esa-sceva/satcom-chunk-collection huggingface dataset:
# A Staring Tracking Measurement Method of Resident Space Objects Based on the Star Tacker
## 3 Results
### Numerical Simulation of the Proposed STM Method
#### 3.2.1 Simulation of the Observation Arcs in One Orbit Period of Staring-  
Tracking Imaging  
The acquisition time of the arc measured by traditional methods is usually less than 20 s in one observation. The observation arc obtained by the STM method is much longer than that of traditional methods. In the process of staring-tracking, the observation satellite adjusts the optical axis direction of the star tracker according to the planned attitude in Section 3.1.1. However, the star tracker cannot always observe the RSO because of the orbital elements error. The RSO can only be observed when the observation angle \(\theta_{st}\) is smaller than the half FOV of the star tracker \(\theta_{fo0/2}\). The simulation results of the relationship between \(\theta_{st}\) and \(\theta_{fov/2}\) in the STM method are shown in Figure 10. Where the blue line represents \(\theta_{st}\), the three straight lines represent \(\theta_{fov/2}\) at 0.5\({}^{\circ}\), 5\({}^{\circ}\), and 10\({}^{\circ}\), respectively. Therefore, the RSO can be measured several times during the orbit period when the blue line is under the straight line. During the first 30 min of the observation period, the measurable arcs at \(\theta_{fov/2}=10^{\circ}\), \(\theta_{fov/2}=5^{\circ}\) are approximately 6 min, respectively. The RSO cannot be observed at \(\theta_{fov/2}=0.5^{\circ}\), because the FOV is too small to catch RSOs.
# Fast Equipartition of Complex 2D Shapes with Minimal Boundaries +
## 3 Problem Formulation  
The 2D Shape Equipartition Problem (2D-SEP) under the minimum intrinsic boundary length [7] is formulated hereafter. Let \(S\) be a given shape, and \(N\) be the given the number of equal-area segments (regions). Let \(R=\{R_{1},R_{2},\ldots,R_{N}\}\) be a segmentation of \(S\). Each region \(R_{i},\ i\in\{1,\ldots,N\}\) should be connected, which means that the pixels in the \(R_{i}\) segment belong to the same connected component. Let \(BD(R_{i},R_{j})\) be the common boundary between the regions \(R_{i}\) and \(R_{j},\ i,j\in\{1,\ldots,N\}\). Then, the optimal segmentation of 2D-SEP \(R^{*}=\{R_{1}^{*},R_{2}^{*},\ldots,R_{N}^{*}\}\) should satisfy the following constraints:  
\[|R_{1}^{*}|=|R_{2}^{*}|=\ldots=|R_{N}^{*}|=\frac{|S|}{N} \tag{1}\]  
where \(|.|\) denotes the cardinality operation, e.g., \(|S|\) gives the area of shape \(S\) (the number of pixels).  
\[R^{*}=\underset{R}{\text{argmin}}\ L(R) \tag{2}\]  
where \(L(R)\) the total intrinsic boundaries' length of segmentation \(R\):  
\[L(R)=\sum_{i=1}^{N}\sum_{j=i+1}^{N}|BD(R_{i},R_{j})| \tag{3}\]  
where \(|BD(R_{i},R_{j})|\) denotes the length of boundary \(BD(R_{i},R_{j})\). In image processing with pixel accuracy, the satisfaction of Equation (1) is impossible in the case that the area of shape \(S\) is not exactly divided by \(N\), and, at the same time, the equality constraint is very hard. Therefore, in this work, we have relaxed Equation (1) as follows:  
\[\sum_{i=1}^{N}||R_{i}^{*}|-\frac{|S|}{N}|\leq\lambda \tag{4}\]  
where \(\lambda\) is set equal to \(2\cdot N+1\), resulting in a mandatory but realistic (feasible) requirement that should be satisfied. This selection can be explained by the fact that \(\lambda\geq\frac{N}{2}\) due to the pre-mentioned pixel accuracy effect. However, since the equality constraint is very hard, we have selected a higher threshold for \(\lambda\), providing a good balance between the constraints. In Section 6.5, we have performed a sensitivity analysis of the parameter \(\lambda\).  
