esa-sceva/satcom-chunk-collection huggingface dataset:
# Adhesion Promoters in Bituminous Road Materials: A Review
## 1 Introduction  
Bitumen (asphalt cement) is the most common petroleum product used in road construction industry thanks to its high adhesive and waterproofing power. [1; 2]. The term \"asphalt mixture\" is a combination of mineral aggregate, bitumen, and some other inorganic and organic additives [3]. Conventional Hot Mix Asphalt (HMA), which is currently produced at elevated temperatures requiring high energy input, consists of crushed mineral particles that are bonded together by bitumen at working temperatures between 150 to 190 \({}^{\circ}\)C. However, current environmental regulations require a greater reduction in emissions caused by the pollution generated by asphalt production for roadpavements. To address these challenging problems, Warm-Mix Asphalt (WMA) technology--which allows a reduction of 20-40 \({}^{\circ}\)C in the temperature at which asphalt mixes are produced and placed compared to conventional HMA mixtures, without lowering the mechanical performance, thus offering a wide range of potential for use in road construction projects--was developed [4; 5]. A powerful variant of WMA technology makes uses of organic additives, provided as formulations based on surfactants, polymers, and adhesion promoters, which are able to enhance coating, workability, and compacting. The suitable amount of additives can be calibrated on the temperature reduction set by the WMA design. Those additives are generally mixed with bitumen before adding the binder into the asphalt mixer.  
One disadvantage of using the WMA technology compared to HMA is connected with lower mixing and compaction temperatures, which make the asphalt more susceptible to residual moisture [5; 6; 7]. Hence, the possible presence of water in the mix, owing to incomplete drying of the aggregates, is detrimental to the adhesive properties of the binder, thereby giving rise to a reduction in adhesion and cohesion [8].  
There are several consequences caused by water damage including shoving, loss of chippings from surface dressings, potholes, cracking due to freeze-thaw damage, and bleeding [9; 10; 11; 12; 13]. As a whole, the main problem on a microscale level is an irreversible failure of adhesion between the binder and the aggregate.  
Additionally, it is very important in the production of WMA to ensure an efficient road-way performance and durability. Indeed, the reduction of the manufacturing temperature, which could not guarantee a complete drying of rocks, may have serious consequences on adhesion quality between asphalt and aggregates. As a contribution to this task, the present review reports highlights of past and present research regarding the effectiveness and application of adhesion promoters (APs), with particular emphasis to their anti-stripping behavior.
## 4 Proposed Design and Method
### Proposed Method and Design for Casually Loading
(**3**) Changing to the loading configuration by tilting and sliding the robot’s stretcher bed to the desired configuration. (**4**) Synchronising the robot’s mobile base and the conveyor belt movements enables the robot to gently load the casualty onto the stretcher bed. (**5**) Once the casualty is correctly positioned on the robot, the stretcher’s strap mechanism securely fastens, stabilises, and safely immobilises the casualty on the stretcher bed. (**6**) Changing back to the compact configuration by sliding up and tilting down the robot’s stretcher bed. (**7**) The robot is ready to transport the casualty to the medical area for further treatment. This procedure involves several major phases:  
1. Relative pose adjustment: The robot aligns its relative pose with respect to the victim in preparation for the loco-manipulation routine. 2. Approaching the target casualty: The robot gently and safely approaches the casualty and makes contact with the casualty's head to initiate loading. 3. Changing to the loading configuration (i.e., sliding and tilting the stretcher bed): The robot's stretcher bed frame tilts up to the desired loading angle and then slides down until it touches the ground in readiness to load the casualty. 4. Loading the target casualty: By balancing the movement of the base and the motion of the belt conveyor, the robot smoothly loads the casualty onboard. 5. Fastening the stretcher strap: Once the casualty is fully onboard, the strapping mechanism secures, stabilises, and immobilises the casualty, minimising the risk of additional harm due to undesired movements. 6. Changing to the compact configuration: Once the casualty is properly secured, the stretcher bed slides up and tilts down to its original compact configuration. 7. Transportation: The robot is now ready for the transportation phase. The casualty is brought to the medical area for further treatment.
