esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementing CAD API Automated Processes in Engineering Design: A Case Study Approach
## 3 Methodology and System Architecture
### Automation Workflow Design  
The automation framework developed in this work was designed to streamline repetitive tasks commonly encountered in mechanical engineering design and documentation  
Figure 3: Block diagram of the code proposed in this study.  
workflows [45]. Utilizing the Autodesk Inventor API, a quantitative and iterative approach was employed to implement a modular and scalable system capable of minimizing manual interventions and improving data traceability. The workflow was compiled using custom scripts developed in Visual Basic for Applications (VBA), enabling the automation of core design operations such as part file creation, folder generation, drawing documentation, and metadata management.  
The core of the design focused on user-guided interactions through input forms, ensuring that essential information is collected consistently and accurately before model generation. This allowed for the seamless transition from raw input to structured output, reducing the risk of error and enforcing standardized documentation procedures. The automation logic was developed to be both hierarchical and reusable, facilitating the deployment of the system across a variety of design scenarios.  
Three representative case studies were used to inform and validate the automation process, selected for their complexity, frequency, and the degree of task repetition involved. These cases, selected from daily operations within the collaborating industrial company, served both as testbeds for iterative code development and, at the same time, as validation scenarios evaluating the robustness and applicability of the proposed solution. The integration of this workflow within Autodesk Inventor not only enabled rapid and repeatable model creation but also ensured alignment with actual engineering demands.  
For clarity and readability, detailed implementation aspects of the automation system have been relocated to Appendix A. This includes the structure and logic of the VBA code, the design of user input forms, the directory and file-naming conventions, and the workflow for generating CAD files and manufacturing drawings. Additionally, screenshots, interface examples, and a breakdown of modular subroutines are provided to support reproducibility and facilitate potential adaptation to other engineering contexts.
## 2 Crack Length Measurement with the DC-Potential Drop Method
### Influence of Temperature and Atmosphere  
The DCPDM can be influenced by several external effects. For example, a change in temperature causes a change in electrical resistance and thus an increase in the measured potential. Because of the  
Figure 2: (**a**) Crack surface with overload marks (red lines). For calibration of the DCPDM, the cracked area is measured and divided by the specimen thickness, resulting in mean crack length (yellow lines); (**b**) the effective distance of the potential grips \(y_{0}\) is achieved by a least square fit of the Johnson equation (Equation (1)) on the measured optical crack length a divided by the specimen width (w).  
low potential values, the temperature must be kept as constant as possible during the experiment. To avoid this problem, [PERSON] [32; 33] uses temperature compensation implemented by means of a reference potential drop measurement on an unloaded sample. However, the main problem when servohydraulic testing machines are used is not the change of the ambient temperature, but the heating of the oil of the testing machine. These heating effects can easily be avoided by a temperature stabilizing period prior to the experiment. Within this period, the temperature of the complete system is levelled out and therefore no undesirable temperature effects in the experiment must be feared.  
Furthermore, other effects are known which might also influence the potential probe measurement. A touching of the fracture surfaces behind the crack tip leads to a formation of electrical contact bridges. This results in a reduction in electrical resistance and, accordingly, a decrease in the measured potential leading to a shortening of the measured crack length [27; 31; 34]. This problem can be avoided by only using the maximum value of the potential within a cycle to determine the crack length since, at least under mode I loading, it can be assumed that there are no contact bridges under maximum loading [22; 35].  
In case of aluminum alloys, this effect is of minor importance due to the rapid formation of an insulating oxide layer on the crack surface, so that the differences between maximum and minimum values within a cycle are very small. Crack propagation measurements under symmetrical tension-compression loading on a SEN-specimen of a particle-reinforced aluminum alloy in dry nitrogen atmosphere, however, show an extreme change of the potential within a cycle due to the lack of oxide layer formation on the fracture surfaces (Figure 3) [36]. The measured potential varies more than 0.3 mV within a cycle which corresponds to a change in the crack length of nearly 3 mm. This example impressively underlines the need to use the maximum value of the potential to determine the crack length.
