esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 1 Introduction
The methods are divided into two categories according to their superpixel generation: clustering-based and graph-based [22]. Here, in order to use affective pixel-wise CNN segmentation models, we present a new pipeline. As seen above, the need for labeled free datasets, together with the presence of SAR image noise, backscattering and geometric distortion are the main issues for pixel-wise segmentation. The previous research efforts have mainly concentrated on speckle noise [23; 24] and on generating labels for these noisy datasets. While SAR images are available for free, reaching the noise-free labeled dataset is still difficult. To solve this problem, we propose a novel process for easily creating a new dataset. The process starts with downloading SAR images, uses CORINE and ends with creating labeled forms ready for SAR image segmentation tasks, as detailed in Section 3. Once we created our dataset, we trained U-Net, PSPNet and HRNet deep learning models effectively for the semantic segmentation task. The overview of our approach is presented in Figure 1.  
Identifying the qualities and shortcomings of deep learning methods is a significant task. To specify which methods are better and how methods differ from each other, we utilize [PERSON]'s test to evaluate the success of the models: U-Net, PSPNet and HRNet. [PERSON]'s test has been employed in various previous research, as discussed in our evaluations. Five machine-learning models were analyzed, with the classification results assessed according to the methods in [25]. In ref. [26], a similar evaluation was performed to compare the class-wise prediction against an extensive database. The essence of this work is explained as follows:  
1. We propose a dataset creation pipeline that can be performed easily;
2. We train the latest deep learning segmentation models, instead of traditional ones, with the newly created dataset;
3. We demonstrate the robustness of the proposed pipeline by training and then comparing results against noisy and noise-free versions of our dataset;
4. We evaluate the performance of deep learning methods with [PERSON]'s test. The rest of this study is structured as follows. We give a brief review of related works in Section 2. In Section 3, the dataset creation pipeline is introduced. In Section 4, models utilized and experimental setups are detailed, step by step. Analyses and results are represented in Section 5. We then conclude this work and make recommendations for future work.
# Design and Testing of Torveastro: An Outer Space Service Robot
## 1 Introduction
LARM2 laboratory at Tor Vergata University in Rome has previously presented basic feasibility studies to provide indications for a feasible design of a first preliminary prototype [15; 16; 17] through performance evaluation utilizing kinematics and dynamics simulations, with results demonstrating the feasibility of Torveastro robot operation and its peculiarities to define an appropriate full design. The mobility performance has been characterized using simulations. The operation efficiency and fundamental performance of the arm-leg limb design's motion have been validated in the laboratory to determine the operation's viability. In this paper, starting from the first preliminary feasibility studies, a novel optimized version is presented, starting from the geometrical design and going through advanced simulation with realistic input modules lab tests. Therefore, this paper presents the new design, simulation, and tests of a novel advanced Torveastro prototype, a space service astronaut robot for the on-orbit operation requirements of space stations that is able to utilize handrail-equipped installations designed for the movement of human astronauts during outer space operations. This paper presents the design, simulation, and preliminary tests of novel advanced Torveastro prototype, a space service astronaut robot for the on-orbit operation requirements of space stations that is able to effectively utilize handrail-equipped installations designed for the movement of human astronauts during outer space operations. Torveastro seeks to build a new service robot capable of assisting and substituting astronauts during Extra-Vehicular-Activity (EVA) on orbiting space stations such as the International Space Station. Typical duties for the robot will include monitoring and maintaining the space station's outer structures. The validation of a terrestrial demonstration prototype is one of the final deliverables of the ongoing project. The project entails the development of innovative limb-like mechanisms and solutions for robot actuators that match the portability and lightness criteria of the space environment.
# A Framework to Understand Attitudes towards Immigration through Twitter
## 4 Methodology
### Attitude Characterisation
#### 4.3.2 Psycholinguistic Characterisation  
The previous lens provided differences in the content published by each attitude. However, a direct content-based approach does not discriminate the expression of emotions in each category. To gain a deeper understanding of attitudes in this direction, we use a well-known psycholinguistic lexicon named \"Linguistic Inquiry and Word Count\" (LIWC) [76]. LIWC was designed to capture emotional, cognitive, and structural components present in text. It is available in several languages. Its Spanish version contains 7515 words classified in one or more of 72 categories that belong in four dimensions:  
