esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Design of _HeaLow_ and Implementation
### Offloading Decision Process in Availability Checker
#### 3.4.1 Fundamental Situation Assumed for Describing Offloading Decision Process  
We assume a situation where there are a main device and \(n-1\) neighbor devices. One neighbor device takes the role of the manager, and all devices are within the same network. \(buffer_{t}^{i}\) indicates the amount of buffered workloads in the work queue of the \(i\)-th device at time \(t\), and the default unit for task is one workload. \(wl_{n}^{i}\) means the \(i\)-th device's \(n\)-th workload. \(dl\_wl_{n}^{i}\) and \(si\_wl_{n}^{i}\) mean the deadline time and the data size of \(wl_{n}^{i}\) respectively. A device conducts sensing and sampling, so a workload consists of samples, and \(ns\_wl_{n}^{i}\) is the number of samples in \(wl_{n}^{i}\). In _HeLow_, \(x\log x\) is the amount of computation for processing the workload composed of \(x\) samples. \(r_{proc}^{i}\) and \(r_{tran}^{i}\) are the processing rate and the transmission rate of \(i\)-th device. In _HeLow_, \(buffer_{t}\), \(r_{proc}\), and \(r_{tran}\) are shared through the manager as explained before. Table 1 lists notations and variables used for _HeLow_.  
In this situation, there are two major cases to be considered. The first case is when a device processes the device's workload, and the other case is when the device is requested to process another device's workload.
## 7 Results and Analysis
### Anomaly Classification in a Kitting Experiment  
#### 7.2.1 Dataset  
The dataset captures sensory-motor signatures of the kitting task under anomalous scenarios as outlined in this paper. A total of 136 anomalous events were recorded in 142 experimental executions across all skills. The proportions for each anomaly class were as follows: TC 15.7%, HC 16.7%, HCO 16.7%, NO 13.0%, OS 16.7%, WC 15.7%, and OTHER 5.6% (see Section 6.1.5 for details).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Kitting Experiment** & \multicolumn{2}{c}{**Testing Information**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**Accuracy**} \\ \hline
**Movements** & **Success** & **Anomalies** & **GD** & **HSD** & **GD** & **HSD** & **GD** & **HSD** & **GD** & **HSD** \\ \hline Home \(\rightarrow\) Pre-prick & 141 & 6 & 1.0 & 1.0 & 0.33 & 0.50 & 0.50 & 0.67 & 0.973 & 0.980 \\ Pre-prick \(\rightarrow\) Prick & 112 & 23 & 1.0 & 1.0 & 0.78 & 0.78 & 0.878 & 0.88 & 0.963 & 0.963 \\ Prick \(\rightarrow\) Pre-prick & 83 & 28 & 1.0 & 0.80 & 0.04 & 0.31 & 0.069 & 0.45 & 0.757 & 0.820 \\ Pre-prick \(\rightarrow\) Pre-place & 13 & 70 & 1.0 & 0.84 & 0.54 & 0.97 & 0.704 & 0.90 & 0.614 & 0.843 \\ Pre-place \(\rightarrow\) Place & 11 & 6 & 1.0 & 0.40 & 1.0 & 1.0 & 1.0 & 0.57 & 1.0 & 0.769 \\ Place \(\rightarrow\) Pre-Place & 8 & 3 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.889 & 0.889 \\ \hline Across all skills: & 368 & 136 & 0.98 & 0.84 & 0.47 & 0.76 & 0.64 & 0.80 & 0.861 & 0.910 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of anomaly detection performance measures between our proposed method and a baseline method.  
Figure 9: The comparison of two anomaly detectors for five robot anomalous executions. Each row represents the same testing execution. _Left column_: results of our hidden-state-based anomaly detector. _Right column_: results of the gradient-based anomaly detector [2]. The ground truth (_anomaly_t_by_human_) is represented by green dashed lines and the detected anomalies are represented by yellow solid lines.
## 3 Channel Modeling of the Ground Electrode Current Field in a Tunnel Environment
### Analysis of the Depth of Penetration of Different Electrode Materials and Shapes  
The penetration depth of the ground electrode through the ground communication method is related to factors such as the conductivity of the formation, the signal frequency, the intensity of the current injected into the ground, the contact impedance between the electrode and the soil, and the material and shape of the electrode.  
