esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussions
### Access Point 5  
The data traffic and energy consumption of Access Point 5 when using each of the evaluated routing protocols are as follows:  
* Shortest-path routing method: Similar to the case of Access Point 3, Access Point 5 only participates in the longest (energy-aware) path. Consequently, the number of bytes transmitted and received as well as the transmission and receiving energies are close to zero, as shown in Figures 7e and 8e as well as Figures 9e and 10e, respectively.
* Proposed SDN-based energy-aware routing method: In the energy-aware routing path, Access Point 5 receives bytes from Access Point 3. The received bytes from Access Point 3 are shown in Figure 8e, whereas the consumed receiving energy is shown in Figure 10e. Subsequently, Access Point 5 transmits the bytes to Access Point 4, as shown in Figure 7e, consuming the transmission energy shown in Figure 9e. As in the case of Access Point 3, Access Point 5 is active from 60 to 160 s and 240 to 400 s. During these periods of time, the transmission and receiving energies increase, whereas during the first 60 s and from 160 to 240 s, the SDN controller selects Access Point 2 to transfer the traffic, and the transmission and receiving energies decrease.  
As discussed above, based on the results shown in Figures 7-10, the traditionally used shortest-path routing method exhausts Access Point 2. Consequently, the battery of Access Point 2 drains, and the transmission, which only uses Access Point 2 to reach Access Point 4 from Access Point 1, is abruptly interrupted at 200 s. The proposed SDN-based energy-aware routing approach outperforms this baseline routing method by enabling the SDN controller to shift the routing path from the default one (shortest path) to the energy-aware path based on the estimation of the energy level of Access Point 2. In particular, Access Point 2, which is energetically critical, is only used when its energy level is high. When its energy level is low, it is skipped, and Access Point 1 is connected to Access Point 4 through Access Point 3 and Access Point 5. In this way, the battery of Access Point 2 is preserved, which prevents its failure, as this would abruptly interrupt the transmission as well as the need for replacing it. Moreover, since the energy consumption of all access points in the network is balanced by the SDN controller's decisions, the transmission lifetime is prolonged, and its duration doubles the one corresponding to the shortest-path routing method.
## 3 Security and Privacy-Preserving Solutions for IoE
### Edge AI and Secure Computing in IoE
#### 3.2.3 Federated Learning-Based Edge AI Solutions  
FL allows edge devices such as IoE sensors, mobile devices, and medical equipment to perform local model training on their own data [23; 25; 65]. This method enables a collaborative learning process by sharing only model updates rather than raw data, thus eliminating the need to send personal data to central servers. As a result, data privacy is preserved, network traffic is reduced, and security risks are minimized [16; 104].  
FL is particularly preferred in domains where privacy is critical, such as healthcare, finance, and industrial IoT [23; 25; 65; 109]. Each device updates its own model locally with its private data and sends the model updates to the central server. However, raw data is never shared. This ensures compliance with data protection regulations such as the General Data Protection Regulation (GDPR) and supports a secure learning process [46; 56; 104; 109].  
As illustrated in Figure 7, edge devices train their local models and send only the model updates to the FL server. This central server then provides a global model to the IoE network, ensuring that an up-to-date and secure AI model is deployed system-wide.  
In the FL architecture, each IoE device performs local model training on its own data [25; 65]. During this process, the data never leaves the device; only model parameters,  
Figure 7: Federated Learning-based IoE security architecture.  
such as weights or gradients, are sent to a centralized or distributed aggregator. This ensures data privacy while simultaneously enabling collective and collaborative learning at a global level [109]. Table 8 illustrates how FL differs from traditional AI models in terms of key criteria such as privacy, latency, and security risk [23; 109; 110].  
Providing high-accuracy learning without accessing centralized data is one of the fundamental pillars of this study. However, the success of FL should not be evaluated solely based on privacy; metrics such as communication overhead, convergence speed, and latency must also be considered. In this context, Table 9 presents a quantitative comparison of different FL approaches.  
Table 9 clearly demonstrates the performance differences between the traditional FedAvg method and more advanced FL techniques [109; 110]. For instance, the FedProx model achieves convergence in fewer rounds due to its resilience to network heterogeneity. Notably, the Quantum-assisted FL (QFL) model achieves higher accuracy with lower communication overhead and reduced latency. This comparison highlights that algorithmic choices in FL architectures directly impact both efficiency and energy consumption.
