esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurement Quality Analysis
### MQA3: Error Source Analysis
The mean value of the clock bias difference between BDS and GPS is 21.070 m. The constant shift between these two systems is due to the measurement being generated using different standards. The BDS user clock bias has a larger standard deviation. With the ionospheric and tropospheric delays removed, the remaining error sources in the pseudorange measurement are satellite clock error, satellite orbit error, multipath error, and receiver thermal noise. Since this experiment used one receiver to receive both signals at the same time, the receiver thermal noise should be the same. Because the surrounding environment had no permanent obstructions (buildings) above the antenna site, multipath error was not considered. The BDS user clock bias is thus noisier due to satellite clock and orbit errors. The main objective of this section is to present the estimation method for each error source. After that, remove these errors and generate a clean pseudorange residual to analyze the performance of the correction estimation in the next section. Figure 21: User clock bias (**Blue**: GPS clock bias; **Red**: BDS clock bias). Figure 22: Pseudorange residuals for all GPS satellites.
## 8 System Design
### Onboard Processing Design, Software and Operations
#### 8.5.3 Ground Station Software  
An understanding of the operations of the ground station is beneficial to understanding the operations of the spacecraft. Figure 5 depicts the logic of the tasking process of the ground station  
Figure 3: Onboard Processing Separation Between Flight and Payload Processing Units.  
software. Figure 6 depicts the logic that applies to the message transmission and receipt functionality of the ground station.  
The tasking process begins with a user tasking request. These requests are validated to see if they can be accommodated within existing schedule constraints. Those that cannot be integrated into the schedule trigger a check to see if a non-impactful change (e.g., one that does not violate the constraints of another job) can be made to allow it to be completed. Finally, if job satisfaction via a non-impactful change cannot be accomplished, the user is prompted to decide whether authorizing an impacted change is desirable. If so, the user is presented with possible jobs to impact, and must select one or more to terminate or reschedule.  
The transmission process is initiated when a message (e.g., a tasking request) is queued for transmission or when a window occurs, where downlinked data and telemetry could be received. The next available transmission window is identified (which could, in a multi-ground station configuration be either a local communications opportunity or an opportunity at a remote station). In the case where the next opportunity is at a remote station, the messages are sent to the remote station (which completes the remainder of this process locally). Irrespective of whether messages are queued to be sent or not, a ground station will wait for the start of the next scheduled (based on orbital projections) communications window.  
Each window begins with an initial handshake message exchange between the spacecraft and the ground station. If this cannot be completed at the scheduled time, the ground station waits a configurable delay period and re-attempts the handshake; this will continue until communication is achieved or the window expires. If the handshake is successful, the ground station processes the initial telemetry from the spacecraft, which would include any critical condition reports, if applicable. If no critical condition reports are received or they do not require a change to the communications window plan, transmission (if there are queued messages) and reception are initiated.  
Throughout the window, queued messages (if they exist) are sent and received by the ground station. For outbound messages, the message is sent, marked as sent and, as acknowledged, marked as acknowledged. Data is received, checked for integrity (a resend request is triggered if the integrity check fails) and processed and stored. This continues until the transmission window concludes.
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 2 Mapping System Overview  
The HD mapping system is comprised of two components: real-time local mapping on the vehicle and global mapping on the cloud server, as depicted in Figure 1.  
Low-cost sensors, such as a monocular camera, an inertial measurement unit (IMU)/global positioning system (GPS), and a wheel encoder, are used to collect data on-board the vehicle. Semantic elements, including lane lines, ground arrows, road stop lines, traffic signs, electrical poles, etc., are extracted from monocular vision. The ego-motion, estimated by the IMU and wheel encoder, is projected onto the 3D world coordinate system to form a local segment map (LSM) using semantic splicing. The global absolute pose of the vehicle can be estimated through GPS/IMU/wheel encoder fusion processing and then uploaded to the cloud server.  
