esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Growing Environment Measurement by the WSN
#### 3.1.2 IoT service for Farmers  
By getting the standardized data from the cloudSense and storing them in our server, we can create a web service that offers various kinds of cultivation indices to the farmers in the field. In this web site, growing environment information, as a primary index, is displayed as a list of numeric values (Figure 12). All the data acquired by the weather station are simultaneously displayed every one hour.  
Figure 11: ER chart of database structure.  
Figure 12: Primary index as a list displayed in web service.  
Figure 10: Design of agricultural IoT as a service for farmersIn addition to the primary index, our web service also provides several secondary indices; AGDD, GST, CI, HI, BEDD, and DI. They are calculated using growing environment data standardized by cloudSense. The validation of the solar duration data that is necessary for the DI calculation validity was made to verify the certainty of this algorithmic program for calculating the solar duration by comparing it with the true value obtained from the solar duration sensor next to the weather station. Figure 13 shows the result of the solar duration data verification. When comparing in the winter, the difference between the true value and the value obtained from the algorithmic program is only 3 to 5%, while when comparing in the summer, the difference between these two values is about 10%. Based on this result, we should modify the solar duration data used in this study.  
In the service for secondary indices shown in Figures 14 and 15, users can select the observation place, type of sensor, observed property, type of index and time period that you want to observe (Figure 14). After they choose, the result of both the chosen secondary index and primary index is graphically shown (Figure 15). Figure 15 shows the result of the chosen primary and secondary indices in the web application developed in this study. In order to verify the accuracy of each secondary index; AGDD, GST, CI, HI, BEDD, DI, we compared each data point about the CI and HI obtained from this service and corresponding data obtained from previous research, then the validity of the secondary index calculated by our service is proved [22].  
Figure 14: Secondary index displayed in web application. Since this application is written in Japanese, English annotation is mentioned with red letter.  
Figure 13: Data verification about solar duration: (**a**) The data acquired in January; (**b**) The data acquired in July.
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 3 Collision Avoidance Constraint Formulation  
In the optimal control problem formulation (Section 2), collision avoidance constraints ensure the vehicle remains within a safe drivable area. This chapter focuses on the techniques for modeling these constraints within the DC region, which explicitly defines the feasible space for trajectory planning. The method for constructing the DC is detailed in the remainder of this section. The core contributions of this work focus on two aspects: the formation and dynamic integration of occupied grids for representing polygonal obstacles (Sections 3.3 and 3.4) and the dynamic construction of the DC region (Section 3.5). Thesecomponents constitute critical steps toward enhancing the efficiency of collision avoidance constraint modeling.
## 3 Data Synthesis and Analysis
#### 3.1.4 Bat Algorithm  
The bat algorithm (BA), introduced by [PERSON] in 2010 [85], is one of the nature-inspired optimization algorithms based on the echolocation behavioral pattern of bats. K-Medoids was combined with the bat algorithm by [PERSON] and [PERSON] [86] for partitioning clustering using the echolocation behavior of bats to determine the initial cluster number. [PERSON], [PERSON], and [PERSON] [87] hybridized the K-means algorithm with a novel dynamic frequency-based bat algorithm variant (DFBPKBA) as a new approach for clustering in a distributed environment with a better exploration and exploitation capability. The MapReduce model in the Hadoop framework was used to parallelize the hybrid algorithm to ensure satisfactory results within a reasonable time limit. [PERSON], [PERSON], and [PERSON] [88] introduced the K-means binary bat algorithm (BKBA) using a generalized K-means-based binarization mechanism applied to the bat algorithm to solve multidimensional backpack problems. [PERSON] and [PERSON] [89] introduce a bat algorithm clustering based on K-means (KMBA) for automated grading of edible birds nest, which produce nearly 86% dataset clustering accuracy compared with the standard bat algorithm. [PERSON] and [PERSON] [90] hybridized K-means and K-medoids with an enhanced shuffled bat algorithm (EShBAT). K-means and K-medoids were used in generating a rich starting population for EShBAT to produce an efficient clustering algorithm.