## 2 System Model  
The studied FSO system consists of \(M\) individual links, including one transmitter; a stationary, ergodic, and memoryless channel; and \(N\) optimally combined receivers (see Figure 1). The channel suffers from atmospheric turbulence channels, inducing additive white Gaussian noise (AWGN) of zero mean and variance \(N_{0}/2\). Thus, the \(n\)-th signal of any individual link, i.e., the signal received by the \(n\)-th photodetector, \(y_{n}\), is described as [8]:  
\[y_{n}=s_{n}x+\widetilde{n}=\eta xI_{n}+\widetilde{n},\ \ n=1,\ldots,N \tag{1}\]  
where \(s_{n}=\eta_{n}I_{n}\) is the instantaneous intensity gain depending on the receiver's effective photo-current conversion ratio, \(\eta_{n}\), and the instantaneous normalized irradiance, \(I_{n}\); \(x\) stands for the modulated, optical signal, receiving either \"0\" or \"1\" values; and \(\widetilde{n}\) represents the atmospheric channel's AWGN. In particular, the normalized signal's irradiance should be considered as a resultant of three components:  
\[I_{n}=I_{T,n}I_{P,n}I_{L,n} \tag{2}\]  
where \(I_{T,n}\), \(I_{P,n}\), and \(I_{L,n}\) refer to the \(n\)-th normalized signal's irradiances due to turbulence, pointing errors, and path loss effects, respectively. Without loss of generality, it is assumed that \(I_{n}=I_{T,n}I_{P,n}\), implying that the deterministic factor \(I_{L,n}=1\).
# Notes on a New Structure of Active Noise Control Systems
## 4 Simulation Case Studies
### Case Study 1
To compare the rate of convergence of the control algorithms in the two discussed structures, the time plots of the discrete-time signals \(e(i)\) and \(e_{0}(i)\) in the classical FxLMS active noise control system structure as well as the time plots of the discrete-time signals \(e(i)\), \(e_{0}(i)\), and \(\hat{e}_{0}(i)\) in the new FeFxLMS active noise control system structure are presented in Figure 9. These are the averaged curves obtained for 100 realizations of noise to be attenuated with the autoregressive parameter equal to 0.999. For the considered noise to be attenuated, the discrete-time error signal \(e_{0}(i)\) in the new FeFxLMS control system structure converged significantly faster than the signal \(e_{0}(i)\) in the classical FxLMS control system structure. The plots also show that application of the new FeFxLMS active noise control system structure also gave an improvement in the full frequency band of system operation during initial adaptation. The values of the resulting noise reduction obtained in the classical FxLMS and new FeFxLMS control system structures are reported in Table 1. It is worth emphasizing that with the new FeFxLMS control system structure, a greater noise reduction (approximately 10%) in error microphone (signal \(e_{0}(i)\)) was observed. No perfect equality of noise reduction obtained seen by the error microphone and adaptive control algorithm was obtained due to a non-minimum phase feature of the error signal measurement path and the nonlinear feedback introduced by the adaptation algorithm [11,12]. Figure 8: Estimated power spectral densities of the discrete-time signals \(d(i)\) and \(e_{0}(i)\) at the error microphone—the classical FxLMS and new FeFxLMS control system structure. Figure 9: Estimated mean square value of the discrete-time signals \(d(i)\), \(e(i)\), \(e_{0}(i)\), and \(\hat{e}_{0}(i)\) (\(e_{est}(i)\))—the classical FxLMS (**a**) and new FeFxLMS (**b**) control system structure.
# An Intelligent Measurement Method and System for Vehicle Passing Angles
## 1 Introduction and Background
During laser data acquisition, there is a large number of laser points on the flat ground. Moreover, the feature points that effectively represent the structure under a vehicle are sparse. These situations result in a much smaller proportion of vehicle chassis and tire point clouds in each frame of the laser data compared to ground point clouds, causing a disproportionate contribution of ground points in the alignment process. Additionally, the point cloud of the flat plane is more susceptible to the noise and vibration from the acquisition equipment, leading to odometry drift [26]. Furthermore, the ground points collected by the LIDAR near the flat ground present multiple circular shapes, and the alignment of circular-shaped point clouds is more likely to result in local optimal solutions, leading to alignment errors and mistakes. Consequently, it becomes challenging to obtain a reliable overall point cloud of the vehicle chassis and tires and even more challenging to obtain passing angle parameters with good data consistency based on that point cloud. This paper proposes a method and designs a system that can be readily deployed to measure vehicle passing angles objectively, efficiently and robustly. Our system utilizes a wheeled robot equipped with a LIDAR and a camera to pass underneath the vehicle, scan and building the point cloud and calculate the passing angles automatically. In our method, we propose a novel point cloud-building approach to address the challenges of building point clouds from underneath a vehicle and a point cloud-based parameter measurement approach to select target points and measure passing angles without the use of manual operations. This article is organized as follows: Section 2 describes the system, Section 3 details the method, Section 4 presents the experimental results, Section 5 provides a discussion of the proposed system and Section 6 concludes this article.
