esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Plane Monolithic Integration of Scaled III-V Photonic Devices
## 3 Hybrid III-V/Si Photonic Crystal Cavity Lasers  
Photonic crystal (PhC) cavities have been explored extensively, as they have proven to be ideal candidates for the realization of high modulation rates and low-threshold laser operation [37; 38; 39]. This is mainly due to their very high quality (\(Q\))-factors (\(>\)10\({}^{6}\)) and small modal volume, where cavity mode volumes approaching the diffraction limit have been widely reported [3; 40]. In a 2D cavity, this mode volume is significantly smaller than the entire structure. Thus, it was proven to be beneficial to also confine the gain region to the central cavity and thereby achieve a high overlap with the cavity mode, additionally avoiding undesired absorption outside of the main cavity region. In references [37; 41], the local placement of gain in a 2D PhC cavity was realized with the use of buried heterostructures. Similarly, in Ref. [20] a 2D Si PhC structure is used with the active material in the form of a III-V nanowire placed in the central cavity.  
Recently, our group demonstrated a novel concept for a one-dimensional hybrid III-V/Si PhC cavity, where we exploit TASE to replace the central parts of a Si PhC structure fabricated on SOI with III-V active materials [33]. This has several advantages: (1) it optimizes the overlap of the optical mode with the active gain material; (2) optical losses are reduced due to a lower absorption in Si; and (3) unlike the other approaches [20; 37; 41], it is self-aligned to the silicon mirror features.  
In the following, we present first the design and fabrication of such hybrid III-V/Si PhC cavities. Then, we explore the impact of the hybrid nature of the cavity, as it is made up of two different materials: silicon and the III-V gain material.
## 4 Case Study
### Forwarding Principle  
Figure 6 shows the process of sending a unicast frame from source node 1 in ring 1 to destination node 10 in ring 3 using the DSP-based approach. QuadBox pairs 1 and 3 are connected to rings 1 and 3, respectively.  
In this case, the DSP algorithm established dual paths between QuadBox pairs 1 and 3. These dual paths are used to forward unicast frames from source node 1 to destination node 10, instead of duplicating and flooding the frames to the whole network as is done using the standard HSR protocol.
## 5 Performance Analysis
### Industrial Significance  
This paper introduces a method combining feature-enhanced down-sampling dynamic graph convolution networks with multi-head attention mechanisms (ADDGCN) for traffic flow forecast. This method is not only theoretically innovative but also of significant  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**Computation Time**} \\ \hline  & **Training (s/Epoch)** & **Validation (s)** \\ \hline GWN & 71.68 & 2.31 \\ \hline AGCRN & 30.31 & 3.58 \\ \hline SCINet & 35.62 & 2.86 \\ \hline STG-NCDE & 84.47 & 9.17 \\ \hline ADDGCN & 39.36 & 3.28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational costs of various models on the PEMS08 dataset.  
Figure 8: Impact of feature channel count on model performance (**PEMS04 (**above**) PEMS08 (**below**)).  
importance in industrial applications. Firstly, by enhancing the interaction between road network node information, the method dramatically increases traffic flow forecast accuracy. This is crucial for traffic control and urban planning in particular, as accurate traffic flow predictions can effectively guide traffic signal control and network optimization, reducing traffic congestion and improving road usage efficiency. For example, precise traffic flow predictions can help traffic managers adjust signal cycles reasonably during peak hours, alleviating the pressure at intersections, reducing vehicle queuing times, and thus enhancing urban traffic fluidity.  
Secondly, improving the accuracy of traffic flow predictions also positively contributes to environmental protection. By reducing traffic congestion, we can effectively lower vehicle emissions, reducing air pollution and greenhouse gas emissions. This holds significant importance for sustainable urban development and addressing climate change. Additionally, more efficient traffic flow management can also help reduce the incidence of traffic accidents, improving road safety.  
Lastly, the precision enhancement in traffic flow forecasting also shows tremendous potential in terms of adaptability for future technologies. With the advancement of autonomous driving and intelligent traffic systems, accurate traffic flow forecasting will become an indispensable part of these technologies. The approach suggested in this study offers a solid foundation for integrating these innovative technologies, heralding a more intelligent and efficient transportation future.