Limited by the precision of low-cost sensors and the computing power of the vehicle-end hardware platform, the LSMs generated through single-vehicle acquisition at one time cannot meet the requirements of semantic element precision and completeness for autonomous driving. To solve this problem, we can repeat LSM mapping for the same road  
Figure 1: The architecture of HD mapping system.  
section many times and then upload LSMs to the cloud for overall fusion mapping, which can offset the observation error of single mapping, to some extent, and greatly improve the mapping precision and semantic completeness. First, the LSMs collected on the same road section are unified to the global world coordinate system. Then, semantic alignment is performed, different types of semantic elements are instantiated, and finally, the global semantic map is generated using vectorization.  
However, the precision of low-cost sensors and the computing power of the vehicle-end hardware platform limit the LSMs' precision and completeness. To address this issue, LSM mapping is repeated multiple times for the same road section and then uploaded to the cloud server for overall fusion mapping. This offsets the observation error of single mapping to some extent and significantly improves the mapping precision and semantic completeness. The LSMs collected on the same road section are unified in the global world coordinate system, and semantic alignment is performed to instantiate different types of semantic elements. Finally, the global semantic map is generated through vectorization.  
Due to the large measurement error and system error of low-cost sensors, we propose a fusion alignment algorithm, GBA_SGICP, based on crowdsourced data, for semantics alignment. we also present an improved density clustering algorithm for semantic aggregation in this paper.
## 4 Simulation and Analysis
### Test Case 2: LE Identification in the Scenario Where a Single Node Contains Wind Farms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Branch \(k\)** & **Number** & \(\mathrm{P}_{k}^{0}\) **(MW)** & \(\mathrm{P}_{k}^{\prime}\) **(MW)** & \(\mathrm{P}_{k}^{\prime}\) **(MW)** & \(\mathrm{P}_{k}^{\prime\prime}\) **(MW)** & \(\alpha\) & \(\beta\) \\ \hline
5-8 & L1 & 315.56 & 11.77 & 10.77 & 14.03 & 0.0929 & 0.1601 \\
8-7 & L2 & 184.45 & 495.62 & 491.60 & 492.09 & 0.0007 & 0.0072 \\
7-6 & L3 & 419.65 & 733.87 & 731.69 & 726.66 & 0.0003 & 0.0099 \\
5-4 & L4 & 140.69 & 11.77 & 11.49 & 14.73 & 0.0244 & 0.2010 \\
4-14 & L5 & 262.45 & 394.49 & 394.72 & 393.45 & 0.0006 & 0.0026 \\
14-13 & L6 & 281.66 & 432.61 & 437.72 & 439.73 & 0.0117 & 0.0162 \\
13-10 & L7 & 289.75 & 427.00 & 431.67 & 434.50 & 0.0108 & 0.0173 \\
10-11 & L8 & 360.25 & 223.22 & 218.56 & 215.50 & 0.0009 & 0.0358 \\
11-6 & L9 & 357.54 & 207.41 & 202.33 & 200.11 & 0.0251 & 0.0365 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The simulation results after the faulty branch 5-6 is removed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Branch \(k\)** & \(\mathrm{G}_{k-26}\) & \(\mathrm{G}_{k-i}^{I}\) & **Branch \(k\)** & \(\mathrm{G}_{k-26}\) & \(\mathrm{G}_{k-i}^{I}\) \\ \hline
5-8 & \(-\)0.0360 & \(-\)0.1122 & 14-13 & 0.3163 & 0.3545 \\
8-7 & \(-\)0.1270 & \(-\)0.0493 & 13-10 & \(-\)0.2892 & \(-\)0.2545 \\
7-6 & \(-\)0.1277 & \(-\)0.0501 & 10-11 & 0.2892 & 0.2520 \\
5-4 & \(-\)0.4922 & \(-\)0.5315 & 11-6 & \(-\)0.3143 & \(-\)0.3523 \\
4-14 & 0.0298 & 0.0629 & 5-6 & 0.1154 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensitivity and generalized sensitivity. Figure 7: Comparisons between two methods. (**a**) Comparison of the APEV and the APMV between the two methods; (**b**) comparison of LEEs between the two methods. LE calculated by the traditional method is 0.0099, which also satisfies the LE identification criterion and is able to correctly judge the cause of branch overload. Figure 7a illustrates that the APEV calculated by the two methods is basically consistent with the APMV. Figure 7b shows that in the case of very small wind power penetration, the APEV calculated by the conventional method still has high accuracy and can still meet the accuracy requirements in LE identification. It is worth stating that the APEV calculated by the proposed method is closer than that calculated by the conventional method, and the error between the APEV and the APMV is smaller.
