esa-sceva/satcom-chunk-collection huggingface dataset:
# A Strawberry Ripeness Detection Method Based on Improved YOLOv8
## 5 Conclusions  
In this study, we proposed an enhanced version of the YOLOv8 object detection model, termed YOLOv8_CDW, specifically designed to accurately and comprehensively identify the ripeness stages of strawberries under complex agricultural environments. In addition to architectural improvements to the detection network, we also introduced a complementary image-processing-based classification strategy to further refine the detection and categorization of strawberry ripeness stages.  
The main contributions and findings of this study are summarized as follows:  
1. The integration of a CA mechanism into the backbone network of the original YOLOv8 significantly enhances the model's capacity to capture fine-grained features by reinforcing inter-channel dependencies within feature maps. This modification allows the model to better represent the subtle visual cues associated with different ripeness stages, such as variations in surface texture and color gradients, thereby improving its discriminative power in challenging scenarios.
2. The incorporation of the Wise-IoU loss function during training enables the model to dynamically adjust the weights assigned to bounding boxes based on their quality. This adaptive weighting mechanism increases the influence of high-quality bounding boxes while effectively reducing the impact of noisy or low-quality samples. As a result, the model better handles the imbalance between easy and hard samples--a common challenge in object detection tasks involving variable natural conditions.
3. Experimental results on a self-collected strawberry dataset demonstrate the superior performance of the proposed YOLOv8_CDW model. It achieved an accuracy of 96.9%, a recall of 93.6%, a mAP@50 of 0.973, and an F1 score of 95.5%, outperforming baseline models including FAST R-CNN, YOLOv5, YOLOv7, and the original YOLOv8 in both accuracy and inference speed. Notably, in visually complex environments where unripe strawberries closely resemble the surrounding foliage in color and texture, YOLOv8_CDW maintained high detection robustness. These results validate the model's ability to support efficient, precise, and real-time fruit detection, which is essential for intelligent agricultural applications such as automated harvesting and field monitoring.  
[PERSON] contributed to writing--original draft, review and editing, validation, software development, methodology design, and conceptualization. [PERSON] was responsible for the review and editing, resource provision, project administration, and funding acquisition. [PERSON] contributed to manuscript review, editing, and validation. All authors have read and agreed to the published version of the manuscript.  
No external funding was received for this study.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# A Survey of the Literature on Order-Picking Systems by Combining Planning Problems
## 1 Introduction
It has forced warehouses to manage large numbers of small orders in limited opportunities [9]. A successful e-commerce ordering process contains multiple different suppliers and programs that need to be quickly implemented. In this regard, completing orders is one of the most important fundamentals to succeed in the on-line business. Moreover, to overcome shortcomings in the ordering process, several aspects should be carefully considered. The easier the process for the end customers is, the more satisfied they would be with you. Furthermore, to decrease the warehouse costs and applying new methods in the internal procurement system, some recent trends need to be more efficiently performed in the procurement industry. Previous studies have focused on order-picking and route selection, and warehousing operations. However, the problems of picking operations are integrated. [PERSON] et al. [9] provided a comprehensive empirical analysis of the choice of order-picking systems (e.g., focus and strategic decision making). [PERSON] et al. [2] focused on a combination of tactical and operational planning problems as well as the way they could support new market developments. In this regard, some issues, such as ergonomics and order tardiness, have been rarely considered by the researchers. On this account, this paper investigated how warehouse managers can take advantage of combining several operational planning issues by reviewing previous articles. This paper sought to answer the following questions through reviewing the scientific literature related to combining order-picking problems, such as cost, equipment, warehousing, assignment, routing, batching, sequencing, and tardiness:  
1. Which components of OPS examined in the previous literature were effective in reducing costs? 2. What is the combination of order picking problems in the past literature that leads to cost reduction?What kind of warehouses were examined to evaluate the impact of the OPS system on cost reduction? Therefore, the present study aimed to investigate the problems affecting OPS and combining planning problems and how these problems have been dealt with in the previous studies. We also intended to review the solution methods used in past literature to assist researchers in future decisions to solve problems. By examining the components of the order-picking process in different warehouses, this study also sought to assist future research to identify lesser-seen problems to reduce warehouse operating costs. The remainder of this paper is organized as follows: Section 2 describes OPS literature taxonomy. Section 3 discusses the research background. Section 4 describes the literature review methodology. Section 5 discusses OPS problem planning. The managerial implications resulting from the literature overview are discussed in Section 6. Finally, Section 7 presents the conclusions.