## 4 Comparative Analysis
In a comparison tip, the proposed AUTO-NN achieves 58.72% of \(RMSE\) (Root Mean Square Error), 62.72% of \(nRMSE\) (Normalized Root Mean Square Error), 48.04% of \(MaxAE\) (Maximum Absolute Error), 48.66% of \(MAE\) (Mean Absolute Error) and 46.76% of \(MAPE\) (Mean Absolute Percentage Error). While compared with the other existing models, the proposed model achieved better results. The electricity produced by the solar panels is sent to the battery via an electronic controller, and the batteries store the energy. DC from the battery is inverted to AC; Electrical loads draw power from these batteries. In a straight-grid system (or grid-tied system), the SPV panels are connected to the public power supply lines through a controller and energy meter. No batteries are used here. Electricity is primarily used to power the household's immediate power needs. When those requirements are met, a power meter sends additional power to the grid. Solar photovoltaic system cells convert only 10 to 14 percent of radiant energy into electrical energy. On the other hand, fossil fuel plants convert 30-40 percent of the chemical energy of their fuel into electrical energy. The conversion efficiency of electrochemical energy sources is as high as 90 to 95%. \[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(Y(i)-Y^{\prime}(i)^{2}}{N}} \tag{30}\]1  
Efficiency in a solar photovoltaic system is about 15%, meaning that for every 100 W/m\({}^{2}\) of radiation with 1 m\({}^{2}\) of the cell surface, only 15 \(W\) is transmitted to the circuit. \[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(Y(i)-Y^{\prime}(i)^{2}}{N}} \tag{30}\]2  
For lead-acid batteries, we can distinguish between two types of efficiency: coulombic efficiency and energy efficiency. During the charging process, which converts electrical energy into chemical energy, the Ah efficiency is about 90%, and the energy efficiency is about 75%  
A large amount of solar and thermal energy makes it a beautiful energy source. This energy can be directly converted into direct current electricity and heat energy. Solar energy is the cleanest, most abundant, and inexhaustible renewable energy available on Earth. Solar panels or photovoltaic systems using panels (SPV panels) are placed on rooftops or solar farms arranged so that solar radiation falls on the solar photovoltaic panels to facilitate the reaction of converting sunlight into electricity. When pure silicon is at 0 K (0 degrees Kelvin-273 degrees Celsius), all positions in the outermost electron shells are occupied due to the absence of covalent bonds between atoms and the absence of free electrons. So the valence band is filled, and the conduction band is empty. Although valence electrons have the highest energy, they require the least energy to remove them from the atom (ionization energy). It can be illustrated with the example of a lead atom. Here the ionization energy (of a gas atom) of the first electron removal is 716 kJ/mol and that of the second electron is 1450 kJ/mol. The equivalent values for Si are 786 and 1577 kJ/mol.
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 5 Performance Evaluation  
In this section, we conduct simulation experiments and analyze the experimental results. First, the experiment setup is introduced. Then, we compare the proposed multipath transmission scheme with other related works from different metrics. Finally, we analyze and discuss the experimental results.
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 3 Proposed Method
### Pix2 pix Dehazing Network with Guided Filter Layer
#### 3.1.3 Discriminator  
The discriminator accepts the output of the generator and determines whether the generated image is a real and clear image. Similar to [26], we designed a neural network, the basic operations of which were convolution, batch normalization and Leaky Rectified Linear Unit (LeakyReLU) activation.
# Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets
## 4 Formal Description of Our Dynamical Model Using Petri Nets
Furthermore, an enabled transition \(t\) may or may not fire depending on whether the guards take place. If there is a guard \(g\) in a transition \(t\), the associated rules must be evaluated. For the above rules of transition enabling, there is a concept related to the capacity of a place \(p\). Initially, it is assumed that each place \(p\) can accommodate an unlimited number of tokens. In addition, it is natural to consider an upper limit to the number of tokens that each place can hold. Each place \(p\) has an associated capacity _K(p)_, the maximum number of tokens  
Figure 1: Petri net of our AQM scheme with initial marking. that \(p\) can hold. For a transition \(t\) to be enabled, there is an additional condition that the number of tokens in each output place \(p\) of t cannot exceed its capacity \(K(p)\) after firing \(t\). Once the structure and rules are presented, the PN depicted in Figure 1 represents our AQM model at initial marking \(M_{0}\) = (1,0,0,0,0,0,0,0,1). There are 10 places (\(n\) = 10, P1,..., P10) and 12 transitions (\(m\) = 12, T1,...,T12). Table 1 shows further details of our PN. Each place shows where the token is located, and the transitions are actions of our AQM model. The guards are on transitions T3, T4, T5, T8, and T9. Regarding the capacity, the places have capacity K = 1 to show how our AQM runs actions on the packet and router. The PN starts on T1 enabled, i.e., there is an incoming packet, the router is ready (P1), and AQM is ready (P10). Once T1 and T2 are fired, our model gets the AQL. As said before, transitions T3, T4, T5, T8, and T9 have guards that restrict their firing. The PN fires T3 or T5 (fill buffer or drop packet) if the AQL is below \(q_{min}\) or over \(q_{max}\), respectively. If the AQL evaluates \(q_{min}\leq AQL\leq q_{max}\) as true, T4 starts and P5 gets the probability to detect where the stabilization is at the average router queue length. The PN fires T8 or T9, not to discard or discard the packet according to the value from _probability_\(p_{max}I_{2}(\alpha,\beta)\). T10 and T11 mean input buffer or reject packet--that is, whether the packet is filled into the buffer or discarded, respectively. T12 is fired when the packet leaves the buffer, and P10 restarts the PN for more incoming packets.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.3 Cramer's V test  
The chi-square test increases in proportion to the number of rows and columns in the contingency table; however, it is limited in that relative comparisons are difficult. Cramer's V test is a new test method derived from the chi-square test. A value closer to 1 in the positive range (0,1) signifies the greatest relevance [45]. Cramer's V test was performed in the same way as the chi-square test, and was used as one of the common major variable extraction methods.
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications
#### 2.2.9 Network Slicing and Dynamic Resource Sharing  
Mobile network capacity maximization is one of the new features introduced with 5G. For critical 5G use cases such as remote surgery and autonomous vehicles, low latency and high throughput are essential. In 6G, both low latency and high throughput are expected to be further enhanced. To ensure high-quality service, the full availability of reserved network slices and spectra for these critical services must be guaranteed and well secured. Each reserved slice in a mobile network can serve various purposes, including massive connectivity, high bandwidth, low latency, and high reliability. This requires robust management of the spectrum and orchestration of network slices, incorporating immutability and traceability features provided by blockchain capabilities. In 6G, resource and spectrum usage is further enhanced, enabling the capacity of mobile operators to be utilized by other operators. This feature necessitates stringent security and interconnection requirements, which can be fulfilled through blockchain interoperability. Figure 5 illustrates a typical architecture used for network slicing and dynamic spectrum allocation. The dynamic spectrum management component records and manages spectrum availability and assigns the available spectrum based on requirements from network slicing acquirers, such as mobile network operators (MNOs), mobile virtual network operators (MVNOs), and over-the-top (OTT) providers. An intermediary broker is used between the mobile operator and custom or blockchain-based solutions. Single intermediary-based solutions are considered an SPoF design, which is serial in nature and ineffective with respect to process usage.  
In [42], surveys on blockchain-enabled dynamic spectrum allocation (DSA) and slicing management were reviewed. Additionally, the authors of [42] evaluated various blockchain platforms to determine whether they can meet the performance expectations of 6G networks. They also focused on a single-chain architecture, which acts as an intermediary between the mobile operator and external parties; however, this architecture represents clear serialization. Through utilizing multiple interoperable blockchains, enhanced parallelization can be achieved.
# Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster
## 3 Methodology
### Neural Network Models & Datasets Used
The use of common lightweight face detection models such as the Haar-Cascade algorithm and SSD for Experiments 2 and 3 was not considered, as the intended focus was solely on comparing state-of-the-art, high-accuracy face detection models on Pi against the rest of the system configurations for the coverage of this research. For Experiment 1, the datasets used were the ImageNetV2 test set and the CIFAR10 test split. The ImageNetV2 dataset is a specialised test set that follows the original ImageNet2012 dataset with the same label space, each comprising 10 test images per class. It is comprised of 10,000 test images which can be paired with an existing model that is pre-trained on the ImageNet2012 dataset for running inference. The CIFAR10 test split is comprised of 10,000 images which are 32 \(\times\) 32 in resolution and are coloured. For consistency reasons, test splits for these two datasets were chosen as the goal was to evaluate the performance across every system configuration rather than evaluating the neural network models used in this paper. For Experiment 2, the WIDER FACE dataset [23], which is a face detection benchmark containing faces taken from the WIDER dataset, was used. The official recommended dataset split is 40% for training, 10% for validation, and 40% for testing. Its _test_ split dataset contains 16,097 images, which are similar in size to the _train_ split. Originally, the plan was to use the _test_ split for executing inference on the WIDER FACE dataset. But during the experiment, the time taken was exorbitantly long for low-end devices where it required almost a week to complete inference on a single iteration on the Pi 4B. Hence, the decision was taken to reduce the dataset size to the _validation_ split, which contained 3220 images. For Experiment 3, the experiment captured frames continuously in a real-time face detection test. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Datasets Used** & **Models Used** \\ \hline Experiment 1 & ImageNetV2 [21], CIFAR10 [22] & InceptionV3, MobileNetV3 \\ \hline Experiment 2 & WIDER FACE [23] & MTCNN [24], RetinaFace [25], YOLOface [26] \\ \hline Experiment 3 & - & MTCNN [24], RetinaFace [25], YOLOface [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Datasets and models used in Experiments 1 to 3.
###### Abstract
Bioinformatics techniques were used to compare plant TPS family members, which can be divided into 8 subfamilies: TPSa (sesquiterpenenes), TPSb (cyclic monoterpenes and hemiterpenes), TPSc (copalyl diphosphate synthases), TPSd (gsymnosperm-specific), TPSe (ent-kaurene synthases), TPSf (other diterpene synthases), TPSg (acyclic monoterpenes), and TPSh (lycopod-specific) [28]. The volatile compounds of orchids contain various terpenes. Monoterpenes, such as linalool, pinence, ylangene, and limonene, are major floral scent compounds in Orchid flowers [20; 29; 30; 31]. Among the monoterpene synthetase genes, linalool synthetase (LIS), gerani synthetase (GES), and \(\beta\)-Basil synthetase (OS) have been studied more, which belong to the TPSg family [22; 32]. To date, only a few TPS genes have been identified in orchids. A total of 34 TPS genes were identified In _Dendrobium officinal_, and these genes were mainly expressed in the flowers, followed by the roots and stems [24]. PbTPS5 and PbTPS10 genes are involved in monoterpene biosynthesis in _Phalanopsis bellina_[33]. In the flowers of the _Freesia hybrid_, a total of 8 TPS genes were identified. Among them, FhTPS1 catalyzed the formation of linalool, whereas FhTPS4, FhTPS6, and FhTPS7 were poly-product enzymes that could recognize both substrates, GPP and FPP [34]. There are many species of _Dendrobium_, but few have flowers that are both fragrant and beautiful. In the experiment, we selected two species of _Dendrobium: Dendrobium moniliforme_ (L.) Sw. (_D. moniliforme_) and _Dendrobium nobile_ \"H1\" (_D._ \"H1\"). _D. moniliforme_ is a wild germplasm widely distributed in the south of China, the Korea Peninsula, northeastern India, and Japan [35]. It grows on tree trunks, valley rock walls, or even cliffs. It has the characteristics of thin stems, small flowers, and a high-scent fragrance of flowers. Unlike _D. moniliforme_, _D._ \"H1\" is a cultivated line with large and bright flowers but only a faint fragrance. By combining volatile constituent determination and transcriptome sequencing analysis of these two different _Dendrobium_ species, the characteristics of volatile constituents in different florescences and organs were analyzed, and the key genes involved in terpenoid synthesis were explored. Furthermore, real-time quantitative PCR (qRT-PCR) was used for the detection of the correlation between transcriptome sequencing and gene expression levels.
## 3 CNN Architecture and Parameter Optimizer
### CNNs
#### 3.1.4 Fully Connected Layer  
The fully connected (FC) layer is functioned as a classifier. The FC layer converts the two-dimensional feature map output by the convolution layer into a one-dimensional vector. The final probability of each label is obtained using Softmax.  
