esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MIMO & Multiple-input multiple-output \\ AP & Air platform \\ SINR & Signal-to-interference-plus-noise ratio \\ WET & Wireless energy transfer \\ BS & Base station \\
5G & Fifth generation \\ EE & Energy efficiency \\
2D & Two-dimensional \\
3D & Three-dimensional \\ WIT & Wireless information transmission \\ LoS & Line-of-sight \\ NLoS & Non-LoS \\ MF & Match filter \\ RF & Radio frequency \\ CSI & Channel state information \\ AoD & Angle of departure \\ PAS & Power angle spectrum \\ AWGN & Additive white Gaussian noise \\ MUI & Multi-user interference \\ SLNR & Signal-to-leakage-plus-noise ratio \\ SE & Spectral efficiency \\ QoS & Quality of service \\ \end{tabular}
###### Abstract  
Predictive maintenance is becoming increasingly important in industry and requires continuous monitoring to prevent failures and anticipate maintenance processes, resulting in reduced downtime. Vibration is often used for failure detection and equipment conditioning as it is well correlated to the machine's operation and its variation is an indicator of process changes. In this context, we propose a novel energy-autonomous wireless sensor system that is able to measure without the use of batteries and automatically deliver alerts once the machine has an anomaly by the variation in acceleration. For this, we designed a wideband electromagnetic energy harvester and realized passive energy management to supply a wireless sensor node, which does not need an external energy supply. The advantage of the solution is that the designed circuit is able to detect the failure without the use of additional sensors, but by the Analog Digital Converter (ADC) of the Wireless Sensor Nodes (WSN) themselves, which makes it more compact and have lower energy consumption. The electromagnetic converter can harvest the relevant energy levels from weak vibration, with an acceleration of 0.1 g for a frequency bandwidth of 7 Hz. Further, the energy-management circuit enabled fast recharging of the super capacitor on a maximum of 31 s. The designed energy-management circuit consists of a six-stage voltage multiplier circuit connected to a wide-band DC-DC converter, as well as an under-voltage lock-out (UVLO) circuit to connect to the storage device to the WSN. In the failure condition with a frequency of 13 Hz and an acceleration of 0.3 g, the super capacitor recharging time was estimated to be 24 s. The proposed solution was validated by implementing real failure detection scenarios with random acceleration levels and, alternatively, modus. The results show that the WSN can directly measure the harvester's response and decide about the occurrence of failure based on its characteristic threshold voltage without the use of an additional sensor.  
energy harvesting; autonomous wireless sensor; passive energy management; weak vibration; electromagnetic converter; wideband; planar spring; voltage multiplier; rectifier; predictive maintenance; failure detection; WSN +
Footnote †: journal: Physics Letters B  
18 January 2022  
1
Footnote 1: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Privacy and Security in Federated Learning: A Survey
## 2 An Overview of Federated Learning
### FL Aggregation Methods  
The aggregation of the local models should result in an improved and more general model. Some of the main state-of-the-art methods are presented below:  
* The first proposal was the Federated Averaging Algorithm (FedAvg), introduced in [3]. Considered the default approach for centralized FL, the central server will generate the global model by averaging all the participating client models. This approach can be considered as gradient descent on the server side. Extensions have been proposed to adapt efficient optimization strategies, such as Adam and Adagrad, to this context [17].  
FedProx [18] was proposed as a generalization of the FedAvg method. It takes into account the variable amount of work to be performed by each client. This also depends on global data distributions across clients, local computational resources, energy, and communication limits. It has been shown that FedProx results in better averaging accuracy than FedAvg in heterogeneous settings.
* The Federated Matched Averaged (FedMa) method [19] aims to update the global model via layer-wise matching and aggregation of inner model components, namely neurons. This approach only works on neural networks due to its specificities. Moreover, it only works on simple neural networks such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)-based models. This method presents good results with heterogeneous learners and better performances than FedAvg and FedProx within a few training iterations.
