esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Morphological Comparison  
Morphological characters of the four wild Corsican hops are described in Table 4.  
Figure 1: UPGMA dendrogram based on Euclidean distance of cultivated and Corsican wild hops. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Accessions/Cultivars** & **H1 ACA3** & **H1 AGA7** & **H1 GA23** & **HIGT14** & **HIGT16** & **HIGT17** \\ \hline SMARAGD Corsica & 211 & 223 & 187 & 212 & 243 & 249 & 165 & 167 & 233 & 233 & 195 & 195 \\ SMARAGD Tettrang & 211 & 223 & 187 & 212 & 243 & 249 & 165 & 167 & 233 & 233 & 195 & 195 \\ CASCADE Corsica & 211 & 211 & 163 & 187 & 241 & 267 & 157 & 169 & 233 & 233 & 177 & 187 \\ CASCADE Tettrang & 211 & 211 & 163 & 187 & 241 & 267 & 157 & 169 & 233 & 233 & 177 & 187 \\ TETTNAMSER Corsica & 211 & 239 & 183 & 209 & 287 & 301 & 167 & 167 & 209 & 231 & 179 & 195 \\ TETTNAMSER Tettrang & 211 & 239 & 183 & 209 & 287 & 301 & 167 & 167 & 209 & 231 & 179 & 195 \\ Ajaccio Wild & 230 & 236 & 167 & 187 & 243 & 265 & 161 & 167 & 229 & 233 & 179 & 187 \\ Oletta Wild & 211 & 233 & 187 & 187 & 295 & 295 & 165 & 165 & 229 & 229 & 179 & 179 \\ Patrimonio Wild & 211 & 239 & 159 & 181 & 243 & 277 & 165 & 167 & 215 & 234 & 187 & 187 \\ Corte Wild & 209 & 231 & 187 & 187 & 295 & 295 & 165 & 165 & 215 & 234 & 179 & 179 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Genetic profiles of ten hop samples (six cultivars and four wild hops). From Table 4 it is possible to observe the absence of anthocyanin coloration in three wild hops from coastal areas of Corsica (Oletta, Patrimonio and Ajaccio). As [PERSON] et al. (2018) explained, the absence of plant pathogens, such as _Tetranychus urtice Koch_, downy mildew, and powdery mildew, makes the biosynthesis of protective anthocyanins unnecessary. Therefore, the absence of these biotic stresses in the studied areas could explain the absence of anthocyanin coloration of the main shoot of these wild accessions. The side shoots of the coastal plants Oletta, Patrimonio, and Ajaccio are short, and the number of cones is low (Table 4). As exposed previously, these three Corsican areas are characterized by high temperatures, high solar exposition, medium to strong winds and low precipitations, leading to a severe hydric stress, especially during the hop growing period. Therefore, this observation supports the correlation between the size of the plant, the number of cones, and water stress exposed by [PERSON] and collaborators (2018). Moreover, even if salt sensitivity of hop plants has not yet been studied, Oletta, Patrimonio and Ajaccio accessions grow in the coastal area, and salt stress could influence the plants' morphological traits (2018). Corte accession, instead, comes from a particular area of Corsica characterized by a humid microclimate caused by its weak winds, its medium precipitations and temperatures and the presence of two big rivers (2018). This leads to the presence, in Corte accessions,
## 5 Experimental Results  
To evaluate the proposed method, we conduct experiments with both a public dataset and a real intelligent vehicle test. TT100K is selected for the dataset experiment. In the real intelligent vehicle test, we collect the data in Zhenjiang, China, including a city expressway and an ordinary urban road. The real intelligent vehicle test covers different routes, weather conditions, and daytimes.
