esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Proactive Routing Protocols
#### 2.1.1 Destination-Sequenced Distance Vector (DSDV)  
This routing protocol is based on the Bellman-Ford-Moore algorithm, with a little modification to render it more suitable for FANETs. In DSDV, each UAV must know absolutely everything about all of the other UAVs connected to the network, due to the up-to-date routing information [20]. This is due to their proactive nature, where the routing table is periodically updated about the complete network. However, these periodic updates can result in routing loops. To solve this problem, DSDV adds sequence numbers with the data packets [21]. The most recently used path with the highest  
Figure 1: Classification of topology-based routing protocols.  
sequence number receives preference over a path with a lower sequence number. The main advantages of DSDV are the practice of sequence numbers and the simplicity, which guarantees the loop-free data transmission among the UAVs [22]. However, the main drawback of this routing protocol is the periodic updates, which create extra signaling overhead during the data transmission. This protocol is not suitable for highly vibrant networks, where the network topology changes more frequently. In addition, it supports single path routing but does not have a multipath routing capability.
###### Abstract  
Background:Mandibular reconstruction remains challenging in oral and maxillofacial surgery due to its complex anatomy and functional requirements. While Deep Circumflex Iliac Artery (DCIA) flaps offer advantages in bone height and natural contour, their application with computer-aided surgical planning remains limited. **Aim**: This preliminary study aimed to evaluate the accuracy and clinical outcomes of mandibular reconstruction using DCIA free flaps with patient-specific surgical guides and customized plates. **Materials and Methods**: A pilot study of five patients who underwent mandibular reconstruction using DCIA free flaps was conducted. Virtual surgical planning was performed using Cone-Beam Computed Tomography (CBCT) and pelvic Computed Tomography (CT) data to design patient-specific cutting guides and reconstruction plates. All surgeries were performed by a single experienced surgeon using standardized techniques. **Results**: The mean deviation between the virtual planning and actual surgical outcomes was 0.73 \(\pm\) 0.23 mm, with maximum deviations ranging from 3.87 to 7.83 mm. All flaps achieved 100% survival, with one case (20%) experiencing screw loosening that required plate removal. This complication led to the modification of the plate design to include six or more holes in subsequent cases. **Conclusions**: Despite limitations including the small sample size and short follow-up period, our preliminary results demonstrate that computer-aided surgical planning with patient-specific devices can significantly improve the accuracy and predictability of DCIA flap mandibular reconstruction, even in challenging revision cases. Further studies with larger patient cohorts are needed to validate these findings.  
DCIA flap; mandibular reconstruction; customized plate; virtual surgical planning +
Footnote †: journal: Applied Sciences
## 4 Experiments
### Data Preprocessing and Augmentation  
In order to enhance the model's generalization capacity, mitigate the occurrence of overfitting and underfitting, and improve its resilience to disturbances such as noise and variations in angles, we performed data preprocessing and augmentation on the facial expression images used in the experiments. Specifically, prior to training, all original images were first resized to a fixed dimension of 256 \(\times\) 256 pixels. Subsequently, random cropping was applied to obtain images of size 224 \(\times\) 224 pixels. Moreover, we employed the Five-Crop strategy to further augment each expression image. This strategy involved cropping sub-images of specified sizes from the top-left, top-right, bottom-left, bottom-right, and center regions of the image, resulting in a five-fold increase in the number of image samples.
## 4 Results  
In this section, several interpretations of the Landsat LST data with respect to the landfill are presented. While these interpretations yield quantitatively different results, the general trends identified by each enable hotspot detection. This unanimity of interpretation suggests that these algorithms are robust with respect to each other and yield similar conclusions regarding the spatial and temporal extents of hotspots determined by others. Here, we use simple stationary statistical metrics (e.g., mean and standard deviation) to demonstrate proof-of-principle use of LST measurements for landfill hotspot detection and monitoring. Future extensions of these approaches, which are described below, may involve more rigorous (thermal) change detection techniques such as cumulative sum control (CUSUM) and related online change-detection algorithms.  