## 4 Discussion
### Impact of LAI/FPAR on MODIS GPP  
In addition to improving the quality of the meteorological inputs, we also introduced an improved LAI dataset (GLASS LAI) to evaluate the impacts of LAI/FPAR on the GPP estimates (_i.e._, NCEP_GLASS GPP). The results show that after replacing MODIS FPAR with GLASS FPAR, substantial improvements were achieved in estimating the eight-day GPP values (Figure 2a,c). The effectiveness in estimating GPP during the summer was also improved (Figure 5a,c). For Cropland and Grassland, the GPP estimates became more accurate after FPAR was improved (Table 4). Based on the comparisons, it was found that the MODIS LAI/FPAR were generally less than the GLASS LAI/FPAR (Table 5). Indeed, the MOD17A2 GPP algorithm generally underestimated the tower-based GPP (Table 3). Therefore, the improved LAI/FPAR compensated for the underestimation caused by MODIS LAI/FPAR to some extent, which resulted in a higher effectiveness of the GPP estimation.  
In this paper, we further estimated GPP by improving the quality of the meteorological inputs and the LAI/FPAR data jointly (_i.e._, Tower_GLASS GPP). We simply replaced the NCEP-DOE Reanalysis II meteorology data with the site meteorology data and replaced MODIS FPAR with GLASS FPAR.  
The results demonstrate that the replacements resulted in the greatest improvements in estimating the eight-day GPP values (Figure 2a,d). However, this type of replacement strategy was not very effective in estimating the seasonal and annual GPP (Table 3). This implies that the GPP estimation accuracy can be greatly improved by simultaneously enhancing the quality of the multiple inputs of the MODIS GPP algorithm. However, it does not mean that improvements can be achieved simply by substituting the input parameters because the overall performance gains are by no means the sum of the individual gains. In the future, we will thoroughly study how to use the synergies among the individual gains to increase GPP estimation accuracy.
## 3 System Models and Problem Formulation  
In this section, we will provide the related system models and formulate the problem for jointly optimizing client clustering, bandwidth allocation, and workload optimization in semi-synchronous wireless FL. Denote the set of clients in the system as \(\mathbf{\mathcal{I}}\); \(i\) is used to index these clients. All the clients in \(\mathbf{\mathcal{I}}\) will be clustered into different tiers and will upload their local models to the FL server via a base station (BS). Denote the set of tiers as \(\mathbf{\mathcal{J}}\); \(j\) is used to index these tiers. In our scenario, we consider frequency division multiple access (FDMA) as the wireless resource-sharing mechanism among various tiers. This implies that each tier will be allotted a distinct frequency band that does not overlap with other tiers, and the bandwidth allocated to each tier will be optimized. Let \(b_{j}\) be the bandwidth allocated to tier \(j\). In addition, the clients within the same tier would share the same frequency band based on TDMA, meaning that the clients would take turns acquiring the entire frequency band to upload their models. As mentioned in Section II, TDMA may introduce extra waiting time for a client to wait for wireless bandwidth to be available. Therefore, if client \(i\) is in tier \(j\), then the latency of client \(i\) in training and uploading its local model to the FL server is  
\[t_{ij}=t_{i}^{comp}+t_{ij}^{wait}+t_{ij}^{upload}, \tag{1}\]  
where \(t_{i}^{comp}\) is the computing latency of client \(i\) in training its local model, \(t_{ij}^{wait}\) is the delay of client \(i\) in tier \(j\) to wait for the frequency band to be available, and \(t_{ij}^{upload}\) is the uploading latency of client \(i\) in tier \(j\) to upload its local model.
