esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 4 Obstacle Avoidance Algorithm and Transverse Controller Design
### Design of MPC Controller with Planning Module
#### 4.2.4 Establish Constraints  
When establishing the constraint conditions, the vehicle dynamics limitations should be taken into account in addition to the control quantity and control increment constraints.  
(1) Control quantity and control increment constraints  
Based on real vehicle testing, it was found that the front wheel of the ferry vehicle turns left to the limit position and from the center position to the right to the limit positionis 20 degrees, so that the rotation angle of the wheel to the left is negative; otherwise, it is positive [25]. Therefore, the constraint condition is set to the following:  
\[-20^{\circ}\leq\delta_{f}\leq 20^{\circ}\]  
\[-0.85^{\circ}\leq\Delta\delta_{f}\leq 0.85^{\circ}\]  
(2) Centroid lateral deflection angle constraint  
Since the road surface driven by the ferry vehicle in the school is a smooth asphalt pavement, it is only necessary to consider that the centroid side slip angle is kept within a reasonable range when the ferry vehicle is driving on a good road surface, and the constraint condition of the centroid side slip angle is as follows [26]:  
\[-12^{\circ}\leq\beta\leq 12^{\circ}\]  
(3) Tire slip angle constraint  
At \(t\) time, the tire slip angle can be expressed as follows when the system's state variables are known:  
\[\begin{array}{c}\alpha_{f,t}=\frac{\dot{y}_{t}+a\dot{y}_{t}}{\dot{x}_{t}}- \delta_{f,t-1}\\ \alpha_{r,t}=\frac{\dot{y}_{t}+b\dot{y}_{t}}{\dot{x}_{t}}\end{array} \tag{37}\]  
According to the tire cornering properties, at tire side deflection angles less than 5 degrees, the tire's side deflection angle is directly proportional to the side deflection force [27], and the following is the restriction condition for the tire slip angle:  
\[-2.5^{\circ}\leq\alpha_{f,t}\leq 2.5^{\circ}\]
## 4 Verification
### Simulation Validation Utilizing Synthetic PMU Data
#### 4.1.1 Absolute Deviations in Optimal Scenarios  
According to the data in Table 1, under optimal scenarios, whether \(f_{\text{sub}}\) varies at an interval of 1 Hz within the range of [5; 45] Hz or \(x_{\text{sub}}\) varies at an interval of 1 within the range of [5; 100] Hz, the relative error of parameter identification of the fitting-based ERA was less than \(10^{-11}\%\), which approximated the outcomes achieved by the conventional ERA  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Test Set** & **SNR (dB)** & **Method** & \(\frac{|f_{\text{a}}-f_{\text{b}}|}{f_{\text{a}}}\) & \(\frac{|g_{\text{b}}-g_{\text{u}}|}{x_{\text{a}}}\) & \(\frac{|f_{\text{amb}}-f_{\text{sub}}|}{f_{\text{sub}}}\) & \(\frac{|g_{\text{amb}}-x_{\text{sub}}|}{x_{\text{sub}}}\) & \(\frac{|g_{\text{amb}}-g_{\text{band}}|}{\phi_{\text{amb}}}\) & \(\frac{|g_{\text{app}}-g_{\text{up}}|}{x_{\text{up}}}\) & \(\frac{|\phi_{\text{app}}-g_{\text{up}}|}{g_{\text{up}}}\) \\ \hline \multirow{6}{*}{\(f_{\text{sub}}\in[5,45]\,\mathrm{Hz}\)} & \multirow{6}{*}{\(\infty\)} & F-ERA (200 ms) & \(0\) & \(10^{-13}\) & \(10^{-13}\) & \(10^{-11}\) & \(10^{-11}\) & \(10^{-11}\) & \(10^{-12}\) \\ \cline{3-10}  & & ERA (200 ms) & \(10^{-9}\) & \(10^{-8}\) & \(10^{-12}\) & \(10^{-8}\) & \(10^{-8}\) & \(10^{-8}\) & \(10^{-8}\) \\ \cline{3-10}  & & ERA (1 s) & \(10^{-11}\) & \(10^{-9}\) & \(10^{-12}\) & \(10^{-10}\) & \(10^{-10}\) & \(10^{-10}\) & \(10^{-10}\) \\ \cline{2-10}  & & F-ERA (200 ms) & \(0.0652\) & \(1.1277\) & \(0.18\) & \(1.327\) & \(3.1766\) & \(1.6314\) & \(4.5545\) \\ \cline{3-10}  & & ERA (200 ms) & \(0.7217\) & \(6.8243\) & \(0.0877\) & \(9.3988\) & \(6.2499\) & \(9.5708\) & \(6.4723\) \\ \cline{3-10}  & & ERA (1 s) & \(0.1321\) & \(7.1142\) & \(0.0052\) & \(0.964\) & \(1.5934\) & \(2.6431\) & \(1.  
