esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Simulations on Jet Impingement  
### Axisymmetric 2D Model  
In order to compare the numerical results with the experimental investigations from the literature, two models of increasing accuracy are developed. The main geometrical parameters of the model are the vertical distance H from the jet nozzle exit to the wall and the nozzle diameter D. Geometries defined by H/D = 2 and 6 are considered with the same Reynolds number, referred to as the nozzle outlet:  
\[Re=\frac{u_{mean}D}{\
u} \tag{1}\]  
The Reynolds number is set at 23,000. The computational domain is made of a pipe of diameter D, a solid wall, an upper surface, and the outlet surface.  
The pipe length is at least 10 D in order to guarantee a fully developed boundary layer at the jet outlet section. In Figure 1, the domain, with components and boundary conditions, is sketched. Structured meshes, generated using an ICEM-CFD package are considered, as shown in Figure 1.  
In Figure 2, the velocity contours are shown. In case the domain does not include the straight pipe, the jet velocity profile at the inlet boundary condition is obtained from a separate simulation of the flow in the isolated pipe. A constant heat flow is imposed along the wall and the atmospheric pressure is set at the outlet sections. According to the literature for similar applications, the v2f turbulence model is selected. The analyses are set to steady flow with an upwind second-order spatial discretization. The simulation convergence is based on the stabilization for the wall temperature. The main heat transfer parameter is the Nusselt number:  
\[Nu=\frac{hD}{k} \tag{2}\]  
where h is the local or average coefficient of convective heat exchange, calculated as follows:  
\[h=\frac{\dot{q}}{\left(T_{\text{turb}}-T_{\text{jet}}\right)} \tag{3}\]  
Figure 1: The 2D axisymmetric computational domain: case H/D = 2.  
Figure 2: Velocity magnitude contours in case H/D = 2.  
D is the nozzle diameter and k is the thermal conductivity of the evolving fluid (air). The values of air thermal properties (thermal conductivity and kinematic viscosity) are evaluated at the fluid inlet temperature. In Figures 3 and 4, the Nusselt number distributions on the wall for cases H/D = 2 and H/D = 6 are respectively shown. The Nusselt number distribution is plotted at distance r from the jet center using the non-dimensional variable r/D. A good match between the present numerical results and the experimental data provided by [PERSON] [17] and [PERSON] et al. [18] is observed. The predicted heat exchange is underestimated with respect to the data from [PERSON] et al. [7].
## 2 Methods/Experimental Approach
### Power Consumption Model
#### Electronic Circuitry Energy Consumption  
Though it is negligible in many cases, energy is also consumed by the NB-IoT node's electronic circuitry. In general, the NB-IoT circuit is made of sensors and signal converters such as ADCs. Most NB-IoT nodes' circuitry operates either in _burst_ or _periodic_ mode. For periodic sensing mode, the NB-IoT node's energy consumption is almost equal to the sensing energy consumption (meaning when the sensing circuit is \"ON\") added with the energy consumed during the \"ON\" to \"OFF\" as well as the \"OFF\" to \"ON\" state transitions. The electronic circuitry energy consumption can, therefore, be expressed as  
\[E_{circuit}=E_{on-off}+E_{off-on}+E_{Sensing}. \tag{21}\]
# HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance
## 2 Related Work
### Probabilistic U-Net  
U-Net delivers the best segmentation performance at the time and has been applied to many medical image domains. However, U-Net follows a one-to-one I/O mode, so it cannot solve the uncertainty of the plausible segmentation hypotheses. Based on U-Net and the conditional variational auto encoder (CVAE) [7], [PERSON] et al. [9] proposed a segmentation network called probabilistic U-Net, which can output an unlimited number of segmentation hypotheses for an input image. The network structure of the probabilistic U-Net is shown in Figure 3a. Similar to the general auto encoder, the CVAE is also symmetrical and consists of two symmetric sub-networks called prior net and posterior net, which are corresponding to the encoder and decoder in an auto encoder. The inputs of the prior net and the posterior net are the medical image and the ground truth of the segmentation. Different from the general auto encoder, the CVAE assumes that the data are generated by some random processes, and the CVAE performs probabilistic inference by learning Gaussian latent variables to model complex distributions in the latent space. In CVAE, the parameters in the network are assigned the meaning of \(\mu\) and \(\sigma\) in a Gaussian distribution. During the training process, the CVAE optimizes a large number of Gaussian latent variables to construct a large number of multidimensional Gaussian distributions to fit a complex distribution.  
