esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Preliminaries
#### 2.2.2 Prompt Engineering  
This paper leverages DeepSeek and prompt engineering to enhance the accuracy of NL2 SQL, utilizing prompt engineering in both the NL2 PY (Natural Language to Python) and PY2 SQL (Python to SQL) components. The logical flowchart of prompt engineering used in this paper is shown in Figure 2. It includes the foundational architecture, supplementary knowledge, sample selection, and reasoning approach. Considering the specifics of the financial industry, we have incorporated financial knowledge and financial data models into the supplementary knowledge section. This greatly improves the model's understanding of the financial industry's characteristics and enhances the matching between database tables and Chinese language inputs.  
Compared with directly converting natural language to SQL (NL2 SQL), the method of converting natural language to Python code (NL2 PY) and then converting Python code to SQL (PY2 SQL) has the following advantages: First, as a general programming language, Python has mighty expressive power and can more flexibly implement complex business logic and algorithms, including advanced features such as loops, conditional judgments, and functions. Second, Python has a wealth of third-party libraries and modules, which facilitates data processing and analysis and improves the reusability and maintainability of the code. In addition, Python's debugging and testing tools are more complete, which is convenient for developers to verify the code and troubleshoot errors. The limitations of NL2 SQL are mainly reflected in the following aspects: First, it is difficult to directly express complex logic and algorithms, especially when it involves multi-table associations, nested queries, or dynamic calculations. Second, the generated SQL statements may be too complex and need better structure and readability, which challenges the maintenance and understanding of the code. Third, SQL lacks advanced data processing capabilities and flexible error handling mechanisms and cannot integrate various data sources and processing processes as quickly as Python. Finally, SQL debugging tools are relatively scarce, and it is not easy to debug complex queries. In summary, the method of NL2 PY and then PY2 SQL can fully utilize the advantages of Python, make up for the shortcomings of NL2 SQL, and better meet complex business needs.
## 2 Basic Theories and Principles
### Untrained Neural Network Architecture
Inspired by the DIP and SPI approaches, we integrate the imaging physics model of ToF into a randomly initialized untrained convolutional neural network to obtain high-quality reconstructed images by interacting with the imaging physics process during network optimization, which allows for low time consumption in data preparation and image reconstruction. In our approach, the reconstruction formula of the target image can be represented by Equation (12),  
\[R_{\theta*}=\arg\min_{\theta}\left\|H_{j}R_{\theta}(z)-y_{j}^{\prime}\right\|^ {2}+\xi T[R_{\theta}(z)]. \tag{12}\]  
As we mentioned, the input \(z\) to the neural network can be a blurred image obtained using conventional reconstruction methods, and \(H_{j}R_{\theta}(z)\) denotes the inner product between each modulation mask and the target object image. During the network iteration process, the network will find the appropriate parameter \(\theta*\) to optimize its network structure. \(\xi T[R_{\theta}(z)]\) is the TV regularization constraint that improves the quality of the reconstructed image, and \(\xi\) is the intensity. In Equation (12), \(\left\|H_{j}R_{\theta}(z)-y_{j}^{\prime}\right\|^{2}\) denotes the error between the experimentally measured bucket signals and the bucket signals estimated by the untrained neural network, which we use as the loss function of the network. According to Equation (12), the neural network weights are continuously updated during the iterative operation to minimize the error between the bucket signals. When the error is smaller, the estimated bucket signal is closer to the real bucket signal, and the network output is closer to the target object image. The image reconstruction process of our proposed method is shown in Figure 2a, where the input image we use is the result of TVAL3 reconstruction. This is because the TVAL3 reconstructed image introduces the physical prior of the target object image,which will accelerate the convergence of the network. When the input image does not have physical prior information, the best reconstructed image can be obtained with the minimum number of iterations. Figure 2b shows the reconstructed image during iterations of the network for different sampling rates and learning rates.