## 3 Results and Discussion
### Analysis of Satellite Images
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CHL\_NN**} & \multicolumn{2}{c}{**TSM\_NN**} & \multicolumn{2}{c}{**ADG443\_NN**} \\ \cline{2-7}
**Items** & **Summer** & **Winter** & **Summer** & **Winter** & **Summer** & **Winter** \\  & **2020** & **2021** & **2020** & **2021** & **2020** & **2021** \\ \hline Cluster 1 & 1.360 & 1.333 & 1.913 & 6.198 & 2.642 & 5.396 \\ Cluster 2 & \(-\)0.460 & \(-\)0.287 & \(-\)0.514 & \(-\)0.231 & \(-\)0.481 & \(-\)0.242 \\ Cluster 3 & 1.453 & 0.857 & 1.582 & 0.176 & 1.396 & 0.295 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cluster Means results from applied statistics. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CHL\_NN**} & \multicolumn{2}{c}{**TSM\_NN**} & \multicolumn{2}{c}{**ADG443\_NN**} \\ \cline{2-7}
**Items** & **Summer** & **Winter** & **Summer** & **Winter** & **Summer** & **Winter** \\  & **2020** & **2021** & **2020** & **2021** & **2020** & **2021** \\ \hline Valid & 91 & 91 & 91 & 91 & 91 & 91 \\ Missing & 0 & 0 & 0 & 0 & 0 & 0 \\ Mean & 8.197 & 5.183 & 33.051 & 14.420 & 1.195 & 3.909 \\ Std. Deviation & 6.710 & 3.769 & 35.000 & 29.320 & 0.918 & 5.111 \\ Minimum & 0.182 & 0.064 & 0.964 & 0.157 & 0.111 & 0.074 \\ Maximum & 21.806 & 21.030 & 100.000 & 232.553 & 4.188 & 32.201 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics of data collected from Sentinel-3B OLCI satellite images. within cluster 2 as the furthest from the source of pollution and contamination (Table 4). Cluster 3 demonstrated high values of CHL_NN (1.453 m\({}^{-3}\)), TSM_NN (1.582 g m\({}^{-3}\)), and ADG443_NN (1.396 m\({}^{-1}\)) in the summer season, demonstrating the incidence of more significant water contamination (Table 4).
## 2 Related Studies and Backgrounds
### Vital Sign Data Mining Techniques  
Data mining is technology to explore and discover knowledge by modeling and searching for relations, patterns, and rules existing in the (big) data [21]. Study on algorithms to extract and mine useful and meaningful information from accumulated data are important, and it has been conducted in various areas, such as finance, medicine, education, and so on. In particular, data mining research for personal healthcare services becomes more and more important and actively carried out [22]. For example, IBM's [PERSON] began to diagnose cancers at human level with artificial intelligence.  
Vital signs that we tackle in the paper are largely divided into two types. The first type is the data by which its value is meaningful, e.g., blood pressure and body temperature, and the second one is time-series data to measure continuously over time, such as electrocardiogram (ECG), acceleration, respiration, and so on, which usually have waveforms. The second type is much more complex to analyze, requiring signal preprocessing. For example, ECG is the signal combined with five waves of P, Q, R, S, and T. One can measure useful information including exercise intensity, stress, and heart rates, and the like from ECG data. In particular, heart rate variability (HRV) extracted from ECG is also a valuable vital sign to get parameters, such as standard deviation of normal to normal interval (SDNN), low frequency (LF), high frequency (HF), and heart rate per minute. One can diagnose arrhythmia from HRV data. Recently acceleration data becomes crucial. Acceleration sensors are attached in many wearable devices and in smartphones, and so, acceleration data will play an important role as big data in a near future, in that they are collected everywhere and easily. Typical examples of the information analyzed from acceleration data are: the number of steps, walking distance, activity patterns of sitting, standing, walking, and running.
