esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 3 IoHT Security Landscape
### Security Vulnerabilities in IoHT Implementations
#### 3.1.2 Communication Protocols  
IoHT devices come with fewer safety checks, and it is the firmware of these devices that have security vulnerabilities like hardcoded keys. The urgency to roll out IoHT cloud platforms and the limited user experience of new IoT applications may result in the development of protocols by I.T. companies having many potential security loopholes. Due to the diversity of IoT devices, it is not easy to develop a standard security protocol for heterogeneous devices and it leads to problems of how to discover and urgently address the security vulnerabilities among IoT devices.
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 4 Throughput Analysis of String and Grid Topology Considering Inter-Network Interference
### Analysis of Grid Network Topology  
In the grid topology, depending on a network position, the number of networks within the carrier-sensing range is different. Therefore, it is necessary to consider the carrier-sensing airtime and the simultaneous transmission time following the network number in the carrier-sensing range. If Network \(i\) is in the m \(\times\) n grid network topology, the network number could be two, three, and four. For the sake of easy presentation, this section investigates the case of m = n = 3. With the other values of m, n, we can similarly deduce each network state.
## Materials and Methods
### Volatile Composition  
For the HS-SPME GC-MS analysis of volatile organic compounds, 3 g of ground paprika P1-P8 was transferred to a 20 mL headspace vial, and 3 mL of NaCl saturated solution was added to enhance the volatile organic compounds in the headspace and to inhibit any enzymatic reactions. The method was developed according to [PERSON] et al., with improvement [1]. The headspace vials were sealed with crimp-top caps with TFE-silicone headspace septa (Thermo Fischer Scientific, Waltham, MA, USA). Each vial was incubated for 20 min at 60 \({}^{\circ}\)C. Afterward, the SPME fiber Divinylblenene/Carboxen/Polydimethylsiloxane (50 \(\upmu\)m DVB/30 \(\upmu\)m CAR/30 \(\upmu\)m PDMS) was exposed for 15 min (60 \({}^{\circ}\)C) at the headspace of the sample to perform the HS-SPME extraction of volatile organic compounds. Furthermore, the extracted volatile organic compounds were desorbed for 7 min from the fiber coating into the Thermo Fischer Scientific Trace 1310 GC gas chromatograph injection port set at 250 \({}^{\circ}\)C. The volatile organic compounds were separated using a DB-WAX capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness, J&W Scientific Inc. (Folsom, CA, USA)). Ultrahigh purity helium was used as a carrier gas at a linear velocity of 1 mL/min. The oven temperature program was as follows: initial temperature of 35 \({}^{\circ}\)C, heated to 180 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), increased to 230 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) and then held at a plateau for 7 min. Mass spectra were recorded in electron impact (EI) ionization mode at 70 eV using a TSQ 9000 MS, Thermo Fischer Scientific mass spectrometer. The quadrupole mass detector, ion source and transfer line temperatures were set at 150, 230 and 280 \({}^{\circ}\)C, respectively. Mass spectra were scanned in the range \(m/z\) 50-450 amu. VOCs were identified by comparing the mass spectra with the NIST 14 database system library and linear retention index. The criteria for compound identification required a mass spectrum matching score of \(\geq\)80%. The results were expressed as a percentage of the relative peak area (% RPA) of a peak in each paprika sample that was calculated by dividing the peak area by the total peak area of all identified peaks in each chromatogram. The total ion chromatogram (TIC) of each sample was used for peak area integration.  
All measurements were conducted in triplicate, and data are presented as the mean \(\pm\) standard deviation.
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 2 Problem Formulation  
The definition of the forward problem can be expressed as  
\[\mathbf{d}=F(\mathbf{m}) \tag{1}\]  
where \(F\) and \(\mathbf{d}\) refer to the physical forward model and the observed data, respectively. \(\mathbf{m}\) is a set of geoacoustic parameters standing for one ocean environment and seabed condition.  
The inversion problem aims at inferring the set of geoacoustic model parameters generating the observed data and can be expressed as:  
\[\mathbf{m}=F^{-1}(\mathbf{d}) \tag{2}\]  
where \(F^{-1}\) refers to the inversion operation.  
