esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
## 3 Path Segmentation-Based Hybrid Caching
### Caching Idea Based on Time Segmentation
#### 3.2.1 Base Station Clustering  
Two neighboring base stations have similar user access time series due to their close location, so we use K-means clustering algorithm to regionalize the base stations. K-means clustering algorithm is an iterative solution clustering analysis algorithm. The step is to randomly select K objects as the initial clustering centers, then calculate the distance between each object and each seed clustering center, and assign each object to the nearest cluster center. Algorithm 1 details the process of base station clustering. _K_class_ represents the number of clusters and Z is the base station location data collection. First, the cluster centers are randomly initialized. Next, cluster each node to the nearest center (step 2). Then, for each class \(j\), recalculate the center of the class, \(c(i)=\arg\min\limits_{j}\lVert Z_{i}-\mu_{j}\rVert^{2}\), of which \(\mu_{j}=\sum_{i=1}^{N}l\{c(i)=j\}Z_{i}\Big{/}\sum_{i=1}^{N}l\{c(i)=j\}Z_{i}\). Repeat step 2 until the cluster center no longer changes significantly. Set V is the final set of different class.
# An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic
## 3 The P-S Multirate Loss Model under Restricted Accessibility  
### The Analytical Model  
We consider again the P-S model and apply the notion of restricted accessibility. To this end, let each state \(j_{1}>0\) be associated with a blocking probability factor, \(pb_{k,l}(j_{1})\). Note that if the available subcarriers for service-class (\(k,l\)) calls are not enough (i.e., when \(j_{1}\geq M-b_{l}+1\)), then \(pb_{k,l}(j_{1})=1\). Similarly, if the system is empty, then \(pb_{k,l}(0)=0\).  
The admission mechanism for a call of service-class (\(k\),\(l\)) in the P-S/res model is the following: (a) if \((M-j_{1}\geq b_{l})\cap(j_{2}+p_{k}b_{l}\leq P)\) then the service-class (\(k\),\(l\)) call is accepted in the cell with probability \(1-pb_{kJ}(j_{1})\) and remains for a service-time which is generally distributed with mean \(\mu^{-1}\), (b) if \((M-j_{1}<b_{l})\cup(j_{2}+p_{k}b_{l}>P)\) then the call is blocked due to subcarriers' unavailability.  
The proof of a recursive formula for the unnormalized values of \(q(j_{1},j_{2})\)'s requires the existence of local balance between states \((j_{1}-b_{l},j_{2}-p_{k}b_{l})\) and \((j_{1},j_{2})\). These two states differ only by one in-service call of service-class (\(k\),\(l\)). The form of local balance equation is as follows:  
\[\alpha_{kl}\big{(}1-pb_{kJ}(j_{1}-b_{l})\big{)}q(j_{1}-b_{l},j_{2}-p_{k}b_{l} )=y_{kl}(j_{1},j_{2})q(j_{1},j_{2}), \tag{10}\]  
where \(y_{kl}(j_{1},j_{2})\) refers to the mean number of service-class (\(k\),\(l\)) calls in state \((j_{1},j_{2})\).  