###### Abstract  
To objectively assess the anti-electromagnetic interference ability of the navigation receiver, the sensitivity criterion of a certain type of navigation receiver is tested under single-frequency continuous wave electromagnetic radiation with an optimized testing method. The experimental results show that it is difficult to guarantee the accuracy of the measurement value of the sensitivity level by using the carrier-to-noise density ratio (C/N0) as the sensitivity criterion, but the initial C/N0 of each satellite can be used as the basis for identifying whether the navigation system has recovered from the interference. The experimental error is dramatically decreased when the sensitivity criterion of \"the sensitive phenomenon appears within the first 4 s, and the loss of positioning lasts for 30 s\" is employed, the variable interference power step size is adopted and all of the satellites C/N0 are required to recover to the initial value after an interference. The critical interference field intensity error can be controlled within 1 dB by using all these measures. The sensitivity law of the navigation receiver is the same under different working signal intensities. It is significantly sensitive in the working frequency bandwidth. It is also quite sensitive in \(-11.5\) MHz\(-55.5\) MHz of the frequency offset range. The positive sensitive bandwidth is about 5 times that of the negative sensitive bandwidth.  
navigation receiver; interference mechanism; sensitivity criterion; variable step size; sensitivity law +
Footnote †: journal: 2022, 12, 204. [[https://doi.org/10.3390/app12010204](https://doi.org/10.3390/app12010204)]([https://doi.org/10.3390/app12010204](https://doi.org/10.3390/app12010204))
## 2 Models and Methods
### Problem Formulation and Discretization Values  
Dynamic programming is based on [PERSON]'s principle of optimality. State variables are discretized temporally and on a scale of values. A graph whose vertices are defined by the value taken by the state variable at a given instant is thus constructed. The path from one vertex to another is associated with a value of the command and a cost from the point of view of the objective function. The path that minimizes the sum of the costs in the graph corresponds to the sequence of the optimal values of the command.  
The associated optimal control problem is given by Equation (26), which is the weighted sum of pollutant emissions and fuel consumption.  
\[J=\sum_{cycle}\Bigg{(}\frac{\dot{m}_{fuel}}{\dot{m}_{fuel,ref}}\delta t+\frac{ \alpha}{3}\Bigg{(}\frac{\dot{m}_{CO}}{\dot{m}_{CO,ref}}+\frac{\dot{m}_{HC}}{ \dot{m}_{HC,ref}}+\frac{\dot{m}_{NO}}{\dot{m}_{NO,ref}}\Bigg{)}\delta t\Bigg{)} \tag{26}\]  
Both fuel consumption and pollutant emissions show extremely different orders of magnitude. To normalize them we consider the simulation of the reference vehicle for which mixture is stoichiometric and spark advance is optimal.  
There are three control variables in the optimal control problem, intake pressure, relative spark advance and fuel/air equivalence ratio. By mixing Equations (5) and (9), we calculate the necessary intake pressure that satisfies Equation (23) and the required engine torque at each time step, thus reducing the problem complexity with two control variables only.  
The state variable is the catalyst temperature as it drives the catalyst overall efficiency and the associated emitted pollutants. The problem is subject to its first-order dynamics expressed in discrete form. It can vary freely during the driving cycle, its initial temperature is ambient temperature and its final value is unconstrained.  
The catalyst monolith temperature varies continuously despite the discrete control variables. To obtain a temperature mesh, it is rounded as expressed by Equation (27). The impact of the mesh precision will be discussed in the following section.  
\[\Delta T_{cata}=\text{round}\bigg{(}\frac{\delta t}{m_{cata}C_{p}}(\dot{Q}_{ amb}+\dot{Q}_{gaz}+\dot{Q}_{reac})\cdot\frac{1}{\delta T}\bigg{)}\delta T \tag{27}\]  
Table 2 summarizes the discretization steps for the state and the control parameters.
## 2 System Architecture and Assumptions
### Orbits  
The orbit parameter is one of the most crucial parameters since it decides the visibility of each satellite with the OGSs considered in the system architecture, as depicted in Figure 1. Initially, satellites across all operational orbits, i.e., LEO, MEO, and GEO are considered. This strategy aims to consider the full spectrum of options for satellite-based quantum communications by assessing their potential and limitations across different operational contexts. For the case of LEO and MEO satellites, the elevation and range of the satellite-OGS channel are calculated as a function of time for different satellite overpasses.  
Figure 1: Schematic of the envisioned architecture integrating urban and terrestrial grid networks across LEO, MEO, and GEO orbits for DS-BB84 QKD downlinks under night-time conditions. The fiber distribution routes from OGS to detection stations are illustrated with yellow lines.
## 4 The TRAJ-Q-GPSR Algorithm
### Pseudo-Code  
The pseudo-code representation of the Traj-Q-GPSR algorithm proposed in this paper is summarized in Algorithm 1.  
