esa-sceva/satcom-chunk-collection huggingface dataset:
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 5 Source Code Analysis
### Intra-Component Analysis
#### 5.2.1 Normalized Error  
ErrorExplainer normalizes the transaction-reverting statements into a unified format and defines them as a _normalized error_.  
The normalized error also contains custom errors, which are structured as a function signature or a type construct, with a name and a list of argument types. The error message is instantiated with a name and argument values when used in a transaction-reverting statement. Hence, before defining the normalized error, we define the error definition context \(\mathbb{D}_{c}\) for a component \(c\) as a map of the following form:  
\[\mathbb{D}_{c}(n)=\hat{a},\]  
where \(n\) is the name of the custom error and \(\hat{a}\) is the argument type list specified in the custom error. This information is used to encode and decode the error message as ABIs.  
Finally, we define the normalized error \(e\) as the record  
\[e\ =\ \{\tau,\ \bar{e},\ \epsilon,\ \phi,\ r\},\]where the five fields have the following semantics:  
* \(\tau\) **(type of the error message)** indicates whether it involves no message, a plain string, or a custom error type \[\tau=\bot\mid\mathtt{string}\mid\mathtt{custom}(n).\] For a custom error type, the full signature is provided by \(\mathbb{D}_{c}(n)\).
* \(\bar{\epsilon}\) **(binary-encoded message)** contains the encoded version of the error message. We define encoding functions for a string (i.e., \(\tau=\mathtt{string}\)) and custom errors (i.e., \(\tau=\mathtt{custom}(n)\)) as \(\mathsf{Str}(\)) and \(\mathtt{Custom}(n,args)\), respectively. Otherwise (i.e., \(\tau=\bot\)), \(\bar{\epsilon}\) is also \(\bot\) because the statement has no error message. This field will be used in matching with the error message in \(\mathbb{U}.\bar{\epsilon}\).
* \(\epsilon\) **(error message text)** is a readable form of a string or custom error extracted from the source code.
* \(\phi\) **(error condition)** is the source code's expression that triggers the error, which is the immediate precondition of the transaction-reverting statement. It depends on whether the transaction-reverting statement is a conditional statement or not (e.g., require and revert, respectively). If we cannot find the error condition, it is \(\bot\).
* \(r\) **(source-code reference)** is for adding code snippets to the error description.
## 5 Experiment Analysis  
In the previous section, the final model was presented based on the analysis results according to the winding method and rotor shape. Therefore, the concentrated winding model with a hollow shaft rotor was selected as the final model, and an experiment was performed using a manufactured model. However, the requirement of speed and torque is higher than for conventional electrical machines; thus, it cannot be directly measured with a dynamometer. Therefore, it is necessary that an air-compressor system is constructed by using mechanical components for evaluation performance of machines. In the case of speed, it was derived through the frequency of the current, and in the case of torque, an impeller was attached to the opposite side and calculated inversely by using mechanical equations. However, impeller shape and efficiency equations for overall systems keep the manufacturer confidential, so we have not written them in this paper.  
Figure 11 shows the back electromotive force (EMF) between lines in the no-load experiment. When considering the 3D structure and the hollow shaft in consideration of the magnet leakage, it can be confirmed that it is most similar to the no-load back EMF of the actual manufactured model. Table 6 shows the load test results. This is a comparison of the experimental results with the application of the harmonic current derived in this study. The applied current and the counter electromotive forces are similar, but in the case of the output, it is slightly reduced.  
Figure 12 shows the corresponding results of the power characteristics according to the speed. The loss determined considering the applied power was defined as the motor system loss, and the electromagnetic loss considering the current harmonics was compared with that obtained experimentally.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & **Back EMF** & **Current** & **Torque** & **Output Power** \\  & **(Vrms)** & **(Arms)** & **(N.m)** & **(kW)** \\ \hline FEM & 175.3 & 25.2 & 1.43 & 12.3 \\ Experiment & 175.5 & 25.5 & 1.41 & 12.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of electromagnetic characteristics obtained using FEM analysis and experiment.  
