esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The present study proposes a comprehensive segmentation method for reconstructing the indoor interiors of a multistory building. The rooms and corridors in each story are segmented by overlapping segmented room-space that is created by a morphological erosion method with cells that are partitioned by extracted wall lines. The space across multiple floors is extracted by using the peak-nadir-peak strategy in a histogram that describes the distribution of points along the z-axis. The ceiling and floor planes are shown as two peaks, and the connected area is shown as a nadir in the histogram. The raw point cloud is considered to be the input source, and the watertight model is represented as the output indoor model. The remainder of this paper is organized as follows. Related works are described in Section 2. The proposed indoor-reconstruction method is described in Section 3. Experimental results for seven real-world datasets and eight synthetic datasets are presented in Section 4. The evaluation results are shown in Section 5, and the conclusions are presented in Section 6.
# Initial Structure Design and Optimization of an Automotive Remote Head-Up Display
## 4 Simulation Design and Results
### Design Example
The imaging position of the virtual image plane in the display of the optical path light map is shown in Figure 10 below. In the process of optical path structure design, the windshield is involved as a reflecting surface. Therefore, the surfaces to be designed and determined are folding mirror M\({}_{1}\), main mirror M\({}_{2}\), and the windshield. Among them, the spacing between each surface, the \(x\) and \(y\) axis rotation, and the \(y\) axis offset are the main parameters; the specific data are shown in Table 3 below. The optimized M\({}_{1}\) and M\({}_{2}\) profile data are shown in Tables 4 and 5. Finally, the vision distance of the virtual image display optical path of the AR-HUD system is 7.5 m, and the virtual vision field angle is 10.145\({}^{\circ}\)\(\times\) 5.072\({}^{\circ}\), which accords with the design goal.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Flavor and Fragrance
#### 3.1.2 Geraniol  
Geraniol is an acyclic monoterpene alcohol with a rose-like odor and a sweet, floral, rose-like taste. It is an important raw material in the commercial fragrance industry. It is reported that various microorganisms can convert geraniol into high-value terpenoid products such as (R)-citronellol, (S)-citronellol, nerol, and neral [109]. Besides its unique scent, it also possesses biological activities like anti-microbial, anti-cancer, anti-oxidant, and anti-inflammatory. Therefore, geraniol is widely used in fields such as food, cosmetics, and pharmaceuticals, exhibiting great commercial value.  
Nowadays, researchers have identified that geraniol synthase (GES) originated from various plants and assembled a geraniol de novo synthetic pathway in microorganisms, such as _E. coli_[26], _S. cerevisiae_[110], _Y. lipolytica_[56], and _Candida glycerinogenes_[111]. However, the toxicity of geraniol to cells limits the production of microbial cell factories. Conversion of geraniol to its acetate form is an effective method to reduce its toxicity. [PERSON] and co-workers combined metabolic engineering, enzyme engineering, ribosomal binding site, and abiotic engineering in _E. coli_ to produce geranyl acetate by using glycerol as feeding stock. To circumvent potential issues of insufficient solubility or low activity of GES, they utilized nucleoside diphosphate-x (NUDIX) instead of GES to produce geraniol. After regulating the MVA pathway flux, they fusion-expressed the efficient GPPS and NUDIX for geraniol synthesis. Then, they knocked out alcohol dehydrogenases andadopted microaerobic cultivation to avoid the conversion of geraniol to geraniol to geraniol. Finally, by introducing the alcohol acyltransferase _Rlt_AAT1, the strain transformed geraniol into the low-toxicity geranyl acetate. The final yield of geranyl acetate reached the highest reported level of 19 g/L [90].
