esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Smart Contract Vulnerability Detection Tools
### Symbolic Execution  
A program analysis approach called symbolic execution [7] examines a program to determine how to allow a certain code area to run. As the name suggests, symbolic values rather than the precise values used in routine program execution are used as input when studying a program using symbolic execution. The analyzer may obtain the corresponding path constraints once the target code has been reached, and it can then utilize the constraint solver to determine the precise values that can activate the target code. The symbolic execution approach is appropriate for smart contract analysis because of the conciseness of smart contracts and the completeness of symbolic execution.  
Oyente [9] analyzes a contract's bytecode using the global Ether market and the contract's bytecode as inputs, identifies any security flaws, and provides the user with the problematic symbolic routes. The Z3 solver [26] is employed in this procedure to ascertain satisfiability. With more precise estimation of the contract's worst-case gas consumption (exchange fees), Oyente intends to assist developers in writing better contracts.  
The first source code to LLVM's [27] bitcode converter was created by Zeus [28], and on top of that, abstract interpretation and symbolic model checking are carried out for quick security testing.  
The symbolic execution of Linux ELF binaries and EVM bytecodes is supported by the vulnerability analysis tool Manticore [29]. The system has the capacity to store and handle symbolic data, including timestamps, using its own EVM implementation. This system may be used to create an automatically produced test input and supports sending a sequence of symbolic transactions to the contract being tested. The test contract is examined for potential vulnerabilities using a set of detectors resembling those found in the Mythril analysis module. These detectors have the ability to use their own memory and respond to a sequence of callbacks at different execution points, such as when starting a new transaction or instruction. The detectors have the ability to capture the location and restrictions of potential vulnerability exploits in the code, which can be included in the test input output. Similar to Mythril, developers can use it as a Python third-party library or directly contact the API.  
A symbolic execution tool based on bytecode parsing is called DefectChecker [30]. Control flow graphs, stack events, and three features are used to create eight distinct protocols for detecting vulnerabilities. Based on the severity of contractual vulnerabilities in prior studies, despite the fact that the symbolic execution technique can produce execution paths with greater accuracy, it also has a number of drawbacks, including the state space's dramatic increase and the challenge of solving constraints that are incredibly complicated. These issues are frequently challenging to resolve.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Extended Finite Element Method (XFEM)
#### 3.1.2 Ansys  
ANSYS, another widely used commercial software package, incorporates XFEM capabilities for crack growth simulation. Its XFEM module provides advanced meshing techniques, crack nucleation and propagation algorithms, and robust contact analysis capabilities. ANSYS allows engineers to model crack propagation in three dimensions, accounting for complex crack geometries and material interfaces. The software's intuitive user interface and extensive documentation make it accessible to users with varying levels of expertise [106; 107; 108; 109]. ANSYS offers a limited free version called ANSYS Student, which provides access to a subset of features suitable for educational purposes but may not fully support advanced fatigue analysis.
# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 3 System Design and Implementation
### OT Layer
Regarding software, the nesting box node was programmed using the Visual Studio Code environment (version 1.87.2) with the PlatformIO extension, which facilitates embedded software development for the chosen microcontroller platform. The environment node, responsible for monitoring temperature and humidity, was programmed using Arduino IDE. On the other hand, for long-range communication, the LoRaWAN gateway used was a MikroTik wAP LR8 Kit, equipped with an Omni LoRa 6.5 dBi antenna. MikroTik's WinBox software (version 3.40 64 bits) was used for the administration, configuration, and maintenance tasks of the gateway. In this sense, for the field tests, the network was configured with a Spreading Factor (SF) of 7, which offers the highest data rate. This choice was appropriate given the favorable conditions of the test environment--short distances and no physical obstructions--and contributed to optimizing energy consumption. The Adaptive Data Rate (ADR) mechanism was also enabled, allowing the device to automatically adjust transmission parameters--such as the Spreading Factor (SF) and the Coding Rate (CR)--based on the signal quality received by the gateway. This ensures efficient communication, minimizing energy usage and reducing the likelihood of interference or packet loss. Under these conditions, all transmissions were carried out using SF7. Regarding the Coding Rate, since the signal quality was consistently good, the CR remained at lower values (e.g., CR 4/5 or CR 4/6), improving transmission efficiency by sending more compact data with less redundancy. In contrast, if the signal quality had been poor, the CR would have increased (e.g., CR 4/7 or CR 4/8), enhancing error correction and the likelihood of successful packet reception, albeit at the expense of energy and bandwidth efficiency. Moreover, transmission power was also managed automatically by the ADR mechanism, and no specific testing was carried out in this regard, as it was beyond the scope of this study. LoRaWAN devices can adjust transmission power across several levels, such as 14 dBm (25 mW) and 12 dBm (15 mW), down to 2 dBm (1.5 mW). In the EU868 frequency band, the maximum allowed transmission power is 14 dBm. Since the nodes were located near the gateway, only the minimum required power was used. As expected under these favorable conditions, the RSSI (Received Signal Strength Indicator) values recorded at the gateway were consistently close to -50 dBm, which is considered excellent for LoRaWAN communications. Finally, the nodes were configured using Over-the-Air Activation (OTAA) and operated under the Class A specification, transmitting only when triggered and remaining in low-power sleep mode otherwise.
