esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 1 Introduction  
The 4 th Industrial Revolution has stimulated the need for innovation in the manufacturing industry, and the intelligent manufacturing system is also becoming an important issue [1, 2, 3, 4, 5]. In addition, the manufacturing paradigm is changing from the mass production of a small range of types to multi-products [6, 7, 8]. For this reason, to adapt to the change in the manufacturing paradigm and meet the needs of consumers, the existing manufacturing process is developing to become more flexible. Manufacturing process data collection and analysis technology are some of the crucial elements of the intelligent factory. With the development of IoT technology, it has become possible to extract data from all manufacturing processes. Based on this vast amount of data, intelligent systems aim to improve productivity and energy efficiency and reduce defect rates [9, 10, 11, 12]. Quality inspection of a product is an important task that can reduce the defect rate in the manufacturing process through various tests before shipment [13, 14]. Product quality inspection can be composed of visual inspection and inner structure inspection. Surface inspection is performed basedon vision systems, such as ultraviolet, microscopy, RGB, and depth imaging [15; 16; 17; 18], while inner structure inspection is performed by X-ray, 3D-CT, and ultrasound [19; 20; 21].  
In the automobile tire manufacturing process, the final inspection stage detects defects in the tire using a visual inspection. In most of these inspections, the operator visually determines whether a tire is defective [22]. Among the errors occurring on the tire surface, the vent spew error is hairs on the tire surface, which are to be cut during the manufacturing process, remaining over a certain length. This error implies that the tool that cuts the hairs on the tire surface needs to be replaced. There are more than 200 points on a single tire surface where a vent spew error can occur.  
Additionally, there are a total of over 100 errors that can appear on the tire surface, including the vent spew error. Inspecting these errors by the naked eye can waste time and human resources. The error detection rate dramatically depends on the operator's skill level, because the size and location volatility of the error is high [23]. Driving a vehicle with poor tire condition can lead to critical accidents, so a more precise error detection method should be applied.  
Therefore, this paper applies a deep learning method to the final quality inspection of automobile tires. For this proposal, we demonstrate the process and architecture as follows: (1) use a depth image of the tire surface as training data, (2) segment the error region using a deep learning model, and (3) preprocess the data by applying the anomaly detection concept for more effective accuracy.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 1 Introduction
### Current Trends in Training Data (TD) Collection  
A large proportion of remote-sensing projects make some use of TD, typically created either using geolocated in situ data [46; 60], by visually interpreting high and/or very high spatial-resolution imagery [61; 62; 26], or by interpreting the images to be classified/modeled themselves, e.g., [55; 56; 63; 64]. Of these collection methods, HR/VIR image interpretation is increasingly common [65], particularly with the rise in crowdsourcing initiatives [22; 66]. As such, mapping is strongly constrained by the creation of TD, which, much like map reference data, are often treated as absolute \"truth\", in that their accuracy is assumed to be perfect [30; 38; 47; 67]. However, multiple sources of error are possible and indeed likely in TD, whether collected in situ or via image interpretation [60].  
The use of large, data-intensive ML algorithms continues to grow in many fields, including remote sensing. Neural networks (NN) represent an increasingly used class of ML algorithms, with more complex NNs such as convolutional neural networks (CNN) producing higher output accuracy [68]. While some forms of ML can function effectively with smaller training datasets, the quality of these data is nevertheless critically important [28; 31; 51]. Additionally, the increasingly popular large-scale, high-complexity NNs require substantially more TD than traditional statistical models, and like many ML approaches are sensitive to noisy and biased data, producing the logistical difficulty of creating very large, \"clean\" training datasets [69; 70; 71].  
Partially to address this need, several recent efforts have been devoted to producing extremely large training datasets that can be used across a wide range of mapping applications, and to serve as comprehensive benchmarks [72; 73]. Similarly, a recent trend has emerged in large-scale mapping projects to employ large teams of TD interpreters, often within citizen science campaigns that rely on web-based data creation tools [74; 75; 22; 76].