utilizing 1 s and 200 ms data windows. Therefore, under optimal scenarios, the ERA both before and after improvement can better perform the SSO parameter identification task.
## 4 System Overview
### Digital High-Speed Connectivity  
The ROACH-2 boards are equipped with four SFP+ ports on a single 10 GbE mezzanine card, where the Ethernet interface is driven by an on-board 156.25 MHz crystal oscillator. This clock frequency is internally multiplied by a factor of 66 within the FPGA, resulting in an effective wire speed of 10.3125 Gbps.  
The CASPER 10 GbE Simulink core facilitates the transmission and reception of UDP packets over IPv4, encapsulated within Ethernet frames. Each Ethernet frame incurs an overhead of 38 bytes for the frame header, 20 bytes for the IPv4 header, and 16 bytes for the UDP header, totaling at least 74 bytes of overhead per packet. To align with 64-bit boundaries, some headers may also include zero-padding, further impacting the effective throughput. The maximum payload size for the CASPER 10 GbE core is 8192 bytes, implemented in the BRAM, with an additional 512 bytes available in distributed RAM for application-specific headers [100].
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 1 Introduction
All these studies made the time window more reasonable in restricting the distribution problem, but they ignored the factors affecting the time window are not only the distribution path but also the production speed. In this paper, the variable of unloading efficiency is introduced in the process of optimizing the distribution route, and the influence of unloading efficiency on the time window is discussed. Briefly, two considerations motivate us to conduct this study. First, efficient loading and unloading should be considered when a large number of goods are loaded into the carriage. Second, the distribution cost with the high loading rate should be involved. In this study, a pallet with telescopic height is introduced as a cargo stacking carrier to expand the type of goods. On the one hand, pallets can be used as a unit to distinguish customers and cooperate with forklifts to improve loading and unloading efficiency. On the other hand, it can solve the pain points of traditional pallets stacked by stable top. A 3D pallet loading-vehicle routing problem (3 DPP-VRP) model with a time window combined with the hierarchy theory was constructed based on the 3D pallet loading constraint, and the influence of the 3D pallet loading scheme on the time window cost was discussed. First, the initial solution of 3D loading was generated by the volume of cargo, and the initial solution of the path was generated through spatio-temporal clustering. Then, the solution process of the pallet loading, vehicle loading, and distribution path was completed by combining with the heuristic solution method of hybrid simulated annealing and genetic algorithm. The main contributions of this paper are as follows. (1) We construct a pallet with a telescopic support structure as the intermediate carrier to improve the handling efficiency of cargo. In this regard, the special pallet is convenient for a forklift truck to load and unload and greatly promotes the handling efficiency of cargo. (2) We design a novel distribution scheme based on the constraints of a three-dimensional pallet loading time window. The scheme can save more time. With the improving speed of the loading and unloading, the proposed scheme can greatly save delivery time and reduce the time cost by optimizing the line and considering the time window. (3) We utilize the empirical data of an express company in Chongqing, China, to implement the proposed approach to demonstrate the analysis.
