esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 4 Results and Analysis of Experiment
### Experimental Design
#### 4.1.5 Best Path Selection  
Normally, a large number of servers and switches are included in DCNs. In a wide DCN, each node has multiple flows. Therefore, accessing the data with reliability and in a simple way is extremely hard in this type of network. To solve this problem, different topologies are used to improve the utilization of bandwidth and reduce network congestion. In this part of the work, two types of DCNs based on the SDN are deployed, as explained in Figures 3 and 4. In the DCN, the performance of the network is a critical aspect; hence, high throughput and sensitivity to packet loss are required. Elephant and mice flows are known as the main types of traffic in a DCN. Therefore, the need to manage the traffic of these types of flow still exists as discussed in the literature review section.  
Many algorithms can provide routing for elephants and/or mice flows. For example, equal-cost multipath routing (ECMP) [39] can be utilized for routing mice but not elephant flows [40]. In this project, the primary aim is to select the best path for each elephant and mice flow. The best path for a flow is the path that achieves the requirements of the flow, such as low latency for mice flows and high bandwidth for elephant flows. A developed  
Figure 6: Steps of Preprocessing.  
Dijkstra algorithm was proposed to find the route based on the type of flow (elephant or mice flows) by employing Algorithms 2, 3, and 5, as shown in Section 3.2. Using a developed Dijkstra algorithm, the links that fulfill the conditions are determined, and the paths that contain the appropriate bandwidth and latency are selected.  
The Dijkstra algorithm was applied to find the shortest path for mice flows, whereas for elephant flows, the Widest- Dijkstra algorithm was used to find multiple paths to route them and record all the available shortest paths. Because it has an appropriate complexity for real-time problems and it gives deterministic results, the Dijkstra algorithm was chosen in this part of the work.
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 5 Discussion
### Future Work  
There is potential research required over the LoRaWAN-based deployment in greenhouse. The most important constraint in the current setup is power consumption. This requires a new design technique such as inclusion of energy harvesting schemes, multidisciplinary approach like machine learning, artificial intelligence and on chip data processing for energy saving. There needs to be a trade-off between the frequency of sending messages to the power required in onboard computing as given in Table 7. Also, we plan to integrate ML technique for improved crop monitoring and extending the lifetime of the network.
## 3 Results
### Performance After Feature Selection  
After applying recursive feature elimination to reduce data dimensionality, the models were re-evaluated using the 16 most relevant features. The RFE method was chosen for its ability to iteratively eliminate less significant features based on their impact on model performance.  
The results showed that model performance remained stable, indicating that removing less important features did not negatively affect accuracy. Specifically, the DT and RF algorithms achieved the highest accuracies, reaching 85% and 87%, respectively. These scores were slightly higher compared to the full-feature models, suggesting that eliminating redundant features enhanced efficiency.  
The XGBoost and SVM models followed with accuracies of 82% and 80%, respectively. Regarding precision, recall, and F1-score, RF and DT also demonstrated superior performance, with an average F1-score of 0.85. Overall, the ML models' performance was considered consistent and effective, demonstrating that reducing the feature set maintained or even slightly improved predictive accuracy.
## 3 The Structure of the Decision Support Platform
### The Profiling Module
#### 3.3.2 Clustering-Based Method in Profiling Process
_Extraction of TECPs_: A TECP will be assigned to each cluster, calculated as the mean of normalized energy consumptions associated with each profile. \[\overline{w}_{j,k}=\frac{\sum\limits_{l=1}^{N_{k}}w_{l,j}^{norm}}{N_{k}};\,\,j =1,\ldots,\,m,\,\mathfrak{m}=\{24,\,48,\,96\},\,k=1,\,\ldots,\,C_{k} \tag{9}\]  
where \(C_{k}\) is the number of clusters resulted from the clustering process; \(\overline{w}_{j,k}\) represents the mean normalized value \(j\) of the energy consumption associated with the TECP corresponding to the cluster \(k\), \(k=1,\,\ldots,\,C_{k}\); \(l\) is the index of the energy consumption profiles, from the matrix \(W\), associated with the cluster \(k\) in the clustering process; and \(N_{k}\) is the number of the energy consumption profiles associated with the cluster \(k\) in the clustering process. The representativeness of the mean can be evaluated based on the variation coefficient:  
\[\
u_{j,k}=\frac{\sigma_{jk}}{\overline{w}_{j,k}}\cdot 100\quad(\%);\,\,j=1,\, \ldots,\,m,\,m=\{24,\,48,\,96\},\,k=1,\,\ldots,\,C_{k} \tag{10}\]  
\[\sigma_{j,k}=\sqrt{\frac{\sum\limits_{l=1}^{N_{k}}(w_{l,j}^{norm}-\overline{ w}_{j
# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 3 Proposed DWT_LSTM Forecasting Method  
### Sketch of DWT_LSTM  
The proposed DWT_LSTM forecasting method has two important components: the LSTM and DWT. The two components are used to implement the strategy of dividing and conquering. Specifically, the proposed method uses DWT to decompose original wind power data into sub-signals and independent LSTMs to learn the temporal relationship from sub-signals, respectively.