## 2 Numerical Simulation
### Downscale Setup  
The downscale string functions as a laboratory experimental rig, providing a valuable platform for investigating drill-string dynamics at a lower scale [32]. This approach offers a unique alternative to full-scale testing by simulating downhole conditions in a controlled, repeatable environment. The key advantages of a downscale laboratory setup include its economic feasibility, allowing multiple tests without the high costs associated with full-scale operations. Moreover, the ease of replicating downhole conditions enables researchers to conduct experiments with high precision and consistency. Additionally, the setup allows for comprehensive data collection, with easy access to both surface and downhole parameter measurements, offering detailed insights into the dynamics of the drill string [48]. Figure 1 presents the torsional vibration setup for the downscale string, designed to replicate the key components of a full-scale drilling operation. The setup included a bit, drill collar, heavy weight drill pipe (HWDP), and drill pipe, all scaled down to mimic real-world drilling scenarios while allowing for a controlled and detailed analysis of torsional vibrations.  
The downscale drill string was modeled with a total length of 15 m, closely replicating the configuration of the experimental setup. The bit diameter was 0.20 inches, and the string included the same components used in the experimental arrangement, ensuring consistency for accurate comparison. Table 2 provides a detailed breakdown of the string's  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Upscale Values** & **Downscale Values** \\ \hline Length & 450 m & 15 m \\ \hline Bit diameter & 5.625 inches & 0.20 inches \\ \hline Rotation speed (RPM) & 0–150 RPM & 0–150 RPM \\ \hline \multirow{3}{*}{Torque} & 500 kgfm & 0.01 kgfm \\ \cline{2-3}  & 1500 kgfm & 0.03 kgfm \\ \cline{2-3}  & 4500 kgfm & 0.09 kgfm \\ \hline Density & 1.2 SG & 0.001 SG \\ \hline Static/Dynamic friction & 0.2 & 0.01 \\ \hline \multirow{3}{*}{Depth of observation} & 0 m (bit) & 0 m (bit) \\ \cline{2-3}  & 100 m & 3.3 m \\ \cline{2-3}  & 300 m & 10 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters comparison.  
Figure 1: Experimental setup for torsional vibration for downscale string.  
components, which consist of the drill bit, drill collar, heavy weight drill pipe (HWDP), and standard drill pipe.
## 3 System Model
### Communication Model  
In order to consider the impact of channel quality on network performance, the real propagation loss and channel impulse response are calculated using the BELLHOP ray model in conjunction with winter sound speed profile data from a specific sea area. The obtained channel impulse response is then applied to a physical-layer OFDM communication system to evaluate the BER of the underwater acoustic channel in conjunction with specific modulation modes and underwater environmental conditions.  
The winter sound speed profile, as depicted in Figure 2, is utilized to describe the sound speed distribution of the deep-sea acoustic channel in a specific sea area. We obtain the number of multipaths \(Npa\) for the known transmitting depth and the known receiving  
Figure 1: The schematic diagram of the network.  
position, as well as the amplitude and delay information in the \(Npa*1\) dimension. The impulse response \(h(t)\) is defined as follows:  
\[h(t)\ =\ A_{0}\delta(t-\tau_{0})+\sum_{u\ =\ 1}^{Npa-1}A_{u}\delta(t-\tau_{u}) \tag{6}\]  
where \(A_{u}\) and \(\tau_{u}\) are the amplitude and delay of the u-th acoustic line at the receiver, respectively. An example of the channel is shown in Figure 3. If the signal at the transmitter is denoted by \(x_{m}(t)\), the received signal of the multipath channel is denoted by the following:  
\[y_{m}(t)\ =\ A_{0}x_{m}(t-\tau_{0})+\sum_{u\ =\ 1}^{Npa-1}A_{u}x_{m}(t-\tau_{u})+n_{c }(t) \tag{7}\]  
where \(y_{m}(t)\) is the superposition of the direct acoustic signal and multiple reflections, and \(n_{c}(t)\) is the channel noise.  
The delay and amplitude of the multipath channel, as calculated by BELLHOP, are incorporated into the physical layer of the OFDM system depicted in Figure 4. OFDM is a modulation technique that divides a high-speed data stream into multiple low-speed subcarriers for transmission. This process has the characteristics of resistance to multipath  
Figure 3: Example of channel (transmit depth 1000 m, receive depth 878.1 m, distance 536.8 m).  
Figure 2: The sound speed profile.  
fading, high-frequency utilization, and strong resistance to jamming. It is therefore suitable for the complex channel environment of underwater acoustic communication.  