## 4 Review: Slant Links Rain Fade Models
### Limitations
#### 4.1.3 Garcia Lopez Model  
The Garcia Lopez Model [34] is based on measurement data from satellite links in Europe, Japan, Australia, and the USA. The model has four coefficient constants, \(c_{1},c_{2},c_{3},c_{4}\), dependent on the geographical area to support the regional climate. However, the physical significance of these coefficients is unknown, and these coefficients do not have any physical meaning. The model is suitable for predictions with elevation angles ranging from \(10^{\circ}\) to \(40^{\circ}\). The detailed steps of the model are given in Algorithm 3.  
```
1 Calculate k and \(\alpha\) /* CCIR [104] */
2 Calculate specific attenuation \(\gamma\gets kR^{a}{}_{0.01}\)
3 if\(\varphi<36^{\circ}\)then
4\(H\gets 4\)
5 else
6\(H_{R}\gets 4-0.075(\varphi-36^{\circ})\)
7 if\(\theta\geq 5^{\circ}\)then
8\(L_{S}\leftarrow(H_{R}-H_{S})/\sin\theta\)
9 else
10 Elevation angle error /* Invalid, if\(\theta\
geq 5^{\circ}\) */
11 Calculate, \(A_{p}\leftarrow\frac{\gamma L_{S}}{[c_{1}+\{L_{S}(c_{2}R+c_{3}L_{S}+c_{4})\}/ \varepsilon]}\)/* \(c_{1},c_{2},c_{3},c_{4}\) are constants */
12 Return \(A_{p}\)
```  
**Algorithm 3**Garcia Lopez Model [34]
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
## 5 Ergodic Capacity  
Apart from the OP and BER performance, the average channel ergodic capacity is another important metric of wireless communication systems. In this section, we discuss the ergodic capacity of the considered asymmetric dual-hop MIMO mixed RF/FSO DF relaying system. The ergodic channel capacity is defined as [21]\(\bar{C}=\mathbb{E}\left[\frac{1}{2}\text{log}_{2}(1+\bar{\omega}\gamma_{eq}) \right]\) where \(\bar{\omega}=1\) for heterodyne detection [22].  
From [23], we can calculate average capacity in terms of the complementary cumulative distribution function (CCDF) of \(\gamma_{eq}\) as  
\[\bar{C}=\frac{\bar{\omega}}{2\ln(2)}\!\int_{0}^{\infty}\left(1+\bar{\omega} \gamma_{eq}\right)^{-1}\!\bar{F}_{\gamma}(\gamma_{eq})d\gamma_{eq}, \tag{21}\]  
where \(\bar{F}_{\gamma}(\gamma_{eq})=1-F_{\gamma}(\gamma_{eq})\  
The product of the two independent Meijer G-functions, which can be expressed in terms of extended generalized bivariate Meijer G-function (EGBMGF) ([16] [Equation (07.34.16.0003.01)]) is used to calculate \(I_{5}\)  
\[\begin{split} I_{5}&=\frac{\bar{\omega}}{2\ln(2)} \int_{0}^{\infty}e^{\left(\frac{-\gamma}{\gamma_{RF}}\right)}\sum_{u=0}^{N_{ \text{t}}N_{\text{r}}-1}\frac{1}{u!}\bigg{(}\frac{\gamma}{\gamma_{RF}}\bigg{)} ^{u}\frac{2^{\alpha_{\text{s}}+\beta_{\text{s}}-2}}{\pi\Gamma(\alpha_{\text{s} })\Gamma(\beta_{\text{s}})}G_{5}G_{1}d\gamma\\ &=\frac{\bar{\omega}}{2\ln(2)}\frac{2^{\alpha_{\text{s}}+\beta_{ \text{s}}-2}}{\pi\Gamma(\alpha_{\text{s}})\Gamma(\beta_{\text{s}})}\sum_{u=0}^ {N_{\text{t}}N_{\text{r}}-1}\frac{1}{u!