## 2 Problem Description and Model Analysis
### Model Analysis
#### 2.2.3 Model Validation  
Based on the joint simulation framework, the stepless flow control system model of the reciprocating compressor was established using the AMESim simulation platform. The key parameters of the DW-12/2 reciprocating compressor test bench were substituted into the above model (Table 1). The experimental and simulation data under different loads were compared according to the average relative error from Equation (14). The results are shown in Figure 5. Under different loads, the full cycle error of the compressor was less than 5%, and the error of different operating processes under different loads was less than 6%, indicating that the fit between the simulation and actual models was high, which provided a simulation basis for the next optimization:  
\[error=\Sigma_{i}^{N}\left(\frac{P_{simulation}^{i}-P_{experiment}^{i}}{P_{experiment} ^{i}}\right)\Bigg{/}N \tag{14}\]  
where \(i\) is the \(i\)th data point; \(P_{simulation}^{i}\) is the \(i\)th simulated pressure data; \(P_{experiment}^{i}\) is the \(i\)th experimental pressure data point; and \(N\) is the total number of points.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Suction pressure (MPa) & 0.1 \\ Discharge pressure (MPa) & 0.3 \\ Rotating speed (rpm) & 300 \\ Valve lift (mm) & 2 \\ Inlet pressure (MPa) & 8 \\ Oil return pressure (MPa) & 0 \\ Return spring stiffness (N/m) & 80,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical parameters of the experimental compressor.  
Figure 5: Model error analysis diagram.
# DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System
## 2 Background
### IoT Security Overview
#### 2.3.2 Artificial Intelligence-Based Security Method  
Artificial intelligence has attracted attention in recent years, especially in the field of IoT security. The following Table 2 summarizes the leading technologies that have been introduced to protect the IoT ecosystem.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**No** & **Year** & **Techniques** & **Dataset** & **Metrics** & **IoT Layer** & **FS** & **Category** \\ \hline
[47] & 2019 & C5 and One Class Support Vector Machine classifier & Bot-IoT dataset and DPCA-WD & Detection rate and False & Network Layer and F1 & Yes & Intrusion Detection System \\ \hline
[48] & 2019 & Multilayer Perceptron (MLP) & ADFA-LD ADFA-LD ADFA-WD & Accuracy, Recall, and F1 & Network Layer and F1 & No & Intrusion Detection System \\ \hline
[49] & 2018 & Recurrent Neural Network (RNN) + Convolutional Neural Network (CNN) & RedIRIS. (CNN) & Accuracy, Precision, Recall, and F1 & Network Layer and F1 & No & A network traffic classifier (NTC) \\ \hline
[50] & 2018 & Deep-learning (DAE + Deep Feed Forward Neural (DFFNN)) & NSL-KDD and UNSW-NB15 & NSL-KDD and UNSW-NB15 and FASE Positive Rate & Network Layer and FASE & Yes & Anomaly Detection \\ \hline
[51] & 2017 & Deep Neural Network + Grid Search Strategy & UNSW-NB15, GDPS. & Accuracy, Precision, Recall and False Alarm Rate. & Transport Layer and FASE & No & Anomaly Detection \\ \hline
[52] & 2017 & Convolutional Neural Networks (CNN) & IoTPOT (CNN) & Detection rates, Accuracy & Network Layer and FASE & No & Light-weight Detection \\ \hline
[53] & 2017 & Recurrent Neural Network (RNN) and the Dataset & ISCX Detection and the CTU-13 & Intrusion Detection, Confusion Matrix and F1 Score & Sensitivity, Specificity, Accuracy & Applications Layer & No & Botnet Detection \\ \hline
[54] & 2016 & Stacked Auto Encoder (SAE) Deep learning & Auto & KDD99 & Detection rates, and False Positive Rate & Transport Layer and FASE & Yes & Intrusion Detection System \\ \hline
[55] & 2016 & artificial neural network (ANN) & Collect the data in Testbed & Detection rates, and False Positive Rate & Network Layer and FASE & No & Anomaly Detection \\ \hline
[56] & 2016 & Artificial Neural Network (ANN) & Simulated network. & IoT & Accuracy, and False Alarm Rate. & Applications Layer & No & Threat Analysis \\ \hline
[57] & 2016 & Machine Learning + Security as a Service &..... & Accuracy, and False Alarm Rate & Transport Layer and FASE & No & Integrated Intrusion Detection System \\ \hline
[58] & 2013 & Hormone-based Service Detection Algorithm (HSDA) & Simulated network & IoT & Stability of the network, Saving energy and Detection rates & Network Layer and FASE & No & Anomaly Detection \\ \hline \hline \end{tabular}
\end{table}
Table 2: Artificial Intelligence-based Security Methods (FS = Features Selection).
