M_{w}}=2/3\times\mathrm{log}\mathrm{M_{0}}-10.7 \tag{2}\]  
At the same time, due to the smaller scale of the JRNF, it may not be applicable to the estimation formula by [PERSON] [49]. [PERSON] et al. developed a generalized seismic moment magnitude scale M\({}_{w}\) to improve the consistency of M\({}_{w}\) for a wider range [51,52]. The M\({}_{w}\), M\({}_{0}\), and M\({}_{w}\)g connection can be made as follows:  
\[\mathrm{M_{w}}=\mathrm{log}\mathrm{M_{0}}/1.36-12.68 \tag{3}\]  
Based on M\({}_{w}\), it replaces the traditional M\({}_{w}\) scale to ensure superior accuracy, particularly for intermediate and smaller earthquakes [52], from which we derived a magnitude of M\({}_{w}\)g 6.95.  
Since the JNRF ceased activity only before the Late Pleistocene at 15.5 \(\pm\) 0.9 ka, it may still be classified as an active fault based on the age criteria of most researchers [53, 54, 55, 56, 57]. Although the fault is not very close to the densely populated area of Ulanqab City, its potential seismic hazard cannot be overlooked. Currently, the human-used land within 2 km of JRNF is mainly agricultural land (Figure 2B), and future construction should still avoid the fault zone.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 3 Methodology
### Temporal Sparse Attention (TSA)
#### 3.1.3 Contextual Feature Compression  
Concatenation  
It is a process of placing several vectors next to each other and combining them into a single vector.  
The output for each head has the following form as a weighted sum of value vectors:  
\[\text{out}_{(h)}=\alpha_{h}V_{h} \tag{8}\]  
Outputs from all heads are concatenated and then passed through a final linear projection:  
\[TSA_{\text{features}}=W_{o}\,\text{concat}(\text{out}_{1},\dots,\text{out}_{h} )+b_{o} \tag{9}\]  
TSA Module Pseudocode: Algorithm 1 describes the TSA module.  
```
1:\(d_{k}\leftarrow\)hidden_dim/num_heads
2:# Compute queries, keys, and values
3:\(Q\leftarrow\)Linear(\(x\_embed,W_{Q},b_{Q}\))
4:\(K\leftarrow\)Linear(\(x\_embed,W_{K},b_{K}\))
5:\(V\leftarrow\)Linear(\(x\_embed,W_{V},b_{V}\))
6:# Reshape for multi-head attention: (batch, num_heads, seq_len,\(d_{k}\))
7:\(Q\leftarrow\)ReshapeAndTranspose(\(Q,\)num_heads,\(d_{k}\))
8:\(K\leftarrow\)ReshapeAndTranspose(\(K,\)num_heads,\(d_{k}\))
9:\(V\leftarrow\)ReshapeAndTranspose(\(V,\)num_heads,\(d_{k}\))
10:# Compute scaled dot-product attention scores
11: Scores \(\leftarrow\)MatMul(\(Q,\)Transpose(\(K\)))/\(\sqrt{d_{k}}\)
12:# Apply sparse attention selection per head
13:for each head in \([1,\)num_heads\(]\)do
14:for each sequence position \(i\) in \([1,\)seq_len\(]\)do
15:\(\max\_\)score \(\leftarrow\)\(\max\)(Scores[head,\(i,:\)])
16: Threshold \(\leftarrow\)\(0.5\times\max\_\)score
17:for each position \(j\) in \([1,\)seq_len\(]\)do
18:if\(\text{Scores}[\text{head},i,j]<\text{Threshold}\)and\(j\
eq\text{arg}\max(\text{Scores}[\text{head},i,:])\)then
19:\(\text{Scores}[\text{head},i,j]\leftarrow-\infty\)
20:endif
21:endfor
22:endfor
23:endfor
24:# Compute attention weights via softmax
25:attention_weights \(\leftarrow\)Softmax(Scores, axis \(=-1\))
26:# Compute weighted sum of values
27:out \(\leftarrow\)MatMul(attention_weights, \(V\))
28:# Concatenate heads and apply final projection
29:out \(\leftarrow\)TransposeAndReshape(out, hidden_dim)
30:\(\text{Tsa\_features}\leftarrow\)Linear(out, \(W_{o},b_{o}\))
31:returnTsa_features
```  
**Algorithm 1** TSA_Module (\(x\_embed\), num_heads, hidden_dim)
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 5 Simulations and Parameter Effects
### Simulation Results
#### 5.2.4 Network Lifetime  
An IoT network or WSN is composed of a variable number of sensors that are deployed in a sensing area. The constrained battery capacity of the sensor nodes is a major factor that impacts the lifetime of such a network [35]. To ensure network continuity, a message should be disseminated evenly among the network nodes. Figure 5 plots the effects of the examined MFWF parameters on the network lifetime, reflecting the ability to ensure even energy dissemination among network nodes as their number increases from 50 to 250. This evaluation metric is represented in the plot as the number of rounds until the death of the last node in the network.  
