esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 3 Security Model
### Security Risks  
As shown in Figure 3, this paper argues that the main security risks in the whole process of data asset sharing and circulation in uncontrolled environments include three levels: unreliable transmission network, uncontrollable cloud storage service and untrusted participants, which can be summarized as follows:  
* There are many uncontrolled factors and strong countermeasures.
* The means of protection are scattered and difficult to implement.
* Cloud storage is untrustworthy, and the security is poor.
* The storage capacity is limited, and the efficiency is low.
* The management means are extensive, and the granularity is coarse.
* The confidence level of control is low, and the audit is difficult.
* The monitoring and control can easily be blocked, and the risk is high.
* The transmission scale is massive, and the rate is low.  
Figure 2: Data asset life cycle model.  
Specifically, when data assets are outsourced, they face some security risks, such as untrustworthy outsourcing service providers, low efficiency of outsourced storage, and the difficulty of completely deleting external data. When multi-party sharing is involved, they face security risks such as unclear ownership boundaries of data assets, inadequate authority management, difficult-to-trace keys, etc. When trusted operations are involved, they face security risks such as low operational confidence, complex post- event traceability, difficult operation audit, etc. When transmitting back across domains, they face some security risks, such as the monitoring of transmission content, the blocking of transmission network, the traceability of the identity of the data transporter, and so on.  
Therefore, by analyzing the security risks faced by data assets in uncontrolled environments, the new security requirements can be summarized as follows.  
* Full cycle and systematic data asset security protection.
* Fine granularity and fine data asset access confirmation.
* Strong credible and auditable data asset operation and control.
* Service and component data asset protection means.
* High security and high efficiency data asset outsourcing storage.
* Anti-blocking, anti-traceability of data assets secure transmission.  
Therefore, it is urgently required to establish a security protection model that can protect the different stages of the whole life cycle of data assets, and provide multi-mode and all-rounded systematic protection for the security risks faced by different stages of the whole life cycle of data assets and different operating scenarios.
# Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback
## 5 Experimental Results
### Case Study of Prompt Refinement via APO-CF  
We examine how APO-CF refines prompts by analyzing training data and adjusting key semantic components to improve classification accuracy. Table 4 presents an example where APO-CF is applied to the M1 prompt using GPT-4o. It summarizes the original and refined prompts, along with the confusion matrix derived from the training data prior to refinement.  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{2}{c}{**Prompt Refinement via APO-CF**} \\ \hline Original Prompt & Does the passage answer the query? Respond with ’Yes’ or ’No’. \\ \hline \multirow{4}{*}{Confusion Matrix} & Confusion Matrix from Training Data \\  & - True Negatives (TNs): 57 \\  & - False Positives (FPs): 3 \\  & - False Negatives (FNs): 22 \\  & - True Positives (TPs): 18 \\ \hline \multirow{4}{*}{Refined Prompt} & To classify a passage as answering the query (”Yes”), ensure that the passage directly addresses the main point of the query with clear and relevant information. Consider a passage as answering if it provides a complete or partial response that logically infers the answer. If the passage does not directly or indirectly address the query, or if the connection is weak or ambiguous, classify it as not answering (”No’).  
The original prompt, \"Does the passage answer the query? Respond with \"Yes' or \"No\", was found to have a high false negative (FN) rate of 22 in the training data, indicating that many valid responses were incorrectly classified as not answering the query. To address this issue, GPT-4o adjusted the prompt by introducing the phrase \"partial response that logically infers the answer\". This modification broadens the interpretation of what constitutes an answer, effectively softening the rigid definition of the term \"answer\" in the original prompt. By allowing a passage that contains partial but inferable responses to be classified as \"Yes\", the model adapts to better capture subtle but valid answer patterns.  
This refinement process demonstrates that APO-CF actively examines training data to fine-tune prompt semantics, ensuring that terms such as \"answer\" are dynamically redefined in a way that improves classification accuracy. A similar case study for GPT-4o-mini can be found in Appendix B, which provides additional insight into how the model-specific characteristics affect prompt refinement.  
