esa-sceva/satcom-chunk-collection huggingface dataset:
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 2 Related Work
The evaluation metrics are CAPEX and bandwidth and delay costs. From another perspective, some researchers address the chain embedding problem when considering other objectives, such as resilient allocation of SFCs. In [11], a model for calculating the availability of the SFC is introduced. The authors present Joint Path-VNF backup model that jointly considers path and VNF backup. Furthermore, they use a priority-based algorithm in order to optimize the composition and mapping of SFCs. In this algorithm, VNF dependency is converted to a VNF priority. There might be several FGs generated according to these priorities. Subsequently, the SFC is mapped on the SN based on an optimal FG. The authors use Alevin [12], an open-source VNE framework written in JAVA, with Barabasi-Albert-based random topology. The evaluation metrics are total data rate, acceptance ratio, max availability, VNF cost, and physical node and link cost. In [13], two main resilience strategies are discussed: VLi and VNF resilience. The VLi resilience strategy tries to calculate a disjoint backup path when mapping a target VIi that connects two VNFs. VNF resilience needs at least two different SNos on which the VNF is allocated. The evaluation focuses on the acceptance ratio and embedding cost. We contribute to the current state of the art in different aspects. First, a policy-based transformation of an abstract AR composes an EVN that includes the physical and virtual application nodes and the VNF requests. Our policies consider preliminary topological, performance, security, and reliability requirements. Second, we integrate location- and domain-awareness into the SFC composition and embedding solutions. These solutions consider dependencies among VNFs, which also reflect the locations and domains. The transformations add security VNFs according to the domains and locations across which the traffic flows. Third, we define a certain use case of these approaches from Industry 4.0 and design the fixed topology that represents it. The last contribution is an embedding heuristic that tries to find a valid solution for the embedding problem while using proactive verification of the path. To the best of our knowledge, this is the first work that addresses the composition of an EVN that combines the application and SFCs, based on multiple policies, and in the context of industrial environments. We do not yet consider optimizing the composed SFC, for example, for resource requirements, which is the focus of chain composition works. For these reasons, it is not straightforward to compare our composition method with existing works.
## 3 Online Processing Parameters Prediction and Reverse Design Framework  
The proposed framework, which is shown in Figure 1, for the online processing parameters design in this paper follows the subsequent workflow: when the collected streaming data do not meet the pre-training quantity, a simple prediction is made. Once the quantity meets the pre-training requirement, concept drift detection is performed on the new streaming data. If concept drift is detected, this portion of data is discarded to prevent incorrect updates to the model. If no drift occurs, the online machine learning model parameters are updated to predict the next round of process performance. The streaming data without concept drift are used to update a Gaussian process regression model and Bayesian optimization is carried out using a Gaussian maximum upper confidence acquisition function with an adaptive learning rate. This process completes the reverse design and recommendation of process parameters. Factory equipment automatically adjusts according to the designed process parameters. Then, new streaming data are collected again for the next round of process parameter recommendations.  
Notation: To make the notations easier to follow, we give a summary of the notations in the following list:  
* is the \(\epsilon-\)insensitive loss function and is defined as \(|Y-f(x)|\epsilon=\max\{0,|Y-f(x)|-\epsilon\}\) for a predicted value \(f(X)\) and a true output \(Y\);
* is the kernel function;
* is the bias;
* is a regularization constant;
* is the weight of \(K(X_{i},X_{j})\);
* are slack variables correspond to the magnitude of this excess deviation for positive and negative deviations, respectively;
* is the \(i\)th
* variable in the upper function;
* is the \(i\)th
* variable in the lower function.
