esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study analyzes the corrosion inhibition efficiency of sodium molybdate (SM) solution on weldment specimens in 1 M HCl, based on H\({}_{2}\) bubbles detection. The detection of the bubbles produced by the corrosion process is achieved by a YOLOv4 deep learning algorithm. The results indicate that the corrosion rate is higher on the weld metal zone than on the base metal zone in the same solution, which might be attributed to the coarser grain of the weld metal zone and the stability of the produced oxide layer. The addition of sodium molybdate was discovered to boost the stability of the oxide layer, hence enhancing the specimens' corrosion resistance. The overall inhibitory efficiency of the sodium molybdate solution was 59% for the weld metal (WM) (0.4 g/L), 52% for the heat-affected zone (HAZ) (0.2 g/L), and 37% for the base metal (BM) (0.2 g/L). The object detection algorithm models showed 97% for the mAP and 0.98 for recall. The minimum average bubble detected for the WM was 0.353 /mm\({}^{2}\) at an SM concentration of 0.4 g/L, while the HAZ was 0.612 /mm\({}^{2}\) at 0.2 g/L, and the BM was 1.055 /mm\({}^{2}\) at 0.2 g/L. The results of the bubbles detection appeared to be consistent with the corrosion experiment outcomes obtained by the potentiodynamic polarization and hydrogen volume measurement tests. This experiment validates the distinctiveness of the different weld zones in terms of the inhibitor concentration required for successful corrosion prevention, as well as the potential of analyzing corrosion using machine learning algorithms for object detection.  
deep learning; hydrogen evolution; carbon steel welding; inhibitor; corrosion +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Network Structure and Available Traffic
#### 3.1.1 Protocols Presentation  
The protocols listed in Table 3 were found to be present in the network traffic. From some of the protocols several different versions were seen, such as was the case with IGMP. The data displayed a diversity in versions, a need for which was not immediately clear. For example the IGMP protocol traffic includes both versions 1 and 2. This coincides with our initial expectation that systems such as this that have been modified over a period of time include redundant versions of protocols that are either forgotten to be present in the network or kept for compatibility purposes due to old and non-updated systems.
# Measurement of Structural Loads Using a Novel MEMS Extrinsic Fabry-Perot Strain Sensor
## 4 Experimental Setup and Discussion  
### Sensor Placement  
The material of the small tested structure is titanium alloy, and its properties are shown in Table 1. Finite element simulation should be carried out on the structure under different loading conditions to determine the optimal placement position of the sensor. The total length of the structure is 11 mm, and the distance between the two holes of the structure is 8 mm. The external static force range of the small tested structure is 0-10 N, while the maximum transient-state force is 20 N. Due to the small size of the structure, it is necessary to consider the concentrated stress caused by structural through-holes. The geometry characteristics and strain distribution are shown in Figure 5.  
As shown in Figure 6, the finite element analysis results can be obtained as follows. (1) When the external load applied on the structure is 10 N, the sensor should located at the position of the structure between 4.173 mm and 6.873 mm in the X axial direction. (2) When the external load applied on the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials Physical Parameter** & **Symbol** & **Value** & **Unit** \\ \hline [PERSON]’s modulus of the structure & \(E_{s}\) & 107 & GPa \\ [PERSON]’s ratio of the structure & \(\
u_{s}\) & 0.34 & \\ Density of the structure & \(\rho_{Ti}\) & 4.62 & g/cm\({}^{3}\) \\ Length of the structure & \(L_{s}\) & 11 & mm \\ Width of the structure & \(W_{s}\) & 1.5 & mm \\ Thickness of the structure & \(T_{S}\) & 0.55 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural material properties.  
Figure 5: Geometry characteristics and strain distribution of tested structure. (**a**) Schematic of the geometry of the tested structure. (**b**) The strain distribution of the structure subjected to 20 N load.  
structure increases to 20 N, the axial coordinate range of the uniform strain field is 4.272-6.773 mm. The larger the load, the smaller the range of the uniform strain field.