Figure 11: Line voltage at no-load condition.  
The results of the experiment indicate that the motor exhibit a similar output power of the analysis results with efficiencies of 92% at the rated speed. However, it can be seen that the loss of most experimental results is slightly higher. In the actual experimental case, it is believed that the difference occurred owing to the addition of friction loss. This approach can thus be considered as a reasonably accurate electromagnetic loss analysis method in the design process before the motor is manufactured.
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
## 2 Materials and Methods
### Parameters Test
#### 2.4.1 The Number of Training Samples  
As we know, insufficient training datasets will lead to poor approximation. Will the model's performance continue to improve as the training data volume increases? According to [PERSON] et al. [31], the performance will improve when the size of the dataset is increased. Conversely, [PERSON] et al. [32] found that the model performance decreased with a higher dataset volume. In this section, we calculated the relative error of nine different training sample numbers for the validation dataset. Two parallel experiments were conducted for each sample size in order to determine the average error, as shown in Figure 7. The figure shows that the error does not continuously decrease with an increase in the number of training samples. However, when the number of samples reaches 5600, the error reaches its lowest value (8.9%).
###### Abstract  
The remote sensing satellite constellation based on micro-satellites is an important means to construct a global and all-sky earth observation system in the future. Therefore, realizing the recognition of aircraft activities on video micro-satellites is a key technology that needs to be solved urgently. In this paper, an efficient algorithm for aircraft activity recognition that can be deployed on video micro-satellites was proposed. First, aircraft detection was performed on the first incoming remote sensing image using a robust DCNN-based object detection model. Then, a multi-target tracking model incorporating geospatial information was built for aircraft tracking and activity recognition. The algorithm was deployed on an embedded AI computer which was a COTS component. The algorithm was verified using remote sensing videos from commercial micro-satellites. Experimental results show that the algorithm can process aircraft targets of different sizes, and is equally effective even with complex environmental backgrounds, lighting conditions, and various movements of the aircraft, such as turning, entering, and exiting. Based on aircraft tracking results and geospatial information, the motion speed of each aircraft can be obtained, and its activity can be divided into parking, taxiing, or flying. The scheme proposed in this paper has good application prospects in the realization of on-orbit event recognition in micro-satellites with limited computing and memory resources.  
micro-satellites; staring-imaging; aircraft tracking; DCNN; embedded AI processor Article
###### Abstract  
The investigation of cultural heritage is a continuous process, in which a large scale of metadata is gradually accumulated, i.e., condition data, intervention data, and the comprehension of building techniques. We take the roof tile of Yangxindian Palace as an example. The total number of roof tiles is 17,154. Their positions are various, as they are equally distributed on double-curved irregular surfaces. Their metadata are multisource and heterogeneous. Therefore, the integration between the model and the data can be complex. This paper aims to generate the M1 (metalevel) model with its graph database based on the M3 (metalevel) model through algorithm modeling. This synergy approach consists of three parts. First is the data collection. The unique identifier is used as the index while investigating onsite, which eventually forms the metadata repository. Second is the algorithm modeling. All the roof tiles are rebuilt in Grasshopper with identifiers. Their metadata are merged through Cypher syntax, which can generate the graph database in Neo4j. Third is the delivery. The IFC model and knowledge graph are connected in real time during the whole process. The significance of this paper is to provide a synergy approach and discuss its efficiency for the integration between a model and data of very large quantities. The results show that it is efficient to express the semantic relationships of both conditions and interventions. It is practicable to lightweight the IFC model and to associate metadata into the graph database, enabling online browsing and querying.  