LST-change interpretations are presented here at three levels of spatiotemporal resolution. This has the advantage of reducing complexity when detecting or monitoring landfills, i.e., there is no need to expend computational resources or human-in-the-loop attention for the observation of baseline thermal behavior, but upon detection of a hot spot, different degrees of spatial resolution can be invoked. Initially, the landfill is treated as a whole system characterized by average, minimum, and maximum LSTs across the entire spatial domain. Time series trends enable the observation of baseline and excursive behaviors, although with no distinct spatial resolutions. Subsequently, the landfill is divided into regions (determined by both historical definitions as well as automated clustering analysis) to resolve the general location of the enhanced thermal activity. For sites like the Bridgeton landfill that are adjacent to sensitive assets (e.g., the radiologically impacted zone indicated in Figure 1), such determination indicates the areal extent that may require action without adding further complexity (quantity, noise, uncertainty) from pixel-resolved (30 \(\times\) 30 m) LST measurements. Lastly, we consider pixel resolved LST measurements of the Bridgeton landfill to further demonstrate the robustness of our analysis. As demonstrated below, each of these analyses based on satellite remote sensing was corroborated by independent ground-truth data and field measurements for this well-studied site [21, 23, 31].
## 5 DFIG Connected at Electrical Grid
### B2B Power Converter Analysis
#### 5.1.2 Modulation Method  
The B2B power converters have as their main component a voltage source converter, which uses pulse width modulation (PWM) for controlling purposes. This technique uses power semiconductors that operate at high frequencies. In this paper, the sinusoidal PWM (SPWM) is used since it is mature technology. In this method a triangular wave is compared with a sinusoidal wave. For the SPWM technique, with an amplitude modulation index less than one, the fundamental voltage amplitude varies linearly with this modulation index, but the fundamental frequency is less. When you have an amplitude modulation index greater than one, the amplitude also increases, leading to overmodu-lation, and the output waveform contains many harmonics. The dominant harmonics in the linear range may not be so in overmodulation; the amplitude of the fundamental component does not vary linearly with the amplitude modulation index. In power applications, the overmodulation region should be avoided as much as possible to minimize distortion in the output voltage. Within the MATLAB-Simulink(r) software, the SPWM technique is developed using some blocks that have already been designed in the master library. The carrier signal operates at 5 kHz and is compared with the modulated signals at 60 Hz.
## 3 Methodology
### Need of Selection of Best-Fit Probability Distributions  
It is generally assumed that exponential distribution is the most suitable distribution to describe the failure behavior of components. The reliability requirements of all types of sub-systems within the system were estimated assuming a constant failure rate (exponential distribution). This assumption is misleading; in actual practice, the components do not always have constant failure rates and therefore an exponential distribution is not representative of most of the systems [44]. Here is a mathematical example: The Time-To-Failure (TTF) data of an electronic sub-system used in a machine tool is collected from the field and given in operating hours as 190, 510, 1359, 1399, 3000, 4561, 8731, and 15,601 (h). Table 4 presents the effects of selected reliability distributions on the reliability metrics. There is a significant variation in the reliability values for the given time, in the design life for the given reliability, and in the MTTF for the selected reliability distributions. The reliability metric and predictions using only an exponential distribution cannot be a true representative of this sub-system if the most suitable distribution is other than exponential, especially Weibull 2-parameter, Gamma, or lognormal. In this case, for the given data, the best-fit probability distribution is a Weibull 2-parameter distribution.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
###### Abstract  
This paper investigates the secure transmission for buffer-aided relay networks in the Internet of Things (IoT) in the presence of multiple passive eavesdroppers. For security enhancement, we adopt the max-link relay selection policy and propose three secure transmission schemes: (1) non-jamming (NJ); (2) source cooperative jamming (SCJ); and (3) source cooperative jamming with optimal power allocation (SCJ-OPA). Moreover, to analyze the secrecy performance comprehensively, two eavesdropping scenarios, i.e., non-colluding eavesdroppers (NCE) and colluding eavesdroppers (CE) are considered. Based on this, by modeling the dynamic buffer state transition as a Markov chain, we derive the exact closed-form expressions of the secrecy outage probability, the average secrecy throughput, and the end-to-end delay for each schemes. The analytical analysis and simulation shows that the SCJ-OPA scheme achieves similar performance as the NJ scheme when the total transmit power is small. On the other hand, when the transmit power is high, the performance achieved by SCJ-OPA is similar to that of SCJ. Thereby, the SCJ-OPA scheme can achieve better performance across the entire total transmit power, which makes up the defects of NJ and SCJ exactly.  