## 8 Performance Analysis
### The TFD Thresholds Parameter Analysis  
Since the size of \(\delta\) affects the maximum road load and search range for path-planning, it has an impact on vehicle path optimization and the resulting travel time. Therefore, it is important to determine an appropriate value for \(\delta\) to minimize travel time. To evaluate the impact of \(\delta\), \(\delta\) is set to different values for further study. It starts at 0.6 and increases in small steps of 0.05. As shown in Figure 5, it is the travel time when \(\delta\) takes different values  
update time interval increases, there is a decrease in the amount of information obtained through both the push and joint push-pull modes, while the pull mode remains relatively stable. This is because the longer update time intervals provide fewer opportunities for vehicles to receive information from the RSUs in the push and joint push-pull modes, while the pull mode relies on vehicles actively requesting information from the RSUs. In terms of performance, the push mode outperforms the pull mode, which can be attributed to the push mode's ability to proactively provide information to vehicles. Additionally, the joint push-pull mode outperforms the push mode, as it combines the advantages of both push and pull modes. The point push-pull mode allows vehicles to receive up-to-date information from RSUs while also being able to actively request information when needed.  
\begin{table}
\begin{tabular}{l c} \hline
**Simulation Parameters** & **Value** \\ \hline Network area & 20 (km\({}^{2}\)) \\ AEV speed(\(V\)) & 25–60 (km/h) \\ AEV mass (\(M\)) & 1200 kg \\ Acceleration of gravity (\(g\)) & 9.8 m/s\({}^{2}\) \\ Rolling friction coefficient (\(f_{r}\)) & 0.009 \\ Air density (\(\rho\)) & 1.2 kg/m\({}^{3}\) \\ Coefficient of air resistance (\(\varphi\)) & 0.335 \\ Frontal area (\(A\)) & 2 m\({}^{2}\) \\ Road slope (\(\theta\)) & 0 deg \\ Weight parameters & \(\alpha=0.5\), \(\beta=6\) \\ \(w_{t}=0.5\), \(w_{e}=0.5\) \\ \hline \end{tabular}
\end{table}
Table 4: Simulation Parameters.  
Figure 5: Comparison of travel time for different TFDs.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 2 Tissue Sources for Preparation of ECM-Based Biomaterials for Skin Repair
### Decellularized Mesothelioma
#### 2.2.3 Decellularized Skin Flaps  
A skin flap consists of healthy skin and adipose tissue (Figure 2A) or skin, fat, and muscle. Clinically, skin flaps are used to cover full-thickness, serious wounds [26] but are not always an option and can result in donor site morbidity and poor aesthetics. Additionally, split-thickness, autologous skin grafts often used to cover large wounds transplant only a small portion of the dermis and are thus subject to scarring and contracture. Full-thickness skin grafts, including epidermis and full dermis, are difficult to obtain but are subjected to less contracture and result in a more functional graft and a better cosmetic final outcome. However, they depend on vascular ingrowth. Therefore, generating a decellularized full-thickness skin flap with a perfusable vascular pedicle could eventually yield substantial regenerative potential. In two recent studies, decellularized skin flaps from animal sources were developed and recellularized and/or integrated into host tissue. [PERSON] et al. [72], using a perfusion protocol combined with agitation, produced a skin/fat decellularized matrix that maintained the native ECM architecture and composition of mainly collagen, GAGs, and vascular endothelial GF and absence of the major class I-histocompatibility complex. Immunohistochemistryal analysis of the matrix showed peripheral nerves and vessel structure maintenance, and angiography confirmed the existence of the vascular pedicle and microcirc networks. Those scaffolds could subsequently be re-populated with human adipose-derived stem cells and endothelial cells and promote neovascularization after implantation in a rat model [72]. [PERSON] et al. [73], using a porcine model, developed a full-thickness decellularized skin flap by applying a perfusion technique. The resulting scaffold comprised a vascular pedicle, maintained the ECM architecture and composition, and could be successfully perfused and anastomosed to the recipient vascular system. The scaffold demonstrated good biocompatibility and ability to support human keratinocyte engraftment and formation of an epidermis with intact barrier function [73]. These results are rather promising and may represent an adequate solution for the treatment of wounds, especially those characterized by loss of tissue volume.
