esa-sceva/satcom-chunk-collection huggingface dataset:
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 3 Scaling up RINA Networks
### Renumbering RINA Networks  
Renumbering IP networks is an expensive procedure that has to be carefully planned and executed to avoid routing, firewall, and connection integrity problems [12]. TCP connections are tightly bound to a pair of IP addresses, and will be disrupted with changing IP addresses. Furthermore, the renumbering process usually leads to stale DNS (Domain Naming System) entries pointing to deprecated addresses [13].  
The use of application names and directories per DIF makes renumbering a RINA network easier. Applications request flows to target application names, which are resolved using the DIF directory to the address of an IPC Process (IPCP). Renumbering assigns a new address (synonym) to the IPCP, giving it 2 addresses. The IPCP sends a message for all its flows that originate at this IPCP to its peer. This notifies the other end of the flow so that it can use the new address. A routing update in the IPCP starts advertising the new address instead of the old one. The DIF's directory is updated so that  
Figure 4: GEANT Network physical connectivity graph and DIF structure used in the experiment.  
Figure 5: RTT (Round Trip Time) for some of the paths involving Ljubljana.  
applications are now mapped to the new address. Over time, all peers will use the new address and the old address disappears.  
ARCFIRE renumbering experiments showed that it is possible to dynamically renumber RINA networks (even multiple DIFs simultaneously), without destroying any flows and with a minimal impact on the QoS experienced by each flow [14]. Initial experimental evidence of this property was already described in [12], but with very limited experimental setup consisting in few flows and systems running in Virtual Machines. ARCFIRE performed a multi-layer renumbering experiment on the imec iLab.t Virtual Wall testbed with 41 physical machines and 6 DIFs, as shown in Figure 6. Each IPCP in each DIF changed its address once every 45 s, while 180 flows between CPE routers were exchanging traffic using a simple ping application. We performed an experiment with and without network renumbering. There was no packet loss or observable change in RTT, as shown in Figure 7.  
In this experiment we analyzed how the complete naming and addressing architecture embodied by RINA allows RINA networks to be renumbered live, without significantly impacting the performance perceived by existing flows or impairing the ability to crate new ones. Renumbering a network with multiple layers today is a maintenance event: it has to be carefully planned, requires humans in the loop, and it takes one or more days to complete. With RINA renumbering can be done life, dynamically, in a fully automated way, and in a time period between tens or hundreds of milliseconds to a few seconds (depending on the network size).
## 2 Materials and Methods
### Electromyography  
An eight-channel Noraxon TeleMyo 2400 system (Noraxon USA Inc., Scottsdale, AZ, USA; 1500 Hz) was used for recording and analysis of biopotentials from the muscles. The activity was recorded for three muscles: PM (Pectoralis Major), AD (Anterior Deltoid), TBlat, and TBlong (Triceps Brachii). Before placing the gel-coated self-adhesive electrodes (Dri-Stick Silver circular sEMG Electrodes AE-131, NeuroDyne Medical, Germantown, TN, USA), the skin was shaved, abraded, and washed with alcohol. The electrodes (11 mm contact diameter and a 2 cm center-to-center distance) were placed along the presumed direction of the underlying muscle fiber according to the recommendations by SENIAM [10]. The EMG signals were sampled at a rate of 1000 Hz. Signals were band-pass filtered with a cutoff frequency of 8 Hz and 450 Hz, after which the root-mean-square (RMS) was calculated. All the electrodes were located on the right side of the participant, regardless of whether this was the dominant side or not. The grounding electrode was placed on the connection with the triceps brachii muscle. Video recording was used for identification of the beginning and completion of the movement. After completion of all the tests in a single day, 2-3 s evaluations of Maximal Voluntary Isometric Contraction (MVIC) of the prime movers in the bench press movement (AD, PM, and TBlong) were performed according to the SENIAM procedure. These evaluations were performed in order to normalize electromyographic records. The analysis was based on peak activity during the bench press exercise from both, the eccentric and concentric phases of the movement. Due to the large amount of data covering three different loads and the right and left sides of the body separately, the analysis was limited only to the analysis of the peak activity of the tested muscles in order to determine the differences between muscle involvement under free load and under pneumatic load.
# Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector
## 3 Theory Overview
### Convolutional Neural Networks  
A CNN receives at the input an image, attributes importance to several objects within the image, and differentiates one from the other. CNN's are composed of several layers of neurons that have  
Figure 1: Types of Fully Connected Networks. (**a**) Single Layer Network; (**b**) Multi Layer Network.  
different weights. The first layer of neurons receives the input data, while the performances that are offered by the network and the accuracy of the outputs are measured by a loss function (e.g., SVM, SoftMax) that is applied to the last neural layer.  