The OFDM system described in this paper employs LDPC code for channel coding, BPSK for modulation and demodulation, and a frequency-based channel estimation and equalization method. In order to eliminate the Doppler effect in the underwater acoustic channel, a time-domain correlation method is employed for Doppler estimation, while an interpolation method is used for Doppler compensation. Based on the positional information of the transmitting and receiving nodes, the BER for known transmitting and receiving depths and distance can be calculated. The bit error rate is inversely proportional to the channel quality. A lower bit error rate indicates a higher channel quality. Figure 5 depicts the 2D BER for a transmitting depth of 800 m, varying receiving depths, and varying receiving distances. As stated in [25], the PDR can be calculated from the BER.
## 7 Discussion  
The comprehensive evaluation of three large language models (LLMs) (ChatGPT, DeepSeek, Grob) within WarehouseGame Training provides clear insights into their suitability for optimizing logistics simulation, considering four critical dimensions: difficulty progression, adaptability, response compliance, and response time. Each model exhibited distinct strengths and limitations that shape their applicability in this context. In difficulty progression, ChatGPT excelled by generating challenges consistently aligned with the requested level, ensuring a coherent experience across all skill levels, from beginner to expert. This consistency is vital for maintaining a clear learning curve. However, its lenient failure tolerances and prolonged assistance in advanced levels could hinder the development of autonomous skills. Grob delivered a well-structured progression, logically increasing complexity, but its reliability was undermined by alignment errors between requested and responded levels, particularly at the advanced stage. DeepSeek exhibited less refined scalability, with abrupt difficulty transitions that could disorient players, especially at medium levels. Regarding adaptability, Grob proved superior in dynamically adjusting challenges based on user performance. Its rapid response to excessive errors, through balanced difficulty reductions and increased failure allowances, minimized frustration and fostered a fluid learning environment. ChatGPT, while implementing more aggressive adjustments, suffered from slower reactivity, potentially prolonging players' exposure to overly challenging tasks. DeepSeek, despite its consistency, was less effective due to insufficient order reductions at medium levels, limiting its personalization capabilities. For response time, Grob emerged as the clear leader, delivering the fast and consistent responses essential for maintaining interactivity in a real-time training environment. ChatGPT performed acceptably but exhibited occasional delays that could disrupt fluidity in dynamic scenarios. DeepSeek, with significantly longer response times, was less suitable for applications requiring immediate feedback. Grob is the most suitable model for gamified simulation, excelling in adaptability and response time and offering robust progression despite alignment issues. ChatGPT is a strong alternative, particularly for lower levels, but it requires improvements in reactivity and rigor. DeepSeek is not recommended, due to its irregular scalability and high latency, which compromise user experience. However, a hybrid approach, using ChatGPT for lower levels and Grob for higher levels, could maximize training effectiveness. ChatGPT would be ideal for beginner and medium levels, where its perfect level alignment ensures a clear and coherent introduction to logistics tasks. As players progress to advanced and expert levels, Grob could take over, leveraging its superior adaptability and fast response times to manage more complex, personalized challenges, provided its alignment errors are addressed.
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 4 Practicality Validation of Polyimide FBGs  
### Theoretical Validation of Polyimide FBGs in Dynamic Measurement  
Another experiment was designed in order to verify the practicality of polyimide FBGs in practical strain measurement. It was a dynamic strain measurement in a reinforced concrete specimen internal explosion experiment. Before the experiment was conducted, the ability of polyimide FBG in dynamic strain measurement needed to be investigated by a theoretical experiment.  
In the theoretical experiment, polyimide FBGs were the same as before. The interrogator used in the experiments was the SM130-700 dynamic interrogator (Micro Optics). The interrogator can identify a fiber grating sensor with wavelength ranging from 1525 nm to 1565 nm. Its wavelength resolution was up to 1 pm and it could achieve synchronous four-channel 2000 Hz sampling. The experiment system are shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**FBG Wavelength**} & **Measured Average Strain** & **Theoretical Average Strain** & \multirow{2}{*}{**Relative Error**} \\  & **Transfer Rate** & **Transfer Rate** & \\ \hline
1520 nm & 96.28\% & 93.72\% & 0.9\% \\
1525 nm & 96.18\% & 93.72\% & 0.8\% \\
1570 nm & 96.18\% & 93.72\% & 0.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of strain transfer.  
Figure 19: Dynamic measurement test system.  
Two polyimide FBGs were bonded to a cantilever beam along the longitudinal axis at the center. The bonding length was 40 mm and bonding width was 2 mm. After the adhesive had solidified and was confirmed to be in good condition, one end of the beam was applied stress to produce vibration.  