Therefore, Chinese and foreign scholars have put forward a lot of ideas and have carried out a lot of research. [PERSON] et al. conducted an electrostatic field analysis of a line source carrying a DC current [22].  
In order to simplify the analysis process, the thick collapsible layer is assumed to be an infinitely uniform conducting medium with a conductivity of \(\sigma\). It is assumed that the pair of transmitting and receiving electrodes can be completely aligned (the direction of the field strength at the receiving point coincides with the baseline of the electric dipole), as shown in Figure 2, where \(l_{b}\) is the distance between the transmitting electrodes and the distance between \(l_{o}\) and the receiving electrodes, and \(d_{e}\) is the penetration distance [23].  
Figure 2: Schematic diagram of the transceiver electrodes.  
Then, the electric field strength in an infinite homogeneous medium can be expressed as:  
\[E=\frac{Il_{b}}{4\pi d_{e}^{3}\sigma}\Big{(}1+\gamma d_{e}+\gamma^{2}d_{e}^{2} \Big{)}e^{-\gamma d_{e}} \tag{1}\]  
In Formula (1), \(\gamma\cong j\omega_{te}\mu\sigma\), then \(\gamma^{2}=j\omega\mu\sigma\), \(\omega\) is the angular signal frequency, and \(\mu\) is the vacuum permeability, \(\mu=4\pi\times 10^{-7}H/m\). At the receiving end, the electric dipole is also used to obtain the voltage between the two poles. By integrating the electric field, the receiving voltage can be obtained as: [24]  
\[V_{o}=\frac{V_{b}l_{b}l_{o}}{4\pi d_{e}^{3}\sigma Z_{c}}\Big{(}1+\gamma d_{e}+ \gamma^{2}d_{e}^{2}\Big{)}e^{-\gamma d_{e}} \tag{2}\]  
In Formula (2), \(V_{b}\) is the emission voltage and \(Z_{c}\) represents the contact resistance of the emitter electrode.
## 2 Experimental Section
### Instrumentation and Synthesis
#### Synthesis of 4-fluoro-\(\beta\)-nitrostyrene **9** [4-fluoro-2-(nitroethen-1-enyl)benzene]  
Following literature procedures [24], a stirred solution of acetic acid (33.5 mL) and ammonium acetate (4.4 g, 57.1 mmol) was added to nitromethane (10 g, 164.0 mmol) followed by 4-fluorobenzaldehyde (2.94g, 23.7 mmol) and the solution was refluxed in an oil bath at 100 \({}^{\circ}\)C for 5.5 hr. The dark orange mixture was then cooled to room temperature and poured into water (100 mL). The pH of the solution was basified with sodium hydroxide solution (2 M), and the product was extracted with ethyl acetate (3 \(\times\) 100 mL). The combined organic extracts were dried over MgSO\({}_{4}\), filtered and concentrated under high vacuum. Recrystallization from 95% ethanol removed the brown oily impurities, giving pale yellow needles, 57% yield and mp. 99-100 \({}^{\circ}\)C. \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)): \(\delta_{\rm H}\) (ppm) 7.94-7.89 (d, \(J\) = 13.81 Hz, 1H, \(\alpha\)C-H), 7.52-7.44 (m 1H, \(\beta\)C-H), 7.52-7.44 (m, 2H, Ar-H), 7.11-7.05 (t, \(J\) = 8.40, 2H, Ar-H). \({}^{13}\)C NMR (300 MHz, CDCl\({}_{3}\)): \(\delta_{\rm H}\) (ppm) 164.9 (d, \(J\) = 255.08 Hz, C-F), 137.8 (C=C, \(\beta\) carbon), 136.8 (C=C, \(\alpha\) carbon), 131.2 (Ar), 126.3 (Ar), 116.7 (Ar), GC/MS \(m\)/z (M\({}^{+}\)) 167.