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 3 Overlapped Apple Images Segmentation
### K-Means Segmentation  
In the apple image, the color difference of apples, branches, and leaves can be better obtained by K-means segmentation. This is an algorithm that classifies objects into K clusters based on color features, where K is a positive integer. The \(K\)-means algorithm is simple and easy to implement. The main adjustment parameter is K. For massive data, the time complexity tends to be linear. The \(K\)-means segmentation algorithm aims to minimize an objective function, in this case the sum of squared error functions. The larger the value, the greater the error. The advantage is that the information provided by the measurement data can be fully utilized. Similarly, the larger the error, the worse the segmentation effect.  
Figure 3: Overlapped images under the Lab color space. (**a**) Lab image; (**b**) L component; (**c**) a component; (**d**) b component.  
The objective function is:  
\[J=\sum_{j=1}^{K}\sum_{i=1}^{N}\|X_{i}^{j}-c_{j}\|^{2} \tag{4}\]  
\[d(X_{i},X_{j})=(\sum_{K=1}^{N}(X_{iK}-X_{jk}))^{\frac{1}{2}} \tag{5}\]  
where \(c_{j}\) is the guess centroid of the same class, \(\|X_{i}^{j}-c_{j}\|\) is a chosen distance measure between the data \(X_{i}^{j}\) and the cluster centroid \(c_{j}\). In this paper, the selected distance metric is Euclidean distance as shown in Equation (4). Euclidean distance is a method of distance calculation that is the easiest to understand. It is derived from the formula for the distance between two points in the Euclidean space. In \(m\) dimensional space, it represents the true distance between two points, or natural length of vector (the point of distance from the origin).  
Each pixel in the image serves as a sample. The detail steps of the \(K\)-means segmentation algorithm are as follows:  
1. Choose the number of clusters: \(K\), according to the different color of apple fruit, leaves, sky, the \(K\) value is 3;
2. Randomly generate K points as initial cluster centers: \(c_{1},c_{2},c_{3},\dots\) ;
3. Assign the distance from each sample to each cluster center and classify each sample to the nearest cluster center;
4. For each cluster, the mean value of all samples is taken as the new cluster center;
5. Repeat the Steps 2 to 4 until the centroids no longer change.  
The segmentation image of the target fruit is obtained, and then the contour is extracted by morphological methods.  
It can be seen from Figure 4 that the K-means clustering algorithm had a good effect on the segmentation of the apple. Among the apples, sky, branches, and leaves, apples include red components the most, so images of the components can be used for the subsequent processing.  
Figure 4: Result of the K-means segmentation: (**a**) original image; (**b**) overlapped apples; (**c**) sky; (**d**) branches and leaves.
# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Multi-Layer Satellite Networks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Scheme** & **Time Delay** & **Packet Loss Rate** &
\begin{tabular}{c} **Calculation** \\ **Overhead** \\ \end{tabular} & **Throughput** \\ \hline Unicast routing [27] & ✓ & \(\bigtimes\) & \(\bigtimes\) & ✓ \\ Geographical routing [28] & ✓ & \(\bigtimes\) & ✓ & \(\times\) \\ Broadcast routing [31] & \(\bigtimes\) & ✓ & ✓ & ✓ \\ MAC-routing [32] & ✓ & \(\bigtimes\) & ✓ & \(\times\) \\ MSVN-SHO,VN-HO [36] & ✓ & ✓ & \(\bigtimes\) & \(\times\) \\ DDRA [37] & ✓ & ✓ & \(\bigtimes\) & \(\times\) \\ LCRA [38] & ✓ & ✓ & \(\times\) & ✓ \\ LCPR [39] & \(\bigtimes\) & ✓ & \(\times\) & ✓ \\ DODR [40] & ✓ & ✓ & \(\times\) & \(\times\) \\ CEMR [46] & ✓ & \(\bigtimes\) & \(\times\) & ✓ \\ DTRB [50] & \(\bigtimes\) & \(\times\) & ✓ & ✓ \\ DPS [51] & \(\bigtimes\) & \(\bigtimes\) & ✓ & ✓ \\ MA-routing [52] & ✓ & ✓ & \(\times\) & \(\times\) \\ DCCR [53] & ✓ & \(\bigtimes\) & \(\times\) & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of partial single-layer satellite routings. LEO/GEO, MEO/IGSO, LEO/MEO/GEO, etc. [56]. The dynamic routing in multi-layer satellite networks is designed with a great deal of freedom due to the diversity of network architectures. The satellites are grouped in different layers according to their distance from the ground control center. Short distances are routed using only LEO satellites, while long distances are routed via MEO satellites or GEO satellites. In addition to grouping, due to the hierarchical structure among different satellite nodes, dynamic routing strategies can be considered by dividing labor among individual satellites, such as dynamically adjusting routes based on delay or link congestion. When the number of routing hops for the LEO layer satellite exceeds a specific delay threshold or when the current link is congested, the system activates the inter-satellite link between the GEO satellite and the LEO satellite before incorporating the GEO satellite into the route. Based on academic and industry dynamic routings for multi-layered satellite networks, they can be classified as SDN-based, QoS-based, and traffic-balancing dynamic routing.
## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Building Future-Ready Infrastructure and Integration Pathways  
This subsection addresses the technological infrastructure and system compatibility barriers that fall under the technological dimension of the TOE framework. Many SMEs struggle with legacy systems, fragmented digital architectures, and limited capacity to adopt AI solutions that require modern, scalable environments. These limitations hinder seamless integration and the long-term scalability of AI initiatives.  
Ref. [112] identify infrastructure limitations and interoperability as the main inhibitors of AI adoption among SMEs. Ref. [97] similarly emphasize technological readiness, including updated IT infrastructure, as a foundational component under the TOE framework. From a regional lens, ref. [27] show that infrastructure and cloud availability significantly impact AI adoption in European SMEs. In emerging economies such as India, ref. [28] highlight the unstable internet and low cloud penetration as key infrastructure bottlenecks. Sector-specific studies, such as ref. [115], confirm that fragmented legacy systems are particularly acute in manufacturing and retail SMEs.  
To address these gaps, shared infrastructure models, such as public-private AI computation centers and cloud-based integration platforms, have been proposed as cost-effective and scalable solutions [119]. These platforms offer SMEs flexible access to advanced AI capabilities without requiring extensive capital investments in on-premise infrastructure.  
To overcome this challenge, SMEs should focus on building flexible and modular infrastructures that support interoperability, gradual deployment, and external collaboration. The following strategies are recommended:  
**A. Adopt Cloud-Based AI Platforms** Leverage scalable cloud infrastructure to reduce initial investment and ensure on-demand access to computing resources.
**B. Collaborate with Public-Private AI Centers** Partner with national or regional AI innovation hubs to access shared infrastructure, expert support and high-performance computing tools.
**C. Use APIs for System Interoperability**  
Deploy APIs to bridge legacy systems and new AI tools, allowing SMEs to avoid full system overhauls while improving compatibility.
**D. Invest in Modular Digital Infrastructure**  
Design infrastructure upgrades in modular phases to support future AI applications without disrupting current operations.  
In summary, investing in future-ready and interoperable digital infrastructure is essential for SMEs to unlock the full potential of AI and ensure long-term scalability and integration success.
# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 3 Performance Evaluation of the 4 DT-Wx Prototype System
### Performance Analysis of MET Data Processing on 4 DT-Wx Prototype System
#### 3.2.1 Scalability and Efficiency Analysis: Speedup and Partition Optimization
Figure 7 compares the total execution time of the serial algorithm with that of the parallel algorithm using the optimal number of partitions for different numbers of API requests (50, 100, 500, 1000, 1500, and 2000). This graph clearly visualizes the effect of parallelization. The execution time of the serial algorithm increases almost linearly as the number of API requests increases, whereas the execution time of the parallel algorithm shows a much gentler slope. This indicates that parallel processing provides significant performance improvements for large-scale data processing. Notably, as the number of API requests increases, the execution time gap between the serial and parallel algorithms widens. For example, at 50 API requests, the difference between the two algorithms is relatively small, but at 2000 API requests, the difference becomes significantly larger. This demonstrates that the benefits of parallelization are more pronounced in large-scale data processing. Additionally, the graph shows the results for the optimized number of partitions for each number of API requests. Therefore, these results reflect the effect of optimized parallel processing tailored to the size of each task rather than simple parallelization. These findings empirically demonstrate that the parallelization strategy of the 4 DT-Wx prototype system functions effectively,  
Figure 7: Comparison of the total execution time between serial and parallel algorithms for BOff and BOn and different numbers of API requests. particularly showing excellent performance in large-scale data processing. Moreover, this graph indirectly indicates the system's scalability, suggesting that the 4 DT-Wx system can efficiently handle increasing API request loads.
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 2 Description of the New Concept Platform
### Deduction of the Pseudo-Rigid Body Equivalent Mechanism
The cables may be arranged in such a way so as to be aligned in the three directions \(\xi_{1}\), \(\xi_{2}\), and \(\xi_{3}\), as illustrated in Figure 2a. * Type (i) cables are the moving elements for Type (g) links. These three cables provide motion for the three CSFH hinges, and so, they regulate the orientation of Type (g) links. For example, a cable pull will induce a rotation that tends to raise the link upward. Three linear actuators \(SPS\) are added to the system and positioned as in Figure 2b in order to replace the three actuated cables directed along the \(\xi_{1}\), \(\xi_{2}\), and \(\xi_{3}\) directions. The three linear actuators are identified in the figure as the ones that are incident to the triple spherical joint \(S_{3}\) positioned at Point B. This replacement is justified by the fact that the three cables are directed along lines that pass through the center \(B\)of the triple spherical joint \(S_{3}\). This point is positioned in the middle of the fixed platform, in correspondence to a level that does not interfere with the CSFH hinges. Finally, it is worth noting that the identification of a PRBM that adequately corresponds to the proposed compliant structure represents the first necessary step to further analyze the new systems, such as workspace and kinematic analysis, kinematic synthesis, and kinetostatic behavior.
# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 2 Related Work
### Conversational AI
Military-domain task-oriented conversational systems can be divided into three types according to their mode of interaction: voice-type, text-type, and interactive interface-type systems. A few successful examples are described below. The Siri chatbot developed by Apple in 2011 began as a part of the \"Cognitive Assistant that Learns and Organizes\" project funded by the Defense Advanced Research Projects Agency (DARPA); by using perceptual and experiential learning, Siri reduced the information overload faced by battlefield commanders. It has since become a virtual voice assistant of national importance. With the development of expert assistant systems and the application of text-based conversational AI in military applications, IBM's Watson system came to be used to provide occupational information to US military members and help them transition from active duty into civilian life. In 1998, DARPA presented a dialogue system based on conversational multimodal interfaces, which allowed its users to perform operational tasks more efficiently [28]. Due to the lack of relevant literature on military dialogue systems, this study summarizes relevant research on military dialogue systems in different periods in the past, as shown in Table 1. For example, [PERSON] et al. [35] in 2006 proposed a spoken dialogue system that can engage in Call For Fire Radio dialogues (Radiobot-CFF) to help train soldiers in proper procedures for requesting artillery fire missions. They provided three modes: fully-automated, semi-automated, and passive mode, as the radio operator in a simulated Fire Direction Center (FDC) takes calls from a forward observer for artillery fire in training exercises. The Hassan system [36] proposed by [PERSON] in 2009 is a set of tactical question-answering dialogue systems, including a management interface for creating dialogue content and a dialogue manager, which can be used to build multiple virtual characters for tactical questions. The experiment consisted of 19 dialogues and 296 utterances. Furthermore, the experts expanded the range of possible responses provided by the virtual character by annotating other candidate utterances according to need. However, the system lacks the capabilities of question generation. MIRIAM is a conversational multimodal interface developed for command-and-control systems proposed by [PERSON] et al. [21]. The system improved situational awareness by providing information in multiple modalities (including audio, images, and text) to clarify the textual ambiguities that often arise in natural language and improve understanding. Therefore, multimodal conversational AI could become an important trend in the design of military conversational systems. The recent example of a successful military conversational AI would be the human-robot navigation system of [PERSON] et al. [20].
