esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Preliminary Study on the Impact of Transmission Power on the Reliability of Wireless Links  
This section analyzes the probability distribution of the delivery of packets. This can be considered a preliminary study that guides the selection of transmission power for ACTOR. This is essential to check the applicability of the UCB algorithms and their variants. Defining tighter upper bounds contributes to minimizing the regret of the UCB algorithm.  
In this experiment, two NRF5340 nodes [33] are positioned at specific distances within each other's line of sight. The two nodes (one as a server and one as a client) are programmed to communicate with different transmission power levels. The client sends 200 batches of packets for each power level to sample the link quality.  
In the literature [34], three classes of wireless links are identified, namely (i) connected, (ii) disconnected, and (iii) the links in the transitional region. The former two demonstrate a deterministic behavior; packets either go through or are dropped. However, for the transitional region, an element of randomness is involved.  
Figure 3 illustrates the probability of delivering the packets being sent over a distance of 1, 10, and 20 m. The box plots show the spread and skewness of the measured data  
Figure 2: Transmission Power Control in TPP.  
by showing their four quartiles. The transitional region is also determined using a shade of gray. It can be seen that the probabilities for delivery of packets associated with all transmission power settings fall with an increasing distance. For example, when the transmitter and the receiver are only one meter apart, the packets are very likely to be delivered even with the lowest power level, and higher transmission power settings almost surely guarantee the delivery of the packets. On the other hand, at a distance of 20 m, even the highest transmission power cannot guarantee 100% PDR.  
Based on the above results, ACTOR blacklists the transmission power settings that lead to disconnected links. Blacklisting speeds up the process of finding the optimal transmission power during the initialization of the protocol, as described in Section 5.2. These results also indicate that, even in simplistic scenarios where only two nodes are involved, a 100% delivery of packets cannot be assumed for higher transmission power settings. This assumption, as far as it is from reality, has been used in the related work to find the best transmission power. In the two-node scenario, this can be due to many reasons, ranging from the shadowing effect to external noise. In a mesh network, the complexity of estimating the link quality tends to increase. These results require us to come up with transmission power control solutions that consider lossy links and the dynamic nature of radio links.
# Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
## 2 Related Work
### Task Offloading in Edge Computing (EC)  
The decision-making factor in task offloading plays a key role in edge networks. Plenty of works have designed efficient algorithms or models. The authors of [8] designed an Energy-optimal Dynamic Computation Offloading (EDCO) scheme by jointly optimizing various parameters. The research has proposed a novel Delay-dependent Priority-aware Task-Offloading (DPTO) strategy for task scheduling [9]. A socially aware dynamic computation offloading scheme was proposed in [10]. Some researchers considered each fog node's time-energy-efficiency and priority to propose a task-offloading scheme called Fair Task-Offloading (FTO) [11]. Some studies have focused on the partial offloading approach to achieve a trade-off between the energy consumption of IoT devices [12] and task-processing delays [13,14]. The authors introduced a new dynamic edge-computing model and designed an online primal-dual algorithm to offload the task when it arrives [29]. In reference [30], a new optimization model to maximize the expected unloading rate of agents is proposed, using the game-theory method. A novel task-offloading method based on meta-reinforcement learning is proposed to improve the generalization ability of the offloading algorithm [31]. Based on a fog resource, a Fog Resource-aware Adaptive Task-Offloading (FRATO) framework is proposed to flexibly select the optimal offloading policy [32]. The authors proposed a computation offloading and resource allocation strategy named Deep Deterministic Policy Gradient (DDPG), based on the deep reinforcement learning presented in [33].  
However, these works did not consider the service that was requested by users, and may not be configured on the execution node. In this work, we introduce a dynamic computation offloading strategy, which takes the intermediate nodes with rich computing resources as the execution nodes in the task. If the execution node does not configure the corresponding service, the service migration process will occur in parallel. The deep reinforcement learning algorithm is used to help the optimal intermediate node make a quick decision.