## 4 Materials and Methods
### In Vitro TPS Assay  
For heterologous expression of _SrTPSs_, pDEST15, and pDEST17 vectors containing _SrTPS_ genes were transformed into _E. coli_ C41(DE3) and grown on LB agar plates containing 100 mg/L of ampicillin. Zero-point four millimolar isopropyl-\(\beta\)-D-thiogalactoside (IPTG) was added to induce the expression of fusion proteins in bacterial cells at 25 \({}^{\circ}\)C for 6 h. His-tagged SrTPS2 and SrTPS3 were purified using Ni-NTA Sepharose resin (Qiagen, Hilden, Germany), whereas GST-tagged SrTPS1, SrTPS4, and SrTPS5 were purified by using glutathione-gaarose resin (Sigma-Aldrich, St. Louis, MO, USA) according to the manufacturer's recommendations. The purified proteins were immediately used for in vitro TPS assay. An in vitro enzyme assay for TPS activity was performed in a 500 \(\upmu\)L reaction volume containing 250 \(\upmu\)L of 2 \(\times\) reaction buffer specific for monoterpene (50 mM HEPES, pH 7.4, 200 mM KCl, 20 mM MnCl\({}_{2}\), 20% (\(\psi\)/\(\psi\)) glycerol, 5 mM dithiothreitol) or sesquiterpene (50 mM HEPES, pH 7.4, 200 mM KCl, 20 mM MgCl\({}_{2}\), 20% (\(\psi\)/\(\psi\)) glycerol, 2 mM dithiothreitol) biosynthesis with about 20 \(\upmu\)g of recombinant protein and 10 \(\upmu\)g of GPP or FPP (Echelon Biosciences, Salt Lake City, UT, USA). The reaction mixtures were mixed gently and carefully overlaid with 500 \(\upmu\)L of hexane (Sigma-Aldrich, St. Louis, MO, USA) to trap volatile products and incubated at 30 \({}^{\circ}\)C for 2 h. As negative controls, the heat-inactivated recombinant proteins were tested. After centrifugation at 1200\(\times\)\(g\) at 4 \({}^{\circ}\)C for 30 min, the hexane upper layer was concentrated to 50 \(\upmu\)L using nitrogen gas evaporator and analyzed by GC-MS.
# Absolute Distance Measurement Based on Self-Mixing Interferometry Using Compressed Sensing
###### Abstract  
An absolute distance measurement sensor based on self-mixing interferometry (SMI) is suitable for application in aerospace due to its small size and light weight. However, an SMI signal with a high sampling rate places a burden on sampling devices and other onboard sources. SMI distance measurement using compressed sensing (CS) is proposed in this work to relieve this burden. The SMI signal was sampled via a measurement matrix at a sampling rate lower than Nyquist's law and then recovered by the greedy pursuit algorithm. The recovery algorithm was improved to increase its robustness and iteration speed. On a distance measuring system with a measurement error of 60 \(\upmu\)m, the difference between raw data with 1800 points and CS recovered data with 300 points was within 0.15 \(\upmu\)m, demonstrating the feasibility of SMI distance measurement using CS.  
self-mixing interferometry; distance measurement; compressed sensing; recovery algorithm +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Absolute Distance Measurement Based on Self-Mixing Interferometry Using Compressed Sensing. _Appl. Sci._**2022**, 12, 8635. [[https://doi.org/10.3390/app1278635](https://doi.org/10.3390/app1278635)]([https://doi.org/10.3390/app1278635](https://doi.org/10.3390/app1278635))  
Academic Editor: [PERSON]  
Received: 24 July 2022  
Accepted: 26 August 2022  
Published: 29 August 2022  
**Published:** Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 6 Ablation Study
### Experiment Setup  
Our ablation study experiments with several variants of the model, each stage adding a particular component. For this, we used unseen fonts and unseen characters for handwritten Korean characters. Details of the ablation study are given below:  