## 3 Results and Discussion
### Corrosion Inhibition According to the Nitrite Dosage
The specimens were selected at one dosage level above the inhibitor dosages, where no visual corrosion was observed in Figures 2 and 3, enabling micro-scale observations of early-stage localized corrosion damage. In all the samples, polishing marks were clearly visible, indicating that the inhibition performance was maintained across the bulk surface. Figure 3: Surface morphology of FCD-500 after immersion tests in solutions containing various inhibitor dosages and 100 ppm of chloride ions at both temperatures, 50 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, for 24 h.  
In the specimens subject to 3000 ppm (Figure 5a) and 5000 ppm (Figure 5b) at 80 \({}^{\circ}\)C, micro-scale corrosion was observed at the matrix surrounding the graphite nodules. The FCD-500 consists of spheroidal graphite nodules embedded in a ferrite-pearilic metallic matrix. In corrosive environments, this structure behaves as a micro-galvanic cell. In such cells, graphite serves as a cathodic site, making the surrounding matrix susceptible to localized anodic dissolution. As shown in Figure 5a, localized corrosion preferentially initiates in the ferrite phase surrounding the graphite nodules, subsequently propagating outward from these regions. Under conditions containing 100 ppm of chloride ions at 50 \({}^{\circ}\)C, the specimen exposed to 11,000 ppm of inhibitor (Figure 5c) exhibited the formation of micro-scale pits. Notably, increasing the inhibitor dosage to 13,000 ppm (Figure 5d) led to a significant reduction in the occurrence and severity of such pits, demonstrating enhanced corrosion inhibition performance at higher dosage levels. This finding suggests that an inhibition dosage of at least 13,000 ppm is required to effectively inhibit the corrosion of FCD-500 material. Figure 4: Weight loss of immersion coupons with varying inhibitor dosages for chloride-free and 100 ppm of chloride ion concentrations. Figure 5: SEM images of the immersion specimens exposed for 24 h to varying conditions: (**a**) 3000 ppm at 80 \({}^{\circ}\)C, (**b**) 5000 ppm at 80 \({}^{\circ}\)C, (**c**) 11,000 ppm + Cl\({}^{-}\) 100 ppm at 50 \({}^{\circ}\)C, and (**d**) 13,000 ppm + Cl\({}^{-}\) 100 ppm at 50 \({}^{\circ}\)C.
## 2 Materials and Methods
### Material Characterisation
#### 2.1.1 Particle Size Distribution (PSD)  
The PSD, determined by static laser light scattering (Saturn DigiSizer 5200), uses a laser in conjunction with a charge-coupled device (CCD) containing over one million detector elements to measure particle size [18]. These detectors are placed to measure the intensity of light scattered by the particles at various angles. Light is scattered by particles in a pattern dependent on their size, shape, refractive index, and wavelength of incident light. Based on the Mie theory, the PSD is calculated from the angle distribution of the scattered light intensity collected by the detectors [18].
## 3 Results and Discussion  
### Microstructure and Grain Size Analysis  
The following Figure 3 depicts the microstructure of the as-received and ECAP Al5083 samples. The as-received alloy had a coarse grain structure. The microstructures demonstrated that microscopically homogeneous, fine-grained structures were formed at a high deformation degree. The grain size was measured using ImageJ software. The as-received sample had a grain size of 145 \(\upmu\)M, which decreased to 102 \(\upmu\)M after the first pass, 65 \(\upmu\)M after the second pass, and 37 \(\upmu\)M after the third pass. The grain refinement occurred due to the deformation produced by the ECAP.  
A decrease in the grain sizes in the cross-section and an increase in the longitudinal section were observed. Figure 2a-d illustrate the elongation and the refinement grains in the tested samples. A decrease in the grain size was primarily seen after the first pass through the channel, due to the original grains of the sample separating into subgrain groups.  