# Friction Behavior of Ceramic Materials for the Development of Bridge-Bearing Friction Materials
## 1 Introduction
Ceramic materials possess excellent durability, wear resistance, chemical resistance, and heat resistance while exhibiting high hardness, high strength, and low coefficients of friction [25; 26; 27]. In addition, ceramics do not deform even after prolonged use, allowing for semi-permanent use, which can significantly reduce the maintenance costs. These properties present a potential solution to the issues of high deformation and corrosion associated with existing friction materials and suggest that ceramics could serve as an alternative to friction materials in bridge bearings, ensuring their efficiency. Although studies on the application of ceramics as friction materials for bridge bearings remain limited, their potential advantages, including high wear resistance, low coefficient of friction, and excellent thermal stability, suggest significant opportunities for future development. As part of this study, we explored these properties through friction tests and discussed how ceramic-based materials could be effectively applied in structural components such as bridge bearings. Recent advancements in material science have led to the development of reinforced and hybrid ceramics by incorporating metals or graphene, thereby enhancing both mechanical strength and flexibility [28; 29; 30]. These hybrid materials effectively address the inherent brittleness of conventional ceramics while maintaining superior hardness and thermal stability. In particular, ceramic-based composites have emerged as promising candidates for structural applications, including seismic supports. These supports enable controlled sliding and energy dissipation during seismic events, ensuring improved resilience and structural integrity in earthquake-prone environments. This unique combination of properties underscores the potential of ceramics in advanced structural applications where reliability under dynamic loading is critical. This study investigates the potential of ceramic materials as alternative friction components for bridge bearings by evaluating their performance characteristics, including low coefficient of friction (COF) and durability. To ensure their suitability for structural applications, comprehensive compression and friction tests were conducted on large-scale bridge bearings incorporating ceramic materials. The experimental results provide critical insights into the feasibility of using ceramics in bridge bearing systems.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## Appendix A Abel Integral Algorithms
### Abel Integral
#### a.2.3 Exp_T Algorithm  
Ref. [24] extended the Equation (1) physically by introducing three new parameters in the \(j\)th level, which are \(P_{1},P_{2},P_{3}\)[26]:  
\[\begin{split} P_{1}&=k_{j}+\frac{k_{j}^{2}\beta_{j}} {2T_{m}}\Big{(}\big{(}a-x_{m}\big{)}^{2}-d\Big{)}-\frac{k_{j}\beta_{j}}{T_{m}} (a-x_{m})\\ P_{2}&=\frac{k_{j}^{2}\beta_{j}}{T_{m}}(a-x_{m})- \frac{k_{j}\beta_{j}}{T_{m}}\\ P_{3}&=\frac{k_{j}^{2}\beta_{j}}{2T_{m}}\end{split} \tag{11}\]  
where the temperature gradient \(\beta_{j}=\big{(}T_{j+1}-T_{j}\big{)}/\big{(}x_{j+1}-x_{j+1}\big{)}\), the subscript \(m\) is the middle point of two successive levels, i.e., \(T_{m}=\big{(}T_{j+1}+T_{j}\big{)}/2\) and \(x_{m}=\big{(}x_{j+1}+x_{j}\big{)}/2\). When \(\beta_{j}=0\), we have \(P_{1}=k_{j},P_{2}=0,P_{3}=0\), which is exactly the exp algorithm. The terminal differential at \(j\)th level is expressed as [26]:  
\[\begin{split}\Delta\alpha=10^{-6}\sqrt{2a}N_{j}\exp\big{(}k_{j} \big{(}x_{j}-a\big{)}\big{)}\bigg{[}erf\Big{(}\sqrt{k_{j}(x-a)}\Big{)}\sqrt{ \pi}\bigg{(}\frac{P_{1}}{k_{j}^{1/2}}+\frac{P_{2}}{2k_{j}^{3/2}}+\frac{3P_{3}} {4k_{j}^{5/2}}\bigg{)}-\\ \sqrt{x-a}\exp\big{(}-k_{j}(x-a)\big{)}\bigg{(}\frac{P_{2}}{k_{j} }+\frac{P_{3}\big{(}2k_{j}^{2}(x-a)+3\big{)}}{2k_{j}^{2}}\bigg{)}\bigg{]}_{x_{ j}}^{3+1}\end{split} \tag{12}\]  
This method alleviated the oscillation. The error of this algorithm is the same as that of the exp algorithm, that is, its exponential decay of refractivity with increasing height. A larger abnormal refractivity has a more significant impact on its value than other algorithms.  