# Adam Algorithm with Step Adaptation
## 4 Numerical Results and Discussion
### Test Functions
#### 4.2.4 Non-Quadratic Function  
The following function was used to analyze the effect of interference on the gradient components:  
\[f_{\text{Q2}}(x,[a\text{max}])=\left(\sum_{i=1}^{n}a_{i}\ x_{i}^{2}\right)^{2},\ \ \ \ \ \ a_{i}=a\text{max}\frac{i-1}{n-1},\ \ \ \ x_{0}=(1,\ 1,\ldots,\ 1). \tag{22}\]  
The initial point is \(x_{0}=(1,\ 1,\ldots,\ 1)\), \(\varepsilon=10^{-10}\), \(n\_max=\) 1,000,000. Table 12 shows the results of minimization.  
\begin{table}
\begin{tabular}{c r r r r} \hline \hline \(\mathbf{r}\) & **ADAM** & **H\_GR** & **H\_MOMENTUM** & **H\_ADAM** \\ \hline
0 & 125,914 & 3,982,257 & 200,396 & 7183 \\
1 & \(5456\times 10^{-5}\) & 3,880,233 & 281,489 & 137,671 \\
2 & \(1320\times 10^{-4}\) & 3,536,635 & 407,108 & 206,566 \\
3 & \(4690\times 10^{-4}\) & 3,502,361 & 565,846 & 309,788 \\
4 & \(9978\times 10^{-4}\) & 3,747,585 & 775,680 & 467,366 \\
5 & \(5281\times 10^{-4}\) & \(1449\times 10^{-10}\) & 1,057,342 & 661,454 \\
6 & \(1434\times 10^{-3}\) & \(1731\times 10^{-9}\) & 1,346,356 & 897,082 \\
7 & \(2083\times 10^{-3}\) & \(1738\times 10^{-8}\) & 1,704,643 & 1,104,140 \\
8 & \(1612\times 10^{-3}\) & \(1264\times 10^{-7}\) & 1,682,106 & 1,420,716 \\
9 & \(4059\times 10^{-3}\) & \(6793\times 10^{-7}\) & 2,210,826 & 1,854,261 \\
10 & \(6697\times 10^{-3}\) & \(2897\times 10^{-6}\) & 2,724,228 & 2,357,420 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Number of iterations for the function \(f_{\text{Z}}(x,\ [a_{max}=100,b_{max}=1000])\) minimization with gradient interference.  
\begin{table}
\begin{tabular}{c r r r r} \hline \hline \(\mathbf{r}\) & **ADAM** & **H\_GR** & **H\_MOMENTUM** & **H\_ADAM** \\ \hline
0 & 42,027 & 29,774 & 22,993 & 1347 \\
1 & 58,005 & 30,091 & 4894 & 4500 \\
2 & 59,829 & 45,441 & 17,907 & 17,888 \\
3 & 74,718 & 79,189 & 39,133 & 38,062 \\
4 & \(1022\times 10^{-4}\) & \(3758\times 10^{-8}\) & 69,850 & 68,934 \\
5 & \(1258\times 10^{-1}\) & \(4687\times 10^{-5}\) & \(1785\times 10^{-9}\) & 110,252 \\
6 & \(2943\times 10^{-1}\) & \(3656\times 10^{-3}\) & \(1657\times 10^{-5}\) & 161,151 \\
7 & \(1271\times 10^{2}\) & \(6545\times 10^{-2}\) & \(4047\times 10^{-3}\) & 224,008 \\
8 & \(5060\) & \(4938\times 10^{-1}\) & \(1643\times 10^{-1}\) & 291,613 \\
9 & \(8679\) & \(2199\) & \(3565\) & 372,003 \\
10 & \(65,182\) & \(7184\) & \(41,457\) & 483,712 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Number of iterations for the function \(f_{\text{Q2}}(x,\ [a_{max}=10,000])\) minimization with gradient interference.  
Here, we can see that the efficiency of the algorithms increase in the following order: ADAM, H_GR, H_MOMENTUM and H_ADAM. The best results are obtained by the H_ADAM algorithm. It copes with all tasks. The ADAM, H_GR and H_MOMENTUM algorithms do not cope in conditions of significant interference.  
