esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Simulation
### Tracking of Simple-Maneuvering Target  
First, a simple-maneuvering target moving with constant acceleration or jerk is considered to validate the above theoretical analysis. For the target with constant acceleration, neither the predicted positions obtained by the \(\alpha\)-\(\beta\)-\(\gamma\) filter nor the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter include bias error due to target motion, because they consider constant acceleration or jerk. Thus, the consideration of constant acceleration motion indicates the difference of the reduction in performance of random errors between these filters. In contrast, the \(\alpha\)-\(\beta\) includes bias error due to acceleration of the target. Thus, the simulation assuming constant acceleration compares the errors of the \(\alpha\)-\(\beta\) filter that consist of both random and bias errors against those of the \(\alpha\)-\(\beta\)-\(\gamma\) filter and the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter, which are affected by random errors only. On the other hand, for the target with constant jerk, the \(\alpha\)-\(\beta\)-\(\gamma\) filter also has bias errors due to the difference between the motion model and the motion of the target. Thus, the simulation assuming constant jerk compares the errors of the \(\alpha\)-\(\beta\)-\(\gamma\) filter that consist of both random and bias errors against those of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter.  
Values of \(T=1\) s and \(B_{\text{x}}=25\) m\({}^{2}\) are set. In terms of the gain settings, we set identical values for the gains that determine the tracking performance index (i.e., we set the same value for \(\delta\) of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter, \(\gamma\) of the \(\alpha\)-\(\beta\)-\(\gamma\) filter, and \(\beta\) of the \(\alpha\)-\(\beta\) filter). Cases of both low and high gain are investigated. In the former, although the smoothing function of filters becomes relatively large, the bias error due to the complicated motion of the target also increases. In contrast, this kind of bias error is suppressed in the latter case. However, the smoothing performance for the random errors decreases. The gain settings in the two cases are as follows:  
* Low gain case: (\(\alpha\)=0.266, \(\beta\)=0.1) for the \(\alpha\)-\(\beta\) filter, (\(\alpha\)=0.738, \(\beta\)=0.165, \(\gamma\)=0.1) for the \(\alpha\)-\(\beta\)-\(\gamma\) filter, and (\(\alpha\)=0.613, \(\beta\)=0.715, \(\gamma\)=0.128, \(\delta\)=0.1) for the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter.
* High gain case: (\(\alpha\)=0.475, \(\beta\)=1.5) for the \(\alpha\)-\(\beta\) filter, (\(\alpha\)=1.354, \(\beta\)=0.560, \(\gamma\)=1.5) for the \(\alpha\)-\(\beta\)-\(\gamma\) filter, and (\(\alpha\)=0.593, \(\beta\)=2.14, \(\gamma\)=0.605, \(\delta\)=1.5) for the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter.
# Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies
## 3 MAT Solutions for PVI Shopping
### Navigation and Product Identification  
Going to the store is not the only navigation challenge for PVI; in-store navigation is also a complex problem [24]. It is challenging for PVI to navigate inside shops and to reach and identify products. It is also difficult for them to get detailed information about products, such as production and expiration date. Additionally, PVI always need help from others, as most shops are not well prepared to help them [11]. As a result, researchers have developed multiple solutions to help PVI navigate and identify products. These solutions are divided into three categories, as shown in Figure 2:  
1. Tag Based Systems: such as Radio-frequency identification (RFID) and Near Field Communication (NFC), which use wireless components to transfer data from a tag attached to an object for the purposes of automatic identification and tracking.
2. Computer Vision Based Systems: some of these systems require unique visual tags such as Quick Response (QR) codes, Barcode, or Augmented Reality (AR) markers to be placed on products. These tags are used for detecting and giving PVI all available details about the products. Other systems do not require tags to be placed on products. Instead, they utilize information about the objects' physical features to identify them.
3. Hybrid Systems: these take the strengths of two or more systems and combine them into a new system to deliver better accuracy and performance [9; 41; 42; 55; 56; 57; 58; 59; 60; 61; 62].