This paper mainly compared two algorithms: direct and exp. We dismissed the lin algorithm because it required the interval to be less than 100 m. Most observations input to FM cannot satisfy this condition. In addition, we only compared the exp_T algorithm with the exp algorithm because their difference is too small to be noticeable if the direct algorithm is combined. In another aspect, we also considered the difference incurred from the interpolation method, including the log-spine cubic interpolation and the log-linear interpolation. Furthermore, the algorithms were compared in experiment 1.
## 4 Materials and Methods  
### Femtosecond Laser System  
The experiments in this work were carried out using a Coherent femtosecond laser system. The seed laser was generated from a mode-locked oscillator and further amplified in a Q-switched amplification cavity. The output laser beam has a spatial Gaussian distribution, and the central wavelength was around 800 nm with a 100-nm spectral width. The pulse duration was about 60 fs with a 1-kHz repetition rate. More information on the laser system can be found elsewhere [6; 22; 29]. In addition, an optical parametric amplifier (OPerA-Solo) was used for the generation of a 2.4-\(\upmu\)m short infrared laser. The samples were placed on a motorised linear stage (VT-80) and translated through the focus of the Gaussian beam with a step size of 0.15 mm. The beam was focused to a minimum beam waist of 16 \(\upmu\)m by an N-BK7 Plano-convex lens with a 60-mm focal length. The pulse fluence was controlled by a half-wave plate combined with a linear polariser, which sets the polarisation to the in-plane direction (p-polarised). All experiments were conducted at the normal incidence.  
We note that in general, \(p\)-Si can be classified as a uniaxial birefringent material with the optical axis aligned along the pores' direction [30; 31; 32]. However, at the normal incidence, there is no electric field component parallel to that axis, and the material can be treated as ordinary. A 45%/55% beam splitter was placed behind the sample, which splits the transmitted signal to two identical detectors, one with a 500-\(\upmu\)m aperture (\(S\approx 0.05\)) in front and one without for the closed and open aperture measurements, respectively. Silicon detectors with a \(5\times 5\) mm detection area were used for the 800-nm Z-scan and PbS detectors for the 2.4-\(\upmu\)m Z-scan.
# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
## 6 Obtained Results
### Application Evaluation
The 802.11p standard has the feature of using short headers with no need for nodes association to communicate in the setting applied, which explains the least delay and latency. Regardless, the IM still performed the best considering the higher amount of successfully delivered messages. Considering the transmission of 480p video frames referenced in the discussion in Figure 7, the performance produced by IM in terms of average end-to-end delay and latency is satisfactory, as it received a \"Very Good\" quality reproduction [44]. However, for the three-node experiment, the observed latency was 29.112 ms, which would cause a reduction in quality to \"Good\", by reducing the video resolution. The higher number of nodes in the network implies more neighboring nodes generating lower averages in  
Figure 9: Packet delivery rate (%). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Average End-to-End Delay (ms)** & **Latency (ms)** & **Flow (n)** \\ \hline
3-node IM & 2.34 \(\times\) 10\({}^{-2}\) & 29.112 & 45 \\
3-node 802.11p & 1.44\(\times\) 10\({}^{-7}\) & 0.008 & 8 \\
3-node 802.11n & 4.50\(\times\) 10\(  
end-to-end delay and latency and a larger amount of message flow, since the messages are being sent in broadcast mode.
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 2 Related Work  
Some attempts have been made to use CNN to solve the homography estimation; these attempts obtained higher estimation precision than the ORB+RANSAC method. [PERSON] et al. [20] proposed four-point homography, which is defined by four pairs of corresponding points between two images. The model proposed in [20] is similar to the VGG (Visual Geometry Group) architecture [21] with eight convolutional layers, one max pooling layer after every two convolutional layers, two fully connected layers and an L2 loss function that is calculated from the square of the difference between the predicted and the ground truth four-point coordinate values. [PERSON] et al. [22] proposed a hierarchical model that is stacked by the twin convolutional regression networks to estimate the homography between a pair of images, and improved the prediction accuracy of four-point homography compared with that of the work by [PERSON] et al. [20].  
