esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Scheme
### Algorithms  
The proposed system could be divided into ten algorithms stated below.  
**Setup:** Taking as input a security parameter \(k\), the algorithm generates system's public parameters _params_ and a master secret key _msk_.  
**ABE-KeyGen:** The attribute key generation algorithm takes as input system parameters _params_, a master secret key _msk_, a user's identity _id_ and the attribute set \(\mathbf{w}\). It generates an attribute private key \(d_{\textit{id, }\mathbf{w}}\) associated with user's identity _id_.  
**KS-KeyGen:** The keyword search key generation algorithm takes as input system parameters _params_, a master secret key _msk_ and a user identity _id_. It generates a keyword search key \(q_{\textit{id}}\) for _id_.  
**Encrypt:** The encryption algorithm takes as input system parameters _params_, an LSSS access structure \(\mathbb{A}\) and a message _msg_. It generates a ciphertext _ct_.  
**Re-Encrypt:** The re-encryption algorithm takes as input system parameters _params_, a ciphertext _ct_ and the private key of PS, say \(\beta\). It generates a re-encrypted ciphertext _ct_*.  
**Index:** The index algorithm takes as input system parameters _params_ and a keyword set _kw_. It produces a secure index _IX_(_kw_).  
**Trapdoor:** The trapdoor algorithm takes as input system parameters _params_, a user's keyword search key \(q_{\textit{id}}\), a user's attribute private key \(d_{\textit{id, }\mathbf{w}}\), a keyword set _kw_ and the private key of PS. It generates a trapdoor \(T_{\textit{kw}}\) corresponding to the keyword set _kw_.  
**Test:** The test algorithm takes as input system parameters _params_, a trapdoor \(T_{\textit{kw}}\) and an index _IX_(_kw_). It will return either an intermediate result \(Q_{\textit{ct}}\) or 0.  
**Pre-Decrypt:** The pre-decryption algorithm takes as input system parameters _params_, a re-encrypted ciphertext _ct_* and the private key of PS, say \(\beta\). It outputs a ciphertext _ct_.  
**Decrypt:** The input of this algorithm includes system parameters _params_, a ciphertext _ct_ with the intermediate data \(Q_{\textit{ct}}\), and the user's attribute private key \(d_{\textit{id, }\mathbf{w}}\). Finally, the algorithm will return an original message _msg_ or an error symbol \(\bot\).
# Combined Channel Estimation with Interference Suppression in CPSS
###### Abstract  
With social characteristics integrated into cyber-physical systems (CPS), the wireless channel has been a complex electromagnetic environment due to the subjectivity of human behaviour. For the low-power and resource-constrained nodes in cyber-physical-social systems (CPSS), minimum research is available focusing on conquering the issues of computational complexity, external interference and transmission fading simultaneously. This study aims to explore channel estimation with interference suppression based on machine learning. A novel channel estimation scheme is proposed, which combined interference suppression in channel impulse response (CIR) of frequency domain with \(K\)-means algorithm and noise cancellation in CIR of time domain with \(K\)-nearest neighbor (KNN) algorithm into an integrated process. Complexity analysis and simulation results showed that the proposed scheme has relatively lower complexity and the performance is proven better than traditional schemes, which meets the requirements of CPSS in complex electromagnetic environments.  
channel estimation; interference suppression; noise cancellation; machine learning; \(K\)-means; KNN +
Footnote †: journal: Sensors
## 5 Materials and Methods
### In Vitro Phytohormone Treatment  
Sterile basal agar plates [1.5% (\(w/v\)) agar] supplemented with 0.02 M sucrose and 0.02 M raffinose were prepared with different phytohormonal treatments in order to test the response of isolated trichome disc cells under _in vitro_ conditions. The hormone concentrations applied were as follows: 20 uM (+)-cis,trans-abscisic acid (Astral Scientific, Taren Point, NSW, Australia; A-1003); 20 uM salicylic acid (Sigma-Aldrich, St. Louis, MO, USA; S7401); 20 uM (\(\pm\)) jasmonic acid (Sigma-Aldrich, J2500); 1 mM sodium diethyldidincarbamate (Sigma-Aldrich, 228680); 20 uM brassinolide (Sapphire Bioscience, Redfern NSW, Australia; 21594); 20 uM enthephon (Sigma-Aldrich, C0143); 20 uM gibberell acid (ChemSupply, Gillman, SA, Australia; GL003); 20 uM indole acetic acid (Biosynth, Staad, Switzerland; FI09866); 20 uM kinetin (Acros Organics, Antwerp, Belgium; 226500050); start point control (CTR_0h); end point control (CTR_24h). Phyto hormones were prepared as 1000X stock solutions dissolved in 100% (\(v/v\)) ethanol and then added to the molten agar post-sterilisation. Equivalent amounts of 100% (\(v/v\)) ethanol were added to molten control agar post-sterilisation as the treatments. While working under sterile conditions in a laminar flow hood, the trichome disc suspension (500 uL) in mannitol buffer (0.2 M mannitol, 0.05 M Tris-HCI, 0.005 M MgCl\({}_{2}\), 0.01 M KCl, 0.0005 M \(\mathrm{K}_{2}\mathrm{HPO}_{4}\), 0.001 M EGTA; pH 6.0) was pipetted onto the petri plates, which were then gently tilted to spread the cells across before air drying and sealing with parafilm.  
