esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Individual Plant-Level Segmentation Based on UAV-LS  
Phenotypic analysis technology based on UAV-LS has garnered significant interest from researchers due to its capability to collect large-scale field crop data in a multi-scale and real-time manner. Upon obtaining the crop point cloud data from the field scene, efficiently extracting individual plants is a crucial step for acquiring their phenotypic traits. Unlike organ-level segmentation tasks, current methods for segmenting plots into individual plants largely depend on traditional machine learning algorithms, with end-to-end deep learning models being relatively scarce.  
Conventional methodologies, such as region-growing algorithms [94], Euclidean clustering algorithms [95], and k-means clustering algorithms [96], have served as important facilitators in the individual plant point cloud segmentation process. In this regard, [PERSON] et al. proposed a columnar space clustering segmentation method for singularly segmenting the plant species of maize, cotton, and oilseed rape [97]. These tactics tend to be predicated on certain predetermined feature information. With the growing adoption of deep learning, certain deep learning-based models have reduced the reliance on such a priori features. For instance, [PERSON] et al. introduced a segmentation strategy that integrates Faster R-CNN with the aforementioned region-growing algorithms to segregate individual maize plants existing in a field context [98]. Despite this advancement, the algorithm still requires synchronization with conventional methodologies to achieve optimal performance, warranting a need for a comprehensive end-to-end network model to independently handle individual plant segmentation. In this experiment, the potential of Mask3D for segmenting plot maize was validated, demonstrating its effectiveness in segmenting individual plant-level point clouds. Compared to organ-level segmentation, extracting individual plants often necessitates a comprehensive exploration of the entire regional point cloud information. By employing sparse convolutions, Mask3D reduces the computational cost, enabling feature extraction from the entire plot point cloud. However, this experiment was limited to maize, and further exploration is required for crops with more complex morphological structures.
###### Abstract  
As the technology of the maritime autonomous surface ship (MASS) systems has geared toward autonomy, the importance of human operations in the shore control center (SCC) has gained in significance. Accordingly, the effects of the training method, including the traditional and new remote operator training methods have to be investigated in terms of MASS navigation safety. Therefore, this study conducted a comparative analysis to prove the effect of onboard training. The findings include the execution of a simulated navigation experiment, the extraction of rudder steering-related features, selection of significant features, and comparative analysis with network graph visualization. The separate results obtained from the \"untrained\" group and \"trained\" group were exhibited as the purpose of research for the effect of onboard training on navigation skills. Then, the authors interpreted the difference in each group allusively in accordance with features considering actual navigation and compared groups using descriptive statistics. Consequently, this study emphasized the importance of proving the effect of training before the new training technologies are used to train MASS remote operators in the future.  
maritime autonomous surface ship; remote operator; effect of training; simulated navigation experiments; comparative analysis +
Footnote †: journal: Applied  
proportion of navigator training than it does now, and the training must include navigation skill enhancement [23; 24].  
Existing navigation skills will have to be reinterpreted in line with the new education and training requirements for MASS [25; 26]. In a similar manner, other research has emphasized the need for newly added training, such as the mandatory requirements of the future version of STCW [17; 27]. Despite the growing debate over the new training, the expected effect of training on navigation skills is yet to be adequately elucidated. Thus, the verification of enhanced navigation skills ought to be conducted, especially before the new method of training is applied. As a result, this study conducted a simulation experiment on two different groups, namely, the \"untrained group\" and the \"trained group,\" of onboard training as preparation for obtaining the newly added type of training. The proposed methods include related materials from the process of experiment execution to the interpretation of test results.  
As a result, this study primarily aims to investigate the impact of onboard training on navigation skills improvement through a comparative analysis of various aspects of navigation evaluation perspectives.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 6 Emerging Human-AI Collaboration Problems in RSA
### Emerging Problem 9: Relighting and Removing Unwanted Artifacts on Live Video  
We have discussed the shaky and low-resolution issues affecting the quality of the live camera feed in Problem 7 and Problem 8, respectively. There are other important aspects of the quality of the live camera feed that can greatly impact the RSA performance. The first issue is the poor lighting condition, either too dark or too bright. As listed in problem G4.(2) in Table 1, low ambient light conditions at night cause the poor quality of the video feed. The second issue is due to the unwanted artifacts on the live video, including unwanted lights (e.g., reflected light, flare or glare) and unwanted contaminants (e.g., dust, dirt, and moisture) on the camera lens. These issues also belong to the external issues of problem G4 in Table 1 as major RSA challenges. Therefore, we identify an emerging research problem of changing the lighting condition (or simply relighting) and removing unwanted artifacts on live video that can be addressed by human-AI collaboration.  