The difference between a CNN and the Fully Connected networks relies in terms of input data, i.e., CNN relies on the assumption that the input data are an image. This assumption leads to specific changes in network architecture. Regarding the Fully Connected networks, the neurons are placed in two-dimensional layouts. Unlike this type of neural networks, the layers of a CNN are developed from neurons arranged in three-dimensional layouts. This essential difference can also be observed in Figure 2.  
Generally, the CNN architecture contains the following types of layers:  
* **Input layer.** At this layer's level, the pixel values from an image of various widths and heights are stored, using three color channels (R--Red, G--Green, B--Blue).
* **Convolution layer.** It calculates the output of neurons that are connected to the input regions. The third dimension of the output changes depending on the number of filters (kernels) used in the convolution operation.
* **RELU layer.** This layer applies an input data activation function, such as \(\max(0,x)\), with the threshold 0. The operations that take place at this layer do not change the size of the input data.
* **Pooling layer.** It performs the subsampling operation along the spatial dimensions (width and height, respectively).
* **Fully Connected (FC) layer.** This layer calculates the results for each class. Unlike all other layers, the neurons in this layer are connected to all of the previous layers' neurons.  
In this way, the CNN network transforms the original image, from the pixel values to all classes' final probabilities. In image processing, these neural networks are predominantly used, because they allow for the use of information from the three image channels in order to extract essential characteristics of the images, which leads to high efficiency in terms of video processing.  
Subsequently, Region-based Convolutional Neural Networks (R-CNN) [38] were implemented, which involve two stages: identifying regions of interest (RoI) in the image containing various objects and independently extracting CNN features from each region. Several versions of these networks were developed, which aimed to improve the performance in terms of both the processing time and method accuracy [39; 40].  
Figure 2: Neuron layouts in different types of neural networks. (**a**) Fully Connected Neural Network; (**b**) Convolutional Neural Network.
## 3 Theoretical Framework  
### Bee Colony Optimization Algorithm  
The Bee Colony Optimization (BCO) algorithm is a nature-inspired optimization scheme based on the foraging behavior of honeybee colonies [15]. BCO imitates the food-foraging behavior of honeybees to find solutions to complex optimization problems. Honeybee colonies show incredible efficiency in searching for food sources in their immediate environment and embark on complex foraging behavior to locate, collect, and bring food (nectar) to the hive [13].  
The honeybee communicates via dance movements and performs a distinctive \"waggle dance\", signaling or sending information about the location of food sources to other bees in the colony [5]. Honeybees share details like the distance, direction, and quality of food sources through these dance movements, letting other bees optimize their foraging routes and efficiently gather nectar. Honeybee colonies collectively work to gather nectar from multiple sources of food [6]. The collaborative activities of individual honeybees contribute to the overall success of the colony in getting enough food for survival and growth.  
Like honeybee colonies, the BCO algorithm focuses on an efficient search for high-quality solutions by coordinating the search behavior of artificial bees [6]. The BCO algorithm's iterative nature and adaptive search technique make it a strong optimization strategy for solving complex optimization problems. The BCO algorithm comprises two main phases, the forward pass and the backward pass, which can be described as follows:
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 5 Conclusions  
Since DFIGs are greatly influenced by the oscillation in the shaft of rotating mass, over-speed of rotor, and transient peak currents during fault, a very detailed DFIG model with two-lumped mass based wind turbine is firstly introduced for investigating the dynamics of wind farms. By using PSAT software, this research work has analyzed the impacts of wind power generation on power system transient stability from different points of view, i.e., what will be the impact on the system response of adding wind farms and changing its location? And what will be the impact if the wind speed changes? The simulation results show that there is a direct relationship between capacity of wind farms and power system transient stability. With the increased integration of wind generation, the transient instability of the power system increases. Moreover, the wind farm locations and wind speed could also affect power system stability, but the negative impacts on the system transient stability can be greatly alleviated by adding capacitors at the generator buses as demonstrated in the case studies.  
[PERSON] and [PERSON] designed the study and mainly wrote the paper; [PERSON] and [PERSON] performed the experiments; [PERSON] revised and proofread the paper. This research received no external funding. This work was supported in part by Beijing Natural Science Foundation (3174057), Support Program for the Excellent Talents in Beijing City (2016000020124G079), the Fundamental Research Funds for the Central Universities (2016 MS14), and the Science and Technology Program of State Grid Corporation of China (SGHE 0000 KXJS1700086). The authors declare no conflict of interest.  