## 5 Applications and Services
### RIS Technologies
#### 5.3.3 Holographic Surface  
The reconfigurable holographic surface (RHS) is a type of RIS [161]. The RHS performance is very similar to that of RIS, with two major differences in the operation and application: (1) RHS consists of metamaterial elements with tunable radiation amplitude, which makes it feasible to tune the radiation pattern of the planar surface. Therefore, beamforming is implemented by controlling the radiation pattern of RHS elements rather than controlling the phase shifts [162], which makes it energy-efficient. (2) The RHS can function as a transceiver rather than only reflecting signals. UAV may be equipped with RHS as RHSeUAV that propagates communication signals generated by the UAV rather than reflecting external signals [12]. This results in two main advantages: (1) compared with UAV-BS, the onboard package of the RHSeUAV is energy efficient as the RHS adjusts the radiation patterns through the holographic surface that obviates active/passive beamforming [162]; (2) compared with the RISeUAV, the RHSeUAV's flight becomes independent of the ground MIMO BSs which adds more freedom for optimal navigation [163].
# Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples
## 1 Introduction
In recent years, with the advancement of electronic technology, Android technology has been widely applied in various geophysical instruments, driving the development of lightweight and intelligent geophysical instruments. Compared to computer-based host systems, Android smartphones offer advantages such as portability, ease of use, aesthetic interfaces, and long battery life, making them more suitable for field exploration in geophysics. For instance, [PERSON] et al. developed acquisition and monitoring software for a Wide-Field electromagnetic receiver based on the Android platform [15]. Building upon the aforementioned research, this study developed an electrical impedance tomography system for rock samples with high-density microelectrodes. The system incorporates low-power and high-precision devices such as the STM32 microcontroller and CPLD (Complex Programmable Logic Device) to ensure measurement accuracy and reduce power consumption. Human-machine interaction is achieved through an Android smartphone, enabling real-time monitoring of the instrument's operational status and facilitating user convenience. Consequently, the need for conventional display panels used in most geophysical instruments is eliminated, resulting in reduced power consumption, improved instrument sealing, and decreased weight. Bluetooth and Wi-Fi wireless communication technologies are employed to eliminate the time-consuming and laborious process of laying cables in challenging terrain conditions, which often leads to tangles, disconnections, and short circuits. The system is designed with 192 current electrodes and 192 potential electrodes, surpassing the typical 16-electrode configuration [16] and 32-electrode configuration [17] used in most EIT systems. This increased electrode count ensures a comprehensive coverage of rock and ore specimens. All electrode channels can be flexibly selected and configured on the Android App according to specific requirements. Once the electrodes are initially deployed, automatic switching and data acquisition can be achieved, enhancing measurement efficiency and increasing the volume of acquired data. Subsequently, three-dimensional inversion imaging of rock and ore specimens can be performed based on the collected data, allowing for a comprehensive representation of their electrical properties. Furthermore, this system also enables fully automated three-dimensional high-density induced polarization imaging of target bodies in water tanks, facilitating related research in electrical exploration. The system can also carry out detailed three-dimensional detection of tree and crop roots [18; 19].
## 2 Non-Gaussian Stress
### Generating of Non-Gaussian Stresses  
The idea behind generating non-Gaussian random signals is to use a monotonic non-linear transformation function \(g(\cdot)\) to convert Gaussian random signals into non-Gaussian signals, which can be described as  
\[y(t)=g(x(t)) \tag{5}\]  
where \(x(t)\) represents a Gaussian random signal with a given PSD. Emphasizing the monotonic transformation means ensuring that the value of \(y(t)\) at any time \(t\) depends solely on the value of \(x(t)\) at that time \(t\).  