In all tests, the algorithms H_ADAM and H_MOMENTUM are almost equivalent except for the last three cases, where the H_MOMENTUM method is somewhat worse than the H_ADAM. Also, in all tests, the ADAM algorithm tends to start looping and shows worse results.
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 3 Methods
### Loss Function with Directional Guidance
The loss function utilized in this study is a combination of the \(L_{1}\) loss function and the SIoU loss function with respective weightings. Specifically, the loss function \(L_{\rm loss}\) is given by the following:  
\[L_{\rm loss}=\lambda_{L_{1}}L_{1}\left(B,\hat{B}\right)+\lambda_{\rm SIoU}L_ {\rm SIoU}\left(B,\hat{B}\right), \tag{6}\]  
where \(\lambda_{L_{1}}\) and \(\lambda_{\rm SIoU}\) are the weights assigned to the \(L_{1}\) loss function and SIoU loss function, respectively.
## 3 Results and Discussion
### Oxide Cross-Section Evaluation  
#### 3.2.1 Oxide Thickness Evaluation  
Representative oxide cross-sectional thickness images of each oxide are shown in Figure 5, and the compiled oxide thickness values calculated for each group are provided in Figure 6. Both groups revealed bi-layered oxide structures. The inner and outer layer regions of oxide thickness were measured separately and are provided in Figure 6A,B. The combined total thickness for each oxide is compiled in Figure 6C. The unpaired \(t\)-test results for the NOJ CP oxides showed a significantly (\(p\) < 0.0001) thicker inner oxide layer, averaging 1.7 \(\pm\) 0.3 \(\upmu\)m, compared to the NOJ MP oxides with an average inner oxide thickness of 1.5 \(\pm\) 0.2 \(\upmu\)m. Similarly, unpaired \(t\)-test results for the NOJ CP oxides showed a significantly (\(p\) < 0.0001) thicker outer oxide layer, averaging 77.7 \(\pm\) 27.8 \(\upmu\)m, compared to the NOJ MP oxides with an average outer oxide thickness of 34.8 \(\pm\) 13.3 \(\upmu\)m. Due to the thicker outer oxide, the NOJ CP oxide also revealed a significantly thicker total oxide thickness value of 78.5 \(\pm\) 27.8 \(\upmu\)m compared to the NOJ MP oxide with a value of 35.5 \(\pm\) 13.3 \(\upmu\)m (\(p\) < 0.0001). Thinner oxide coatings are generally associated with improved adhesion strengths with the titanium substrate due to reduced internal residual stress and enhanced interfacial bonding [10; 40; 41].  
Figure 4: Representative FTIR spectra for each oxide group. Each oxide revealed PO4\({}^{3-}\) peaks (1050 cm\({}^{-1}\)) and CO3\({}^{2-}\) peaks (875, 1450, and 1570 cm\({}^{-1}\)). The lack of characteristic peaks for synthetic hydroxyapatite at 3570 cm\({}^{-1}\) and the corresponding presence of AB-type substitution peaks for CO3\({}^{2-}\) confirmed the formation of bone-like carbonated apatite in both oxides.  
Figure 5: Representative cross-sectional SEM images of each oxide group.
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 5 Evaluation
### Network Environment Setup  
The network topology is a simple FatTree topology, as shown in Figure 6. All the switches, except a common layer 3 Huawei router between R1 and R3, are our own programmable switches implemented based on a Protocol Oblivious Forwarding (POF) switch [42]. The controller is implemented based on an ONOS controller. We implemented the whole routing process based on the POF protocol and corresponding instruction sets. These applications can support hybrid routing with the current IP infrastructure through OSPF.  
The key components are GNMRS and, in particular, LNMRS. We implemented LNMRS, which can maintain name and NA mapping for resolving the ID to addresses locally,  
Figure 6: Network topology in the evaluation.  
and synchronize a portion of the mappings of the ID and NA to GNMRS to be accessed globally.  
