esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 3 Results and Discussion
### Effect of Ag Nanowires on Response Time of PSLC Devices  
The response time of PSLC with different Ag nanowire concentration with 30 V, 1 kHz square wave driving is shown in Figure 6a. Here, both the turn on or rising time from clear state to scattering state (an interval from 90% transmittance to 10% transmittance) and the turn off or falling time shows the same variation tendency with threshold voltage as the Ag nanowire concentration increases. The turn on time of PSLC first decreased to about a half and then increased to about double of undoped PSLC as Ag nanowire concentration increased. However, the change of turn off time was very limited, with all around 5 ms.  
Figure 6b shows the comparison of temporal response of a pure PSLC and a PSLC with 0.02 wt.% Ag nanowire doping. There was no noticeable difference in the turn off time of the samples, but the turn on time of PSLC with 0.02 wt.% Ag nanowire doping was only 12.9 ms, i.e. 41% lower than the undoped PSLC.  
Figure 6c shows the input signal and output signal of a PSLC with 0.02 wt.% Ag nanowire doping. It shows the Ag nanowire doped PSLC has very fast response to electric field. The turn off process of a PSLC device is elastic response, during which the negative LC molecule reorientation is driven by the vertical alignment layer and vertical aligned LC network. This process is almost linear and the Ag nanowire doping had very little influence on it. The turn on process is dielectric response. Ag nanowire doping can lower the ionic impurities in LC, which would enhance the electric field and thus increase the speed of dielectric response. On the other hand, Ag nanowire doping can increase the polar anchoring energy in the PSLC [37], which would also shorten the turn on time.  
Figure 6: Response time of the Ag nanowire doped PSLC according to the Ag nanowire concentration: (**a**) response time according to the Ag nanowire concentration; (**b**) comparison of response time of pure PSLC and 0.02 wt.% Ag nanowire doped PSLC; and (**c**) input signal and output signal of PSLC with 0.02 wt.% Ag nanowire.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 4 Simulation Setup and Performance Evaluation  
### Simulation Setup  
The proposed PUTPOSH and some of the existing related approaches were implemented and simulated using Matlab. The existing approaches: (1) reactive handoff--DFHC [18]--(2) proactive handoff--fuzzy logic-based spectrum handoff (FLSH) [47]--and (3) hybrid handoff--DSA [53]--are selected to simulate with PUTPOSH. Two diverse Poisson procedures are used to produce PUs and SUs [20] in the CRN at constant time. The overall service time and the inter-arrival time are measured as non-integer instances of users. The data transfer of the low priority CRUs can be interrupted by the high priority PUs. To avoid the collision of the same priority CRUs during the channel access, the first come first serve scheduling strategy is used.  
The parameters utilized to achieve simulation outcomes are listed in Table 2. The simulation was executed multiple times (generally about 1000 times) and the frequent values are taken to plot different graphs. We presume the packet length of SUs and PUs was 10 bytes for conducting the experiments. The overall service time of CRU included the waiting time, the processing time of the channel, the data transmission time and the sensing time respectively. The dealing out time of the channel is supposed to be 0.05 msec [10,12]. The arrival rates of PU and CRUs were followed by the Poisson processes. For simplicity, the arrival rate value of CRU was fixed to 0.1 and compared with the parameters at dissimilar values i.e., 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, and 0.08 of PU interarrival rates. This disparity aspect helped us understand the behavior of a CRU in the proposed PUTPOSH scheme with various interarrival rates of the PU. Moreover, the overall service time of CRU and PU was taken as 0.5 and 0.4, respectively, and the order of the PU was considered a superior precedence than SU. Therefore, the overall service time of CRUs depends on the arrival rate of the PU and different arrival rates of PUs may effect the overall service time.  
The simulation starts with the recognition of a PU by a CRU at a momentarily engaged licensed band. The CRU may make a decision to ensure a handoff to the next available channel or to stay and wait at the ongoing channel. The decision of transferring communication to a new vacant channel or may waiting in an ongoing channel depends firmly on the service time. When handoff was achieved, the overall service time for reactive and proactive decision was considered. With the overall least service point, CRU makes the decision to execute reactive or proactive handoff.  