Multiplying both sides of (10) by \(b_{l}\) and summing over \(k\) and \(l\) we obtain the following formula for the recursive calculation of \(q(j_{1},j_{2})\)'s in the P-S/res model:  
\[\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}\alpha_{kl}\big{(}1-pb_{kJ}(j_{1}-b _{l})\big{)}q(j_{1}-b_{l},j_{2}-p_{k}b_{l})=j_{1}q(j_{1},j_{2}), \tag{11}\]  
or  
\[q(j_{1},j_{2})=\left\{\begin{array}{ll}1,&\mbox{for $j_{1}=j_{2}=0$}\\ \frac{1}{j_{1}}\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}\alpha_{kl}b_{l}\big{(} 1-pb_{kJ}(j_{1}-b_{l})\big{)}q(j_{1}-b_{l},j_{2}-p_{k}b_{l})\\ &\mbox{for $j_{1}=1,\ldots,M$ and $j_{2}=1,\ldots,P$}\end{array}\right.. \tag{12}\]  
Based on the unnormalized values of \(q(j_{1},j_{2})\), we can determine the CBP \(B(k\),\(l)\) of service-class (\(k\),\(l\)) via:  
\[B(k,l)=\sum\limits_{\{(j_{1}+b_{l}>M)\cup(j_{2}+p_{k}b_{l}>P)\}}G^{-1}q(j_{1}, j_{2})pb_{kJ}(j_{1}), \tag{13}\]  
while \(E(k\),\(l)\), BP, SU, and PU are calculated via (6)-(9), respectively.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 6 GitHub Actions CI/CD Pipelines
### GitHub Actions Workflows
#### 6.3.2 Deletion Workflow (delete-infra.yml)  
Complementing the deployment pipeline is the deletion workflow, which enforces complete lifecycle management of infrastructure. Infrastructure as code is not just about  
Figure 4: Deployment pipeline steps in the GitHub portal.  
provisioning but also about safe, reliable, and auditable teardown. The deletion workflow ensures that resources can be cleanly removed when they are no longer needed, thereby avoiding orphaned resources, unexpected costs, and security liabilities. The design ensures that the infrastructure lifecycle is fully automated from creation to destruction, enabling ephemeral environments for development and testing that mirror production with minimal manual effort, as shown in Figure 5[29].  
Workflow steps:  
* Checking out the Repository, which retrieves any required configuration;
* Azure Login, which authenticates securely using the same credentials, ensuring only authorized SPN can initiate deletions;
* Removing Managed Identity roles and Resource Group Deletion, which removes the MI-assigned roles and then deletes the entire resource group and all contained resources, providing a predictable and complete cleanup process. The process allows the pipeline to finish immediately while the deletion proceeds asynchronously in Azure.  
A dedicated deletion workflow ensures the clean and secure teardown of infrastructure components once they are no longer needed. This workflow removes role assignments, deletes the managed identity, and destroys resource groups used for ACR and infrastructure.  
The full delete-infra.yml GitHub Actions workflow is provided in Appendix 1.  
Both workflows utilize the workflow_dispatch trigger, enabling manual and auditable releases. This choice enables teams to carefully control when infrastructure changes occur in different environments (development, QA, production).  
Figure 5: Deletion pipeline steps in the GitHub portal.
## 2 Proposed Method
### Content Owner's Work  
#### 2.1.1 Generating of the Linear Regression-Based Predictor  
In the proposed method, to vacate a large amount of embedding room, an accurate pixel predictor is required to generate concentrated prediction errors. To generate the predictor, a linear regression model is trained based on the original image. Figure 2 shows that the linear regression model predicts one pixel by using its three neighboring pixels, and the predicted value is calculated as follows:  
\[p_{x}=w_{0}+w_{1}p_{1}+w_{2}p_{2}+w_{3}p_{3}. \tag{1}\]  
To acquire the applicable four coefficients, i.e., \(w_{0}\), \(w_{1}\), \(w_{2}\), and \(w_{3}\), for the original image, the linear regression model in Equation (1) is trained using the training dataset that was constructed from the original image. The training dataset was constructed as follows. Figure 3 shows that the original image is divided into two parts, i.e., (1) the reference pixels that contain all pixels in the first row and the first column of the original image and (2) the predictable pixels that contain the other pixels of the original image. Denoting the training set as \(\mathbf{D}=\{\mathbf{X};\mathbf{Y}\}\), Figure 2 shows that the target set \(\mathbf{Y}\) consists of all predictable pixels,  
Figure 1: This is a figure. Schemes follow the same formatting.  