## 2 Background and Methods
### Bayesian Search  
Bayesian search (also known as Bayesian optimization) aims to approximate unknown functions with surrogate models such as Gaussian processes. The main difference between Bayesian optimization and other search methods is that it incorporates prior beliefs about the underlying function and updates it with new observations. Bayesian optimization attempts to collect the most informative observations at each iteration by balancing exploration and exploiting. In addition, Bayesian optimization is a sequential design strategy for the global optimization of black-box functions that do not take any functional form. It is often used to optimize expensive evaluation functions. Since the objective function is unknown, the Bayesian strategy is to treat it as a random function and place a prior on it. There are three remarks about Bayesian search: (1) the prior captures beliefs about the behavior of a function; (2) after collecting the function evaluations processed as data, the prior is updated to form the posterior distribution of the objective function; and (3) the posterior distribution is used to construct the acquisition function that determines the next query point. Note that Bayesian optimization is built on tree-structured Parzen estimator models that take into account the generative process [33].
## 3 Results
### AOFAS Score  
The AOFAS score (American Orthopedic Foot and Ankle Society Score) analyzes pain, function (limitations in daily-life activities and need for aids); maximum distance it can travel, in hundreds of meters; walkable areas; gait abnormalities; sagittal movement (flexion + extension); reafoot movement (inversion + eversion); ankle and reafoot stability (anteroposterior, varus, and valgus); and alignment [21]. These items were assessed in 16 studies. The range of the mean preoperative AOFAS score was 53 to 78, and the range of the mean postoperative AOFAS score was 85 to 100 among all the studies reviewed. The AOFAS score significantly improved following surgery in every study that was examined. Between groups, we can assess that:  
- Group B patients improved their AOFAS score from 51.5 to 94.3 (129 patients).  
- Group A patients improved their AOFAS score from 69 to 94 (118 patients).  
- Group A+ patients improved their AOFAS score from 70 to 88 (34 patients).  
- Group C patients improved their AOFAS score from 67 to 89 (41 patients).  
- Group D patients improved their AOFAS score from 73 to 100 (17 patients).  
The group analyzed in Saragas shows only a post-operative AOFAS score of 85 without analyzing the pre-operative one [35].
# An Improved Lightweight Model for Defect Detection on Paths in Images
## 3 Results and Analysis
### Feature Fusion Network Lightweight Improvement Experiment  
To assess the effectiveness of the lightweight design in the improved feature fusion network, independent experiments were performed and compared against the original baseline. Additionally, the lightweighting effect was compared with the BiFPN [29] module, which demonstrates excellent model performance. The BiFPN module improves model performance by leveraging bidirectional feature fusion and weighted fusion mechanisms, simplifying the network structure to achieve lightweight effects. Table 5 presents the detailed experimental results.  
As illustrated in Table 5, compared to the original YOLOv8s algorithm model, the model that introduced BiFPN not only showed a decrease in accuracy, but also had a very limited optimization in terms of parameter count and model size. However, the model that introduced CCFM, although also experiencing a slight decrease in [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95, was able to significantly reduce the parameter count and model size, with reductions of approximately 3.8 M and 7.7 m, respectively.  
The results demonstrate that the proposed methods successfully simplify the network structure, achieving the goal of lightweighting the feature fusion network. It enables the model to deliver excellent performance and accuracy when handling complex tasks, enhances YOLOv8s' detection capabilities for scale variations and small targets, significantly reduces the parameter count, and substantially decreases the size of the trained model.