The ocean environment and seabed can be parameterized as an N-layered structure with four geoacoustic parameters (layer thickness, density, compression wave velocity, and shear wave velocity): \(\mathbf{m}=[h_{i},\rho_{i},V\rho_{i},V_{\mathrm{s}i}]_{i=0}^{N}\).  
A general workflow of inversion is illustrated in Figure 1. The terminologies are introduced as follows:  
* The environment consists of a physical forward model for calculating the replica, the observed data, and a misfit function for measuring the mismatch between the observed data and the replica. It receives a set of selected parameters from the agent and provides feedback to the agent.
* An agent is an operator that samples from the parameter space following its search strategy and interacts with the environment. During each iteration of inversion, the agent will log the feedback from the environment, the instant best solution, and the related information.
* A parameter space is a multi-dimensional space defined by the search bounds.  
The inversion is an iterative process. It starts with an initialization that defines a prior geoacoustic model and the original search bounds based on prior knowledge. During each iteration, the agent will sample from the parameter space. The environment will receive the selected parameters, correspondingly create a replica, and provide the misfit as feedback for the agent. The iteration stops once the termination criteria are met.  
Figure 1: The general workflow of geoacoustic inversion.  
As shown in Figure 1, the existing optimization methods (GA, DE, and ASSA) role as the search strategy for controlling the agent to explore the parameter space and determine the best solution at the end. Specifically, GA [6] and DE [7] are two heuristic search algorithms inspired by the evolution of natural species. ASSA [8] is a hybrid optimization method that combines the downhill simplex and the simulated annealing methods. More details about GA, DE, and ASSA can be found in [6; 7; 8], respectively.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### Performance Comparison
#### 5.4.1 Malicious Node Detection Accuracy  
Detection accuracy is defined as the percentage of adversarial nodes correctly identified and isolated by the system. As illustrated in Figure 5, the proposed framework consistently outperforms existing approaches across all evaluated scenarios. In low adversary densities, i.e., 5-15 malicious nodes, all methods show comparable accuracy, with our framework achieving up to 89%, which is within 3% of the best-performing baseline. However, as the proportion of malicious nodes increases, the advantage of our method becomes more prominent.  
Figure 4: The cost to compute FTS when moving from a small network (50 nodes) to a large network (250 nodes), reported in ms.  
In medium-scale attack scenarios, i.e., 20-30 malicious nodes, the proposed method achieves up to 95% detection accuracy, marking a +2.1% gain over [PERSON] et al. [33], +1.0% over [PERSON] et al. [40], and +2% over [PERSON] et al [39]. This improvement is due to the integration of ledger-anchored behavior metrics (transaction confirmation rate and reattachment frequency), which enhance the distinction between honest and dishonest activity beyond traditional network-level observations.  
Under large-scale attacks, i.e., 35-50 malicious nodes, our framework achieves detection rates of up to 98.2%, compared to 96.0% for [PERSON] et al. [33], and 95.2% for both [PERSON] et al. [40] and [PERSON] et al. [39]. This translates to a +2.2% absolute improvement over the best baseline ([PERSON] et al.) [33] and a +3.0-3.2% gain over the others. These gains highlight the robust scalability of our trust mechanism under increasing adversarial influence.
# High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector
## 4 Improved Detection Method
### Modulation of Beacon Light  
The direct detection method produces errors because when the electric signals pass through the amplifiers, direct current offsets are brought in. In order to solve this problem, we can adopt the modulation method.  
When we apply intensity modulation to the beacon light in the transmitter, we can get the following expressions [13]:  
\[E_{s}(t)=A[1+d(t)]\cos\omega t \tag{5}\]  
\[P_{s}=\overline{E_{s}^{2}(t)}=\frac{A^{2}}{2}\Big{[}1+2d(t)+d^{2}(t)\Big{]} \tag{6}\]  
Here, \(E_{s}\) represents the beacon light field, and \(P_{s}\) represents the average power of light field. \(A\) is the intensity of beacon light; \(d(t)\) is the modulating signal.  