## 3 System Configurations  
### Basic Configuration  
For our system, we assume that the welding method of the side body is the modular build method [17] and the transfer policies in all sublines are asynchronous transfer [18]. To evaluate the reconfiguration strategies for underbody lines in an automotive body shop, we defined the abstract model of the automotive body shop as shown in Figure 1, and the following assumptions were applied to the system:  
1. Both the engine and electric cars are produced for the same car model. The total target production volume is fixed, but the individual production volume can be changed according to the product mix.
2. All sublines except for the underbody lines are typically shared. However, two types of underbody lines exist: one for the engine car and the other for the electric car. The layout of the underbody lines for the engine car is similar to the traditional layout. However, the structure of underbody lines for the electric car is designed based on the concept of a cell system owing to the low production volume. When the production volume of the electric car increases, we can install additional cell lines in parallel.
3. Buffers exist between two sublines (total number of buffer locations is 14); however, no buffer exists between two successive stations in a subline.
4. The process times (\(PT\)) of all stations in sublines for the upper body (side body and main body) and the opening parts are constant and known as one time unit (minute) because a body shop is a highly automated manufacturing system.
5. The \(PT\)s of the underbody lines (cells) can be changed according to the change in the product mix of two types of cars. The total workload is fixed; hence, the process time of a workstation is determined by the number of work stations. We assume that a perfect line balancing is possible.
6. Only one mode of time-dependent failure exists for all workstations, and the distributions of time-to-failure (uptime) and time-to-repair (downtime) are exponentially distributed.
7. No starvation occurs in the first stations, and no blocking occurs in the final stations. The first stations denote stations without predecessors, whereas the final stations are stations without successors.  
Table 2 shows the basic data of the two underbody lines. The total workload of the underbody line of the engine car (\(TW_{1}\)) is 24, and that of the electric car (\(TW_{2}\)) is less than \(TW_{1}\). This assumption is reasonable because the underbody structure of the electric car is simpler than that of the engine car. Hence, we set \(TW_{2}\) to 21, as shown in Table 1; however, it can be changed from 16.8 to 24 for developing a meta-model, as shown in Table 3. Subsequently, the total number of stations for the engine car (\(NS_{1}\)) is 24, and that for the electric car (\(NS_{2}\)) is three. Hence, the process times of a station (\(PT_{1}\) and \(PT_{2}\)) can be calculated using Equation (1), of which the values are one and seven, respectively.  
\[PT_{i}=\frac{TW_{i}}{NS_{i}} \tag{1}\]
## 1 Introduction
The experiments show that ADRC has obvious advantages in step response, overshoot, noise suppression, disturbance suppression, and low-speed motion. [PERSON] [15] uses the ADRC to stabilize the ring structure resonance of the photoelectric platform servo system. The simulation results show that the ADRC suppresses the influence of structural resonance and improves the speed tracking accuracy effectively. [PERSON] [16] used ADRC in the speed control loop to solve the suppression of interference in the photoelectric pod, and then realized the stability of the optical axis. [PERSON] [17] uses the particle swarm algorithm (PSO) and the population search algorithm (SOA) to carry out the design of the ADRC parameters and compares the control effect of PID. The results show that the ADRC method optimized by SOA has a higher control performance. [PERSON] uses ADRC to solve the mechanical resonance problem caused by the flight system under the mechanical impact [18]. [PERSON] designed an improved ADRC to improve the control performance of a fast mirror (FSM) in a large measurement noise environment [19]. [PERSON] proposed an ADRC paradigm that consists of modifying the structure of the ESO by defining the input signal as an additional state variable of the controlled object [20]. [PERSON] established formal conditions of equivalence between the PID and ADRC, and devised a step-by-step procedure of transitioning from the PI/PID to the error-based ADRC [21]. [PERSON] presented an ADRC strategy for the trajectory tracking problem of unmanned tracked vehicles (UTV) in the presence of high slippage disturbance dynamics to prove that the ADRC-based UTV control achieved better trajectory tracking performances than the PI/PID control structure under different working conditions in the presence of high slippage dynamics and measurement noise [22]. [PERSON] demonstrated a new generation of adaptive disturbance rejection control (ADRC), which is more robust against disturbances than the conventional ADRC [23]. The above methods are to improve ADRC control by adding a Kalman filter, feed-forward controller, and PSO parameter setting in the ADRC control structure, without changing the order of ADRC. At the same time, it is also proved that the ADRC is more suitable for engineering applications than PID. In this paper, through the parametric identification of the model of the photoelectric pod, a fourth-order tracking differentiator and a fifth-order extended state observer are designed. Through simulation verification, the stability performance of HO-ADRC, increasing by 145.17%, is better than that of PID. Interms of the disturbance suppression and noise removal ability, HO-ADRC is also better than PID.