# Hybrid Mode Reconfigurable Antenna with V-Shaped Extrudes for Cognitive Radio Applications
## 4 Conclusions
**Data Availability Statement:** All data are available in this paper. **Acknowledgments:** [PERSON] and [PERSON] would like to thank the Department of Electronics and Communication Engineering, Manipal University Jaipur, Jaipur, India, for providing the simulation facilities at the Electronics and Simulation Lab. **Conflicts of Interest:** All the authors declare that there are no conflicts of interest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Substrate** & **Area (mm\({}^{2}\))** & **No. of Frequency Band** & **Operating Frequency (GHz)** & **Bandwidth (GHz)** & **Hybrid Re-configuration** \\ \hline
[24] & FR4 & 2400 & 3 & 2.4/3.5/5.8 & 2.37–2.51/3.28–3.54/4.96–6.43 & No \\
[26] & PET & 1829 & 2 & 2.36/3.64 & 2.27–2.45/3.5–3.77 & No \\
[27] & Polyamide & 456 & 3 & 2.4/3.8/5.6 & 2.33–2.47/3.68–3.92/5.38–5.75 & No \\
[30] & RO4350B & 675 & 3 & 2.3/4.5/5.8 & 2.29–2.39/4.29–4.57/5.64–5.96 & No \\
[31] & FR4 & Not given & 2 & 1.97/2.53 & 1.97–2.53 & No \\
[32] & Roger RO4350B & 3664 & 2 & 2.4/5.4 & 2.21–2.79/5.27–5.56 & Yes \\
[33] & FR4 & 2500 & 4 & 4.5/4.8/5.2/5.8 & Not given & Yes \\ Proposed & FR4 & 1152 & 5 & 1.5/2.6/3.2/5.1/8.1 & 1.4–1.5/2.7–5.9/7.8–8.3 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis for proposed antenna.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
###### Abstract  
In Public Safety Networks (PSNs), the conservation of on-scene device energy is critical to ensure long term connectivity to first responders. Due to the limited transmit power, this connectivity can be ensured by enabling continuous cooperation among on-scene devices through multipath routing. In this paper, we present a Reinforcement Learning (RL) and Unmanned Aerial Vehicle-(UAV) aided multipath routing scheme for PSNs. The aim is to increase network lifetime by improving the Energy Efficiency (EE) of the PSN. First, network configurations are generated by using different clustering schemes. The RL is then applied to configure the routing topology that considers both the immediate energy cost and the total distance cost of the transmission path. The performance of these schemes are analyzed in terms of throughput, energy consumption, number of dead nodes, delay, packet delivery ratio, number of cluster head changes, number of control packets, and EE. The results showed an improvement of approximately 42% in EE of the clustering scheme when compared with non-clustering schemes. Furthermore, the impact of UAV trajectory and the number of UAVs are jointly analyzed by considering various trajectory scenarios around the disaster area. The EE can be further improved by 27% using Two UAVs on Opposite Axis of the building and moving in the Opposite directions (TUOAO) when compared to a single UAV scheme. The result showed that although the number of control packets in both the single and two UAV scenarios are comparable, the total number of CH changes are significantly different.  
clustering; d2d communication; disasters; energy conservation; network lifetime; public safety networks; reinforcement learning +
Footnote †: journal: _Sensors_  
1
Footnote 1: email: [EMAIL_ADDRESS]
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 3 Preliminary and Problem Definition
### Problem Definition  
This study focuses on addressing the few-shot fault-diagnosis problem in the federated scenario, and it is primarily based on the following two assumptions:  
1. Sufficient labeled data can be gathered from various source domains, whereas the target domain possesses only a limited amount of labeled data.
2. Ensuring data privacy across different domains is essential, and it is imperative to prevent any interaction between local data of different domains.  