# Source-Based Size-Resolved Optical Properties of Carbonaceous Aerosols
###### Abstract  
In this study, the source-based optical properties of polydisperse carbonaceous aerosols were determined from PM2.5 concentrations measured at a Global Atmospheric Watch station in South Korea. The extinction and absorption coefficients of carbonaceous aerosols were calculated using the Mie theory and assuming a lognormal size distribution. Based on the mass concentration from the EPA's Positive Matrix Factorization (PMF) receptor model, which considers five source identification and apportionment factors (biggic source, local biomass burning, secondary organic aerosol, transported biomass burning, and mixed sources), the source-based size-resolved mass extinction and absorption efficiencies were estimated for each source using a multilinear regression model. The results show that the source-based optical properties depend on the aerosol size and physicochemical characteristics of the chemical compounds. The long-range transport of biomass burning (LBB) aerosol, which has a mass concentration of 20%, holds a 12.1-23.1% total extinction efficiency--depending on the size and refractive index--in the range of 0.1-0.5 um in geometric mean diameter and humic-like substances (HULIS) imaginary refractive index of 0.006-0.3. Biogenic sources of aerosols with small diameters have higher mass absorption efficiencies (MAE) than other sources, depending on the size and refractive index.  
a 2021 11434 1143 1143 1143 143  
samples [11]. A regional Global Atmospheric Watch (GAW, 36\({}^{\circ}\)32\({}^{\prime}\) N; 126\({}^{\circ}\)19\({}^{\prime}\) E, 45.7 m above sea level) station was established by the Korean Meteorological Administration on Annyeon Island on the midwestern coast of South Korea. One of the goals of the GAW station is to monitor the variations in components that might affect climate change.  
Many studies have been conducted to understand the effects of aerosols on the climate. For example, optical properties, such as radiative forcing, have been characterized using direct measurements [12; 13]. Chemical composition-based optical properties are also important for understanding the contribution of aerosol species and their effects on climate [7; 14; 15]. In addition, source-based aerosol optical properties can provide information about their effects on the climate [16]. [PERSON] et al. [17] measured the carbonaceous aerosol compounds in PM\({}_{2.5}\) samples and determined the major factors affecting the carbonaceous organic compounds at the Annyeon GAW station using a receptor model. In this study, the source-based mass extinction efficiency of polydisperse aerosols was estimated from source apportionment-based chemical species-resolved mass contribution results [17]. The size-resolved extinction and absorption coefficients of carbonaceous aerosols were calculated using the Mie theory and the mass extinction and absorption efficiencies of each of the five sources of the EPA's Positive Matrix Factorization (PMF) model were estimated using a multilinear regression model.
## 3 Effect of Structural Sealant and Supporting Frame
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Mode**} & **Measured** & \multirow{2}{*}{**Equation (1)**} & **Model 1** & **Model 2** & **Model 3** \\ \hline
1 & 6.730 & 7.816 (\(m=1\), \(n=1\)) & 7.817 & 7.534 & 7.005 \\
2 & 13.833 & 17.957 (\(m=1\), \(n=2\)) & 17.981 & 17.026 & 14.282 \\
3 & 18.369 & 21.125 (\(m=2\), \(n=1\)) & 21.159 & 20.007 & 19.095 \\
4 & 22.310 & 31.266 (\(m=2\), \(n=2\)) & 31.306 & 28.421 & 21.226 \\
5 & 28.178 & 34.858 (\(m=2\), \(n=3\)) & 34.999 & 32.387 & 27.420 \\
6 & 32.663 & 43.307 (\(m=3\), \(n=2\)) & 43.496 & 39.638 & 31.877 \\ \hline \hline \end{tabular} Note: the values in the parentheses are \(m\) and \(n\) in Equation (1).  
\end{table} 
Table 4: Calculation results of natural frequency of sample B. The results show that the influence of structural sealant and a supporting frame on the dynamic characteristics of panel unit is not negligible, and the natural frequency of panel units of hidden frame curtain wall is less than that under a simply supported boundary condition. Other test models, such as the one in which the panel units are directly installed on the ground [13] by fixing with fixture, and another one in which the I-steel with high stiffness was adopted [14], ignored the influence of the supporting frame and concluded that the panel's natural frequency was between the results under the simply supported and fixed boundary conditions. It does not happen in real engineering. Therefore, it is unreasonable to take the natural frequency of a simply supported boundary as the threshold value to identify the damage of sealant of the panel unit in actual engineering.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Data Collection Modalities
#### 3.1.5 Contextual Modalities  
Contextual data provides environmental or spatial information that influences learning, such as seating arrangements, physical proximity, location, and ambient conditions. These are often collected through positioning sensors, manual observations, or system metadata [52; 61; 62]. For example, spatial positioning with Pozyx tags or motion capture enables analysis of embodied interaction in classrooms [52; 61]. Observational notes and metadata (e.g., timestamps, roles) further enrich interpretation by situating learner actions in real-world or virtual environments [57; 63].