Keywords:buffer-aided relay; physical layer security; Internet of Things (IoT); secrecy performance +
Footnote †: journal: Applied Sciences
# A Game Model and Fault Recovery Algorithm for SDN Multi-Domain
## 4 SDN-MARA Algorithm
### Design of the SDN-MARA Algorithm
#### 4.3.1 Fault Controller Detection Algorithm  
The paper adopts the adaptive fault detection protocol proposed by [PERSON] et al. [24], which can be applied to the SDN control plane. The idea is to use the delay of heartbeat messages for fault judgment and analysis, thereby achieving controller fault detection. Thisdetection process is dynamic and can be self-adjusted and optimized according to network conditions. This paper identifies suspected faulty controllers based on this protocol.  
The process of detecting SDN controller faults based on this protocol is as follows: (1) In the SDN network, collect data related to the controllers, including controller log information, performance indicators (processing ability, capacity), network traffic, heartbeat information, etc. (2) Clean, filter, and organize the collected data. (3) Use a linear regression model for monitoring and judgment. During real-time monitoring, collect relevant data from the controllers and use the trained model to classify or predict the data. When an abnormality is detected, the fault detection mechanism is triggered, and corresponding measures are taken to handle the fault. The flowchart of the SDN controller fault detection algorithm is shown in Algorithm 1.  
```
Input: Controller status information (INFOc), heartbeat packet delay information (INFOh) Output: Predicted fault status of the controller y_pred
```  
1. Initialize linear regression model model = Initialize();
2. Define regression equation \(y_{\text{pred}}=\theta_{0}+\theta_{1}*\text{INFOc}+\theta_{2}*\text{INFOh}\);
3. Train model with training dataset trainingData = GetTrainingDataset() features = ExtractFeatures(trainingData) TrainModel(model, trainingData, features)
4. get \(\theta_{0},\theta_{1},\theta_{2}\) \(\theta_{0},\theta_{1},\theta_{2}\) = GetParameters(model)
5. **For each** test sample t in TestDataset: testData = GetTestDataset()
6. **For each** t in testData: y_pred = Predict(model, t)
7. **If** \(\mid\)y_pred \(-\) t.trueLabel \(\mid>\) \(\theta\): Mark t as faulty
8. **Return** y_pred ```
**Algorithm 1**SDN Controller Fault Detection Algorithm  
In the above table, INFOc signifies the status of the controller, INFOh denotes the heartbeat delay information, and \(\theta\) is a scaling factor. The switches are under the direct control of the SDN controllers within their designated control domains. Upon detecting a faulty controller, the switches that it controls are promptly labeled as out-of-control.
## 3 Method
### Search Space
#### 3.1.2 Search Space for FPN  
We extract three features of different spatial sizes from the backbone and pass them through FPN. As shown in Figure 2, we adopt nodes to denote feature maps and edges to denote operations. Each normal cell is a supernet that is independently searched during the search process. Similar to the search space of backbone, there are 7 candidate operations. Similar to the backbone, we introduce architecture parameters \(\hat{\mathbf{\gamma}}\) to denote the importance of candidate operations for each normal cell, whose normalized weights are denoted as \(\mathbf{\gamma}=softmax(\hat{\mathbf{\gamma}})\). To introduce the feature of nodes in a normal cell, we take the \(j\)-th node as an example without loss of generality. The feature of node \(v_{j}\) is \(z_{j}=\sum_{i<j}\sum_{o\in\mathcal{O}}\gamma^{o}_{i,j}\cdot e^{o}_{ij}(z_{i})\), where \(\mathcal{O}\) is the candidate operation set, \(z_{i}\) is the feature of node \(v_{i}\) (a predecessor of node \(v_{j}\)), and \(e^{o}_{ij}\) denotes the operation \(o\) on edge \(e_{ij}\) that connects node \(v_{i}\) and \(v_{j}\). After searching, only two connections are preserved for each node, and only one operation for each connection is selected according to the magnitude of \(\mathbf{\gamma}\).