After the photoelectric effect and passing through BPF, the photocurrent can be expressed as:  
\[I=\frac{e\eta A^{2}}{hv}d(t)=Kd(t)+n(t) \tag{7}\]
## 4 Experiments
### Comparison Results with Classical Virtual Staining Methods  
We compared our model with four other classical and recent virtual coloring methods to further validate the effectiveness and time efficiency of the network in grayscale image  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **FID\(\downarrow\)** & **LPIPS\(\downarrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** \\ \hline Ours w/o Loss & 67.8005 & 0.1866 & 0.9086 & 20.3208 \\ Ours w/o ECR & 57.3747 & 0.1372 & 0.9180 & 19.8297 \\ \hline Ours & 49.3686 & 0.1334 & 0.9241 & 20.4459 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative results for ablations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise** & **FID\(\downarrow\)** & **LPIPS\(\downarrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** \\ \hline \(6\times 10^{-4}\) & 56.4709 & 0.1327 & 0.9220 & 20.3231 \\ \(6\times 10^{-3}\) & 56.4854 & 0.1343 & 0.9214 & 20.3286 \\ \(6\times 10^{-2}\) & 56.5298 & 0.1378 & 0.9203 & 20.4288 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of our method under the different noise conditions.  
Figure 9: The qualitative comparison results between the two ablation experiments and the proposed method. (**a**) Input, (**b**) Ours w/o Loss, (**c**) Ours w/o ECR, (**d**) Ours, (**e**) Ground Truth.  
coloring. Reinhard [42] and Macenko [43] are classic methods for image color transfer, while CFPM [16] and CFFPM [44] are recently proposed methods for FPM virtual staining based on color transfer. The above method was applied to our dataset for training to achieve virtual coloring of FPM single-channel grayscale images. The experimental results show that the proposed method has advantages in terms of color and time.  
We randomly selected three images for display in Figure 10, and through visual analysis, it can be seen that the method proposed in this article has achieved the best results in terms of color and content. In our data, the [PERSON] and Macenko methods performed poorly, with a wide difference in color styles and an inability to accurately stain cells at the appropriate locations. CFPM and CFFPM had relatively good results in distinguishing and coloring areas with different shades of color, but the overall color was relatively light and did not meet the expected goals. Moreover, CFFPM introduced significant artifacts. In conclusion, the deep learning method proposed in this paper had the best experimental results.  
Specific evaluation indicators are shown in Table 5. We choose the number of iterations and the inference time as indicators to measure the time efficiency. Our proposed approach has the least time cost and the best performance. CFFPM requires iteration and takes the longest time. The Reinhard method is also fast in time, but ineffective.
# Spectrum Based Power Management for Congested IoT Networks
## 2 Related Work  
As the standard for IoT is currently in the development phase, power-efficient communication techniques are in demand to facilitate long-lasting battery life on IoT devices. At least ten years battery life is expected in order to minimize the overall maintenance cost [9; 10]. So, to extend the battery life of IoT devices it is important to decrease the packets delivery time delay. Lower latency improves power wastage caused by retransmission and  
Figure 2: Proposed solution.  
wait time [4; 11]. Furthermore, solving problems like retransmission and delay without having good spectrum management in congested IoT environment is almost impossible. Similarly, a billion varieties of IoT packets are being sent to the routing system, the default coordinator process method cannot identify the best channel while communicating [12; 13]. Wireless sensors in IoT have limited resources in terms of memory and data processing capability. That is why most of the methods proposed to implement spectrum management at IoT device level are not successful. It is proved by multiple studies [3; 9; 14; 15; 16; 17; 18; 19] that use of multichannel not only improve the performance of the network but also lower power consumption of the overall network by significant amount.  
Currently, there are limited power management methods from spectrum perspective. As a result, IoT data is being lost in all the chaos--unable to pass up important data [13; 20; 21; 22]. Our proposed solution to minimize use of power by implementing effective spectrum management method at FREDEVPLA level is a good solution in solving all above mentioned problems for congested IoT environment.  
