esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 2 Defining the Deployment Problem
### Satellite Dispersal and Phasing
In the final stage, the drag configuration is no longer defined by convention but instead is determined analytically for each satellite using the phasing equations above. The disengagement sequence inherently creates multiple branches where the two satellites resulting from a disengagement are coupled through the same initial conditions. For example, in an eight-satellite system, four primary branches emerge:  
* Branch 1:
* \(S_{0,0}\to S_{1,1}\to S_{2,3}\to S_{3,6},S_{3,7}\);
* \(S_{0,0}\to S_{1,1}\to S_{2,2}\to S_{3,4},S_{3,5}\). * Branch 2: \(S_{0,0}\to S_{1,0}\to S_{2,1}\to S_{3,2},S_{3,4}\). * Branch 3: \(S_{0,0}\to S_{1,0}\to S_{2,0}\to S_{3,1}\). * Branch 4: \(S_{0,0}\to S_{1,0}\to S_{2,0}\to S_{3,0}\). Figure 2 illustrates these branches in terms of angular separation and phase times. Figure 3 shows the phasing control for the final stage of each branch. Solving the phasing times in Branch 1 involves the most complexity, as the initial conditions of each stage are heavily dependent on the final conditions of the previous stage. In Branch 2, this complexity decreases because the solution becomes insensitive to the final conditions of Stage 1 (i.e., \(k=1\)). This occurs because regardless of the time spent phasing \(S_{1,1}\), \(t_{k=1}\), the initial separation angle of the satellites in Stage 2 will always be zero, with its rate of change determined by the disengagement \(\Delta v\) and the orbital velocity at disengagement. In Branch 3, this insensitivity extends to include the final conditions of Stage 3. Lastly, Branch 4 requires no consideration, as the initial and final conditions are always \(\left[\dot{\theta}\ \dot{\theta}\right]=\left[0\ 0\right]\) by definition. In other words, the optimal solution for \(S_{3,\ 4\text{--}7}\) determines the value of \(\theta_{1,1}\), and the optimal solution for \(S_{3,\ 2\text{--}7}\) determines their respective \(\theta_{2,i}\). As the number of satellites increases, the disengagement sequence becomes increasingly complex. While the fundamental mathematical formulation remains consistent, the computational complexity grows due to the increased number of interactions and constraints that must be resolved. Specifically, the number of cases to analyze follows a logarithmic relationship, increasing with \((1+\log_{2}n)\).
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 3 Method
### HMM-Based KPI Anomaly Detection  
The Hidden Markov Model is a time series probability model that includes a hidden state and an observation state. The implicit state is the actual state within the system, and the observation state refers to the state that can be directly observed, and which has a correlation with the implicit state. The HMM can be described by five elements, including two state sets and three probability matrices. These five elements are the hidden state set \(S\), the observation state set \(V\), the initial observation probability \(\pi\), the hiddenstate transition probability matrix \(A\), and the observation state transition probability matrix \(B\). Generally, HMM is represented as \(\lambda=(A,B,\pi)\).  
The Baum-Welch algorithm [30] is often used to update the parameters of the HMM. [PERSON] [31] inferred the most likely explicit state in HMM. In this paper, the Baum-Welch algorithm is used to update the initial parameters of the HMM, and then the HMM is trained to obtain the most relevant and abnormal of the current KPIs. Finally, the anomaly detection of KPIs is realized. This paper uses HMM combined with the KPI correlation matrix obtained in the previous section to realize the detection of abnormal KPIs.
### Internet of Things  
The term 'IoT' is also termed as 'Internet of Objects'. IoT devices are of varying sizes and capabilities of electrical or electronic devices that are capable of connecting to the web. IoT devices can be utilized in a variety of settings, including those shown in Figure 2, including residence, manufacturing, environment, healthcare, electricity, and communication.  