## 4 Analysis of Experimental Results
### Precise Identification Technology for Emergency Airdrop Stations
#### 4.2.5 Visual Analysis
(a) In solving the false detection problem, APCR-YOLOv8 accurately distinguishes between actual and false targets, reducing the false positive rate and improving overall detection accuracy. (b) For the problem of missed detection of tiny targets, APCR-YOLOv8 enhances the detection capabilities for small and difficult-to-detect targets which were initially ignored by YOLOv8, thanks to its finer extraction and aggregation of target features. (c) In dealing with the repeated detection problem, APCR-YOLOv8 significantly reduces repeated detections of the same target, showing higher stability and robustness in the detection process. (d) Regarding detection confidence, APCR-YOLOv8 improves the confidence level of detections from 0.65 to 0.89 for the same frame, indicating a significant enhancement in the reliability of target recognition in complex backgrounds. In summary, the APCR-YOLOv8 algorithm demonstrates significant advantages in several key areas, including reducing false positives, minimizing missed detections of small objects, eliminating duplicate detections, and enhancing detection confidence. These improvements make APCR-YOLOv8 more accurate and robust for emergency airdrop station detection tasks, providing a more reliable solution for practical applications.
# Investigating Attention Mechanism for Page Object Detection in Document Images
## 5 Results and Discussion  
This section discusses the experimental results of the model trained using various backbones and object detection heads. The standard metrics, i.e., Precision (7), Recall (8), F1-score (9), and AP (10) for IoU (11) levels with a range of [0.5, 0.95], are used to compare baselines and the proposed pipeline. All the models are trained for a 1x schedule (12 epochs) with a batch size of 8, on RTX3090 GPUs. Lightweight backbones, such as ResNet-50 or the tiny versions of ConvNeXt and Swin-Transformer, are used for the experiments. The reason for choosing lightweight backbones is to adhere to the goal of this paper to show the importance of the attention mechanism with a minimal backbone. Furthermore, tiny versions of ConvNeXt and Swin Transformers contain a similar number of parameters as ResNet-50, while providing additional benefits of improved saliency over the extracted features. For all the experiments discussed in this section, the baseline model is chosen as Faster-RCNN, with ResNet-50 as the backbone to adhere to the goal of this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**No of Classes**} & \multicolumn{2}{c}{**No of Images**} \\ \cline{3-4}  & & **Train** & **Test** \\ \hline DocBank & 13 & 400,000 & 50,000 \\ PublayNet & 5 & 335,703 & 11,245 \\ IIIT-AR-13K & 5 & 9333 & 1955 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The data distribution of datasets used for the experiments. These datasets are divided into train and test sets, with major classes, generally found in documents, spanning over both training and test sets.
## 3 The Spatial Finite Helical Motion for Compound Transmission  
Generally, there are three motion transmission types of the gear pair combination of cylindrical gear and curve-face gear. The specific models and fundamental properties of three cases are summarized in Table 1. For example, in Case II, the cylindrical gear as the input element rotates about the fixed input axis. The curve-face gear outputs the finite helical motion in space, which rotates about and moves along the fixed output axis. The specific models and fundamental properties of these three cases are shown in Table 1.  
In Table 1, 1# gear is the driving gear, and 2# gear is the driven gear. The Case I is mainly applied to the generation of curve-face gears by shaper as described in Section 2.2. In the process of generation, the two tooth surfaces are in line contact at every instant. The Cases II and III are mostly used to transmit mechanical motion and power. However, the two tooth surfaces in Case II and III are usually in point contact, which is less sensitive to misalignment. In this regard, Case II is a deceleration mechanism with an output of spatial finite helical motion, while the Case III is an acceleration mechanism with the output of planar complex curved motion.  
Consider again coordinate system \(S(O-XYZ)\), \(S_{1}(O_{1}-x_{1}y_{1}z_{1})\) and \(S_{2}(O_{2}-x_{2}y_{z}z_{2})\) are rigidly connected to the engine base of machine tool \(f\), cylindrical gear 1 and curve-face gear 2, respectively. Without any loss of generality, the screw axis \(\underline{\underline{S}}_{1}\) of the input reference frame \(S_{1}\) is selected such that it coincides with the \(Z\)-axis of the fixed reference frame \(S\). The screw axis \(\underline{\underline{S}}_{2}\) of the output reference frame \(S_{2}\) is aligned with the \(Y\)-axis of the fixed reference frame \(S\). The included angle \(\theta_{1}\) between the \(X\)-axis and \(x_{1}\)-axis, determined as the angular displacement of cylindrical gear, is a counterclockwise rotation about the fixed \(Z\)-axis. The clockwise rotation angle \(\theta_{2}\) and displacement \(l(\theta_{1})\) of the curve-face gear  
Figure 5: The left involute tooth profile of the curve-face gear in a cycle.  
coordinate system relative to the fixed coordinate system \(S\) along the \(Y\)-axis are the angular displacement and axial displacement of the curve-face gear, respectively.  