1. Baseline (A): The original FUNIT implementation for few-shot font generation.
2. A + Patch Encoder (B): Added a patch encoder to the baseline.
3. B + Patch Attention (C): Introduced a patch-based attention mechanism on top of Variant B.
4. C + Triple Loss (D): Included a triplet margin loss to Variant C.
5. D + Style Fidelity Loss: Integrated a style fidelity loss into Variant D.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Acc (C)**\(\uparrow\) & **Acc (S)**\(\uparrow\) & **LPIPS**\(\downarrow\) & **SSIM**\(\uparrow\) & **FID**\(\downarrow\) \\ \hline Baseline (A) & 74.89 & 8.14 & 0.260 & 0.477 & 153 \\ A + Patch Encoder (B) & 76.35 & 30.15 & 0.253 & 0.512 & 134 \\ B + Patch Attention (C) & 78.18 & 42.43 & 0.211 & 0.523 & 117 \\ C + Triple Loss (D) & 96.91 & 53.72 & 0.182 & 0.578 & 98 \\ D + Style Fidelity Loss & 97.16 & 78.42 & 0.132 & 0.758 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative Results of the Ablation Study: This table presents the performance metrics of different model variants. The progression from the baseline (A) through subsequent enhancements illustrates the impact of each added component on model performance.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Training Time** & **Inference Time** & **GPU Memory** \\  & **(h)** & **(s/Image)** & **(GB)** \\ \hline FUNIT & **28** & **0.09** & **10.5** \\ DM-Font & 68 & 0.21 & 12.0 \\ MX-Font & 86 & 0.32 & 18.0 \\ FM-GAN & 144 & 0.60 & 21.0 \\ Patch-Font & 74 & 0.11 & 18.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational efficiency of font generation methods.
# An Intelligent TCP Congestion Control Method Based on Deep Q Network
## 4 Framework
### Problem Formulation  
The TCP congestion control process can be abstracted as a partially observable Markov decision process, which is defined as a quintuple [S, A, R, P, \(\gamma\)]. Where S is the set of all states. \(s_{t}\in\) S is the state observed at time \(t\), and the initial state is \(s_{0}\); A is the set of actions, and \(a_{t}\in\) A is the action taken at time \(t\); R is the reward function defined as  
\[\mathrm{R}(s_{t},a_{t})=\mathrm{E}(\mathrm{R}_{t+1}|s_{t},a_{t}) \tag{2}\]  
Figure 1: Q-learning vs. DQN.  
Figure 2: TCP–DQN framework.  
According to Equation (2), if the agent is in state \(st\) at time-step \(t\), and selects the action at, then the reward \(\text{R}_{t+1}\) is received from the environment at time-step \(t+1\); P is the transition probability matrix, defined as  
\[\text{P}^{a}_{ss^{\prime}}=\text{P}(s_{t+1}=s^{\prime}|s_{t}=s,a_{t}=a) \tag{3}\]  
Equation (3) represents the probability from the state \(s^{\prime}\) to the state \(s\) if the action \(a\) is chosen at time \(t\). \(\gamma\in[0,\,1]\) is the discounting factor, which reflects the decay rate of the future rewards. The discount factor reflects the design idea of the reinforcement learning algorithm, which means the reward that can be obtained immediately is preferred. Whereas the reward obtained in the future will be decayed by a certain proportion.  
The reinforcement learning algorithm starts from an initial state \(s_{0}\). According to the observed state \(s_{t}\), the action \(a_{t}\) is selected by a policy function \(\pi\left(a_{t}\,|\,s_{t}\right)\). According to the state transition matrix P (\(s_{t+1}\,|\,s_{t},a_{t}\)), the reinforcement learning algorithm gets the reward \(\text{R}_{t+1}\) from the environment. The goal of reinforcement learning is to optimize the strategy function to maximize the expected reward, which is defined as  
\[\begin{split}\text{G}_{t}=\text{R}_{t}+\gamma\text{R}_{t+1}+ \gamma^{2}\text{R}_{t+2}\ldots+\gamma^{T}\text{R}_{t+2}\\ =\sum_{k=t+1}^{T}\gamma^{k-t-1}\text{R}_{k}\end{split} \tag{4}\]  
In Equation (4), T is the time-step at which the episode terminates.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 2 System Architecture
### ESP32-CAM Sensors  
The ESP32-CAM is the primary sensing device, equipped with fire detection modules, such as temperature sensors and cameras [12]. The sensor nodes are deployed throughout an industrial environment and are configured to collect real-time environmental data. The ESP32-CAM, which supports image capture, is essential for identifying anomalies, such as the presence of fire [13]. Sensors are strategically placed, ensuring comprehensive coverage. These sensors are configured to autonomously and continuously monitor their surroundings for signs of fire. When an anomaly is detected, the sensor captures an image, processes the data locally, and transmits the information through the wireless network.