Each circuit and node contains five and three variables of interest, respectively (although more than 30 variables are monitored, reported, and stored in the system). That required nearly 4000 univariate forecasting models capable of producing hourly forecasts for a one-week horizon. Additionally, as in most data-science endeavors, data is noisy and contains outliers and missing information. It is also worth noticing that, due to CFE's data governance policies, developing our solution based on specialized services from public cloud providers was not an option. Therefore, MIRD was implemented entirely on-premises, based on a fast and resilient software architecture capable of dealing with the huge-scale and high-speed requirements posed by the problem. Additionally, fundamental components of that architecture had to take care of both challenging data-preprocessing operations (facing many types of data disturbance across the data-acquisition stage) and the urgent need to balance forecasting accuracy and speed. In that context, the main contributions of this research are as follows:  
* The complete implementation of a large-scale machine-learning (ML) pipeline focused on short-term PQ indices forecasting for a regional-size, state distribution grid. * We developed our ML pipeline with the integration of modern, widely tested, open-source computing frameworks such as the big-data unified engine Apache Spark or the scientific computation library SciKit Learn. * To our knowledge, no other regional power distribution grid in Latin America has made a parallel effort in PQ indices forecasting or early-warning operations. The rest of the article is structured as follows: Section 2 presents an account of work found in the literature that deals with the problem of short-term PQ indices forecasting. Section 3 describes the overall architecture of MIRD as a data pipeline centered on large-scale ML operations. Sections 4 and 5 present in detail the two most important components of the MIRD pipeline: data preprocessing and forecasting. Section 6 presents examples of the results of the MIRD operation. Finally, Section 7 presents the conclusions and lines of future work.
# Generative Image Steganography via Encoding Pose Keypoints
## 4 Evaluation and Comparisons
### Security  
As previously mentioned, communication subject to monitoring may compromise security. An attacker would need to detect the existence of covert communication and then decrypt or destroy the information to obtain secret messages. However, the method in this paper generates common model clothing images as the covert carrier. The images do not directly embed information, so they are less likely to arouse suspicion and have a high level of security.  
In addition, an effective method of information hiding should withstand various steganalysis tools. Most of the current methods can be detected by steganalysis tools that use the changes in pixel values resulting from the secret message-hiding process. However, the method in this paper does not embed information by modifying the pixel values of the image. Instead, it uses secret information to drive the generation of human pose keypoints, thereby generating images of people with target poses. Therefore, the proposed method has a good ability to resist steganalysis. Table 5 compares the detection results of this method and other methods under steganalysis. To train XuNet [9] and YeNet [10], we selected 5000 real images from the above datasets for each steganographic method. Then, we generated 15,000 stego-images for each steganographic method containing three types of hiding payload. Next, we used the trained XuNet and YeNet to test the steganographic method's anti-steganalysis ability. We evaluate the anti-steganalysis abilities of different methods by \(P_{E}\). The \(P_{E}\) is computed by  
\[P_{E}=\frac{1}{2}(P_{FA}+P_{MD}) \tag{7}\]  
where \(P_{FA}\) and \(P_{MD}\) represent the false alarm rate and missed detection rate, respectively. When \(P_{E}\) reaches 0.5, the performance of anti-steganalysis ability is optimal. It should be noted that the false alarm rate refers to the probability of misjudging a normal situation as abnormal during the detection process. The miss detection rate, on the other hand, refers to the probability of failing to detect an actual abnormal situation during the detection process.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attacks** & & **[PERSON] [11]** & **[PERSON] [15]** & **[PERSON] [23]** & **[PERSON] [36]** & **[PERSON] [18]** & **Proposed** \\ \hline Gaussian & \(\sigma\)(0.001) & 5.57\% & 48.67\% & 0.12\% & 9.39\% & 0 & 0.64\% \\ \cline{2-8} Noise & \(\sigma\)(0.005) & 5.82\% & 45.92\% & 0.52\% & 1.15\% & 0.57\% &