As the CVAE can learn a complex probability distribution in the latent space, an unlimited number of random samples can be sampled by the learned distribution. In the probabilistic U-Net, the features of the generated sample are then concatenated to the output of the last layer in U-Net. Finally, three 1 \(\times\) 1 convolutions are applied to combine the concatenated information and map it to the desired number of classes. The probabilistic  
Figure 2: U-Net structure. Each dark green box corresponds to a multi-channel feature map. Light green boxes represent copied feature maps. The arrows denote the different operations. Up-conv 3 \(\times\) 3 represents upsampling of the feature map followed by a 3 \(\times\) 3 convolution.  
U-Net has two characteristics. Firstly, it can model the joint probability of all pixels in the medical image, so the produced segmentation maps can interpret the entire images consistently. Secondly, it models a complex distribution by all training samples, so it also learns the segmentation hypotheses with low probabilities. Due to the above characteristics, the probabilistic U-Net delivers better performance than network ensembles [3], M-heads [5; 6], and other related methods.
###### Abstract  
Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains. However, previous methods mostly construct semantic representations by learning from words directly, which is limited in domain adaptability. In this study, we enhance the domain adaptability of the model by utilizing the distributional signatures of texts that indicate domain-related features in specific domains. We propose a **Multi**-level **D**istributional Signatures based model, namely MultiDS. Firstly, inspired by pretrained language models, we compute distributional signatures from an extra large news corpus, and we denote these as domain-agnostic features. Then we calculate the distributional signatures from texts in the same domain and texts from the same class, respectively. These two kinds of information are regarded as domain-specific and class-specific features, respectively. After that, we fuse and translate these three distributional signatures into word-level attention values, which enables the model to capture informative features as domain changes. In addition, we utilize domain-specific distributional signatures for the calibration of feature representations in specific domains. The calibration vectors produced by the domain-specific distributional signatures and word embeddings help the model adapt to various domains. Extensive experiments are performed on four benchmarks. The results demonstrate that our proposed method beats the state-of-the-art method with an average improvement of 1.41% on four datasets. Compared with five competitive baselines, our method achieves the best average performance. The ablation studies prove the effectiveness of each proposed module.  
domain-generalized few-shot learning; text classification; distributional signature; meta-learning 2023
# Context-Based Energy Disaggregation in Smart Homes
## 4 Disaggregation Algorithm
### The Probabilistic Data Model  
The observation of the devices'consumption traces has underlined that most of them usually switch from a power consumption value to few others during each period of use; every trace can thus be considered as a set of transitions from a consumption level to the subsequent one. Consequently, the mean value of each power level with its associated variance can be regarded as a state.  
The Hidden Markov Model (HMM) is a probabilistic learning method for time series where the information about the past is transmitted through a single discrete variable, precisely named \"hidden state\" [8]; in this work, the HMM represents the power consumption evolution as a sequence of states. Such Markov processes are labeled starting from the outputs; analyzing the observed state, the algorithm assess what is the most likely Markov model hidden state capable of generating the observed output. Each device, thus, has been modeled through an HMM according with the power states and the transition matrix which determines the probabilities of each state to evolve in another. HMMs have been treated as Factorial HMM as described in [8] to consider the independent utilization overlaps of each device; the observed output is thus composed as a state additive function of the different hidden states.  
The single hidden Markov model, with its conditional independencies, is graphically represented in Figure 1a, where a sequence of observations \(\{Y_{t}\}\) with \(t=\{1,...,T\}\), is modeled by a probabilistic relation with a sequence of hidden states \(\{S_{t}\}\), and a Markov transition structure connecting the hidden states [8].  