Figure 19: Generator speeds for systems with 4 capacitors.  
Figure 20: Generator angles for systems with 4 capacitors.
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 2 System Overview  
In this section, the components of the designed system are explained in detail.  
In Figure 2, the same icons as in Figure 1 are used to illustrate the hardware and the software components of the designed Winet system. The algorithm of the Winet warning system runs in the Cloud. Data from the sensor are sent by microcontroller board with the NB-IoT communication and energy-harvesting module. There is also an in-house developed add-on board with a display connecting all environmental sensors. The end-user reads the results and gets warnings from the provided cloud front-end and back-end application with appropriate dashboard and visualizations. In addition, a mobile application was also developed.
# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 3 Results
### Validation of Multivariate Models
If the performance of our multivariate regression model using hourly averaged input data is compared with the model performance by ANN-sig, SVR, random forest and multivariate regression using daily averaged data, it has much higher accuracy of calculation than the models performed by [PERSON] and [PERSON] [36] and [PERSON] [37]; however, our model performance has relatively lower accuracy than [PERSON]'s models. Our future research will be carried out using both artificial neural network models and multivariate regression models and the calculation results from the models will be compared with each other. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Name**}} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Before (Hourly) YS**} & \multicolumn{4}{c}{**During (Hourly) YS**} & \multicolumn{4}{c}{**After (Hourly) YS**} & \multicolumn{4}{c}{**Daily/Seasonal/**} \\ \cline{3-11}  & & **PM\({}_{10}\)** & **PM\({}_{2.5}\)** & **NO\({}_{2}\)** & **PM\({}_{10}\)** & **PM\({}_{2.5}\)** & **NO\({}_{2}\)** & **PM\({}_{10}\)** & **PM\({}_{2.5}\)** & **NO\({}_{2}\)** & **PM\({}_{10}\)** & **PM\({}_{2.5}\)** & **NO\({}_{2}\)** \\ \hline \multirow{4}{*}{(1) Jeon and Son, 2018 [40]} & ANN-sig & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} \\ \cline{2-11}  & SVM & & & & & & & 0.751 & \\ \cline{2-11}  & Random F & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} \\ \cline{2-11}  & Multivariate & & & & & & 0.849 & \\ \cline{2-11}  & ANN-sig & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} \\ \cline{2-11}  & SVM & & & & & & 0.962 & \\ \hline \multirow{4}{*}{(3) Lim, 2019 [38]} & ANN-sig & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} \\ \cline{2-11}  & SVR & & & & & & & 0.962 & \\ \cline{2-11}  & Multivariate & & & & & & & 0.958 & \\ \hline \multirow{4}{*}{(4) Choi and Choi, 2021 [41]} & Multivariate & 0.983 & 0.998 & 0.916 & 0.998 & 0.941 & 0.998 & \\ \cline{2-11}  & ANN-sig & 0.954 & 0.866 & 0.873 & 0.941 & 0.960 & 0.975 & 0.930 & 0.953 & 0.917 & \\ \cline{2-11}  & ANN-tanh & 0.974 & 0.958 & 0.953 & 0.971 & 0.983 & 0.930 & 0.953 & 0.960 & 0.959 & \\ \cline{2-11}  & Multivariate & 0.961 & 0.909 & 0.896 & 0.948 & 0.977 & 0.875 & 0.920 & 0.947 & 0.903 & \\ \cline{2-11}  & Multivariate & 0.957 & 0.906 & 0.886 & 0.936 & 0.982 & 0.866 & 0.919 & 0.945 & 0.902 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Prediction performance using different models from different scientists regarding the yellow sand event (YS), with correlation coefficients (Pearson’s r). Reference: ANN-sig (Artificial neural network-sigmoid), ANN-tanh (Artificial neural network-tanh), SVM (Support vector machine), RF (Random forest), Multivariate (Multivariate regression) and SVR (Support vector regression).
# A Phase Correction Model for Fourier Transform Spectroscopy
###### Abstract  
In Fourier transform spectroscopy (FTS), the conventional Mertz method is commonly used to correct phase errors of recovered spectra, but it performs poorly in correcting nonlinear phase errors. This paper proposes a phase correlation method-all-pass filter (PCM-APF) model to correct phase errors. In this model, the proposed improved phase correlation method can correct linear phase errors, and all-pass filters are applied to correct the residual nonlinear phase errors. The optimization algorithm for the digital all-pass filters employs an improved algorithm which combines the subtraction-average-based optimizer (SABO) and the golden sine algorithm (Gold-SA). The proposed PCM-APF model demonstrates high correction precision, and the optimization algorithm for the filters converges faster than traditional intelligent optimization algorithms.  