## 3 Results  
### Accuracy of Saildrone Observations  
The accuracy of salinity measurements by each of the three saildrones deployed was assessed by comparing individual salinity sensor (SBE37 and RBR) measurements on board each vehicle. For our analysis we use the SBE37 data as recommended by the ATOMIC Cruise Report [40]. Table 1 provides statistical analysis for the duplicate sensors-mean difference, standard deviation difference (STD) and root mean squared error (RSME). There is a consistent small offset (~0.06 psu) between the means of the SBE37 and RBR data on each vehicle that is attributed to a constant calibration offset. The standard deviations are ~0.002 psu, indicating that variability is consistently observed by both sensors. Additionally, the root mean squared error (RMSE) values for each vehicle are less than 0.07, which indicates that the two sensors produced quite similar datasets. These results confirm the consistency and accuracy of saildrone salinity data across sensors highlighting that salinity data from calibrated saildrone salinity sensors are ideal for validating conventional remote sensing techniques in tropical coastal regions.
## 5 Experimental and Simulation Results
The experimental scenario is shown in Figure 10.  
A single Stanford FS725 Rubidium Frequency Standard provided the ISL ground simulation unit with the 10 MHz and 1 PPS reference. The ISL engineering prototype 10 MHz reference was provided by another Stanford FS725. The latter one additionally provided the 10 MHz reference for two Agilent 81150A pulse function arbitrary generators (Agilent, Kuala Lumpur, Malaysia) The ISL prototype had a 50 MHz work clock, \(f_{c}\), from a single-channel 81150A. It obtained the 1 PPS and the 10.23 MHz input from a dual-channel 81150A. The frequency adjustment was realized by controlling the output frequency of the dual-channel 81150A 10.23 MHz and 1 PPS. Meanwhile, the phase adjustment was realized by simultaneously changing the cable length of the 10.23 MHz. Furthermore, the uncertainty of the initial phase, \(p^{\prime}_{0}\), was realized by repeatedly switching on the FS725 and measured by the Agilent DSO93004L digital storage oscilloscope. In addition, DC power was supplied to the ISL prototype. The ranging results processing personal computer (PC) (Lenovo, Beijing, China) handled the prototype work instructions and measurement data acquisition. The Stanford SR620 time interval and frequency counter monitored the real-time clock error, \(\Delta t\), and output it to the ranging results processing PC. The initial connection state shown in Figure 10 was deemed an unexecuted the FPA state; its ranging value is shown in Figure 11. We simultaneously extended the cable by 8 m (32.42 ns) to the 1 PPS and the 10.23 MHz of the ISL prototype and decreased the 10.23 MHz by 85 mHz. The pseudo-range measurement results with and without the phase compensation sensor are shown in Figure 11,\(c\), respectively. Figure 9: Ranging results with the 1 PPS in the skip-area. Figure 11: Collected actual comparison ranging data. (**a**) non-execution of the FPA without the phase compensation sensor; (**b**) execution of the FPA without the phase compensation sensor; (**c**) execution of the FPA with the phase compensation sensor. Figure 10: Experimental scenario of field data collection and ranging comparison. As shown in Figure 11, the measured ranging data are in accordance with the simulation results. For question 2, we powered off and on the FS725 of the ISL prototype five times without the phase compensation sensor, as well as with the phase compensation sensor. Then, we collected the ranging data, as shown in Tables 2 and 3, respectively. The phase of the 1 PPS at the clock 10.23 MHz, \(p_{0}^{\prime}\), could be read by the digital storage oscilloscope DSO93004L. As per Tables 2 and 3, after phase compensation sensor, the five times of ranging results were maintained at approximately 773.385 ns, which ensured the consistency of ISL ranging.