## 3 Methodology
### Wide-ShuffleNet
[64] introduced skip connections that bypass one or many layers (see Figure 2 in reference [64]). Skip connections create the fundamental unit of the residual network, which is considered a residual module [71]. It maintains the feature data over all layers, having longer networks while preserving low parameter numbers. Alternately of getting a desired mapping (indicated as H(x)), the network with skip connection gets a residual mapping (indicated as F(x) + x, with F(x) = H(x) - x). Skip connections make identity mapping and add the result to the output of the two convolutional layers (see Figure 2 in reference [64]). Next, we apply the long variant of the skip connections to extend the width of the DL architecture. An architecture with a long residual connection converges more quickly and offers excellent performance (see reference [72]). A long residual connection helps the network increase the accuracy as it enhances reused features through the entire network. It also helps the network to get the detailed and general characteristics of objects. A one-by-one convolution layer is inserted in the shortcut connection to create a long residual connection between different sized layers, making the size of two inputs of the additional layer equal. Figure 3 shows the final architecture of the proposed wide-ShuffleNet. ShuffleNet 1, 5, and 13 are stride units, whereas the others are the nonstride units. In three skip connections, we use three skip convolution layers at the same kernel size of 1 \(\times\) 1 to connect the input layers of ShuffleNet units: 1, 5, 13, and an output layer of the ShuffleNet units: 4, 12, 16,  
Skip convolution layers: 1, 2, 3, 4, and 5 have 136, 272, 544, 544, and 544 filters, respectively. The stride for all skip convolution layers is equal to 2. Figure 3: Proposed wide-ShuffleNet. Additionally, we insert the batch normalization (BN) layer after every skip convolution layer due to some reasons. DL training process becomes quick when applying BN. Increasing the deep of the network means that the training process gets more challenging because of many problems faced while training. The architecture provides greater test data precision with the BN layer than the original model (without this layer). BN decreases the internal covariate shift; thus, improving the performance of the network. The classification accuracy significantly increases when applying BN. The position of the BN layer is after the convolutional layer and before the leaky ReLU layer. This structure speeds up the training and decreases testing and training time [73]. Furthermore, we replace all ReLU layers in all ShuffleNet units with the leaky ReLU layers. Leaky ReLU gives better results than the ReLU activation function (see reference [74]).
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### AI-Based Voice Assistant Module  
To increase the robustness of fall detection and provide user-centered interaction, the system integrates a voice assistant module that is triggered immediately after a fall is detected. This module serves a dual role: verifying the user's state post-incident and initiating appropriate emergency procedures based on the response.  
(a) Activation Trigger  
The voice assistant is activated only when the fall detection logic confirms a potential fall event. Once a fall is flagged, the assistant issues an audible prompt using the pyttsx3 text-to-speech engine.  
This interaction simulates a real-world caregiver inquiry and ensures the system seeks user input before triggering emergency protocols.  
Figure 1: Pose detection results using dual-camera input: (**a–d**) examples showing accurate identification of the “Standing” and “Fall detected!” postures from different viewpoints.  
Figure 2: Real-time detected postures with operation maintained despite one camera failure: (**a**) Bent over, (**b**) Sitting.
# Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine
## 1 Introduction
measured the specifications of low-noise amplifiers at \(-\)40 \({}^{\circ}\)C, 27 \({}^{\circ}\)C, 75 \({}^{\circ}\)C and 100 \({}^{\circ}\)C [22]. This method can be more accurate in knowing the specifications of RF amplifiers at different temperatures, but the measurement process takes more time and is also constrained by the measurement environment to measure RF amplifier specifications at each temperature. Therefore, it becomes an urgent problem to know the specification of an RF amplifier at any temperature in each temperature range by measuring a few critical temperature points. In this paper, temperature characteristic variation measurements are performed for two given RF amplifiers and are based on the data from these measurements. An extreme learning machine (ELM) is applied to model the critical specifications of an RF amplifier concerning temperature. It can be used to predict the variation in the critical specifications of the RF amplifier in each temperature interval. The modeling results show that the accuracy of this model is two orders of magnitude higher than that of the model in the literature [19] for the same number of temperature measurement points, and the selection and distribution of temperature points have little effect on the accuracy of this model. The organization of this paper is specified as follows. Section 2 gives a brief description of the structure and the measurement setup of the temperature characteristics of the two RF amplifiers. Applying ELM to model the temperature characteristics of two RF amplifiers is described in Section 3. Section 4 provides a detailed discussion and analysis of the modeling results. Finally, the main conclusions of this paper are summarized.