The plates were incubated in an environmental chamber (Sanyo, Osaka, Japan; MLR-350H) set to 26 \({}^{\circ}\)C with 24 h light (200 umol m\({}^{2}\) s\({}^{-1}\)) for a period of 25.5 h. Continuous light was used to maintain GT disc cells in a state where cannabinoid is most active [33]. Each plate was considered a biological replicate, with four replicates prepared per treatment (\(n=4\)). After incubation, trichome disc cells were harvested from each plate using a cell scraper, resuspended in 900 uL of mannitol buffer and then split into aliquots. Trichome disc cells were spun down at 4 \({}^{\circ}\)C 12,000\(\times\)\(g\) for 2 min to remove the supernatant before flash freezing in liquid nitrogen and storage at \(-\)80 \({}^{\circ}\)C.
## 4 Mid-Layer Software EMIDE for User-Friendly Application Development Environment
### Performance Evaluation
#### 4.4.3 Application Code Execution Efficiency  
In the ContikiDL and TinyLD, the final executed code, which is generated after the dynamic linking process, is the pure machine code. Thus, the application code execution efficiency is high. In the simpleRTJ and nanoVM, the final executed code is the Java byte code. This code needs to be interpreted, thus the execution efficiency is low. To compare the execution efficiency of the machine code and the byte code, the time between the calling of the application system-call function and the executing of the corresponded system function in the simpleRTJ and the ContikiDL is computed, and the result is shown in Table 7. From this result, it can be known that the execution efficiency of the machine code is 34.6 times higher than that of the simpleRTJ Java byte code. Therefore, less energy resource will be consumed if the machine code is used for the application image.  
In EMIDE, the machine code is also used, but the access from the application system-call function to the system service function is done through an intermediate jump table, thus the application code execution efficiency is a bit lower if compared with that of ContikiDL. The execution efficiency proportion of these two mechanisms can be modeled as follows:  
\[P=E_{\mathit{EMIDE}}/E_{\mathit{ContikiDL}}=(10N+S)/S=1+(10N/S) \tag{3}\]  
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c} \hline \multirow{2}{*}{\
\begin{tabular}{c} **APP Code** \\ **Type** \\ \end{tabular} } & \multirow{2}{*}{**Mechanism**} & \multicolumn{3}{c}{**Execution Status (Execution in Loop for 2,000,000 Times)**} \\ \cline{3-6}  & & **Voltage (V)** & **Current (mA)** & **Time (S)** &
\begin{tabular}{c} **Energy Cost** \\ **(mJ)** \\ \end{tabular} & **Proportion** \\ \hline Machine code & ContikiDL & 3 & 10.5 & 14 & 441 & 1 \\ \hline Java byte code & simpleRTJ & 3 & 10.5 & 485 & 15,277.5 & 34.6 \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of the application code execution efficiency.  