Figure 4: Time stream of the CRU transmission in the handoff scheme.
## 4 Control Design
### Adaptive Sliding Mode Servo Control  
In the actual work of the hydraulic servo control system, there are some uncertain factors such as the perturbation of the elastic modulus of hydraulic oil, hydraulic oil leakage and friction of moving parts. They will directly affect the stability and dynamic characteristics of the hydraulic servo control subsystem. Therefore, the dynamics of FFS with uncertainties will be from Equation (21) to  
\[\frac{d\tau_{f1}}{d\sigma_{1}}+\widetilde{A}_{0}\tau_{f1}+F\ =\ \widetilde{C}_{0}I_{f1} \tag{78}\]  
where \(F\ =\ \Delta\widetilde{A}\tau_{f1}-\Delta\widetilde{C}_{f1}\) is the overall uncertainty, and it is bounded.  
If \(\hat{F}\) is the estimated value of \(F\), define the estimated error  
\[\widetilde{F}\ =\ F-\hat{F} \tag{79}\]  
We define the tracking error vector  
\[\begin{array}{c}e_{f}\ =\ \tau_{f1d}\ -\ \tau_{f1}\\ \hat{e}_{f}\ =\ \dot{\tau}_{f1d}-\dot{\tau}_{f1}\end{array} \tag{80}\]  
then select a sliding surface  
\[s_{f}\ =\ e_{f} \tag{81}\]  
**Theorem 3.**_Considering the hydraulic servo FFS, the ASMC is presented as_  
\[I_{f1}\ =\ \widetilde{C}_{0}^{-1}[\frac{d\tau_{f1}}{d\sigma_{1}}+\widetilde{A}_{0 }\tau_{f1}+\hat{F}+\hat{\xi}\text{sgn}(s_{f})] \tag{82}\]  
\[\dot{\hat{F}}\ =\ \Lambda^{\text{T}}s_{f} \tag{83}\]  
_Then, the closed-loop FFS is uniformly ultimately stable._  
**Proof.** Considering the [PERSON] theory, we define the third positive definite energy function  
\[V_{3}\ =\ \frac{1}{2}s_{f}^{\text{T}}s_{f}\ +\ \frac{1}{2}\widetilde{F}^{ \text{T}}\Lambda^{\text{T}}\widetilde{F} \tag{84}\]  
Differentiate (84) with respect to time, and we can have  
\[\begin{array}{c}\dot{V}_{3}=s_{f}^{\text{T}}s_{f}+\widetilde{\hat{F}}^{ \text{T}}\Lambda^{-1}\widetilde{F}\\ =s_{f}^{\text{T}}(\frac{d\tau_{f1}}{d\sigma_{1}}+\widetilde{A}_{0}\tau_{f1}+F -\widetilde{C}I_{f1})-\hat{F}^{\text{T}}\Lambda^{-1}\widetilde{F}\\ =s_{f}^{\text{T}}(F-\hat{F}-\hat{\xi}\text{sgn}(s_{f}))-s_{f}^{\text{T}}\Lambda \Lambda^{-1}\widetilde{F}\\ =-s_{f}^{\text{T}}\hat{\xi}\text{sgn}(s_{f})\\ \leq 0\end{array} \tag{85}\]  
From (85), \(V_{3}(s_{f}(t))\leq V_{3}(s_{f}(0))\), so \(s_{f}(t)\) is bounded. Considering the Lyapunov function as \(V_{4}=-s_{f}^{\text{T}}s_{f}\), the integration of \(V_{4}\) can be obtained as \(\int_{0}^{\infty}V_{4}(\tau)d\tau\leq V_{3}(s_{f}(0))-V_{3}(s_{f}(\infty))\leq\infty\). Therefore, it is concluded that error can asymptotically converge to zero as \(t\rightarrow\infty\) based on Barbalat Lemma.  