Keywords:HBIM; Grasshopper; Neo4j; graph database; algorithm modeling; metalevel; ontology; asset management; heritage conservation; Yangxindian Palace +
Footnote †: journal: Applied sciences
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 1 Introduction
### Related Research
#### 1.2.1 Related Work Concerned with EDA  
The adoption of event-driven architecture (EDA) in the development of smart cities has become a widely applied concept in recent research. This trend highlights the versatile application of EDA across various fields, demonstrating its potential to address a broad spectrum of challenges within the smart city paradigm. The diversity observed in these studies reflects EDA's flexibility and its capability to enhance systems in multiple domains, from healthcare, which directly impacts human health and safety, to urban traffic management, aiming at optimizing flow and increasing transportation efficiency. Although these approaches differ in their technical implementations, they share a core objective: leveraging advanced technology to improve human life, whether through safeguarding human health or promoting environmental sustainability.  
This work by [PERSON], [PERSON], and [PERSON] [6] introduced an event-driven IoT architecture for data analysis of reliable healthcare applications, including context, event, and service layers. Furthermore, the study presents complex event processing (CEP) as an innovative solution, integrating automated intelligence within the event layer to enhance the system's responsiveness and decision-making capabilities. This contrasts with our approach, which integrates Elixir and MQTT for an edge-based programming framework. While CEP offers advanced data processing capabilities ideal for healthcare applications, our methodology leverages Elixir's robust concurrency and MQTT's efficient message handling, tailored to the dynamic nature of smart transportation systems. This divergence highlights the adaptability of event-driven architectures across varying domains. The next work by [PERSON] et al. [7] also utilized complex event processing (CEP) systems. Differently, it introduced Geo-TESLA, an advanced complex event processing language tailored for smart city applications, enhancing the detection and reporting of complex events within urban settings by leveraging spatial data types and operations.  
Another work that combines event-driven architecture and smart city is the work by [PERSON], [PERSON], [PERSON], and [PERSON] [8]. They offered an approach for spatiotemporal capabilities in information services for smart cities and developed a reference architecture of event-driven applications. This work demonstrates the feasibility, performance, and scalability of event-driven applications in real-time processing and detecting geographic events, leveraging IoT technologies. [PERSON] [9] offered an event-driven focusing service (EDFS) method that uses cyberphysical infrastructures for emergency response in smart cities.
## 4 Discussion
This is evident from the GPS registered data, which shows a difference in the total distance, but no differences when the playing time was considered. This can be further explained by the lack of neuromuscular fatigue seen in the lower-limb power as detected by the CMJ. The CMJ showed a pattern of increasing successively over the games. The increase in values is contrary to previously published literature in different sports [50; 51; 5]. This finding could be explained by a couple of reasons. Firstly, a reduced, five-minute warm-up was performed in the pre-match 1 testing, focusing mainly on joint mobility exercises for the lower limb, since they went on to perform the match-specific warm-up after the tests, thus, warm-up intensity could have been insufficient. Secondly, the increase in the CMJ height could be linked to greater activation of the muscles of the tibialis anterior, gastrocnemius, and soleus muscles as a result of the large volume of medium-intensity runs performed during the match [52], which in turn, results in an optimal stiffness of the ankle and better transmission of the nerve impulses due to the increase in muscle temperature of active muscles [53]. These muscles play an important role in determining CMJ performance [54], and a greater activation could result in a better performance. However, it is important to note that the role of post-activation performance enhancement (PAPE) effects [55] elicited by actions during the game is unlikely for a couple of reasons: PAPE has a short-term, acute effect lasting for about five to thirty minutes [56], and the fact that no differences were seen between post-match 2 and 48 h post-match 2 values. Not only in the case of CMJ values, but looking at all data, it appears that the 48 h of recovery permitted a complete recovery following participation in consecutive matches. Moreover, in other studies featuring field hockey [57] or ice hockey players [58], PAPE has had minimal or no effect on performance.