Furthermore, in our experimental results, we allow comparisons between different shape sizes and scales by using the normalized total intrinsic boundary length (\(NL(R)\)) of the segmentation \(R\), defined as the ratio of \(L(R)\) to the outer boundary length of the object \(|BD(S)|\).  
\[NL(R)=\frac{L(R)}{|BD(S)|} \tag{5}\]
## 7 Discussion and Future Trends
Future studies should focus on hybrid experimental-numerical approaches, where high-fidelity microstructural imaging (X-ray \(\mu\)CT, SEM) is integrated with simulation-based failure predictions to refine fracture and deformation models. Another critical research direction involves fiber-reinforced composites, where current models fail to fully capture fiber breakage, alignment errors, and void formation [67, 68]. More advanced multi-scale fiber orientation models should be developed to predict fiber motion during deposition, ensuring optimized mechanical properties and structural integrity. For fiber-reinforced composites, while CFRCs have demonstrated exceptional mechanical performance, they introduce manufacturing challenges related to fiber placement, matrix bonding, and deposition precision. Current topology optimization frameworks have improved load distribution and material efficiency, but they remain limited by fiber misalignment during printing [71, 72]. Future research should develop automated path-planning algorithms that adaptively adjust fiber orientation based on in situ stress distribution data. Additionally, real-time reinforcement monitoring using AI-driven vision systems could improve defect detection in large-scale composite printing [12]. Lastly, large-scale applications in aerospace and automotive manufacturing require further investigation into void formation, pressure fluctuations, and thermal expansion effects in Large Area Additive Manufacturing (LAAM). While models have been developed to predict fiber-induced pressure variations and flow disturbances, they require more accurate fiber-matrix interaction models [8]. Future work should integrate non-Newtonian flow effects and high-resolution fiber-resin interface simulations to improve prediction accuracy for industrial applications.
## 2 Related Work
### Fuzzy Local Information C-Means (FLICM)  
The FLICM [24] model was presented by [PERSON] et al. FLICM does not need to manually input a number of parameters but directly introduces fuzzy parameters to the objective function. The expression of the fuzzy parameter is given by  
\[G_{ki}=\sum\limits_{j\in N_{k},j\
eq k}\frac{1}{d_{kj}+1}\big{(}1-u_{ij}\big{)} ^{p}\big{\|}x_{j}-v_{i}\big{\|}^{2}, \tag{4}\]  
where \(x_{j}\) represents the pixel within the neighborhood, \(v_{i}\) represents the cluster center, \(u_{ij}\) represents the membership matrix, and \(d_{kj}\) represents the Euclidean distance between the \(j\)-th and \(k\)-th pixels. After introducing fuzzy parameters, the objective function of FLICM can be represented as  
\[J_{m}=\sum\limits_{i=1}^{N}\sum\limits_{k=1}^{c}\Big{[}u_{ki}^{m}\|x_{i}-v_{k} \|^{2}+G_{ki}\Big{]}, \tag{5}\]  
where \(u_{ki}\) is deduced by  
\[u_{ki}=\left(\sum\limits_{j=1}^{c}\Bigg{(}\frac{\big{\|}x_{i}-v_{k}\big{\|}^{2 }+G_{ki}}{\big{\|}x_{i}-v_{j}\big{\|}^{2}+G_{ji}}\Bigg{)}^{\frac{1}{m-1}} \right)^{-1}. \tag{6}\]The expression of \(v_{k}\) is given by  
\[v_{k}=\frac{\sum\limits_{i=1}^{N}u_{ki}^{m}x_{i}}{\sum\limits_{i=1}^{N}u_{ki}^{m}}. \tag{7}\]  
FLICM has strong robustness to noise, but it is not always effective in practical applications and incorrect segmentation may occur at the boundaries of different tissues. The reason for this is that the gray values of two adjacent tissues are greatly similar, so a wrong value of \(G_{ki}\) will be obtained in both tissues.
# Examining User Perception of the Size of Multiple Objects in Virtual Reality
## 1 Introduction
### Background  
Perception of depth and size with different 3D displays is an essential area of investigation and well studied. [PERSON] and [PERSON] [13] investigated the human perception of layout and distance. They identify nine sources of information for people to determine layout and their relative utility at different distances: occlusion, relative size, relative density, height in the visual field, aerial perspective, motion perspective, binocular disparities, convergence and accommodation.  
A common technique for investigating size perception with an HMD is having a participant perform a size-matching or size-judgement task. The general findings of [PERSON] et al. [7] suggested that there was a difference in the participants' size judgements between VR and physical environments. Experiments by [PERSON] et al. [14] were consistent across the participant population, and results showed that scene complexity and stereovision could have a significant impact on user performance in virtual environments to make precise judgments on the size of virtual objects. They found contrary to other studies, motion parallax, either from the virtual environment or by user movement, might not be a significant factor in determining a users' performance. [PERSON] et al. [6] employed size-judgement tasks to measure the perceived size of virtual objects displayed on a large screen monitor. They determined the size of an object in the VE is underestimated compared to an object in the real world. This difference was smaller when viewed in stereo. Egocentric [15] distances in virtual environments are frequently under perceived by up to 50% of the actual distances. [PERSON] [15] investigated these effects while walking through a VE, and they found an under-perception of egocentric distance. This section will explore these concepts in more detail.