The nonlinear transformation function used in this paper is in the form of a hyperbolic sine function and its inverse [19; 20]:  
\[g(x)=\begin{cases}e^{\theta_{1}x}-e^{-\theta_{2}x}&K_{u}>3\\ \ln\left(\theta_{1}x+\sqrt{\left(\theta_{2}x\right)^{2}+1}\right)&0<K_{u}<3 \end{cases} \tag{6}\]  
When the parameters \(\theta_{1}\) and \(\theta_{2}\) in Equation (6) are equal, it indicates that the skewness of the signal \(y(t)\) is zero. In this paper, non-Gaussian signals with both a mean and skewness equal to zero are considered. In this case, the approximate closed-form solution for kurtosis control in Equation (6) is  
\[\theta_{1}=\theta_{2}=\begin{cases}3.48(K_{u}-3)^{0.171}-2&K_{u}>3\\ 2.36e^{3-K_{u}}-2&0<K_{u}<3\end{cases} \tag{7}\]
# Indoor-Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
## 4 Quantum Machine Learning Approaches
### Quantum Distance-Based Classifier  
The second approach we present is a classification algorithm. Here, data points are classified based on their distance to the training data. The algorithm takes as input \(M\) training data points \(\mathbf{x}_{i}\in\mathbb{R}^{N}\) with binary labels \(y_{i}\in\{-1,1\}\). The goal of the algorithm is to assign a label \(\tilde{y}\) to a new data point \(\mathbf{\tilde{x}}\). Classification is based on the classification function  
\[\tilde{y}=\text{sgn}\left(\sum_{m=0}^{M-1}y_{m}\left[1-\frac{1}{4M}|\mathbf{\tilde {x}}-\mathbf{x}_{m}|^{2}\right]\right). \tag{10}\]  
This method requires an additional preprocessing step to get the data in the required format. To be able to process the data by the qubits, they have to lie on a unit sphere. The implementation is explained in detail in [41]. The quantum algorithm can be implemented using \(2\cdot(log_{2}(F)+log_{2}(N)+1)\) qubits, where \(N\) is the number of training data points and \(F\) is the number of features of the data. In Figure 11, the resulting assignments are shown for \(N=256\) and \(F=2\), which can be implemented on a quantum device using 20 qubits. In this figure, the projection on the unit sphere has been reverted before being plotted. Due to this projection on the unit sphere, it can be seen that classification of a data point depends only on the angle between the different features.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**N** & **Model** & **Cost Function** & **F1** & **Acc** & **Pr** & **Rc** \\ \hline
225 & 2-qubit & 0.4167 & 1 & 1 & 1 & 1 \\  & 4-qubit & 0.3329 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of performance indicators after 350 optimisation iterations.  
Figure 10: Resulting assignments of the variational classifiers: 4-qubit model.  
This approach is especially powerful if the initial quantum state is given, for instance, as a result from a quantum process or using a quantum RAM, as was also proposed by [42]. In that case, the complexity is constant \(\mathcal{O}(1)\), independent of the number of features and the number of data points. If the quantum state has to be generated explicitly, the complexity is \(\mathcal{O}(NF)\). Due to the probabilistic nature of quantum computing, the obtained result is probabilistic and multiple evaluations of the quantum state are required. The results are shown in Table 2.
# Detecting and Mitigating Attacks on GPS Devices
## 5 Detection Techniques and Their Comparison
### GPS Spoofing Detection
The authors of [100] utilized a satellite imagery matching technique to detect GPS spoofing attacks in UAS applications. In this approach, DeepSIM uses the onboard camera to capture images of the terrain below and then compare those to existing satellite-based imagery to determine its position, which can then be compared with the position determined via GPS. A discrepancy between the two is illustrative of GPS spoofing. The authors utilized four different DL models to achieve image matching based on whether satellite imagery or aerial imagery was available for the area, including Distance Threshold, Siamese ResNet, Semi-Siamese Network, and Siamese SqueezeNet. The authors reported a GPS spoofing detection rate of over 95% using this approach. A potential limitation of this type of approach is the ability to capture all possible conditions in the DL training datasets to reflect different light conditions (e.g., night versus day), changes in seasons (e.g., foliage changes), manmade or natural-induced changes to terrain, etc.
# E-Eye-Based Approach for Traceability and Annuality Compliance of Lentils
## 3 Results and Discussion
### SPORT Classification According to the Harvesting Year, the Origin, and the Edibility
#### 3.2.1 Classification of the Samples According to Their Geographical Origin  
At first, a model to discriminate among the three geographical origins of the lentil samples was built and validated. To this purpose, the 560 samples were split into a training and a test set by means of the Duplex algorithm [41] applied category-wise with a 70:30 splitting ratio. Accordingly, 392 samples (88 AQ, 70 COL, and 234 CDN) were included in the training set, while the remaining 168 (38 AQ, 30 COL, and 100 CDN) were left out to constitute the external test set. The training samples were then used to build the SPORT classification model, whose optimal complexity, estimated in cross-validation, resulted to be 11 and 3, for the mean-centered and autoscaled colorgram, respectively. The results are summarized in Table 3.  