This completes the proof. \(\square\)
## 2 Methods
### Functional Models for the Determination of the Satellite Acceleration
#### 2.1.3 Orbit Differentiation Method  
For the time series of satellite positions \(r(t)\), the polynomial is expressed as:  
\[r(t_{0}+\tau_{i})_{N}=\sum_{j=0}^{N}c_{j}\tau_{i}^{j} \tag{12}\]  
where \(r(t_{0}+\tau_{i})\) is the satellite position; other symbols are mentioned above.  
Satellite accelerations can be represented as a linear combination of the satellite positions for the constant sampling interval. The degree of the polynomial should be even and the points for the interpolation should be odd, which guarantees a symmetrical distribution around the central time at which the polynomial is evaluated [26]. In fact, the sampling interval \(\Delta t\) of the PKI orbit and the carrier phase observations is not constant. There are two main reasons for choosing the sixth-order seven-point differentiation operator with constant coefficients. First, the differentiation with the constant coefficients is more efficient than the differentiation with non-constant sampling interval, which must estimate the coefficients individually for the seven input points. Second, the percentage of the carrier phase observations with the maximum adjacent epoch difference of continuous seven points greater than \(6\times 10^{-6}\) s is less than 1%, which can be ignored. Accordingly, we only used the data with the adjacent epoch difference less than \(6\times 10^{-6}\) s. Further, we tested the accuracy of the unequally sampled differential accelerations derived with constant differentiation coefficients in inertial reference frame (IRF), as seen in Table 1. The data used for the test is from November 1 2009 to January 11 2010 (71 days). According to the Table 1, the accuracy of the differential accelerations is less than \(3.039\times 10^{-7}\), which satisfies the requirement of the gravity field modeling.
## 4 Evaluation
### Key Results
#### 4.2.3 Scalability  
In this experiment, the proposed framework demonstrated robust scalability as follows:  
* Device count: Performance remained stable with up to 1000 edge devices. Training time increased slightly to 84.40 s per epoch, while latency rose to 350 milliseconds.
* Data volume: The framework efficiently handled varying data sizes from 10 GB to 100 GB, maintaining accuracy and efficiency.  
From the blockchain impact on scalability perspective, the blockchain's decentralized ledger allowed seamless integration of additional devices without compromising security or increasing risks of bottlenecks.
## 4 Discussion  
### SIF Differences between Vegetation Types  
To understand the SIF values between needle and broadleaf plants, we selected the highest 5% (among all 1519 SIF points) and lowest 5% (among all 1482 points) SIF values of each Julian day and analyzed the spatial distribution of high and low SIF values. We found that 77.75% (1181 out of 1519) of the highest SIF values and 69.23% (1026 out of 1482) of the lowest SIF values were distributed in the southern and northern Yilehuli Mountain, respectively (Figure 11). Photosynthesis between coniferous and broad-leaved forests is different because of the differences in leaf characteristics and canopy structure [30; 48]. The average temperature in Lingnan is higher than that in Lingbei because of the lower latitude and the souherly aspect, and these are other reasons for higher SIF. Therefore, SIF increased due to the increase in photosynthesis and temperature. Compared with coniferous forests, broad-leaved forests usually have a larger leaf area, higher rates of photosynthesis [48], and then higher GPP and SIF, which is an important conclusion of this research. This result is in good agreement with the findings of [PERSON], et al. [30], who argued that different vegetation types are usually characterized by different SIF values. In general, the highest SIF values are found in croplands, followed by broadleaf and needleleaf forests [30]. Temperature and perpetually frozen soil in northern Greater Khingan Mountains may also be the reason for the lower SIF values. The northern Greater Khingan Mountains are a permafrost region [49] with poor plant growth and low photosynthetic activity.  
Figure 10: Scatter plot between SIF and temperature at a 16-day time scale.
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
## 1 Introduction
### Human-Following Robot  
Following behind the human, side-by-side with the human, and following in front of the human are the main categories of the human-following robot [25]. Following the target human from behind is investigated in the previous works [6]. In recent years, studies about following side-by-side and following in front are explored. Side-by-side walking is a human-like behavior and enables the robot to accompany the human, which is applied for social robots [26; 27; 28]. Following in front of a human is consistent with human behavioral patterns like pushing a cart and allows the human to check the robot at all times when the robot carries valuable items. We highlight the front-following studies since it is the same pose as our human-guided stroller.  