Fourier transform spectroscopy; phase correction; phase correlation method; all-pass filters; meta-heuristic algorithm +
Footnote †: journal: Applied
## 3 Experimental Setup
### Evaluation Metrics  
To compare and analyze the proposed algorithm, we use four common evaluation metrics: accuracy, recall, F1-score, and Intersection over Union (IoU). The formulas for these metrics are as follows:  
\[\text{Pre}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{15}\]  
\[\text{Rec}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{16}\]  
\[F1=\frac{2\text{Pre}\times\text{Rec}}{\text{Pre}+\text{Rec}} \tag{17}\]  
\[\text{IoU}=\frac{\text{TP}}{\text{FP}+\text{TP}+\text{FN}} \tag{18}\]  
Among them, TP (true positive) denotes true instances, TN (true negative) denotes true-negative instances, FP (false positive) denotes false-positive instances, and FN (false negative) denotes false-negative instances.  
The specific meanings of these indicators in the farmland change detection task are as follows:  
* Precision: This indicates how well the areas of change correctly detected by the model match the actual areas of change. A high precision means that the model has a low false alarm rate, i.e., that most of the detected change areas are actual changes.
* Recall: This is a measure of how well the model covers the areas of change that it detects versus the actual areas of change. A high recall means that the model is able to detect more of the actual change area. A high recall is especially important in the task of change detection in agricultural fields, because omission (failure to detect an actual area of change) often has more serious consequences than misdetection (misclassifying an unchanged area as a change). Change detection in farmland is important for  
Figure 3: HRSCD dataset.  
agricultural management and decision-making, and ensuring that all areas of change are detected can help farmers and agricultural managers take timely action.
* F1-score: This is a metric that combines precision and recall, and it is their reconciled average. A high F1-score indicates that the model performs well in terms of both accuracy and completeness, being a balance between precision and recall.
* Intersection and Union Ratio (IoU): This measures the degree of overlap between the region of change detected by the model and the actual region of change. A high IoU indicates that the region of change predicted by the model highly overlaps with the actual region of change, reflecting the overall detection performance of the model.  
With these evaluation metrics, we can comprehensively and objectively assess the effectiveness of the proposed model in the task of farmland change detection, thus verifying its superiority and usefulness.
## 5 Numerical Simulations
### Simulation Results and Analysis
#### 5.2.3 Case 3  
In the third case, the position and the attitude of UAV base are not controlled. Similar to the second case, the pose disturbance acting on UAV base is considered. However, the robotic manipulator is constrained to move from initial state \(\mathbf{q}^{0}=[-10,30,40,60,30,75]\) to desired state \(\mathbf{q}^{f}=[10,20,50,75,45,90]\) with unit of degree. Furthermore, the UAV base is required to move from initial state \(\mathbf{x}_{b}^{0}=[-5(\text{deg}),5(\text{deg}),-5(\text{deg}),0(\text{m}),0(\text{ m}),0(\text{m})]\) to desired state \(\mathbf{x}_{b}^{de}=[0(\text{deg}),0(\text{deg}),0(\text{deg}),0(\text{m}),0(\text{ m})]\). The cost function in (24) includes the UAV disturbance (21) and the angular variations of joints (22), shown as \(\Gamma=\Gamma_{1}+\Gamma_{1}\). The