This example illustrates how a specific error pattern can be directly addressed through targeted semantic modifications in the prompt. The revised instruction enables the model to more flexibly interpret partially correct responses, reducing misclassifications. As shown in the next section, this single refinement leads to a measurable improvement in [PERSON]'s \(\kappa\), confirming the practical effectiveness of the APO-CF approach.
## 4 Results and Discussions
### Results of Axle-Box Vibration Acceleration Caused by Wheel Polygons in Time-Domain
#### 4.1.2 Effect of Vehicle Speed  
Figure 5 shows the maximum vertical axle-box accelerations as a function of OOR amplitude for a polygonal wheel with four waves and with 24 waves around its circumference and for train speeds from 200 to 350 km-h\({}^{-1}\). Figure 6 shows the calculated axle-box acceleration as a function of harmonic order of polygonized wheel and for different vehicle speeds and an OOR amplitude of 0.10 mm.  
Figure 5 shows that the growth per unit OOR amplitude of the vertical axle-box acceleration increases monotonically with vehicle speed. The calculated results shown in Figure 6 indicate that the maximum vertical axle-box acceleration is linear in harmonic order of the polygonal wheel and that this growth rate also increases with increasing train speed. However, the harmonic order has much less effect on the growth rate than does the OOR amplitude. At high speeds, the OOR amplitude of polygonal wheels is what most affects the axle-box acceleration.  
Figure 5: Vertical axle-box acceleration as a function of OOR amplitude at different speeds for (**a**) a 4 th-order polygonized wheel and (**b**) a 24 th-order polygonized wheel.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Speed(km-h\({}^{-1}\))**} \\ \cline{2-7}  & **200** & **225** & **250** & **275** & **300** & **325** & **350** \\ \hline
**OOR Amplitude (mm)** & \multicolumn{6}{c}{**Growth Rate (m-s\({}^{-2}\))**} \\ \hline
0.01 & 0.38 & 0.45 & 0.62 & 0.75 & 0.97 & 0.99 & 1.05 \\
0.02 & 0.63 & 1.15 & 1.31 & 1.45 & 1.58 & 1.64 & 1.97 \\
0.03 & 1.51 & 1.75 & 1.82 & 1.97 & 2.17 & 2.64 & 3.73 \\
0.04 & 2.01 & 2.43 & 2.55 & 2.75 & 3.12 & 3.20 & 4.14 \\
0.05 & 2.94 & 3.12 & 3.52 & 3.78 & 4.40 & 4.98 & 5.28 \\
0.06 & 3.22 & 3.67 & 4.49 & 5.28 & 5.80 & 5.89 & 6.24 \\
0.07 & 4.06 & 4.38 & 4.87 & 5.58 & 6.34 & 6.92 & 7.13 \\
0.08 & 4.43 & 4.58 & 5.25 & 5.90 & 6.60 & 7.05 & 7.78 \\
0.09 & 5.01 & 5.73 & 6.29 & 7.08 & 7.84 & 8.20 & 8.68 \\
0.10 & 5.45 & 6.25 & 6.98 & 7.55 & 8
# Robust Algorithm Software for NACA 4-Digit Airfoil Shape Optimization Using the Adjoint Method
## 1 Introduction  
Determining the geometric shape of an aircraft's wings is crucial for optimizing its aerodynamics and performance. Adjusting geometric constraints can improve various aspects, such as aerodynamic performance, flight distance, and flight time. The first steps in wing design involve defining the wing's geometry and selecting an appropriate airfoil type based on specific parameters. Special mission requirements may require designing and manufacturing the necessary parts and equipment to meet different operating conditions. The wing and airfoil are critical components, and numerical optimization techniques can be divided into two main categories: gradient-free and gradient-based methods. When dealing with large-scale aerodynamic optimization problems with multiple variables, the adjoint method is the most effective approach, particularly when the number of design variables exceeds 100 [1, 2].  