## 2 Preliminaries  
### Mechanical Structure and Three Working Modes  
The mechanical configuration of the NQTAUV is illustrated in Figure 1, where the rotors are mounted on the tip of the hydrofoil. The servos tilt the rotors, separately generating vector thrust. Each rotor generates force \(f_{i},i=1,2,3,4\), and the servo tilts the rotor to angle \(\alpha_{i},i=1,2,3,4\).  
The NQTAUV has three working modes: hover mode, transition mode, and level cruise mode. These are illustrated in Figure 2. The NQTAUV takes off, operating first in hover mode. In this mode, it can move slowly in a horizontal direction. The rotors then tilt to a certain angle, which is usually \(60^{\circ}\), to generate forward thrust to accelerate until it reaches a speed at which the lift force generated by the hydrofoil can balance the weight of the vehicle in the water, which describes the transition mode. After that, the rotors tilt to \(90^{\circ}\), and the NQTAUV operates in level-cruise mode. Unlike neutral-buoyancy vehicles, the NQTAUV must overcome less drag force [9], resulting in high efficiency and speed.
## 2 Materials and Methods
### Flammability, Combustion, and Thermal Characteristics of Flame Retardant Coatings  
The LOI test was performed using an HC-2C oxygen index instrument (China). Three coating layers were applied on 130 mm \(\times\) 6.5 mm \(\times\) 3.2 mm sized plywood sheets using the dip-coating method and analyzed according to the ASTM D2863 standard procedure. The UI-94V test was conducted according to the ASTM D 3801 standard procedure. Before performing this study, dip-coated specimens were pre-treated at 23 \(\pm\) 1 \({}^{\circ}\)C/50 \(\pm\) 5% relative humidity for 2 days, then 70 \({}^{\circ}\)C for 7 days using a hot air oven, and then cooled in a desiccator for a minimum of 4 h. The sample specimens of dimensions 125 mm long, 13 mm wide and 3.1 mm thicknesses were reinforced in a vertical position and a flame was applied to the bottom of the sample specimen. The flame was obtained by adjusting the gas supply, and flame height was adjusted to 20 mm. Finally, the rate of combustion of the composite coatings was determined by the vertical burning test followed by UL-94 V standards in an air atmosphere. The combustion behaviors, peak heat release rate (PHRR) and total heat release rate (TRR), were determined by a microcalorimeter instrument (Make Fire Testing Technology, United Kingdom, and Modal 11311). The test was conducted in accordance with the ISO 5660 guidelines. For this study, 4-5 mg of each sample was put into a thin-walled, quartz capillary crucible, kept in a sample holder, and heated at a constant rate of 260 K/min with a temperature range of 90 to 800 \({}^{\circ}\)C. Thermogravimetric analysis was conducted on a thermogravimetric analyzer (Perkin-Elmer) instrument at a linear heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\) in a nitrogen environment. The range of the temperature was kept at 30 to 800 \({}^{\circ}\)C. The sample weight was taken at about 10 mg.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Formulation**} & **Ammonium** & **Chitosan** & **Melamine** & **Boric Acid** & **ZrO\({}_{2}\) NPs** & **Bisphenol:Hardener** \\  & **Polyphosphate** & & & & & **(2:1)** \\ \hline F0 & 15 & 5 & 5 & 15 & 0 & 60 \\ F1 & 15 & 5 & 5 & 15 & 1 & 59 \\ F2 & 15 & 5 & 5 & 15 & 2 & 58 \\ F3 & 15 & 5 & 5 & 15 & 3 & 57 \\ \hline \end{tabular}
\end{table}
Table 1: The ratio of formulation epoxy resin nanocomposites in _v/v_ %.