The state can take one of \(K\) discrete values, \(S_{t}\in\{1,...,K\}\), which have been extracted from the occurrence histogram composed by several power traces for each appliance, through the use of a clustering technique (_i.e._, Gaussian Mixture Model). The transition matrix has therefore a \(K\times K\) dimension and represents the state transition probabilities, \(P(S_{t}|S_{t}1)\). Figure 1b shows the additive FHMM in which each independent HMM for each monitored device evolves in parallel; the sequence of observed output \(\{Y_{t}\}\) represents the aggregate hidden states; the algorithm thus estimates which is the most probable sequence of Markov hidden states that could have produced that output.
## 4 Experimental Results and Analysis
### Validation of Wind Speed Prediction Algorithms
The MHSA improves the model's ability to adapt to different seasons and wind speed change patterns, and it effectively enhances the accuracy and stability of the prediction, indicating that the method can effectively deal with the complexity and volatility of wind speed data. Table 3 was calculated based on the wind speed data from the official website of Iowa State University. According to a comparison of the prediction results across different wind farms, the proposed model continued to exhibit a strong predictive performance at other wind farm locations. The prediction results are shown in Figure 12.  
Based on the wind speed data from March 20 in Table 2, the proposed model showed a superior performance in predicting the wind speeds at both the Inner Mongolia and Iowa wind farms, with R\({}^{2}\) values of 0.98 and 0.99 and NSE values of 0.97 and 0.99, respectively. By analyzing the daily wind speed data from both wind farms and comparing the results with the other models, it can be verified that this method remains applicable across different geographic locations, highlighting its strong adaptability in predicting data from different areas. The prediction results for the different wind farm test sets, as shown in Figure 13, reveal that the error fluctuates with changes in the test set length. The evaluation metrics for the different training sets are provided in Table 4. When the training set length is 70%, the  
Figure 12: Model predictions for the Iowa wind farm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictive Model** & _RMSE_ & _MAE_ & _MAPE_ & \(\mathbf{R^{2}}\) & _NSE_ \\ \hline CEEMDAN-RIME-MHSA-BiLSTM & 0.12 & 0.08 & 1.09\% & 0.99 & 0.99 \\ EMD-BiLSTM & 0.54 & 0.32 & 6.17\% & 0.93 & 0.92 \\ CNN-BiLSTM & 0.69 & 0.48 & 8.32\% & 0.88 & 0.86 \\ GRU & 0.87 & 0.61 & 12.14\% & 0.80 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of single-day wind speed prediction for Iowa wind farm. CEEMDAN-RIME-MHSA-BiLSTM model reduces the KRMSE by 4% and 10% compared to training set lengths of 50% and 60%, respectively. Similarly, the KMAE decreases by 6% and 17%, while the KMAPE drops by 2.45%. The experiment shows that the CEEMDAN-RIME-BiLSTM short-term wind speed prediction model achieves a strong predictive accuracy and stability with an appropriately sized training set. In particular, when the training set length reaches 70%, the model's prediction accuracy is higher, with NSE values closer to 1, showing a closer fit to the actual values. Figure 13: (**a**) Prediction with 50% training set length; (**b**) prediction with 60% training set length; (**c**) prediction with 70% training set length.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies  
The discussion presented in this section aims to provide answers to Research Question 2: What are the techniques and strategies currently used in distributed learning?  
In our discussion, we focus on three main aspects, namely: (i) the system architecture, (ii) how the ML tasks are distributed among the devices, and (iii) the underlying adopted techniques. We classify the several approaches used in distributed learning based on these three aspects. We identified nine groups of techniques and strategies, described in what follows: Federated learning; Model partitioning; Right-sizing; Edge pre-processing; Scheduling; Cloud pre-training; Edge only; Model Compression; and Other techniques.
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## 4 Simulation Analysis  
### Simulation Conditions  
Considering the extremely high accuracy requirements for a lever arm in POS, it is difficult to find another more accurate benchmark to evaluate the estimation results. As an expedient, we carried out a series of simulation tests instead of real tests to verify the proposed method. Considering the effect of the time-asynchrony error, we assume that the aircraft flies at 50 m/s uniform speed, and the angular motion excitations are added into the simulation trajectory. The initial values needed for the trajectory are given as:  
* Initial position: latitude, longitude and altitude [34.2\({}^{\circ}\); 108.9\({}^{\circ}\); 400 m];
* Initial Velocity: \(v_{0}^{n}=[0;50;0]\) m/s;
* Initial Attitude: pitch, roll and heading [0; 0; 0]\({}^{\circ}\).