## 1 Introduction
Additionally, the precise measurement of VA consumption can be a challenge due to the complexity of the technology and the need to consider multiple factors in the energy calculation. On the other hand, a combination of different models may be required to provide accurate energy consumption predictions in varied situations. Table 1 abstracts the characteristics of the studies related to this work, identifying those that explored each research line associated with this project. Thus, the main contributions of this work are as follows:  
* Presentation of a connection architecture and a prototype that enables energy savings in VAs through the implementation of cache memory supported by AI, achieving experimental results, demonstrating savings and an additional level of security;
* Development of a metric to determine the energy consumption of VAs when user responses are obtained from local network cache memory;
* Comparison of consumption between passive and active states for two types of VAs;
* Proposal of a mathematical model of the process performed by VAs, from which a metric was developed to compare VA energy consumption;
* Energy-saving solution for using VAs in remote locations with limited electrical resources and low Internet connectivity;
* The proposal minimizes the response time for queries made to VAs, improving UX. The article's structure is as follows: Initially, a description of the methodology and materials used in the work is presented, as well as the mathematical model and prototype implementation. Subsequently, the results obtained are presented and analyzed, and finally, the discussion and conclusions of this work are shown.
# Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
## 3 Optimization of UAV Position and Power Allocation
### Power Allocation Optimization  
The sum rate maximization problem can be rewritten as follows when the UAV's position is fixed.  
\[\text{P4}:\] \[\max_{\mathbf{P}}\frac{1}{2}\sum_{n=1}^{N}\log_{2}\left(1+\frac{p _{n}{A_{n}}^{\prime}}{{A_{n}}^{\prime}\sum\limits_{k\
eq n}^{N}\alpha_{k,n}p_{ k}+A_{n}B_{n}+C_{n}}\right)\] (20) \[s.t.\] ( 9a )- ( 9b ), ( 9e )- ( 9f ).  
P4 is hard to handle due to its objective function and is not strictly concave-convex according to the Lagrangian dual transform technique proposed in [24]. The optimization problem in P4 can be expressed as:  
\[\max_{\mathbf{Y},\mathbf{P}}\frac{1}{2}g(\mathbf{P},\mathbf{Y}). \tag{21}\]where \(\mathbf{Y}\) refers to \((\mathrm{Y}_{1},\ldots,\mathrm{Y}_{N})\) and \(\mathrm{Y}_{n}\) is introduced as the auxiliary variable and was introduced for each ratio term in the objective function in P4, and  
\[\begin{split} g(\mathbf{P},\mathbf{Y})=\sum\limits_{n=1}^{N}\log_{ 2}(1+\mathrm{Y}_{n})-\sum\limits_{n=1}^{N}\mathrm{Y}_{n}+\underbrace{\sum \limits_{n=1}^{N}\frac{(1+\mathrm{Y}_{n})p_{n}A_{n}{}^{\prime}}{p_{n}A_{n}{}^{ \prime}+A_{n}{}^{\prime}\sum\limits_{k\
eq n}^{N}\alpha_{k,n}p_{k}+A_{n}B_{n}+ C_{n}}}_{sum-of-riotterm}.\end{split} \tag{22}\]  
By setting \(\partial g/\partial\mathrm{Y}_{n}=0\), we can obtain:  
\[\mathrm{Y}_{n}{}^{*}=\frac{\log_{2}\!e\big{(}p_{n}A_{n}{}^{\prime}\big{)}+b \Bigg{(}A_{n}{}^{\prime}\sum\limits_{k\
eq n}^{N}\alpha_{k,n}p_{k}+A_{n}B_{n} +C_{n}\Bigg{)}}{A_{n}{}^{\prime}\sum\limits_{k\
eq n}^{N}\alpha_{k,n}p_{k}+A_{ n}B_{n}+C_{n}}. \tag{23}\]  
where \(b=\log_{2}\!e-1\).  
By substituting (23) into (22) and adopting a quadratic transform algorithm in [32], the sum-of-ratio term can be solved in (22). Finally, the objective function in P4 can be transformed into:  
\[\begin{split} g(\mathbf{P},\mathbf{Y}^{*},\boldsymbol{\mathcal{Z}} )=\sum\limits_{n=1}^{N}\log_{2}(1+\mathrm{Y}_{n}{}^{*})-\sum\limits_{n=1}^{N} \mathrm{Y}_{n}{}^{*}\\ +\sum\limits_{n=1}^{N}\Bigg{(}2\mathcal{Z}_{n}\sqrt{(1+\mathrm{Y} _{n}{}^{*})p_{n}A_{n}{}^{\prime}}-\mathcal{Z}_{n}{}^{2}\Xi(\mathbf{P})\Bigg{)},\end{split} \tag{24}\]  
where \(\Xi(\mathbf{P})=p_{n}A_{n}{}^{\prime}+A_{n}{}^{\prime}\sum\limits_{k\
eq n}^ {N}\alpha_{k,n}p_{k}+A_{n}B_{n}+C_{n}\).  