The task is characterized as a few-shot learning fault-diagnosis problem in the target domain, including \(C^{T}\)-way, \(K\)-shot, and \(M\)-test. This involves a labeled support set, denoted as \(S^{T}\), and an unlabeled query set, denoted as \(Q^{T}\). In \(S^{T}\), each class consists of \(K\) samples, while each class in \(Q^{T}\) comprises \(M\) samples. The samples from the query set and support set are, respectively, represented as \((X^{T}_{S_{x}},y^{T}_{S_{x}})_{a=1}^{N^{T}_{S}}\) and \((X^{T}_{Q_{x}})_{a=1}^{N^{T}_{Q}}\). Here, \(N^{T}_{S}\) equals \(C_{T}\times K\), \(N^{T}_{Q}\) equals \(C_{T}\times M\), and \(K\) is a small value. In the context of fault diagnosis, \(x\) represents the vibration acceleration signal obtained from specific mechanical equipment, with a length of \(L\), while \(y\) represents the corresponding fault type. In each fully labeled source domain dataset, all included samples are denoted as \((X^{S}_{x},y^{S}_{a})_{a=1}^{N^{S}}\), where \(N^{S}\) is a large value. The ultimate objective is to train a model utilizing tasks from various source domains, enabling identification of the fault types for samples in the target domain's query set \(Q^{T}\) based on the support set \(S^{T}\), which consists of only a small number of samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Layer** & **Size/Stride** & **Channal** & **Padding** \\ \hline
1 & Conv1 \& Pool1 & \(64\times 1/16\times 1\) & 16 & yes \\
2 & Conv2 \& Pool2 & \(3\times 1/1\times 1\) & 32 & yes \\
3 & Conv3 \& Pool3 & \(3\times 1/1\times 1\) & 64 & yes \\
4 & Conv4 \& Pool4 & \(3\times 1/1\times 1\) & 64 & yes \\
5 & Conv5 \& Pool5 & \(3\times 1/1\times 1\) & 64 & yes \\
6 & RConv1 \& UpSample1 & \(64\times 3/1\times 1\) & 64 & yes \\
7 & RConv2 \& UpSample2 & \(64\times 3/1\times 1\) & 64 & yes \\
8 & RConv3 \& UpSample3 & \(32\times 3/1\times 1\) & 64 & yes \\
9 & RConv4 \& UpSample4 & \(16\times 3/1\times 1\) & 32 & yes \\
10 & RConv5 \& UpSample5 & \(1\times 64/16\times 1\) & 16 & yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structure of the convolutional denoising auto-encoder.  
Figure 3: Illustration of the auto-encoder.
## 5 Fault Location in Direct Current (DC) Grid Having Cable and Overhead Line Segments
### Identification of Dominant Natural Frequency
#### 6.3.2 Multiple Signal Classification  
Natural frequencies are extracted from complex traveling waves by the application of higher resolution spectra estimation tool. A parameter-based method known as multiple signal classification is employed for extraction of higher frequency components of natural frequency. In this method, data is considered as a sinusoidal component of noise. Higher resolution spectrum is achieved by dividing the signal eigen vector into signal and noise subspaces. A sampled signal is expressed as:  
\[x(n)=\sum_{i=1}^{p}\begin{array}{c}A_{i}e^{j(2\pi f_{i}n+\varphi_{i})}+ \omega(n)\\ n=1,\ 2,\ \ldots,\ N\end{array} \tag{46}\]  
where \(p\) is the number of harmonics. \(A_{i}\), \(f_{i}\), \(\phi_{i}\) are the amplitude, frequency, and inception angle of the \(i^{th}\) harmonic component. \(w(n)\) is noise. Auto-correlation of matrix of \(X(n)\) is given as:  
\[R_{xx}=E\big{\{}X(n)X^{T}(n)\big{\}} \tag{47}\]  
where \(X(n)\) is a \(m\times N\) matrix and given by:  
\[X(n)=[x(n)\ x(n+1)\ldots x(n+m-1)]^{T} \tag{48}\]  
The weight of each component of frequency is given by power spectrum and mathematically as:  
\[P(f_{i})=\ \frac{1}{\sum_{k=p+1}^{m}\left|s(f_{i})^{T}v_{k}\right|^{2}} \tag{49}\]  
Figure 8: Flowchart of natural frequency-based protection for high-voltage direct current (HVdc) transmission lines.  
where \(s(f_{i})\ =\left[1\ e^{/2\pi f_{i}}\ \dots\ e^{/2\pi(N-1)f_{i}}\right]^{T}\).  