[PERSON] et al. and [PERSON] et al. [20; 22] focused on estimating the homography between pairs of image_patch_a and image_patch_b with centers being roughly aligned, which we call center-aligned image pairs. However, in our task, we need to estimate the homography between image_a and image_patch_b when their centers are not necessarily aligned, as shown in Figure 2. We call these non-center-aligned image pairs, and they are more difficult, as we do not know which region of image_a should be matched with image_patch_b. The homography estimation of a center-aligned image pair is a special case of the homography estimation of a non-center-aligned image pair.  
Because deep networks require many data to train from scratch, we took a similar approach as that used by [PERSON] et al. and [PERSON] et al. [20; 22] to generate a seemingly infinite dataset for the homography estimation of center-aligned and non-center-aligned image pairs from the Microsoft Common Objects in Context (MS-COCO) [23] dataset. The trained models were then directly used for the homography estimation of climbing robot wall maps.  
Figure 2: Center-aligned and non-center-aligned image pairs. image_patch_a is the rectangle crop of image_a around the ground truth green quadrilateral, and image_patch_b is the warping of the ground truth green quadrilateral.
## 6 Implementation Experiments
### Demonstration Experiment of Basic Indoor AR Navigation Functions for the Museum  
To test the navigation functionality of the framework, we set up several navigation service routes from the entrance to the target exhibits on the first floor of the Mineral Industry Museum and used the prototype to follow the routes to see if the current position is tracked accurately and the geofences are triggered correctly. For example, the route to a target exhibit (Rhodochrosite) is composed of three geofences as waypoints, each having a radius of 0.75 m to cover the width of the path, as illustrated in Figure 13. The red dotted line represents the planned walking route, and the blue dots represent the actual movement trajectory data recorded with the prototype during the experiment. The navigation service is provided as necessary when the movement trajectory data falls within the geofence boundary area, which successfully triggers the direction updates.  
Figure 14a illustrates that upon the user's entry into the exhibition area from the corridor--crossing the boundary of the geofence located at the exhibition's entrance--a check-in event is immediately triggered. The virtual 3D arrow, which guides the user's movement, then changes direction, signaling through the AR screen that the user should turn left. Simultaneously, a voice narration instructs the user to make a left turn.  
Figure 12: VIO-based positioning accuracy evaluation by moving modes.  
Figure 13: Examples of geofences for indoor AR navigation at the Mineral Industry Museum (1F), Akita University. The number (0.75 m) beside each geofence shows its radius. The red dotted line indicates the route to the user’s destination, and the blue dotted line represents the user’s trajectory.  
As depicted in Figure 14b, as the user approaches the target exhibit, the 3D sign immediately points toward it upon geofence check-in, and a voice narration introduces the exhibit. This demonstrates that a framework leveraging spatial design and high-accuracy indoor positioning can effectively realize a practical indoor AR navigation system.  
The virtual 3D arrow is 2 m long and is positioned 5 m away from the smartphone camera. It is designed to occupy approximately 60% of the screen's width. The smartphone's field of view is 60\({}^{\circ}\), divided equally with 30\({}^{\circ}\) to both the left and right. The rationale behind the 5 m placement is founded on a design principle that provides audio guidance roughly 5 s in advance, followed by a succinct narration (e.g., \"Please turn right\") lasting for about 2 s and, thus, allowing the user 3 s to react. The user is assumed to walk at a speed of 1 m/s.
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
###### Abstract  
The energy yield of vehicle-integrated photovoltaics (VIPV) differs from that of standard photovoltaics (PV). It is mainly by the difference of the solar irradiance onto the car roof and car bodies as well as its curved shape. Both meaningful and practical modeling and measurement of solar irradiance for VIPV need to be established, rather than the extension of the current technologies. The solar irradiance is modeled by a random distribution of shading objects and car orientation with the correction of the curved surface of the PV modules. The measurement of the solar irradiance onto the car roof and car body is done using five pyranometers in five local axes on the car for one year. The measured dynamic solar irradiance onto the car body and car roof is used for validation of the solar irradiance model in the car.  
photovoltaic; electric vehicles (EV); plug-in hybrid vehicles (PHV); standardization; car roof; flexible PV; performance modeling; rating Article