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 2 Fundamental Principles
### Deep Reinforcement Learning  
Deep reinforcement learning (DRL) [34] applies deep learning combined with reinforcement learning, in which neural networks are used to handle complex structures and datasets. Typically, DRL is used in tasks requiring learning to make decisions in an environment that is subject to variation and uncertainty. It has various applications in multiple fields, such as gaming [35], robotics [36], stock trading [37], medicine [38], energy management [39], and traffic management [40].  
For DRL [41], the Q-function, also called the Deep Q-network (DQN), is a widely used method. A neural network that replays data and learns from experience can approximate the Q-value, which is a performance measure for a given action based on the state and action. It is then applied to determine the best action in the current situation.  
The main equations of the DQN are the Q-function and loss function.  
\[Q(s,a)=r(s,a)+\gamma maxQ(s^{\prime},a^{\prime}) \tag{2}\]  
Equation (2) is known as the Bellman equation [42]. The equation is used to update the Q-value of the current action in state \(s_{t}\), including the immediate reward and the discounted Q-value [29], where \(\gamma\) represents the discount factor, \(r(s,a)\) represents the reward value received after taking an action in state \(s_{t}\), and maxQ refers to the Q-value of choosing the action with the highest value among all possible actions in the next state \(s_{t+1}\).  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(Q(s,a)-q(s,a))^{2} \tag{3}\]  
For the DQN, the implemented loss function [42] is the mean squared error (MSE) according to Equation (3). It is calculated by comparing the predicted Q-value with the actual Q-value, where n is the total amount of data, q(s, a) is the predicted Q-value from the neural network, and Q(s, a) is the actual Q-value according to Equation (2).
## 2 Materials and Methods
### Obturation Quality Assessment  
The obturation quality was evaluated according to the standard-of-care, density and length parameters. Density was classified as adequate in the absence of vacuoles and inadequate in the presence of vacuoles or heterogeneous canal filling. The obturation length was classified as short when more than 2 mm away from the apical exit, adequate when 0-2 mm from the apical exit, and long when material extended beyond the limits of the acrylic canal. Strict criteria were applied; that is, the obturation quality was only considered adequate when both length and density filled the requirements, and inadequate when any or both conditions were inadequate. Three independent observers assessed the obturation quality: two undergraduate senior students (final year students) and an experienced Endodontics professor. The inter-observer correlation, as well as the global agreement, were determined.
## 3 Inspection Robot Platform and Data Resources
### Safety Attire Dataset  
This study constructs a custom dataset focusing on detecting safety helmets and vest usage among workers. The dataset is designed for safety monitoring applications in industrial environments such as construction sites and manufacturing plants. All images are annotated under the official YOLO labeling format. The dataset is compatible with mainstream object detection frameworks. The LabellImg 1.8.6 tool is employed to label each image during the annotation process. The dataset includes four categories: helmet (wearing a helmet), no-helmet (not wearing a helmet), vest (wearing a safety vest), and no-vest (not wearing a safety vest). The detailed annotation statistics are summarized in Table 1.  