## 6 Simulation Results
### Performance Results
#### 6.2.2 GEO and Non-GEO Scenarios  
Several tests were carried out in a GEO scenario (with a negligible Doppler shift and no Doppler rate) and adding a Doppler shift (up to \(\pm 10\) kHz) and Doppler rate (up to 400 Hz/s) in order to approximate as much as possible a LEO condition.  
The MSD-based receiver showed good performance in demodulating most of the defined waveforms in both a GEO and LEO environment. Some problems arose with the most challenging waveform TFI#33, denoted by the highest spreading factor value (256), only in the LEO environment. Indeed, the combination of Doppler shift and Doppler rate coupled with the long burst size, made the demodulation highly time consuming and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**WF** & **SF** & **MODCOD** & **C/N [dB]** & \
\begin{tabular}{c} **Measured PLR** \\ **No Power Randomized** \\ \end{tabular} &
\begin{tabular}{c} **Measured PLR** \\ **Power Randomized** \\ \end{tabular} \\ \hline TFI\#25 & 16 & QPSK1/3 & \(-\)11.4 & 0.03553 & 0.00338 \\ \hline TFI\#9 & 32 & QPSK1/3 & \(-\)15.4 & 0.08752 & 0.00000 \\ \hline TFI\#4 & 64 & BPSKI1/3 & \(-\)19.9 & 0.57831 & 0.00005 \\ \hline TFI\#5 & 64 & QPSK1/3 & \(-\)16.6 & 0.54291 & 0.00054 \\ \hline TFI\#7 & 64 & QPSK2/3 & \(-\)11.2 & 0.00764 & 0.00155 \\ \hline TFI\#6 & 64 & QPSK1/2 & \(-\)13.7 & 0.00084 & 0.00064 \\ \hline TFI\#17 & 128 & QPSK1/3 & \(-\)21.1 & 0.16193 & 0.00043 \\ \hline TFI\#33 & 256 & QPSK1/3 & \(-\)23.2 & 0.01181 & 0.00776 \\ \hline TFI\#9 and TFI\#5 & 32 & QPSK1/3 & \(-\)10.0 & 0.23406 & 0.00155 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured PLR comparison with or without power randomization.  
resource intensive; however, it should be noted that the demodulation results outperformed the benchmark for all the waveforms except for the TF#33 in the presence of a Doppler shift-only (\(\pm 20\) kHz) and/or Doppler rate-only (400 Hz/s).  
Thanks to a channel emulator that was integrated as part of the traffic generator in the test-bed, the demodulation performance was evaluated even in the case of mobile terminals with a speed up to 130 km/h in both GEO and LEO scenarios. The test was successful, in terms of handled traffic load and PLR at the working point, with the application of a Perez-Fontan mobile fading channel [19]. Figure 15 shows the trend of the PLR which reached the \(10^{-3}\) threshold after four IC loops.
## 2 Material and Methods
### Experimental Designs and Preparation of the Samples  
The experimental layout of the experimental designs is shown in Figure 1. Firstly, a central composite design (CCD) with three variables (\(a_{w}\), fat content, and pressure) and five levels, with an \(a_{w}\) ranging from 0.86-0.94, fat from 10-50%, and pressure level from 347-852 MPa, as described in [PERSON] et al. [9; 12], was followed. Secondly, to characterize subsequent behavior of the pathogens under DCH \(a_{w}\) values around the growth/no-growth interface, a full factorial design (FFD) with 2 variables (\(a_{w}\) and pressure) was conducted in DCH with an \(a_{w}\) of 0.94-0.98 and with a fixed fat content of 30%. The factors and levels of the design were selected to cover a wide range of physicochemical characteristics of DCH [16].  