For the issue of poor lighting conditions, there exist extensive AI-based methods in the literature on illumination estimation and relighting [107] in computer vision and computer graphics. Basically, the original lighting of the scene in the video can be recorded as high dynamic range (HDR) light probes [155]; then the lighting of the original video can be accordingly adjusted to be pleasant to view. These methods cannot be directly applied to RSA services, because the scene after relighting may look quite different from what other pedestrians see and may mislead the agent to make wrong judgments. This issue can be addressed in a human-AI collaboration framework, where the agent can define the type of the scene (e.g., indoor or outdoor) and specify the strength of the relighting effect.  
For the issue of unwanted artifacts, there are also AI-based methods for removing unwanted lights [156] and unwanted contaminants [157] in the CV literature. These methods train deep neural models with synthesized datasets to detect unwanted lights (e.g., scattering flare, reflective flare) or unwanted contaminants and restore the unwanted parts with view synthesis. When applying to the RSA services, AI-based methods can be further improved with the human-AI collaboration framework. On the one hand, the agent can ask the user to act to reduce the influence of the unwanted artifacts (e.g., move the camera direction to avoid flare, or wipe the lens to clean some contaminant). On the other hand, the agent could identify the unwanted artifacts on the live video feed and mark the area for the AI algorithm to conduct restoration. The AI-detected contaminant can be approved or denied by the agent. In this way, the removal results would be better than AI-only methods.  
In addition, the study of Problem 9 can help address Problem 4 (recognizing texts on irregular surfaces), where light reflection or refraction is one of the main challenges in recognizing texts on irregular surfaces.
## 1 Introduction
### History of Glucose Sensors  
[PERSON] and [PERSON] developed the first-generation of glucose sensors using an electrochemical method to monitor the oxygen consumed by the catalyzed enzyme. A thin layer of GOx enzyme, immobilized over the oxygen electrode, had been developed for that purpose [19]. The reaction could be described by the following equations:  
\[Glucose+O_{2}\to glucose\ acid+H_{2}O_{2} \tag{1}\]  
Reductive detection of oxygen consumption could be calculated by applying a negative potential to the cathode electrode:  
\[O_{2}+4H++4e-\to 2H_{2}O \tag{2}\]  
In second-generation glucose sensors, mediators (soluble redox-active molecules have abilities of rapid and reversible redox reactions in which closure of the electrons between the redox center at the enzymatic active site and electrode surface) are developed to be used [20; 21]. The following equations describe the second-generation glucose biosensors:  
\[glucose+GOx(FAD)\to gluconic\ acid+GOx(FADH_{2}) \tag{3}\]  
\[GOx(FADH_{2})+2\text{M}(\text{ox})+2e^{-}\to GOx(FAD)+2\text{M}(\text{red})+2 H^{+} \tag{4}\]  
\[2\text{M}(\text{red})\rightarrow\ 2\text{M}(\text{ox})+2e^{-} \tag{5}\]  
Figure 2: Different types of carbon allotropes crystalline. (**a**) Fullerene—C60, (**b**) graphene monolayer, (**c**) carbon nanotube (CNT), (**d**) carbon nano-onion, (**e**) nanodiamond, (**f**) carbon nanohorn.  
where M(ox) and M(red) are the oxidized and reduced forms of the mediator. The third-generation glucose biosensors operated with a direct electron transfer between the enzymatic redox center and electrode, which create high sensitivity and reproducibility without any mediators and at low potential [22; 23; 24]. The mechanism of this biosensor can be understood by the following reactions:  
\[glucose+GOx(FAD)\to gluconic\ acid+GOx(FADH_{2}) \tag{6}\]  
\[GOx(FADH_{2})+2e^{-}\to GOx(FAD)+2H^{+} \tag{7}\]  
Figure 3 shows a schematic of the three generations of glucose sensors.