The focus of this paper is on robust aerodynamic shape optimization; we present an algorithm that differs from other similar articles. While other articles rely on commercial software or algorithms that do not consider flexible airfoil capabilities, this study takes into account the airfoil's global geometry instead of solely modifying the mesh points of its boundary without regard to its flexibility. Building upon previous research by [PERSON] et al. [3], who utilized the pressure distribution around the airfoil to determine its shape [3], this work uses design parameters and flight conditions to determine the airfoil's shape, which is a more adaptable and versatile approach.  
The primary objective of this study is to develop an algorithm that considers critical parameters, such as chord length, maximum thickness, and maximum camber, for flexible NACA-4-digit airfoils. The algorithm employs a unique reverse design approach to determine the shape of the NACA-4 digit airfoil based on the surrounding pressure distribution. During each iteration of the algorithm, it checks if the pressure distribution can generate NACA-4 digits while imposing constraints to ensure that the variables remain within acceptable ranges.  
In this study, we conducted a comprehensive review of relevant literature and clarified the adjoint equation and optimization algorithm. The flow solver was validated by comparing its results to those obtained through numerical solutions. Finally, we present the results of the multipurpose optimization approach applied to an airfoil in a given regime, followed by a detailed discussion of the proposed algorithm's performance.
## 2 Materials and Methods
### Feature Extraction  
In medical image diagnostic systems, the next important step is to compute the most relevant features. Researchers are paying much attention to compute the most relevant features [17]. In the field of medical imaging problems, the medical data are collected without sacrificing the result quality [25; 26; 27; 28; 29; 30; 31]. To capture the most relevant properties, we computed GLCM features.  
Gray-Level Co-Occurrence Matrix (GLCM)  
In the machine learning (ML) techniques, the most important step is to compute most relevant features to capture the maximal hidden information present in the data of interest. The GLCM features were applied from an input image by applying transition on two pixels with gray-level. GLCM features were originally proposed by [32] in 1973, which characterized texture utilizing diverse quantities acquired from second-order image statistics. Two steps are required to compute the GLCM features. Firstly, the pair-wise spatial co-occurrence of image pixels are separated by distance, d, and direction angle, \(\theta\). A spatial relationship between two pixels is created for neighboring and reference pixels. In the second step, GLCM features are computed with scaler quantities, which utilize the representation of numerous aspects of an image. This process produces the gray-level co-occurrence matrix that contains several gray-level pixel combinations in an image of interest or the specific portion of an image [32]. The resultant GLCM matrix comprises MxM, where M denotes the gray-level numbers in an image. To compute the GLCM, we utilized the distance, d = {1, 2, 3, 4}, and angle, \(\theta\) = {0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 135\({}^{\circ}\)}, for directions.  
Consider the pixel probability, \(P(i,j,d,\theta)\), indicates the two pixels' probability separated by a particular distance having gray-levels \(i\) and \(j\)[33; 34; 35]. The GLCM-based texture features were composed of contrast, sum of square variance, cluster shade [36], correlation [37], and two values of homogeneity [37; 38; 39]. GLCM features have been successfully utilized in the classification of breast tissues [40], and many other medical imaging problems [36; 41; 42; 43] detailed in [44; 45; 46].
## 3 Overvoltage Simulation Model Including Variable Capacitance
### Overvoltage Simulation Model Including Variable Capacitance  
In this paper, PSCAD software (version:PSCAD-v46)has been used to simulate the operating overvoltage. This software is suitable for the electromagnetic transient analysis of the power system. It can better simulate the transient process in the power system and has the ability to deal with the complex power system model and high frequency response. The simulation model of the HX\({}_{\text{D}}\)1 electric locomotive's roof high-voltage system is shown in Figure 6. The high-voltage equipment mainly includes a pantograph, a high-voltage transformer, a vacuum circuit breaker, and an arrester, where C\({}_{\text{ij}}\) (i = 1,2; j = 1,2,3,4) represents the capacitance to ground of the wires and high-voltage equipment, \(R_{\text{mn}}\) (m = 1,2; n = 1,2,3) represents the equivalent resistance of the connecting wires, \(Z_{1}\) and \(Z_{2}\) are surge arresters, \(Z_{3}\) represents the high-voltage connector between two cars, and S\({}_{1}\) and S\({}_{2}\) represent the locomotive load.  