```
0: Global trajectory information \(\mathcal{T}\), current time \(t_{k}\), node energy, communication radius \(R\)
0: Updated Q-states and next-hop routing decisions
1:/*Initialization*/
2:for each UAV node \(i\)do
3: Initialize the current state \(s\) based on \(\mathcal{T}\)
4: Construct the two-hop neighbor table \(\mathcal{N}_{i}^{(2)}(t_{k})\)
5: Initialize the Q-table \(Q(s,a)\gets 0\)
6: Initialize parameters \(\alpha=\alpha_{\text{max}}\) and \(\gamma=\gamma_{\text{max}}\)
7:endfor
8:/*Packet Processing Logic*/
9:if A packet is pending for transmission then
10:if Queue is at maximum capacity. then
11:drop the packet
12:else
13: compute the estimated sending time \(t_{\text{send},p}\)
14:if\(\mathcal{N}_{i}(t_{\text{send},p})=\emptyset\)then
15: drop the packet
16:else
17: enqueue the packet
18:endif
19:endif
20:endif
21:/*Q-Learning Routing Decision*/
22:while network is active do
23:for each UAV node \(i\)do
24:if\(E_{\text{res},i}(t_{k})\geq E_{\text{threshold}}\)then
25:if the queue is non-empty then
26:if there's a closer neighbor then
27: route based on Q-learning action space
28: forward the packet
29: compute immediate reward via (24).
30: update the Q-table
31: update \(\alpha\) and \(\gamma\) via (33) and (35)
32:else
33: encountering routing voids
34: perimeter forwarding by right-hand rule
35:endif
36:endif
37:else
38: node \(i\) departs the network and returns to base
39:endif
40:endfor
41:endwhile
```  
**Algorithm 1** Traj-Q-GPSR Algorithm
## 4 Application in Tunnel Smooth Blasting
### Damage in Large Scope Estimated by PPV  
In terms of the extent of large-scale damage, various estimation methods are currently available, e.g., the PPV method [67], the criterion of tensile and compressive shear failures [68], the method of release ratios of displacement [69], and damage criterion using Mazars' model [70]. The PPV method is the most widely used in practical on-site measurement. However, it is usually firstly measured in the far-field (more than 10 m away from the explosion source), and then the PPV in the near-field can be inferred using empirical formulae, which is difficult to estimate accurately. In this study, PPV is measured numerically, and can be more directly obtained in the near-field around the boreholes. It is known that the instability of the tunnel mainly depends on the degree of damage degree to the tunnel vault. Therefore, it is necessary to explore the attenuation law of the PPV in the vault area. As is shown in Figure 16, three zones are chosen to measure the PPV outwards from the borehole walls. The measured PPV values of the corresponding zones can be clearly recognized from the attenuation curves named 'Left', 'Mid', and 'Right' in Figure 17, in which the top left zone has a similar attenuation tendency to the top right zone and attenuated faster than the middle top zone. According to the PPV damage criterion for bedded rock [71] and the PPV damage criterion for unlined rock tunnels [72], the lowest damage extent is incipient damage whose threshold is about 70 cm/s for hard rock. Therefore, the damage scopes of the three positions above can be estimated from Figure 17, in which the damage scope of the middle top is 151.7 cm, the top left is 160.0 cm and the top right is 155.2 cm.  
Through a comprehensive study of the PPV values of the whole surrounding rock mass, four groups of gauging points in the surrounding rock mass are measured, which are 0.5, 1.0, 1.5, and 2.0 m away from the design contour of the tunnel. Each group has 29 gauging points uniformly distributed around the design contour (see Figure 16). The PPV curves for different distances are presented in Figure 18. It can be seen that the PPVs with values around 0.5 m have a larger diversity during the 29 gauging points, which is probably caused by the effect of the joints close to the boreholes. In light of the changes of the PPV curves in Figure 18, the PPV values of the four different distances can be sorted into four concentrated ranges, which are shown in Table 3, and the damage degrees are estimated according to the PPV damage criteria [71;72].  
Figure 16: Positions of gauging zones and gauging points.  
Figure 17: Presentation of peak particle velocities (PPVs) obtained from numerical study on a log-log scale.  
Figure 18: PPVs for different distances obtained from numerical study.