# Activation-Based Pruning of Neural Networks
## 2 Related Work  
### Low-Rank Matrix Approximation  
Various pruning methods [2; 10; 11] have resulted in sparse matrices, low-rank matrix approximation, or a hybrid of both. [PERSON] et al. [2] compressed deep neural networks by simultaneously pruning both network weights and connections to reduce computational cost and memory usage by inducing a regularization term that encourages sparsity during training. Weights with small magnitudes are pruned based on a specified threshold. This technique results in sparse weight matrices. [PERSON] et al. [10] proposed a novel method called sparse low rank (SLR) to compress the dense layers of deep neural networks by improving upon truncated singular-value decomposition (SVD). The key idea is to induce structured sparsity into the decomposed matrices from SVD based on the significance of the input/output neurons. Neuron significance is estimated by absolute weights, activations, or a change in cost when removed. [PERSON] et al. [11] proposed a method called SVD training, which first decomposes each layer into the form of its full-rank SVD and, then, performs training on the decomposed weights. Low rank is encouraged by applying sparsity-inducing regularizers on the singular values of each layer. Singular-value pruning is applied at the end to explicitly reach a low-rank model. Activation-based pruning achieves sparsity in low-rank matrix approximation by assigning scores to each neuron to identify significant and insignificant neurons. Our method avoids the computationally expensive process of decomposing matrices using SVD, making it advantageous for large matrices.
## 4 Simulation and Analysis
### Comparison of Different Loop Bandwidths  
The selection of the bandwidth is significant for a carrier tracking loop, so we first simulate the HD-VLL of different loop bandwidths.  
Assuming a scenario of relative sinusoidal motion between the transmitter and receiver, the velocity is [20\(sin(10t)\) + 30] m/s, the acceleration is 200\(cos(10t)\) m/s\({}^{2}\), and the jerk is \(-2000 sin(10t)\) m/s\({}^{3}\). Hence, the maximum acceleration and maximum jerk are 200 m/s\({}^{2}\) and 2000 m/s\({}^{3}\). In practical scenarios, the motion between the transmitter and receiver does not frequently reach maximum acceleration and jerk. However, we can observe the behavior of algorithms under continuously varying acceleration and jerk, including extreme cases by simulating sinusoidal motion. This periodic variation enables us to continuously test and evaluate the algorithm at different time points, providing a more comprehensive understanding of its performance under varying acceleration and jerk conditions.  
The frequency hopping rate is set to 100 khops/s, and the carrier-to-noise ratio (CNR) of the received signal is set to 30 dB Hz. Different bandwidths of 3 Hz, 5 Hz, and 8 Hz are adopted, respectively. The velocity estimate curves and velocity error curves of different bandwidths for the HD-VLL are shown in Figure 9.  
Figure 9: The tracking results of different bandwidths for the HD-VLL. **(a)** The velocity estimate curves of different bandwidths for the HD-VLL. **(b)** The velocity error curves of different bandwidths for the HD-VLL.  
Figure 9 shows that the HD-VLL loses lock with the bandwidth of 3 Hz, and for the bandwidths of 5 Hz and 8 Hz, it is able to lock and enter steady-state tracking. The larger the bandwidth, the higher the dynamic the HD-VLL can adapt to. However, as the bandwidth increases, the steady-state tracking error also becomes larger, which will worsen the PLL tracking performance in HD-VLL-SA-PLL structures. Thus, we adopt 5 Hz for the HD-VLL in subsequent simulation.
# Method to Measure Light Spot Position in Beacon-Less Inter-Satellite Communication System
## 3 Method of Separating the Two Light Signals
### Cyclic Cross-Correlation Process  
As integration time \(T\) approaches infinity, that matches Equation (18). However, in practice, the sampling data are truncated. The normal cross-correlation (NCR) operation after sampling is expressed as:  
\[r(m)=\frac{1}{N}{\sum_{n=0}^{N-1}s(n)z(n+m)} \tag{19}\]  
The data length after truncation is \(N\). To match Equation (18), we can adjust \(N\) as:  
\[N=M_{4}\cdot\frac{f_{s
## 5 Experiment and Result Analysis
### Experimental Process and Result Analysis
#### 5.3.2 Generation of Pedersen Commitments and Range Proofs  
Taking the previously described transaction as an example, in order to achieve the hiding of the transaction amount, both parties of the transaction generate the Pedersen commitments for the relevant amounts and the range proofs for the output amounts based on Bulletproofs, respectively. The specific process is as follows.  
