esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 5 Proposed System
### Involved Technologies
#### 5.1.2 Wireless Sensor Networks (WSN)  
Wireless Sensor Networks (WSNs) are networks of small, inexpensive sensor nodes that use wireless communication to gather and send data from their surroundings. These sensor nodes can monitor physical or environmental variables because they are furnished with a variety of sensors, including temperature, humidity, light, motion, and gas sensors. The primary goal of WSN is to connect IoT-based devices (such as those belonging to a patient) in order to offer helpful information whenever and wherever it is required, such as in a hospital [4].  
WSNs have a number of benefits over conventional wired sensor networks, including quick deployment, adaptability, scalability, and affordability. Numerous industries, such as environmental monitoring, industrial automation, healthcare, agriculture, transportation, and smart cities, all find use for them. WSNs can increase their capabilities and potential applications by merging them with other internet technologies [15].  
A wide range of opportunities for improving data gathering, analysis, and decision-making capabilities are made possible by the integration of WSNs with other internet technologies. WSNs can aid in creating intelligent and interconnected systems in various sectors by utilizing the power of connection and cutting-edge computation [4].
## 3 Results and Discussion
### Mechanical Properties
Figure 14 shows the variation in the energy-absorbed values attained from the impact test of the 1.6511, 1.6511 Si2, 1.6511 Si2V, and 1.6511 Si2M samples. Impact testing is an indirect method for obtaining fracture toughness [42; 43]. Since the fracture toughness mostly depends on the possibility of blinding the crack tip by secondary phases and changing the crack path due to grain boundaries [44], the impact test results of the samples, shown in Figure 14, were studied from these two perspectives. Sample 1.6511 Si2V has the highest volume fraction of retained austenite among the samples; despite the larger grain size due to the possibility of simultaneous blinding and the TRIP phenomenon resulting from its retained austenite, it was able to absorb great energy before failure. Rare earth elements, besides decreasing the grain size, which leads to increased fracture toughness [45; 46; 47; 48; 49; 50; 51; 52], also increase the grain boundary strength [36]. It seems that there is an increase in energy required for the fracture of the 1.6511 Si2M sample compared to the standard sample, and the 1.6511 Si2 sample has a higher grain boundary volume and higher grain boundary strength compared to other samples. Moreover, the rare earth elements reduce the distance between the dendritic arms, leading to improved mechanical properties regardless of other cases [38]. The 1.6511 sample in the standard state (without Si) absorbs the lowest amount of energy before fracture, which can be due to the simultaneous lack of retained austenite (or in other words, CFB matrix) and the presence of carbide in the structure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Yield Strength (MPa)** & **Ultimate Tensile Strength (MPa)** \\ \hline
1.6511 & 713 \(\pm\) 3 & 915 \(\pm\) 4 \\
1.6511 Si2 & 858 \(\pm\) 4 & 965 \(\pm\) 4 \\
1.6511 Si2M & 878 \(\pm\) 4 & 980 \(\pm\) 4 \\
1.6511 Si2V & 787 \(\pm\) 3 & 1063 \(\pm\) 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mechanical properties, including the yield stress (YS, with 0.2% offset) and ultimate tensile strength (UTS) of the samples.