## 4 Discussion
As the model temperature bias is large in the QTP area because of its complex topography [60], these results may imply that the background uncertainty and the QTP's complex terrain contribute to the larger temperature bias in the QTP region. Figure 12b indicates that the mean pressure bias between COSMIC and radiosonde is reasonable. Furthermore, the pressure bias and RMSE profiles for 28-37 N latitude regions show that the worst bias profiles mostly occur in the QTP (figures not shown). The authors tend to think it is also related to the background uncertainty and the QTP's complex terrain, and future work is still necessary to explore this issue. The mean vapor pressure bias and RMSE between COSMIC and radiosonde increase as height decreases below 10 km, and the negative vapor pressure bias is clear below 5 km (see Figure 12c). Note that the mean bias and RMSE profiles for refractivity are in the same situation, and the negative refractivity bias occurs below 5 km (see Figure 12d). These results support the idea that the large refractivity discrepancy between COSMIC and radiosonde in the lower troposphere is related to more humid conditions and super-refraction [62; 65]. Note that the sample selection and data-matching method may impact the results of comparison between COSMIC and radiosonde measurements. Table 1 presents the mean temperature bias and standard deviation (RMSE) between COSMIC and radiosonde data reported in some previous studies [4; 51; 66; 67]. Regional differences are found in temperature bias and RMSE. Figure 12: The mean (red), best (green) and worst (blue) bias and RMSE profiles of COSMIC retrievals compared to radiosonde. The solid lines indicate the biases and the dotted lines are for RMSE. (**a**) Temperature; (**b**) pressure; (**c**) vapor pressure; (**d**) refractivity. Moreover, the differences in temperature bias and RMSE are also related to the data-matching method and height levels of the profile. Additionally, the larger temperature RMSE reported in this study may come from the lower troposphere and in the 30-40 km height levels, and future work is necessary in order to make a safe conclusion.
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 2 MVDR-HBT Localization Principle
### Principle of Broadband Sound Source Localization Using MVDR-HBT  
A flowchart of MVDR-HBT broadband signal localization is shown in Figure 2.  
From the above narrowband principle, it can be concluded that by performing FFT on the broadband signal \(\mathbf{p}_{b}\), we can obtain  
\[\text{FFT}(p_{bi})=[p_{bi}(f_{0}),p_{bi}(f_{1}),\cdots p_{bi}(f_{n})] \tag{13}\]  
The term \(\text{FFT}(\cdot)\) represents the N-point fast Fourier transform (FFT), \(n=N/2\).  
After frequency division processing, the broadband signal is converted to a narrow-band signal, and the value of \(\mathbf{w}_{MVDR}(\theta,f_{j})\) is calculated for each frequency component in the narrowband signal.  
\[\mathbf{w}_{MVDR}(\theta,f_{j})=\frac{\mathbf{R}_{xf_{j}}^{-1}\mathbf{a}(\theta,f_{j})}{ \mathbf{a}^{H}(\theta,f_{j})\mathbf{R}_{xf_{j}}^{-1}\mathbf{a}(\theta,f_{j})} \tag{14}\]  
Based on Equation (14), we can obtain the received signals \(\mathbf{p}_{b}^{\prime}(f_{j})\) corresponding to each sub-band frequency of the MVDR-weighted microphone signals:  
\[\mathbf{p}_{b}^{\prime}\big{(}f_{j}\big{)}=\mathbf{w}_{MVDR}(\theta,f_{j})\odot\text{ IFFT}\big{(}p_{b}\big{(}f_{j}\big{)}\big{)} \tag{15}\]  
where IFFT denotes the inverse fast Fourier transform and \(\odot\) represents the Hadamard product, which is defined as element-wise multiplication of corresponding positions.  
Taking the weighted signals of each sub-band frequency of every microphone and averaging them, we obtain  
\[\mathbf{p}_{bi}^{\star}=E\big{[}\mathbf{p}_{b}^{\prime}\big{(}f_{j}\big{)}\big{]} \tag{16}\]  
Figure 2: Broadband Signal Localization Process Flow Diagram of MVDR-HBT.  