# A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
## 3 Results and Discussion
### Current State of IoT for Agriculture
Research in Embu County, Kenya tested different proximal soil sensors to estimate soil properties and composition on smallholder plots and provided management recommendations to farmers [57]. The measurements showed considerable variation, both within plots and regionally, indicating a need for management recommendations based on both local measurements and regional soil maps. Another application implemented a tracing system for smallholders who raised live poultry that were traded in Vietnam to prevent the spread of avian influenza [75]. Low-cost radio-frequency identification (RFID) tags on transport cages and electronic tag readers at markets tracked birds from farm-to-market, revealing that birds passed through up to six intermediate traders, creating a high risk of virus transmission. Many commercial entities serving smallholder farmers use the IoT to track assets and interface digitally with customers; for example, tractors and implements for hire by Hello Tractor in West Africa and EM3 AgriServices in India [83], and irrigation equipment by Agriworks, Futurepump, and SunCulture in East Africa [84]. Enrollment in these services is enabling more productive growing; however, low digital literacy is a barrier that requires a mix of traditional and technology-enabled engagement with farmers. A notable case is the use of sensors to monitor borehole pump reliability for potable and irrigation water supply in East Africa, which is discussed later in this article.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 3 System Model
### Deep Reinforcement Learning Module  
Deep reinforcement learning is a deep learning algorithm that involves two main components, including the environment and the agent. The agent selects an action according to the environment state and receives the reward and the next state from the environment. The main goal of the DRL algorithm is to train the agent to select the actions in the environment that maximize its rewards. The environment for DRL is designed as a Markov decision process, so it can only solve Markov decision process problems. DRL is divided into off-policy methods and on-policy methods. Off-policy methods use different strategies to collect experiences and use these experiences to improve other target strategies. On-policy methods directly collect experiences based on the current policy and use these experiences to improve the policy. Compared to off-policy methods, on-policy methods, because they cannot use the experiences of old policies, need to interact with the environment more frequently to collect experiences, which leads to a decrease in learning efficiency. However, because they are updated according to a consistent policy, the updates obtained are more effective and the decision-making performance stability of the new policy is higher. Typical off-policy methods for SDN include DQN [19]. Typical on-policy methods for SDN are, for example, the AC [20], DDPG [21], and PPO [26], which are called policy gradient algorithms (PG). Compared to DQN, PPO has greater stability, which makes PPO perform better on complex problems than DQN. Transitioning from traditional policy gradient algorithms like DDPG and AC to PPO is driven by the quest for stability and efficiency. While DDPG excels in continuous action spaces, it is sensitive to hyperparameters; AC leverages asynchronous updates for diverse data but faces challenges in distributed training. PPO, on the other hand, employs a clipping mechanism to prevent large policy updates, offering a more stable and consistent learning experience. This makes PPO often outperform DDPG and AC without intricate tuning or handling asynchronous complexities (response to Reviewer 4, comment 10). Additionally, PPO usually requires less hyperparameter tuning, making it more convenient than DQN. In this work, we chose the on-policy PPO algorithm as our decision-making module and propose a TBPPO. We will provide specific descriptions of the algorithm in Section 4.5.
## 3 Silicon Photonics
### Waveguides  
Waveguides are a core element of integrated photonic circuits. Their most important characteristic is their propagation loss, for which the dominant cause is the sidewall roughness [18]. This parameter is difficult to estimate and requires special conditions to observe. Figure 7 shows an SEM micrograph of the same view field inspected by different SEM detectors. Adjustment of the microscopy conditions enables correct detection and characterization of the sidewall roughness.  
Figure 6: Passive device manufacturing. Schematic representation of the main process steps: (**a**) SOI wafer as starting material, (**b**) silicon dioxide hard mask deposition, (**c**) silicon structuring by time-controlled shallow etching, (**d**) complete silicon etching in selected areas, (**e**) silicon dioxide top cladding deposition with Chemical-mechanical polishing (CMP) planarization.  
The sidewall roughness development starts from the lithography process and is transferred into the silicon by dry etching. Different types of the process could be used to reduce silicon waveguide sidewall roughness, such as photoresist reflow [19], more isotropic silicon etching [20], or sidewall roughness smoothing postprocessing [21]. In our manufacturing process, we used the latter-smoothing of completely etched waveguides by oven wet oxidation at 1050 \({}^{\circ}\)C to create 26 nm of thermal silicon dioxide. This process consumes 12 nm of silicon and sufficiently planarizes the sidewalls following its diffusion mode. The transmission loss measured on these passive devices is reduced from 7 dB/cm down to 1.5 dB/cm after applying this process (Figure 8a,b). In addition, the spectral noise clearly improved in all spectra. Moreover, as seen in Figure 8d,c, the slow-light waveguides (discussed in detail in Section 5) feature a much deeper band gap after thermal oxidation, which is a clear indication of the sidewall roughness reduction.  