When overvoltage occurs during pantograph dropping, arcs are generated between the catenary-side contact and the vehicle-side contact (Figure 6). The RC element in the model represents the equivalent distributed parameter line of the connecting wire within the high-voltage equipment on the roof.  
Figure 5: Equivalent circuit of pantograph and catenary.  
Figure 6: Locomotive simulation circuit.  
The simulation software (PSCAD-v46)provides modular functions, allowing users to design circuits with different functions as independent modules and encapsulate these circuits into simulation model to achieve a certain function. The three functional modules designed in this paper are the pantograph-catenary distance control module, the arc burning detection module and the arc resistance and variable capacitance calculation module. This combination of three packaged modules can finally realize the numerical calculation of output arc resistance and variable capacitance. Then, the results are converted into resistance and capacitance types of signals input into the arc between the catenary side and the vehicle side.
###### Abstract  
In this paper, we proposed a high-efficiency 2.45 GHz and 5.8 GHz dual-band rectenna for wireless energy harvesting (WEH). First, a dual-band dielectric resonant antenna (DRA) was designed. A 4-element DRA array was further developed using a compact feeding network. The measured gains of the DRA array were 5.5 dBi at 2.45 GHz and 12.9 dBi at 5.8 GHz. Then, a new type of impedance regulation stub (IRS) was introduced that significantly improved the conversion efficiency of the rectenna. The result showed that the conversion efficiency of the rectifier was 66% and 62% at 2.45 GHz and 5.8 GHz, respectively, when the input power level was 10 dBm. Furthermore, the proposed rectenna was validated to activate a standard BQ25504 DC-to-DC boost converter and charge a supercapacitor. The results demonstrated that the proposed rectenna could be an appropriate solution for WEH applications.  
wireless energy harvesting (WEH); dielectric resonant antenna (DRA); dual-band; rectenna +
Footnote †: journal: Applied  
antenna. A 4-element DRA array is further developed, which can achieve high gain in the dual operating band. Finally, a gain of 5.5 dBi and 12.9 dBi, respectively, at 2.45 GHz and 5.8 GHz, is obtained. To further improve the efficiency of the rectifier circuit, a novel IRS-loaded impedance matching network is used which makes it possible to Achieve better performance than in other works. At 2.45 GHz and 5.8 GHz, the proposed rectifier achieves the conversion efficiencies of 66% and 62%. The rectenna is fabricated and measured. The measurement results demonstrate that the proposed rectenna has high conversion efficiency. In addition, the proposed rectenna is integrated with a DC-DC boost circuit. The experimental result verifies that the proposed rectenna has significant advantages in gain and conversion efficiency, and it is suitable for RF energy harvesting technology.
## 4 Discussion  
### Effects of Static Stress on the Formation Time and the Type of Initial Crack  
Figure 9 shows the formation time and type of initial cracks under uniaxial static compressive stress. The formation time of the initial cracks without static stress condition was 50 us after explosion. With the increase of uniaxial static stress, the formation time  
Figure 8: The crack network at different times during crater blasting under an SSR of 0.4. **(a–i)** The crack network after 0, 150, 240, 300, 400, 500, 600, 700, and 890 μs of explosion, respectively.  
of initial cracks were delayed. When the SSR reached 0.4, the time of the initial cracks' appearance suddenly was delayed even more, with the time of the first observed cracks being after 150 \(\upmu\)s. The abovementioned experimental results indicate that static stresses tend to cause a time delay of the cracks' appearance. Therefore, the effects of static stress on the formation time of cracks should be considered during blasting excavation of deep, high geo-stress rock engineering. In other words, the appropriate millisecond blasting time should be selected according to the in situ stress, and this would be helpful in improving the blasting effect.  
