esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Technology has become a necessity for humanity to control the essential aspects required for development and evolution. As people have grown fond of technology and integrated it into every activity of their daily routine, increasing the comfort and confidence toward its results, it would be prudent to use technology to build and simulate the required features and characteristics of an environment for the elderly and disabled people to fulfill their basic desires and enhance their productivity and creativity within society.  
The motivation behind the research began gradually from the moment we noticed articles about the initiations occurring worldwide to help people of determination become integrated into social tasks, such as schools and hospitals. A recent newspaper article [1] was published discussing the obstacles encountered by people of determination inside shopping stores and how such an essential task can become very difficult to perform.  
Thus, we were motivated to utilize the extent of our knowledge combined with the recent research to construct a system that would bring us closer to a solution.  
One of the most desirable qualities that individuals aspire to maintain is obtaining whatever is needed to facilitate their life. Old and disabled people may face difficulties and obstacles in fulfilling this desire. Shopping could be a very weary and exhausting activity that they need to practice daily. Obtaining items from shelves, waiting in a long cashier line, and having difficulty accessing knowledge about the products are obstacles that they may face. Our service robot aims to solve most of these problems and facilitate the shopping process for people with disabilities.  
Some of the existing solutions that could be considered to assist the situation would be personal assistants provided by the market store and online shopping websites. Many people may feel uncomfortable asking a person to do everything desired while shopping, and in some situations, it may produce awkwardness and unease. Online shopping does not provide the typical shopping experience that people may want. It would not be easy to buy items that are not available physically and deal with restoring purchases. Online shopping is not instant and may take time to obtain items purchased physically. Our proposed system aims to provide a new meaning of shopping through which disabled people can acquire the ability to have the shopping experience with comfort and ease.  
Our approach proposes smart systems determined to facilitate obtaining items from high shelves, reduce overcrowding at cashier lines, and have high reliability.  
The remainder of the paper is organized as follows: Section 2 discusses the literature review. Section 3 introduces the materials and methods. In Section 3.1, we present the system overview. In Sections 3.3 and 3.5, we present our robotic arm control mechanisms. The scanning and validation systems proposed are presented in Sections 3.4 and 3.6. We present the testing results for each of the sub-systems in Section 4. Finally, the conclusions are drawn in Section 5, and future improvements are proposed in Section 6.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis
### Resonant Constellation Configuration Analysis for Distinct Regions
#### 4.1.1 Near-Earth Region  
Given that high-altitude satellites in near-Earth orbits typically exhibit orbital radii of approximately 36,000 km, the simulation spherical boundary was configured at 40,000 km to ensure complete orbital encapsulation. To achieve the optimal trade-off between computational load and precision requirements, a coarse-grid discretization scheme was implemented during the preliminary screening phase. Therefore, the target region was divided into longitude intervals of 60\({}^{\circ}\) and latitude intervals of 30\({}^{\circ}\). Applying the aforementioned algorithm, five optimal and five suboptimal constellations were identified for this region. The optimal configurations and their corresponding spatiotemporal average DOP values and standard deviations are listed in Table 2. A schematic diagram of the best-performing resonant constellation configuration and the temporal average distribution of DOP values across grid points in the target region are illustrated in Figure 5.  
Table 2 and Figure 5 indicate there are minimal performance differences between the Rank 1 and Rank 2 resonant constellations, with mean DOP values both below 17 and a standard deviation below 4. These two configurations incorporate L2 southern/northern NRHO orbits and periodic orbits of the same type at L4 and L5. In contrast, configurations Rank 3-Rank 5 consist of L1 or L2 southern/northern Halo orbits combined with L4/L5 periodic orbits, exhibiting mean DOP values between 22 and 25.
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 5 Results
### Experimental Results Analysis
#### 5.2.2 Different Message Generation Intervals  
The impact on each metric is observed by varying the message generation interval at a cache size of 50 M. Each routing technique performs noticeably better as the generation interval gets longer.  
As shown in Figure 4, when there are too many messages, MPCM moves out messages with lower utility values in accordance with the message utility values, leaving enough space for messages with high utility values, and on this basis, a certain success rate will be guaranteed. MPCM maintains a better state at intervals greater than the 40s, and the delivery success rate is no longer affected. Several other algorithms perform poorly because they are unable to handle excessively packed communications. With an increase in the message generation interval, MPCM performs fairish in terms of latency, and its overall latency is lower than that of several other three algorithms. Since the messages stay in the cache for a long time as the generation interval rises, the network overhead also rises gradually. When the generation interval exceeds the 20s, MPCM and RDR are largely unaffected, but the overhead of the other three algorithms continues to rise. The Epidemic algorithm has the highest number of dropped packets, and a large number of message copies in the network are discarded due to cache limitations and long survival times.  