1. Standard linguistic processes: articles, prepositions, pronouns, etc.
2. Psychological and affective processes: positive and negative emotions, with subcategories such as anger and anxiety.
3. Relativity: time, verb tense, motion, space.
4. Personal matters: sex, death, home, occupation, etc.  
We will focus our analysis on LIWC categories that are possibly associated with some of the factors shaping empathetic and threatening attitudes towards immigration. With regard to _empathy_ attitudes, we consider the following LIWC categories as relevant: affective processes, positive feeling and emotions, optimism and energy, humans, social, family, and inclusion. With regard to _threat_ attitudes, we consider the following LIWC categories as relevant: anger, anxiety, negative emotions, inhibition, death, body, job, and money. We estimate the association between attitudes and LIWC categories. First, we define the association of LIWC category \(c\) to attitude \(a\), \(L_{c}^{a}\), as the relative frequency of words in \(c\) with respect to all terms in the discussion per attitude:  
\[L_{c}^{a}=\frac{\sum_{\tau\in c}f_{\tau}^{a}}{\sum_{\tau\in T}f_{\tau}^{a}},\]  
where \(T\) denotes the vocabulary, and \(f_{\tau}^{a}\) the total frequency of vocabulary term \(\tau\) by accounts with attitude \(a\). To explore these associations, we visualise them using Generalised Word Shift Graphs [77]. These visualisations summarise each attitude according to the differences in association between attitudes and LIWC categories, with the aim of describing the emotional and semantic aspects of attitude formation.
# Fabrication of Smart Materials Using Laser Processing: Analysis and Prospects
## 4 Laser Surface Modification by Direct Laser Interference Patterning (DLIP)
The variables h, dx, and dy denote the height of the structure and the dimensions of the elliptically shaped focal spot of the laser beam, respectively, while \(\alpha\) represents the angle between the laser beams [126]. Designing materials with micro- and nano-scale two- and three-dimensional structures introduces a new opportunity for tailoring material properties and enhancing functionalities. The complexity of such designs requires advanced fabrication techniques, particularly with high resolution on the nanoscale. DLIP emerges as an innovative solution for high-speed patterning of periodic structures. This method involves overlapping multiple laser beams on the material's surface, facilitating rapid and precise customization of material surface microstructures and topographies at industrially relevant scales [133]. Laser-textured surfaces, gaining prominence for their diverse applications and industrial scalability, have been extensively studied in research with the purpose of creating functional surfaces. The study outlined in [134] demonstrates that combining structures with varying feature sizes, produced different techniques like DLIP, direct laser writing, and LIPSS fabrication methods not only enhances specific functions but also imparts multiple functionalities to materials. This includes improvements in surface wetting properties, resistance to ice formation, self-cleaning capabilities, antibacterial performance, and the enhancement of optical properties in polymer materials, designed for manufacturing solar cells and organic light-emitting diodes. Thus, it has been demonstrated that direct laser interference patterning is a progressive method to fabricate defect-free periodic micro- and nanostructures for a wide range of materials, opening up new possibilities for the formation of functional surfaces. DLIP has been successfully implemented using different pulsed laser sources, processing systems and configurations. Because of the flexibility of DLIP in terms of fabricated geometry, sizes, and its compatibility with a wide range of materials, numerous applications become feasible.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 4 Use of USVs in Environmental Disasters with Contaminants
### Biological Hazards
affirm, sampling from multiple water columns is important not only in the monitoring process but also in facilitating mechanistic modeling and understanding of the development of HABs. With a marine microscope imaging system with automated cell characterization capability attached to a solar-powered Wave Glider, [PERSON] et al. [162] acquire crop and geo-tag phytoplankton images. Such images allow a near real-time detection of harmful algal species as well as the calculation of their population density. Data is then sent to land for analysis via mobile phone or satellite communications. The collected data provides input for computational models to advance the understanding of phytoplankton and allow better forecasting of HAB events. Other USVs containing HAB detection capabilities include the solar-powered Lake Wivenhoe USV [153; 154; 155; 156], which is a twin-hull catamaran capable of navigating in complex inland water reservoirs and measuring a range of water quality properties. The 16 ft long solar-powered catamaran is also capable of collecting water column profiles while in motion.
## 4 Simulations and Experiments
### Simulations
#### 4.1.1 Simulation Setup
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Nominal mass \(J_{n}\) (\(\text{kg}\cdot\text{m}^{2}\)) & 0.0021667 \\ Nominal damping \(B_{n}\) (\(\text{N}\cdot\text{s}/\text{m}\)) & 0.15 \\ Proportional gain of PD \(K_{p}\) & 500 \\ Derivative gain of PD \(K_{d}\) & 30 \\ DOB cut-off frequency \(g_{dab}\) (Hz) & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of control system in simulation. In this simulation, RMSE in Equation (27) is applied to compare the control error between position response and command. Where \(y_{k}\) is position command, \(\hat{y}_{k}\) is position response applying corresponding feedback and \(m\) is number of sampling points.