The results reported in Table 3 show how the SPORT classification model led to very good results not only in the calibration stage but also when it was applied to the external validation samples. Indeed, an average correct classification rate of 88.0% was achieved, corresponding to the correct prediction of 87% of the AQ samples, 90% of the COL images, and 87% of the CDN lentils. The model results were also very specific, with values always higher than 92%. When looking at the directions of the misclassifications, it could be observed how the wrongly predicted COL samples were all assigned to the CDN category, whereas for the other two classes, misclassifications were equally distributed among the other categories.  
It might be useful to emphasize that, in contrast to previous works [23; 26] aimed at discriminating among Italian landraces by image analysis, the present study demonstrates the method's versatility by correctly distinguishing between typical varieties from relatively close areas that also span several production years.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 2 Description of the Dataset  
### Study Area  
The Yellow Sea green tide has been breaking out for 15 years and has become a regular marine ecological hazard along the Chinese coast. The study area was the Yellow Sea (119\({}^{\circ}\)-126\({}^{\circ}\)E, 31\({}^{\circ}\)-38\({}^{\circ}\)N), as shown in Figure 1 (bathymetry data in Figure 1 are from ETOPO1). To validate the accuracy under different environments, we partitioned the Yellow Sea according to its water depth, as shown in Figure 1a according to the water depth of the Yellow Sea. Such partitioning mainly referred to [PERSON] et al., who argue that the normalized difference vegetation index (_NDVI_) will be more or less affected by muddy water, and shallow areas (seawater depth of \(<\)\(-\)35 m) would affect _NDVI_ to some extent, and that environmental factors would cause uncertainty in the extraction results on satellite images [23]. As seen in Figure 1b, we chose the accuracy assessment areas A1-A3, B1-B3, and C1-C3 corresponding to the three partitions of A, B, and C.
# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 3 Detailed Description of the Method
### Adaptive KLT Tracker and Simplified KLT Matcher
The parameters (\(d_{0}\), \(v_{0}\), \(\alpha_{0}\), \(c_{d}\), \(c_{v}\), \(c_{\alpha}\), and \(b\)) are empirically set to be (20 pixels, 1.0 m/frame, 0.02 rad, 1, 10, \(-100\), and 17), respectively. Meanwhile, the window size \(B_{k}\) has the lower and upper bounds of 5 and 49, respectively. Note that the adaptive window size is determined using the first-order approximation. An interesting future direction would be to explore a nonlinear model for the JAF and obtain the above parameters in a learning framework. Figure 4: (**a**): Feature point tracking results in frame 99 on sequence 00 of the KITTI odometry dataset. The yellow arrow indicates the optical flow vector of the feature point. (**b**): Relationship between the disparity and the optical flow for a turning maneuver; regardless of the size of the disparity, all features have large motion. Each blue dot corresponds to a feature point in (**a**). Figure 3: (**a**): Feature point tracking results in frame 0 on sequence 00 of the KITTI odometry dataset. The yellow arrow indicates the optical flow vector of the feature point. (**b**): Relationship between the disparity and the optical flow when the translation velocity is fast and the yaw angle is small; features with a large disparity are prone to large motion and features with a small disparity are prone to small motion. Each blue dot corresponds to a feature point in (**a**). After the tracking step, for a rectified stereo frame, stereo matching can be conducted strictly along the epipolar line. Combining the KLT method with the epipolar constraint, the component of the displacement \(\mathbf{d}\) in the row direction is approximately equal to zero. This means that the SKM looks for feature correspondences only in the column direction. Thus, the SKM between the left and right images \(l_{k}^{L}\) and \(l_{k}^{R}\) in frame \(k\) can be represented as follows:  
\[d=\underset{d}{\operatorname{argmin}}\iint_{W}\Big{[}l_{k}^{L}(u,v+d)-l_{k}^{ R}(u,v)\Big{]}^{2}\omega(u,v)dudv \tag{3}\]  
where \(d\) is the disparity. The resulting set is also projected to 3-D space via triangulation. Afterward, when estimating the camera pose in frame \(k+1\), the input to the new AKT is no longer the output of the SURF detector but the output of the SKM, i.e., the FL This means that the new SURF feature detection will be taken into account only if the number of new tracked features is lower than a predefined threshold. Due to the FI, the AKT, and the SKM, the approach presented here can avoid both feature detection and feature-based stereo matching as much as possible. Consequently, the computational time is considerably reduced.