1. Parameter initialization  
The initialization information for generating elements G, H, and the reasonable range of amounts \(\left[2^{lower},2^{upper}\right]\) is shown in Figure 6.  
After testing, the time consumed for initialization of dual public key pairs, calculation of stealth address and construction of output note for transaction users are 133.096 ms, 9.779 ms and 12.162 ms respectively, all in milliseconds.  
#### 5.3.2 Generation of Pedersen Commitments and Range Proofs  
Taking the previously described transaction as an example, in order to achieve the hiding of the transaction amount, both parties of the transaction generate the Pedersen commitments for the relevant amounts and the range proofs for the output amounts based on Bulletproofs, respectively. The specific process is as follows.  
1. Parameter initialization  
The initialization information for generating elements G, H, and the reasonable range of amounts \(\left[2^{lower},2^{upper}\right]\) is shown in Figure 6.  
Figure 4: Initialization of the receiver’s dual key pair.  
Figure 5: Construction of the payment note.  
Figure 6: Initialization of parameters related to the generation of Pedersen commitments and range proofs.  
3. The recipient generates the Pedersen commitments for the relevant amounts and range proofs based on Bulletproofs The receiver [PERSON] generates the Pedersen commitment \(C_{4}\) for the payment amount \(v_{4}\) (the information related to its calculation results is shown in Figure 9) and generates the range proof \(\pi_{4}\) for the output amount \(v_{4}\). The calculation results are shown in Figure 10. The time consumed by both the sender and the receiver to generate Pedersen commitments for the relevant amounts based on the algorithm _Commit_ is about 16 ms, and the time consumed by the algorithm _RangeProof_ to generate range proofs for the output amounts is about 640 ms. The time consumed for the relevant operations is in the millisecond range, and the size of the range proof is only about 2.8 kb, which is efficient in proving the legitimacy of the transaction amounts.  
Figure 8: The sender generates the Bulletproofs-based range proof for the output amount \(v_{5}\).  
Figure 7: The sender generates the Pedersen commitments for the relevant amounts.
# Biosynthesis of Silver Nanoparticles Using _Tabernaemontana ventricosa_ Extracts
###### Abstract  
Nanoscience and nanotechnology have been rapidly developing due to the increased use of nanoparticles in several fields including health (antibacterial agents), medicine, chemistry, food, textiles, agricultural sectors, and nanofluids. The study aimed to biologically synthesize AgNPs using leaf and stem extracts of _Tabernaemontana_ _rentricosa_. The AgNPs were successfully synthesized and verified using UV-visible spectroscopy; however, the synthesis of the AgNPs was more efficient using the leaf extracts rather than the stem extracts. The energy-dispersive X-ray (EDX) analysis showed that the elemental silver (Ag) content was much higher using leaf extracts compared to the stem extracts. The AgNPs synthesized using both leaf and stem extracts were analyzed using scanning electron microscopy (SEM), and high-resolution transmission electron microscopy (HRTEM), and images displayed spherical, ovate, and triangular-shaped nanoparticles (NPs), which varied in particle size ranging from 16.06 \(\pm\) 6.81 nm to 80.26 \(\pm\) 24.93 nm across all treatments. However, nanoparticle tracking analysis (NTA) displayed much larger particle sizes ranging from 63.9 \(\pm\) 63.9 nm to 147.4 \(\pm\) 7.4 nm. The Fourier transform infrared (FTIR) spectral analysis observed functional groups such as alcohols, phenolic compounds, aldehydes, alkanes, esters, amines, and carboxylic acids. Our study suggests that medicinal plant extracts can be used for the effective economical production of AgNPs due to their efficient capping; however, further studies are necessary to determine the possible function groups and phytochemicals within _T._ _rentricosa_ that are responsible for the synthesis of AgNPs.  
biosynthesis; nanoscience; silver nanoparticles; bio-inspired synthesis; _Tabernaemontana ventricosa_