In the network topology, our in-network storage modules are deployed in every POF switch, which are connected with 10 Gbps optical fiber. In addition, there is a common layer-3 IP router in the path from R1 to R3. This can simulate the hybrid network environment, and is also used for traffic control, because our new protocol is implemented based on the raw socket in a Linux environment and the network throughput in the Linux server can only achieve 4 Gbps on average. Two servers, namely the producer and the consumer, are connected to the network using 10 Gbps optical fiber. All POF switches are configured for in-network storage with a size of 10 TB.
## 5 Experiment of Imaging Detection
### Analysis of the Results of the Test for Imaging Detection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Image Gray Level (\(f\))**} \\ \hline
**Transducer Frame** & **Hole \(A\)** & **Hole \(B\)** & **Hole \(C\)** & **Hole \(D\)** & **Hole \(E\)** \\ \hline
1 & 53.75 & 85.00 & 97.00 & 81.25 & 162.5 \\
2 & 37.70 & 83.63 & 97.34 & 72.66 & 150.8 \\
3 & 36.66 & 86.46 & 99.05 & 76.61 & 150.5 \\
4 & 33.40 & 87.56 & 92.52 & 70.41 & 148.5 \\
5 & 30.65 & 81.52 & 96.85 & 67.17 & 128.8 \\
6 & 29.03 & 74.08 & 89.18 & 58.99 & 122.2 \\
7 & 28.12 & 68.08 & 80.26 & 55.56 & 109.8 \\
8 & 27.34 & 67.49 & 79.73 & 52.54 & 102.1 \\
9 & 27.98 & 67.79 & 83.69 & 50.87 & 103.4 \\
10 & 28.08 & 67.06 & 84.69 & 50.93 & 109.4 \\
11 & 27.30 & 64.61 & 82.43 & 50.54 & 111.6 \\
12 & 28.80 & 65.80 & 85.20 & 52.84 & 114.4 \\
13 & 30.31 & 67.22 & 86.85 & 54.11 & 116.9 \\
14 & 32.08 & 68.69 & 89.23 & 56.77 & 119.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multi-frame image gray level testing data of left-rank arc sparse array scatterer. The data in Table 5 show that as the rank of the received aperture increases, the signal-to-noise ratios of hole \(B\), hole C, hole \(D\), and hole \(E\) are all increased to different degrees. Figure 8 reveals an \"inflection point\" when the rank is nine, corresponding to approximately 64% of the full rank of the left-rank receiving aperture. At this point, all defect scatterers exhibit a distinct upward trend in signal-to-noise ratio. The 11 th, 12 th, 13 th, and 14 th frames of the left-rank receiving aperture demonstrate consistent increasing trends, indicating a progressive improvement in the signal-to-noise ratios to varying degrees. This increase in signal-to-noise ratio is beneficial for enhancing the imaging process. The present study specifically addresses the issue of multi-frame imaging using the left-rank arc sparse array in ultrasonic guided waves for welded structural plates. However, it is important to note that the related topics of multi-frame imaging using the right-rank arc sparse array and the inner-rank arc sparse array would require separate investigations and can be explored in future research articles.