Figure 2: Predictable pixel and its three neighboring pixels.  
and the feature set \(\mathbf{X}\) consists of the three neighbor pixels of each predictable pixel. For an original image that has the size of \(H\times W\), the training set \(\mathbf{D}=\{\mathbf{X};\mathbf{Y}\}\) is constructed as:  
\[\mathbf{D}=\left\{p_{i,j},p_{i,j+1},p_{i+1,j};p_{i+1,j+1}|1\leq i\leq H-1,1\leq j\leq W -1\right\}, \tag{2}\]  
where \(p_{i,j}\) is the pixel at the coordinates \((i,j)\) of the original image.  
Based on the training set \(\mathbf{D}\), a linear regression model \(\mathbf{LM}=\{w_{0},w_{1},w_{2},w_{3}\}\) can be trained by any linear regression algorithm. Using the model \(\mathbf{LM}\) as the predictor, the prediction errors of all predictable pixels can be calculated and encoded to vacate room in the original image. The details are provided in Section 2.1.2.
###### Abstract  
The emergence of dual frequency global navigation satellite system (GNSS) chip actively promotes the progress of precise point positioning (PPP) technology in Android smartphones. However, some characteristics of GNSS signals on current smartphones still adversely affect the positioning accuracy of multi-GNSS PPP. In order to reduce the adverse effects on positioning, this paper takes Huawei Mate30 as the experimental object and presents the analysis of multi-GNSS observations from the aspects of carrier-to-noise ratio, cycle slip, gradual accumulation of phase error, and pseudorange residual. Accordingly, we establish a multi-GNSS PPP mathematical model that is more suitable for GNSS observations from a smartphone. The stochastic model is composed of GNSS step function variances depending on carrier-to-noise ratio, and the robust Kalman filter is applied to parameter estimation. The multi-GNSS experimental results show that the proposed PPP method can significantly reduce the effect of poor satellite signal quality on positioning accuracy. Compared with the conventional PPP model, the root mean square (RMS) of GPS/BeiDou (BDS)/GLONASS static PPP horizontal and vertical errors in the initial 10 min decreased by 23.71% and 62.06%, respectively, and the horizontal positioning accuracy reached 10 cm within 100 min. Meanwhile, the kinematic PPP maximum three-dimensional positioning error of GPS/BDS/GLONASS decreased from 16.543 to 10.317 m.  
Android smartphone; precise point positioning; multi-GNSS; stochastic model; robust Kalman filter; gradual accumulation of phase error +
Footnote †: journal: Sensors
## 5 Simulation and Performance Analysis
### Experimental Evaluation Indicators  
The experimental evaluation indicators in this study encompass ACCuracy (ACC), RECall (REC), PREcision (PRE), False Alarm Rate (FAR), F1-score, Area Under Curve (AUC), and Mean Absolute Error (MAE). Higher values of ACC, PRE, REC, F1-score, and AUC are indicative of superior classifier performance. The formulas for the aforementioned parameters are provided below.  
\[A\text{CC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}},\text{ REC}=\frac{\text{TP}}{\text{TP}+\text{FN}},\text{ PRE}=\frac{\text{TP}}{\text{TP}+\text{FP}},\text{ FAR}=\frac{\text{FP}}{\text{TN}+\text{FP}},\]  
\[\text{F1-score}=2\times\frac{\text{REC}\times\text{PRE}}{\text{REC}+\text{ PRE}},\text{ MAE}=\frac{1}{N}\times\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}|\]  
where \(\hat{y}_{i}\) is the predicted value, \(y_{i}\) is the actual value, and the parameters are from the following confusion matrix, as shown in Table 4.  
The AUC represents the area under the receiver operating characteristic curve (ROC).