Figure 8: (**a**) Transmission spectra of three waveguides having different lengths (L = 2.8 mm, 8.7 mm and 14.6 mm) for cut-back analysis of the propagation losses. (**b**) Propagation losses (dB/mm) of rib waveguides extracted with the least mean square method, allowing us to find the best linear regression by minimizing the sum of squared errors. The propagation losses are reduced to 0.15 \(\pm\) 0.05 dB/mm (mean value) after our developed thermal oxidation process was applied. The error bars on the propagation loss values at each wavelength arise from the goodness of the linear fit with respect to the measured data set. (**c,d**) Comparison of slow-light waveguide band gap (**c**) before and (**d**) after our thermal oxidation process was applied. The slow-light waveguides featured a much deeper band gap after the oxidation, which is another clear sign of the achieved sidewall roughness reduction.  
Figure 7: SEM micrograph of a ridge waveguide. Sidewall roughness is invisible using one detector (**left**) and visible using another (**right**).
# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 4 Results
### Correlations of Dynamap Predictions with Measurements  
In the following, we discuss the tests performed to evaluate Dynamap predictions based on Equation (10). The locations of the measurement sites are reported in Table 4, and have been shown by the filled yellow circles within Z9 in Figure 6. The measurement sites have been selected in order to test the system in complex scenarios where the noise from roads belonging to different groups may contribute to the local noise level. We have used the updating times: \(\tau=(5,10,15,30,60)\) min, and all time series have been normalized according to procedure P2. The validation of Dynamap predictions are based on the calculation of the associated correlation coefficients, Equation (17), between predictions and measurements. The calculations have been performed between  
* Case 1: A single measurement at site \(s_{j}\) with the corresponding Dynamap prediction.
* Case 2: The mean group values, \(\left<L_{\text{eq}}^{(\tau)}(t,s(s_{j}))\right>\), for the measurements and the mean Dynamap predictions, for all groups \(g_{i}\).  
The results are shown in Figure 12.  
Figure 12: Correlation coefficients between Dynamap predictions and some field measurements vs. update time \(\tau\) [min], obtained within the six groups (\(g_{1}-g_{6}\)), according to normalization procedure P2 (see Figure 10). The black lines illustrate the cross-correlations between the mean group equivalent noise level measurements and respective mean Dynamap predictions, \(\left<L_{\text{eq}}^{(\tau)}(t,s(g_{i}))\right>\)[93]. The location of the site measurements, (s1–s21) is displayed in Figure 6 (see also Table 4).  
As one can see, the correlation coefficients systematically increase with the integration time \(\tau\), but, at low times, such as for \(\tau=5\) min and \(\tau=10\) min in Case 1, the correlations are quite low. However, as it is apparent from Figure 12, Dynamap predictions present good correlation results with measurements, especially at higher integration times (\(\tau>10\) min). This result can be expected since high updating times \(\tau\) reduce high frequency fluctuations letting low frequency periodicities emerge. Furthermore, correlation performance is enhanced at all integration times when, within each group, one computes the correlation coefficient between mean Dynamap predictions and corresponding mean values of measurements. This is consistent with the fact that Dynamap noise description has a statistically-based approach and, therefore, its performance is enhanced when averaging over a sufficient number of observations.  
The results shown in Figure 12 are quite illustrative as they indicate a typical range of variability of Dynamap predictions from the real measurements. It can be used as a starting point also for the search of better site locations of the fixed noise stations within Z9. An attempt can also be made to connect these correlation results with the mean noise level deviations reported in Tables 6 and 7. This remains to be investigated.