In order to have an estimation of the device lifetime for a given battery capacity, network configuration, and transmission frequency, we need to understand the power consumption of an IoT device during (1) initial turn-on, (2) connected state, (3) re-transmission and (4) sleep/listen state. To the best of our knowledge, literature research is more focused on optimizing energy efficiency through different scheduling schemes, which is not on solving the cause [4; 5; 10; 11; 12; 23; 24]. Instead, this work is based on empirical evaluation of real deployments, which shed light on the relation between power consumption and spectrum management in congested IoT environment.  
The physical test-bed of our testing sets our paper apart from other resource management simulations. We pieced together IoT weaknesses and developed a way to choose a channel with lower interference which resulted in lower power usage within the IoT sensor level. It is important to mention that this work mainly focus on minimizing power utilization of remote IoT sensors not there coordinators (CN). So, power consumed for energy scan purpose is not under consideration in our result.
## 4 Experimental Results and Discussion
It can be seen from the experimental results that the maximum phase sensitivity of the sensor cold be increased nearly nine times when the initial phase of the sensor was modulated. As shown in Figure 8, it can be seen that the maximum phase sensitivity of the sensor did not correspond to \(V_{\rm x}=\pm 0.785\) V, and the minimum value did not correspond either to \(V_{\rm x}=0\) V or to \(V_{\rm x}=\pm 1.57\) V. The main reason is that the insertion loss in the 2 \(\times\) 2 coupler and the power-coupling coefficient was not 0.5, which led to a left deviation of \(3\pi/25\) on the experimental fitting curve in comparison with the ideal situation. \begin{table}
\begin{tabular}{c c} \hline \hline \(V_{\rm x}\) **(V)** & **Amplitude of Fundamental Voltage** \\ \hline
0 & 0.00660 \\ \(\pm 0.1\) & 0.01910 \\ \(\pm 0.2\) & 0.02873 \\ \(\pm 0.3\) & 0.04117 \\ \(\pm 0.4\) & 0.04509 \\ \(\pm 0.5\) & 0.05245 \\ \(\pm 0.6\)  
In addition, there were two obvious singular values which can be observed in Figure 8. One of them, corresponding to \(V_{\rm x}=\pm 1.57\) V, is normal because the sum of the bias phase and the initial phase was a litter greater than \(2\pi\) and therefore out of the range of the fitting curve. However, the value of \(V_{\rm x}=\pm 0.785\) V was abnormal. In order to further investigate this problem, we reduced the intensity of AE signals and performed similar experiments with \(\Delta\phi\in[\pi,2\pi]\), obtaining a similar value. Thus, this phenomenon may be related to the inherent characteristics of the Y-branch waveguide and needs to be further studied in the future.
## 4 Results and Discussion
### Comparison with the Existing Approaches by Varying Map Sizes and Obstacle Counts  
This scenario is defined with \(W\) at sizes ranging between 100 m \(\times\) 100 m \(\times\) 300 m-1000 m \(\times\) 1000 m \(\times\) 400 m. It encompassed 50 maps with distinct obstacle counts (e.g., 5-50). For the sake of simplicity and rational comparisons, we categorized all maps into 10 distinct groups considering both map size and obstacle strengths, as given in Table 1. The locations for \(s\) and \(t\) were marked in alternate places for every test/map. Furthermore, the obstacle density in \(W\) varied on each map. The ApVL algorithm [60] processed only obstacles that were on the \(\overline{l_{o}}\) and generated a dense graph to find \(P\). However, the ApVL algorithm has higher complexity, and it produced a non-taut \(P\) in most scenarios due to the connected obstacles in urban environments, as shown in Figure 8b. The RRT*-AB algorithm restricts the space, but it explores many locations while finding \(P\). The \(P\) generated by this algorithm had a longer length, and computing time was immense. In contrast, the proposed  
Figure 8: Example of the paths produced by the proposed method, ApVL, and RRT*-AB algorithms. PP method processed fewer obstacles and employed an ESWG to find a \(P\) with good quality. The complete information about maps utilized in this particular scenario and the mean computing time for task modeling by our PP method and its comparisons with the two previous algorithms are given in Table 1.  