In fact, gear pairs are usually used for speed reduction, and mechanical power is commonly and easily transmitted by a fixed axis with uniform rotation, therefore, the following work will focus on Case II, which is generally the case in gearing. Actually, different from the common curve-face gear transmission formed by non-circular gear and curve-face gear with variable transmission ratio [41], the gear pair combination of cylindrical gear and curve-face gear in Case II is defined as curve-face gear compound transmission.  
Following, screw theory is used as the basis for the geometric foundation to parameterize the kinematic geometry of motion transmission between intersecting axes.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## Materials and Methods
### Field Site and In Situ Crop Measurements
#### 2.1.1 Experimental Trial Plot Description  
The field experiment included a total of 50 winter wheat (_Triticum aestivum_ L.) trial plots located approximately 3.8 km south of the village of East Saltoun, East Lothian, Scotland (55 deg52'50.5\" N, 2 deg50'12.2\" W; 170 m above sea level). The trial plots, with dimensions of \(2\times 10\) m, had a Latin square experimental design and, in order to induce variation in LAI, LCC and CCC, comprised five different levels of N application--0, 50, 100, 150 and 200 kg N ha\({}^{-1}\) (Figure 1) --each with five replicates for two common soft group 4 winter wheat varieties. These two wheat varieties--Revelation and Leeds--were included on the 2017/2018 recommended lists published by the U.K.'s Agriculture and Horticultural Development Board (AHDB) for cereals crops (2018).  
Each of the wheat plots were sown on 30 September 2017, in a roughly southwest to northeast direction with a seed rate of 340 seeds/m\({}^{2}\) and harvested on 25 August 2018. The soil was of the Humbie soil series with a loam texture. The N fertilisation of plots was carried out as a split application--50% of the total N was applied on 22 March 2018 and the remainder was added on 26 April 2018, which corresponded to growth stages (GS) 24 and 31, respectively. A herbicide based on the active ingredients picolinafen and pendimethalin was applied at GS11 on 27 October. A robust fungicide programme based on the active ingredients triazole, chlorothalonil, cylfluenamid, proquinazid, succinate dehydrogenase inhibitor and azoxystrobin, was also applied to all plots at four growth stages (GS30 on 16 April, GS32 on 9 May, GS39 on 30 May and GS65 on 22 June) to keep all diseases to a minimum level throughout the growing season.
# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 5 Design
### Global-Parent Selection
Instead of introducing a new message and adding much to the communication overhead, a new DIO message option is introduced to the global-DIO base object. It is referred to as the Load and Stability Information Option (LSIO), which complies with the standard ICMP options. It contains all LSOF information in a limited number of bytes appended to each global-DIO message. Upon the reception of a global-DIO message, a global-node retrieves the LSOF information contained in the LSIO option. To store this information, the global-node maintains a global multi-DODAG list in its local cache. A new entry is added to the list for each nearby global-neighbor in the global-DODAG. The entry is kept updated every time a relevant global-DIO message is received. The list is made accessible for the global-parent selection process. The dynamic multi-DODAG algorithm is also provided with open access to this information to enable making informative decisions, as explained later.
## 2 Methods
### Global Positioning System Variables  
To record the load, the global navigation satellite systems Apex (STATSports, Newry, Northern Ireland) was used. The system's transmitter was placed in a specially designed west at the back and lower part of the neck (between the shoulder blades). The transmitter was activated and recorded the player's movements during the match. After the game ended, the transmitter was placed in a specialized company device, transferring the recorded data to a computer, where the file was then exported in CSV format. The speed zones used were the following:  
* Zone 1: 0.1-7.19 km/h (rest, walking)--very low intensity
* Zone 2: 7.2-10.99 km/h (jogging)--low intensity
* Zone 3: 11-14.39 km/h (jogging)--moderate intensity
* Zone 4: 14.4-19.79 km/h (running)--high intensity 1* Zone 5: 19.8-25.19 km/h (fast running)--high intensity 2
* Zone 6: \(\geq\)25.2 km/h (sprinting)--very high intensity  
For accelerations (ACCs), those greater than 2 m/s\({}^{2}\) were used, and for decelerations (DECs), those greater than \(-\)2 m/s\({}^{2}\) were used.