## 1 Introduction
Para rubber plantations in addition to main socio-economic benefits, can offer ecosystem services like land recuperation, soil conservation, carbon sequestration, and water quality improvement. Furthermore, the rubber industry is influenced by social, political, economical, and ecological factors, which require a multidisciplinary approach for sustainable exploration. In recent years, there has been an increased interest in exploring approaches for a holistic evaluation of rubber tree abundance and its biomass estimations which is beyond the field investigations performed in plantations which have limitations because the traditional method of acquiring rubber plantation data, such as field surveys, over large areas can be time consuming, costly, and labor intensive. Remote sensing technologies are increasingly being used to account for aboveground biomass spatial variability and to predict future land use scenarios in many vegetation environments [7]. Although this technology has been relatively infrequently applied to management activities in Thailand, it is widely used in temperate and boreal regions [8]. Satellites such as MODIS and Sentinel are an effective technology for addressing these issues in large-scale assessments, with associated advantages as: (1) The ability to estimate biomass for small and large areas [9; 10], which is highly crucial in forest systems where field work is challenging to conduct due to topography, climate, and/or forest structure [11]; (2) gathering data in short time cycles, which enables time-series data [10], that is not possible with ground plot data for periods shorter due to labor and cost constraints [11];and (3) collecting data at a range of scales, from regional to local, connected with the spatial resolution of imagery [9; 10]. Consequently, remote sensing as a technique is readily available for the assessment of para rubber biomass resources at the local, regional, and global scales. This technique complimented with modeling is a highly appropriate measure for greater insight into evaluating para rubber biomass and would yield useful information about the dynamics of the rubber sector. The objective of the present exploration is to estimate the theoretical above ground biomass potentials of para rubber using the net primary productivity in the study area. This will bring to light the distribution trend and potential shares of rubber tree biomass across the province, and it is hoped that this study will lead to new insights of investigations on the identified biomass fractions.
## 3 Special Cases of Weight Functions
### Local Convergence
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & \(f\) & \(n\) & \(x_{n}\) & \(|f(x_{n})|\) & \(|x_{n}-\alpha|\) & \(|\epsilon_{n}\epsilon_{n-1}^{6}|\) & \(\eta\) & \(p_{n}\) \\ \hline \multirow{4}{*}{**EM1**} & & 0 & 0.01 & 0.00989984 & 0.0100000 & & \\  & & 1 & \(-1.33986049407934\times 10^{-12}\) & \(1.339\times 10^{-12}\) & \(1.339\times 10^{-12}\) & \(1.339860494\) & 1.296296296 & 5.99282 \\  & \(f_{1}\) & 2 & \(-7.5000087961687\times 10^{-72}\) & \(7.500\times 10^{-72}\) & \(7.500\times 10^{-72}\) & \(1.296296296\) & & 6.0000 \\  & & 3 & \(-2.30714514410106\times 10^{-427}\) & \(2.307\times 10^{-427}\) & \(2.307\times 10^{-427}\) & \(2.307\times 10^{-427}\) & & \\ \hline \multirow{4}{*}{**LK1**} & & 0 & 2.0 & 0.0907026 & 0.0206799 & & \\  & & 1 & \(1.97932014655603\) & \(7.783\times 10^{-13}\) & \(1.786\times 10^{-13}\) & \(0.002284503784\) & 0.00248336214 & 6.02152 \\  & & 2 & \(1.97932014655621\) & \(3.520\times 10^{-79}\) & \(8.081\times 10^{-80}\) & \(0.002483362140\) & & 6.0000 \\  & & 3 & \(1.97932014655621\) & \(0.0\times 10^{-299}\) & \(3.015\times 10^{-328}\) & & \\ \hline \multirow{4}{*}{**EMS**} & & 0 & 1.53 & 0.023961 & 0.0407963 & & \\  & & 1 & \(1.57079629958835\) & \(2.058\times 10^{-8}\) & \(2.721\times 10^{-8}\) & \(5.902375791\) & 7.190518106 & 6.06171 \\  & & 2 & \(1.57079632679490\) & \(2.208\times 10^{-45}\) & \(2.919\times 10^{-45}\) & \(7.190518106\) & & 6.0000 \\  & & 3 & \(1.57079632679490\) & \(3.367\times 10^{-207}\) & \(4.450\times 10^{-287}\) & & \\ \hline \multirow{4}{*}{**LK6**} & & 0 & 0.73 & 0.0381041 & 0.00104506 & & \\  & & 1 & \(0.7195493668682969\) & \(2.31\times 10^{-11}\) & \(7.703\times 10^{-12}\) & \(5.913012409\) & 6.120642565 & 6.00757 \\ \cline{1-1}  & \(f_{4}\) & 2 & \(0.719549366870672\) & \(3.837\times 10^{-66}\) & \(1.278\times 10^{-66}\) & \(6.120642565\) & & 6.00000 \\ \cline{1-1}  & & 3 & \(0.71954936870672\) & \(1.20621\times 10^{-327}\) & \(1.507\times 10^{-327}\) & \(
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 4 Study 2: Visual-Haptic Guidance Design and Evaluation
### Results
#### 4.2.3 User Preference  
After the test, the participants needed to complete a questionnaire about their preference and comments. A one-way repeated-measure ANOVA across the guidance feedback types found a significant effect on preference (\(F_{2,22}=22.17,p<0.001\)). Post hoc Bonferroni pairwise comparisons showed that the preferences of Haptic (7.25) and Visual-Haptic (7.75) were significantly higher than that of Visual (4.25) (\(p<0.01\)), as shown in Figure (a)a. A one-way repeated-measure ANOVA found significant main effects for workload using the NASA TLX (\(F_{2,22}=4.90,p<0.05\)), as shown in Figure (b)b. Post hoc Bonferroni pairwise comparisons showed that the workload of Visual-Haptic (23.89) was significantly lower than that of Visual (34.31) (\(p<0.05\)).  