## 2 Proposed \"Hybrid BEM-p-y Curve\" Method
### Pile Modelling
\[a_{ij} =\sum\limits_{k=1}^{i-1}\left[\left(\frac{(l_{k}-l_{k-1})^{3}}{3E_{p}l_ {k}}+\frac{(z_{j}-l_{k})\cdot(l_{k}-l_{k-1})^{2}}{2E_{p}l_{k}}\right)+\left(\frac {(l_{k}-l_{k-1})^{2}}{2E_{p}l_{k}}+\frac{(z_{j}-l_{k})\cdot(l_{k}-l_{k-1})}{E_{p }l_{k}}\right)\cdot(z_{i}-l_{k})\right] \tag{3}\] \[\qquad\qquad\qquad+\left(\frac{(z_{i}-l_{i-1})^{3}}{3E_{p}l_{i}}+ \frac{(z_{j}-z_{i})\cdot(z_{i}-l_{i-1})^{2}}{2E_{p}l_{i}}\right)\quad\text{if}\; z_{i}<z_{j}\] \[a_{ij} =\sum\limits_{k=1}^{j-1}\left[\left(\frac{(l_{k}-l_{k-1})^{3}}{3E _{p}l_{k}}+\frac{(z_{j}-l_{k})\cdot(l_{k}-l_{k-1})^{2}}{2E_{p}l_{k}}\right)+ \left(\frac{(l_{k}-l_{k-1})^{2}}{2E_{p}l_{k}}+\frac{(z_{j}-l_{k})\cdot(l_{k}-l _{k-1})}{E_{p}l_{k}}\right)\cdot(z_{i}-l_{k})\right]\] \[\qquad\qquad\qquad\qquad+\left(\frac{(z_{j}-l_{i-1})^{3}}{3E_{p}l _{j}}+\frac{(z_{i}-z_{j})\cdot(z_{i}-l_{j-1})^{2}}{2E_{p}l_{j}}\right)\quad \text{if}\;z_{i}\geq z_{j}\]  
In Equation (3), \(z_{i}\) and \(z_{j}\) represent the distance between the fixed node in Figure 2 and the point along the beam in which the displacement is considered and the distance between the same fixed node and the point where the load is applied, respectively. On the other hand, \(l_{k}\), represents the distance between the fixed node and the lower part of block \(k\), and \(E_{p}l_{k}\) is the flexural rigidity of block \(k\) (Figure 3). Equation (3) was obtained using the commonly used approach for analyzing beams of variable flexural rigidity ([PERSON] [29], [PERSON] and [PERSON] [30]). This method consists of two steps: in the first step the beam (i.e., the pile) is divided into segments, and each segment is studied separately, and in the second step boundary conditions and continuity are imposed.
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 5 Comparison of Simulation Results
### Convergence Analyses
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DRL Algorithm** & **CSM-TD3** & **TD3** & **PPO** & **DQN** \\ \hline Hidden layer dimension & 256 & 256 & 256 & 256 \\ Batch size & 256 & 256 & 256 & 256 \\ Discount factor & 0.99 & 0.99 & 0.99 & 0.99 \\ Soft update coefficient & 0.05 & 0.05 & \(\times\) & \(\times\) \\ Policy noise & 0.2 & 0.2 & \(\times\) & \(\times\) \\ Noise clipping range & 0.5 & 0.5 & \(\times\) & \(\times\) \\ Policy update frequency & 2 & 2 & 2 & \(\times\) \\ Priority exponent & 0.6 & 0.6 & 0.6 & 0.6 \\ Learning rate & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters used in different DRL algorithms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Path-Planning Parameter** & **Parameter** \\ \hline Vehicle mass (kg) & 1477 \\ Vehicle yaw inertia (kg \(\cdot\) m\({}^{2}\)) & 1536.7 \\ Boundary length (m) & 8 \\ Angular velocity range of obstacles (rad/s) & [0.2, 0.4] \\ Radius of obstacles (m) & 0.5 \\ Radius of obstacle motion path (m) & 2.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: System parameters of the environment. policies were relatively limited, making it difficult to maintain consistent performance improvements. Thus, although TD3 gradually surpassed the DQN during convergence, its overall stability and final convergence performance were still inferior to those of CSM-TD3. CSM-TD3, on the other hand, demonstrated a consistently increasing trend in average returns throughout the training process, with relatively small fluctuations. This indicates that the algorithm possesses strong sample efficiency and stable convergence performance, attributable to the improvements made to TD3 through the introduction of causal models and a state-masking mechanism. Specifically, CSM-TD3 employs an SEM to identify and leverage state features that have direct causal effects on control inputs, using a state-masking vector to filter out irrelevant features that do not contribute to decision making, reducing noise and spurious correlations. Moreover, the incorporation of causal consistency constraints and causal consistency loss enables the effective learning of causal relationships, which significantly enhances CSM-TD3's policy optimization compared  
Figure 5: Comparisons of the average return obtained from the DQN, TD3, and the proposed CSM-TD3 when the obstacle is stationary. Figure 6: Comparisons of the average return obtained from the DQN, TD3, and the proposed CSM-TD3 when the obstacle is moving. to the traditional TD3 algorithm. The comparative results of different DRL algorithms are presented in Table 3. Compared to the DQN, the proposed CSM-TD3 improved the convergence performance by 51%, and compared to TD3, it achieved an improvement of 37%.