Figure 3: Comparison of success rate, latency, overhead and packet loss with different buffer size.
###### Abstract  
This study presents the design and validation of an integrated fire safety system that leverages edge AI, hybrid sensing, and precision suppression to overcome the latency and collateral limitations of conventional smoke detection and sprinkler systems. The proposed platform features a dual-mode sensor array for early fire recognition, motorized ventilation units for rapid smoke extraction, and a 360\({}^{\circ}\) directional nozzle for targeted agent discharge using a residue-free clean extinguishing agent. Experimental trials demonstrated an average fire detection time of 5.8 s and complete flame suppression within 13.2 s, with 90% smoke clearance achieved in under 95 s. No false positives were recorded during non-fire simulations, and the system remained fully functional under simulated cloud communication failure, confirming its edge-resilient architecture. A probabilistic risk analysis based on ISO 31000 and NFPA 551 frameworks showed risk reductions of 75.6% in life safety, 58.0% in property damage, and 67.1% in business disruption. The system achieved a composite risk reduction of approximately 73%, shifting the operational risk level into the ALARP region. These findings demonstrate the system's capacity to provide proactive, energy-efficient, and spatially targeted fire response suitable for high-value infrastructure. The modular design and SmartThings Edge integration further support scalable deployment and real-time system intelligence, establishing a strong foundation for future adaptive fire protection frameworks.  
edge AI fire detection; residue-free clean suppression agent; hybrid smoke--heat sensing; motorized 360\({}^{\circ}\) suppression nozzle; energy recovery ventilation (ERV); SmartThings Edge integration; ISO 31000-based fire risk assessment
## 5 Results and Discussion
### Post-Processing Optimizations
At the same time, the authors acknowledge that future work needs to be carried out to improve the performance of the proposed method. Currently, the state of the art for mobile-based solutions for cervical cancer screening is still far from reaching clinical usage due to the particular limitations of this scenario, like lack of large publicly mobile-acquired datasets, limited mobile-acquired image quality, and the restricted computational power of mobile-based solutions for portable microscopic screening. In particular, the requirement of locally executing the detection models on mobile devices dramatically limits the selection of suitable meta-architecture/backbone combinations, which must be simultaneously lightweight and compatible to run on mobile devices. These limitations should be consid \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Score Threshold** & **Class** & **AP** & **Recall** & **Accuracy** & **Specificity** & **F1** & **Youden** & **TP** & **FP** & **FN** \\ \hline \multicolumn{10}{c}{_Before Optimization_} \\ \hline \hline \multirow{4}{*}{0.500} & ASC-US & 0.1073 & 0.1824 & 0.9221 & 0.9314 & 0.0547 & 0.1137 & 31 & 932 & 139 \\  & LSIL & 0 & 0 & 0.9953 & 0.9997 & 0 & \(-\)0.0003 & 0 & 4 & 61 \\  & ASC-H & 0 & 0 & 0.9970 & 0.9993 & 0 & \(-\)0.0003 & 0 & 10 & 31 \\  & HSIL & 0.1524 & 0.1714 & 0.9950 & 0.9972 & 0.1500 & 0.1686 & 6 & 39 & 29 \\  & Sqm. Normal & 0.8023 & 0.6864 & 0.6518 & 0.1990 & 0.7855 & \(-\)0.1147 & 8767 & 781 & 4006 \\ \hline \multicolumn{10}{c}{_After Optimization_} \\ \hline
0.660 & ASC-US & 0.1098 & 0.0529 & 0.9859 & 0.9969 & 0.0804 & 0.0498 & 9 & 45 & 161 \\  & LSIL & 0 & 0 & 0.9955 & 0.9997 & 0 & \(-\)0.0003 & 0 & 4 & 61 \\  & ASC-H & 0.0600 & 0.0645 & 0.9957 & 0.9977 & 0.0597 & 0.0622 & 2 & 34 & 29 \\  & HSIL & 0.1758 & 0.2286 & 0.9942 & 0.9961 & 0.1600 & 0.2247 & 8 & 57 & 27 \\  & Sqm. Normal & 0.8271 & 0.7943 & 0.7065 & 0.0878 & 0.8258 & \(-\)0.1180 & 10,145 & 1652 & 2628 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance metrics for the best-performing model before and after the score threshold optimization. ered when comparing the performance and maturity of such mobile-based solutions with solutions that operate under optimal, well-controlled laboratory conditions, which usually have access to images acquired with high-end microscopic equipment and unrestricted computational resources.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 5 Anthropometric and Morphological Approaches for HRTF Personalisation