As shown in Figure 20, \(l_{1}\) = 45 mm, \(l_{2}\) = 240 mm, \(l_{3}\) = 60 mm, \(a_{1}\) = 9.18 mm, and \(c\) = 45.9 mm. The thickness of the cantilever beam \(h\) = 35 mm, the density of the beam \(\rho\) = \(7.8\times 10^{3}\) kg/m\({}^{3}\) and [PERSON]'s modulus of the beam E = 200 GPa.  
When \(x<l_{1}\),  
\[\frac{d^{2}(y_{1}(x))}{dx^{2}}=\frac{M_{1}(x)}{EI_{1}(x)}=\frac{12x}{Eah^{3}} \tag{15}\]  
When \(l_{1}\leq x\leq l_{1}+l_{2}\),  
\[\frac{d^{2}(y_{2}(x))}{dx^{2}}=\frac{M_{2}(x)}{EI_{2}(x)}=\frac{12x}{Eb(x)h^{3}} \tag{16}\]  
The cross section:  
\[b(x)=\frac{x-l_{3}}{l_{3}}a \tag{17}\]  
When \(l_{1}+l_{2}<x<l_{1}+l_{2}+l_{3}\),  
\[\frac{d^{2}(y_{3}(x))}{dx^{2}}=\frac{M_{3}(x)}{EI_{3}(x)}=\frac{12x}{Ech^{3}} \tag{18}\]  
\[w^{2}=\frac{\int_{0}^{l}EI(x)\big{(}y^{\prime\prime}(x)\big{)}^{2}dx}{\int_{0 }^{l}m(x)y^{2}(x)dx} \tag{19}\]  
The theoretical basic frequency of the cantilever beam is 52.96 Hz.  
As shown in Figure 21, the basic frequency of cantilever measured by FBGs without coating is 51.28 Hz. The relative error between the theoretical and the measurement is 3.2%. The experiment verifies the capacity of polyimide FBGs in dynamic measurement.  
Figure 21: Strain of the cantilever beam measured by FBGs.  
Figure 20: Cantilever beam and bonding diagram.
# Benchmarking Numerical Methods for Impact and Cratering Applications
## 4 Results and Discussion
### Implications for Modeling Fracture  
For impact cratering cases in the strength-dominated regime, strength and damage also play a role in the crater formation process [1; 2]. Thus, modeling the 5 km/s normal impact simulations, which lie in the strength regime, with the inclusion of strength and damage models can provide additional insight into simulating cratering processes.  
The FLAG simulations used a constant yield stress with linear hardening behavior to model both target and impactor, and damage was modeled using the Johnson-Cook model [61]. The yield stress was 276 MPa, and the hardening parameter was 0.1 [62]. The HOSS simulations also used a constant yield stress with linear hardening. This simulation used a yield stress of 276 MPa and a linear hardening of 113.3 MPa. In addition, the properties of cohesive bonds between the elements were assigned such that fracture occurred once the stress in the material surpassed its ultimate tensile strength (310 MPa).  
The particle velocity and damage metrics are presented in Figures 13 and 14, respectively. Snapshots are shown at 0.4 s and 0.8 s. Because of the difference in damage formulations, it is difficult to directly compare across codes. Thus, the damage in FLAG is termed 'continuum damage', as it indicates damage incurred within a cell/zone. Damage in HOSS is termed 'discrete damage,' as it indicates damage incurred between two cells/zones. In both cases, a damage value of 0 represents completely undamaged material while 1 represents a fully damaged region. In HOSS, fully damaged regions directly correspond to fractures or cracks evolving within the material. Damage in FLAG is more homogenized, so there is not a direct correlation with a discrete crack network.  
While the FLAG and HOSS particle velocities shown in Figure 13 are relatively similar, the damage from both codes, shown in Figure 14, is very different. HOSS shows a significant amount of damage developing and evolving in the wake of the shockwave as it travels through the medium. In FLAG, however, only highly damaged regions in the crater ejecta are visible at these early times. The FLAG simulation did continue to run to 3.0 s, and more damage is evident at later simulation times, below the crater floor and surrounding the crater walls, similar to the shape of the damaged region in HOSS at early times (see Appendix A).  