* Lever arms to be estimated: \(\mathbf{L}_{DI}^{b}=[-0.3;0.1;0.2]\) m, \(\mathbf{L}_{OG}^{b_{c}}=[0.5;-2.0;2.0]\) m.  
To verify the observability analysis results in Section 3.2, two simulation trajectories with different rotation sequences are designed as shown in Table 1. The total time of one simulation trajectory is 150 s, and the related rotation angular rates in two trajectories are all set to 10\({}^{\circ}\)/s.  
As shown in Table 1, the only difference between the two trajectories is that steps 2 and 3 are exchanged, which means that the change of matrices \(\mathbf{C}_{b}^{b_{c}}\) and \(\mathbf{C}_{b_{c}}^{n}\) occur in a different time sequence. The rotation angles of ISP gimbals and vehicular attitudes are shown in Figure 2.  
Referring to the system POS AV610 produced by Applanix [4; 35], we simulated an aviation-grade SINS and a CDGPS receiver with sensor specifications, including initial errors, process noises, and measurement noises listed in Table 2. Without loss of generality, the IMU data is sampled at 100 Hz, the ISP encoder data outputs at 20 Hz, and the GPS positioning information at 1 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Steps** & **Start Time** & **Trajectory 1** & **Trajectory 2** \\ \hline Step 1 & 0 s & Keep stationary 30 s & Keep stationary 30 s \\ \hline Step 2 & 30 s & \
\begin{tabular}{c} Rotate inertially stabilized platform (ISP) \\ heading gimbal 90\({}^{\circ}\) \\ 60 s \\ \end{tabular} & Rotate vehicular heading 90\({}^{\circ}\) \\ \hline Step 3 &
\begin{tabular}{c} 90 s \\ 120 s \\ \end{tabular} & Rotate vehicular heading 90\({}^{\circ}\) \\ \hline Step 4 & 124 s & Keep stationary to 150 s & Keep stationary to 150 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The design of simulation trajectories.
## 5 Conclusions  
### Summary  
In this research, we proposed a method for the data-acquisition synchronization of a high-speed sensor network system using sensors, such as tactile, vision, acceleration, and gyro sensors. In the proposed method, each sensor node refers to its own local clock, and all of the clocks in the network are globally synchronized by PTP. The global clock synchronization can be performed with an error of ten microseconds or less. The error is considered to be significantly smaller than the sampling time (less than 2 ms) for data acquisition. Subsequently, we suggested a method of data-acquisition synchronization based on the clock synchronization. The timing error between the sensor nodes for data acquisition was also evaluated, and we confirmed the precise synchronization of data acquisition with errors less than 15 \(\upmu\)s. Because it is considered that this synchronization error is significantly smaller than the sampling time, we conclude that the proposed method is effective for data-acquisition synchronization in a sensor network system.  
Moreover, the higher sampling rate (>1 kHz) in the developed sensor network system consisting of a tactile sensor, acceleration sensors, and a gyro sensor can be achieved; additionally, we verified the performance of the sensor network system experimentally. The features of the developed high-speed sensor network system are the following: good data synchronization, high-speed sampling rate, and general-purpose property. In particular, the data synchronization contributes to improving the performance of some applications.
## 2 Methodology
### Entropy  
Entropy is a method of quantitatively evaluating the degree of randomness using the probability distribution of data [25]. Entropy includes marginal entropy, conditional entropy, and joint entropy. Marginal entropy proposed by [25] is the amount of uncertainty of discrete random variable X, as shown in the following equation.  
\[\mathrm{H}(\mathrm{X})=-\sum_{n=1}^{\mathrm{N}}\ \mathrm{p}(x_{n})\ln\mathrm{p}(x _{n}), \tag{1}\]  
where \(\mathrm{p}(x_{n})\) is the empirical probability of \(x_{n}\) and \(\mathrm{H}(\mathrm{X})\) is the uncertainty of marginal entropy X. \(n\) is the number of the class intervals of X.  