}\bigg{(}\frac{1}{\gamma_{RF}}\bigg{)} ^{u}(\tilde{\gamma}_{RF})^{u+1}\\ S&\bigg{[}\bar{\omega}\tilde{\gamma}_{RF},\ \frac{(\alpha_{\text{s}}\beta_{\text{s}})^{2}}{16}\frac{\gamma_{RF}}{\tilde{ \gamma}_{FSO}}\bigg{|}\bigg{|}\bigg{[}\begin{matrix}1,0\\ 0\end{matrix}\bigg{|}\begin{matrix}u+1\\ 0,0\end{matrix}\bigg{]}-\bigg{|}\bigg{[}\begin{matrix}1,1\\ 1,1\end{matrix}\bigg{]}\begin{matrix}0\\ 1,5\end{matrix}\bigg{|}\begin{matrix}4,1\\ 1,5\end{matrix}\bigg{|}\begin{matrix}4,1\\ 1,0\end{matrix}\bigg{]}.\end{split} \tag{24}\]  
After some mathematical manipulations  
\[\begin{split}\tilde{C}&=\frac{\bar{\omega}}{2\ln(2)} \sum_{u=0}^{N_{\text{t}}N_{\text{r}}-1}\frac{1}{u!}\bigg{(}\frac{1}{\tilde{ \gamma}_{RF}}\bigg{)}^{u}\tilde{\gamma}_{RF}^{u+1}\times\\ &\bigg{(}G_{2,1}^{1,2}\bigg{[}\bar{\omega}\tilde{\gamma}_{RF}|^{- \underline{u},0}\bigg{]}-\frac{2^{\alpha_{\text{s}}+\beta_{\text{s}}-2}}{\pi \Gamma(\alpha_{\text{s}})\Gamma(\beta_{\text{s}})}\times\\ S&\bigg{[}\bar{\omega}\tilde{\gamma}_{RF},\ \frac{(\alpha_{\text{s}}\beta_{ \text{s}})^{2}}{16}\frac{\tilde{\gamma}_{RF}}{\tilde{\gamma}_{FSO}}\bigg{|} \bigg{|}\bigg{[}\begin{matrix}1,0\\ 0,0\end{matrix}\bigg{]}+1\bigg{|}\bigg{[}\begin{matrix}1,1\\ 1,1\end{matrix}\bigg{]}\begin{matrix}0\\ 1,5\end{matrix}\bigg{|}\begin{matrix}\Big{[}\begin{matrix}4,1\\ 1,5\end{matrix}\Big{]}\begin{matrix}1\\ \Delta 1,0\end{matrix}\bigg{]}\bigg{)}.\end{split} \tag{25}\]
## 2 System Model
### Architectures for Forwarding NB-IoT Frames over a Satellite Relaying Node
#### 2.2.2 Case 2: Decode-and-Forward Relaying of NB-IoT Frames  
Similar to _Case 1_ (see Section 2.2.1), the optical receiver of the satellite removes the time-packed interference (see Section 2.3) but, instead of remapping the 4-PAM modulation to QPSK directly, it computes the LLRs of the 4-PAM modulation that are used by the soft-Viterbi decoder to detect the message bits transmitted from the gateway (see Section 2.4). After that, the estimated message bits \(\widehat{b}_{\text{gw,sat}}\) are re-encoded, QPSK mapped, OFDM modulated, and forwarded to the corresponding IoT device. There, the NB-IoT receiver computes the LLRs of the receive QPSK symbols, to use them in the soft-Viterbi algorithm to detect the transmitted bits from the gateway \(\widehat{b}_{\text{gw,iot}}\).  
Let \(\widehat{s}_{\text{ntp,sat}}[n]\) be the estimated symbols of the non-time-packed 4-PAM modulated symbols at the satellite. Then, the following step of _Case 2_ consists in computing the two LLRs per modulated symbol of the 4-PAM constellation, denoted as \(LLR_{b_{0}}\) and \(LLR_{b_{1}}\) (see Section 2.3). After that, the soft-Viterbi decoder at the satellite estimates the original transmitted bits \(\widehat{b}_{\text{gw,sat}}\), re-encodes them to regenerate the coded NB-IoT bits \(c_{\text{ntp,sat}}\), maps them to QPSK symbols \(s_{\text{ntp,sat}}\), and follows as in _Case 1_ (see Figure 3).