# Recent Progress in Synthesis and Photonic Applications of Two-Dimensional Bismuthene
## 2 Structure and Properties of Bismuthene
### Structure and Electronic Properties  
According to the theoretical results of the first-principles calculation, bismuthene exhibits three possible phases, i.e., \(\alpha\)-, \(\beta\)-, and \(\xi\)-phases, with comparable lowest average binding energy. However, until now, only \(\alpha\)- and \(\beta\)-bismuthene have been successfully realized in the laboratory, owing to their energetic stability [15; 33]. As shown in Figure 1a,b, \(\alpha\)-phase bismuthene has a honeycomb, wrinkled crystal structure, and \(\beta\)-phase bismuthene possesses a crooked layered structure. Both \(\alpha\)- and \(\beta\)-bismuthene exhibit a layered structure, with weak van der Waals (_vdWs_) interactions between adjacent layers, enabling the straightforward acquisition of bismuthene by the mechanical exfoliation method. It has been reported that \(\alpha\)-phase bismuthene exhibits non-trivial 2D topological insulator properties, with a large bulk bandgap at room temperature [15]. Compared to the \(\alpha\)-phase type, \(\beta\)-phase bismuthene is more stable, with the ability to sustain a pristine morphology and layer structure, even under temperatures up to 400 \({}^{\circ}\)C (Figure 1c) [34]. Moreover, because of the difference between the in-plane and out-of-plane inter-atomic distance, \(\beta\)-bismuthene shows clear anisotropic properties (12%). Excluding \(\alpha\)-phase and \(\beta\)-phase bismuthene, bismuth atoms can also form buckled square or octagon rings, which have been confirmed by molecular dynamics calculations [35].  
Previous theoretical studies based on the density functional theory (DFT) have been carried out to study the band structures of bismuthene for evaluating their electrical characteristics [36; 37; 38]. According to the computation results, \(\alpha\)-bismuthene and \(\beta\)-bismuthene are direct bandgap semiconductors, with bandgaps of 0.36 eV and 0.99 eV, respectively (Figure 1d). In addition, the absorption coefficient of both \(\alpha\)-bismuthene and \(\beta\)-bismuthene were higher than \(10^{4}\) cm\({}^{-1}\), and the high mobility of bismuthene (\(10^{2}\)-\(10^{3}\) cm\({}^{2}\)/V\(\cdot\)s) was obtained using the acoustic phonon limited mobility approach [39]. Furthermore, by employing HSE06 method to revise the band structure, \(\beta\)-bismuthene shows a new indirect band gap feature [37]. Meanwhile, the Rashba spin-splitting phenomenon was also observed in the valence band maximum states. The Rashba energy can be modulated by applying in-layer biaxial strain, owing to the buckling structure of \(\beta\)-bismuthene. Importantly, \(\beta\)-bismuthene can undergo a transition from indirect band gap to direct band gap, as well as from semiconducting to semimetallic characteristics by enforcing the biaxial strain. In addition to monolayer bismuthene, multi-layer bismuthene was also confirmed to demonstrate a transition from semiconductor to semimetalic [40].
## 2 Materials and Methods
### 165 rRNA Gene Analysis  
#### 2.3.1 DNA Extraction, PCR Amplification, and 165 rRNA Gene Sequencing Analysis  
Duplicate black-spot samples were obtained from two lotus tubers at field NI (Table 1). These samples were stored at \(-80\)\({}^{\circ}\)C prior to DNA extraction. DNA extraction was performed using a FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer's protocol. PCR amplification of the 165 rRNA gene was performed using the universal forward primer (Univ515F: 5\({}^{\prime}\)-GTGCCAGCMGCGCGGTTAA-3\({}^{\prime}\)) and the reverse primer (Univ909R: 5\({}^{\prime}\)-CCCCGYCAATTTTTRAGT-3\({}^{\prime}\)) [14]. PCR was performed according to the protocol used in a previous study [15]. PCR products were purified by a QIAquick PCR purification kit (Qiagen, Valencia, CA, USA) following the manufacturer's protocol. The purified 165 rRNA genes were sequenced using the MiSeq Reagent kit v3 and the MiSeq system (Illumina, San Diego, CA, USA). Raw 165 rRNA gene sequences were analyzed using QIIME 2 ver. 2021.4 [16]. Quality trimming, primer sequence removal, paired-end assembly, and a chimera check were performed using DADA2 [17]. After
# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 3 Proposed Studies