On account of the multi-pass ECAP, the dislocation augmentation speed for the first pass was much bigger than that of the dislocation obliteration because of the little dislocation density. The material grains along these lines can be essentially refined. The grain refinement process went on as further passes occurred, because of the expansion of the internal energy and the dislocation density. The internal energy expansion caused the crystalline recovery and the recrystallization formation, such that the grain refinement was diminished step by step after a few passes. The grain refinement development procedure of the specimen was again changed for the diverse pressing routes. The accumulation and the equalization of the dislocations were likewise unique for Route C. The investigation of the deformation and the dislocation advancement might very well show that the materials with a nanostructure can be acquired by the ECAP process. The grain refinement procedure can be portrayed as consistently unique recuperation and recrystallization. From the viewpoint of a microstructure examination, the grain refinement procedure aims to control the dynamic evening out of the age and the demolition of the dislocations [47].
## 2 Background Information on the Two Real Cases
### Case 1: Gottingen (Germany) 1993
In addition to the above information, Prof. [PERSON] ([PERSON] was Germany's leading expert in forensic facial reconstruction at the Institute of Forensic Medicine at the University of Bonn), MD, described particular features of possible facial injuries at the time. Specifically, Prof. [PERSON] described an oval bone defect on the left maxilla (upper jawbone) (6 mm below the inferior orbital rim (lower edge of the eye socket) and 20 mm from an imaginary midline, counting from the left side) with evidence of complete bone healing. This defect includes areas where sensory nerves run from large parts of the left side of the face. From all indications, it could be a facial injury or a consequence of inflammatory reactions following the opening of the left maxillary sinus. The bony injury could have resulted in scarring just below the left eye, which would have been visible on a living person and might have been conspicuous. A nerve injury to the infraorbital (area located below the eye socket) nerve could also have resulted in partial trophic disturbances on the left side of the face. During the medical treatment of a possible injury and the removal of the injured tissue, the surgical wound could have been closed with surgical sutures. This might leave a scar that looks like a line. Overall, according to Prof. [PERSON], MD, the bone injury provides too little information about the shape of a possible scar. Based on all this conclusion, Prof. [PERSON] carried out a manual soft tissue reconstruction of the face of the unknown deceased in 1994 (Figure 1). This reconstruction will later be used to compare traditional methods with a digital reconstruction based on an optimized method of computer-aided FSTR. This study received the necessary permissions (approval dated 17 December 2024) from the Gottingen Public Prosecutor's Office. Figure 1: Procedure and results of the manual facial soft tissue reconstruction of the unknown decedent from Case 1. The following illustrations show the procedure and results of manual facial soft tissue reconstruction performed by Prof. [PERSON] in 1994 (the illustrations are own figures). **(A)** Illustration of the skull, reconstructed from CT images. **(B)** Reconstructed soft tissue of the unknown deceased. The illustration was also based on CT images. **(C)** The final reconstruction was further individualized with hairstyle and clothing. The illustration shows a 3D model that has been created with the help of photogrammetric methods.
## 3 Results
### Classification Tree
#### 3.2.2 Interpretation of the Classification Tree Results  
The final tree resulting from the application of the modeling procedure described above is shown in Figure 2. In an attempt to facilitate the interpretation of the results, except for the predicted category (yes or no) in each node, the final tree also includes the percentage and the frequency of each category. These percentages and frequencies derived from the complete database and not only from the training set.  
The results of the classification tree show that the most influential characteristic that affects the intention to shift towards bike-sharing systems due to a sophisticated mobile app is age, which is at the top of the tree. More specifically, it seems that the likelihood of shifting towards bike-sharing systems due to a mobile app is much higher for those who are aged between 18 and 54 comparing to those who are aged over 54. Also, it appears that potential users would be more easily affected by the mobile app in comparison with the existing users of the ThessBike system. The occupation of travelers is also a characteristic that differentiates the intention of users of all ages. Regarding the frequency of using private cars, there is a more positive attitude among respondents who use a car for their trips, but not on a daily basis. Finally, no safe conclusions can be drawn for the impact of the education level and the income on users' intention, since in these lower levels of the tree the model works with limited observations.  