[PERSON] et al. [29] proposed a holonomic motion model to allow the robot to stay in front of the human. [PERSON] et al. proposed a natural following model using LRFs and IMU sensors to monitor movement in the gait cycle [30]. [PERSON] et al. proposed a finite-time controller to control an omnidirectional Lane robot to the desired pose using the user's leg motion [31]. These works suffer from a common limitation; the lack of direct instructions from the user to the robot makes it difficult to handle situations where the users change their behavior suddenly, such as sharp turns.  
To solve this issue, anticipatory control methods were proposed to enable the robot to predict the human's next behavior [5; 25]. [PERSON] et al. proposed to estimate the user's next action based on the current heading of the person and the position of the obstacle [32]. [PERSON] et al. used the human head pose as an implicit indicator and predicted the human future pose based on the Bayesian technique and fused the predicted result with the other human pose-predicted result using Gaussian fusion. The proposed method was evaluated in a simulation with a comprehensive route design and achieved good results; however, it sometimes failed when passing constrained walk paths [33]. Using only anticipatory methods is not stable to keep the robot in the user's FOV because of ambiguity in the user's intention. Robots automatically attempt to reach a point in front of the person without direct human instructions in the above studies, which causes tracking failure in some cases.
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
## 5 Discussion
### Axial and Radial Vibration Characteristics  
An analysis of Figures 4-6 reveals that, in the tunnel on the construction side, vibration displacement in the three directions follows the order of axial > radial > vertical. During shield tunneling, the majority of vibration energy propagates along the axial direction, resulting in the most intense axial vibrations. In contrast, radial and vertical vibrations, while weaker, still show noticeable responses.  
For the adjacent tunnel, distinct vibration characteristics are observed. Data from Figures 8-10 indicate that vibration displacement follows the order of radial > axial > vertical. Radial vibrations are particularly pronounced, especially near the vibration source. In the construction-side tunnel, vibration energy propagates further along the axial direction due to the thrust and cutting forces of the shield machine. In contrast, vibration energy in the adjacent tunnel follows a shorter radial path, leading to higher energy concentration. These findings suggest the need for differentiated vibration control measures for both the construction-side and adjacent tunnels to optimize construction planning.
## 1 Introduction
### Tree Cover in the Australian Context  
An accurate record of existing tree cover is particularly important in Australia, as it has lost nearly 40% of its forests ([PERSON] et al., 2016). Prior to the work presented here, tree cover was mapped over NSW using Landsat  
Figure 1: Examples of native trees present across New South Wales (NSW), Australia. (**A**) Remnant trees in the cleared agricultural land of the central west; (**B**) Open woodland in the semi-arid north west; (**C**) Sclerophyll forest on the east coast; (**D**) Rainforest in the north east; (**E**) The locations of each example.  
data (25-30 m pixels), based on methods developed for the Queensland (QLD) Statewide Landcover and Trees Study (SLATS) [25]. The SLATS method relied on a model between field measurements of foliage projective cover (FPC) and normalised Landsat data [36]. FPC is the percentage of ground area covered by the vertical projection of foliage from tree crowns, and is commonly used in Australia [37; 38]. Changes in FPC over time can be useful for detecting clearing, and distinguishing persistent tree foliage from temporary ground cover and understorey foliage.  
Landsat based maps were a great improvement on previous products, however significant small patches of vegetation were still not mapped [29; 39]. [PERSON], [PERSON] and [PERSON] [29] compared tree cover derived from time series 25 m Landsat data [28], 10 m SPOT4 data, and 0.15 m aerial photography, and found that tree cover mapped from sensors with pixel sizes greater than 10 m did not accurately detect small remnant patches of vegetation. Landscape fragmentation, product scale and minimum mappable unit were shown to influence the magnitude of error.