# MOCA: A Network Intrusion Monitoring and Classification System
## 5 Measurement Results
### Multi-Class Classification Accuracy
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MOCA (Top-20)**} & \multicolumn{3}{c}{**MOCA (Top-7)**} & \multicolumn{3}{c}{**MOCA (Top-5)**} & \multicolumn{3}{c}{**RF (10) [2]**} \\ \hline
**Class** & **Prec** & **Rec** & **F1** & **Prec** & **Rec** & **F1** & **Prec** & **Rec** & **F1** & **Prec** & **Rec** & **F1** \\ \hline DoS GoldenEye & **1.00** & **1.00** & **1.00** & 0.99 & **1.00** & 0.99 & 0.99 & 1.00 & 0.99 & **1.00** & 0.99 & 0.99 \\ DoS Hulk & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & 0.99 & **1.00** & 0.99 & 0.99 & **1.00** & 0.99 \\ DoS Slowhttptest & **1.00** & **1.00** & **1.00** & 0.97 & 0.96 & 0.97 & 0.86 & 0.85 & 0.86 & 0.83 & 0.83 & 0.83 \\ DoS slowloris & **1.00** & **1.00** & **1.00** & 0.98 & 0.98 & 0.98 & 0.93 & 0.93 & 0.93 & 0.98 & 0.98 & 0.98 \\ FTP-Patator & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & 0.99 & 0.99 \\ Heartbleed & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** \\ PortScan & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & 0.99 & **1.00** & 0.99 \\ SSH-Patator & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & 0.99 & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** \\ \hline (Brute Force) & 0.97 & 0.97 & 0.97 & 0.95 & 0.91 & 0.93 & 0.94 & 0.90 & 0.92 & 0.58 & 0.45 & 0.52 \\ (Sql Inj) & **1.00** & **1.00** & **1.00** & 0.86 & 0.92 & **1.00** & 0.86 & 0.92 & **1.00** & 0.80 & 0.89 \\ Web Attack XSS & 0.93 & 0.92 & 0.93 & 0.92 & 0.89 & 0.91 & 0.99 & 0.87 & 0.93 & 0.68 & 0.76 & 0.72 \\ \hline Bot & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & 0.83 & 0.79 & 0.81 \\ DDoS & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & 0.99 & 0.99 \\ Infiltration & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & **1.00** & 0.88 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results of MOCA vs. previous work [2] based on CICIDS2017 dataset. Figure 2: Performance of multi-class classifier trained by top-k selected features: (**a**) CICIDS2017; (**b**) CICDDOS2019.
## 5 Feasibility Study
### Entering and Exiting a Place  
The proposed LTC system uses a single door entry checkpoint for each room to track the users' mobility. The architecture of the door entry node, depicted in Figure 5, depends on the RFID reader, and it does not utilize any other kind of sensors to help identify whether the user is entering or exiting the room. This is meant to simplify the design and the installation of the door entry node. We designed the tracking database's schema to maintain fields that hold the identities of the current door entry checkpoint and the previous door entry checkpoints. Knowing these two identities of door entry checkpoints and the truth table that is manually configured during system startup based on the floor plan of the premise, we can determine whether the user is entering or exiting a place. Moreover, we set up a fallback extension for invalid unmapped entries in the truth table. Invalid entries may be due to system glitches or missed RFID tag readings when the users enter or exit places. The fallback extension number can be configured as the default extension number of the user or as an arbitrary extension number (i.e., the receptionist extension). Figure 15  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RPi 4 B**} & \multicolumn{2}{c}{**RPi 3 B+**} & \multicolumn{2}{c}{**RPi Z  W**} & \multicolumn{2}{c}{**RPi Z  W**} \\ \cline{2-9}  & **Passthrough** & **Trans.** & **Passthrough** & **Trans.** & **Passthrough** & **Trans.** & **Passthrough** & **Trans.** \\ \hline \multicolumn{9}{l}{**Forward (Phone-to-RPi)**} \\ \hline Max jitter & 9.35 & 10.49 & 11.87 & 10.97 & 12.05 & 13.85 & 15.33 & 16.02 \\ (milliseconds) & 7.08 & 6.45 & 8.09 & 6.88 & 9.96 & 10.13 & 11.58 & 13.69 \\ RTP Packets & 132,486 & 115,715 & 111,460 & 65,414 & 101,623 & 37,211 & 8688 & 5058 \\ Expected loss (\%) & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \multicolumn{9}{l}{**Reverse (RPi-to-Phone)**} \\ \hline Max jitter & 5.07 & 5.98 & 5.90 & 8.43 & 8.85 & 9.32 & 10.02 & 20.36 \\ (milliseconds) & 0.65 & 0.68 & 0.70 & 0.73 & 0.98 & 0.85 & 0.90 & 3.5 \\ RTP Packets & 132,450 & 115,624 & 111,365 & 65,369 & 101,577 & 37,188 & 8610 & 5049 \\ Expected & 132,450 & 115,624 & 111,365 & 65,369 & 101,577 & 37,188 & 8610 & 5049 \\ Packet loss (\%) & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: VoIP call quality measurements during passthrough and transcoded VoIP call testings for different Raspberry Pi boards.  