## AI-Enabled 6G Network Challenges
### Intelligent Routing  
Because of the high user density expected in the 6G network, sophisticated algorithms and methods will be needed to meet user demand. Additionally, it is anticipated that the 6G network would support a wide range of complex ultra-low latency applications, including holographic imaging, haptic communication, Tele driving, Tele Robotics, and others. When a call is dropped due to a lack of resources, the network experiences an extra delay that is unsuitable for latency-sensitive applications like haptic communication. The issue could be resolved by adding a Wireless Intelligent Router (WIR) to the network. The router will keep an eye on the network in cases of congestion, and reroute users as necessary. In order to meet customer demand, the WIR is a trained router that uses reinforcement learning to connect to the closest accessible access point. It makes sure that the user receives the optimal power allocation based on the desired application [32].
## 3 Proposed Algorithms
### Extension to the IP Scheme  
The objective function and constraints of the STAR-RIS's IP scheme may contain multiple locally optimal solutions, making it challenging for traditional optimization methods to guarantee the identification of a globally optimal solution. The CCCP method can be used as an iterative algorithm for solving non-convex optimization problems, and its basic idea is to decompose a complex non-convex problem into a series of easier-to-solve convex optimization subproblems. The CCCP method ensures the convergence of the algorithm by constructing a discrete-time dynamical system that guarantees a monotonically decreasing value of the objective function at each iteration. This is particularly important for complex optimization problems in IP schemes, where the optimal solution can be achieved within a reasonable number of iterations. Specifically, we use the CCCP method to decompose the non-convex independent phase shift constraints into a series of convex sub-constraints to gradually approximate the global optimal solution.  
With given \(\theta_{l}\), the optimization of \(\mathbf{w}_{l}\) is as the same as CP scheme. The main difference is without the constraint (6f). In accordance with CCCP, the constraint (6c) and (6d) is decomposed into the subsequent expression:  
\[Tr(\mathbf{D}_{1,l})\leq 2 Tr(\theta_{l}^{(n)}\theta_{l}^{H})-Tr( \theta_{l}^{(n)}(\theta_{l}^{(n)})^{H}), \tag{18}\] \[\left[\begin{array}{ccc}\mathbf{D}_{1,l}&\mathbf{\Theta}_{l}& \mathbf{\theta}_{l}\\ \mathbf{\Theta}_{l}^{H}&\mathbf{D}_{2,l}&\mathbf{\theta}_{l}\\ \theta_{l}^{H}&\theta_{l}^{H}&\mathbf{1}\end{array}\right]\geq 0, \tag{19}\]  
where \(n\) denotes the number of iterations, \(\mathbf{D}_{1,l}\in\mathbb{H}^{M\times M}\) and \(\mathbf{D}_{2,l}\in\mathbb{H}^{M\times M}\) are slack variables. With fixed \(\mathbf{w}_{l}\), the optimization of \(\theta_{l}\) can be solved by CVX. More details can be referred to [28]. Then, optimization of IP scheme can be formulated as follows:  
\[\min_{\theta_{l},\mathbf{w}_{l}}\sum_{l}\phi_{l}R_{l}^{SEC} \tag{20a}\] \[\text{s.t.}\quad\eqref{eq:1},\eqref{eq:2},\eqref{eq:3},\eqref{eq:4}, \eqref{eq:5},\eqref{eq:6}. \tag{20b}\]  
The overall process of the CCCP method is illustrated in Algorithm 2 below.  
```
1:Set feasible variables \(\left\{\mathbf{w}_{l}^{(0)},\mathbf{\theta}_{l}^{(0)}\right\}\) and \(q=0\).
2:repeat
3: (a) Obtain \(\left\{\mathbf{w}_{T}^{(q)},\mathbf{w}_{R}^{(q)}\right\}\) via solving (6a)-(6f) using CVX, with fixed \(\left\{\mathbf{\theta}_{l}^{(q-1)},\mathbf{w}_{l}^{(q-1)}\right\}\). (b) Obtain \(\left\{\mathbf{\theta}_{T}^{(q)},\mathbf{\theta}_{R}^{(q)}\right\}\) via solving (20a) and (20b) using CVX, with fixed \(\left\{\mathbf{w}_{l}^{(q)},\mathbf{\theta}_{l}^{(q-1)}\right\}\). (c) \(q\gets q+1\). (d) Calculate WSSR \(R^{(q)}\) by (6a).