## 11 Current Studies and Future Directions
### Research Directions  
Overall, the research directions of smart cities are diverse and require multidisciplinary collaboration. By prioritizing these research areas, cities can become smarter and more sustainable. We can summarize the research directions from the information and communication technology perspective as follows [130; 131]:  
1. Developing AI/ML algorithms to assist smart city applications  
Creating advanced AI/ML algorithms is a top priority in the race to improve smart city applications. These algorithms are needed to overcome the typical difficulties of smart city technology implementation, including restricted hardware capacity and resource limits. The future directions of AI/ML for smart cities include the following two main categories:  
1. Algorithms for overcoming resource and hardware limitations. In order for smart city applications to run smoothly on devices, mainly IoT devices, with limited processing power, it is crucial to build AI/ML algorithms that are both lightweight and efficient with resources. This involves optimizing algorithms for edge computing environments to save end nodes' resources. This includes deploying techniques, such as model quantization and pruning, to reduce the size and complexity of AI/ML models so they can be implemented for devices with limited resources. Another way to overcome resource limitations is to seek an efficient way of using them. AI/ML can play a major role in this direction by designing novel methods for dynamically allocating resources according to demand to optimize performance while reducing resource usage. This includes using reinforcement learning to develop adaptive resource allocation schemes for IoT-based smart city applications. 2. Algorithms for enhancing communication network efficiency. AI/ML can significantly improve the network performance of different smart city applications, mainly to meet the demands of 5G. Algorithms for controlling and predicting network traffic are critical components, allowing for the intelligent distribution and allocation of network resources based on consumption patterns. Another critical aspect is the development of algorithms for assessing data reliability. AI/ML algorithms can analyze and verify
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 4 Experimental Environment and Results
### Discussion  
The rearrangement process is important for the compression process provided. We conducted experiments in two ways to evaluate important algorithms. At first, the amount of loss was measured as the text was deformed. Both RMSE and RC (rearrangement cost) values were measured. We also assessed the simplification results, which measure the additional performance of important algorithms. The best result was achieved using a voxel size of 1.55. We were able to reconstruct the point-cloud efficiently with the least deviation, providing higher or similar file sizes to other methods. Existing simplification algorithms are heavily influenced by the input model and parameters, resulting in high deviations or small deviations and poor performance.  
In particular, the RMSE value increased significantly from 0.5, which is the unit that generally constitutes a point-cloud. Measured when the voxel size was 0.5 units (e.g., k = 1.0, 1.5, 2.0) on a dataset with a fixed minimum distance of 1, the proposed algorithm aimed to eliminate chaotic errors. All RMSEs were significantly increased. In general, a certain number of RMSE values are expected to appear in all voxel units in the measured arbitrary dataset.  
Finally, we implemented deduplication and conducted an experiment to extract duplicate data using a point-cloud with a preprocessing algorithm. Experimental results were identified by the contours of the 3D model. This is because there was an empty data structure in the point-cloud. It is presumed that the outline shape was generated in the form of a subset of the target frame, because the overlapping vertices of the reference frame of the target contour were extracted. The results extracted using the algorithm were found to increase by 16.85% compared to 6.63% previously when k = 2.0 (i.e., the range of the largest experiment).
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 7 IoT and Blockchain Integration
### Application Areas of IoT and BCN Integration
#### 7.1.1 IoT Devices and IoT Applications' Security Enforcement  
* Trust Platform: As discussed in the previous section, a blockchain network is a platform that supports transaction and process verification and public audit of the data stored in the temper-detectable blockchain ledger. Thus, the blockchain network has the potential to be used as a trusted platform for several IoT-related applications. [PERSON] et al. [54] presented a blockchain-based trust architecture for building end-to-end trust for various IoT-based applications. [PERSON] et al. [55] proposed a decentralized trust framework called IoT Passport for cross-platform collaborations using blockchain to enforce trusted interactions between IoT devices across platforms.
* Authentication and Access Control: Access control is a mechanism of mapping computational resources with users with appropriate access, including reading, writing, and executing. Because the blockchain network guarantees the integrity of stored data, access control mechanisms for IoT devices and applications are built and encoded on the blockchain ledger. [PERSON] et al. [56] used an identify-based data access control model, BDAC, to provide fine-grained data access control for IoT systems. [PERSON] et al. [57] proposed an enhanced authentication and access control method for IoT devices to add features, such as decentralization, secured authentication, authorization, and scalability. [PERSON] et al. [58] proposed an attribute-based collaborative access control scheme on top of a blockchain for IoT devices. Recently, [PERSON] et al. [59] proposed blockchain-based IoT access control mechanisms that are claimed to provide critical features, including resource management, access rights transfer, permission enforcement, attribute management, and scalability. [PERSON] et al. [60] proposed using the blockchain network to manage and monitor IoT devices. Previous works from [61; 62; 63] were heavily focused on using Blockchain for IoT access control management.