Figure 14: LTC implementation scenarios: a small office and a large enterprise.
# Two Efficient Derivative-Free Iterative Methods for Solving Nonlinear Systems
## 3 Computational Efficiency
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \(\rho\) & _a(m)_ & _p(m)_ & _C(u, m)_ \\ \hline \(\psi_{1}\) & 2 & \(m(m+1)\) & \((m^{3}-m)/3+2m^{2}\) & \(C_{1}=m(m+1)\mu+(m^{3}-m)/3+2m^{2}\) \\ \(\psi_{2}\) & 4 & \(3m^{2}\) & \(2(m^{3}-m)/3+7m^{2}\) & \(C_{2}=3m^{2}\mu+2(m^{3}-m)/3+7m^{2}\) \\ \(\psi_{3}\) & 6 & \(m(2m+3)\) & \(2(m^{3}-m)/3+6m^{2}\) & \(C_{3}=m(2m+3)\mu+2(m^{3}-m)/3+6m^{2}\) \\ \(\psi_{4}\) & 4 & \(2m(m+1)\) & \((m^{3}-m)/3+6m^{2}+2m\) & \(C_{4}=2m(m+1)\mu+(m^{3}-m)/3+6m^{2}+2m\) \\ \(\psi_{5}\) & 6 & \(m(2m+3)\) & \((m^{3}-m)/3+9m^{2}+4m\) & \(C_{5}=m(2m+3)\mu+(m^{3}-m)/3+9m^{2}+4m\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computational cost of the iterative methods. Then, we compare the \(CEI\) of the iterative methods with different convergence order by the following theorem:  
**Theorem 3**. : _We have 1. \(CEI_{5}>CEI_{4}\) for all \(m\geq 2\) and \(\mu>\frac{m^{2}\ln^{2/3}+18m\ln^{4/3}+(17\ln^{2}-5\ln^{3})}{6(m\ln^{3/2}+\ln^{ 3/4})}\). 2. \(CEI_{4}>CEI_{1}\) for all \(m\geq 8\) and \(\mu>0\)._  
Proof. : 1. From the expression Equation (31) and Table 1,We get the following relation between \(\psi_{4}\) and \(\psi_{5}\)  
\[R_{5,4}=\frac{\ln(\rho_{5})C_{4}(\mu,m)}{\ln(\rho_{4})C_{5}(\mu,m)}=\frac{\ln^ {6}}{\ln^{4}}\frac{2m(m+1)\mu+(m^{3}-m)/3+6m^{2}+2m}{m(2m+3)\mu+(m^{3}-m)/3+9m^ {2}+4m} \tag{34}\]  
We consider the boundary \(R_{5,4}=1\). The boundary can is given by the following equation  
\[\mu=\mathrm{H}_{5,4}(m)=\frac{m^{2}\ln^{2/3}+18m\ln^{4/3}+17\ln^{2}-5\ln^{3}} {6(m\ln^{3/2}+\ln^{3/4})} \tag{35}\]  
where \(CEI_{5}>CEI_{4}\) over it (see Figure 1). The boundary Equation (35) cut axes at points \((m,\mu)=(13.888,\ 0)\) and \((2,\ 4.7859)\). Thus, we get that \(CEI_{5}>CEI_{4}\) since \(R_{5,4}>1\) for all \(m\geq 2\) and \(\mu>\mathrm{H}_{5,4}(m)\). 2. The relation between \(\psi_{1}\) and \(\psi_{4}\) is given by  
\[R_{4,1}=\frac{\ln(\rho_{4})C_{1}(\mu,m)}{\ln(\rho_{1})C_{4}(\mu,m)}=\frac{\ln ^{4}}{\ln^{2}}\frac{m(m+1)\mu+(m^{3}-m)/3+2m^{2}}{2m(m+1)\mu+(m^{3}-m)/3+6m^{ 2}+2m} \tag{36}\]  
Subtracting the denominator from the numerator of Equation (36), we have  
\[\frac{1}{3}m(m^{2}-6m-7) \tag{37}\]  
The Equation (37) is positive for \(m>7\). Thus, we obtain that \(CEI_{4}>CEI_{1}\) for all \(m\geq 8\) and \(\mu>0\).
## 5 Detection and Damping Methodology  
The proposed methodology summarized in Figure 2 allows us to detect inter-area oscillations, dynamically activate POD-P controllers, and deactivate them once oscillations are mitigated, all within the DSPF environment. The control logic relies on two key variables derived from the FFT output: Enable_POD and Internal_variable.  