Figure 12: Memory cost of the EJVM, the DLM, the EMIDE and the Atmel OTAU.  
where \(N\) represents the number of the system-call functions in the application program, _10_ represents the clock cycle of each entry in the jump table, \(S\) represents the clock cycle of the whole application code (includes the sub-functions). Provided that \(S\) is _1000_ and \(N\) is \(6\), then the \(P\) will be _1.06_.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 5 Performance and Complexity Analysis
### SSS Search  
The main task of the SSS Search is to identify the PCI_1 which along with PCI_2 from PSS Search gives PCI. The SSS Search is computationally less complex than PSS Search due to the small correlation size and hence, there is no need for down-sampling. We analyze the effect of WL on the probability of detection of PCI_1, referred to as \(P^{SSS}\), for different SNRs. We consider four different WL as shown in Figure 13 and corresponding execution time and resource utilization results are given in Table 2. It can be observed that SPFL, half-precision floating-point (HPFL) and fixed point architecture with 24 bits offer nearly identical detection performance. On the other hand, execution time decreases by half as we move from SPFL to fixed-point architecture. In addition, there are significant savings in resource utilization as well. Further savings in resource utilization is possible using a fixed-point architecture with WL of 16 bits but it incurs slight degradation in detection performance. When compared to PSS search, SSS search is significantly faster and requires lower on-chip memory due to a smaller correlation size. The higher number of DSP48s and LUTs is due to the serial-parallel realization of correlators as a complete SSS search involves 336 correlation operations compared to 3 in the PSS search.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **SPFL** & **HPFL** & **Fixed Point** & **Fixed Point** \\  & & & **[24,2]** & **[16,2]** \\ \hline
**Execution Time (\(\mu\)s)** & 33.4 & 18.8 & 16.6 & 16.6 \\ \hline
**On Chip Memory** & & & & \\ (18 KiloBytes BRAMs) & 58 & 49 & 44 & 31 \\ \hline
**Embedded Multipliers** & & & & \\ (DSP48s) & 651 & 573 & 573 & 558 \\ \hline
**6-input Look-Up-Table** & & & & \\ (LUT) & 84,052 & 33,696 & 32,574 & 28,878 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution Time and Resource Utilization Comparison for SSS Search.  
Figure 12: Effect of WL on the probability of detection, \(P^{PSS}_{D}\), for PSS search architecture.
# Privacy Framework for the Development of IoT-Based Systems
## 4 Related Work
### IoTS Security and Privacy Vulnerabilities and Attacks with Countermeasures  
Attacks against IoTSs are aimed at compromising users' private information, which can lead to serious breaches. With this in mind, we reviewed a large number of studies addressing security threats in IoTSs. However, we found limited research that specifically focuses on the privacy aspect of IoTSs.  
[PERSON] et al. [25] discussed IoTSs and their three-layer architecture (i.e., physical, network, and application layers), and also provided an overview of cyberattacks targeting these layers. They identified heterogeneity as a major security and privacy concern within IoTSs.  
Likewise, [PERSON] et al. [26] presented a thorough review of current cybersecurity threats and vulnerabilities in IoTSs, as well as appropriate countermeasures. They addressed the vulnerabilities at different layers of the IoTS reference model. Furthermore, the authors proposed the use of blockchain technology for secure data transfer between IoT devices. Despite their detailed discussion of IoTS cybersecurity attacks, privacy-related issues were not specifically addressed. In a related effort, [PERSON] et al. [27] proposed a forensic investigation framework for IoT infrastructures to support incident analysis and enhance future security by capturing information on successful attacks.  
A study by [PERSON] et al. [28] examined IoT security risks and the solutions that have been proposed so far. Their focus was placed on key security requirements, such as authorization and confidentiality. Their research highlighted the effectiveness of hybrid approaches, such as combining blockchain with artificial intelligence (AI), to enhance the security of IoTSs and defend against various threats. However, they also noted that, despite these advances, IoTS security remains an open challenge due to the constantly evolving nature of IoTSs.  
Similarly, a study by [PERSON] et al. [29] provided a basic understanding of IoTSs and the main privacy and security concerns raised by their rapid expansion. They also presented security primitives and solutions to ensure secure communication and protect user data. The researchers highlighted that IoTSs cannot benefit from traditional security measures due to the large number and variety of sensors, limited resources, and unique system architectures used. Their study proposed a multi-layered security architecture that includes peer-to-peer data protection and robust encryption techniques tailored to resource-constrained sensors to prevent unauthorized access to user information.