Figure 7: Mean reaction time, selection time, head rotation degree, and head movement distance results of Secondary Task in Study 2.  
Based on user feedback, haptic feedback was found to elicit quicker responses compared to visual feedback, thereby reducing the time users spent in locating and following visual arrow indicators. Users reported that haptic feedback was direct and permitted rapid reactions with minimal cognitive load, thus reducing mental and visual demands. Users also reported being able to respond faster when relying solely on haptic feedback. Conversely, visual feedback had some drawbacks, including continuously shifting a user's attention, obstructing their line of sight, and being impacted by a user's visual attention and fatigue.  
In our experiment, haptic feedback only provided left and right directional cues. According to participants, providing more precise target location information would be beneficial. The vibration area was reportedly large, leading to some users taking time to discern if the direction was left or right. Therefore, users expressed a preference for a reduced vibration area, which would make the determination of the vibration direction more accurate. Moreover, users generally favored stronger haptic feedback settings.
## 2 Multiaxial Fatigue Assessment Method
### Fatigue-Damage Accumulation Rule  
Equations (1)-(3) provide the fatigue-life calculation method under constant stress-amplitude loading. However, actual projects are mostly subjected to loading with variable stress amplitudes. Therefore, proper accumulation rules must be introduced to account for each instance of fatigue damage and to finally obtain the fatigue life with the help of the definition of critical damage.  
Although various nonlinear fatigue-damage accumulation rules have been proposed for single or multiaxial fatigue problems, their complex formulations have prevented their widespread application [23; 24; 25]. In comparison, the traditional linear fatigue-damage rule, Miner's rule [26], has also been the most popular due to its simple and reasonable formulation. Therefore, Miner's rule is applied in this paper to calculate the accumulated fatigue damage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Advantage** & **Disadvantage** \\ \hline Fatemi-Socie (FS) & Taking into account the shear strain and normal strain on the critical plane & Without considering the influence of average stress effect \\ Wang–Brown (WB) & Considering the influence of mean stress & Ignoring the impact of the cyclic hardening effect \\ SWT (Smith–Watson–Topper) & Having a good life prediction effect & Improper for pure torsion and multiaxial fatigue \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of typical critical-damage-plane functions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Nominal Stress**} & \multicolumn{2}{c}{**Shear Stress**} & \multirow{2}{*}{**Angle**} & \multicolumn{2}{c}{**Experimental**} & \multirow{2}{*}{**Prediction**} & \multirow{2}{*}{**Difference**} \\  & **Amplitude (MPa)** & & & (\({}^{\circ}\)) & & **Results** & **Results** \\ \hline
1 & 449 & 282 & 90 & 29.900 & 31,038 & 3.81 \\
2 & 354 & 334 & 90 & 35,700 & 26,540 & \(-\)25.66 \\
3 & 126.491 & 95.507 & 30 & 420.261 & 283.118 & \(-\)32.63 \\
4 & 158.114 & 119.384 & 30 & 63,584 & 52,745 & \(-\)17.05 \\
5 & 126.491 & 100.0 & 45 & 275,527 & 212,599 & \(-\)22.84 \\
6 & 158.114 & 125.0 & 45 & 57,004 & 41,195 & \(-\)27.73 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation of the applied critical plane approach [16].