## 4 Experiments
### Analysis and Discussion
#### 4.5.2 Convergence of SOBT  
ROBT only takes a few thousand steps to converge, empirically proven to be suboptimal. The curve in Figure 4 shows that our method can deliver greater and continued benefits with more steps and high-quality training samples. To achieve the optimal performance, we break down the whole training schedule into three stage. We utilize SOBT to generate back-translated sentence pairs at both fine-tuning stages while only using CLOS  
\begin{table}
\begin{tabular}{c l l l l l l l l l l} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Method**} & \multicolumn{10}{c}{**Ar-De-Fr-Nl-Ru-Zh**} \\ \cline{3-11}  & & \multicolumn{2}{c}{**English \(\rightarrow\) X**} & \multicolumn{3}{c}{**X \(\rightarrow\) English**} & \multicolumn{3}{c}{**Zero-Shot**} \\ \hline  & & **BLEU** & **METEOR** & **TER** & **BLEU** & **METEOR** & **TER** & **BLEU** & **METEOR** & **TER** \\ \hline (1) & Pivot [21] & - & - & - & - & - & - & 12.98 & 36.54 & 69.22\% \\ (2) & MNMT [21] & - & - & - & - & - & - & 3.97 & 12.05 & 95.89\% \\ (3) & 2 + ROBT [21] & - & - & - & - & - & - & 10.11 & 32.11 & 74.73\% \\ \hline (4) & Pivot & 23.45 & 46.65 & 52.95\% & 35.16 & 67.43 & 38.26\% & 13.82 & 38.55 & 65.54\% \\ (5) & MNMT & 22.53 & 46.28 & 53.46\% & 34.96 & 66.55 & 39.63\% & 3.09 & 10.77 & 96.22\% \\ (6) & 5 + Pivot & - & - & - & - & - & - & 13.45 & 37.43 & 67.44\% \\ \hline (7) & mRASP2 & 22.22 & 44.56 & 55.89\% & 34.87 & 63.74 & 39.93\% & 12.44 & 33.33 & 74.01\% \\ (8) & 5 + ROBT & 22.10 & 44.67 & 55.52\% & 34.53 & 63.34 & 39.93\% & 10.60 & 33.33 & 74.01\% \\ (9) & 5 + POBT & 22.47 & 45.63 & 54.62\% & 34.72 & 64.46 & 39.82\% & 12.74 & 35.02 & 71.99\% \\ (10) & 5 + SOBT & 22.96 & 46.02 & 52.69\% & 35.10 & 68.94 & 37.64\% & 13.88 & 41.41 & 61.66\% \\ (11) & Ours & **23.78** & **47.52** & **51.13\%** & **35.44** & **69.99** & **36.95\%** & **15.80** & **44.75** & **60.07\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance on the OPUS-100 dataset. The dataset only present 15 zero-shot directions for Arabic, Chinese, Dutch, French, German, and Russian, as applying bilingual NMT to each language pair is resource-consuming. The best results, indicated in bold, are statistically significant against all other results, for \(p<0.05\).  
methods at the substitution fine-tuning stage based on a better initialization to achieve the optimal performance.
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 5 Implementing the Proposed Model  
Digitalization has become a fundamental aspect of modern healthcare, transforming traditional medical practices into digital formats. This transition involves utilizing electronic systems to collect, store, and analyze patient data, ultimately enhancing efficiency, accuracy, and accessibility in medical processes [55]. Moreover, AI-driven data collection, analysis, and assessment are increasingly integrated into healthcare, enabling more precise and reliable outcomes [49,50]. As discussed in the previous section, the cyclical approach is essential in structuring medical records. Therefore, integrating this approach into EMRs is a crucial consideration. Digitalization sometimes necessitates modifications to existing processes to ensure optimal implementation.  
During EMR deployment, IT specialists frequently encounter a recurring request from physicians: \"We need to determine which referral (treatment cycle) a specific data field (e.g., lab results, medical item) belongs to in the patient's treatment\". Addressing this requirement is essential to prevent medical errors and ensure data integrity. To resolve this issue, our study proposes a structured model of sequential data forms that delineates treatment cycles within EMRs, enhancing clarity and decision-making.