## 4 Experiment Results
### SHAP Value Analyses
#### 4.1.4 Single Model Results Analysis
Over-fitting due to data imbalance was not observed. In the performance evaluation in Section 3.2, the best models of each attack type were selected as base models to design a stacking-based ensemble model. The base models independently detect flooding, fuzzy, and replay attacks from the input CAN Traffic Data and deliver these detection results to the meta model shown in Table 21. The decision tree-based metamodel synthesizes the detection results of each base model to determine whether an attack has occurred in a binary sense. As a result of analyzing the SHAP value interaction and Pearson correlation of the detection results of each base model, it is estimated that the correlation and similarity between the detection results of the replay and fuzzy models are high.
## 4 Result Analysis and Comparison with FTA
### Result Comparison between FTA and STPA  
FTA can fully exploit the accident caused by component failure. At the same time, it can depict the chain of events that caused accidents due to the failure of the underlying components. In the above analysis, both STPA and FTA were used in the analysis of escalator reversal accidents. In order to prove the feasibility of the STPA method, Table 8 starts from different objects and compares the results of the two methods. Excluding some of the more subtle aspects, it can be seen that the STPA can identify the cause of the accident that the FTA can identify, but at the same time there are some reasons that are caused by the interaction of the component objects and components considered from the STPA-specific STAMP theory. Emergence reasons. Although the results of STPA analysis may have been considered or are unlikely to occur, these control theories are still one of the reasons that may cause major accidents.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Object** & **Identified by Both FTA and STPA** & **Identified by STPA Only** \\ \hline
**sensors** & Sensors damaged &
\begin{tabular}{c} Unreasonable installation location; \\ Signal from sensor is disturbed \\ \end{tabular} \\ \hline
**PLC** & Component of PLC damaged &
\begin{tabular}{c} overspeed threshold setting; \\ Command delay; \\ Error algorithm \\ \end{tabular} \\ \hline
**Brake** & \
\begin{tabular}{c} Component of brake damaged; Braking \\ force is small \\ \end{tabular} &
\begin{tabular}{c} Braking signal trigger setting; \\ Unreasonable trigger threshold; \\ short braking duration \\ \end{tabular} \\ \hline
**Driving spindle** & Component of driving spindle damaged & Long time load \\ \hline
**Passengers** & Heavy load (high traffic flow) &
\begin{tabular}{c} Reaction time of passengers; \\ Mental states of passengers; \\ Long traveling time \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Analysis result comparison between fault tree analysis (FTA) and system-theoretic process analysis (STPA).  
Figure 14: Fault tree of escalator overturned accident.
## 4 Experiments and Results
### Model of Process Agents  
In this model, software agents were considered as static in each node gateway. Each agent is coupled inside the node-gateway. For the platform's development it didn't grow all of a software agent's functionalities as it can be seen in [13], because that was not the mark of this work. it grew in the simulator the change of messages among the agents. The NS-Agents were divided in NS-Agent (it Sends), that it has the specific function of sending messages for NS-Agent (it Receives), with all of the information about the amount of devices contained in the network. That is made by message change that is better detailed in the subsection below.  
The model of the node gateway in the simulator OPNET contains all of the functions of a gateway, in other words, from of the access to the physical layers (Wireless_Lan_Mac), of network, of transport to the application layer.  
For the message exchange among the agents grew up a format of specific package containing the following fields (source address, destination address, amount of mesh routers nodes, amount of clients nodes, amount of gateway nodes and total of all nodes in the network). Each node-gateway have these fields locally. Later these fields are encapsulated in a package that is sent of times in times for all of the neighboring nodes of a certain gateway, with the intention of obtaining the list of the size of the network always updated.  
In function of the method used for sending of these packages to be flooding, a time of sending of the updating of the list should be configured, that can vary according to the accumulation of messages in the network. The configured standard time is measured in seconds, therefore to every 50 seconds thegateways-nodes send their information, avoiding like this the accumulation of exchange of the agents' messages.  
The NM-Agents are also divided in NM-Agent (it Sends) and NM-Agent (it Receives) and they have the function of monitoring the nodes gateways in mesh that they are active or not in the network. The NM-Agent (it Sends) has the function of contacting NM-Agent (it Receives) of times in times. That process form the self-cure base proposed for this dissertation, however that capacity still was not implemented in this work.