IoT can be defined from several perspectives in the literature. Figure 3 shows the elements and paradigm of IoT [1,2]. In the case of the things-oriented vision, the aim of IoT is to become smart through the collaboration and focus of both virtual and physical bodies. These devices see, hear, think, share information, and perform tasks by coordinating decisions [2]. The Internet-oriented vision focuses on the development of IP-based networks so that things can connect and communicate with each other [3]. In the IoT systems, the demand of high volume data flows from and to sensors or smart entities emerges semantic-oriented visions [4]. In the IoT system based on service-oriented vision, IoT intelligent services and applications based on the three perspectives mentioned above are concentrated [5].  
Six main elements are required to deliver the functionality of IoT, as shown in Figure 3[2]. Among six elements, identification is crucial for naming and matching services with their demand. IoT devices capture data by sensing and send data to the cloud/database for analysis. The element of communication is used to bind heterogeneous objects simultaneously to serve specific digital services. WiFi [6], Bluetooth [7], Zigbee [6,8], MQTT [9], IEEE 802.15.4, OPC-UA, NFC, Z-wave, LoRaWAN [10], SigFox, and LTE-Advanced are a few examples of communication protocols. Hardware elements such as microcontrollers, microprocessors, system-on-chip (SoCs), and Field-programmable gate array (FPGAs) are used for processing. Processing units and software applications are used for computing. The combination of both hardware and software elements represents the  
Figure 2: IoT Application.  
brain of IoT. The ultimate goal of IoT is to deliver the services at such a level so that it is capable of providing services anytime, anywhere, to anyone.  
Generalized IoT system architecture has four layers which are Perception, Network, Processing, and Application layer which is shown in Figure 4[11,12]. The devices in the perception layer such as sensors of different types, Radio-frequency identification (RFID) scanners, surveillance cameras, Global Positioning System (GPS) modules, conveyor systems, industrial robots, etc., are responsible for monitoring conditions, collecting sensory data, etc. Different communication systems like WiFi, Bluetooth, Zigbee, LTE, etc., with protocols like IPv4, and IPv6 consist of the network layer responsible for data transfer to the next layer's processing system [13]. Typically, cloud servers and databases in the processing layer are responsible for data analyzing, computing, decision making, and storing a huge amount of data. As per the requirement of users, the application layer delivers the specific needs of the end-users.
## 3 Results and Discussion
### Numerical Simulation Analysis  
#### 3.4.1 Model Construction  
Numerical simulation is an important method to verify the rationality of model test results. Finite difference software was used to construct a numerical model with the same size as the prototype. As shown in Figure 13, the slope dimension is \(221\times 45\times 142\) m (length \(\times\) width \(\times\) height). The anchorage system was arranged on the slope surface, using steel bars with a yield strength of 400 MPa and a radius of 16 mm as anchor bolts. A fixed displacement boundary condition was applied at the bottom of the slope. A fixed normal displacement boundary condition was applied at the toe of the slope, the back side of the slope, and the left and right sides. In the mechanical calculation, the Mohr-Coulomb model was used to analyze the elastic-plastic mechanical behavior of the geological body.  
Figure 13: Modeling construction.  
Considering the difference in fracture development between strongly weathering and weakly weathering sandstone, a structural plane was established in the numerical model. The internal friction angle dominates the shear behavior of the structural plane. Considering the most unfavorable situation, the internal friction angle = 35 \({}^{\circ}\) was determined according to the parameters of the strongly weathering sandstone. In the fluid calculation, the structural plane can allow the seepage of groundwater, and the exposed position was set as the leakage boundary condition.  
The physical parameters of the geological body are summarized in Table 7, including density \(\rho\), elastic modulus \(E\), cohesion \(c\), internal friction angle \(\varphi\), Poisson's ratio \(v\), permeability coefficient \(k\), and porosity \(n\).  
The loading scheme of the numerical simulation is shown in Table 8. During Stage-1 to Stage-3, the process of rainfall enhancement was simulated by gradually increasing rainfall intensity. The fluid influx region was set at the strong-weak weathering interface (Figure 13). During Stage-4, the inflow condition of \(5\times 10^{-4}\) m\({}^{3}\)/s was applied to each mesh in the fluid influx region to simulate the hydraulic fluctuation.  