We can obtain the following closed-form solution for variable \(\boldsymbol{\mathcal{Z}}\)  
\[\begin{split}\mathcal{Z}_{n}{}^{*}&\!=\!\frac{\sqrt {(1+\mathrm{Y}_{n}{}^{*})p_{n}A_{n}{}^{\prime}}}{\bigg{(}p_{n}A_{n}{}^{\prime} +A_{n}{}^{\prime}\sum\limits_{k\
eq n}^{N}\alpha_{k,n}p_{k}+A_{n}B_{n}+C_{n} \Bigg{)}}.\end{split} \tag{25}\]  
Finally, by substituting (25) into (24), P3 can be rewritten as the standard convex optimization problem.  
\[\begin{split}\mathrm{P5}&:\max\limits_{\mathbf{P}} \frac{1}{2}\mathfrak{g}(\mathbf{P},\mathbf{Y}^{*},\boldsymbol{\mathcal{Z}}^{* })\\ s.t.&\eqref{eq:
## 5 Performance Evaluation of Our Routing Protocol
### Comparison with Different Loads  
Figure 10 shows the variations of the performance metrics with varying traffic load. The number of channels is assumed to be 12. The number of source routers are assumed to be 10 for Figure 10. Figure 10 also shows a significant improvement of the performance metrics in case of JRCA, as compared to the single channel and random channel selection scheme. The improvement in comparison to random channel selection scheme is less evident with light network load, however the difference clear (\(\sim\)20% improvement in network throughput, and \(\sim\)40% reducing in traffic delay) when the traffic load increases. In the case of JRCA with four NICs, the network throughput increases almost linearly by about five times, when the traffic load increases from 35 KBps to 185 KBps. On the other hand, the traffic delay increases by \(\sim\)10 times, due to more channel access delay with a higher network load.  
Figure 9: Comparison of (**a**) throughput (**b**) delivery ratio (**c**) delay (**d**) jitter with different number of flows for multi-channel WOBAN with multiple gateways.
## 3 Proposed Signal Processing Algorithm
### Normalization and cross-correlation  
As a preliminary procedure, the output of photodetector signals \(I_{S}\left(\Phi_{P}\right)\) and \(I_{R}\left(\Phi_{P}\right)\) are sampled and normalized respectively into fringe scan \(i_{S}(n)\) and \(i_{R}(n)\):  
\[i_{S}\left(n\right)=\exp\Biggl{\{}-\Biggl{[}\frac{2\pi\bigl{(}n-n_{S}\bigr{)} /\,f_{S}}{\pi L_{C}\,/\,\lambda}\Biggr{]}^{2}\Biggr{\}}\cos\Bigl{\{}2\pi\bigl{(} n-n_{S}\bigr{)}/\,f_{S}\,\,\,\Bigr{\}}+w_{S}\left(n\right) \tag{4}\]  
and:  
\[i_{R}\left(n\right)=\exp\Biggl{\{}-\Biggl{[}\frac{2\pi\bigl{(}n-n_{R}\bigr{)} /\,f_{S}}{\pi L_{C}\,/\,\lambda}\Biggr{]}^{2}\Biggr{\}}\cos\bigl{\{}2\pi\bigl{(} n-n_{R}\bigr{)}/\,f_{S}\,\,\Bigr{\}}+w_{R}\left(n\right) \tag{5}\]  
where \(n_{S}\) is zero order fringe peak sample number for sensing FFPI, \(n_{R}\) is zero order fringe peak sample number for reference FFPI, \(f_{S}\) is the sample rate in samples per fringe (or samples/fringe). A normalization procedure is carried out by removing the d.c. component (constant \"1\" in Equation 1 and Equation 2) of each fringe scan. \(w_{S}(n)\) and \(w_{R}(n)\) are the white noise related to the fringe scan \(i_{S}(n)\)and \(i\)\({}_{R}(n)\) respectively with zero mean and variance \(\sigma_{w}^{2}\). The phase delay between the sensing FFPI and the reference FFPI is defined in terms of samples as:  
\[n_{d}=n_{S}-n_{R} \tag{6}\]  
After normalization, \(i_{S}\left(n\right)\) and \(i_{R}\left(n\right)\) are cross-correlated into \(i(n)\) and normalized again. Cross-correlation \(i(n)\) can be expressed in mathematical form with its zero order fringe peak \(p_{0}\) at \(n\)=\(n_{d}\) as shown in Equation 7.  