## 4 Classification into RPN Groups by Analysis of Risk Priority Number (RPN)
### RPN Groups
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Description** & **Probability (P)** & **Sensitivity (S)** & **Detection (D)** & **RPN Score** & **RPN Group** \\ \hline Crossing & 5 & 5 & 3 & 75 & C \\ Switch toes & 3 & 5 & 3 & 45 & C \\ Joint bolt & 4 & 4 & 3 & 48 & C \\ Heel block bolt & 4 & 3 & 2 & 24 & C \\ Normal rail & 3 & 2 & 3 & 18 & B \\ Switching bolt & 3 & 2 & 3 & 18 & B \\ Wooden tie & 2 & 4 & 2 & 16 & B \\ Closure rail & 2 & 2 & 3 & 12 & B \\ Stock rail & 2 & 2 & 3 & 12 & B \\ Crossing baseplate & 3 & 2 & 2 & 12 & B \\ Check bolt & 2 & 2 & 3 & 12 & B \\ Check rail & 2 & 2 & 2 & 8 & A \\ Check baseplate & 2 & 2 & 2 & 8 & A \\ Baseplate & 2 & 2 & 2 & 8 & A \\ Screw spike & 1 & 3 & 2 & 6 & A \\ Closure joint & 2 & 2 & 1 & 4 & A \\ Normal joint & 2 & 2 & 1 & 4 & A \\ Insulation joint & 2 & 2 & 1 & 4 & A \\ Closure rail baseplate & 1 & 2 & 2 & 4 & A \\ Switch toes baseplate & 1 & 2 & 2 & 4 & A \\ Bearing tie & 2 & 2 & 1 & 4 & A \\ Crossing joint & 1 & 2 & 1 & 2 & A \\ U clip & 1 & 2 & 1 & 2 & A \\ Coil spring & 1 & 2 & 1 & 2 & A \\ \hline \end{tabular}
\end{table} 
Table 9: Overview of RPN groups based on the types of turnout components.
## 4 Application to an Additive Manufacturing Benchmark Test
### Deep Neural Network (DNN) Surrogate Modeling  
A surrogate model was constructed using DNN regression to build a surface map between the input and the output data set. The DNN has six hidden layers, where the first and the last layers have 800 neurons and the middle four layers have 500 neurons each. To construct our DNN model, we used Matlab's deep learning toolbox.  
In the present study, we considered five uncertain parameters, where three are the machine parameters and two relate to material properties. To change the values of [PERSON]'s modulus and the Poisson ratio, we multiplied their baseline values by factors \(cY\) and \(cv\), respectively. The input variables were laser speed, layer thickness, hatch spacing, \(cY\), and \(cv\). A dataset was generated with a sample of 360 input variables using a Sobol sampling technique. Initially, the sample set responses were obtained using the finite element model, and the normal strains were calculated at z = 9.536 (build direction). Later, the outputs from the 360-sample set were used to construct a DNN model, where 70 percent of the data was used to train the DNN, 15 percent was used to test it, and another 15 percent was used to validate the DNN model. Figure 13 shows the loss drop during training, testing, and validation of the DNN model, and Figure 14 shows the plot between the exact and predicted values during training, testing, and validation of the model, using x-strains. The DNN model was developed separately to train and predict the outputs of each normal directional strain at z = 9.536. Figure 15 compares the DNN outputs and the simulation outputs for each directional strain at z = 9.536.  
Figure 11: The NIST AMB2018-1 bridge geometry [9].  
Figure 12: Comparison of the part deflection results from experimental and simulation data.  
Figure 14: Comparison of the exact and predicted results during training, testing and validation of the DNN model.  
Figure 13: Error graph during the training, testing, and validation of the DNN model.  
Once the neural network surrogate model is developed, it can be used to perform statistical and calibration studies. For this purpose, a sample of sufficient size must be generated. We used the Monte Carlo method to perform the convergence analysis using standard deviation (Std) for different numbers of sample sets, including 12, 48, 84, 120, 192, 264, 336, 516, 732, 1020, 1524, 2028, 2532, 3000, 4008, 5016, 8004, and 9588, and the standard deviation was calculated for each sample set. The convergence study was performed for individual directional strains at two locations. Figure 16a,b shows the results for the x strain at 15 and 45 mm, while Figure 16c-f present convergence plots for the y and z directional strain at the 15 and 45 mm locations, respectively. From the figures, we concluded that a set of 6000 samples was enough to perform the sensitivity and uncertainty analysis for an SLM process, as described in the following sections.  