## 4 Case Study
### ANN Model Construction  
The five selected features (one for the HD and the others for the TB) from the previous section are used as the input nodes of the ANN, and the corresponding fault severities of the HD and TB as the output. Since there are 100 data points available for each of the 11 fault conditions, the total number of data points is \(100\times 11=1100\). The model is trained  
Figure 12: Histograms of (**a**) best feature and (**b**) worst feature for HD fault. t-SNE plots of (**c**) four best features and (**d**) four worst features for TB fault.  
using 70% of the datasets and the model accuracy is evaluated by the test datasets (30%). Note that in the model, the severity of each component is transformed into the health index (HI) so that it is 0 for the normal and 1 for the most severe condition. Hence, the HD fault levels of \(Lv_{0}\), \(Lv_{1}\), and \(Lv_{2}\) correspond to 0, 0.5, and 1, and the TB fault levels of \(Lv_{0}\), \(Lv_{1}\), \(Lv_{2}\), and \(Lv_{3}\) to 0, 1/3, 2/3, and 1. The structure of the ANN is given by a single hidden layer to avoid overfitting and reduce computational complexity. For the activation function, tan-sigmoid is selected. The number of nodes in the hidden layer is optimized through 5-fold cross-validation. For this purpose, the training datasets are further divided into training and validation sets by a ratio of 4 to 1. The ANN model is trained with the training set while the accuracy is calculated using the validation sets. The process is repeated five times by choosing the sets at a ratio of 4 to 1 at random, from which the average accuracy is obtained for each hyper-parameter. As a result, the model showing the highest accuracy is chosen among the number of nodes from 1 to 50, which is 22. Finally, the ANN is trained using the training datasets with the optimized parameter and applied to the test dataset to predict the HI of each data. The histograms of the error between the predicted (output) and true (target) HI for the 330 test data sets are given in Figure 13 for the HD and TB, respectively. The root mean square error (RMSE) of these are also calculated, and are 0.0275 and 0.0902.
## 3 Message Digests for Simulatable Signatures
### Proposed Construction  
We next propose an OAEP-based construction allowing the simulation of digest-signature tuples in such a way that the digest component of simulated tuples can be linked to a piece of data whose length can be chosen by the simulator, but its actual value cannot. Given an existing simulatable signature scheme, the construction is as follows:  
1. Let \(M\) be the \(m\)-bit message to be signed.
2. Let \(l\) be the length of the digests signed by the signature scheme.
3. Generate a random \(l\)-bit bitstring \(r\) and compute \((X,Y)=\text{OAEP}_{m,l}(M,r)\).
4. Compute a digital signature over \(Y\), namely \(\text{Sign}(Y)\).
5. Send the \(\{X,Y,\text{Sign}(Y)\}\) tuple to the receiver. Such a tuple is validated as follows:  
1. Validate the \(\{Y,\text{Sign}(Y)\}\) digest-signature tuple under signer's public key.
2. Compute \((M,r)=\text{OAEP}_{m,l}^{-1}(X,Y)\) so as to get message \(M\).  
Note that a tuple \(\{X,Y,\text{Sign}(Y)\}\) can be transformed into \(\{X^{\prime},Y,\text{Sign}(Y)\}\) with \(X^{\prime}\
eq X\) while the signature validation still produces a positive result. In such a case, the computation of \((M^{\prime},r^{\prime})=\text{OAEP}_{m,l}^{-1}(X^{\prime},Y)\) leads to a piece of data \(M^{\prime}\
eq M\). This is not an issue in our construction as long as \(M^{\prime}\) cannot be chosen by the manipulating party, as it has been stated in Lemma 1. The length of \(M^{\prime}\) corresponds to that of \(X^{\prime}\) (or it is \(k_{1}\) bits shorter if plaintext-awareness has been set).  
Similarly, any party can generate a simulated \(\{Y^{\prime},\text{Sign}(Y^{\prime})\}\) tuple for the underlying signature scheme and then choose any \(X^{\prime}\) component. The resulting \(\{X^{\prime},Y^{\prime},\text{Sign}(Y^{\prime})\}\) tuple will also result in a positive validation. As in the previous remark, this is not an issue since the resulting \(M^{\prime}\) is obtained pseudo-randomly.  