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model
### Anti Detection Module
#### 4.2.1 Distributed Simulation  
It is regular for the data flow of normal distribution in the network, and the distribution function is \(F(x)\). In SACT, the reversible transformation function \(tan(x)\) of distribution function \(F(x)\) of delay sequence is constructed by random simulation method. Then modulating the delay sequence corresponding to watermark information. The steps are as follows:  
1. The delay sequence \(T_{i}(i=1,2,\cdots,n)\) needs to be within \(U(0,1)\), to be modulated into a delay sequence satisfying \(F(x)\) by reversible transformation function \(tan(x)\). In fact, the delay sequence \(T_{i}(i=1,2,\cdots,n)\) comes from the set \(E=\{T:T_{\min}+n\cdot\Delta,n=0,1,\cdots,K\}\). \(T_{\min}\) represents the minimum interval delay among the packet interval arrival delays. \(\Delta\) represents the step length between the arrival delays of adjacent packets, which is not uniformly distributed on \((0,1)\). When the sender and receiver synchronize the code table through the controller, \(T_{\min}\) and \(\Delta\) are determined. The delay sequence \(T_{i}(i=1,2,\cdots,n)\) is represented by vector \((j_{1},j_{2},\cdots,j_{n})j_{i}\in[0,1]\).
2. A random vector \((r_{1},r_{2},\cdots,r_{n})\) is generated by pseudo random code generator \(RAND(x)\). And vector \((j_{1},j_{2},\cdots,j_{n})\) is converted into random vector \((j_{1}^{\prime},j_{2}^{\prime},\cdots,j_{n}^{\prime})\) uniformly distributed over \((0,1)\) by Equation (8). \[j_{i}^{\prime}=(j_{i}+r_{i})\bmod 1\] (8)
3. Finding out the probability density function \(f(x)=F^{\prime}(x)\) of the distribution function \(F(x)\), its domain is \((a,b)\). The domain is divided into \(n\) parts: \((a=a_{0}<a_{1}<a_{2}<\cdots<a_{n}=b)\), and the probability \(p\) of each interval is shown in Equation (9). \[p_{i}=\int_{a_{i-1}}^{a_{i}}f(x)dx\] (9)
4. Similarly, the interval \((0,1)\) is divided into \(n\) parts: \((0=m_{0}<m_{1}<\cdots<m_{n}=1)p_{i}=m_{i}-m_{i-1}\). The vector \((s_{1},s_{2},\cdots,s_{n})\) is used as new delay sequence of packets after calculating by Equation (10). \[s_{i}=\frac{(a_{i}-a_{i-1})(j_{i}^{\prime}-m_{i-1})}{m_{i}-m_{i-1}}+a_{i-1},j_{ i}^{\prime}\in(m_{i-1},m_{i}]\] (10)  
The watermark is modulated by distribution simulation so that the delay sequence of packets conforms to the normal distribution.
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
## 5 Potential Techniques for Extraction of Bioactive Compounds from Avocado Peel
### Centrifugal Partition Extraction (CPE)  
Centrifugal Partition Extraction (CPE) is a centrifugal-field-based multi-stage liquid-liquid extraction process. The partition coefficients between the two liquid phases are used to extract the specified components [98]. CPE is derived from centrifugal partition chromatography (CPC) with a column design characterized by fewer cells of larger volume. The purification of phenolic chemicals from marine resources is the most common application of this approach [99; 100]. CPE was employed to recover bioactive phenolic compounds from the brown seaweed _Sargassum muticum_. It was shown that the total phenolic content of the CPE extract was higher than that of the PLE and SFE extracts. The total phenolic compound content in CPE was double that of PLE, which had the second-highest concentration [101]. Still, more research is needed to investigate alternate methodologies, improve operating conditions, and apply them to different agricultural products, including avocado peel.  