DCHs were aseptically deboned in the laboratory. The lean part (with an \(a_{w}\) of 0.85 and 6.7% fat) was aseptically separated from the fat part, and each part was separately minced under aseptic conditions (minced lean showing pH 5.7 and 5000 ppm of water-phase lactic acid from endogenous origin). DCH matrices with the \(a_{w}\) and fat content adjusted in accordance with the target values of the experimental-design trials were prepared as described in [PERSON] et al. [9; 12]. Briefly, to adjust the \(a_{w}\) of the product, the appropriate volume of distilled water was added to the minced lean part, mixed, and equalized until homogenization to reach the target \(a_{w}\). The inoculum was added to the distilled water immediately before mixing with the minced lean part. Afterwards, the proper quantity of minced fat corresponding to each trial was added to the inoculated lean samples. The actual \(a_{w}\) of the samples was verified with Aqualab(tm) equipment (Series 3, Decagon Devices Inc., Pullman, WA, USA). The DCH was distributed in 15 g-samples and vacuum-packaged in PET/PE plastic bags (with oxygen permeability \(<\) 50 cm\({}^{3}\)/m\({}^{2}\)/24 h and water vapor permeability \(<\) 15 mg/m\({}^{2}\)/24 h; Sacoliva S.L., Barcelona, Spain). For each trial, two DCH sample replicates were prepared.
# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 5 WASN Deployment Methodology
### Installation of New Nodes in an Active System  
The process to add nodes to the acoustical network is systematic and can be done whenever a new sensor is required. Note that the system needs a minimum of one node to work. Steps to add a node are described below:  
1. **Node assembly:** Place the heat sink on top of the Raspberry, connect the microphone to the central unit and put both components inside the box. The assembly must look as it does in Figure 5.
2. **Node wiring:** Plug the node to the power source. The box can now be closed with a M4 screw.
3. **Static IP configuration:** connect the running node to a computer using an Ethernet cable to set up the WiFi mesh with an unused IP and static DHCP and obtain the WiFi MAC address (following the steps of [46]).
4. **Update the core node:** add the MAC address of the node to the configuration file of the core so it recognizes the new node as part of the mesh using its MAC.
5. **Microphone calibration:** calibrate the microphone to 1 kHz, 94 dB and set the calibration factor to obtain a referenced sound pressure level.
## 1 Introduction
The proposed method is more effective than the conventional method, which uses mutual information and correlation coefficients in selecting five features [15; 16]. In this paper, we compare the performance of the proposed method using only the correlation coefficient in various environments, the conventional method using mutual information and correlation coefficients, and three methods using only mutual information. In addition, we confirmed the performance of the proposed method with the addition of four kinds of sixth-order cumulants with large variability in the low signal-to-noise ratio (SNR) environment besides the second and fourth-order cumulant. In order to evaluate the proposed method, the representative value was selected from various cumulants by using each method and two kinds of the simulation were conducted. In the first simulation, in order to find the effective features values, we ranked the cumulants based on the calculation from each method. Then, we sequentially measured the classification performance by excluding the feature values one by one. In the second set of simulations, in order to measure the classification performance according to the group, the cumulants were divided into three groups (top, middle, and bottom) based on the ranking obtained from the efficient features extraction method. The following is a summary of how each group is divided. 1. Top group: the five highest important representative values of each method. 2. Middle group: the five medium important representative values of each method. 3. Bottom group: the five lowest important representative values of each method. The cumulants in each group were used as the input data of the DNN algorithm to measure the classification performance. The three AMC environments that use the features of each group as input values were implemented and the superiority of the proposed method was confirmed according to the group performance. The rest of the paper is organized as follows. In Section 2, we explain the features and data analysis method. In Section 3, we introduce the proposed method and the conventional method. In Section 4 we describe the DNN structure used in this paper and present the simulation results. Finally, Section 5 provides the conclusions of the paper.