### Research Contribution  
In this paper, we proposed an energy-efficient framework to enhance the wireless sensor network security and detect anomaly detection. The TEAHR framework is designed to improve the security and performance of WSNs by dynamically assessing the trustworthiness of sensor nodes based on multiple criteria. The core idea behind TEAHR is to integrate trust evaluation with energy-aware routing to create a robust and efficient network. In the proposed framework, by considering factors such as energy consumption, packet forwarding effectiveness, consistency in behavior, and adherence to network protocols, the TEAHR framework provides a comprehensive approach to managing Trust and enhancing the trustworthiness of WSNs. The critical components of TEAHR are as follows:  
* Energy Trust: Energy trust refers to the evaluation of a node's remaining energy levels as a measure of its operational trustworthiness and expected lifespan. In WSNs, energy efficiency plays a critical role due to the limited power resources of sensor nodes, which are often powered by tiny batteries. Nodes with higher energy reserves are more likely to continue functioning reliably, making them more trustworthy within the network. * Forwarding Trust: Forwarding Trust assesses a node's effectiveness in forwarding packets to their intended destination. In a WSN, the ability of a node to reliably forward data is crucial for maintaining data integrity and ensuring that information reaches the intended recipients. A node's forwarding Trust is determined by monitoring its packet forwarding history, including metrics such as the number of successfully
# An Intelligent Condition Monitoring Approach for
## 4 Application and Results
### Experimental Rig Setup and Data Collection  
#### 4.1.1 Experimental Setup  
The shearing experiment was carried out on a simulated spent fuel shearing machine that can simulate the working processes (e.g., gripping, feeding and shearing of fuel assemblies) of the real shearing machines. The simulated fuel assembly was designed according to the structure of AFA-2G fuel assembly, and was simplified and reduced in scale. The structure of the fuel rods were made of 304 stainless steel, and the fuel rods were filled with Al\({}_{2}\)O\({}_{3}\) ceramic blocks instead of UO\({}_{2}\). The simulated shearing machine and fuel assembly are shown in Figure 5 and 6. The main parameter settings of the simulated shearing experiment are shown in the Table 1. In addition, an InLine 1000 high-speed camera (made by Fastec Imaging Corporation, San Diego, CA, USA) was used to record the shearing process through an observation hole designed on the simulated shearing machine.
## 7 Time Domain Optimization  
In the two previous sections, the question of how lidar scan patterns can be optimized for control has been addressed using a few different metrics. This section now deals with the question \"how can the lidar data processing and feedforward controller be optimized to improve the performance of wind turbine control?\". To provide a basic understanding of LAC, the section focuses on the following two aspects:  
* **Filtering**: Measurement filtering is crucial. No filtering or insufficient filtering of uncorrelated frequencies will cause unnecessary control actions, which will likely result in increased loads. By filtering out correlated frequencies, however, possible benefits of LAC algorithms will not reach their full potential.
* **Timing**: Temporal information about the inflowing wind field is necessary for control purposes. Because this information is available ahead in time, the lidar-assisted control action needs to be synchronized with the disturbance impact on the rotor.  
The remainder of the section is organized as follows: Section 7.1 provides the basic background to understand the main objectives of the section. Thereafter, the feedforward collective pitch controller is introduced followed by a short description of the filter used for the lidar wind preview signal in Section 7.2. Finally, Section 7.3 summarizes the results from the time domain optimization.
## 2 Materials and Methods
### Specim Preparation  
Two GHs (Equia Forte HT Fil and Riva Self-Cure), a zirconia-reinforced GIC (Zirconomer), a silver-reinforced GIC (Riva Silver), and a giomer (Beautiful II) were used in the study. Both GHs were tested either with coating as recommended by the manufacturers or without coating to test if the coating would affect their antibacterial activity. A nanohybrid resin composite was used as control.  