LeNet-5 and AlexNet contain fewer layers and simple architecture compared with other deeper CNNs. Among them, AlexNet has not only been presented good performance in many applications, but also allows color images as input, such as computed tomography images. Therefore, with data augmentation and parameter optimizer implementation, AlexNet might be a suitable network architecture used in this study. AlexNet consists of five convolutional layers, three pooling layers, three FC layers, and Softmax with 1000 outputs. The aim of this study was to classify lung CT images into benign or malignant tumors. Thus, the transfer learning technique was applied to change the last FC layer to two outputs. The AlexNet architecture is illustrated in Figure 7 and Table 3 lists the details of the AlexNet.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Input**} & \multicolumn{4}{c}{**227 \(\times\) 227 \(\times\) 3**} \\ \hline \multicolumn{2}{c}{**Layer**} & \multicolumn{2}{c}{**Feature Map Size**} & \multicolumn{2}{c}{**Kernel Size**} & \multicolumn{2}{c}{**Stride**} & \multicolumn{2}{c}{**Activation Function**} \\ \hline \multirow{2}{*}{Layer 1} & C & \(55\times\) 55 \(\times\) 96 & \(11\) & \(4\) & ReLU \\  & Pool & \(27\times 27\)\(\times\) 96 & \(3\) & \(2\) & ReLU \\ \hline \multirow{2}{*}{Layer 2} & C & \(27\times 27\)\(\times\) 96 & \(5\) & \(1\) & ReLU \\  & Pool & \(13\times 13\)\(\times\) 256 & \(3\) & \(2\) & ReLU \\ \hline \multirow{2}{*}{Layer 3} & C & \(13\times 13\)\(\times\) 384 & \(3\) & \(1\) & ReLU \\ \hline \multirow{2}{*}{Layer 4} & C & \(13\times 13\)\(\times\) 384 & \(3\) & \(1\) & ReLU \\ \hline \multirow{2}{*}{Layer 5} & C & \(13\times 13\)\(\times\) 256 & \(3\) & \(1\) & ReLU \\  & Pool & \(6\times 6\times 256\) & \(3\) & \(2\) & ReLU \\ \hline \multirow{2}{*}{Layer 6} & FC & \(9216\) & - & - & ReLU \\ \hline \multirow{2}{*}{Layer 7} & FC & \(4096\) & - & - & ReLU \\ \hline \multirow{2}{*}{Layer 8} & FC & \(4096\) & - & - & ReLU \\ \hline \multirow{2}{*}{Output} & - & \(2\) & - & - & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the AlexNet architecture.  
Figure 6: ReLU activation function.
## 3 Modeling Methods
### Artificial Neural Networks  
ANNs have gained a lot of acceptance among researchers due to their generalization capabilities, especially in prediction problems. They represent a network of multiple processing units (referred to as neurons), which estimate weights and biases for each input parameter to minimize the partial least square of error. For each neuron, the weights and coefficients are calculated for the entire dataset without the restriction of statistical significance [28]. The weights for neurons depend upon the equations which are chosen as activation function for the neuron. These neurons serve as parallel processing units and have the ability to capture unknown complex variations in the output variables. Due to this, ANNs are used as unsupervised learning algorithms [29]. ANN models can be represented as networks, as shown in Figure 3.  
In the illustration above, \(Yi\) is the output for each processing neuron, and an ANN may contain several neurons. The final output, '\(Y\)', is the combination of outputs from all hidden neurons. The numbers of hidden layers and neurons were not known beforehand. These were determined by observing the accuracy of predictions for multiple combinations [30].  
For the current study, seven neurons arranged in a single hidden layer were identified to produce most optimum results. To achieve this result, number of neurons in the hidden layers was changed from 1 to 10 and its effects on MSE for validation dataset were observed, which is shown in Figure 4. Validation dataset comprises of randomly selected samples from the available dataset which is used for determining the appropriateness of model architecture. The model architecture is not selected on the basis of accuracy for training dataset to ensure that the model can be robustly used for unknown values. It was observed that MSE with seven neurons produced the optimum results. It should be noted that MSE is used as the default for determining weights and biases for hidden neurons and this is the reason it was used for determining optimum number of hidden neurons. This number  
Figure 2: Hyperplanes for SVMs.  
depends upon the complexity and nature of modeling problem and the the trial-and-error method, as described, is generally used to determine the appropriate number of hidden neurons [31].