\[i(n)=\sum_{k=-\infty}^{\infty}\!\!i_{R}\left(k\right)i_{S}\left(n+k\right)\]  
\[=\exp\!\left[-\left[\frac{2\pi\!\left(n-n_{d}\right)/f_{S}}{\pi\!\!L_{C,\mathit{ eff}}\ /\ \!\lambda}\right]^{2}\right]\cos\!\left\{2\pi\!\left(n-n_{d}\right)/f_{S}\right\} +w(n) \tag{7}\]  
In Equation 7 the effective coherence length \(L_{C,\mathit{eff}}\) is given as \(\sqrt{2}L_{C}\)[17] and \(w(n)\) is a white noise of \(i(n)\). Exponential term in Equation 7 is termed as \"the envelope\" of cross-correlation \(i(n)\). This cross-correlation improves _SNR_ at zero order fringe peak up to 14 dB [17]. One disadvantage related to cross-correlation is the broadening of peak from \(L_{C}\) to \(\sqrt{2}L_{C}\) due to the cross-correlation of two (almost) identical Gaussian envelope signals [17]. For example, this results in higher \(SNR_{\min}\) in Equation 6 requiring 6 [dB] more than before cross-correlation when \(L_{C}\)=\(26\lambda\), but this will be compensated by 14 dB _SNR_ improvement at the zero order fringe peak [17]. After cross-correlation, the task of the signal processing algorithm becomes to find a zero order fringe peak \(p_{0}\)=\(n_{d}\) (global maximum) of cross-correlation \(i\left(n\right)\) correctly.
# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 2 Materials and Methods
### Case Study on Sentinel-2
#### 2.2.4 Aliasing Artifacts in Sentinel-2 Data
Plastic patch of size 10 m \(\times\) 10 m.The triangle represents the average of the corresponding dataset and the orange line, the median. Figure 7: Results of all the possible jitters for each band by selecting the maximum reflectance values. Plastic patch of size 30 m \(\times\) 30 m. The triangle represents the average of the corresponding dataset and the orange line, the median. a \(30\,\mathrm{m}\times 30\,\mathrm{m}\) patch and from a \(10\,\mathrm{m}\times 10\,\mathrm{m}\), we can observe that the aliasing effect is less significant for larger patches. We generated images with plastic patches made out of a common plastic material and of various sizes (from 1 m to 150 m). We assessed the threshold above which aliasing does not occur. For rectangular shapes, the minimum size required to overcome aliasing for bands with GSD = 10 m is approximately \(25\,\mathrm{m}\times 25\,\mathrm{m}\) whereas for the bands with GSD = 20 m, the required size is \(40\,\mathrm{m}\times 40\,\mathrm{m}\). For circular shapes, the minimum size required to avoid aliasing is R = 15 m for the bands with GSD = 10 m and R = 25 m for bands with GSD = 20 m. All results are summarized in Table 2. We therefore recommend to have these orders of size in mind when using S2 data in order to avoid aliasing, but they are not strict limits. A plastic patch of size \(23\,\mathrm{m}\times 23\,\mathrm{m}\) will be under the size limit but the impact of aliasing might not be significant or at least, less important than for a \(10\,\mathrm{m}\times 10\,\mathrm{m}\) patch.
## 7 Discussion
When compared to tests without encryption, it was found that introducing AES encryption does not necessarily or significantly increase video latency. This is because of the FPGA-based high speed implementation of the AES core using a BITW approach. The LabVIEW-FPGA-based implementation of 128 bytes and 256 bytes AES was achieved at 200 MHz, while 512 bytes AES implementation was achieved at 160 MHz and throughput achieved for all mentioned block sizes was in the range of Gbps [15]. Adding two additional nodes near the camera and a node on the receiving end of the stream, proved to have a greater influence on the latency values than the encryption process itself. Based on the presented results with encryption, it is clear that using H264 encoding over TCP outperforms the other cases in terms of video latency.