A customized cylindrical Teflon mold (2 \(\times\) 8 mm) was used for the preparation of the specimens. The Teflon mold was placed on a glass plate and the bottom surface was covered with a Mylar strip (S.S. White Limited, Middx, UK). Specimens were prepared according to the manufacturers' instructions as follows:  
_Equia Forte HT Fil_ (n:30): Equia Forte HT capsule was activated and mixed in auto-mixer (Softly, Satelec Acteon, Merignac Cedex, France). The material was immediately inserted into the mold, covered with a Mylar strip on the top, and then condensed against a glass plate with a constant finger pressure for the duration of the setting process (2 min, 30 s).  
_Equia Forte HT Fil with coating_ (n:30): The specimens in this group were prepared as described above and, when the setting was completed, Equia Forte Coat was applied using a micro-tip applicator without air blowing, and light-cured for 20 s using an LED curing unit (440-480 nm,1500 mW/cm\({}^{2}\), Radii plus, SDI, Victoria, Australia).  
Figure 1: Study Protocol.  
Riva Self-Cure_ (n:30): After the capsule was mixed for 10 s in auto-mixer, it was placed into the Teflon mold, covered with the strip on the top, and then condensed against a glass plate with a constant finger pressure for the duration of the setting process (2 min).  
Riva Self-Cure with coating_ (n:30): The specimens were prepared and coated as described above.  
Zirconomer_ (n:30): The specimens in this group were hand-mixed. Powder/liquid ratio was 3.6/1.0 (2 scoops:1 drop), working time was 1 min 30 s (from start of mixing), and setting time was 3 min.  
Riva Silver (n:30): After the capsule was activated, it was mixed in auto-mixer for 10 s and transferred into the mold with its own applicator. Then, it was covered with a Mylar strip with slight pressure. Working time was 1 min and 30 s and setting time was 5 min.  
Beautiful II_ (n:30): The giomer restorative was inserted into the mold with a flat-surface hand instrument, covered with strip, and polymerized using an LED curing unit for 20 s.  
G-Aerial A'Chord_ (n:30): The specimens of this group were packed into the molds by compressing between two glass slides and Mylar strips and then polymerized for 20 s.  
All specimens were polished immediately using a sequence of aluminum-oxide discs (medium, fine, and superfine) (Sof-Lex, 3M ESPE, MN, USA) for 30 s, at 20,000 rpm. Specimens were washed with distilled water and air-dried for 10 s after each step. Discs were replaced with new ones after every three specimens to obtain surfaces with homogeneous characteristics. Following polishing, specimens were sterilized in an autoclave at 121 \({}^{\circ}\)C for 15 min and directly subjected to antibacterial activity [23].
## 3 Mission Analysis
### Ground Station  
The ground station is scheduled to be situated at Beihang University (39.9824\({}^{\circ}\) N, 116.3488\({}^{\circ}\) E) for the nanosatellite visibility analysis. On the one hand, backup ground stations can be employed to maintain radio contact if the primary ground station (Beihang University) faces a problem and will be unable to function. Commands may also be allowed from these redundant ground stations for the continuity of the operations plan. On the other hand, additional amateur radio stations may be permitted to access, download, and decode data from some nanosatellites. As a result, the frequencies and operational modes of this nanosatellite may be made public for this purpose.  
The designed ground station is composed of a UHF/UHF ground station and can be located in the satellite center of Beihang University (BUAA). Figure 11 shows this ground station block diagram and Figure 12 shows its actual implementation with the Yagi antenna.  
As shown in Figure 12, the ground station at BUAA is a modular system that is installed specifically for nanosatellites employing UHF radio frequencies in LEO. Using a steerable antenna system, the ground station can autonomously track certain nanosatellites. All hardware (polarization switch, moto driver, antenna controller, radio station, and TNC), as well as specialized software, were included in the complete installation in the mission computer and control computer.  
Figure 11: Ground station block diagram.