## 1 Introduction
### The Technical Background of CDP Synthesis Technique and the Significance of Synthesizing PNDs
The resulting deposit can encompass a range of materials including metals, semiconductors, alloys, and nanocomposites. The advanced iCVD polymers now have the capability of controlled anisotropy in the physical structure and chemical composition and are expected to pave the way for interface engineering toward innovative nanoparticle designs. Currently, polymer nanoparticles with a hemisphere (domed) shape, known as PNDs, are receiving significant research attention. This innovative nanoparticle design holds the potential to enhance drug delivery and improve the efficacy of precision medicine, showcasing remarkable attributes in drug delivery and biocompatibility. The size and shape of PNDs could potentially be critical factors influencing the stability and solubility of encapsulated cargo, facilitating membrane transport, and extending circulation time to enhance safety and efficacy in nanoparticle drug delivery systems.
## 4 Discussion
### Slip Displacement  
The value of shear stiffness is the smallest at the beginning of tests, and it increases with the slip of the fault block. The fault block slip is unstable when the shear stiffness value is less than the critical stiffness value. The critical stiffness is low when the fault block slip displacement is short. As the shear slip displacement increases, the critical stiffness gradually increases. This result is similar to that obtained by [PERSON] in 2016 [28]. The research used a 3 mm thick layer of quartz mud to simulate fault gouge and found that the critical stiffness increased first and then remained stable with slip displacement.  
The interplay of asperity interlocking and gouge lubrication may be the main reason for the variation in shear stiffness. At the beginning of tests, the interaction between the asperities on fault surfaces is poor, making the small value of shear stiffness. As the displacement of the slip increases, the interaction between asperities becomes strong, and  
Figure 9: Two asperities interaction modes. (**a**) The state of the asperities before the slip of the fault block. (**b**) Climbing mode: When the normal stress is small, the asperities will not break. The friction behavior is mainly dominated by the overriding/climbing between the asperities. (**c**) Fracture mode: Fracture of the asperities occurs due to large shear stress when the normal stress is large. Fracture mode associated with the production of fault gouge [15].  
the contact area between fault surfaces increases due to the high asperities being sheared, which leads to a rapid increase in shear stiffness.  
Fault gauge is continuously generated during the fault block slip, and it can lubricate the fault. Furthermore, the lubrication effect would be enhanced as the fault gouge particles break up and tend to be spherical in shape. Therefore, as the displacement of the slip increases, although the asperity interlocking gradually increases, the shear stiffness remains constant.  
Two kinds of surfaces of the sandstone samples used in the test studies are rough at small scales but flat at large scales. Therefore, the sample first experience a transient, high wear rate followed by a considerably lower steady-state wear rate [47; 62]. We believe that the amount of fault gouge remains constant when the slip displacement of the fault block is large. The fault gouge is continuously crushed and compacted under normal stress, which leads to an increase in the fault gouge compactness and the shear stiffness. The shear stiffness is more likely to enter the slowly increasing stage with high normal stress and tight compaction effect of fault gouge.  
As the displacement of the slip increases, the particle size of the fault gouge decreases. A high-strain shear zone appears in the fault gouge and is parallel to the fault surface boundary, which obviously weakens the fault and decreases shear stiffness [35; 47]. High normal stress is more conducive to the formation of high-strain shear zones. Therefore, the value of shear stiffness decreases rapidly in the final stage when the normal stress is large.
## 3 Results
### Physical Meaning of PBLM-EDLC Parameters  
The suitability of a simulation model depends on its performance and the degree of difficulty when the user evaluates the main parameters of the model. The value of R\({}_{\text{s}}\) is easy to find from the manufacturer's data (\"ESR\" in Table 3). In other cases, the measurement nodes for voltage are outside EDLC (the overall device is composed of several commercial banks, and cable links have some resistance, R\({}_{\text{L}}\) in Figure 9). In this case, the value (\(R_{S}+R_{L}\)) is very easy to find when EDLC changes from charge to discharge because the change in voltage is due to the value \((R_{S}+R_{L})Ig_{0}\).  