Figure 13: Particle velocity from a \(5\times 10^{3}\) m/s normal impact in which strength and damage are considered in the material models. Snapshots are shown at 0.4 s (**a**,**b**) and 0.8 s (**c**,**d**) and are calculated from 3D simulations from FLAG and HOSS. (**a**,**c**) show particle speed from 3D FLAG simulations, and (**b**,**d**) show particle speed from 3D HOSS simulations. The length of the horizontal green bar shown inside the crater represents a distance of 1000 m.
## 4 Discussion
### Potential Discrepancy between GPP\({}_{\text{EC}}\) and GPP\({}_{\text{MOD}}\)  
Figure 4 shows the inconsistency between GPP\({}_{\text{MOD}}\) and GPP\({}_{\text{EC}}\) at the three sites, which can be attributed to three aspects: (a) input parameters such as FPAR data and meteorological conditions [49]; (b) the uncertainties in the MOD17A2H GPP algorithm [2]; and (c) the spatial mismatch between remotely sensed pixels and EC footprints [58; 59]. In the past few decades, a wide network of sites have been established across various ecosystems and climate regions; for example, AmeriFlux, Integrated Carbon Observation System, National Ecological Observatory Network, and FluxNet [60; 61]. These EC sites provide potential opportunities to annually update the cropland sites in the land cover maps, redefine the MOD17 cropland parameters and greatly improve GPP\({}_{\text{MOD}}\) at regional or even global scales. To the best of our knowledge, China has the largest area of the rice-wheat-rotation croplands in the world. In China, these croplands are distributed widely along the Yangtze River Basin (Figure 1, inset map), covering around 13 Mha in total [24]. This rotation cropland system is a non-negligible part of the agrocosystem. While the MOD17 algorithm defines only 11 land cover classes, i.e., one type of cropland, one type of woodland, two types of grasslands, two types of shrubland and five types of forests [49]. Therefore, the large discrepancy between GPP\({}_{\text{MOD}}\) and GPP\({}_{\text{EC}}\) over the rice-wheat-rotation cropland indicates that the parameters in the MOD17 product should be modified and more types of cropland (e.g., double-cropping or mixed-cropping systems) should be defined. Meanwhile, the MOD17 GPP product has a fine resolution with 500 m, and so the high-quality MOD17 GPP product can be employed to accurately assess the ecosystem's carbon cycle and agricultural productions. In particular, out-of-academy precision agriculture or commercialized precision agriculture put forward higher requirements for the accuracy evaluation of GPP products. Nowadays, EC-based calibration of MODIS products is a common method. In the present work, due to the study being limited to three EC sites over rotation cropland areas in eastern China, the observations cannot be used to stand for the whole area. Therefore, we proposed the machine learning-based GPP prediction
## 2 Materials and Methods
### Optimization
#### 2.4.5 Position and Orientation Errors  
The end-effector of the manipulator has a coordinate system (CS) placed inside the gripper. There is a target CS that moves along the task trajectory during the simulation. Inverse kinematics in CoppeliaSim are set up so that the CS of gripper follows the target CS. Depending on the simulated manipulator, the inverse kinematics are either able or unable to converge to the target CS and errors in position and orientation may occur. The errors are measured at each simulation step and summed according to the following Equations (3) and (4):  
\[\varepsilon_{\text{pos}}=\sum_{\text{i}=0}^{\text{n}}\bigl{(}|\text{x}_{\text {ef}}-\text{x}_{\text{tar}}|+\bigl{|}\text{y}_{\text{ef}}-\text{y}_{\text{tar}} \bigr{|}+|\text{z}_{\text{ef}}-\text{z}_{\text{tar}}|\bigr{)} \tag{3}\]  
\[\varepsilon_{\text{ori}}=\sum_{\text{i}=0}^{\text{n}}\bigl{(}|\text{\alpha}_{ \text{rel}}|+|\beta_{\text{rel}}|+|\gamma_{\text{rel}}|\bigr{)} \tag{4}\]  
where i is a simulation step; n is a total number of simulation steps; x\({}_{\text{ef}}\), y\({}_{\text{ef}}\), and z\({}_{\text{ef}}\) are coordinates of the end-effector; x\({}_{\text{tar}}\), y\({}_{\text{tar}}\), and z\({}_{\text{tar}}\) are coordinates of the target; \(\text{\alpha}_{\text{rel}}\), \(\beta_{\text{rel}}\), and \(\gamma_{\text{rel}}\) stand for the relative orientations between the end-effector and the target coordinate frames.