# Advanced Brain Tumor Segmentation Using SAM2-UNet
###### Abstract  
Image segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The analysis of medical images is a specialized domain in computer vision and image processing. This process extracts meaningful information from medical images that helps in treatment planning and monitoring the condition of patients. Deep learning models like CNN have shown promising results in image segmentation by identifying complex patterns in the image data. These methods have also shown great results in tumor segmentation and the identification of anomalies, which assist health care professionals in treatment planning. Despite advancements made in the domain of deep learning for medical image segmentation, the precise segmentation of tumors remains challenging because of the complex structures of tumors across patients. Existing models, such as traditional U-Net- and SAM-based architectures, either lack efficiency in handling class-specific segmentation or require extensive computational resources. This study aims to bridge this gap by proposing Segment Anything Model 2-UNetwork, a hybrid model that leverages the strengths of both architectures to improve segmentation accuracy and consumes less computational resources by maintaining efficiency. The proposed model possesses the ability to perform explicitly well on scarce data, and we trained this model on the Brain Tumor Segmentation Challenge 2020 (BraTS) dataset. This architecture is inspired by U-Networks that are based on the encoder and decoder architecture. The Hiera pre-trained model is set as a backbone to this architecture to capture multi-scale features. Adapters are embedded into the encoder to achieve parameter-efficient fine-tuning. The dataset contains four channels of MRI scans of 369 glioma patients as T1, T1 ce, T2, and T2-flair and a segmentation mask for each patient consisting of non-tumor (NT), necrotic and non-enhancing tumor (NCR/NET), and peritumal edema or GD-enhancing tumor (ET) as the ground-truth value. These experiments yielded good segmentation performance and achieved balanced performance based on the metrics discussed next in this paragraph for each tumor region. Our experiments yielded the following results with minimal hardware resources, i.e., 16 GB RAM with 30 epochs: a mean Dice score (mDice) of 0.771, a mean Intersection over Union (mIoU) of 0.569, an \(S_{\alpha}\) score of 0.692, a weighted F-beta score (\(F_{\beta}^{\text{cn}}\)) of 0.267, a F-beta score (\(F_{\beta}\)) of 0.261, an \(E_{\phi}\) score of 0.857, and a Mean Absolute Error (MAE) of 0.04 on the BraTS 2020 dataset.  
m 2020 IEEE/RSRS  
medical image segmentation; BraTS 2020; brain tumor segmentation; deep learning; segment anything model-2; U-Network; CNN
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 8 Electrospray Propulsion System/Electrospray Thrusters
### Design Considerations and Technologies  
In an electrospray thruster, typically, the extraction of charged particles is done through two regimes: the cone-jet regime, in which the meniscus (of the propellant) breaks up into droplets; and the ionic regime where pure ions are extracted. The specific impulse observed in ionic regime is greater than in cone-jet regime [96]. A thruster is typically designed to operate in only one of the two regimes, and the regime defines the specific thruster: either a colloid thruster or a field emission thruster.  
Electrospray thrusters use ionic liquids as propellants as they do not require heating, have low operating voltage, high conductivity in the pure state and negligible vapor pressure [99; 100].  
Figure 7: Schematic of an Electrospray Propulsion System.  
Formamide, propylene carbonate, water, Tri-Ethylene Glycol (TEG) solutions doped with Sodium Iodide (NaI), 1-Ethyl-3-Methyl Imidazolium bis(tri-fluoro-methyl-sulfonyl) imide ([Emim][Im]), formamide, tri-butyl phosphate, 1-Butyl-3-Methyl Imidazolium Di-Cyanamide ([Bmim][DCA]) and 1-Ethyl-3-Methyl Imidazolium Tetra-fluoro-borate (EMIBF4) are some of the propellants used in electrospray systems [93; 100; 101; 102; 103]. Liquid metals like cesium, gallium and indium have also been used as propellants due to their high atomic mass and a low ionization potential [104].  
An individual electrospray emitter operates in the milli-watt (power) and generates thrust in the order of micro-newtons, therefore, an array of emitters is required to form the thruster that can yield the desired thrust [93; 100]. Conventionally, electrospray thrusters have used metallic emitters, but pure silicon has also been used to make emitters with the help of Deep Reactive Ion Etch (DRIE) technology and micro-fabricated components [105]. However, using silicon poses a problem with being able to conduct electrons to or from the sprayed fluid (propellant). This problem can be countered by either providing a conductive film surface over the silicon (by deposition process) or by highly doping the silicon to create a conductive silicon material [105]. Table 7 provides the summary of surveyed electrospray thrusters and their performance parameters. One of the listed systems has a CubeSat heritage and another one has flown on a bigger spacecraft.
# Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
## 3 Test Platform and Software Flow
### System Structure
The vibration analysis software running on the Raspberry Pi stores the raw waveform data and the statistics after digital signal processing from each sensor in a MySQL database on the Raspberry Pi. At the same time, all or part of the data are uploaded to the server via the Internet.
# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 1 Introduction
### Contributions of This Work  
Taking into account the state of the art, we highlight the following points as being the contributions of our work:  
* A module for detection and for attitude estimate of an AUV dock station based on a single camera and a 3D target: for this purpose, a target was designed and constructed whose physical characteristics maximize its observability. The developed target is a hybrid target (active/passive) composed by spherical color markers which could be illuminated from the inside allowing to increase the visibility of the markers at a distance or in very poor visibility situations. It was also designed an algorithm for detecting the target that responds to needs of low computational cost and that can be run in low power, low size computers. A new method for estimate the relative attitude was also developed in this work.
* A novel approach for tracking by visual detection in a particle filtering framework. In order to make the pose estimation more resilient to markers occlusions, it was designed and implemented a solution based on Particle Filters that considers geometric constraints of the target and constraints of the markers in the color space. These specific approaches have improved the pose estimator performance, as presented in the results section. The innovation in our proposal for the tracking system consists of the introduction of the geometric restrictions of the target, as well as the restrictions in the color space as a way to improve the filtering performance. Another contribution is the introduction, in each particle filter, of an automatic color adjustment. This allowed not only to reduce the size of the region of interest (ROI), saving processing time, but also to reduce the likelihood of outliers.
* It was developed a method for real-time color adjustments during the target tracking process, which improves the performance of the markers detector through better rejection of outliers.
* It was designed and implemented an experimental process, with Hardware-in-the-loop, to characterize the developed algorithms.
* A guidance law was designed to guide the AUV with the aim of maximizing the target's observance during the docking process. This law was designed from a generalist perspective and can be adapted to any system that bases its navigation on monocular vision, or another sensor whose field of view is known.
## 3 Theory and Methods
### Signal Analysis  
Before signal analysis, data preprocessing is typically required to prepare the dataset for further analysis. The preprocessing steps include interpolation (using Lagrange polynomials to ensure equidistant data points for time series analysis), detrending (subtracting the mean of the dataset), noise removal (applying third-order and higher-order cumulants to suppress low-frequency components and enhance high-frequency signals), and additional detrending (using least squares fitting to remove upward low-frequency trends). These preprocessing steps can be performed using tools such as Matlab 2024 or Acycle 2.7. After preprocessing, the data are subjected to wavelet transform and spectral analysis in Matlab to extract Milankovitch cycle information from the lacustrine fine-grained sediments of the Yixian Formation, enabling a detailed analysis of sedimentary cycles.  
Spectral analysis decomposes complex multi-frequency signals in the time domain into sine wave components of different frequencies. Essentially, it transforms spatial domain signals into the frequency domain [19]. Common spectral analysis methods include FastFourier Transform (FFT), the Maximum Entropy Method (MEM), and the Multi-Taper Method (MTM). In spectral analysis, the relative amplitude indicates the strength of the GR signal. A higher relative amplitude corresponds to stronger cyclicity, meaning that a particular stratigraphic thickness appears more frequently in the region. The purpose is to assess whether high-amplitude, non-random frequency bands align with Milankovitch cycle periods. By analyzing the amplitude values and corresponding frequencies in the spectrum, the cycle thickness for a given frequency in the strata can be calculated as 1/frequency. Since Earth's orbital signals enter the sedimentary system in a non-linear manner and may be coupled with other signals, spectral analysis alone cannot fully determine whether a distinct Milankovitch cycle exists in the studied interval, necessitating further wavelet analysis. Wavelet transform extracts multi-scale information embedded in well log signals, enabling cyclic information segmentation and extraction [20]. Ultimately, by analyzing the relationship between the cyclic thickness corresponding to orbital periods and energy clusters in the wavelet spectrum, it is possible to determine whether the studied strata exhibit orbitally controlled sedimentary cycles.