Figure 16: _Cont._  
Figure 15: Test outputs for the y and z strains.
# Lifetimes of Used Nuclear Fuel Containers Affected by
## 3 Sulphate-Reducing Bacteria Activity
### Reaction Rate Constant  
The analytical solution of (4a) describes an exponential decay:  
\[S=a_{0}exp\big{(}-k_{\rho}t\big{)} \tag{5}\]  
where \(a_{0}\) can be determined from the initial conditions and for \(k_{\rho}\) an optimization procedure was used to find the optimal value of \(k_{\rho}{}^{*}\) that minimizes the error of the predicted reduction with respect to the observed data. Two objective functions are tested, the squared differences and the square of log differences.  
\[\left\{\begin{array}{l}\underset{k_{\rho}}{\text{Min}}\sum\limits_{i}\left( d_{i}-\hat{d}_{i}\right)^{2}\\ s.t.\\ \hat{d}_{i}=a_{0}\exp\big{(}-k_{\rho}t_{i}\big{)},\ \ \forall i\end{array}\right. \tag{6}\]  
The optimization model (6) is minimized using the bracketing method, which is an iterative approach and where \(d_{i}\) is the concentration of \(SO_{4}^{2-}\) after time \(t_{i}\) obtained from the experiment. This model yields an optimal kinetic constant of \(k_{\rho}{}^{*}=5.9631\times 10^{-9}[1/s]\).  
The second optimization model:  
\[\left\{\begin{array}{l}\underset{k_{\rho}}{\text{Min}}\frac{1}{2}\sum \limits_{i}\left[ln(d_{i})-ln\left(\hat{d}_{i}\right)\right]^{2}\\ s.t.\\ ln\left(\hat{d}_{i}/a_{0}\right)=-k_{\rho}t_{i},\ \ \forall i\\ k_{\rho}\geq 0\end{array}\right. \tag{7}\]  
has a closed optimal solution given by  
\[k_{\rho}=\frac{\sum_{i}t_{i}\,ln\left(\frac{a_{0}}{d_{i}}\right)}{\sum_{i}t_{ i}^{2}}\]  
and the optimal kinetic constant in this model is \(k_{\rho}{}^{*}=6.1614\times 10^{-9}[1/s]\). This rate constant is adopted since it refers to the most unsafe situation and because it yielded a smaller error in two out of three error measures (see Appendix A).  
Therefore, the optimal reduction of sulphate describing the results from experiments in [18; 19] is  
\[\frac{\partial S}{\partial t}=-6.16\times 10^{-9}\ S \tag{8}\]  
in units [M/s].
# Super Twisted Sliding Mode Observer for Enhancing Ventilation Drive Performance
## 3 Experimental Setup and Results  
### Experimental Setup  
Simulation experiments were conducted in MATLAB/Simulink 2019a to evaluate the performance of the proposed control scheme. The Runge-Kutta technique was used to solve differential equations, with a sampling time of \(T_{s}=10\)\(\upmu\)s and a converter switching frequency of 15 kHz. The pole-zero cancellation method was employed to determine the control constraints for the vector control method. These parameters were applied consistently in both simulation and experimental validation. The PI parameters for the two current loops were identical in the simulation. For the STSMO controller, the proportional and integral gains were set to \(k_{p}=485\), \(k_{i}=1485\) for the current observer and \(K_{p}=4\), \(K_{i}=100\) for the speed observer.  
For a fair comparison, the switching gains were set identically for the simulated and experimental STSMO, with \(a_{2}=a_{4}=1000\). The other gains for the STSMO were set to \(a_{1}=a_{3}=200\). The cutoff frequency in Equation (28) was selected as \(\omega_{c}=25\), and the restraining level \(L=0.25\) was aligned with the actual flux range. Figure 2 illustrates the vector control system for the sensorless drive based on the STSMO.  
The experimental platform, equipped with the STSMO, was designed to evaluate the performance of the speed estimator in controlling the speed of an IMD. The platform consists of a 5.5 kW ventilation system and an AC drive module equipped with an intelligent power element. Figure 2 illustrates the block diagram of our experimental validation setup. The AC drive module comprises a diode rectifier and an insulated-gate-bipolar-transistor-based three-phase inverter. The apparatus utilized in the experiment is depicted in Figure 3.  