## 3 Key Challenges
### Underwater Communication  
If we category the underwater communication method by potential transmission media, there are mainly four categories: underwater acoustic, optical, electromagnetic wave, and wired communication [25]. However, the underwater acoustic communication performance is quite different for radio communication, as described in Table 1. Due to the high attenuation, the acoustic is the fundamental way of underwater long-range wireless communication [26; 27]. For optical and magnetic induction, it is mainly used for short-range communication [28; 29]. Although they suffer high attenuation and are sensitive to underwater environment, a significant bandwidth can be utilized compared to acoustic communication [30]. Ignoring the cost and effort for the deployment of an underwater cable, wired communication is the most stable way to provide high-speed communication.  
To design and build an underwater CPNT system, medium and long distance wireless communication is the fundamental condition, which makes the acoustic wave the best choice. A higher data rate can ensure that sufficient effective information is transmitted, helping to increase positioning performance and service quality, which makes it an important point that should be considered in system design.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results
### Reflection Coefficient  
The base value of the reflection coefficient is assumed to be \(-10\) dB, which means that 10% of the incident power is reflected, i.e., 90% of the power is received by the antenna, which is considered to be perfect for mobile communication [24; 33]. The proposed antenna has a resonance at 28.00 GHz with a reflection loss of \(-22.50\) dB, as shown in Figure 5. The S\({}_{11}\) parameter was obtained by powering the antenna using the edge port. The antenna has an operating bandwidth of 5.57 GHz, which gives a relative operating bandwidth of 19.89%. The bandwidth determined from the results of computer simulations is slightly smaller than the theoretical bandwidth determined on the basis of Relation (13), due to the inset feed used as a power supply. Nevertheless, this method of supplying power to a microstrip antenna provides us with better impedance matching (lower reflection factor value) for a resonance frequency.  
Figure 5: The reflection coefficient as a function of frequency for the proposed antenna working in the fifth generation (5G) system.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
###### Abstract  
Accurate and timely segmentation of liver trauma in computed tomography (CT) images is essential for effective diagnosis and management in emergency medicine. This review examines advancements in liver segmentation techniques from 1993 to 2024, focusing on deep learning models and their impact on improving diagnostic accuracy for liver injuries. Early methods relied on basic image processing, which faced limitations due to noise, intensity variations, and complex abdominal anatomy. The advent of deep learning has transformed this domain, with architectures such as UNet, UNet++, UNet3+, multiscale large kernel (MSLUNet), and Swin-Unet achieving significant improvements in segmentation precision. Additionally, generative adversarial networks (GANs), including conditional GAN and pixel-to-pixel (Pix2 Pix) GAN, have further enhanced image quality and detail, addressing deficiencies in traditional methods. This review provides a comparative analysis of these models, highlighting their strengths and limitations in liver injury segmentation.  
medical image segmentation; deep learning; convolutional neural networks (CNN); abdominal trauma analysis Article
# Research and Development of Inland Green and Smart Ship Technologies in China
## 3 Current Inland Green and Smart Ship Technology in China
### Inland Ship Technology
#### 3.1.2. Maneuverability in Shallow Water  
Inland ships must frequently dock and undock, and thus, high maneuverability is required. Further, the water depth of some voyages is shallow relative to the ship's draft. Previous research found that shallow water increases additional mass and hydrodynamic effects, reduces propeller efficiency due to vortices at the stern of the ship, and negatively impacts maneuverability [25]. Combining theoretical analysis, numerical simulations, and model tests, the motions of inland ships at different speeds and residual water depths was simulated. Through analysis of the flow field around the test hull of the virtual plane motion mechanism, changes in the ship attitude and motion characteristics with respect to the speed and residual water depth were obtained. Equations were developed to evaluate the maneuverability of inland ships in shallow water channels. Using turning and Z-shape model tests, the parameters that characterized the ship's maneuverability, such as the longitudinal distance, slewing diameter, and overtaking angle were studied. The results provide a basis for the navigation of ships in shallow water areas.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 7 Security Analysis
### Security Attributes
#### 7.1.2 Privacy  
We analyze privacy using two subcategories \"Voter Privacy\" and \"Ballot Privacy\".  