# Analyzing Delay and CO Emissions: A Simulation Study of the Median U-Turn Method at Intersections
###### Abstract
[PERSON] [27] and [PERSON] [28] calculated the pollutant emissions of vehicles at intersections by establishing  
Figure 1: Intersection of Dongguan Road and Hongwei Road in Dongguan city in Guangdong province. pollutant emission models. [PERSON] et al. [29] studied the difference in carbon dioxide emissions at roundabouts instead of signalized intersections using a portable emission measurement system (PEMS). [PERSON] et al. [30] studied the impact of optimization measures for intersection traffic organization on emissions using the VSP method. [PERSON] [31] studied the impact of signal optimization measures for intersections on emissions using the VSP method. [PERSON] [32] studied the pollutant emissions at the left turn by using an adjacent opposing lane using the VSP method. From the above literature, it can be seen that some research has been conducted on signalized intersections considering pollutant emissions as objectives, but there are few studies considering pollutant emission indicators in traffic organization of using the U-turn method at an intersection. In summary, the traffic and pollutant emission conditions suitable for applying the median U-turn method at an intersection are not clear enough yet. This paper intends to study the quantitative relationship between the two indicators of average delay and CO emissions and the traffic flow composition including through traffic and left-turn traffic to clarify optimal conditions for implementing the median U-turn method at an intersection. The findings of this study will provide a reference for the intelligent design of intersections utilizing a median U-turn design.
## 4 Secure Cooperation
### Secure Dispatching Service
#### 4.1.2 Authenticity in UAN  
Encryption without authentication is insecure [22]. For example, an adversary may flip bits in unauthenticated ciphertext and cause predictable changes in the plaintext that receivers are not able to detect. To address this vulnerability, UAN always authenticates messages, but encryption is optional. Message confidentiality is necessary only when there is some information that has to be kept secret. UAN uses MACs to address authentication only. In contrast, it uses the transformation \(E_{e}(m\|h(m))\) when it addresses both encryption and authentication [22]. More specifically, UAN uses SHA-256 both as hash function and to build keyed hash functions (HMAC) [22,23]. Security of hash functions is directly related to the length of the digest. However, as a digest is appended to the message, it becomes another source of message expansion and consequent communication overhead. For instance, SHA-256 has a 256-bit output, which is about 2.46 times the size of the payload of cooperation algorithm messages. For this reason, UAN features a trade-off between security and performance by using 4 bytes digests resulting from truncating the real hash function value. Using such a short hash function value is not detrimental to security [24]. An adversary has \(1\) in \(2^{32}\) chances to blindly forge a digest. If an adversary repeatedly tries to forge it, he/she needs \(2^{31}\) trials on average. However, the adversary cannot perform trials off-line. This means that the adversary has to validate a given forgery only by sending it to an authorized receiver. This implies that the adversary has to send \(2^{31}\) messages in order to successfully forge a single malicious message. In a conventional network this number of trials is not large enough. However, in a underwater acoustic network this may provide an adequate level of security. An adversary can try to flood the network with forgeries, but on a 2 kbps channel with 184-bit messages, he/she can only send about 11 attempts for second. Thus, sending \(2^{31}\) messages requires around 75 months, _i.e._, about 6 years. Battery-operated vehicles have not enough energy to receive that many messages. Furthermore, the integrity attack would translate into a denial of service attack since the adversaryneeds to occupy the acoustic channel for a long time. Fortunately, it is feasible to detect when such an attack is underway. UAN uses a simple heuristic: vehicles could signal the base station when the rate of digest/MAC failures exceeds some predetermined threshold.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
###### Abstract  
Accurate organ-level segmentation is essential for achieving high-throughput, non-destructive, and automated plant phenotyping. To address the challenge of intelligent acquisition of phenotypic parameters in tomato plants, we propose TSINet, an end-to-end dual-task segmentation network designed for effective and precise semantic labeling and instance recognition of tomato point clouds, based on the Pheno4D dataset. TSINet adopts an encoder-decoder architecture, where a shared encoder incorporates four Geometry-Aware Adaptive Feature Extraction Blocks (GAFEBs) to effectively capture local structures and geometric relationships in raw point clouds. Two parallel decoder branches are employed to independently decode shared high-level features for the respective segmentation tasks. Additionally, a Dual Attention-Based Feature Enhancement Module (DAFEM) is introduced to further enrich feature representations. The experimental results demonstrate that TSINet achieves superior performance in both semantic and instance segmentation, particularly excelling in challenging categories such as stems and large-scale instances. Specifically, TSINet achieves 97.00% mean precision, 96.17% recall, 96.57% F1-score, and 93.43% IoU in semantic segmentation and 81.54% mPrec, 81.69% mRec, 81.60% mCov, and 86.40% mWCov in instance segmentation. Compared with state-of-the-art methods, TSINet achieves balanced improvements across all metrics, significantly reducing false positives and false negatives while enhancing spatial completeness and segmentation accuracy. Furthermore, we conducted ablation studies and generalization tests to systematically validate the effectiveness of each TSINet component and the overall robustness of the model. This study provides an effective technological approach for high-throughput automated phenotyping of tomato plants, contributing to the advancement of intelligent agricultural management.  