The simulatability of the proposal can be disabled by setting the plaintext awareness feature to OAEP. This is because the pseudo-random piece of data \(M^{\prime}\) obtained by simulation does not meet this feature.
## 2 Materials and Methods
### Policy Gradient-Based Deep Reinforcement Learning
#### 2.3.3 Advantage Function and Baseline  
The advantage function, denoted as \(A(s,a)\), is a crucial concept in reinforcement learning that measures the relative value of taking a specific action in a given state compared to the average value of actions in that state. Therefore, the advantage function measures how much better or worse an action is compared to the average action in the same state according to the policy. Mathematically, the advantage function is defined as the difference between the action value function \(Q(s,a)\) and the state value function \(V(s)\), as follows:  
\[A(s,a)=Q(s,a)-V(s) \tag{5}\]  
A baseline is introduced to further decrease the variance in the policy gradient estimation without affecting the expected gradient. A baseline is a function, \(b(s)\), that estimates the expected value of the advantage function for a given state. In practice, the state value function \(V(s)\) is often used as a baseline. By subtracting the baseline from the estimated \(Q\)-values, the resulting advantage values used in the policy gradient updates have reduced variance. This is because the difference between the \(Q\)-value and the baseline captures the relative importance of an action in a particular state, making the gradient estimation more focused on the action preference, rather than the absolute value.  
Importantly, introducing a baseline does not affect the expected value of the gradient. Consider the policy gradient theorem, as follows:  
\[\
abla J(\theta)=\frac{\partial J(\theta)}{\partial\theta}=E_{\tau}\bigg{[} \sum_{t=0}^{T}\frac{\partial log\pi(a_{t}|s_{t},\theta)}{\partial\theta}*Q^{ \pi}(s_{t},a_{t})\bigg{]} \tag{6}\]  
\[Q^{\pi}(s_{t},a_{t})=A^{\pi}(s_{t},a_{t})+V^{\pi}(s) \tag{7}\]  
\[\
abla J(\theta)=\frac{\partial J(\theta)}{\partial\theta}=E_{\tau}\bigg{[} \sum_{t=0}^{T}\frac{\partial log\pi(a_{t}|s_{t},\theta)}{\partial\theta}*(A^{ \pi}(s_{t},a_{t})+V^{\pi}(s))\bigg{]} \tag{8}\]  
When a baseline \(b(s)\) is subtracted from the \(Q\)-value, the new advantage estimate becomes \(Q(s,a)-b(s)\). If \(b(s)=V(s)\), the expected value of the gradient is now given by the following:  
\[E_{\tau}\bigg{[}\sum_{t=0}^{T}\frac{\partial log\pi(a_{t}|s_{t},\theta)}{ \partial\theta}*A^{\pi}(s_{t},a_{t})\bigg{]}=0 \tag{9}\]  
Therefore, adding or subtracting a baseline does not change the expected gradient, but it significantly reduces the variance, thus, leading to more efficient learning.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Mathematical Foundations of DDPM  
The DDPM is built upon a mathematical foundation that combines elements from probability theory, stochastic processes, and deep learning [45]. This foundation enables DDPM to model complex data distributions, denoise images, and generate realistic samples [46].