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
## 2 Related Work
Subsequently, the medical image watermarking and the doctor's fingerprint are visually encrypted using the Integer Wavelet Transform (IWT) along with an ordinary reference image. IWT is reversible without any loss by extracting the fingerprint from the encrypted medical image and matching it with the doctor's fingerprint. In this way, the EHR and the medical image are encrypted into an apparently insignificant image, and sender authentication is ensured by the doctor's fingerprint. In [37], a medical image with digital watermarking based on fingerprints has been proposed. In the proposed method, the hash of the region of interest (ROI) of the medical image, fingerprint, and patient's record (EHR) has been embedded into the medical image. The EHR has been encrypted using the elliptical cryptography (ECC) algorithm, and then a hexadecimal string has been obtained. The minutia points of the fingerprint image have been calculated, and then a binary string has been obtained. The hexadecimal string of the hashed medical image, the hexadecimal string of the EHR, and the binary string of the fingerprint have been concatenated into a single string, which is embedded in the original medical image. In this way, the watermarking image has been obtained. The biometric data used in these approaches has been embedded in the medical system without any protection. The original biometric data can be obtained simply by decoding the medical image in case of white-box attacks. The proposed scheme realizes visually meaningful image encryption by protecting both medical images and biometric data through a chaotic encryption method based on random mapping and a random permutation key. The watermarking process has been applied after the encryption process to obtain the visually meaningful encrypted image and then ensures:  
* a visual protection from black-box attacks;
* encryption protection from white-box attacks;
* an image authentication through a physician's fingerprint
## 7 Conclusions  
To sum up, the purpose of this research work is the design and development of an Intelligent Product Service System for improving the remote maintenance support, taking into consideration the energy demands and adapting the shopfloor scheduling. Ultimately, the purpose of the IPSS is the creation of suitable communication channels between the OEM, the customer/end-user, and the energy supplier. Consequently, as a result of the data exchange between the above-mentioned parties, the MRO operations can be adaptively introduced to the schedules of the customer and the OEM, thus decreasing disruption of the production line. Further to that, an additional service is provided, in order to utilize the big amount of data produced at the shopfloor. The purpose of this service is to calculate a near-optimum schedule for the maintenance and manufacturing activities, in an attempt to reduce the energy expenditures of the manufacturing companies, as well as to reduce the load of the power grid during peak days.  
The applicability of the proposed IPSS has been tested in Engineered-to-Order (ETO) products, such as injection molds. However, given that minor amendments are applied to the framework supporting the IPSS, it can be applied in a variety of other industries/products. One of the key findings of the research is that by establishing sufficient communication channels between the nodes of a supply chain, i.e., the end-user, the OEM, and the energy provider, the efficiency of manufacturing processes can be improved. Therefore, in the future, the framework developed as part of the IPSS can be further improved to include more nodes of the supply chain. With the utilization of the alternative generation algorithm, a wider set of criteria can be examined, thus providing more accurate solutions.  
Taking into consideration the latest developments in the industrial and market landscapes, and the recent acts towards the environmental impact of manufacturing activities, the proposed PSS-based framework will also be further elaborated in order to calculate the CO2 emissions of the manufacturing and maintenance activities. Likewise, besides the calculation of the emissions, the implementation of an optimization algorithm for facilitating engineers to adapt the production schedule targeting the reduction in the environmental footprint of manufacturing processes is necessary.  
All authors have participated in the modeling of the research project. More specifically: D.M., supervisor; J.A., research, conceptualization, software, and original draft preparation. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work has been partially supported by the H2020 EC funded project \"An Integrated Collaborative Platform for Managing the Product-Service Engineering Lifecycle--ICP4 Life\" (GA No: 636862). The authors declare no conflict of interest.