Finally, we substitute the result into Equation (12) to obtain  
\[C_{\text{max}}=\frac{\left\langle{{p_{bi}}^{*}(\theta,t)\cdot{p_{bj}}^{*}(\theta,t+ \Delta T)}\right\rangle}{{\left\|{{p_{bi}}^{*}(\theta,t)}\right\|\cdot{\left\|{{p _{bj}}^{*}(\theta,t)}\right\|}}} \tag{17}\]
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 3 Component Modeling
### Transmitter Modeling  
Models for transmitters in POF-based communications systems have to account not only for temporal/frequency characteristics, but also for spatial ones. In fact, the intensity launched into the fiber \(p(\theta,z=0,t)\), which is required to solve the Gloge power-flow equation, is an important modeling consideration, especially when dealing with short fiber segments where the SSD is not reached [31; 49]. It is quite common to specify the launch as a Gaussian intensity profile dependent on external angle, \(\theta_{ext}\), i.e., the angle outside the fiber, related to the internal or propagation angle \(\theta\) by \(\theta_{ext}=\text{asin}(n_{core}\sin(\theta))\):  
\[p(\theta_{ext})=e^{-\frac{\theta_{ext}^{2}}{2\sigma_{ext}^{2}}} \tag{12}\]  
where \(\sigma_{ext}=FWHM_{ext}/\left(2\sqrt{2\ln(2)}\right)\). In this expression, \(FWHM_{ext}\) is the full-width at half maximum, specified in degrees. Similar expressions can also be used as a function of internal angle. This way, different means of describing the launch intensity can be used to satisfy the needs of most system designers: either launch intensity as a function of external or internal angle: \(p(\theta_{ext},z=0)\) and \(p(\theta,z=0)\), respectively, or encircled angular flux: \(EAF(\theta_{ext})\) or \(EAF(\theta)\).  
The EAF is defined as the fraction of the far-field power contained within a given solid angle [48]. The EAF is related to an intensity distribution \(p(\theta)\) via the following equation:  
\[EAF(\theta)=\frac{2\pi\int\limits_{0}^{\theta}p(\theta^{\prime})\sin(\theta^{ \prime})d\theta^{\prime}}{2\pi\int\limits_{0}^{\theta_{\text{max}}}p(\theta^{ \prime})\sin(\theta^{\prime})d\theta^{\prime}} \tag{13}\]  
which produces the EAF as a function of the internal angle and is consistent with a spherical integration of far-field intensity when there is no azimuthal dependence. \(EAF(\theta_{ext})\) is related similarly to \(p(\theta_{ext})\).  
Both the optical intensity and the EAF can be used to describe the spatial distribution, not only at the launch, but also at any fiber length. Figure 2 shows examples of the normalized intensities and EAFs for three different sources with FWHM of \(5^{\circ}\), \(15^{\circ}\) and \(30^{\circ}\) at the input and after a 150 m SI-POF. For this length, the graphs show that the intensity distributions and the EAFs for all input sources are very similar, which indicates it is close to the SSD.  
Figure 2: Normalized intensities (**a**) and encircled angular flux (EAFs) (**b**) as a function of the width of the Gaussian intensity profile as emitted by the source (\(L=0\) m) and at the output of a 150 m SI-POF.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 3 Experimental Results and Analysis
### Weight Selection Experiment  
To determine the weight coefficients in our R-IoU and optical flow fusion model, we conducted tests to select these weight coefficients. Due to the limited number of submissions allowed by MOTChallenge for official user results, we used the latter half of the MOT17 training set as the standard for weight selection tests, based on the SDP detector provided by the official.  
We selected weights with high MOTA and IDF1 scores and a low number of ID switches as the optimal weight coefficients. Here, \(\alpha\) represents the weight coefficient for the IoU feature similarity matrix, and in the table, we use \(\beta\) to denote \((1-\alpha)\), which represents the weight coefficient for the optical flow feature similarity matrix. From the experiments in Table 1, which tested each training set of MOT17-SDP, it was found that to achieve both the highest MOTA and IDF1 scores and the fewest ID switches, the weight \(\alpha\) should be selected as 0.8 and \(\beta\) as 0.2. This combination of weights proved to be relatively optimal. Through the weight selection experiment, when \(\alpha\) is set to 0.8 and \(\beta\) to 0.2, the three evaluation metrics of the MOT17-SDP training set all reached the ideal values we desired.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\alpha\)\(\beta\) & **0** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **IDs \(\downarrow\)** \\ \hline
1.0 & ✓ & & & & & & & 91.374 & 84.823 & 339 \\
0.9 & & ✓ & & & & & 91.364 & 84.838 & 342 \\
0.8 & & & ✓ & & & & **91.404** & **85.744** & **320** \\
0.7 & & & & ✓ & & & & 90.822 & 83.809 & 399 \\
0.6 & & & & & ✓ & & & 86.489 & 74.332 & 1050 \\
0.5 & & & & & & & ✓ & 80.950 & 69.320 & 1550 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weight selection test based on the latter half of the MOT17 training set (\(\uparrow\): The higher the parameter, the better. \(\downarrow\): The lower the parameter, the better. \(\checkmark\): indicates selection. Bold font: indicates the best value in that column).