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## 3 Remote Sensing Camera with a Harmonic Diffractive Lens
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Sony IMX249** & **e2V EV76C570** \\ \hline Quantum efficiency, \% & 70 & 47 \\ Temporal dark noise, e & 7 & 22 \\ Dynamic range, dB & 74 & 50 \\ Signal to noise ratio, dB & 45 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Sony IMX249 and e2V EV76C570 sensors. entropy to the image, harmonic lenses enable effective image restoration with a trained neural network, introduced in Section 4.2. Figure 3: (**a**) A microscope image of our 10 mm diameter, 70 mm focal length diffractive lens, (**b**) a profilogram measured radially from the center, and (**c**) predicted shift of the principal lens focus as a function of the light wavelength (numerically modeled using our proprietary software Harmony [46]. While one team focused on the design and manufacturing of our harmonic optical element, another team designed a lens housing. Figure 4 shows a 3D model of the camera casing (Figure 4a), the exterior of the computing unit (Figure 4b), and the layout of individual components inside the camera casing (Figure 4c). In the assembly, shown in Figure 4, the casing of the camera (1) has been implemented as a lightweight single-piece component. The design allows for adjustments of the camera lens and IR filter position with a female thread inside the casing. The lens unit can also move inside the cylinder (2) through a threaded coupling to adjust the distance between the harmonic lens (10) and the CMOS sensor (3). After adjustment for the best contrast, the lens is fixed in the position with a locknut (6). The IR filter (9) eliminates the IR flare of the CMOS sensor. Figure 5a shows the camera casing before the assembly next to the diffractive lens mounted to the lens frame. Figure 5b shows the camera mounted inside the CubeSat frame. Figure 4: The design of the harmonic-lens-based video camera for the small satellite 3U CubeSat: (**a**) a 3D model of the video camera; (**b**) the exterior of the computing unit (3D model); (**c**) the video camera in section view (3D model). 1—lens unit; 2—casing; 3—CMOS sensor; 4—processor casing; 5—Jetson Nano processor; 6—locknut; 7—a mount for the lens and IR filter; 8—a nut; 9—an IR filter; 10—harmonic lens. A lens with a focal length of 70 mm and an EV76C570 sensor of 7.2 mm \(\times\) 5.4 mm dimensions provides an angle of view of approximately 6\({}^{\circ}\), which at an altitude of 570 km corresponds to a field of view of 59 km. Therefore, with the sensor resolution of 1600 \(\times\) 1200 pixels, the theoretical ground sampling distance (GSD) is 36 m/pixel, while the conventional onboard camera has a theoretical GSD of 270 m/pixel. However, the actual resolution depends on the efficiency of the image reconstruction algorithms and is effectively lower than the theoretical maximum.
## 4 Experiment and Results
### Hyperparameter Study  
For the introduced preprocessing method, two new hyperparameters in (1) need to be set before the training, \(\tau\) and \(m\). Both hyperparameters were selected quantitatively by comparing the results using the MICC-SRI as shown in Table 6. The best result values are used when choosing a \(\tau\) value of 0.97 and a mean value of 0.01. For the best dynamic removal performance, we selected 0.97 for \(\tau\) and 0.01 for the noise mean value \(m\).  
Compared to the results without the preprocessing applied (\(\tau=1,m=0\)), it implies that preprocessing helps the network's learning. In other words, limiting the maximum value of the labels helps the discriminator learn the desired features with no discriminative factor. Moreover, the noise factor helps the learning of the network, but it does not mean that the noise factor is indispensable.
# Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network
###### Abstract
Therefore, this method only measures the absolute value of the piston error between the sub-mirrors and cannot identify the specific spatial relative positions of each sub-mirror. Aimed at this problem, based on the established theoretical relation between the _MTF_ and the piston error, this paper does not use the polynomial fitting method as it adopts a hybrid artificial neural network to measure the piston error. Although the system _MTF_ is the same when the absolute value of the piston error is the same, despite the sign of the piston error being different, the system point spread function (_PSF_) has a significant difference. Therefore, we first use the Resnet network to learn the mapping relationship between the system _PSF_ and the sign of the piston error, and then use the BP (back-propagation) neural network to learn the mapping relationship between the system _MTF_ and the absolute value of the piston error; thus the precise measurement of the piston error can be realized by the combination output of the two networks. The reason for using a hybrid artificial neural network is that the system _MTF_ cannot distinguish the sign of the piston error, and it is quite difficult to realize piston error detection with a wide range and with high precision directly using a single neural network from the focal plane image of the system. For example, when the detection range is [\(-200\)-\(200\)] um and the detection accuracy is required to be 0.01 um, for a segmented telescope with \(N\) sub-mirrors, the output classification of a single neural network is about 40,001\({}^{N-1}\). When the Resnet network is used to detect the sign of the piston error, the outputs are only divided into \(2^{N-1}\) classes, so the network size and the training difficulty can be greatly reduced. When calculating the specific value of the piston error, since the theoretical relationship between the _MTF_ and the piston value is established, a BP neural network is used to realize the high-precision detection of the absolute value of the piston error. In the following, we describe the imaging system model, the generation of training data, and the implementation of the Resnet and BP networks used in our study in Section 2. Then, the results are presented in Section 3, including the simulation results for the 2-pupilsegmented system and the 4-pupil segmented system, as well as a comparison of the results of our hybrid network and those of other single networks. Finally, concluding thoughts are offered in Section 4.