* Privacy and Integrity of IoT data: Privacy and integrity are the essential properties of the IoT ecosystem. [PERSON] et al. [64] proposed Shamir's threshold cryptography for protecting the privacy of the IoT data stored in the cloud. In this model, the end-users request decryption keys from the blockchain network to decrypt the encrypted data in the cloud. [PERSON] et al. [65] proposed using hardware-derived functions known as  
Figure 6: IoT and blockchain network integration models.  
physical unclonable functions (PUFs) to detect counterfeit IoT devices. [PERSON] et al. [66] simulated a system of assuring and detecting IoT data integrity using a distributed blockchain system. [PERSON] et al. [] discussed a blockchain-based method to monitor the topographic integrity of an IoT network.
# Early Detection and Stability Assessment of Hazardous Rock Masses in Steep Slopes
## 2 Brief Report on Engineering Background and Geological Setting  
The research area is situated in the eastern part of Jingxing County, Shijiazhuang City, Hebei Province, China, with geographical coordinates of 114.26\({}^{\circ}\) east longitude and 38.04\({}^{\circ}\) north latitude. The total area covers approximately 113,547 square meters. Influenced by the Shi-Tai Railway, it is located within a secondary branch of the Taihang Mountains, characterized by low hills ranging from 160 to 257 m above sea level. The terrain is relatively gentle, featuring a few isolated hills with rounded peaks and gullies between 5 and 10 m deep (as depicted in Figure 1).  
Figure 1: Geographical location of the study area. (**a**) The geographical location of the research area; (**b**) schematic diagram of the location of Hebei Province within China.  
The railway slope in the study area exhibits a relatively gentle gradient, ranging from 30\({}^{\circ}\) to 45\({}^{\circ}\), while the lower cutting section of the slope is steeper, with a gradient between 70\({}^{\circ}\) and 90\({}^{\circ}\). Surface water shows signs of infiltration due to the water-scarce and arid environment, resulting in sparse vegetation dominated by low shrubs. The strata mainly consist of non-metamorphic or mildly metamorphic marine sedimentary rocks on a platform. The top layer is covered with gray chert-bearing dolomites, followed by purple and grayish-white medium-grained quartz sandstone, then purple medium-grained quartz sandstone, and finally quartz sandstone and dolomite with gravel at the bottom.  
Located in the transitional zone between the Taihang Mountain uplift and the Hebei Plain depression, the research area experiences a semi-humid to semi-arid monsoon climate within the north temperate zone. The rainfall within the study area was constantly monitored throughout 2021. The elevation of the Taihang Mountains influences an annual precipitation exceeding 600 mm in this area with an inter-annual variation rate as high as 26%. The difference in rainfall between wet and dry years can reach up to 1000 mm. Please refer to Table 1 for average annual climate characteristics.  
Affected by the monsoon climate, the annual distribution of precipitation in the study area is uneven. Winter precipitation only accounts for 2% to 3%, approximately 13 to 18 mm; in spring, it accounts for 12% to 13%, around 60 to 70 mm; during summer, it comprises 65% to 67%, reaching up to 310 to 360 mm; and autumn contributes about 17% to 18%, or roughly 80 to 100 mm [33]. The seasonal rainfall in the study area is depicted in Figure 2, primarily occurring from June to September, and sudden heavy rain may directly result in hazardous rock collapse disasters.
## 5 Numerical Experiments and Analysis  
In this section, a PSU in China is used as the research object to carry out the nonlinear modeling of the pump turbine [8]. A total number of 1125 data points are extracted from the pre-processed WH and WM characteristic curves using the improved Suter transformation (Figure 2). Thus, 1125 data pairs are generated for constructing an AE-PLS-ELM model. In addition, 90% of the data pairs (1012 points) are employed as the training samples and the remaining 10% (112 points) as the test sample.
## 5 Experimental Findings
### Difficult Cases
#### 5.4.2 Case with Blurred Boundary Mass  
From Figure 14, it is possible to appreciate how blur severely compromises the contrast of the original image, making lesion segmentation a very challenging task. As mentioned above, because of the strong uncertainty in boundaries delineation, the manual segmentation (green contour) identifies a very simple and smooth shape into which the lesion is included. Of course, this kind of strategy allows the identification of lesion's location, but also includes into the ROI a lot of false positives. On the other hand, the semi-automatic mask (red contour) identifies a more precise region avoiding the misclassification of FP pixels. Even in this case, because of the imperfect area, overlap area-based metrics will not yield a high score.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 2 Related Work and Problem Statement
AHH-VBF, Adaptive Hop-by-Hop Vector-Based Forwarding; HydroCast, Hydraulic-pressure-based anyCast; DBR, Depth-Based Routing; iAMCTD, improved Adaptive Mobility of Courier nodes in Threshold-optimized DBR; E-CARP, Energy-efficient Channel-Aware Routing Protocol; ARCR, Adaptive Relay Chain Routing.