weight matrix \(\mathbf{M_{1}}\) is assigned as \(\mathbf{M}_{1}=\left[\mathbf{E}_{3},\ \mathbf{O}_{3};\ \mathbf{O}_{3},\ \frac{1}{\sin(\frac{\pi}{300})}\mathbf{E}_{3}\right]\). The weight matrix \(\mathbf{M_{2}}\) is assigned as \(\mathbf{M}_{2}=\frac{1}{\sin(\frac{\pi}{300})}\mathbf{E}_{6}\). \(\mathbf{E}_{3}\) and \(\mathbf{E}_{6}\) are respectively 3-order and 6-order unit matrices. Figure 13 shows the variations of cost function. The proposed HPSO algorithm stops when \(k_{\text{eq}}=101>100=k_{\text{max}}\), where \(N=285\) and the final cost value is 1.1895. Figure 14 shows the time histories of joint angles, meeting aforementioned conditions. Figure 15 shows variations of joint velocities with the value of \(\left(3.97\times 10^{-13},\ 7.79\times 10^{-14},\ 1.54\times 10^{-13},\ 1.01 \times 10^{-13},\ -4.73\times 10^{-13},\ -2.26\times 10^{-13}\right)\)\((\text{deg}/\text{s})\) at final time. Figure 16 shows the attitude and the position variations of UAV base. The actually planned UAV attitude and position are \(\left(-5.77\times 10^{-3},\ -6.21\times 10^{-3},\ -3.56\times 10^{-6}\right)\)\((\text{deg})\) and \(\left(-1.34\times 10^{-5},\ 4.49\times 10^{-6},\ -8.92\times 10^{-6}\right)\)\((\text{m})\), respectively. It means the disturbance order magnitude acting on UAV base is \(-3\). Therefore, the planned error is within the predefined threshold range, where \(\left\|\left[-5.77\times 10^{-3},\ -6.21\times 10^{-3},\ -3.56\times 10^{-6} \right]\right\|=8.4679\times 10^{-3}<\epsilon(1)=0.1\) with the unit of degree and \(\left\|\left[-1.34\times 10^{-5},\ 4.49\times 10^{-6},\ -8.92\times 10^{-6} \right]\right\|=1.6712\times 10^{-5}<\epsilon(2)=0.1\) with the unit of meter. The error vector \(\delta\mathbf{q}\) in Equation (18) is \(\delta\mathbf{q}=[1.27,1.07,-0.11,-0.47,-1.51,-2.21]\times 10^{-10}(\text{deg})\). According to the simulation results, the joints move smoothly and the AEROM system successfully arrives at the desired state. Conclusively, the joint trajectories in aforementioned three cases are all smooth. Furthermore, the generated results are applicable to controlling the AEROM system and meeting the requirements in Section 3.
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
## 2 System Model and Protocol Descriptions
### Throughput of PUs
\begin{array}{l}B=exp\Big{(}-\frac{2\varphi_{p}}{\rho_{{\rm P}}\lambda_{{ \rm P}}}\Big{)}+exp\Big{(}-\frac{\varphi_{p}}{\rho_{{\rm P}}\lambda_{{\rm P}} \lambda_{{\rm P}}}\Big{)}+exp\Big{(}-\frac{\varphi_{p}}{\rho_{{\rm P}}\lambda_ {{\rm P}}\lambda_{{\rm P}}}\Big{)}\Big{(}1-exp\Big{(}-\frac{\varphi_{p}}{ \rho_{{\rm P}}\lambda_{{\rm P}}}\Big{)}\Big{)}\\ +exp\Big{(}-\frac{\varphi_{p}}{\rho_{{\rm P}}\lambda_{{\rm P}}}\Big{)}\Big{(} 1-exp\Big{(}-\frac{\varphi_{p}}{\rho_{{\rm P}}\lambda_{{\rm P}}}\Big{)} \Big{)}\Big{(}exp\Big{(}-\frac{\varphi_{p}}{\alpha_{{\rm P}}\lambda_{{\rm S}} \lambda_{{\rm P}}-\varphi_{{\rm P}}\lambda_{{\rm S}}\rho_{{\rm S}}\lambda_{{ \rm S}}}\Big{)}-exp\Big{(}-\frac{\varphi_{p}}{\rho_{{\rm P}}\lambda_{{\rm P}} \lambda_{{\rm P}}}\Big{)}\Big{)}\\ +\Big{(}exp\Big{(}-\frac{\varphi_{p}\varphi_{{\rm P}}\lambda_{{\rm P}}}{ \rho_{{\rm P}}\lambda_{{\rm P}}}\Big{)}\Big{(}1-\frac{\varphi_{p}\lambda_{{\rm P }}}{\rho_{{\rm P}}\lambda_{{\rm S}}\lambda_{{\rm P}}+\rho_{{\rm P}}\lambda_{{ \rm P}}}\Big{)}+\frac{\varphi_{p}\lambda_{{\rm P}}}{\varphi_{{\rm P}}\lambda_ {{\rm P}}}\Big{)}\\ \times exp\Big{(}-\frac{2\varphi_{p}}{\rho_{{\rm P}}\lambda_{{\rm P}}\lambda_ {{\rm P}}}\Big{)}\Big{(}1-exp\Big{(}-\frac{\varphi_{p}}{\rho_{{\rm P}} \lambda_{{\rm P}}\lambda_{{\rm P}}}\Big{)}\Big{)}.\end{array}  \tag{27}\]