Based on the distribution of pore water pressure, slope displacement and bolt axial force were calculated. In the fluid calculation, when the saturation of the geological is less than 1, pore water pressure is regarded as 0. The fluid flow behavior in porous media is described by [PERSON]'s law [42]:  
\[q_{\rm i}=-k_{\rm i}k_{0}\big{(}p-\rho_{\rm i}x_{\rm i}g_{\rm i}\big{)}_{J}, \tag{4}\]  
where \(q_{\rm i}\) is the seepage velocity vector, \(p\) is the pore water pressure, \(k_{\rm i}\) is the permeability coefficient tensor of porous media, \(k_{0}\) is the coefficient related to saturation, \(\rho_{\rm f}\) is the fluid density, \(x_{\rm i}\) is the head height position, \(g_{j}\) is the gravity acceleration vector, and subscript \(l\) represents the directional gradient.
## 2 Engineering Background and Model
### Fluid-Solid Coupling Theory  
The coastal areas of China belong to the soft soil areas. The change of the underground pressure head in the soft soil will lead to elastic-plastic deformation, and during the excavation of the foundation pit with a waterproof curtain, the excavation of the foundation pit will cause the difference of the water head inside and outside the pit.  
The deformation of a saturated porous medium caused by fluid withdrawal is theoretically described by the three-dimensional fully coupled poroelasticity model [31]. The theory considers the stress-strain relationship between the fluid and the solid skeleton and its mode of motion separately [32].  
The complex situation is better reflected in the calculation of the Biot consolidation theory [7; 33]. Biot theory presents a three-dimensional, fully coupled pore elastic model, which is widely used in calculations for numerical simulations. The Biot theory assumes that all layers around the pit are homogeneous, fully saturated, and that the soil particles and pore water are incompressible and describes the deformation of saturated porous media caused by fluid withdrawal under [PERSON]'s law [34] and is applicable to the problem of deformation of porous media caused by the process of foundation pit precipitation. The groundwater seepage in the model follows [PERSON]'s theorem. Based on [PERSON]'s consolidation theory, the two-dimensional fluid-solid coupling equation can be expressed as:  
\[-G\
abla^{2}w_{x}-\frac{G}{1-2v}\cdot\frac{\partial}{\partial_{x}}\bigg{(} \frac{\partial w_{x}}{\partial_{x}}+\frac{\partial w_{y}}{\partial_{y}}\bigg{)} +\frac{\partial u}{\partial_{x}}=0 \tag{2}\]  
\[-G\
abla^{2}w_{y}-\frac{G}{1-2v}\cdot\frac{\partial}{\partial_{y}}\bigg{(} \frac{\partial w_{x}}{\partial_{x}}+\frac{\partial w_{y}}{\partial_{y}}\bigg{)} +\frac{\partial u}{\partial_{y}}=-\gamma_{t} \tag{3}\]  
\[-\frac{\partial}{\partial_{t}}\bigg{(}\frac{\partial w_{x}}{\partial_{x}}+ \frac{\partial w_{y}}{\partial_{y}}\bigg{)}+\frac{1}{\gamma_{w}}\bigg{\{} \frac{\partial}{\partial_{x}}\bigg{(}k_{x}\cdot\frac{\partial u}{\partial_{x} }\bigg{)}+\frac{\partial}{\partial_{y}}\bigg{[}k_{y}\bigg{(}\frac{\partial u }{\partial_{y}}+\gamma_{w}\bigg{)}\bigg{]}\bigg{\}}=0 \tag{4}\]  
\(G\) is [PERSON]'s modulus; \(\
abla^{2}\) is Laplace operator; \(w_{x}\), \(w_{y}\) refer to the displacement along the \(x\), \(y\) direction; \(v\) is [PERSON]'s ratio; \(u\) is pore water pressure; \(\gamma_{t}\), \(\gamma_{w}\) are the unit weight of soil and water; \(k_{x}\), \(k_{y}\) refer to the permeability coefficient along the \(x\), \(y\) direction.  