## 3 Legal Challenges Posed by Mega-Constellations to Existing International Space Law Regime
### Principle of Equitable Access
#### 4.1.1 Legal Framework and Significance of Equitable Access Principle
In other words, this implies that other space players will not be able to use specific orbits that these space actors predominantly possess, use, or occupy due to their actions. The reason for this is that, since it would be risky to try and share the specific orbit, forcing them to choose other altitudes or orbits, no other operator would dare to use those specific orbits where hundreds of satellites are already operating as part of a constellation.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 7 Service-Oriented Architectures Approaches for WSNs
### Healthcare Approaches  
The Service Layers Over Light Physical Device (SYLPH) architecture [64] consists of layers added over the application layer in each WSN stack [64]. SYLPH is a unique architecture that helps in integrating SOA with WSNs that can be used to build a system based on Ambient Intelligence (AI) for maintaining patient information, which was presented in [64]. The AI provides an intelligent distributed system, allowing effective communication irrespective of location and time [64]. The SYLPH gateway is connected to different sensor networks by using various hardware interfaces. It enables two device types (either the same or different) to work together, such as ZigBee and Bluetooth devices. The system improves the healthcare monitoring of home-bound patients through a prototype system. The drawback of SYPLH is that it has not been tested in real-time.  
Similarly, in [65], a unique framework based on SOA with Wireless Body Sensor Networks (WBSNs) and Web Services (WB) was proposed. The framework provides healthcare services to monitor elderly people and allow doctors and nurses to access patient information. This framework provides a mechanism to keep the healthcare data secure and private, based on the authentication mechanism which decides to allow or reject the user access request. This service helps elderly individuals by carrying a very lightweight and efficient biosensor. The feature of this framework includes reduced memory space, interoperability of service, maintenance cost through storing strange data in a central server, a fast response time, increased privacy, and throughput. The limitations of this framework include overhead, due to its use of XML and SOAP.  
The concept of SOA is used in tele-monitoring. SunShine is integrated with distributed WSNs and the internet to perform complex tasks [66]. SunShine is a web-based system that manages data after collecting it, by analyzing the sensing data to see if it's normal or not. However, applying SOA enables the creation of a Web Management System (WMS) for SunShine, providing flexible and reusable architecture. It can easily extend the sensing region coverage in web-based software design and monitor patients at all the times. The authors do not provide any security method to keep the patients' data secure at all times, especially communication between clients and their doctors. Patients' information is not sent or updated securely.  
Correspondingly, the architecture of a tele-monitoring system can remotely monitor patient data. It has the ability to support efficient retrieval of information and addresses the QoS for visualizing data. SOA-based data architecture for healthcare monitoring with assistance from an algorithm that uses Extract Transform and Load (ETL) and Oracle Business Intelligence Enterprise Edition (OBIEE) is introduced in [67]. The drawback of this architecture is that it does not support heterogeneous sensors.
## 5 Discussion
This study has found that the identification and extraction of the spatial filters reduces any potential for this bias to occur as the spatial matrix eigenvectors minimise residual autocorrelation based on the spatial structure of the study area at varying scales, and can be regarded as patterns of independent spatial dimensions, culminating in the almost complete elimination of residual spatial autocorrelation and therefore mitigating parameter estimation bias and helping to account for unexplained spatial patterns. For the field of mass appraisal, this identification of the spatial structure can help more accurately identify local submarket fluctuations, leading to better ratio studies and more uniform, equitable, and accurate valuations which can help save costs associated with inequity. Further the incorporation of ESFs can greatly reduce the amount of time it takes to create multiple sub-models or a flexible global model, making it more efficient for mass appraisal purposes. Existing research has examined the role of ML within mass appraisal [18; 25; 26; 27; 28], with early arguments critiquing the 'black-box' nature of the model outputs, and despite improvements in the reporting of 'importance' plots providing information for assessors [35; 36; 37; 38] remains challenging for wholesale uptake within mass appraisal practice given the complexity and repeatability of these types of algorithms. This study shows that the application of Regularized regression incorporating spatial filters is a more obvious choice for the assessment community, and for taxpayers, as the ESF approach provides a foundation for including location providing market professionals and policymakers with a more readily and understandable methodology for applying spatial analysis in a more standardised and explainable hedonic framework for understanding housing markets and for applications seeking to harness such understanding, such as automated valuation modelling for mortgage lending, or mass appraisal of residential values for property taxation purposes.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 3 Deep Learning Techniques
### Enabling ML in the Mobile Edge  
Performing ML within edge devices can take advantage of contextual data available such as cell load, user location, connection metadata, application types, local traffic patterns, and allocated bandwidth. Latency for responses from traditional cloud-computing centers over the wide-area network hinders network and services key performance indicators (KPI). In addition, performing ML tasks at the edge can reduce the load on the core network. To take full advantage of the mobile edge computing and ML collaboration benefits, ML models must be designed to use minimal resources and still obtain useful and accurate results as they are applied to scale across expansive communication networks.  