### Anthropometry Matching
Their approach requires minimal inputs, photos of both ears and head dimensions, and involves several steps: identifying ear landmarks, calculating anthropometric distances, transforming ear shapes into a standard format, and adjusting HRTFs based on head and ear scales. To train the CNN, the authors create a dataset of 741 hand-labelled ear photos, expanding and augmenting these to produce a total of 2964 images, including synthetic ones generated from computer-rendered ear meshes. They employ a nearest-neighbour search to match input ears based on four computed distances. Once a matching database ear is found, the corresponding HRTF is scaled to account for differences in ear and head size by resampling based on the relative ear lengths and adjusting the ITD. A head-and-torso (HAT) model is used to further fine-tune the personalised HRTFs, improving spatial accuracy. Most matching techniques rely on traditional methods, while the adaptation and regression approaches described in the following sections are based on machine learning. Results consistently show that these ML methods outperform conventional techniques. However, ML models are generally data hungry, with their effectiveness highly dependent on access to large, high-quality datasets, while generating personalised HRTFs with the techniques introduced in Section 4 can be relatively straightforward, noted minor inconsistencies between generated and measured HRTFs have been noted, underscoring the importance of comprehensive datasets like SONICOM [90] in ensuring model reliability and accuracy. Works like [152] emphasize how ample training data are crucial for these models, as increased data consistently enhance model performance.
## 5 Experimental Verification and Analysis
### Case Study II: Fault Diagnosis of Gearbox  
#### 5.2.1 Data Set Description  
Our bevel gear fault simulation test bench (as shown in Figure 15) is used to further verify the effectiveness of this method. The experimental equipment consists of a motor, coupling, gearbox, magnetic powder brake and controller. The vibration acceleration sensor is arranged on the gearbox housing above the faulty part, and the location of the fault gear is also shown in Figure 15. The specific structural parameters of the tested gear are shown in Table 5. A total of 7 fault types are set in this test. Among them, there is one kind of broken tooth fault, which is recorded as C1; Slight and moderate pitting faults are recorded as C2 and C3, respectively; Slight and moderate crack faults are recorded as C4 and C5; And minor and moderate wear faults, respectively recorded as C6 and C7. The specific information on faulty gears is shown in Table 6. It should be noted that C1 faulty gear actually comes from an industrial reduction gearbox. Due to overload during operation, 1 broken tooth occurs. Here, the damage degree 1 represents the number of broken teeth as 1. The two levels of pitting faults are measured by the number of pits on the tooth surface, with level 1 representing four pits and level 2 representing six pits. Except for C1, other faulty parts were obtained by manual processing. The vibration signal of the gearbox surface is collected through the data acquisition instrument. The sampling frequency is set to 25.6 kHz, the gear speed is set to 1100 rpm, and the vibration signal of each fault type is collected for 30 s.  
Figure 14: Diagnosis results using different parameters of Case Study I: (**a**) Different training sample percentage, (**b**) Different \(\tau\).  
Figure 15: The bevel gear fault simulation test bench.  
Broken teeth in gears usually generate significant impacts, so periodic shock waveforms appear in the time-domain vibration signal, and the number of times the shock wave occurs is the rotational frequency of the gear. Gear pitting can cause regular impact components in the time domain of the gear signal, but the characteristics are different from those of broken teeth. When the gear undergoes uniform wear, its vibration signal waveform tends to approach a square wave. The differences in these fault characteristics are reflected in the different signal complexity in the vibration signal. Therefore, using the proposed RMDE for analysis should achieve effective results.
# UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station
## 3 The Dynamic Model of EOGS
\tag{22}\]  
It can be found from the Equation (24) that the \(P_{O_{2}}^{E}\) has no direct relationship with factors such as the length of the system running time and the number of astronauts. Substituting Equations (14)-(17) into Equation (22), and the coefficients can be defined as following:  
\[ \begin{array}{l}k_{11}=\frac{R_{4}T_{c}}{M_{O_{2}}V_{c}},k_{12}=\sqrt{2\rho _{0}}C_{d1}A_{o1}\beta_{1},k_{21}=\frac{R_{4}T_{c}}{M_{air}V_{c}},k_{22}=\frac {R_{4}T_{c}}{M_{air}V_{c}}(w_{N_{2}}^{cabin}+w_{CO_{2}}^{cabin}),\\ k_{31}=\frac{R_{4}T_{c}}{M_{H_{2}}(V_{c}+V_{c})},k_{32}=\sqrt{2\rho_{H_{2}}}C_ {d2}A_{o2}\beta_{2},k_{41}=\frac{R_{4}T_{c}}{M_{c}V_{c}}\end{array}  \tag{23}\]  
The state equation of the EOS can be written as follows:  
\[\left\{\begin{array}{l}\dot{x}_{1}=k_{11}k_{12}\sqrt{(x_{4}-x  
system composed of random variable \(X\) with Gaussian white noise \(W(k)\) and observation \(Z\) with Gaussian white noise \(V(k)\) can be described as follows:  
\[\left\{ \begin{array}{l}X(k+1)=f(x(k),W(k))\\ Z(k+1)=h(x(k),V(k))\end{array} \right. \tag{26}\]  
The specific process of UKF algorithm are shown in Figure 5.  
1.The UT is performed first to obtain the sigma point set and the corresponding permissions  
2.The one-step prediction of 2n+1 Sigma points is calculated, and then the one-step prediction mean and covariance matrix of the system state quantity are calculated. 3.According to the above one-step prediction value, by using the UT again, the new Sigma point set and its predicted values can be obtained  
4.Then, the mean and covariance predicted by the system are obtained by weighted summation  
5.the Kalman gain matrix is calculated, and the status update and covariance matrix update of the system can be calculated  
\[K(x+1)=P_{x_{i}x_{i}}P_{z_{i}x_{i}}^{-1}\] \[\hat{X}(k+1\,|\,k+1)=\hat{X}(k+1\,|\,k)+K(k+1)[Z(k+1)-\hat{Z}(k+1 \,|\,k)]\] \[P(k+1\,|\,k+1)=P(k+1\,|\,k)-K(k+1)P_{z_{i}z_{i}}^{T}K^{T}(k+1)\]  
Figure 5: The specific process of UKF algorithm. According to the steps of the UKF algorithm, the state equation and observation equation of EOGS, i.e., Equations (24) and (25), are iteratively operated according to the method in Figure 5, and the partial pressure and total pressure in the cabin can be obtained by real-time estimation of oxygen.
## 3 Results
### Performance of Final ML Models
#### 3.3.1 Weights
\begin{table}
\begin{tabular}{c c c c c} \hline
**Weight Metric** & \(\mathrm{MCO_{gs}}\) & \(\mathrm{PGAP_{std}}\) & PGAP-ML\({}_{\mathrm{reg}}\) & PGAP-ML\({}_{\mathrm{clus}}\) \\ \hline \(\mathrm{Rectum D_{mean}}\) & 3.46\% \(\pm\) 0.999\% & **4.18**\% & **4.82**\% \(\pm\) **2.32**\% & **4.17**\% \(\pm\) **0.257**\% \\ Bladder D\({}_{\mathrm{mean}}\) & 1.10\% \(\pm\) 0.421\% & 1.18\% & 1.24\% \(\pm\) 0.368\% & 1.14\% \(\pm\) 0.404\% \\ PTV Conformality & 8.86\% \(\pm\) 2.252\% & **10.7**\% & **11.3**\% \(\pm\) 3.22\% & 9.559\% \(\pm\) 1.75\% \\ Rectum D\({}_{\mathrm{max}}\) & 0.163\% \(\pm\) 0.0800\% & **0.102**\% & **0.107**\% \(\pm\) **0.0341**\% & **0.102**\% \(\pm\) **0.00820\%** \\ PTV Dose Falloff & 0.926\% \(\pm\) 0.390\% & **0.695**\% & **0.649**\% \(\pm\) **0.552**\% & **0.705**\% \(\pm\) **0.0153**\% \\ Bladder D\({}_{\mathrm{max}}\) & 0.487\% \(\pm\) 0.239\% & 0.459\% & 0.400\% \(\pm\) 0.106\% & 0.481\% \(\pm\) 0.116\% \\ Bowel V\({}_{\mathrm{36.0 Gy}}\) and V\({}_{\mathrm{45.6 Gy}}\) & 0.0575\% \(\pm\) 0.00204\% & **0.0559**\% & **0.0551**\% \(\pm\) **0.00243**\% & 0.0568\% \(\pm\) 0.00123\% \\ Higher Goals & 85.0\% \(\pm\) 3.02\% & **82.6**\% & **81.4**\% \(\pm\) **3.59**\% & 83.8\% \(\pm\) 1.82\% \\ \hline \end{tabular}
\end{table} 
Table 8: Summary of PG relative weights. Values are mean averages across the the validation dataset \(\pm\) one standard deviation. Boldface indicates statistically significant differences from \(\mathrm{MCO_{gs}}\) at the 95% level. Figure 3: Plots showing relative weight difference from \(\text{MCO}_{\text{gs}}\) for the validation dataset. Bar chart are order patient 21–40 and box plot represent the overall distribution.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 2 BDS Integrity Concept
### Integrity Definition
Among them, the ground integrity monitoring approach provides users with integrity information through HS, SIF, DIF, SISA, and other parameters in the message; SAIM can provide users with integrity information through message parameters or non-standard PRN codes. In addition, the NTE tolerances of GPS, Galileo, and BDS are broadcasted to users as message parameters, while the NTE tolerance of GNLOASS is fixed to 70 m.