The computing time for modeling the UAV environment shown in Table 1 is the total time needed to constrain the space, an ESWG construction, and an ESWG's expansion for path-quality improvements. From Table 1, we can see that time surged with an increase in the map size and obstacle counts. Through extensive comparisons with prior PP algorithms, our method lowered computing time for task modeling by 15.05% on average. The pathfinding results (i.e., time needed and path length) and their comparisons with the two existing algorithms are depicted in Figure 9.  
Both computing time and path length are the mean of five maps in each map's group (given in Table 1) with arbitrary obstacles' placement. The simulation results emphasize that, for each method, there is a surge in the computation time with the increase in \(W\)'s complexity. Moreover, the proposed PP method shows 15.4% and 33.6% curtailment in mean computing time compared to the ApVL algorithm and the RRT*-AB algorithm, respectively. In path lengths, the proposed method shows 5.34% improvements compared to the ApVL algorithm. Moreover, average improvements in the path lengths compared to the RRT*-AB algorithm are 6.34%. These results highlight that the proposed PP method is superior in terms of both computing time and path length over prior algorithms. Figure 9: (**Left**) computing time: proposed method versus existing algorithms; (**Right**) path lengths: proposed method versus existing algorithms.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 2 Related Work  
### IoT Anomaly Detection  
Anomaly detection in IoT environments is crucial for identifying irregular patterns that may indicate security breaches, system malfunctions, or unsafe conditions. Recent studies have explored various machine learning approaches to enhance anomaly detection in smart homes. For instance, a 2023 study proposed a machine learning-based anomaly detection approach using classifiers such as Random Forest, decision tree, and AdaBoost, tested on the UNSW BoT IoT dataset [5]. Another 2023 research study focused on improving unsupervised anomaly detection within smart home environments through advanced learning methods [6].  
Despite advancements, traditional cloud-based anomaly detection solutions face latency, privacy, and bandwidth challenges. Transmitting data to centralized servers can introduce delays, pose privacy risks, and strain network resources. These limitations underscore the need for decentralized approaches that process data locally, reducing dependence on cloud infrastructures.  
Recent developments in deep learning-based anomaly detection have introduced models such as Variational Autoencoders (VAEs), transformer-based architectures, and hybrid CNN-LSTM approaches that outperform traditional machine learning techniques in time-series analysis [7]. These models leverage sequential dependencies in IoT sensor data to improve anomaly detection accuracy, making them well suited for smart home applications.  
However, most existing works focus either on centralized deep learning or single-model edge approaches, lacking integrated solutions that combine statistical and deep learning techniques with full edge compatibility. Our work fills this gap by proposing a hybrid LSTM-AE and Isolation Forest framework designed for embedded inference. This fusion allows us to benefit from the strengths of both models while mitigating their individual weaknesses.  
Unlike prior works, we focus on implementing quantized, power-efficient versions of these models to support real-time inference in constrained smart home environments.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 7 Conclusions
We have incorporated a utility-based model to capture such demand, but it is a multifactorial phenomenon which may not be fully captured by any single model, including the one we have presented here. In addition, the dynamics of such demand may change in the presence of a competitor company offering similar services. This can be incorporated into the model. Computational capacity is another limitation of this research project. Conceptualization, S.K. and A.B. ; methodology, S.K. and M.Z.-S.; software, A.B. and S.K. ; validation, S.K., A.B. and M.Z.-S.; formal analysis, S.K., A.B. and M.Z.-S.; investigation, S.K. and A.B. ; resources, S.K. and M.Z.-S.; data curation, S.K., A.B. and M.Z.-S.; writing--original draft preparation, A.B. and S.K. ; writing--review and editing, S.K., A.B. and M.Z.-S.; visualization, S.K., A.B. and M.Z.-S.; supervision, S.K., A.B. and M.Z.-S.; project administration, S.K. ; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. This research was funded by [PERSON]. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflict of interest.