There are various advantages to CPE and CPC. The essential advantage is that, unlike HPLC, this separation technique does not necessitate changing the chromatographic column between analyses. Furthermore, no silica must be used or recycled because the stationary phase is a liquid. In most cases, only a tiny amount of the stationary phase is needed for extraction. All of these benefits result in a reduction in the expenses associated with extractions. CPC, on the other hand, is a technique known as \"soft separation\". Indeed, there is no irreversible adsorption, resulting in a 90% recovery rate. The molecules to be removed are also subjected to minimal or no denaturation. These compounds have a purity percentage of >99%. The CPC, on the other hand, has some limitations. It has a low efficiency in terms of theoretical plate height, which is one of its drawbacks. Furthermore, its extractions can only reach the milligram mass level of an analyte, whereas other separation techniques can reach the microgram level. A robust seal is also required for the CPC to function correctly [102].
# Theoretical Framework for Characterizing Strain-Dependent Dynamic Soil Properties
## 2 Explicit Formulas to Describe Nonlinear-Hysteretic Response  
### Constitutive Model: Stress-Strain Relation  
Considerable progress in modeling such hysteretic nonlinear behaviors of granular materials has been made in the geophysics community. The constitutive models are derived within the mathematical framework of the Preisach-Mayergoyz space representation from a unit physical mechanism [19; 20; 21; 22]. This study uses the relevant stress-strain relationship of soils for one-dimensional cyclic shear motion [23; 24; 25; 26]. In particular, the classical nonlinearity terms (higher order power series in strain) are neglected  
\[\tau_{H}=G_{\text{max}}\Big{\{}\gamma-\frac{\alpha}{2}\Big{[}2\Delta\gamma \cdot\gamma-\text{sgn}\Big{(}\dot{\gamma}\Big{)}\Big{(}(\Delta\gamma)^{2}- \gamma^{2}\Big{)}\Big{]}\Big{\}} \tag{1}\]  
where Gmax is the shear modulus in the limit of infinitesimal strain, \(\gamma\) is the shear strain, \(\alpha\) is a non-dimensional parameter that measures the degree of hysteretic nonlinearity, \(\dot{\gamma}(=\partial\gamma/\partial t)\) is strain rate, \(\Delta\gamma\) is shear strain amplitude, and sgn(x) is signum function.
## 4 Application: Case Study
### Processing  
This step generates the polarity calculation. Two approaches were used: LBA using VADER [91], and MLA using SVM and NB classifiers. In the first approach, the installation was performed with the line of code > pip install vaderSentiment and imported with the vaderSentiment import SentimentIntensityAnalyzer. The values were normalized for polarity between [\(-\)1,1] [60] and a word was determined as positive if the related value was greater than 0.05, as negative if the negative value was less than \(-\)0.05, and as neutral if the value was greater than or equal to \(-\)0.05 and less than or equal to 0.05. The overall polarity of opinions was calculated by summing the determined polarities. Regarding the classification, the program generated a general polarity label. SVM and NB were used for the machine learning approach. The results of both methods are shown in Figure 4.  
It can be observed that in the two approaches and three methods implemented, opinions were positively oriented toward the restaurant. In addition, the negative percentage of polarity ranged from 6% to 14%. Neutrality was present in the opinions to a minor degree.
# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
## 5 Analysis of Results  
### Experimental Environment and Configuration  
The experimental environment utilized in this study was Python 3.10.2, Torch 2.0.0 framework, Cuda 11.2; the GPU model was NVIDIA Tesla P100 16 GB (NVIDIA, CA, USA), using two cards; the training parameters set for the experimental model were as follows: the initial learning rate parameter was set to 0.01, the input image size was set to 640 \(\times\) 640 pixels, the batch size was 16, the epoch was set to 300 rounds, workers were set to eight, and the number of early stopping rounds was set to 100 rounds.  
The dataset used for training was self-collected and manually annotated, making the dataset more closely aligned with the actual problem to be solved in this paper. The majority of this training set comprised small targets in the early stages of fires, and the dataset used manual annotation, effectively deleting invalid images and making the annotation of fire targets more accurate. The final number of valid datasets was determined to be 4234, and the dataset was divided into training sets, validation sets, and test sets at a ratio of 8:1:1.