The lifetime of the simulated network increases linearly as the number of nodes increases from 50 to 250 for each of the MFWF parameters. This shows that as the number of nodes in the network increases, the network has a higher chance of staying alive longer because more options for dissemination are presented. The longevity of the network is most evident when the next-node distance parameter (\(f2\)) is controlled, where large variations can be seen as the number of nodes grows to 250. This is because a node consumes less energy when sending a message to a neighbouring node  
Figure 4: The impact of parameter activation on the average percentage of saved rebroadcasts as the number of nodes increases from 50 to 250 nodes.  
that is closer to it, and hence, this prolongs the lifetime of the node and the network. The impacts of the node density (\(f4\)) and sink distance (\(f3\)) on the network lifetime are similar, with the activation of the node density parameter (\(f4\)) outperforming that of the sink distance parameter (\(f3\)) as the number of nodes increases to 250. However, this increase is not as evident as that seen with the activation of the next-node distance parameter (\(f2\)). The residual energy parameter (\(f1\)), by itself, has no effect on the lifetime of the network. By contrast, the activation of the message priority parameter (\(f5\)) is seen to be fruitful, as the network lifetime increases with a growing number of nodes in the network.
# Physiological and Behavioral Changes of Passive Fatigue on Drivers during On-Road Driving
## 4 Discussion
### Research Question 3: Measurements for Passive Fatigue in Different Stages  
The third research question asked about indicator changes for the early versus late stage of passive fatigue. This question is not only important to uncover the theoretical progressive process of passive fatigue, but also comes from urgent industry needs for the development of a Driver Monitor System and the development of machine learning algorithms.  
Fatigue detection indicators can be categorized into four types, according to machine learning needs and the pairwise comparisons in the last three columns of Table 4, that is, early indicators and late indicators, or short-period indicators and long- period indicators.  
Early indicators are changes observed at early stage of fatigue, often minor levels of fatigue, for example, after relatively short driving time, or when SSS = 3 with a label of \"_Awake, but relaxed; responsive but not fully alert_\" [21]. Early indicators are operationally defined as changes occurring before the first 60 min, when Stage I vs. II are significant in Table 4. In contrast, late indicators are changes observed at late stage of fatigue with severe levels of fatigue, for example, when SSS = 6 (\"Sleepy, woozy, struggling to sleep; prefer to lie down\") or 7 (\"No longer struggling to sleep\"). Late indicators are operationally defined as changes occurring after 60 min, when Stage II vs. III are significant in Table 4. Early indicators should be used if the DMS users are conservative or the losses from fatigue are extremely high, for example, a vehicle carrying explosives. Late indicators are recommended for users who are liberal, or the risks of fatigue are low, or when false alarms of the DMS are bothersome. As summarized in Table 4, steering controls (measuredin standard deviation of steering angle and steering hold frequency) are early indicators; vertical gaze dispersion and speed measurements (both mean speed and speed compliance) are late indicators.  
The categorization of short-period indicators and long- period indicators is based on the time windows in which data are averaged over. Short-period indicators refer to indicators which are significant when comparing short time windows, but not significant anymore if larger time windows are compared. Short-period indicators are often behaviors occurring less frequently, such as steering control on highway. Long-period indicators are data which are significant when averaged and compared after a large time window. In this study, short-period indicators are operationally defined as indicators which are significantly when comparing either Stage I vs. II or stage II vs. III, but insignificant when comparing Stage I vs. III. According to Table 4, steering controls (measured in standard deviation of steering angle and steering hold frequency) and vertical gaze dispersions are short-period indicators; speed measurements (both mean speed and speed compliance) are long-period indicators.