## 3 Results and Discussion
### Compressive Strength  
Figure 8 shows the compressive strength of CNT-reinforced cementitious composites at 28 days. CRC01 exhibited the highest compressive strength in the graph, and the compressive strength decreased as the CNT content increased. CNTs were expected to increase the early age strength because they act as nucleating agents and accelerate early age hydration, but the strength was found to decrease as the CNT content increased. [PERSON] et al. [47] reported that the strength increased and then decreased as the CNT content increased. As a result, they suggested suppressing hydration, agglomerating CNT, and increasing air voids due to the excessive supply of CNTs. The porosity measurement results confirmed that entrained and entrapped air voids increased when the CNT content increased. [PERSON] et al. [48] also reported that compressive strength is highest when the addition of CNTs is 0.1% (binder wt.%) among 0.0-0.2% CNTs-reinforced cement composites. Figure 9 shows the agglomerated CNT in CRC05. When CNTs are agglomerated, as shown in the figure, they act as a vulnerable cross-section and decrease the strength. Therefore, it is important to disperse CNTs so that they cannot be agglomerated to improve their mechanical properties through the addition of CNTs.  
Figure 8: The compressive strength of CNT-reinforced cement composites.  
Figure 7: The heat flow and cumulative heat releases of CNT-reinforced cement composites: (**a**) Heat flow; (**b**) Cumulative heat release.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
###### Abstract  
The paper presents a multiaspect analysis of multivalues and the broadband nature of system oscillation. By analyzing the ambient signal caused by random small disturbances during the normal operation of interconnected power grids, many system operation characteristics can be obtained. The traditional signal processing method cannot extract the information from ambient signals effectively. Aiming at the problem of broadband oscillation mode superposition and the difficulty of extracting information from ambient signals, an iterative adaptive variational mode decomposition (IA-VMD) method is proposed based on frequency domain analysis and signal energy. Additionally, the IA-VMD method, combined with a bandpass filter and the Prony algorithm, is used to realize the modal identification of broadband oscillation and ambient signals. Simulation experiments show that the IA-VMD method has good adaptability, antinoise characteristics, and a certain significant engineering application value as well.  
modal identification; ambient signal; low-frequency oscillation; broadband oscillation; IA-VMD method +
Footnote †: journal: Applied sciences
## 4 Results
### Logistic Regression (LR)  
This section summarizes the findings relevant to the Logistic Regression (LR) algorithm, explaining the individual contributions and their influence on the overall outcomes of the federated learning architecture that was put into place.  
1. Global vs. Local Model Training: The remarkable global and local performance of the logistic regression model is shown in Figure 6, which also shows its usefulness in binary classification problems. The model shows remarkable accuracy rate of 93% with precision, recall, and F1 score values of 92%, 95%, and 93%, in that order, globally. This emphasizes its ability to classify events and effectively document accurate, positive outcomes. At the local level, both local models consistently achieve high levels of accuracy, precision, recall, and F1 score. Some measures show tiny gains. Specifically, Local LR 2 is notable for its increased accuracy of 94%, while it has a slightly lower precision of 91%. However, the global logistic regression model performs better than both local models when evaluating the combined results, confirming its superiority across all measures. By adopting an integrated perspective, the global model emerges as the most advantageous option, capitalizing on the advantages of local variations to obtain superior overall performance in binary classification tasks.
2. Confusion Matrix of LR: The confusion matrix displayed in Figure 7 provides a detailed assessment of the effectiveness of the logistic regression model. The model has impressive accuracy in its predictions, as seen by the significant number of occurrences accurately identified as either absence (13,339 instances) or presence (14,601 instances) of selective forwarding assaults, indicated by true labels 0 and 1, respectively. In addition, the model has a significantly low misclassification rate, with just 1252 examples improperly classified as not having selective forwarding attacks and 808 occurrences mistakenly labelled as having such attacks. The little difference between the predicted and real labels highlights the model's strong performance and ability to accurately distinguish between normal network behaviour and malicious selective forwarding actions. Strong sensitivity and specificity--qualities essential for preserving the integrity and security of IoT installations depending on the RPL protocol--are shown by the model's precision. Thus, our results provide a solid foundation for the use of FL-based detection systems, a potent technique of fortifying IoT networks against the dangerous threat of selective forwarding attacks.  
Figure 6: LR training model.