The manufacturer also gives a range of values for the EDLC capacitance (Table 2), so if the model is physically based, the capacitance seen in tests should be close to these values. Neglecting \(R_{P}\) in the first round, because the energy losses in EDLC are very low, we can define an apparent (Equation (12)), or instantaneous capacitance at each time t, \(C_{ap}(u_{C})\), of the parallel branch in Figure 8 as  
\[C_{ap}(u_{C})=\frac{Ig_{0}}{dU_{C}/dt} \tag{21}\]  
Values of CC can be obtained from the EDLC differential Equation (10) as  
\[C_{ap}(u_{C})=C_{0}+K_{P}u_{C}^{2} \tag{22}\]  
Figure 12: Comparisons between the actual values (in black) of the voltage u\({}_{\text{c}}\)(t) and the fitted ones (in red) for Scenario_1: (**a**) Trial of charges at 5 A; (**b**) trial of charges at 33 A; (**c**) trial of discharges at 5 A; (**d**) trial of discharges at 30 A.  
If a single EDLC module (e.g., the 500 F module tested in the laboratory, see Table 2) should perform in the voltage range from 0 to 16 V (17 V maximum, Table 2). The average capacitance (\(C_{av}\)) at the range of voltage is performed by  
\[C_{av}=\frac{1}{17}\int_{0}^{17}CC(u_{C})du_{C}=C_{0}+\frac{1}{3}K_{P}17^{2}=C_{ 0}+96.3K_{P} \tag{23}\]  
This relation is very close to the quantity \(M=100\cdot K_{P}+C_{0}\) that provides the optimal combination of parameters for the grid searching (see Section 3.1).
## 2 Materials and Methods
### Implementation of the Hygro-Thermal Model for Stress-Laminated Timber Deck in Abaqus Code
The monitoring equipment (Figure 5) collects data about the loading of the high-strength steel bars, temperature and humidity of the wood at different depths from the surface, and temperature and humidity of the air from the weather station positioned on the bridge. The collected data is processed directly on the embedded computing unit and regularly transmitted to the central monitoring server over the internet. Figure 4: Sorliveien Bridge in Norway. (**a**) Side view with location of sensors (red circle). (**b**) Cross section showing the SLT deck with 333 mm thickness. Figure 5: Sorliveien Bridge. Detail of the bottom deck with the monitoring equipment. Three load cells were installed to the prestressing bars loaded to 227 kN, on the northeast side of the bridge. The measurements from load cells are not discussed in this paper, because they are not directly needed for the hygro-thermal simulations. Temperature and relative humidity were measured by ten integrated humidity-temperature sensors Vaisala Humitter 50Y [27]. This type of sensor has an operating range from \(-40\)\({}^{\circ}\)C to \(+60\)\({}^{\circ}\)C and from 0 to 100% of the \(RH\). Its length is 70 mm and the diameter 12 mm. Nine sensors were installed in three different depths from the bottom surface (20 mm, 166 mm and 308 mm) and three different planks, and one additional sensor was measuring the temperature and relative humidity of the ambient air. The FEM model for the Sorliveien deck is a 3D slice of the lamella, with the width of 48 mm, height 333 mm and thickness 5 mm. The weather data in Figure 6 was the primary information needed for the hygro-thermal simulation, because the boundary conditions of the model are based on the external \(RH\) and \(T\). The numerical analysis is carried out from August 2005 until the end of January 2010, since in this period the measurements are continuous. The initial relative humidity and temperatures are equal to those of the air (\(RH_{0}=65\%\), \(T_{0}=26\)\({}^{\circ}\)C) and the initial moisture content in equilibrium with \(RH_{0}\) is \(MC_{0}=13.3\%\).