## 4 Security and Privacy in Edge Frameworks
### Machine Learning in Error Detection and Correction
These ML-based predictive modeling approaches are critical for accurate circuit reliability estimation and for designing robust DCIM systems [52]. Quantum systems represent an emerging research field where ML techniques are increasingly employed in Quantum Error Correction (QEC). Methods such as supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning (DL) are used to improve the detection, classification, and correction of errors in quantum systems. The integration of ML significantly reduces computational latency, enhances the robustness of QEC protocols, and offers scalability for future large-scale quantum computing [53]. Furthermore, ML-based error detection and correction methods are applied across various domains. In real-time weather forecasting, ML models correct prediction errors by learning from discrepancies between model forecasts and observational data, thereby enhancing forecast accuracy. Deep learning (DL) techniques have been used to detect errors in medical datasets, such as mislabeled laboratory samples, helping to maintain data integrity, which is vital for accurate diagnoses and treatments. Additionally, chatbots and virtual assistants employ ML to detect and correct errors in user interactions, improving communication accuracy. By learning from past interactions, ML models enhance response relevance and reduce mistakes over time.
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 2 Design of the Micro-Phasor Measurement System
### Time Synchronization  
To identify the phasor of the transformers, the sampling signals of the phasor identification system at the reference and measuring sides have to be synchronized. Signals were first received  
Figure 1: Voltage signal isolating circuit.  
Figure 2: Voltage sampling circuit.  
by GPS module and then output as 1 PPS, which then initiated the TCXO to produce a highly stable synchronous clock.  
GPS reception varies according to weather and barriers such as buildings. Although the influence of barriers on outdoor transformers is inconspicuous, numerous distribution transformers are established in basements because most of the distribution systems in the city are underground. GPS signals from basements are substantially sheltered by the buildings, which hinders GPS reception. To identify phasor of distribution transformers in basement, the TCXO is used to maintain the 1 PPS when synchronizing the GPS signals in an outdoor space area where signal reception is satisfactory before entering the basements. Once time synchronization was completed and reached the basement, the GPS module could be switched off and TCXO generates clock source to maintain 1 PPS for measurements. Crystal in the TCXO could age and offset, adjustment for sampling signal was required to adequately compensate. In a high-temperature environment such as distribution room in the basement, the adopted TCXO could aptly adjust signals according to current temperature to enhance the precision of sampling signals.
## 1 Introduction
A correlation-based graph convolutional network (C-GCN) for audiovisual emotion recognition task is introduced in [18]. Reference [19] introduces an audio-visual fusion model of deep learning features with a Mixture of Brain Emotional Learning. In this method, a CNN as well as recurrent neural network (RNN) were employed. So far, not much attention has been given to the generalization and significance of incorporating federated learning procedures. Motivated by the COVID-19 pandemic, [PERSON] et al. [20] made a first step in this direction by introducing a framework for monitoring the emotional state of an individual without sending data to a centralized server. The latter was achieved by incorporating federated learning techniques based on weighted averaging and utilizing both audio and video modalities. The principal goal of their research was to predict the state of depression during the post-processing phase, after classifying emotions based on the outputs from the audio and video modalities. To process video signals, [PERSON] et al. employed a convolutional neural network. On the other hand, an ensemble of seven machine learning classifiers has been utilized to process audio signals. In the end, they briefly described the procedure for combining these modalities and employing federated learning, without providing multimodal results to validate the approach. Another multimodal emotion recognition model, accompanied by federated learning, was proposed by [PERSON] et al. [21]. They introduced a federated learning method for real-time classification of emotional states from multimodal streaming. Their focus was primarily on utilizing physiological data captured from wearable sensors. Apart from multimodal approaches from [20; 21], which addressed specific objectives, there is only a limited number of federated learning-based methods for unimodal emotion detection systems that consider either the video modality [22] or the audio modality [23; 24]. Our approach represents a step further in the field of privacy-preserving emotion recognition, as we conduct an experiment focused on the application of federated learning to the multimodal audiovisual emotion recognition task, considering relatively small classification models that can be deployable at the edge.
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 6 Experimental Results
### Energy Consumption  
In this experiment, the lifetime of the monitoring nodes was examined. The nodes keep transmitting all the time until they run out of battery. The results are shown in Figure 13. In a real scenario, the nodes will not transmit all the time, however, in this experiment, the upper bound limit of the system was examined. According to the experimental results, in both periods, the wireless nodes manage to transmit for more than 160 h, before running out of energy.