## 4 Numerical Results and Sensitivity Analysis
### Numerical Example
On the other hand, as presented in Figure 3, the obtained optimal solution indicated that each new product included for each period \(t\) a percentage \(\tau\)\({}^{*}\)(\(t\)) of remanufactured parts to reduce the manufacturing costs. Hence, the mean during the planning horizon of the optimal remanufacturing rates \(\tau\)\({}^{*}\)(\(t\)) was 0.16. Nevertheless, the obtained optimal remanufacturing rate \(\tau\)\({}^{*}\)(\(t\)) fluctuated from one period to another following the optimal total demand (\(D_{p}\)\({}^{*}\)(\(t\))+ \(D_{o}\)\({}^{*}\)(\(t\))) of the dual-channel supply chain, which in turn depended on the market size \(A\)(\(t\)). Therefore, the maximal optimal remanufacturing rate \(\tau\)\((t\)) was achieved during the 7 th period while the lowest remanufacturing effort was obtained during the 10 th period. Thus, the market size was an essential parameter in determining not only the demand but also the impacts of the remanufacturing effort and pricing strategy. Adding to that, the optimal total profits generated during the planning horizon by the manufacturer, the pharmacy and the online platform were \(\pi_{M}\)\({}^{*}\) = 6,743,220 \(mu\), \(\pi_{p}\)\({}^{*}\) = 1,410,746 \(mu\) and \(\pi_{O}\)\({}^{*}\) = 876,647 \(mu\), respectively. However, for the same market (\(1-\rho\) = \(\rho\) = 0.5), the online platform generated 38% less total profit than the pharmacy. In fact, even for similar demands and pricing strategies, the online channel paid a total commission of 273,585 \(mu\) in return for the AS provided by the pharmacy, which explained the gap in the total profit between both distribution channels.
###### Abstract  
To improve the performance of fee-space optical communication systems, this paper analyzes the performance of a relay-aided hybrid fee-space optical (FSO)/radio frequency (RF) cooperation system based on a selective combination and decoding forward transmission scheme. In this system, the FSO sub-link experienced Malaga turbulence with pointing errors and the RF sub-link suffered Nakagami-m fading. Firstly, the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end output signal-to-noise ratio (SNR) of the relay-aided hybrid FSO/RF system are derived. Then, using the extended generalized bivariate Meijer's G-function (EGBMGF) and the approximate analytical formula of the generalized Gauss-Laguerre integral, mathematical expressions of the end-to-end average bit error rate (ABER) and outage probability of the relay-aided hybrid FSO/RF system with different subcarrier intensity modulation and different detection schemes are derived. Through a simulation analysis of the system, the results show that compared with the other three modulation technologies, the hybrid FSO/RF direct link and relay-aided hybrid FSO/RF system with coherent binary phase shift keying (CBPSK) modulation have the best bit error performance. Compared with direct detection, the hybrid direct link and relay-aided hybrid system with coherent detection can significantly improve the communication performance. Increasing the RF fading parameter \(m\) can further improve the bit error and outage performance of the hybrid direct link and relay-aided hybrid system; the hybrid direct link can significantly mitigate the degradation of communication performance in the FSO system caused by pointing errors, and the relay-aided hybrid system can further improve the communication performance; under weak turbulence conditions, the impact of pointing errors on the performance of the relay-aided hybrid system can even be ignored. The greater the total number of paths in the relay-aided hybrid system, the better the communication performance of the system; however, the more hops, the worse the performance of the system. The outage probability of the hybrid direct link and relay-aided hybrid system are very sensitive to the decision threshold, and the larger the decision threshold, the worse the outage performance. The transmission distance of different hybrid direct links has little impact on the performance of hybrid direct links and relay-aided hybrid systems. Improving the signal-to-noise ratio of RF sub-links significantly improves the performance of hybrid direct links and relay-aided hybrid systems.  