Currently, ML training and inference tasks within mobile edge computing are partially inhibited by comparatively smaller storage capabilities and limited power supplies in edge devices than those found in industrial cloud data centers. In response, ML within the mobile edge computing has been enabled by two main enhancements:  
1. Efficient ML models specialized to require less energy, memory, or time to train, and
2. Distributed ML models that distribute the training and inference tasks between large data centers and smaller edge devices for parallel processing and efficiency.
## 3 Underlying Terminology and Concepts
### Analysis Ready Data  
A pragmatic approach to the interoperability of satellite-based EO data consists in identifying a minimum set of requirements, to which all data providers should adhere, in order to ease the synergistic use of products from a variety of sensors. The Space Agencies have started addressing this issue with increasing priority through the definition of a new paradigm in satellite products: Analysis Ready Data (ARD). The definition of ARD is still not fully consolidated by the various data providers, since several interpretations were formulated. Specifically, when confronted with the emerging commercial data providers, the ARD concept becomes debatable, and it was the subject of discussion during the recent ESA Very-High Resolution (VHR) Workshop [64]. On the other hand, there is a shared understanding of the underlying basic principle, namely, ARD is meant to reduce the user's effort in the preparation and preprocessing of satellite data by providing a globally and temporally consistent stream of data, calibrated and processed to a standardised level, enabling the users to directly generate added-value information from the spatial data, e.g., maps, time-series.  
This basic principle translates into a number of requirements on the data processing and products definition, such as:  
* Radiometric and geometric calibration issues should be solved following best practices approaches.
* A minimum level of processing should be applied in order to lower the bar of expertise on the user side. For optical sensors, this typically includes cloud screening and atmospheric correction.
* Spatially explicit per-pixel quality flags should be provided to enable users to perform informative processing on suitable good quality data.  
There are on-going international efforts to further tailor these general principles, providing detailed requirements for ARD. In particular, the CARD4L initiative [31; 65] is the most advanced and is progressively becoming the standard within the Institutional Space domain. The CARD4L requirements were defined for a number of product families, among which the land surface reflectances are extremely relevant for discussing the Cal/Val needs for satellite-based Level-2 products. The last reprocessed Landsat-8 dataset, generated by the United States Geological Survey (USGS) adhere to the CARD4L product specifications for surface reflectances [66], and the same will be achieved with the upcoming ESA Sentinel-2 reprocessing activity.
## 3 Envelope Tracking Transmitter Experimental System  
### High Efficiency and Wideband Hybrid Envelope Amplifier  
To evaluate the application of the PA circuit, the overall ET transmitter experimental system was set up for testing. Since the overall efficiency of the ET system is roughly the product of efficiencies of the EA circuit and the RF PA circuit, a high efficiency and wideband EA circuit is another key part of the ET transmitter. The designed EA circuit in this work has a hybrid structure, which is composed of a linear stage, a sensing stage, and a buck switcher stage [20; 21], as shown in Figure 8a. The reason for using the hybrid architecture relies on the characteristics of the input envelope signal [22].  
The designed EA is tested with a 7.5 \(\Omega\) resistive load for efficiency and accuracy verification. A group of 6.7 dB PAPR 16 QAM signals with 20 MHz, 40 MHz, and 60 MHz modulation bandwidths are employed to generate the envelope signals. The efficiency of the EA modulator is measured and shown in Figure 8b. Using 40 MHz 6.7 dB PAPR signal, it achieves an efficiency of 82% at the average output power of 18.3 W and over 70% efficiency when the output power is larger than 5 W. In addition, the small signal 3 dB bandwidth of the EA circuit is 209 MHz, which implies that it can support wide bandwidth envelope inputs.