Calculation method: According to study [12], the yield criterion of the soil around the pit needs to be determined first, and in this paper the Moore-Coulomb criterion is used to calculate the stress-strain of the soil in different layers. Boundary conditions and initial pore water pressure conditions are set, parameters are input, and meshing is carried out before the calculation.
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 3 Experimental Results
### Fairness Index Verses Different UEs  
Figure 8 shows the fairness index for various UEs. From the obtained results, the QuAS and PSS mostly perform in a similar way. The main distinction between these two methodologies is convolution. Using the proposed ITFDS scheduling, the complexity is reduced, and the fairness is improved. The fairness index is computed using the values of normalized throughput and the total number of dynamic connections in LTE. The fairness is another parameter for producing the quality of service, the user service requests are very essential to transit the large amount of data. The fairness value has been increased in the proposed technique that allocates more amounts of resources whenever the total users are increased.
# Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse
## 3 Engineering Situation and MS System  
### Geological Conditions  
The geological structure of the Yakes mining area is relatively uncomplicated, with well-preserved and stable top and bottom slate layers surrounding the ore body. Within the ore-bearing rocks, there is a diverse range of rock types, primarily comprising dunite, dunite peridotite, and peridotite. Occasionally, there are occurrences of pure peridotite and pyroxene. The ore body in this region primarily contains copper and nickel. The primary mining method employed is sublevel caving, with the shrinkage stoping method utilized for the thinner ore bodies situated at the edges and corners of the main ore body. The ore body within the designed mining range is buried within the elevation range of 980-277 m, and the design adopts underground mining and vertical shaft development methods. The designed production scale is 1.32 million tons/year, and mining operations are currently being carried out in two layers of the ore body: 430 m and 410 m. During the mining process of the 30# ore body, large gaps between the upper goat and the top of the goat are easily formed, resulting in a thin column between the goat and the 32# ore body, which may intersect. At the same time, the surrounding rock of the bottom structure is significantly broken, has severe mud deposition, and has developed joint surfaces, and local areas have already exhibited slope fragmentation and roof fall.
### First-Generation (1G) Technology  
In the 1980s, an analog telecommunications system known as first-generation (1G) wireless telephone technology was unveiled. Speech services were transmitted analogously by first-generation mobile devices. NTT (Nippon Telegraph and Telephone) introduced the first commercial 1G cellular network in Japan in 1979, first in the Tokyo metropolis and then in Europe in 1981. Cellular networks could not be utilized to place calls across international borders, although 1G systems did offer handover and roaming capabilities. This was a blatant flaw in the initial generation of mobile networks. Features of 1G technologies were similar, only supporting analog systems, and adopting the Frequency Modulation (FM) schemes; they had a low capacity, since they employed FDMA multiplexing and 1G was limited to voice calls exclusively. However, 1G cellular networks also had several drawbacks. The analog signal transmission led to poor voice quality and frequent call drops. The lack of encryption made these networks highly insecure, making it easy for eavesdroppers to intercept calls. The devices themselves were bulky and expensive, with limited battery life. Moreover, the network coverage was sparse, often resulting in poor connectivity in less populated areas [3].
###### Abstract  
This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human-machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% \(\pm\) 1.4% accuracy, 68.7 ms \(\pm\) 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (\(\kappa\) = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems.  
adaptive mobility systems; gesture recognition; IoT sensor fusion; high-dimensional machine learning; adaptive chassis design; safety-critical AI +
Footnote †: journal:  
_Article_
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 5 Conclusions  
A method for the pose measurement of non-cooperative spacecrafts based on binocular and TOF camera collaboration was proposed. Firstly, a joint calibration method between the binocular camera and TOF camera based on a circular calibration board was conducted. The reprojection error demonstrated the effectiveness of the proposed calibration method. Then, the initial pose method was mainly divided into feature extraction, data association, and Kalman model suppression. The frame-to-frame ICP registration and pose correction based on key frames were carried out during the point cloud tracking and pose optimization. The pose results of the proposed method, stereo vision method, and the traditional FPFH method were compared in the ground verification experiment. The experimental results show that the position error of the proposed method was within 1 cm, and the angle error was within 1 degree in a low-speed rotation process. The position and angle error were within 1.5 cm and 1.4 degrees during the high-speed rotation conditions, respectively. The proposed method had a certain accuracy and robustness when chasing the target satellite, especially for a satellite with an unknown structure. In further studies, the relative orbital and attitude dynamics should be considered in the Kalman filtering process, and the proposed method should be implemented on the embedded platform to verify the real-time performance.  