To continuously monitor system signals, our methodology employs an FFT algorithm that detects frequencies within the inter-area oscillation range (0.1-1 Hz). The FFT operates within a floating window of 5 s, recalculating every 0.5 s.  
Under normal operating conditions, Internal_variable and Enable_POD are zero. If the oscillatory magnitudes calculated by the FFT exceed the predefined threshold of 15 mHz, meaning that an inter-area oscillation has occurred, the Internal_Variable changes to 1. The FFT output is updated every 0.5 s, and a new value of Internal_Variable is set and compared to its previous value to verify consistency and minimize false positives. Activation occurs (Enable_POD = 1) instantaneously when the FFT calculated for two consecutive cycles equals one (Internal_Variable = 1).  
A counter is implemented to manage the deactivation of the POD-P controller (Enable_POD = 0). Deactivation occurs when the FFT value remains at zero for two consecutive cycles (Internal_variable = 0). Additionally, if no change is detected in Internal_variable for 5 s (Counter = 10), the Enable_POD variable transitions to a deactivated state, ensuring that the system has fully stabilized before disabling the controller.  
Figure 2: Methodology flowchart.  
Upon activation, the POD-P controller generates a corrective signal, dP_POD (see Figure 1, Section 2), which dynamically adjusts the active power setpoint of the PPC. This modification allows the PV plants to contribute to damping the detected oscillations by modifying their active power output. As a result, the oscillatory behavior of the system is effectively mitigated, restoring system stability.
## 4 Experiments and Results
### Baseline Systems
#### 4.3.2 DCT-Based Model  
We also trained the model by using DCT coefficients to represent the \(F_{0}\) contour within the syllable. This model and the proposed model were trained using the same input features but their output features were different. Based on past studies [23; 24], we used 10 DCT coefficients \(C=[c_{0},c_{1},...,c_{9}]\) to represent the \(F_{0}\) contour of each syllable, where \(c_{0}\) represents the mean of \(F_{0}\) over the syllable and the other coefficients represent the curve of \(F_{0}\) within the syllable. The network structure with the best performance consisted of three feedforward layers with 512 nodes per layer, where the top two hidden layers had a BLSTM structure, and each combined 128 forward units with 128 backward units. As with the frame-based model, the model was trained with a learning rate of 0.0001 and a mini-batch size of 64.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AES & Advanced Encryption Key Standard \\ AI & Artificial Intelligence \\ AMQP & Advanced Message Queuing Protocol \\ BR-PUF & Bistable Ring PUF \\ CN-PUF & Carbon Nanotube-Based PUF \\ CO-PUF & Computational Optical PUF \\ CRP & Challenge Response Pair \\ DDoS & Distributed Denial-of-Service \\ DL & Deep Learning \\ DP & Differential Privacy \\ DPU & Deep Learning Processor Unit \\ EC & Edge Computing \\ FL & Federated Learning \\ FPGAs & Field Programmable Gate Arrays \\ GDPR & General Data Protection Regulation \\ HD & Hamming Distance \\ HE & Homomorphic Encryption \\ HMAC & Hash Message Authentication Code \\ ICN & Information-Centric Networking \\ IDS & Intrusion Detection System \\ MCC & Mobile Cloud Computing \\ MEC & Multi-Access Edge Computing \\ MECCA-PUF & Memory Cell-Based Chip Authentication PUF \\ ML & Machine Learning \\ MNO & Mobile Network Operators \\ MQTT & Message Queue Telemetry Transport \\ MVL-PUF & Multiple-Valued Logic PUF \\ NEM-PUF & Nano-Eectromechanical PUF \\ ONNX & Open Neural Network Exchange \\ OpenVINO & Open Visual Inference and Neural Network Optimization \\ PE-PUF & Process and Environmental PUF \\ PH-PUF & Photonic PUF \\ PUFs & Physically Unclonable Functions \\ RF-DNA-PUF & Radio-Frequency Certificates of Authenticity \\ RRAM-PUF & Reconfigurable Resistive RAM PUF \\ RTMS & Realtime Traffic Monitoring Systems \\ SAC & Strict Avalanche Condition \\ SASE & Secure Access Service Edge \\ SC-PUF & ScanPUF \\ SDN & Software Defined Networking \\ SEACOD & Selective Encryption and Component-Oriented Deduplication \\ TERO-PUF & Transient Effect Ring Oscillator PUF \\ VMs & Virtual Machines \\ WBI & Web-Based Intermediaries \\ \end{tabular}