## 3 Results and Discussion
### Colour Measurements
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Fresh** & **Freeze-Dried** & **Conv-40** & **Conv-60** \\ \hline L* & 75.70 \(\pm\) 1.71 b & 79.66 \(\pm\) 1.85 a & 57.56 \(\pm\) 1.82 c & 58.45 \(\pm\) 3.78 c \\ \hline a* & 5.21 \(\pm\) 0.95 d & 10.48 \(\pm\) 1.80 c & 17.42 \(\pm\) 0.86 b & 18.92 \(\pm\) 1.14 a \\ \hline b* & 27.26 \(\pm\) 1.77 d & 30.28 \(\pm\) 2.22 c & 33.91 \(\pm\) 2.22 b & 37.85 \(\pm\) 1.84 a \\ \hline \(\Delta\)E & - & 7.25 & 22.85 & 24.25 \\ \hline BI & 5.03 & 9.40 & 22.28 & 20.90 \\ \hline \end{tabular}  
* Fresh—fresh fruit; Freeze-Dried—freeze-dried fruit; Conv-40—convection-dried fruit at 40 \({}^{\circ}\)C; Conv-60—convection-dried fruit at 60 \({}^{\circ}\)C; within each line, values with the same letter are not significantly different at \(p<\) 0.05 ([PERSON]’s post hoc test).  
\end{table} 
Table 2: Colour parameters in L*a*b* space of fresh and dried quince. temperatures, which can be explained by the dark colour of convection-dried fruits. In addition, as the heat treatment time increases, the intensity of browning of the dried fruit also increases. Therefore, for the extended drying time at 40 \({}^{\circ}\)C (16 h), there were no statistically significant differences in the colour of quince flesh dried at this temperature compared to drying at 60 \({}^{\circ}\)C.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 4 Experiments
### Ablation Study  
#### 4.6.1 Effect of Components in High-Frequency Information Preservation (HFIP) Block  
We selected the ResNet-50 network as the backbone of the human pose estimation task and utilized low-resolution images with a resolution of \(128\times 96\) as input to evaluate the contribution of each unit in the module. As shown in Table 4, the model integrated with DFACC achieves a 0.46% improvement in mAP over the original model, indicating the necessity of encoding high-frequency components. Moreover, the model with DHFL achieves a 0.74% improvement over the original model, which demonstrates the effectiveness of the DHFL component in supplementing high-frequency information. The integration of both units improves the model's performance by a total of 1.48%, achieving optimal results and validating the effectiveness of the proposed HFIP block.
## 2 Related Work
### Pose Optimization Related
#### 2.1.2 Joint Pose Optimization Methods  
Joint pose optimization refers to the methodology that does not rely on feature points and optimizes the camera pose during the neural network training process. NeRF\(-\)[23] introduced a framework that jointly optimizes camera parameters and neural radiance field parameters, enabling scene reconstruction in situations with camera disturbances or unknown camera parameters. Around the same time, BARF [24] adopted a framework similar to NeRF- and theoretically linked it to 2D image registration, adding the theoretical understanding for joint pose optimization. The authors also found that high-frequency pose data impede pose training. Hence, a coarse-to-fine training strategy was adopted. GARF [25] presented a Gaussian activation neural radiance field, enabling 3D scene reconstruction without utilizing positional encoding. We also demonstrated the superiority of direct methods when handling low-texture areas. GNeRF [26] introduced a method combined with GAN to estimate camera pose under conditions wherein the camera pose is entirely unknown and scene conditions may be complex. However, this approach relies on a camera sampling distribution not far from the true distribution to prevent training failures. The research on the mentioned methods has contributed to the methods of joint pose optimization. However, challenges persist, such as the high demands for initial pose estimation and the relatively low efficiency of reconstruction, which significantly hinder the advancement of research, impede development efforts and limit the practical application of related algorithms.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 2 Related Work
### UAV SLAM during the Landing Process  
A UAV can employ the SLAM technique to estimate its location by generating a map based on essential data acquired from the surrounding environment. SLAM generates a 3D map of the environment and determines the UAV's position, thereby facilitating its identification of suitable landing locations. This approach [25; 26] primarily uses image or LIDAR data as the main information source, although it is complemented by GPS data for positioning and mapping. In these methods [27; 28; 29], overlapping sections of images or LIDAR data from different time stamps are compared to estimate the trajectory of the UAV and construct maps. Particularly in outdoor conditions, it is likely that some characteristic information or sensor data are missing between the two images/datasets with different time stamps, which increases the risk of the UAV losing its position within a short time. A more stable position estimate can be generated by integrating image features with laser characteristics, which enables the UAV to execute a landing with greater precision along the provided path.
## 2 Theoretical Supports
### GMPE Parameters  
The ground motion prediction model is generally developed using statistical regression of observed ground motion intensities. The GMPE models are capable to give a PDF of ground motion, showing the relation with different GMPE parameters like rupture distance, magnitude, fault type, site conditions, etc. [1]. Ground Motion Prediction Equations (GMPEs) are sometimes referred to as attenuation models. Twenty-one GMPE models have been taken to quantify this tool, where these models differ with distance. All of them are listed in Table 1.