# Effect of Surface Roughness on Early Stage Oxidation Behavior of Ni-Base Superalloy IN 625
## 3 Results
### Post-Exposure Analyses
#### 3.2.2 SEM Analysis  
The images of the cross-sections of polished and ground IN 625 after air exposure at 950 \({}^{\circ}\)C up to 24 h is presented in Figure 4a,b, respectively. From the images captured at lower magnification one can clearly see, that on the polished surface a thicker oxide scale was formed than on the ground  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Polishing (1 μm)** & **Grinding (220 gri)** \\ \hline D & 1.0018 & 1.0084 \\ LSFC & 1.8 & 8.4 \\ L\({}_{\text{R}}\) 1 μm & 1.02 & 1.05 \\ SRC & 15.8 & 12.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of surface roughness evaluation using conventional procedures.  
Figure 3: Quantified GD-OES depth profiles showing the atomic concentration of the given elements as a function of sputtering time determined for (**a**) polished (1 μm); (**b**) ground (220 gri) IN 625 after isothermal oxidation performed at 950 \({}^{\circ}\)C for 24 h in laboratory air.  
sample. The SEM analysis performed at a higher magnification (inner images) showed, that on both samples, mainly Cr\({}_{2}\)O\({}_{3}\) formed as an outer oxide scale, below which precipitates of internally oxidized aluminum are present. However, on top of the chromia scale formed on polished alloy, a layer of Cr, Mn, and Ni-mixed oxide is present. In both cross-sections, the Nb-rich phase, which most probably is Ni\({}_{3}\)Nb, is present at the oxide scale/alloy interface, however this interface is more convoluted in the case of ground material (Figure 4b). Moreover, several voids are observed at the near-surface region on the ground sample. All observations using SEM are in a very good agreement with the results obtained using GD-OES. The average oxide scale thickness measured on the polished sample is 3.87 \(\upmu\)m, while on ground it is 2.74 \(\upmu\)m.
# A Network-Based Method to Analyze EMI Events of On-Board Signaling System in Railway
## 2 Network-Based Modeling Framework and Method  
### Problems Description  
The idea for the method presented in this paper was derived from a similar expression used in system engineering. The EMC problem of the system refers to the process in which EM disturbance sources (internal and external) affect the normal operation of the electrical and electronic functions of the corresponding parts through the transmission coupling relationship within the system. According to this description, it can be defined as:  
\[D_{EMC}=(S,C,T,F) \tag{1}\]  
where \(D_{EMC}\) denotes a definition function, \(S\) is the EM disturbance source in the specific EM environment, \(C\) represents the coupling path, and \(T\) is the equipment in the system. F denotes the function of the equipment or the system. Thus, the representation of EMC issues is very similar to the network as shown in Figure 1, where the left part is the description of the EMC problem including sources, coupling, and OTCS equipment in EMU. The right part is made up of the nodes and edges with different meanings of EMC elements.  
According to the definition in Equation (1), the EMC problem is a network structure composed of different EMI events as shown in the upper of Figure 2. So, it can be regarded as a network structure formed by nodes and edges. In Figure 2, sources 1 and 2 (node \(v_{1}\) and \(v_{2}\)) affect equipment 11 and 12 (node \(v_{11}\) and \(v_{12}\)) through two different paths, which represent different EMI events. Each node can represent the elements of EMC issues, such as EM disturbance sources and sensitive equipment. The edge indicates the propagation path that an EM disturbance source affects the target equipment through different coupling ways, such as the conduction and radiation. Each path from the source to the device can be represented as an EMI event. However, all of the EMI events will not occur at the same time due to different scenarios. If every EMI event of the system can be expressed through a network, it will be helpful in the EMC analysis at the design, verification, and application phases.  
Figure 1: An analogy between the EMC problem and network.