If there are two random variables X and Y, conditional and joint entropies are calculated through conditional and joint probabilities. When variable Y is given, the entropy of variable X is conditional entropy \(\mathrm{H}(\mathrm{X}|\mathrm{Y})\), as shown below.  
\[\mathrm{H}(\mathrm{X}|\mathrm{Y})=-\sum_{n=1}^{\mathrm{N}}\ \sum_{m=1}^{ \mathrm{M}}\ \mathrm{p}\big{(}x_{n},y_{m}\big{)}\ln\mathrm{p}\big{(}x_{n}\big{|}y_{m} \big{)}, \tag{2}\]  
where \(n\) and \(m\) of X and Y are the number of class intervals. \(\mathrm{P}\big{(}x_{n},y_{m}\big{)}\) is the joint probability and \(\mathrm{p}\big{(}x_{n}\big{|}y_{m}\big{)}\) is the conditional probability. Joint entropy \(\mathrm{H}(\mathrm{X},\mathrm{Y})\) is calculated from the joint probability of random variables X and Y.  
\[\mathrm{H}(\mathrm{X},\mathrm{Y})=-\sum_{n=1}^{\mathrm{N}}\ \sum_{m=1}^{ \mathrm{M}}\ \mathrm{p}\big{(}x_{n},y_{m}\big{)}\ln\mathrm{p}\big{(}x_{n},y_{m} \big{)}. \tag{3}\]  
Weights can be applied according to the purpose and study characteristics determined by the experimenter. The weight may be independent of or dependent on \(\mathrm{p}(x_{n})\). Weight entropy is given by the following equation [26].  
\[\mathrm{WH}(\mathrm{X})=-\sum_{n=1}^{\mathrm{N}}\ \mathrm{w}_{n}\mathrm{p}(x _{n})\ln\mathrm{p}(x_{n}), \tag{4}\]  
where \(\mathrm{w}_{n}\) is weight of event. Weight entropy is applicable to conditional and joint entropy. Rain gauge stations with high entropy are important as they have high uncertainties in rainfall observation.  
Figure 1: Research flowchart.  
The most common entropy calculation method estimates uncertainty from the empirical probability distribution of data. Several approaches have been developed and used to compensate for the shortcomings of these methods. In this study, three more methods were considered, other than the \"emp\" method using empirical probability.  
\"mm\" is the bias-corrected empirical entropy estimator proposed by [27]. \"shrink\" is a shrinkage estimate of the entropy of a Dirichlet probability distribution [28]. \"sg\" is the entropy of a Dirichlet probability distribution proposed by [29]. In this study, entropy was calculated using the entropy, contentropy, and multiinformation functions of the infotheo package [30].
## 5 Experimental Results and Analysis
### Description of the Benchmark Datasets  
Currently, only few public benchmarks in the intrusion detection domain can be utilized to evaluate the performance of the IDS, such as KDD Cup 99 [46; 47], NSL-KDD [21; 22], UNSW-NB15 [25; 26; 27], ADFA [48], Kyoto 2006+ [49], ISCXIDS2012 [50], and CICIDS2017 [51]. The KDD Cup 99 dataset is from UCI (University of California, Irvine) Machine Learning Repository and is an upgraded version of the DARPA98 dataset. The KDD Cup 99 dataset has a large size and includes a huge number of duplicate and redundant records. The huge duplicate records can cause the classifier to bias toward the frequently occurring records. The NSL-KDD dataset is a modified version of the KDD Cup 99 dataset and removes redundant, duplicate and irrelevant data records in the KDD Cup 99 dataset. UNSW-NB15 is a new dataset created by the Cyber Range Lab of the Australian Cyber Security Center (ACCS) in 2015 through the IXIA PerfectStorm tool to generate a hybrid of real modern normal activities and synthetic contemporary attacks. The UNSW-NB15 dataset is more in line with today's complex network environments. The ADFA dataset covers Linux and Windows system logs and is designed for HIDS (Host-based Intrusion Detection System) evaluation. In addition, Kyoto 2006+, ISCXIDS2012 and CICIDS2017 datasets contain only two types of data records, normal and abnormal. Therefore, we selected the NSL-KDD and UNSW-NB15 datasets to evaluate the proposed method.