## 2 State of the Art--Ethernet Based Fronthaul Networks
In a study [30], multipath routing schemes were expedited for performing the dynamical load balancing and improving the congestions in TSNs by monitoring and maintaining the various performance indicating parameters. However, the existing QoS-aware routing schemes focus on searching the feasible routes subject to single or multiple QoS constraints, and select the best routes with no consideration of the frame level queuing delays, which is the case in the fronthaul bridged networks due to the simultaneous transmissions of the globally synchronized eCPRI bursts. Moreover, a high control messages overhead with the legacy routing schemes [31] due to the dynamic selection of the paths and continuous monitoring of the networking parameters further increases the delays, which makes them unsuitable for the E2E latency constraints of the EFBNs. The study [18] proposes a queuing model to improve the delays in optical fronthaul bridged networks. In this study, [PERSON] et al. proposed a low latency routing (LLR) mechanism based on the Markov chain Monte Carlo (MCMC) method, which searches the paths for individual streams by considering the traffic load on the different links. This study showed that the legacy QoS-aware routing schemes such as the constraint-based shortest path bridging (SPB) [32] become inefficient to satisfy the latency requirements in the MFH bridged networks, and schemes such as the LLR are required to satisfy the MFH requirements. However, the LLR scheme also yields intolerable E2E latencies, and becomes ineffective at complying with the eCPRI requirements once the traffic load increases to a certain level. This is because the LLR scheme uses a single path for transporting the time-sensitive flows, which are usually the shortest neighboring paths. Under the high load conditions, these alternative paths also get congested and cause intolerable E2E latencies for the eCPRI flows. Moreover, the LLR scheme lacks the mechanism to minimize the queuing delays of eCPRI streams of equal priority at the bridging nodes, which is highly desirable for alleviating the E2E latencies and successfully deploying the leveraging Ethernet-based fronthaul bridged networks in 5G cloud-RANs.
# Development of Path Planning Tool for Unmanned System Considering Energy Consumption
###### Abstract  
Increasing the flight endurance of unmanned aerial vehicles (UAVs) has received attention recently. To solve this problem, two research topics have generally appeared: Shortest-path planning (SPP) and remaining-flying-range estimation. In this work, energy-efficient path planning by considering the distance between waypoint nodes, the minimum and maximum speed of the UAV, the weight of the UAV, and the angle between two intersecting edges is proposed. The performances of energy-efficient path planning (EEPP) and generic shortest-path planning are compared using extended-Kalman-filter-based state-of-charge and state-of-power estimation. Using this path-planning tool and considering energy consumption during flight operation, two different path plans can be obtained and compared in advance so that the operator can decide which path to choose by consulting a comparison chart. According to the experimental results, the EEPP algorithm results in 0.96% of improved SOC leftover and 11.03 (W) of lowered SOP compared to the SPP algorithm.  
EEPP; EKF; path planning; SOC; SOP Article
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 7 Influence of Different Structural Systems
### Shear Walls  
The research literature offers crucial insights into the performance of both steel and reinforced concrete shear walls under the combined impact of seismic activity and fire. An in-depth analysis of these behaviors highlights substantial challenges and considerations that must be addressed in the design and evaluation of such structures.
# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 3 System Design and Implementation  
The Purdue model, originally proposed for manufacturing systems [35], has been adopted to design the system architecture, given its widespread use in Industrial Internet of Things (IIoT) environments [36]. The Purdue model is a structural model that concertssegmentation. It consists of several hierarchical levels, each with a specific function: LEVEL 4/5--enterprise or corporate, LEVEL 3--supervision or operation, LEVEL 2--control or communications, LEVEL 1--process devices, and LEVEL 0--field devices or physical process. This layered architecture provides a clear organizational structure that facilitates communication and integration across different levels of a control system. Additionally, it enhances system security and reliability by limiting access and interaction between the different layers. The system architecture is divided into the following two main layers:  
* OT (Operational Technology): Encompasses the devices that interact with the environment, such as sensors, controllers, and communication gateways (covering levels 0 to 2).
* IT (Information Technology): Manages data processing, storage, and visualization tools to support decision-making processes (covering levels 3 to 5).  
The system architecture following the layer distribution of the Purdue model is shown in Figure 1.  
The system's modular design facilitates a flexible and scalable architecture. It is based on open-source principles, with each component developed as an independent module that is interconnected using open standards. The microcontrollers employed in this  
Figure 1: System architecture based on the Purdue model, illustrating the separation between OT and IT layers.  
project are Arduino and/or ESP32-based, chosen to facilitate hardware modularity. On the software side, the system uses a layered architecture, APIs, and open protocols to separate application logic into independent and reusable components. The main advantage of this modular design is that it allows the system to be easily adapted to changing requirements, extended with new functionalities, and scaled as needed.  
Table 1 presents the selected technologies used in the developed system, organized according to the layers and levels of the Purdue model. The design and development of the system components are subsequently described following this OT and IT layer structure, beginning with the nodes and concluding with the applications.