3D point clouds; tomato; deep learning; plant organ segmentation; plant phenotyping +
Footnote †: journal: Applied Sciences  
1
## 3 Materials and Methods
### Questionnaire Survey  
The minimal research sample was calculated based on the number of inhabitants of the municipality, the required level of confidence, and the rate of deviation. The calculation was provided by an online calculator [66] with the level of confidence established at 95% and the rate of deviation 6%. At least 266 respondents need to have a confidence level of 95% that the real value is within \(\pm\)6% of the surveyed value, as in Table 3 below.  
The questionnaire was submitted to inhabitants or commuters of Zilina municipality. Together, 354 respondents attended the questionnaire survey. The questionnaire was created to collect the bike-sharing service users' opinions about the current situation as well as about the bike-sharing improvements. The introductory question was aimed at social status. The following questions crucial for the study were formulated as follows:  
\begin{table}
\begin{tabular}{l c} \hline Population size & 80,342 \\ Confidence level & 95\% \\ Margin of error & 6\% \\ \hline Research sample & 266 respondents \\ \hline \end{tabular}
\end{table}
Table 3: The calculation of the minimal research sample for the survey.  
Q1: How would you generally assess the level and condition of cycling infrastructure in the city of Zilina (for example, the quality or connectivity of the cycling routes)? (4-level assessment answers: high quality--sufficient quality--below average quality--non-sufficient quality).
* Q2: What factors do you consider key factors for deciding whether to use shared bikes? Select a maximum of 4 options from: price for the service (including free ride), the quality of the cyclist infrastructure, possibility of using the electric bike in the service, bicycle equipment, reachability of the bicycle, current traffic situation in the city, other:...
* Q3: Do you consider the current distribution of 32 shared bike stations in the city of Zilina sufficient based on the municipality plan?
* Q4: Do you consider the current number of bicycles in the system (195) sufficient, or would you welcome the gradual addition of more to increase the availability of the system?
* Q5: In which parts of Zilina do you feel a lack of shared bike stations, and would you like to see them added? Select a maximum of 5 options from: Banova, Borik, Budatin, Bytcica, Hajik, Hliny, Mojsova Lucka, Povazsky Chlmec, Rosinky, Solinky, the city center of Zilina, Strazov, Trnove, Vlince, Zavodie.
* Q6: In some districts, bike-sharing stations are either missing or severely underrepresented, limiting service accessibility. Select up to 4 of the listed city districts where bike sharing should primarily be expanded. Select from: Banova, Budatin, Bytcica, Mojsova Lucka, Povazsky Chlmec, Rosinky, Strazov, Trnove, Zavodie.
* Q7: Do you have any suggestions for placing the bike-sharing stations in Zilina?  
The questionnaire was aimed at the users of the bike-sharing service in Zilina, and it was distributed by social networks during April 2024, at the start of the season 2024. Non-users were directed to the end of the questionnaire.