In terms of voter privacy, the front-end and election provider has no access to the voter identities beyond pseudonyms (VID: unique key allocated by TTP). Voters only submit the VID to the front-end as a proof of identity during the authentication phase. They only need to reveal their real identity to the TTP during the registration phase. Apart from the TTP, no party within the proposed system is able to link a VID to its individual voter.  
Regarding ballot privacy, the PVPBC voting system guarantees that no party, at any point of the election, is able to reveal how a particular voter voted. While the front-end is collecting votes, the only link between the voter and their vote in plaintext is the VID key which acts as a pseudonymous identity. Mapping a VID key to the corresponding voter can only be carried out by the TTP, which is responsible for registering eligible voters. Therefore, ballot privacy is maintained with respect to the PVPBC voting system in three ways: the front-end has no access to the voters' true identities; election service providers have no access to both voters and votes; and, finally, the TTP has no access to the plaintext votes linked to their VIDs.  
Similar to the Selene protocol, the front end provides the assurance that the published verifiability information does not compromise the privacy of the vote, provided that the requirement that each teller node is independent is met. This is maintained by the front end.
## 2 Longitudinal Equivalent Continuous Model  
The LECM is the predominant model for shield tunnels, providing a method to calculate the equivalent bending stiffness \(EI_{\theta eq}\) for shield tunnels when subjected to longitudinal bending moments.  
When subjected to longitudinal bending moment M, the upper segmental ring stretches while the lower segment compacts (Figure 1a). The angle between the centerlines of adjacent segmental rings, known as the relative turning angle (\(\theta\)), the equivalent bending stiffness \(EI_{\theta eq}\) over the analysis region \(l_{s}\) is:\[EI_{\theta eq}=\frac{Ml_{s}}{\theta} \tag{1}\]  
The LECM gives the expression \(EI_{\theta eq}\) as [27]:  
\[EI_{\theta eq}=\frac{Ml_{s}}{\theta}=E_{c}l_{s}\frac{cos^{3}\varphi}{cos\varphi+ \left(\frac{\pi}{2}+\varphi\right)sin\varphi} \tag{2}\]  
where \(E_{c}\) is the segment's elastic modulus, Pa; \(l_{s}\) is the moment of inertia, m\({}^{4}\); \(\varphi\) is the neutral axis angle, rad. As shown in Figure 1b, \(\varphi\) satisfies the following equation:  
\[\varphi+cot\varphi=\pi\left(\frac{1}{2}+\frac{k_{r}l_{s}}{E_{c}t}\right) \tag{3}\]  
where \(k_{r}\) is the density of longitudinal bolts, \(k_{r}=\frac{K_{c}}{2\pi r}\), N/m\({}^{2}\); \(K_{r}\) is the stiffness of a single longitudinal bolt, \(K_{r}=\frac{nE_{c}A_{b}}{l_{b}}\), N/m; \(l_{s}\) is the width of the segment, m; \(E_{c}\) is the elastic modulus of the segment concrete, Pa; \(t\) is the thickness of the segment, m; \(n\) is the number of bolts, \(E_{b}\) is the elastic modulus of the bolt, Pa; \(A_{b}\) is the bolt cross-sectional area, m\({}^{2}\); \(l_{b}\) is the bolt length, m.  
Also, the LECM satisfies the following assumptions:  
1. Flat cross-section assumption.
2. The relative position of the neutral axis in the cross-section does not change along the longitudinal direction.
3. The tensile side of the segmental ring and longitudinal bolts, together with the tensile force, only the compression side of the segmental ring is under pressure; longitudinal bolts are not stressed.
4. The preloading force of longitudinal bolts is not considered.
5. The discrete distribution of longitudinal bolts along the segmental ring is simplified to an equivalent continuous uniform distribution.
6. Both the segment and the bolts are in the elastic stage.