4:until\(R^{(q)}-R^{(q-1)}<\kappa\).
5:return Outputs
```  
**Algorithm 2** CCCP Method to Solve Problem (20a) and (20b)
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
###### Abstract  
As automated driving system (ADS) technology is adopted in wheelchairs, clarity on the vehicle's imminent path becomes essential for both users and pedestrians. For users, understanding the imminent path helps mitigate anxiety and facilitates real-time adjustments. For pedestrians, this insight aids in predicting their next move when near the wheelchair. This study introduces an on-ground projection-based shared eHMI approach for autonomous wheelchairs. By visualizing imminent motion intentions on the ground by integrating real and virtual elements, the approach quickly clarifies wheelchair behaviors for all parties, promoting proactive measures to reduce collision risks and ensure smooth wheelchair driving. To explore the practical application of the shared eHMI, a user interface was designed and incorporated into an autonomous wheelchair simulation platform. An observation-based pilot study was conducted with both experienced wheelchair users and pedestrians using structured questionnaires to assess the usability, user experience, and social acceptance of this interaction. The results indicate that the proposed shared eHMI offers clearer motion intentions display and appeal, emphasizing its potential contribution to the field. Future work should focus on improving visibility, practicality, safety, and trust in autonomous wheelchair interactions.  
autonomous wheelchairs; shared eHMI; human in the loop; motion intentions; on-ground projections +
Footnote †: journal: Applied Sciences
## 2 Materials and Preparation Process  
### Preparation of EMWM  
EMWM is an elastic porous metallic material made of metal wires that are braided or twisted and then stamped. EMWM exerts energy dissipation through compression and rebound when subjected to external impact or sustained vibration. As an anisotropic material, there is a significant difference in vibration damping properties between its molding and non-molding directions. The preparation process of plate EMWM specimens is shown in Figure 1.  
It plays an essential role in the physical properties and mechanical performance of EMWM by metal wire characteristics. Owing to the good corrosion resistance and excellent high-temperature resistance of 304 stainless steel, it is used as a raw material for the preparation of which the wire diameters are 0.1 mm, 0.2 mm, and 0.3 mm. Metal wire is supplied by Wuxi Lianhuan Alloy Co., LTD (Wuxi, China). The mechanical properties of the metal wire are shown in Table 1[23; 26].  
A spiral coil is made of metal wire by using the winding equipment. The specific operation process is as follows: Firstly, the mandrel is fixed on the clamping device, and the wire is transferred to the mandrel through the guide wheel with a set path. Under the action of rollers, it is crushed into a spiral coil. According to different wire diameters, the motor can be adjusted to control the rotating speed of the mandrel.  
The spiral coil is stretched with equal pitch, and then the blank is wound by spindle rotation. Blank winding equipment effectively controls the preparation of EMWM and ensures that consistent specimens are obtained during experiments. Reasonable design of stamping models for EMWM blanks stamping molding process is critical. The EMWM blank is loaded into the mold and stamped under the action of the press.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Genre**} & **Density** & **Elongation** & **Yield** & **Elastic** & **Tensile** \\  & **kg/m\({}^{3}\)** & **/\%** & **Strength** & **Modulus** & **Strength** \\  & & & **/MPa** & **/GPa** & **/MPa** \\ \hline
06 Cr19 Ni10 & \(7.93\times 10^{3}\) & \(\geq\)40 & \(\geq\)205 & 193 & \(\geq\)520 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mechanical properties of the metal wire.  
Figure 1: Preparation process of plate EMWM specimens.  
The surface of specimens is accompanied by oil and dust during the preparation process. These impurities will prevent the brazing material from coating the surface during the brazing process, resulting in a weak soldering surface. Consequently, impurities on the surface can be efficiently removed by ultrasonic cleaning to ensure close contact between the wire and the brazing material. To begin with, an appropriate amount of industrial acetone is added during the cleaning process to assure that the impurities on the surface of the metal rubber are fully interacted with. The industrial acetone is then replaced to continue the cleaning and the process is repeated at least three times to be certain that the impurities are eliminated completely.