Peaks of the frequency spectrum is obtained by the expression of power spectrum \(P(f_{i})\). These peaks help in determination of dominant component of natural frequency. This dominant component of natural frequency is then employed for determination of distance of fault from converter station in MT-HVdc systems.
# A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
## 2 Representation of Grid-Stiffener Model
### Fundamental Level Set Function Based on RBFs  
The radial basis functions (RBFs) [34; 35; 36; 37; 38] are utilized here to construct the fundamental level set functions \(\gamma(\mathbf{x})\) and \(\theta(\mathbf{x})\), i.e.,  
\[\gamma(\mathbf{x}) =\sum_{i=1}^{m}\alpha_{i}\varphi(\mathbf{x},\mathbf{q}_{i}) \tag{8}\] \[\theta(\mathbf{x}) =\sum_{i=1}^{m}\beta_{i}\varphi(\mathbf{x},\mathbf{q}_{i}) \tag{9}\]  
where \(\alpha_{i}\) and \(\beta_{i}\) represent the coefficients of the \(i\)-th RBF; \(\mathbf{q}_{i}\) is the knot of the \(i\)-th RBF; \(m\) stands for the total number of RBFs. The RBF at knot \(\mathbf{q}_{i}\) is denoted as  
\[\varphi(\mathbf{x},\mathbf{q}_{i})=\varphi(\left\|\mathbf{x}-\mathbf{q}_{i}\right\|) \tag{10}\]  
where the \(\|\cdot\|\) represents the Euclidean norm.  
In this study, the compactly supported RBF (CS-RBF) with \(C^{2}\) continuity is utilized. It is expressed as [37]  
\[\varphi(t)=(1-t)_{+}^{4}(4t+1) \tag{11}\]  
where \((\cdot)_{+}\) represents \(\max\{\cdot,0\}\); \(t\) is formulated as  
\[t(\mathbf{x},\mathbf{q}_{i})=\frac{1}{d_{s}}\sqrt{\left\|\mathbf{x}-\mathbf{q}_{i}\right\|^{2 }+\kappa^{2}} \tag{12}\]  
where \(d_{s}\) is the support radius of CS-RBF; \(\kappa\) is set as \(10^{-4}\) to prevent division by zero.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 4 Simulation Results
### Imperfect CSI  
In this subsection, we consider the effect of imperfect CSI at the BS. Specifically, we assumed that perfect CSI, i.e., \(\{\hat{H}_{k,m}^{n},\forall k,n,m\}\), was available for each user through channel estimation and reporting to the BS via the signalling channel. However, the CSI obtained by the BS might not be accurate due to the limited feedback. For simplicity, the imperfect CSI obtained by the BS was formulated as:  
\[\hat{H}_{k,m}^{n}=\hat{H}_{k,m}^{n}+\varepsilon, \tag{36}\]  
for \(k=1,2,\cdots,K\), \(n=1,2,\cdots,N\) and \(m=1,2,\cdots,M\), where \(\varepsilon\) is the CSI error, \(\varepsilon\sim\mathcal{CN}(0,MSE)\) and \(MSE\) is the mean square error of the CSI error, \(MSE=\mathbb{E}\Big{[}|\hat{H}_{k,m}^{n}-\hat{H}_{k,m}^{n}|^{2}\Big{]}\).  
Figure 8: The comparison of CCDF for QPSK modulation with \(M=2\) and \(N=64\).  
Figure 7: The average mutual information per sample. (**a**) \(M=2\), \(N=64\); (**b**) \(M=4\), \(N=256\).  
Under this condition, the OFDM system and the proposed scheme used the imperfect CSI to perform the precoding instead, i.e., \(\{\hat{H}_{k,m}^{n},\forall k,n,m\}\).  