# Realistic Multi-Agent Formation Using Discretionary Group Behavior (DGB)
## 5 Conclusions and Future Works
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Avoidance Alg.**} & \multirow{2}{*}{\begin{tabular}{c} **Grouping** \\ **Technique** \\ \end{tabular} } & \begin{tabular}{c} **Tested on** \\ **Challenging** \\ **Situations** \\ \end{tabular} & \multirow{2}{*}{\begin{tabular}{c} **Groups’** \\ **Density** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Members’** \\ **Connectivity** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Formation** \\ **Changes** \\ **in Critical** \\ **Situations** \\ \end{tabular} } \\ \hline \multirow{2}{*}{Our method} & RVO2 & \begin{tabular}{c} Modified ORCA \\ (DGB) \\ \end{tabular} & Yes & Small/Big & No & No \\ \hline [PERSON] et al. [1] & RVO & \begin{tabular}{c} Modified \\ Velocity obstacle \\ \end{tabular} & Yes & Small/Big & Yes & Yes \\ \hline [PERSON] et al. [5] & SFM & Modified SFM & No & Small & Yes & Yes \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Karamouzas and \\ Overmars [7] \\ \end{tabular} } & Velocity-selection &
\begin{tabular}{c} Velocity- \\ selection \\ approach \\ \end{tabular} & Yes & Small & No & Yes \\ \hline [PERSON] et al. [20] & Ray casting technique & Slot-locking & Yes & Small & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between our method and the three recent similar models.
## 5 Performance Analysis
### CASE 1: MANET Nodes with \(\Delta T_{i}\) Track Records
The corresponding FP detection rate remains \(\approx\)20% for the \(\sigma_{R}\) range considered, provided \(\rho_{max}\leq 0.6\) because the Q-test compares the difference between the two largest \(\Delta T_{i}\) values in relation to the difference between \(\Delta T_{MAX}\) and \(\Delta T_{MIN}\), which will be approximately constant, regardless of the interval, provided the \(\Delta T_{i}\) values are linearly distributed. When \(\rho_{max}=0.9\), the FP rate rises because the queuing delay of a node increases rapidly as \(\rho\) tends to 1, and the \(\Delta T_{i}\) distributions are no longer linear. This means that a \(\Delta T_{i}\) value produced by a node with a high network traffic load can easily become confused with a tampered \(\Delta T_{i}\). Realistically however, even a FP rate of \(\approx\)30% is still a satisfactory outcome since FP detection does not automatically mean that a route between a source and destination node cannot be established, but rather that an alternative route must be chosen other than the shortest path in terms of HC.
## 4 Performance Evaluation
### Performance Parameters
To evaluate the energy consumption by the mobile anchor, we first need to determine the value of the path length and the amount of energy needed to move one meter, which can be calculated from the following equation:  
\[E_{traveling}=path_{l}*E_{d}+E_{dissipated\_send} \tag{15}\]  
where \(E_{traveling}\) is the energy dissipated through travelling, \(path_{l}\) is the path length of the mobile node, \(E_{d}\) is the energy dissipated through moving 1 m which is calculated to be 2 J [22; 36], and \(E_{dissipated\_send}\) is the energy consumed during sending. The dissipated energy of one mobile anchor node is only dissipated through sending messages at each pausing point, and it is calculated from the following equation:  
\[E_{dissipated}=messages_{send}*E_{send} \tag{16}\]  
where \(messages_{send}\), \(E_{send}\) are number of sending messages and energy dissipated for sending one message, respectively. The energy dissipated for sending one message is calculated from this equation:  
\[E_{send}=P_{send}*\frac{packet\_size}{data\ rate} \tag{17}\]  
where \(p_{send}\) is the sending power, and its value is 18.5 m In ESP 32, the value of data rate is 100 Mbps, and we assume packet size 25 kbps. In Node MCU, to evaluate the power consumed by the mobile anchor, we first need to obtain the value of the volt and current consumption of the two 18,650 rechargeable batteries, which are connected in series. The nominal capacity is 3800 mAH and nominal voltage is 3.7 V, and the values of voltage and current are measured using MAX471 Voltage and Current Sensor Module shown in Figure 10 The equations used for voltage and current measurement are as follows:V = Analog reading of sensor value \(*\) (8.4/4095) (18)  
I = Analog reading of sensor value \(*\) (1/4095) (19)  
The equation for power is : \(\mathrm{P}=(\mathrm{V}*\mathrm{I})\) (20)  
Power dissipated is converted to energy using this equation:  
\[\mathrm{E}_{(\mathrm{J})}=\mathrm{P}_{(\mathrm{W})}*\mathrm{T}_{\mathrm{sec}} \tag{21}\]  
where E, P, T is the energy dissipated, power dissipated and time of dissipation, respectively.