Figure 1: Schematic view of the proposed STSMO.
# Squill: Testing DBMS with Correctness Feedback and Accurate Instantiation
## 3 Design of Squill
### Correctness-Guided Mutation
#### 3.1.1 Correctness-Focused Seed Selection  
In DBMS fuzzing, most of the seed increments come from valid seeds. Valid seeds can trigger deeper logic of DBMS than those with syntactic and semantic errors, exploring more paths. In addition, valid seeds have a higher probability of generating valid seeds, producing more crashes. Based on the conclusion above, we propose a correctness-focused seed selection strategy. We mutate valid seeds first, then seeds with semantics-error, and finally seeds with syntax-error. Because mutating valid seeds is more likely to generate  
Figure 4: Overview of Squill.  
valid seeds, leading to more path exploration and bug finding. The process of seed selection is shown in Algorithm 1.  
```
1:// Run when fuzzer generates new seed
2:functionupdate_bitmap_score(new_seed)
3:for i from 0 to MAP_SIZE do
4:// trace_bits is the bitmap of current seed
5:if trace_bits[i] is not 0 then
6:if top_rated[i] is not 0 then
7:if new_seed has better correctness than top_rated[i] then
8: top_rated[i] = new_seed;
9:endif
10:// Compare speed and size with top_rated[i]
11:// when they have same correctness
12:if new_seed has the same correctness as top_rated[i] then
13:if new_seed is faster and smaller than top_rated[i] then
14: top_rated[i] = new_seed;
15:endif
16:endif
17:else
18: top_rated[i] = new_seed;
19:endif
20:endif
21:endfor
22:endfunction
```  
**Algorithm 1** Correctness-focused seed selection.  
We implement correctness-focused seed selection based on AFL's original seed selection mechanism. AFL updates top_rated whenever it finds a new seed top_rated is an array that has the same size as the bitmap, where each value records the seed with the highest score on the corresponding edge in the bitmap. The faster, smaller seed will have a higher score. Then, AFL uses a greedy algorithm to select a minimum subset of seeds that contain all edges in the bitmap from seeds recorded in top_rated. Seeds in this subset are marked as favored. The favored seeds have a higher probability of mutating. AFL uses this mechanism to reduce the seed queue and improve the efficiency of fuzzing.  
In the original seed selection mechanism, AFL prioritizes fuzzing faster and smaller seeds. This mechanism tends to select more syntax-error or semantics-error seeds to participate in fuzzing. The seeds with syntactic or semantic errors usually have a faster execution speed, as they terminate at the syntactic and semantic check phase of DBMS. The subsequent phases of DBMS, such as the optimization and storage phase, are often time-consuming. In our method, we preferentially update seeds with better correctness into top_rated, as shown in Algorithm 1 Line 7-9. We define that valid seeds are better than semantics-error seeds, which are better than syntax-error seeds. We first compare the correctness of seeds and then consider their execution speed and size only if they have the same correctness (Line 12-16). It ensures that valid seeds are mutated preferentially.
# Dielectric Characterization of Chinese Standard Concrete for Compressive Strength Evaluation
###### Abstract  
Dielectric characterization of concrete is essential for the wireless structural health monitoring (SHM) of concrete structures. Guo Biao (GB) concrete refers to the concrete mixed and cast in accordance with the Chinese standard. Currently, China is the largest producer and consumer of concrete in the world. However, minimal attention has been paid to the dielectric properties of GB concrete. This paper presents the results of the dielectric constant of GB concrete, where three regression models have been used to present the measurement data from 10 MHz to 6 GHz. The objective is to provide a data set of nominal values of the dielectric constant for ordinary GB concrete. The final goal is to facilitate a compressive strength evaluation via the measured dielectric constant. Measurements of the dielectric constant and compressive strength for five types of ordinary concrete have been undertaken, after 28 days of curing. As the main contribution in this work, the correlation model between the compressive strength and dielectric constant of GB concrete is realized.  
dielectric characterization; dielectric constant; Chinese standard concrete; Debye model; Jonscher model; structural health monitoring (SHM) +
Footnote †: journal: Applied Sciences