As the numerical results show in Figure 9, the effect of imperfect CSI was subtle when the MSE was small, for example, \(MSE=-20\) dB. Specifically, compared to perfect CSI at the BS, the BER performance loss with \(MSE=-20\) dB, \(-15\) dB and \(-10\) dB at BER \(=10^{-4}\) were 0.19 dB, 0.58 dB and 2.39 dB for the OFDM system and 0.27 dB, 0.83 dB and 3.06 dB for the proposed scheme, respectively. Both schemes produced a similar performance loss and were robust at a moderate level of CSI error.
## 1 Introduction
In the recent past, sophisticated synthesis strategies for the preparation of Fe\({}_{3}\)O\({}_{4}\)-Au nanocomposites have facilitated the realization of diverse magnetoplasmonic nanoparticles, including core-shell, flower-like and dumbbell-like nanoheterostructures [17]. For instance, Fe\({}_{3}\)O\({}_{4}\)@Au core-shell nanoparticles exhibit an encouraging potential for biomedical applications [18; 19; 20; 21; 22], since the gold surface provides biocompatibility and, furthermore, may be easily functionalized in aqueous solutions with thiol-containing biomolecules. Unfortunately, the complete gold shell also limits the radio-enhancing performance, because it completely blocks the catalytic activity of the Fe\({}_{3}\)O\({}_{4}\) nanoparticle surface. In contrast, dumbbell-like Au-Fe\({}_{3}\)O\({}_{4}\) nanoheterodimers should be perfectly tailored for their application as radio-enhancing agents, since such nanoheterodimers unify the high-Z material and the Fe\({}_{3}\)O\({}_{4}\) catalyst surface [23; 24]. However, the major drawback of the Au-Fe\({}_{3}\)O\({}_{4}\) nanodumbbells concerns their surfaces, which are irreversibly stabilized with oleic acid, completely hampering their solubility in aqueous solution. The surfactants oleic acid and oleylamine are essential for the synthesis of Au-Fe\({}_{3}\)O\({}_{4}\) nanodumbbells, which only succeeds through thermal decomposition of an iron precursor (e.g., Fe(CO)\({}_{5}\), Fe(acac)\({}_{3}\)) on AuNPs in the presence oleic acid and oleylamine at temperatures above 300 \({}^{\circ}\)C [16; 25; 26; 27]. In this contribution, we report a novel, simple, one-pot synthesis route enabling the preparation of water-soluble, non-toxic patchy Fe\({}_{3}\)O\({}_{4}\)-Au nanoparticles, which exhibit an encouraging radiosensitizer potential for cancer therapy. The patchy surface architecture emerges from growing gold nanoclusters on the Fe\({}_{3}\)O\({}_{4}\) nanoparticle surface. Surface-grown Au nanopatches are expected to enhance the radio-catalytic activity of the Fe\({}_{3}\)O\({}_{4}\) nanoparticles due to interfacial communication [26]. X-ray irradiation of intracellular Fe\({}_{3}\)O\({}_{4}\)-Au nanoparticles may result in Fe\({}_{3}\)O\({}_{4}\) surface-catalyzed production of ROS and the generation of O\({}_{2}\)\({}^{\bullet}\) near the Au surface, which is significantly enhanced by interfacial electron transfer and excess electronic charges (hot spots) at the Au surface [27]. To attain biocompatibility and water solubility, the as-synthesized patchy Fe\({}_{3}\)O\({}_{4}\)-Au nanoparticles (Fe\({}_{3}\)O\({}_{4}\)@Au pNPs) were successfully stabilized with 1-methyl-3-(dodecylphosphonic acid) imidazolium, forming a self-assembled monolayer on the nanoparticle surface. Different kinds of tumor cells (MCF-7 and A549) loaded with Fe\({}_{3}\)O\({}_{4}\)@Au pNPs and exposed to X-radiation in a single dose of 1 Gy were shown to increase the relative ROS concentration by more than 130%. The associated dose-modifying factors (DMF) reached values of 0.448 and 0.422, demonstrating the excellent performance of the Fe\({}_{3}\)O\({}_{4}\)@Au pNPs as X-ray dose-enhancing agents.