Figure 1: Inspection robot.  
The dataset encompasses a variety of environments, including daytime, night-time, strong illumination, indoor and outdoor scenes, as well as both close-range and long-range views. The training, testing, and validation sets are split according to a 7:2:1 ratio.  
This paper aims to enhance the robustness of the model. We also ensured the diversity of the dataset. The images are collected from multiple factory environments and construction sites. These locations are distributed across different regions. The dataset includes various lighting conditions. These include natural light, indoor lighting, and night-time illumination. It also contains diverse backgrounds. These include clean factory workshops, cluttered work areas, and warehouse zones. Such diversity reduces the risk of overfitting to a narrow visual context. The types of safety equipment in the images vary. These differences reflect the diversity of industrial safety conditions in the real world. We deliberately included such variations. This enhances the generalizability of the dataset. Three trained annotators labeled all the images. They follow a standardized annotation protocol. This protocol defines four safety attire categories: helmet, vest, no-helmet, no-vest. For any disagreements, the annotators reach a consensus through discussion. We apply common data augmentation techniques during training. These include random brightness adjustment, contrast adjustment, and the addition of Gaussian noise. These methods improve the model's ability to generalize across different visual conditions.  
This paper collects 3966 images from real-world field photography and publicly available web resources for training and validation. The dataset annotations are illustrated in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Attire Classes** & **Number of Labels** \\ \hline \hline  vest & 3619 \\ no-helmet & 4678 \\ helmet & 4204 \\ no-vest & 4723 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attire classes and labels.  
Figure 2: Safety attire detection dataset.
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 3 Simulation Study
### Simulation Study 1: Two Items per Testlet  
#### 3.1.1 Method  
In this simulation study, item responses with local dependence are simulated according to the 2 PL model with a normal copula model for item residuals. We study the situation containing \(I=12\) items. All items are arranged in testlets consisting of two items. Hence, there are six testlets for the case of 12 items.  
The item discriminations \(a_{i}\) for the 12 items are 0.8, 1.5, 1.3, 1.5, 1.1, 1.6, 0.7, 0.9, 1.1, 1.7, 0.8, and 0.9. The item intercepts \(b_{i}\) are chosen as \(-0.2\), 0.0, 0.3, 0.7, \(-1.2\), 2.2, 0.2, 1.1, \(-0.3\), 1.8, 2.1, and \(-1.1\). The item parameters can also be found in the R code for this Simulation Study 1 located at [[https://osf.io/xjp54](https://osf.io/xjp54)]([https://osf.io/xjp54](https://osf.io/xjp54)) (accessed on 18 March 2024).  
Locally dependent item responses are simulated by assuming a normal copula model [31] for the residuals. The residual correlation matrix \(\Sigma^{*}\) is defined by  
\[\Sigma^{*}=\left(\begin{array}{cccc|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|  
different analysis models were specified. First, MML and PML estimation applied to all items were specified under the (incorrect) local independence assumption. The resulting estimation methods are denoted as MML1 and PML1, respectively. Second, MML and PML were specified with a normal copula model for item residuals (denoted as MML2 and PML2). Third, PML was specified by excluding item pairs stemming from the same testlet (denoted as PML3). All estimation methods have been implemented in dedicated R functions that can be found at [[https://osf.io/xjp54](https://osf.io/xjp54)]([https://osf.io/xjp54](https://osf.io/xjp54)) (accessed on 18 March 2024). The R script for the simulation can also be found at this link.  
In total, 3000 replications were conducted. We computed the empirical bias, the empirical standard deviation, and the coverage rates at the confidence level of 95% for all estimated item discriminations \(a_{i}\) and item intercepts \(b_{i}\). To summarize the simulation results, we computed the average absolute bias, the average standard deviation, and the average coverage rate across all item discriminations and item intercepts, respectively.