Furthermore, the uniaxial static stress also changes the type of initial cracks as is shown in Figure 9. When the SSR is 0 or 0.1, the initial cracks are randomly distributed radial cracks. When the SSR is 0.15, the radial cracks that are parallel to the static stress are longer than the radial cracks that are perpendicular to the static stress. However, when the SSR reaches 0.2 and above, the only initial cracks are those radial cracks that are parallel to the static stress, and the radial cracks perpendicular to the static stress are inhibited by the static stress and do not appear. With the increase of uniaxial static stress, the type of initial cracks also changes. The above experimental results show that the static stress promotes the initiation and propagation of radial cracks that are parallel to its own direction and inhibits the initiation and propagation of radial cracks that are perpendicular to its own direction.  
It should be noted that the mechanism of the delay in the appearance of initial cracks under uniaxial static stress needs to be further studied, which would be helpful for the reasonable selection of the delay time during millisecond blasting.
# Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs
## 2 Related Work
### Cooperative Routing Protocols
The performance tradeoffs of cooperation-based schemes are given in Table 3.  
The discussed cooperative routing protocols [21; 22; 23; 24; 26; 27] have a high ratio of void zones, resulting in a high packet drop ratio. In order to avoid void zones and to enhance the network throughput, Co PAMP is proposed in this paper, which maximizes the packet delivery ratio and avoids the void regions. It is discussed in Section 3.
# Remote Sensing for Irrigation of Horticultural Crops
## 3 Sensors and Platforms
### Remote Sensing Platforms
#### 3.2.4 Ground-Based Platforms  
The more common ground-based platforms (up to 50 m) are tripods, towers, poles, and cranes, as well as instruments mounted on vehicles. They are characterized by different land coverage: short (50-100 m), medium (150-250 m), and long (up to 1 km). Platforms may be temporary or permanent and are usually used to support research projects, even for micro-meteorological measurements like, for example, for Bowen-ratio and Eddy-covariance [94].  
Important data can be also collected in the field using vehicle-mounted instruments. [PERSON] et al. [95] have studied how to enhance crop-water-use efficiency by reducing irrigation losses and enhancing soil water availability. The authors underline that abiotic and biotic factors can increase the yield gap through a reduction of water use and of crop yield potential. Emerging technologies, such as wireless communication, may be integrated with soil and plant sensors (variable-rate irrigation equipment, and decision support systems).  
[PERSON] et al. [96] discussed different examples of the use of sensors and their networks for agriculture and irrigation purposes. [PERSON] et al. [97] developed an automatic irrigation system with significant water conservation (i.e., up to 30-60%). [PERSON] and [PERSON] [98] worked on precision irrigation control of self-propelled, linear-movement and center-pivot irrigation systems, controlled by wireless technology. To improve water use efficiency, [PERSON] et al. [99] implemented a smart irrigated system piloted by a wireless data acquisition network to acquire environmental (climate) and soil moisture data. [PERSON] et al. [100] set a drip-irrigation-control system characterized by remote sensing data acquisition. [PERSON] et al. [101] devised a site-specific sprinkler irrigation system equipped with a real time sensor (using Bluetooth and GPS technologies) and control software based on soil moisture, temperature and weather information data. [PERSON] and [PERSON] [102] and [PERSON] et al. [103] improved the above noted irrigation system with _\"in-field data feedback to support the decision-making and real-time monitoring of irrigation operations via Bluetooth wireless radio communication\"_. [PERSON] et al. [104] studied soil moisture sensors to support the decision-making irrigation management scheduling. The study was aimed at determining threshold values of soil water characteristics (matric potential and available soil water content), using appropriate values of leaf water potential for pepper, melon, spring tomato and winter tomato crops.