Conceptualization, L.H. and H.D.; methodology, L.H. and D.S.; Validation, L.H., D.S. and A.S.; formal analysis, S.Z.; investigation, H.P.; resources, H.P.; data curation, L.H., D.S. and A.S.; writing--original draft preparation, L.H.; writing--review and editing, D.S. and H.D.; supervision, S.Z.; funding acquisition, H.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Preliminary Research Foundation of Equipment, grant number: 3050404030, and the Innovation Program CX-325 of the Shanghai Institute of Technical Physics.  
Figure 20: Comparison of the computational time between the proposed method and the FPFH method in rotation experiments: (**a**) low-speed experiment; (**b**) high-speed experiment.
## 5 Evaluation and Results
### Access Outcome Analysis and Performance Metrics  
We present the distribution of access decisions made during the simulation. The granted and denied request counts highlight how the implemented ABAC policies filtered user-resource combinations based on defined attribute conditions.
# Physicochemical and Sensory Evaluation of Yanggaeng Treated with Roasted Guateman Coffee Extracts
## 3 Results and Discussion
### Hunter's Color Properties of Yanggaeng
The yellowness/blueness (\(b^{\bullet}\)) values remained positive and increased with increasing G concentration across all roasting levels. Specifically, \(b^{\bullet}\) values increased from 2.47 to 9.93 in group L, 2.33 to 10.30 in group M, and from 1.23 to 8.37 in group D, reflecting an overall enhancement in yellowness. The \(\Delta E\) values calculated for each group, representing the color difference from the CON sample, are as follows: for the L group, \(\Delta E\) values were 7.71 \(\pm\) 0.64, 7.71 \(\pm\) 0.19, and 8.98 \(\pm\) 0.11; for the M group, \(\Delta E\) values were 10.66 \(\pm\) 1.14, 9.57 \(\pm\) 0.29, and 14.67 \(\pm\) 0.34; and for the D group, \(\Delta E\) values were 12.03 \(\pm\) 0.40, 13.60 \(\pm\) 0.32, and 14.90 \(\pm\) 0.24. The \(\Delta E\) values indicate that significant color differences occurred as the G concentration increased, with darker roasts showing higher \(\Delta E\) values, suggesting a greater perceptible color change compared to the CON. The color analysis results demonstrated that the incorporation of G significantly modified the color properties of Yanggaeng, with the roasting levels and G concentrations exerting distinct effects on \(L^{\bullet}\), \(a^{\bullet}\), and \(b^{\bullet}\) values. The color transformation of coffee beans during roasting is attributed to several chemical reactions that occur upon heating. Amino acids and reducing sugars in coffee beans react to form melanoidins during roasting [27]. The Maillard reaction, which is primarily active during light roasting, initiates the initial browning process [28]. As the roasting temperature increases, sugars undergo caramelization, leading to further browning and decomposition into simpler compounds [29; 30]. Consequently, the color of coffee deepens to a darker brown as roasting progresses. Additionally, moisture loss during dark roasting concentrates the remaining compounds, intensifying the color of coffee [31]. These findings indicate that the chemical components of coffee extracts significantly affect the color properties of Yanggaeng. Understanding these changes is crucial for developing coffee-infused desserts with desirable visual and sensory characteristics.
## 3 SDWSNs and IEEE 802.15.4e TSCH  
We consider an architecture based on SDWSN over TSCH, and this section presents the main concepts of these technologies.