Concluding, from the tree presented in Figure 2, it can be demonstrated that:  
* the group of people that has the highest probability to shift towards the bike-sharing system owing to the mobile app, is the one that includes potential users aged between 18 and 54 (green box),* the group of people that has the lowest probability to shift towards the bike-sharing system owing to the mobile app, is the one that includes people older than 54 years old, who are retired or they are occupied as private employees (red box),
* regarding the existing users, the group of people that has the highest probability to be positively affected by the mobile app is the one that includes people aged between 18 and 54 that they are occupied as state employees or they are students.
## 2 Literature Survey
### Research Problems  
The related work presented in this literature survey covered more than ten years, and the primary focus was either the approach to enhancing oil pipeline leak detection systems or highlighting the LoRa network performance with a limited ratio of enhancement. During this research, the following issues and challenges were identified:  
* A real-time transient model (RTTM) was developed as a robust technique for accurate leak detection, while the integration with LoRaWAN for real-time monitoring presents challenges in the achievement of energy efficiency.
* Current LoRa-based monitoring and leak detection systems lack the power optimization needed for continuous and reliable operation. This issue decreases the node lifespan and affects the feasibility of continued leak detection.
* The standard LoRaWAN does not efficiently balance power consumption and the high-rate data transmission required by RTTM-based oil pipeline leak detection systems.
* According to the achievements in related and previous work, this research reports the need for an optimized LoRaWAN model that deploys different aspects of machine learning techniques to reduce the node power consumption with the target of reaching a 40% reduction with respect to the baseline power consumption of the standard LoRa network configuration, as well as the need to enhance the node packet delay and number of packets lost over the entire network, while maintaining the reliability, which is necessary for stable real-time leak detection and localization operations.
# Coupling Motion and Energy Harvesting of Two Side-by-Side Flexible Plates in a 3D Uniform Flow
###### Abstract  
The fluid-structure interaction problems of two side-by-side flexible plates with a finite aspect ratio in a three-dimensional (3D) uniform flow are numerically studied. The plates' motions are entirely passive under the force of surrounding fluid. By changing the aspect ratio and transverse distance, the coupling motions, drag force and energy capture performance are analyzed. The mechanisms underlying the plates' motion and flow characteristics are discussed systematically. The adopted algorithm is verified and validated by the simulation of flow past a square flexible plate. The results show that the plate's passive flapping behavior contains transverse and spanwise deformation, and the flapping amplitude is proportional to the aspect ratio. In the side-by-side configuration, three distinct coupling modes of the plates' motion are identified, including single-plate mode, symmetrical flapping mode and decoupled mode. The plate with a lower aspect ratio may suffer less drag force and capture less bending energy than in the isolated situation. The optimized selection for obtaining higher energy conversion efficiency is the plate flapping in single-plate mode, especially the plate with a higher aspect ratio. The findings of this work provide several new physical insights into the understanding of fish schooling and are expected to inspire the developments of underwater robots or energy harvesters.  
three-dimensional simulation; fluid-structure interaction; passive flapping; flexible plates; energy harvesting +
Footnote †: journal: Applied Physics Letters B
# A Study on PF-IFF-Based Diagnosis Model of Plant Equipment Failure
## 4 Case Study
### Identifying Abnormal Patterns Related to Each Failure Mode
#### 4.3.2 Calculating the Relationship between Patterns and Failure Modes  
In this step, the PF-IFF value, which is a criterion for identifying the pattern associated with the failure mode, was calculated. A high PF-IFF value means that a particular pattern is highly relevant for a particular failure mode. Table 4 shows the results of calculating the PF-IFF values between the failure modes (bearing damage, leakage, misalignment) and the cluster of feature vectors in this study.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 1 Introduction
### Organization  
This paper is organized as follows. We introduce the scenario of in-network caching for streaming content delivery and our assumptions in Section 2. Then, we present the RXI and RXI-based cache replacement algorithm in Section 3. In Section 4, we evaluate the RXI-based algorithm in two scenarios in comparison with the LRU, LFU and TLP-TTH. Finally, a summary and the conclusions of this study are presented in Section 5.