relay-aided hybrid FSO/RF system; atmospheric turbulence; Nakagami fading; pointing errors 2023236191. [[https://doi.org/10.3390/s23136191](https://doi.org/10.3390/s23136191)]([https://doi.org/10.3390/s23136191](https://doi.org/10.3390/s23136191))
## 3 Results
### Optimized Electrode Parameters  
Figure 5 shows the volume percentage of beneficially, over-, and understimulated tissue in the defect domain in dependence on the electrode length \(h_{\mathrm{el}}\) for an exemplary stimulating voltage of \(\varphi_{\mathrm{stim}}=1\,\mathrm{V}\).  
It can be observed that the electrode length has a strong impact on the volume of stimulated tissue. Over the whole range of \(h_{\mathrm{el}}\) the stimulated volumes vary by factors of ca. 2.2 (understimulation), 2.4 (beneficial stimulation), and 12 (overstimulation). Generally, with increasing electrode length the volume of beneficially stimulated tissue (\(5\,\mathrm{V/m}\leq|\underline{\mathbf{E}}|\leq 70\,\mathrm{V/m}\)) increases linearly, reaching a plateau at around \(23\,\mathrm{mm}\)-\(27\,\mathrm{mm}\). At \(h_{\mathrm{el}}=25\,\mathrm{mm}\) the volume of beneficially stimulated tissue reaches a maximum with 54% of the defect volume being stimulated. Smaller and larger electrode lengths lead to slightly reduced volumes of beneficially stimulated tissue. The volume of understimulated tissue (\(|\underline{\mathbf{E}}|<5\,\mathrm{V/m}\)) generally shows an inverse behavior: It decreases from ca. 77% to ca. 35% with increasing electrode length. The volume of overstimulated tissue (\(|\underline{\mathbf{E}}|>70\,\mathrm{V/m}\)) in the considered domain is generally about one order of magnitude smaller than the volumes for beneficial and understimulation. It increases from ca. 1% to ca. 12% with increasing electrode length.  
Based on the optimum electrode length \(h_{\mathrm{el}}=25\,\mathrm{mm}\), the stimulation potential applied to the electrode was also optimized numerically. For the optimal stimulation amplitude, we obtain \(\varphi_{\mathrm{stim,opt}}=0.523\,\mathrm{V}\). With this parameter setting, approximately 49% of the tissue in the defect is stimulated beneficially, 49% is understimulated and 2% is overstimulated.
# The _E. coli_ Global Regulator DksA Reduces Transcription during T4 Infection
## 3 Results
### Deletion of dksA Results in an Increase in Phage Progeny per Infection  
Plaque size is affected by several factors, including the number of phage progeny (i.e., burst size), diffusivity, absorption rate, and length of the latent period [50; 51]. To assess whether the larger plaque size observed in the absence of DksA or ppGpp arose from an increase in the number of phage progeny, we performed burst size experiments for T4 wt infections of B606 wt, B606 \(\Delta\)_dksA_, and B606 ppGpp\({}^{0}\). We found that the absence of DksA increased the burst size \(\sim\)2-fold (Figure 2a,b), while the absence of ppGpp had no statistically significant effect (Figure S4). In addition, we observed no drastic change in latent period for any of the infected strains (Figure 2a,c and Figure S4a). We conclude that the increase in plaque size for T4 wt in the absence of DksA can be attributed to an increase in phage progeny per infection. Conversely, the increase in plaque size in the absence of ppGpp is not related to burst size or latent period.
# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 3 Methodology
### Justification for Selecting Constraints Programming
#### 3.4.4 Computational Efficiency  
While machine learning and deep learning approaches can handle larger-scale problems, they are often computationally expensive and require significant amounts of training time and data preprocessing. For smaller-scale problems like the MRO task scheduling problem discussed in this paper, these approaches may be unnecessarily complex and resource-intensive.  
The computational efficiency of constraint programming is another key reason for its selection. As shown in the experimental results, our CP-based method is able to generate near-optimal schedules in a matter of seconds to minutes, even as the number of tasks increases. For example, scheduling 24 tasks across 6 workplaces can be completed in under 74 s on a standard PC setup. This is considerably faster than many machine learning approaches, which could take hours or days to train, depending on the dataset size and the complexity of the model. CP provides an optimal solution with significantly lower computational overhead.
## 5 New Trends and Future Research Directions in Enterprise Networking
### Edge Computing and 5G Integration  
Edge computing is becoming an essential component of enterprise networking due to the explosion of Internet of Things (IoT) devices and the demand for real-time data [91; 105]. With the proliferation of data usage across various fields, the entire world is gradually drifting towards digitalization. Edge computing lowers latency and preserves bandwidth by processing data closer to its source. This will ultimately promote optimal performance of sectors such as finance, healthcare, and manufacturing that rely on quick responses.  
The adoption of 5G networks further enhances edge computing, since it offers low-latency and high-speed connectivity [106; 107]. This helps to facilitate quicker data transfer between devices, enabling decision-making and real-time analytics closer to the data source, as illustrated in Figure 11. While cloud technology proves to be very efficient in data processing, the bandwidth required to send data limits its application. Hence, edge computing readily emerges to remedy the data transfer bottleneck regularly encountered by cloud computing during computation [108; 109]. However, it is noteworthy that edge computing also performs the function of a data producer in addition to data consumer role at the cloud.  
Figure 11: Illustration of edge computing architecture and example.  
Figure 10: NaaS management and service functions.  
The edge computing architecture is categorized in Table 2 based on the deployment types and roles played for the successful implementation of its objectives. In addition, some software platforms, such as virtualization, data analytics [19], and edge orchestration, help to simplify the complex nature of edge computing.
## 2 Methodology
### Coarse-to-Fine Spatiotemporal Correlation Computation Scheme
#### 2.3.1 Coarse Disparity Estimation  
The left images were used as reference images and the right images were used as targets. The reference images are divided into small horizontal and vertical grids by a fixed step interval, and the disparity at the cross point in the reference image is obtained by searching for the matching point in the target image. After the correlation threshold is set, the pixel with the maximum correlation value is identified as the homologous point. A smaller matching window can improve the computational speed of the matching-point search, but a larger window can greatly reduce the error rate of matching because matching tends to be easier. At this stage, the coarse disparity of each grid point is only used as a guide value for the subsequent fine disparity calculation; hence, a relatively large correlation window was chosen to reduce the matching error rate. The grid-point interval is consistent with the size of the coarse matching window in order to ensure full coverage of the initial disparity. The search range \([d_{c}^{\min},d_{c}^{\max}]\) of the disparity is determined by the depth region of the measured object. Disparity consistency and sequential constraints were employed to remove outliers. To further reduce the computation of disparity, a disparity propagation strategy [39] is introduced to narrow the search range to a smaller  
Figure 4: Block diagram of the fast stereo-matching method for 3D face reconstruction.  
radius, \(r_{c}^{p}\). Because our measurement target is a human face, the disparity calculation satisfies the continuous and slowly changing constraint. Therefore, after the previous effective matching disparity was obtained, the disparity search range was also reduced to a certain interval when searching for adjacent matching points. Note that the correlation computation at the current stage may still cause some missing disparity, thereby creating holes. It is thus necessary to interpolate the disparity map to fill-in the missing disparity. According to the aforementioned continuity assumption, because the disparity of a reliable grid point, \(p_{g}\), has been acquired, the disparity of its next adjacent point can be initialised using the disparity of \(p_{g}\) by up-sampling.